Social Capital – Its measurement and relationship with health and

communitization of health centers in Nagaland, India

by

Avril Kaplan

A dissertation submitted to Johns Hopkins University in conformity with the requirements for

the degree of Doctor of Philosophy

Baltimore, Maryland

April 2018

ii

Abstract

Social capital and health is a highly studied relationship in public health. Researchers are

interested in social capital in order to understand how personal connections and relationships

influence health. The concept has meaning for both individuals and communities: for individuals,

it is the resources available through personal relationships; for communities, it is collective trust,

norms and networks that facilitate coordinated action for mutual benefit. Social capital is

hypothesized to influence health through a variety of mechanisms. For individuals, it could help

people acquire health related information, instrumental support that helps them overcome

barriers to seeking care, or affective support that reduces stress. For communities, it could lead to

social contagion or informal social control that influences health-related behaviors, or the ability

to mobilize people to work towards shared goals. The theme connecting the three papers in this

dissertation is the relationship between social capital and health in the northeastern state of

Nagaland, India.

Social capital is a relevant topic of study in Nagaland. Features of the state – including

the strong tribal bonds, numerous community groups, and remote nature of villages – suggests

that communities may have high levels of social capital. Furthermore, in 2002 the government

established a cross-sectoral policy, the Communitisation of Public Institutions and Services Act,

which aimed to leverage social capital in Naga villages to improve the quality of government

services. In the health sector, the policy established committees at government health facilities

that included both community representatives and health workers. The idea behind the act was

that communities with high social capital could mobilize to make improvements to their services.

The three papers in this analysis used data from a cross-sectional survey of 1642

households, 97 health facilities and 179 health workers. The Department of Health and Family

iii

Welfare, Nagaland conducted these surveys in 2015 through the World Bank-funded Nagaland

Health Project.

Paper 1 examines the association between social capital and self-rated health. We used

multilevel confirmatory factor analysis to assess the construct validity of a 9-item scale at two

levels. We found that our scale measured two constructs at an individual level (cognitive and

structural social capital) and one overarching social capital construct at the community level.

We then found that higher community social capital was associated with worse self-rated health

in a given community.

Paper 2 assesses four implementation outcomes of health facility committees that were

established through the Commmunitization Act: fidelity, acceptability, appropriateness and

feasibility. We used data from all three surveys, as well as data from 61 in-depth interviews with

committee members and health workers. We found that there was variation in how the

committees were implemented, and that many had not been implemented as planned.

Communitization was widely accepted by committee members and health workers. Yet, some

respondents expressed that relying on community donations to compliment gaps in government

funding was difficult due to economic constraints within their communities, and that withholding

health worker salaries may not be an effective way to motivate absent staff given challenges they

faced in their everyday work.

Paper 3 builds off the first two. We used multilevel-structural equation modeling to

examine the relationship between our validated measure of community social capital and an

index of health committee functioning. We found that there was a positive, but not statistically

significant, association between the two. We also found that facilities that had more female

iv

members and received government funding and supervision visits were associated with better

functioning committees.

Findings across the three papers suggest that social capital may not always function as

expected. First, many researchers have found that higher social capital has a positive association

with self-rated health, although there is a growing body of studies that have found a negative

association. Second, the Communitization Act was designed under the premise that communities

with high social capital would take action through the health committee to improve government

services. Our findings suggest that beyond social capital, health committees need meaningful

engagement with the government to take action and improve their services.

This dissertation contributes to the existing body of social capital research. It is the first

study to examine social capital and health in Nagaland. It is among the first to apply multilevel

latent variable modeling to the study of social capital, and use social capital theory to assess

health committees. The modeling techniques used in this dissertation are applicable to other

researchers examining community level constructs with individual level data, and have the

potential to investigate the complex pathways through which social capital influences health.

More research that examines the relationship between social capital and health committees in

other settings could help demonstrate how social capital translates into effective interventions.

This is an area that holds critical insights into the true utility of social capital to reach tangible

health system impacts.

v

Committee of Thesis Readers

Committee members:

Krishna D. Rao, PhD, MSc (Advisor)

Assistant Professor, Department of International Health

David Bishai, MD, PhD, MPH

Professor, Department of Population, Family and Reproductive Health

Lorraine Dean, ScD

Assistant Professor, Department of Epidemiology

Caitlin Kennedy, PhD, MPH

Associate Professor, Department of International Health

Qian-Li Xue, PhD

Associate Professor, School of Medicine

Alternate committee members:

Rupali Limaye, PhD, MPH

Assistant Scientist, Department of International Health

Alden Gross, PhD, MHS

Assistant Professor, Department of Epidemiology

vi

Acknowledgements

My experience writing this dissertation was so much more than I ever imagined – it was

exciting, challenging, and humbling. It would not have been possible without the support of

many people.

To my advisor, Krishna Rao, your insight over the past four years has been instrumental

to my learning. You always managed to strike a balance between challenging me to do my best

work while helping me survive the program. My skills and outlook on public health have been

transformed through your mentorship. I look forward to seeing not only the impact of your own

research, but that of the next generation of students you advise.

To my colleagues that I met through the Nagaland Health Project – Patrick Mullen, Dr.

Nandira Changkija, Aarushi Bhatnagar and Dr. Thomas Keppen – thank you for making this

dissertation possible. A special thank you goes to Meyajungla Longchar, Yamosoba

Longkumer and Akangla Longkumer who helped me collect data. For the rest of my career, it

will be hard to top the time that we spent together working on this project. Nagaland will always

have a special place in my heart.

To my professors at Johns Hopkins – David Bishai, Lorraine Dean, Caitlin Kennedy and

Qian-Li Xue – your input has strengthened this work tremendously and pushed me greatly in my

thinking. A very special thank you goes to Ronald Heck at University of Hawaii at Manoa, who

answered my cold email about multilevel structural equation modeling. I would still be stuck in

the weeds of this analysis if it were not for your insight.

To all of my family, but especially my parents who supported me through a decade of

university, you made this dream a reality. To my mom – who has been my role model for what

vii

determination, focus and hard work looks like – you went above and beyond to help me finish

this dissertation.

To Zach, this has been quite the journey. I would not have been able to do this without

your encouragement through the tough times of this program, and your belief in me when I

doubted myself.

And lastly, to Eve. Nothing was better than taking breaks from my analysis and building

forts, taking nature walks and going to the park with you. Like others here have done for me, I

hope to provide you with inspiration and support to pursue your passions to the highest level.

viii

Table of Contents

List of Tables ................................................................................................................................. x

List of Figures ............................................................................................................................... xi

1. Introduction ............................................................................................................................ 1 1.1 Origins of social capital research .................................................................................................... 1 1.2 Social capital research in India ........................................................................................................ 3 1.3 Gaps in social capital and health research ....................................................................................... 5 1.4 Organization of this dissertation...................................................................................................... 8

2. The State of Nagaland ......................................................................................................... 10 2.1 Social capital in Nagaland ............................................................................................................. 10 2.2 Health sector in India .................................................................................................................... 14 2.3 Health in Nagaland ........................................................................................................................ 18 2.4 Social capital and health policy in Nagaland ................................................................................ 22

3. Social capital and self-rated health in Nagaland, India: Application of multilevel latent

variable modeling to study a classic relationship in public health (Paper 1) ......................... 29 3.1 Introduction ................................................................................................................................... 30 3.2 Background ................................................................................................................................... 31 3.3 Methods ......................................................................................................................................... 42 3.4 Results ........................................................................................................................................... 53 3.5 Discussion ..................................................................................................................................... 66 3.6 Conclusions ................................................................................................................................... 72

4. Communitization of health centers in Nagaland, India: have health facility committees

been implemented as planned? (Paper 2).................................................................................. 74 4.1 Introduction ................................................................................................................................... 75 4.2 Background ................................................................................................................................... 76 4.3 Methods ......................................................................................................................................... 78 4.4 Results ........................................................................................................................................... 86 4.5 Discussion ................................................................................................................................... 104 4.6 Conclusion ................................................................................................................................... 110

5. Does social capital influence the functioning of health facility committees? A

quantitative analysis in Nagaland, India (Paper 3) ................................................................ 111 5.1 Introduction ................................................................................................................................. 112 5.2 Background ................................................................................................................................. 113 5.3 Methods ....................................................................................................................................... 120 5.4 Results ......................................................................................................................................... 136 5.5 Discussion ................................................................................................................................... 141 5.6 Conclusions ................................................................................................................................. 147

6. Conclusion .......................................................................................................................... 148 6.1 Summary of findings ................................................................................................................... 148 6.2 Contributions to existing research ............................................................................................... 150 6.3 Policy implications ...................................................................................................................... 151 6.4 Areas for further research ............................................................................................................ 153

ix

Annex 1: Summary of social capital research in India ............................................................... 156

Annex 2: Comparison of multilevel regression modeling and multilevel structural equation

modeling ..................................................................................................................................... 164

Annex 3: Comparison of original SASCAT and modified SASCAT ........................................ 169

Annex 4: Exploratory data analysis for relationship between social capital and self-rated health

..................................................................................................................................................... 172

Annex 5: Results of multilevel regression analysis examining association between social capital

and self-rated health .................................................................................................................... 174

Annex 6: Results of multilevel regression analysis examining association between social capital

and self-rated health stratified by sex ......................................................................................... 176

Annex 7: Summary of quantitative data (paper 2) according to implementation outcome ....... 183

Annex 8: Descriptive statistics for health committee functioning index ................................... 186

Annex 9: Results of linear regression analysis examining association between social capital and

health committee functioning ..................................................................................................... 188

Annex 10: Exploratory data analysis and linear regression model diagnostics for association

between social capital and health committee functioning .......................................................... 189

Annex 11: Mplus code for final models ..................................................................................... 198

References .................................................................................................................................. 201

Curriculum Vitae – Avril Kaplan ........................................................................................... 219

x

List of Tables

Table 2.1: Geographic location of the tribes of Nagaland ........................................................... 10 Table 2.2: Population and health indicators in Nagaland and India ............................................ 21 Table 2.3: Annual state and central government funding for health facilities in Nagaland ........ 24 Table 3.1: Glossary of social capital terms .................................................................................. 34 Table 3.2: Summary of social capital dimensions measured through surveys ............................ 36 Table 3.3: Coding of nine survey items to measure structural and cognitive social capital ........ 45 Table 3.4: Characteristics of study participants by self-rated health ........................................... 54 Table 3.5: Descriptive statistics for social capital items by self-rated health .............................. 55 Table 3.6: Model fit statistics for single level social capital measurement models ..................... 57 Table 3.7: Interclass correlation coefficient for social capital items ........................................... 58 Table 3.8: Polychoric correlation of social capital items within and between communities ....... 59 Table 3.9: Model fit statistics for multilevel social capital measurement models ....................... 60 Table 3.10: Standardized factor loadings for single and multilevel social capital measurement

models ........................................................................................................................................... 61 Table 3.11: Multilevel structural equation model results for relationship between social capital

and self-rated health ...................................................................................................................... 65 Table 4.1: Summary of in-depth interview participants .............................................................. 82 Table 4.2: Role of health facility committee and government under Communitization of Health

Centers Act.................................................................................................................................... 87 Table 4.3: Recommended and actual composition of health facility committees ....................... 88 Table 4.5: Availability and condition of infrastructure and equipment at government health

facilities ....................................................................................................................................... 103 Table 5.1: Pandey's ten mantras of social capital ...................................................................... 119 Table 5.2: Variables included in health committee functioning index ...................................... 128 Table 5.3: Summary of health committee functioning determinants ......................................... 137 Table 5.4: Structural equation model results for relationship between community social capital

and health committee functioning index ..................................................................................... 139

xi

List of Figures

Figure 2.1: Map of India and districts of Nagaland ..................................................................... 11

Figure 2.2: Organization of government health services in India................................................ 16

Figure 3.1: Path diagram of single level measurement model ..................................................... 47

Figure 3.2: Path diagram of multilevel measurement model ....................................................... 48

Figure 3.3: Path diagram of single level social capital measurement model ............................... 57

Figure 3.4: Path diagram of multilevel social capital measurement model ................................. 62

Figure 3.5: Path diagram of relationship between social capital and self-rated health with

covariates ...................................................................................................................................... 64

Figure 4.1: Process to select health facilities for qualitative data collection ............................... 81

Figure 4.2: Implementation outcome framework ........................................................................ 83

Figure 5.1: Conceptual framework for determinants of health committee performance ........... 121

Figure 5.2: Path diagram of association between social capital and health committee functioning

index ............................................................................................................................................ 134

Figure 5.3: Path diagram of relationship between social capital and health committee

functioning index with covariates ............................................................................................... 140

1

1. Introduction

1.1 Origins of social capital research

The central theme of this dissertation is the relationship between social capital and health

in Nagaland, India. The concept of social capital originated outside the field of public health.

The first known use of the term was by Lyda Judson Hanifan in 1916. Hanifan, a school

supervisor in West Virginia, used the term to describe the role of communities in improving

educational outcomes (1). The concept did not initially gain traction among researchers.

Although many other scholars used the term – including Jane Jacobs (1961), Glenn Loury

(1977), Pierre Bourdieu (1986), James Coleman (1988), Ekkehart Schlicht (1993), and Alejandro

Portes (1996, 1998 and 2000) – it was Robert Putnam‘s conceptualization of social capital in his

analysis of democracy in Italy (1993) and the United States (1995 and 2000) that made social

capital a household term (2-13). Putnam‘s work was so powerful that it inspired President of the

United States Bill Clinton to propose an approach to confront the ―suffering‖ civil life and

―badly frayed‖ community bonds in America during his 1995 State of the Union Address (14).

Since the 1990s, and largely attributed to Putnam‘s work, there was a marked increase in the

level of interest and amount of research conducted on social capital (15-17).

A series of working papers published by the World Bank in 1998 provide insight into

why there was, and continues to be, immense interest in social capital (18). The authors

described that the traditional approaches to achieve sustainable development focused strongly on

building and leveraging natural, physical and human capital. Yet, these approaches often

overlooked ―the way in which the economic actors interact and organize themselves to generate

growth and development‖ (18). The authors suggested that the ―missing link‖ between the three

2

types of capital and economic growth was social capital. In essence, they described that social

capital may be the reason why some communities were more prosperous than others. Prosperity

was defined broadly, in terms of economic development, achieving income and gender equality,

and improving health outcomes.

The concept of social capital entered into the public health realm under similar

assumptions, namely that greater levels of social capital could be related to better health

outcomes. In 1997, Kawachi et al. published the first paper to examine social capital and health

(19). In their analysis of 39 US States, the authors hypothesized and also found that higher state-

level income inequality was associated with lower levels of social capital and increased mortality

(including total mortality, death from coronary heart disease, malignant neoplasms and infant

mortality).

Since this first study, researchers have proposed a series of mechanisms through which

social capital could influence health. These mechanisms include the spread of information that

informs health; receipt of instrumental support (e.g. cash, transportation) that could help reduce

barriers to seeking care; exchange of affective support that could reduce stress; influence of

individual behaviors and habits through social contagion (the behavior of others) and informal

social control (a community‘s ability to informally maintain social order); and the ability to

mobilize people to undertake collective action (20-22).

The idea of social capital – that individuals can access resources through their personal

relationships and that the common norms, social networks and trust within a community

facilitate coordinated action for mutual benefit – is relevant in many different settings. Where

initially there was a lot of focus around social capital in the United States, studies have now

3

expanded globally and have been conducted across a range of low, middle and high-income

countries and multiple disciplines.

1.2 Social capital research in India

Within India, multiple studies have examined social capital and health. DeSilva et al. led

two social capital and health studies in Andhra Pradesh in 2007, one that examined the

association between social capital and nutritional outcomes, and another that examined social

capital and mental health (23,24). In their nutritional study, the authors found inconsistent

results. When they examined one measure of social capital, citizenship activities, they found that

―children whose mothers are involved in some citizenship activities have lower height-for-age z-

scores,‖ which was contrary to their hypothesis that social capital would increase height-for-age

(23). However, when they used a different measure of cognitive social capital (which they

defined as perceptions of the quality of social relationships such as trust and social harmony)

they found that ―high compared to medium or low levels of maternal cognitive social capital are

associated with increased height-for-age and weight-for age z scores‖ (23). In their mental health

study, the authors found that the same measure of cognitive social capital was associated with

reduced odds of anxiety and depression.

Next, Sivaram et al. examined the association between social capital and HIV stigma in

Chennai (25). Their 2009 study found that among men and women who visited community based

alcohol outlets, people who reported membership in community groups, perceived that their

neighborhood had high levels of collective action and reciprocity, and had trust in health workers

who provided care for sexually transmitted disease had lower levels of HIV stigma.

In 2014, Story studied the association between social capital and utilization of maternal

health services (26). This study used nationally representative data (from the 2005 Indian Human

4

Development Survey) and concluded that social capital that bridged people of different

backgrounds was positively associated with use of antenatal care services, professional delivery

care, and childhood immunizations. However, Story found that social capital working to bond

people of similar backgrounds was negatively associated with antenatal care and childhood

immunizations, and positively associated with professional delivery care. In 2017, Story and

Carpiano examined the association between social capital and child nutrition (27). Using the

same data source, they found that households with greater wealth were associated with higher

social capital and that social capital that bridged people of dissimilar backgrounds within a

community was associated with reduced odds of a child being underweight.

After Story, Rawal et al. examined the relationship between social capital and self-rated

health among older people in Chandigarh in 2017 (28). Using a cross-sectional survey, the

authors found that social capital was positively and significantly associated with mental health.

However, there was no association between social capital and physical health.

Lastly, in 2018, Vikram studied the association between social capital and child nutrition

(29). Using the same data source as Story, she found that social capital that bridged people of

dissimilar backgrounds was positively associated with better child nutrition, whereas social

capital that bonded people of similar backgrounds had a negative association. When she

examined these relationships at a community, rather than household level, she found that social

capital that bridged people of dissimilar backgrounds was still associated with better outcomes,

but it was dependent on the level of economic development within a community.

Across the social capital and health studies in India, the measures of social capital used

and the outcomes studied were not consistent. Furthermore, social capital did not consistently

have a positive relationship with health.

5

Outside of the public health literature, there has been interest in examining the role of

social capital in India more broadly. Annex 1 presents a summary these studies. Besides health,

social capital in India has been studied in relation to economic development, governance and

democracy, environment, microfinance, water and sanitation, disaster recovery and land

ownership. A common discussion within this body of literature is about social hierarchy and

gender in the Indian context (30-33). For example, Pai described, ―In India, segmentation has

created deep-seated divisions, which are an important determinant of social capital and political

action in the countryside. Thus, a culture of distrust historically has developed among the various

hierarchical segments of society‖ (30). A finding from a different social capital study in India

described, ―Building social capital is not easy. It is not enough to create public space and believe

that human beings will naturally become citizens leaving behind their traditional hierarchies and

positions‖ (34). While there were some themes that emerged across the existing body of social

capital research, making generalizations is challenging in a large and diverse country like India.

One such illustration of the difficulty of generalizing insights on social capital research is its

application in the state of Nagaland. As the next chapter will discuss, Nagaland is unique from

the rest of India, leading to important considerations for the study of social capital in the region.

1.3 Gaps in social capital and health research

This dissertation was designed to build upon gaps in global research conducted within the

public health arena. Multiple reviews have summarized the body of literature examining the

association between social capital and physical health (21), mental health (35,36) access to

health services (37), health related behaviors (38), and health inequality (39,40). Findings from

these studies consistently demonstrated that the majority of social capital studies:

6

Used inconsistent definitions of social capital. The definition provided by Putnam,

which described social capital as the networks, norms and trust that facilitate coordinated

action for mutual benefit, was the most commonly cited definition of social capital within

the public health literature (16). However, multiple other definitions of social capital

have been applied in empirical studies. The way that social capital was defined had

implications for the level at which it was measured and the ways in which it could impact

health. Putnam conceptualized social capital as a community level resource, which has

benefits for everyone within the community. However, other scholars, such as Bourdieu

and Portes, have defined social capital as an individual level attribute as well (4,11).

When researchers do not use a consistent definition of social capital, they are not

studying the same concept.

Used inconsistent measures of social capital. Measuring social capital has not been

standardized (41). The majority of scales and indices that measured social capital in low

or middle-income countries were adapted from those used in high-income countries.

However, few tools underwent cultural adaptation, validation or tests of reliability (41).

Lacking a consistent measure of social capital has made cross-country comparisons a

challenge.

Measured social capital at an individual level, and then developed an aggregate

measure of social capital at the community level by calculating a community

average. Despite researchers‘ recognized need to develop better community level

measures of social capital, most researchers measured social capital at the individual

level and then aggregated these results to reach a community level construct (42).

7

Were quantitative and cross sectional in nature. Few studies used longitudinal data or

qualitative methods (21,37). As a result, few studies empirically demonstrated how social

capital was associated with heath.

Increasingly took into account the contextual effects of social capital through the use

of multilevel models. Given that social capital could be considered an individual, as well

as community level attribute, multilevel studies have become a popular research method.

Multilevel models allow a researcher to examine the independent effects of community

social capital beyond individual level social capital. Yet, multilevel models were more

commonly applied in social capital studies that took place in high-income countries (43).

Took place in high-income countries. DeRose and Kim both concluded that there was

limited evidence examining the association between social capital and health from low or

middle-income countries (21,37). Story‘s review of social capital and health studies in

low-income countries between 1990 and 2011 identified 14 studies that had an outcome

of interest related to physical health or health behaviors, and attempted to measure social

capital (43). Among these studies, 12 were in Africa and two were in South Asia.

Examined the relationship between social capital and physical health. The majority

of studies focused on the relationship between social capital and physical health, with

fewer studies examining the relationship between social capital and mental health, access

and use of health services, health related behaviors or health inequality. Studies that

examined the relationship between social capital and physical health generally showed a

positive association between individual level social capital and self-rated health (21).

However, in studies where social capital was also examined at a community level, the

association with self-rated health was mixed (21). Two components of social capital that

8

were frequently associated with better self-reported health were trust and associational

membership. A growing body of literature has also found a negative association between

social capital and health (44).

Had limited applicability for health programming. Since the majority of studies

examined the statistical association between social capital and self-rated health, a current

criticism of the body of research is that it has not resulted in tangible evidence to improve

health promotion, health services or health outcomes (45). A key research question

missing from the existing body of literature is how social capital can be built or leveraged

to improve health?

1.4 Organization of this dissertation

This dissertation includes three papers that target some of the gaps in the existing body of

social capital and health research. The first paper presents a new way to measure social capital,

using multilevel confirmatory factor analysis, so that measurement is better aligned with the

multilevel nature of the construct. The validated measure of social capital is then applied to

examine the classic relationship between social capital and self-rated-health in Nagaland. This is

among the first studies to use multilevel latent variable techniques to study social capital.

The second paper examines the implementation outcomes of the health facility committees

that were established through Nagaland‘s Communitisation of Public Institutions and Services

Act in 2002. The premise of the Communitization Act was to leverage social capital in Naga

communities. Through the Act, health committees were established at government clinics and

within communities. The health facility committees included community representatives and

health providers, and aimed to promote health and improve the quality, responsiveness and use

9

of government health services. The Communitization Act is a rare example of a health system

policy that was designed to leverage community social capital.

The final paper builds upon the first two. The primary objective is to determine whether

social capital is associated with better functioning health facility committees. The secondary

objective is to identify features of the health facility, committee and community that are

associated with better functioning committees. While there is also a deep literature that examines

community participation and health committees, this is among the first studies to use social

capital theory to examine the functioning of health committees.

10

2. The State of Nagaland

2.1 Social capital in Nagaland

Situated in the northeast of India, Nagaland is geographically remote from the majority of the

country. Today, of the approximately two million people living in the state, 71% of people live

in rural areas (46). Most people in Nagaland (87%) are affiliated with one of sixteen recognized

tribes (46). The origin of the Naga tribes is debated (47). Each tribe has its own dialect and

unique customs, and many people have and continue to live in villages that are homogeneous by

tribe. Table 2.1 maps each of Nagaland‘s eleven districts to its predominant tribe. In 1836,

American missionaries traveled to Nagaland and began the spread of Christianity (48). This

explains in large part why today, the majority (88%) of people throughout the state identify as

Christian (46).

Table 2.1: Geographic location of the tribes of Nagaland

Tribe District/Subdivision

1. Angami Kohima

2. Ao Mokokchung

3. Chakjesang Phek

4. Chang Tuensang

5. Kachari Dimapur

6. Khiamniungam Noklak in Tuensang

7. Konyak Mon

8. Kuki Dimapur; Peren

9. Lotha Wokha

10. Phom Longleng

11. Pochury Meluri in Phek

12. Rengma Tseminyu in Kohima

13. Sangtam Kiphire; Tuensang

14. Sumi Zunheboto

15. Yimchungru Shamator in Tuensang and Kiphire

16. Zeliang Peren Source: (47)

11

Figure 2.1: Map of India and districts of Nagaland

12

Nagaland has had conflict since before India‘s independence, arising from resistance to

British rule and then resistance to becoming a state of independent India (49). When the British

arrived in Nagaland in 1832, there were a series of violent raids while the Naga‘s attempted to

maintain their control of the region. Ultimately, the British established control over parts of the

modern day state, but certain regions, including Mon and Tuensang in the northeastern section of

the state, remained ‗Unadministered Areas‘ through the 1920s. When India gained Independence

in 1947, Nagaland was not part of the country. Factions of the population tried to remain

independent from India, which resulted in violence. Located in a strategic position to link India

and China, Nagaland ultimately became the 16th

state of India in 1963. The Nagas gained

special protections in the Indian constitution that would preserve their law, institutions and

practices. However, since this time, there have been groups within Nagaland that continue to

fight for independence, and multiple agreements have been signed with the Indian government to

try to bring peace to the state; the most recent peace agreement was signed in August 2015. The

Indian military still maintains a strong presence in Nagaland. The history of resistance to outside

control shapes social identity today. An analysis conducted in 2015 found that people within

Nagaland had a stronger sense of identity with their tribe and as a Naga than with the rest of

India (50).

That which differentiates Nagaland from the rest of India, beyond its identity drawn

along tribal lines and a sense of regional independence, includes the preservation of tribal laws

and institutions within the Indian constitution. This means that villages in Nagaland continue to

be run according to community governance structures. The Village Council, which consists of

people elected from within the community, has the authority to make a wide range of decisions –

from settling land disputes to making decisions about criminal cases (51). The Village

13

Development Board consists of both men and women from within the community, and is

responsible for village development (51). Many state government initiatives and schemes are

decentralized and implemented in villages through the Village Development Board. The power

of these entities to deal with village administration was summarized by Shimray in his analysis

of local governance in Nagaland. Shimray described:

“It is the duty of the Village Council to frame rules and regulations regarding civil,

criminal, and tradition related cases, they also ensure that offenders are duly punished. Most of

the disputes are settled within the village by the Village Council based on their framed existing

laws and regulations...The council also has full powers to deal with the internal administration

of the village, maintenance of law and order” (51).

Such a unique governance structure in Nagaland may enable these village entities to reinforce

common norms within the community that shape behaviors and facilitate cooperation among

people.

The main driver of the economy in Nagaland is agriculture, which may also play a role in

fostering social capital in the state. The hilly terrain throughout Nagaland makes farming a

challenge, so over time, the Nagas have relied on Jhum practices (52). Jhum is a labor-intensive

farming technique that has been passed down through generations and involves first burning land

and then rotating where agricultural products are cultivated on an annual basis. Agriculture is so

prominent in the lives and social structures of the Nagas that many villages throughout the state

are sustained entirely by the products that they cultivate (52). The labor-intensive farming

practice requires cooperation from an entire community. For example, according to a recent

government and United Nations Development Program report, ―Jhum is central to not only the

survival needs but to the very existence, thinking and psyche of the inhabitants of the state‖ (52).

14

Together, these cultural, geographic and socioeconomic characteristics of Nagaland may

have contributed to high levels of social capital in Naga villages. However, there has yet to be a

study of social capital and health in the state.

2.2 Health sector in India

The 1946 Bhore Committee report, which was released one year prior to India‘s

Independence, outlined the vision for the health sector in India. The report stated:

“The maintenance of the public health requires the fulfillment of certain fundamental

conditions, which include the provision of an environment conducive to healthful living,

adequate nutrition, the availability of health protection, preventive and curative, to all

members of the community irrespective of their ability to pay for it and the active-co-

operation of the people in their own health” (53).

This statement touches upon fundamental principles of the health system in India: that access to

health services should be available to all and people should be involved in health promotion,

disease prevention and curative care. Based on the report, India‘s public sector health system

was designed according to the Beveridge model: financed by tax revenues, delivery of basic

services through government facilities, and payment of health providers through the government

payroll.

Since the 1946 Bhore Committee report, India has developed multiple health system

policies that have sought to update and drive this vision. In 1983, the government passed its first

National Health Policy, which set the goal to deliver primary health care to all by 2000 (54). The

second National Health Policy, established in 2002, expanded on the principles of the first policy

by calling for greater government expenditures on health, partnerships with the private sector and

decentralization of health sector management to the district and community levels (55). In 2005,

the National Rural Health Mission was established to implement the 2002 National Health

Policy. The goals of the initiative were to improve access to health services for India‘s mostly

15

rural population (70% of Indians live in rural areas), and improve health outcomes for women

and children (56). The initiative also aimed to increase the availability of human resources by

creating a new cadre of community health workers, the Accredited Social Health Activists, and

by improving government infrastructure. A core pillar of the National Rural Health Mission was

to further incorporate community members into the management and financing of health

services. Hence, the initiative established Village Health, Sanitation and Nutrition Committees

and Rogi Kalyan Samiti (Patient Welfare Committees) that incorporated community members

into health promotion and disease prevention activities, as well as health facility management.

In 2013, the National Rural Health Mission became the National Health Mission, as it expanded

its scope to also target the urban poor. The urban poor in India include one-third of the country‘s

population, and are growing three times faster than the national population growth rate (57).

Health is a state subject in India (58). At the national level, the Central Ministry of Health

and Family Welfare is responsible for setting national guidelines and standards, and managing

programs of national importance (i.e. medical education, prevention and control of major

communicable diseases, monitoring the quality of drug manufacturing) (58). At the state level,

Ministries are responsible for implementing health programs, delivering health services and

managing public health and sanitation (58). Government services throughout India are delivered

through a large network of facilities, including Sub-Centers, Primary Health Centers,

Community Health Centers and District Hospitals (59). Details about each of these facilities are

presented in Figure 2.2.

16

Figure 2.2: Organization of government health services in India

Source: (59)

Delivering high quality health services to 1.2 billion people – one-fifth of the world‘s

population – through the public sector is a challenge. Numerous studies have documented issues

related to poor quality of health services in India (60-62). For example, in a study of seven

neighborhoods in Delhi using clinical vignettes, Das and Hammer found that in 50 to 75 percent

of cases studied, doctors provided harmful treatment to patients.1 They also concluded that the

poor had access to less competent providers as compared to the rich, with the availability of

MBBS doctors (formally trained doctors) more than doubling when moving from poorer to

wealthier neighborhoods (63). In a different study, Rao et al. used clinical vignettes to examine

the technical quality of health providers. They found that at Primary Health Centers in

1 For diarrhea, viral pharyngitis, depression and preeclampsia

17

Chhattisgarh, 61% of Medical Officers and Rural Medical Assistants made appropriate

prescriptions for the health conditions they were treating, whereas this figure was only 51% for

AYUSH Medical Officers2 and 33% for pharmacists and nurses (64).

Despite India‘s vision to provide basic health services to all citizens free of cost in the

public sector, today, the majority of health services throughout the country are paid through out-

of-pocket expenditures and delivered through the private sector. In 2014-15, total health

expenditure (THE) for the country accounted for four percent of Gross Domestic Product (GDP)

and Rs. 3,826 per capital (roughly US$60) (65). Government health expenditures accounted for

one percent of GDP, which is substantially lower than other BRICS3 countries, and 29% of THE

(65,66). Out-of-pocket expenditures accounted for the majority of THE (63%), whereas private

health insurance expenditure accounted for a very small proportion of THE (4%) (65). The

majority of current health expenditures for 2014-15 were spent in private hospitals and clinics

(31%) and at pharmacies (29%), followed by government hospitals and clinics (21%) (65).

Across India, there are notable trends in population health since 1990. Overall, the

country is advancing along the epidemiologic transition with the prevalence of communicable

diseases decreasing while the prevalence of non-communicable disease rises. However, among

the ten leading causes of disease burden remain diarrheal disease, respiratory infections, iron-

deficiency anemia, preterm birth complications and tuberculosis (67). Injuries, and specifically

road injuries and self-harm, have also increased in nearly all states (67). While maternal and

child malnutrition has decreased, it was still responsible for 15% of the total disease burden in

2016 (67). Likewise, there have been major improvements in water and sanitation. Once the

second leading risk factor for disease in 1990, it dropped to the seventh leading cause (67).

2 AYUSH = Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homeopathy.

3 Brazil, Russia, India, China, South Africa

18

However, in 2016, unsafe water and sanitation was still responsible for 40 times the disease

burden per person in India as compared to China (67). Contrary to water and sanitation, which is

improving across the country, outdoor air pollution is worsening. Air pollution in India is among

the highest in the world (67).

2.3 Health in Nagaland

Health outcomes and health service delivery vary widely across India‘s 29 states and

seven union territories. The remote and rural nature of Nagaland makes health service delivery a

challenge. As of 2009, Nagaland had 396 Sub-Centers, 126 Primary Health Centers, 21

Community Health Centers, and 11 district hospitals (68). As of 2012-13, 79% of villages were

within 3 kilometers (km) of a Sub Center, whereas 77% of villages were within 10 km of a

Primary Health Center (69). Contrary to other states in India, private sector health facilities

(both hospitals and clinics) have only recently opened in Nagaland, and are primarily

concentrated in three of the state‘s urban districts – Kohima, Dimapur and Mokokchung (70). In

2015-16, the public sector was the main source of care for three-fifths of households in Nagaland

(71). More people in rural areas (64%) than urban areas (50%) reported that the public sector

was their main source of care, and people were more likely to seek care at private hospitals

(30%) than private clinics (6%) (71).

Table 2.2 summarizes key population and health indicators in Nagaland in 2015-16 and

2005-06, and in India for the same years. There are also health disparities across Nagaland‘s

eleven districts. Overall, health outcomes are better and health service use is greater in the three

more urban districts (Dimapur, Kohima and Mokokchung). The more remote districts, which are

located in the eastern part of the country (Kiphire, Longleng, Mon and Tuensang), have worse

outcomes overall.

19

Key service utilization indicators in Nagaland are low. In 2015-16, only 15% of women

had at least four antenatal care visits, as compared to 51% nationwide (71). Furthermore, 33% of

women had institutional births, which was less than half of the national average of 79% (71).

Only 21% of women used a modern method of contraception, which again, was less than half of

the national average of 48% (71). Similarly, only 36% of children 12-23 months old were fully

immunized, as compared to the national average of 62% (71).

The story changes when we examine health outcomes – while service utilization

indicators in Nagaland are below their respective national averages, health outcomes tend to be

above national averages. In 2015-16, the infant mortality rate was 29 deaths per 1000 live births,

as compared to the national average of 41 deaths (71). For under-five mortality, the rate in

Nagaland was 37 deaths per 1000 live births, whereas it was 50 deaths nationwide (71).

Nutritional outcomes in Nagaland were also better than the national average. The proportion of

children under five years who were stunted was 29%, as compared to 38% nationwide, and 11%

were wasted, as compared to 21% nationwide (71).

Similar to India, Nagaland is moving along the epidemiologic transition. Disability

Adjusted Life Years (DALYs) lost to non-communicable diseases increased over the past ten

years, whereas DALYs lost to communicable, maternal, perinatal and nutritional causes

decreased (67). The leading causes of death have also changed. While the leading causes of

death were lower respiratory infection, ischemic heart disease and HIV respectively in 2005-06,

they were ischemic heart disease, cerebrovascular disease and lower respiratory infection

respectively in 2015-16 (67).

Certain health risk factors in Nagaland are either above national averages, or have

increased substantially over the past ten years. Tobacco use has been steady since 2005-06, but

20

was much higher than the national average in 2015-16, particularly among women (28% of

women used tobacco in Nagaland versus 7% in India) (71). Alcohol consumption, which is

banned throughout the state, was also higher than national averages for both genders in 2015-16

(71). While obesity was lower than the national average for men and women, prevalence

increased by nearly 10% from 2005-06 for both sexes (71).

Other indicators of note are related to the status of women, which appears to be higher in

Nagaland than in other parts of India. A large proportion of women in Nagaland attended school

(81% as compared to 69% nationwide in 2015-16), and were literate (81% in Nagaland as

compared to 68% nationwide in 2015-16) (71). Nationally, there was a gap in literacy between

women and men in 2015-16, with 68% literacy for women as compared to 86% literacy for men

(71). However, the gap was much smaller in Nagaland with 81% literacy for women versus 86%

literacy for men (71). Furthermore, a lower proportion of women in Nagaland were married

before the age of 18 years, and 97% of women in Nagaland reported that they participated in

household decisions, as compared to 84% in India (71).

Overall, the health indicators in Nagaland reveal that there are disparities in service use

and outcomes across districts. In 2015-16, service utilization in Nagaland was lower than the

rest of India, but health outcomes were higher than their respective national averages. Similar to

the rest of India, the disease profile is changing in Nagaland, with the burden of communicable

diseases being replaced by non-communicable diseases.

21

Table 2.2: Population and health indicators in Nagaland and India

Indicator Nagaland India

2015-16 2005-06 2015-16 2005-06

Bu

rden

of

dis

ease

DALYs lost to communicable, maternal,

perinatal and nutritional causes (%) 32.2 50.5 32.7 47.1

DALYs lost to non-communicable diseases

(%) 57.2 40.8 55.4 42.3

DALYs lost to injuries (%) 10.6 8.6 11.9 10.6

Infa

nt/

Ch

ild

Children12-23 months fully immunized (%) 35.7 21.0 62.0 43.5

Children under 5 years who are stunted (%) 28.6 38.8 38.4 48.0

Children under 5 years who are wasted (%) 11.2 13.3 21.0 19.8

Infant mortality rate (per 1000 live births) 29 38 41 57

Under-five mortality rate (per 1000 live births) 37 65 50 74

Mate

rnal Mothers who had 4 antenatal care visits (%) 15.0 12.1 51.2 37.0

Institutional births (%) 32.8 11.6 78.9 38.7

Total fertility rate

(children per woman) 2.7 3.7 2.2 2.7

Ad

ult

Women who use any kind of tobacco (%) 27.5 28.1 6.8 10.8

Men who use any kind of tobacco (%) 69.4 67.9 44.5 57.0

Women who consume alcohol (%) 3.3 3.5 1.2 2.2

Men who consume alcohol (%) 39.0 38.5 29.2 31.9

Women who are overweight or obese (%) 16.2 6.4 20.7 12.6

Men who are overweight or obese (%) 14.0 5.7 18.9 9.3

Gen

der

Literate women (%) 81.0 75.2 68.4 55.1

Literate men (%) 85.6 83.1 85.7 78.1

Population (female) age 6 years and above

who ever attended school (%) 81.0 68.5 68.8 58.3

Women age 20-24 years married before age 18

years (%) 13.3 21.4 26.8 47.4

Currently married women who usually

participate in household decisions (%) 97.4 96.9 84.0 76.5

DALY: Disability adjusted life year

Sources: (67,71)

22

2.4 Social capital and health policy in Nagaland

In 2005, Village Health, Sanitation and Nutrition Committees and Rogi Kalyan Samitis were

established in villages and at health centers across India through the National Health Mission.

However, prior to the National Health Mission, health committees were established in Nagaland

through a state specific policy. The Communitization of Public Institutions and Services Act,

which began implementation in 2002, is a cross-sector policy that aims to improve the delivery

of government services. Policymakers envisioned that the Communitization Act would be

successful in Nagaland because they believed that villages throughout the state had high social

capital (72). According to the government‘s Handbook on Communitisation of Health Centres,

―The State is rich in ‗social capital‘ having community spirit in abundance and existence of

traditional community institutions such as Village Council and Village Development Board in

every village. Communitization was introduced to harness this ‗social capital‘ in order to vitalize

public institutions‖ (73). Hence, the policy was in part based on the idea that given the

opportunity through the health committees, communities would work together collectively to

make improvements to the quality and responsiveness of their health services.

In the health sector, the Communitization Act established health facility committees that use

community representatives to manage services at Sub Centers, Primary Health Centers and

Community Health Centers. When a village does not have a health facility, a Village Health

Committee is established to spread information about health promotion and disease prevention.4

The health facility committees have three main functions: to take ownership and management of

4 Within Nagaland, health committees associated with Community Health Centers (CHC) and Primary Health

Centers (PHC) are called Health Center Management Committees (HCMCs). The committees associated with Sub-

Centers (SC) or those that are not affiliated with any facility are called Village Health Committees. For this

dissertation, we refer to the committees associated with SCs, PHCs and CHCs as health facility committees.

23

health centers, to promote preventive health through education and action, and to popularize and

encourage traditional medicines and its practitioners (73).

The health facility committees and Department of Health and Family Welfare both have

specific roles to manage health service delivery. These roles were outlined in the

Communitization Act for Sub Centers, Primary Health Centers and Community Health Centers

(available in the Department of Health and Family Welfare‘s Handbook on Communitisation of

Health Centres) (73). According to the Act, once the government posts providers to health

facilities, the committees are responsible for supervising their performance, ensuring that they

are present at their post, arranging or making improvements to staff accommodation, granting

leave and distributing their salaries. If a provider is routinely absent, the committees have

authority to withhold staff salaries through the ‗No Work No Pay‘ principle. This principle

enables the committee to suspend a health worker‘s salary for a specified period of time, and

repurpose the funds to make improvements to the health facility. The authority given to health

facility committees in Nagaland to withhold salaries is greater than the authority given to Rogi

Kalyan Samitis elsewhere in India.

The central and state government both provide funds for the health centers that are

transferred to bank accounts managed by the committees. When Communitization first began,

the state government developed the State Communitization Committee (SCC) to allocate funds

to procure medicines and maintain the committees on an annual basis (74). The SCC also

provides funding for staff salaries for a period of three months, whereas funds for infrastructure,

equipment and medicines are provided in annual grants. In addition to SCC funding, the central

government provides funds for health centers through the National Health Mission (74). The

committees have discretion over how untied funds are used, with the National Health Mission

24

providing general guidelines (75).5 After 2005, committees associated with Primary Health

Centers and Community Health Centers became eligible for funding through the national Rogi

Kalyan Samitis initiative. These funds are to be used at the discretion of the committee ―for

smooth functioning and maintaining the quality of services‖ (76). Table 2 summarizes the

funding for each of the committees by source in 2009.

Table 2.3: Annual state and central government funding for health facilities in Nagaland

Health

facility type

SCC (State

funding)

NHM (Central government funding) Total

Untied funds Maintenance

funds

Rogi Kalyan

Samitis

SC Rs. 10,000 Rs. 10,000 Rs. 10,000 N/A Rs. 30,000

PHC Rs. 125,000 Rs. 25,000 Rs. 50,000 Rs. 100,000 Rs. 300,000

CHC Rs. 150,000 Rs. 50,000 Rs. 100,000 Rs. 100,000 Rs. 400,000 Source: (74)

The committee is also responsible for raising funds from within the community, from the

Village Council, Village Development Board or other private sources. The funds raised should

support health facility operations and bridge any gap between the funding provided by the

government and required by the community to deliver health services. The committee has control

over how funds raised from within the community are used. However, they must maintain a book

of accounts, which is to be inspected by the government on a quarterly basis. Furthermore, the

committee must report any salary deductions made through the ‗No Work No Pay‘ principle

directly to the government on a quarterly basis.

5 According to the government operational guidelines, untied funds are for ad-hoc payments for cleaning the facility,

transport of emergency cases and samples, purchase of consumables (bandages, medicines), purchasing of

disinfectants, supplies for environmental sanitation, payment of electricity and water bills, payment for sitting

arrangements, safe drinking water, heating and cooling, sterilization equipment, emergency lighting, making and

displaying information and educational materials, organizing stakeholder meetings and repairing furniture. Untied

funds cannot be used for employee salaries, incentives, vehicle purchases, equipment purchases. The governments

operational guidelines state that annual maintenance funds can be used for minor modifications and repairs to

buildings, providing boundary walls or fencing, septic tanks, water storage, whitewashing, electric installation

works, arrangement for biomedical waste, improvement to the path to the institution, landscaping, making payments

for electricity and water, ad-hoc cleanliness.

25

The health facility committees play a role in procuring medicines and recording health

information. The government provides a list of required medicines, and the committee is

responsible for purchasing these medicines from any retail store or provider using funds

distributed by the government. If the facility has a shortage of medicines, then the committee is

responsible for raising additional revenue required to purchase these medicines. The committee

is also responsible for tracking vital statistics of patients and reporting them to the state

government. The government then aggregates these data for planning purposes.

One of the key objectives of the Communitization Act is to decentralize health system

governance to the community level. Hence, each health facility committee is responsible for

developing and executing an annual plan, which is based on their assessment of village needs.

The committee has authority to devise and execute this plan, whereas the government plays an

oversight role. The Chief Medical Officer for each District is responsible for overseeing

Communitization, along with the District Coordination Committee, which audits committee

expenditures on an annual basis. The government has the mandate to train committee members

on their role, and to incentivize high performance by recognizing well-functioning committees

and specific members on an annual basis.

The committees support service delivery at their facility by ensuring that infrastructure

and equipment is operational and maintained. They also play a role in arranging transportation

for emergency cases, which links community health workers and primary care services offered at

SCs to higher levels of care at PHCs and CHCs. Furthermore, the committees are charged with

making health services more responsive to local expectations by working with the community to

develop the indigenous health care system, which is based on tribal medicinal practices. The

26

government‘s role to improve services is to review and approve any additional services offered

by traditional providers.

Communitization of health centers began in 2002 through a series of awareness campaigns

and trainings for committee members. At this time, the government encouraged implementation

to occur from the bottom up, allowing communities to decide whether they want to partake in the

initiative (77). For a health center to become ―Communitized,‖ the government and health center

sign a Memorandum of Understanding, and a committee is then established (73). The

Communitization Act also outlines the composition of each health facility committee to ensure

that it represents the community. Committee members represent a variety of different community

groups, and should include at least one woman. For all health facility committees, members

should serve for a period of three years and should hold at least one meeting every three months.

The Village Council in the community where the facility is located elects the Chairman for

health committees at Sub Centers. Health committees that serve at Primary Health Centers and

Community Health Centers are supposed to include the Chairmen from the committees within

their catchment area. These Chairmen then elect the leader for the committee at the Primary

Health Center or Community Health Center at their first meeting.

Four existing studies have examined the health facility committees in Nagaland (74,78-80).

From these studies, we know that there is variation in how the committees are functioning and

we have some evidence about why some committees are not functioning well.

In 2009, the Directorate of Health and Family Welfare conducted an impact assessment

of the health facility committees at 70 health centers (10 CHCs, 22 PHCs and 38 SCs)

across all of Nagaland‘s 11 districts (74). This is the only study that has examined health

committees in each district, but data collection was completed six years before this study.

27

The report concludes, ―The beneficiaries [of the policy] indicated improvements in access

to health care facilities due to Communitization‖ (74).

In 2014, Broome conducted a case study of Communitization of Health Services in

Mokokchung and Kohima, which are two of the more urban districts within the state (78).

Using in-depth interviews with policymakers, committee and community members,

Broome found that ―Communitisation of health services has improved the reach of the

health services to rural areas as well as awareness about health issues among the local

villagers‖ (78). Broome further concluded, ―Nowhere has the situation worsened after

Communitisation‖ (78). Despite these positive findings, the author recognized some of

the limitations of the initiative: she found that government officials reported that progress

was slower in eastern regions of the state and that local healers had not widely been

incorporated into the initiative.

Another assessment in 2014 conducted by Oxford Policy Management in four districts –

Phek, Tuensang, Dimapur and Kiphire – painted a bleaker picture of the Act (79). The

authors reported inefficient use of resources, and a dominant role of health staff rather

than committee members in health facility management. They found that some

committees had limited engagement with the wider community, a strong focus on

curative interventions rather than prevention, and a sense of hopelessness about the state

of health services.

Most recently, in 2017, Tushi and Kaur examined input, process and output indicators of

health facility committees in the district of Mokokchung (80). The authors found that

health committees were in place and were completing their administrative duties.

Furthermore, they report that infrastructure, equipment and outpatient service availability

28

was satisfactory. However, they found that shortages in funding and doctors hampered

the initiative.

Despite these existing studies and the fact that the Communitization Act has been in effect

since 2002, there is still a recognized need throughout the state to improve the way that the Act is

implemented so that it can have a greater impact on public sector services (81,82).

29

3. Social capital and self-rated health in Nagaland, India:

Application of multilevel latent variable modeling to study a

classic relationship in public health (paper 1)

Abstract

Social capital and health is a widely studied relationship in public health. Yet over the past

twenty years, there has been an ongoing debate about how to define and measure the construct.

Leading researchers in this field have proposed that social capital should be studied as a

multilevel construct: for individuals, it is the resources made available through personal

relationships and connections, and for communities it is the norms, networks and trust within a

community that facilitates coordinated action for mutual benefit. This study applies a new

technique to examine the classic public health relationship between social capital and self-rated

health: multilevel latent variable modeling. First, we used multilevel confirmatory factor

analysis to examine the construct validity of a nine-item social capital scale in Nagaland, India.

This approach allowed us to separately validate our scale at an individual and community level.

We found that social capital was best represented by a structural factor and a cognitive factor at

the individual level, and one overarching social capital factor at the community level. We then

used multilevel structural equation modeling to examine the relationship between our validated

measure of social capital and self-rated health. We found that community social capital had a

negative and statistically significant association with self-rated health in a given community.

Individual structural and cognitive social capital were not significantly related to self-rated

health. This study fits among a growing body of research that has found a negative association

between social capital and health.

30

3.1 Introduction

Within the past two decades, the relationship between social capital and health has become

one of the most widely studied associations in public health (83). Researchers are interested in

studying social capital to understand whether and how social relationships impact health

outcomes. However, a key challenge with this body of research is defining and measuring social

capital (83,84). In addition, the majority of social capital research has taken place in high-income

countries, with research in low and middle-income countries now growing (21,37,43). In this

study, we examine the definitions of social capital that stem from the fields of political science

and sociology, and discuss existing approaches to measure the concept. We then apply a new

technique to the study of social capital – multilevel confirmatory factor analysis – to assess the

construct validity of a nine-item social capital scale in the context of Nagaland, India. Finally, we

apply our validated scale to examine the classic relationship between social capital and self-rated

health in Nagaland using multilevel structural equation modeling.

This is among the first studies to apply multilevel latent variable procedures to the

analysis of social capital. The approach presented in this paper is applicable to researchers

studying social capital and other community level constructs measured with individual level

data. It is also the first study to examine the relationship between social capital and self-rated

health in Nagaland and only the second study to examine this relationship in India (28). The

unique features of Nagaland, such as the strong connections of its people stemming from their

tribal affiliations, the longstanding community governance structures and the remote nature of

communities, make the study of social capital and health within the state meaningful. This study

elucidates the effect of social relationships and contextual environment on health, which is an

important consideration when designing interventions that address social determinants of health.

31

3.2 Background

3.2.1 Definitions of social capital

Social capital is a widely debated topic (85-87). Researchers have neither come to

consensus about where the concept originated nor its official definition. Today there remain

multiple ways to define the construct (88). Bjørnskov et al. provide a comprehensive summary

of social capital definitions that have been applied across multiple disciplines (see page 1228)

(84). However, in this paper, we focus on the definitions established by Pierre Bourdieu (4),

Alejandro Portes (11), James Coleman (5,6) and Robert Putnam (8-10), as there is general

agreement that their definitions are most commonly applied in public health research (16,84).

Bourdieu defined social capital as ―the aggregate of the actual or potential resources

which are linked to possession of a durable network… which provides each of its members with

the backing of the collectivity-owned capital, a ‗credential‘ which entitles them to credit, in

various senses of the word‖ (4). Bourdieu‘s definition largely viewed social capital as the

resources made available to individuals through their social networks. These resources could be

access to information, opportunities, financial assistance or the ability to influence others. While

Bourdieu viewed social capital primarily as an individual attribute, he also recognized that an

individual‘s social capital depended on the size of their social network, and the collective

resources possessed by other people within their network. In this sense, Bourdieu conceptualized

social capital as a private good that is shaped by the wider community.

Similar to Bourdieu, Portes defined social capital as an individual‘s ―ability to secure

benefits through membership in networks and other social structures‖ (11). Portes provided a

framework for both the sources and consequences of social capital. He described two sources.

First, consummatory sources arise from people‘s feelings of obligation to behave in a specific

32

way because of value introjection (internalizing the beliefs of others) or due to bounded

solidarity (the feeling that a group has common fate). Second are instrumental sources, which he

explained could be reciprocity exchanges (exchanges based on intangible social goods), and

enforceable trust (motivation to act according to group expectations to gain an advantage). He

described the positive consequences of social capital as the observance of social norms, family

support and network-mediated benefits. Unique from the other scholars, Portes also described the

potential negative aspects of social capital including: (1) restricted access to opportunities for

specific individuals within a network; (2) restrictions on individual freedom; (3) excessive claims

on specific group members (some group members are under more stress because they have to

provide support to others in their network); and (4) downward leveling norms (when group

solidarity is focused on an opposition to mainstream society).

Coleman defined social capital by its function, stating that ―it is not a single entity, but a

variety of different entities having two characteristics in common: they all consist of some aspect

of social structure, and they facilitate certain actions of individuals who are within the structure‖

(5). Like Bourdieu, Coleman viewed social capital as the resources made available to

individuals within a network. However, he expanded the concept of social capital to incorporate

features of a public good. Coleman proposed that social capital creates social obligations, norms

and expectations that facilitate actions of people living in a community, such as the spread of

information or the ability to enforce sanctions. This aspect of social capital could have positive

and negative externalities for all people living within a community.

Finally, Putnam‘s definition of social capital built on Coleman‘s communitarian

perspective. Putnam noted that people within communities were better off when they cooperate

to achieve collective interests (10). He therefore defined social capital as ―features of social

33

organization, such as networks, norms, and trust, that facilitate coordination and cooperation for

mutual benefit‖ (9). According to Putnam, social capital is a community attribute. People living

in communities where there are dense associations, active groups, norms of reciprocity and high

trust have a benefit of being able to work together efficiently.

To further assist in defining the concept, social capital has been categorized into sub-

dimensions (89). In public health, researchers commonly use these descriptions when they

measure the construct. Bain and Krishna classified social capital into structural and cognitive

forms (90). Structural social capital refers to features of the networks through which people

socialize and is commonly described as ―what people do‖ (91). Structural social capital can be

objectively verified, and enables people to interact, develop social ties and build their networks

(17,83). Examples of structural social capital include the nature, types of activities and density of

institutions and networks that an individual possesses, or that exist within and between

communities. Cognitive social capital refers to the quality and nature of social interactions and

can be thought of as ―what people feel‖ (91). Cognitive social capital incorporates people‘s

perceptions and values, and is therefore subjective (83). Examples of cognitive social capital

include trust between individuals and within institutions, solidarity and norms of reciprocity that

an individual possesses. Additional categories for social capital include bonding, bridging and

linking (10,92). Bonding social capital describes associations between people of similar social

background and status –for example family members or neighbors – whereas bridging describes

associations across people of different social background – for example different socio-

demographic groups or ethnicities, and linking describes associations between people with

different levels of authority or power.

34

Table 3.1 summarizes the definition of each type of social capital and provides an

example of how it can be measured using indicators that were applied in this study. Note that

certain measures of social capital fall under more than one type. For example, trust is a measure

of cognitive social capital. However, it can be considered a form of bonding social capital if it

measures trust in people of similar backgrounds, bridging social capital if it describes trust in

people of dissimilar backgrounds, or linking social capital if it measures trust in people with

different levels of power. While there are many definitions of social capital, Kawachi

emphasized that the definitions have two common features: they describe social capital as a

resource, and they emphasize that social capital is generated through social connections (22).

Table 3.1: Glossary of social capital terms

Term Definition Examples

Structural

social

capital

Presence or absence of formal

opportunity structures or

activities in which individual

actors might develop social ties

and build social networks.

Membership in community groups

Interaction and support from individuals

Talking with authorities about community

problem

Joining with community to address

community problem

Voting

Cognitive

social

capital

Measures that assess people‘s

perceptions of trust, reciprocity

and support.

Trust in neighbors, leaders, strangers

Social cohesion with community

Bonding

social

capital

Resources that are accessed

within networks or groups having

generally similar characteristics

(i.e. class, race/ethnicity, age).

Membership in groups with people of

similar backgrounds

Support from/trust in people with similar

background

Bridging

social

capital

Social resources that may be

accessed across groups of

different socioeconomic or socio-

demographic characteristics

Membership in groups with people of

different socioeconomic backgrounds

Support from/trust in people with different

socioeconomic background

Linking

social

capital

Social resources that may be

accessed across formal or

institutionalized structures of

authority and power.

Membership in groups in a position of

leadership/power

Support from/trust in people in a position

of leadership/power Source:(17)

35

3.2.2 Measurement of social capital

Without an agreed upon definition of social capital, measurement of the concept has been

inconsistent and critics have questioned whether researchers are really studying the same concept

(85). In an attempt to clarify parameters around the concept of social capital, Harpham proposed

topics that have been considered social capital, but ―can be more correctly and usefully regarded

as intermediate variables between social capital and health‖ (93). These topics include sense of

belonging, enjoyment of area, desirability to move, and neighborhood quality, security and

crime.

There have been several attempts to assess the body of social capital research across a

variety of disciplines to identify how applied researchers have conceptualized the construct

(41,94,95). Table 3.2 presents the components of social capital identified by three recent reviews.

We classified the components as being either structural or cognitive social capital.6 Not

surprisingly, each of the authors concluded that existing studies cover different components of

social capital. Engbers concluded that social capital is merely an ―umbrella term‖ that

researchers have used to describe multiple different constructs related to social relationships and

interactions (94). These findings underscore the importance for researchers working in this space

to specify their definition of social capital and to elaborate on the unique components that they

intend to measure.

6 Each component identified in these studies could also be classified as bonding, bridging or linking social capital.

For example, depending on the nature of personal relationships, they could bond together people with similar

characteristics (class, race/ethnicity, age), bridge people with different socioeconomic or socio-demographic

characteristics, or link people across formal structures of authority and power.

36

Table 3.2: Summary of social capital dimensions measured through surveys

Engbers (2016) Scrivens (2013) Agampodi (2015)

Scope of review Review of 17 large

sample datasets within

the United States

Review of 50 surveys

from Organization for

Economic

Cooperation and

Development (OECD)

countries

Review of 46 studies

examining social

capital and health in

low and middle

income countries

Structural components

of social capital Formal

membership and

participation

Altruism and

political

engagement

Informal

interactions

Personal

relationships

Social network

support

Civic engagement

Social support

Collective action

Community

participation

Group

membership

Social

relationships

Social networks

Volunteering

Cognitive components

of social capital Trust

Shared norms

Trust and

cooperative norms

Social trust

Sense of

belonging

Reciprocity

Social cohesion

Solidarity

Optimism Sources: (41,95,96)

Within the body of research that examines the relationship between social capital and

health, the level at which social capital is studied has also been inconsistent. There has been a

long debate about whether social capital is an individual or community level attribute (97).

Kawachi and colleagues proposed that it is both (83). As a result, they have recommended

studying social capital within a multilevel analytic framework, where individual level data are

aggregated to develop a community level measure of social capital (83). Multilevel models

allow researchers to simultaneously model community level measures of social capital with

individual level measures. This enables researchers to understand whether social capital has a

contextual effect on individual health outcomes above and beyond an individual (or

37

compositional) effect (83). Stated differently, multilevel models allow researchers to assess

whether there is a relationship between community level characteristics (i.e. the community‘s

social capital) and health that differs from characteristics of people living in these communities

(i.e. each individual community member‘s social capital).

There are three common approaches to develop individual level measures of social

capital, which are then used to develop aggregated community level variables. First is to use a

single indicator of social capital (i.e. group membership) (98,99). This approach is limited.

Since social capital is a complex construct and existing theory indicates that it is multi-

dimensional, the use of one indicator to measure social capital does not capture the full domain

of the construct.

A second approach is to use a non-validated scale (i.e. to calculate a score across multiple

indicators that researchers assume measure social capital) (98,100-103). When researchers do not

validate their measures, they do not confirm whether their items tap into common underlying

constructs of social capital, such as those presented in Table 3.2. Hence, when researchers take

this approach, they continue down the path of vaguely defining and measuring social capital,

without assessing if there is any common variance among the measures.

A third approach is to use exploratory or confirmatory factor analytic methods to develop

a social capital scale. This approach improves upon the latter two because social capital is a

latent construct – it cannot be measured directly. As a result, the ideal approach is to use

multiple indicators to capture the full domain of the construct, and then examine the co-variation

among these indicators (104). Factor analytic techniques are used to examine the co-variation

among indicators, which we assume is caused by the latent construct. Any variation that is

unique to the indicator is assumed to be measurement error of the latent construct. Hence, factor

38

analysis helps a researcher isolate indicators that are strong measures of a latent construct, as

compared to those that have high measurement error and should be removed from the analysis.

In addition, factor analysis is used to assess the structure of a latent variable, for example,

whether it is uni-dimensional or multi-dimensional. Exploratory factor analysis is an inductive

and data driven approach that is used when a researcher does not have a preconceived theory

about how specific indicators measure the underlying latent construct (104). Alternatively,

researchers use confirmatory factor analysis when they are testing whether a hypothesized factor

structure fits the data (104).

In all three approaches, it is common for individual level measures of social capital to be

group mean centered (to measure variation within a community), and included in a multilevel

regression analysis with a community level measure of social capital (to measure variation

between communities). The community level measure is mean aggregated from the individual

level measures in a given community (26,105-115). More details about using multilevel

regression as an approach to study social capital are presented in Annex 2. This multilevel

regression approach has been widely applied to study social capital and health globally. In a

rapid search, we found 201 articles that used this approach to study the relationship between

social capital and health between 2001 and 2017.7 Despite the popularity of this approach, for the

majority of studies that take place in low or middle-income countries, social capital data have

been collected from individuals and assessed at an individual level only (41).

The third approach is the strongest method identified to develop a community level

measure of social capital using individual level data. Yet, it has limitations. Even when an

individual level social capital scale is validated using factor analysis, the validity of the scale

7 We searched ((social capital[Title/Abstract]) AND health[Title/Abstract]) AND multilevel[Title/Abstract] in

PubMed on November 1, 2017.

39

may not hold at the community level. Deriving community level variables using single level

factor analysis is subject to atomistic fallacy – the relationship between variables at an individual

level may not be the same at the community level (116-118). In addition, if researchers conduct

factor analysis without taking into account nested data structures, their analysis will also have

inaccurate standard errors for the indicator‘s factor loadings (119).

Multilevel factor analysis (ML-FA) is one approach to examine construct validity of a

scale at two different levels. The approach has been applied in educational (120-123),

organizational (124-126) and health (127,128) research, but it has not yet been widely applied to

the study of social capital. Single level factor analysis is based on the analysis of a total

correlation or variance co-variance matrix of observed variables, whereas multilevel factor

analysis decomposes this matrix into a pooled within group covariance matrix and a between

group covariance matrix (119). ML-FA therefore takes into account that social capital can vary

within a community as well as between communities.

3.2.3 Social capital and health in Nagaland, India

While the relationship between social capital and self-rated health has been studied in

many contexts, the results across country contexts are not always consistent (129). A potential

reason for the inconsistent findings is due to the complex set of pathways through which social

capital may influence health. These hypothesized pathways differ depending on whether social

capital is conceptualized as an individual level construct, where people are able to obtain

resources from within their social networks that would otherwise be unavailable, or as a

community level construct, where people in communities can work together more efficiently to

achieve collective goals.

40

As an individual level construct, social capital can influence health by providing people

with affective support, instrumental support, and rapid spread of information (20,22,83).

Affective support can positively affect health by reducing an individual‘s stress level, or

enhancing their ability to cope with difficult life events (130). Aye et al. argue that when people

live in areas where the delivery of government health services and market systems are weak, they

will rely on their social networks to access resources that are unavailable to them (131). Hence,

people may use their personal relationships to access instrumental support (e.g. cash, provision

of transportation) and overcome barriers, such as treatment and transportation costs, to seeking

care. Thiede argues that a key component of spreading information is trust: people are more

likely to share information and confide in others with whom they have trusted relationships

(132). Hence, people living in communities with high social capital may more rapidly spread

information that shapes decisions related to health and health seeking behavior.

As a community level construct, social capital can influence health through social

contagion, informal social control and collective efficiency (22,83). Social contagion is when

behaviors spread quickly through a tightly knit network, whereas informal social control occurs

when people in tight knit communities rapidly and efficiently sanction others in their community

who exhibit behaviors that go against common norms (22,83). Hence, behaviors in a community

with high social capital are likely shaped by the behaviors of others and common norms within

the community. Mohnen et al. identify five behaviors that could be shaped by the community,

and that would have a profound impact on health: smoking, alcohol consumption, sleep patterns,

nutritional habits and physical activity (133). Collective action is when people are willing to

intervene for the benefit of the wider community (83). Collective action could impact health

when the community takes action to improve their physical environment (e.g. by taking action to

41

improve sanitation facilities), or to ensure that health services are delivered effectively (e.g. by

ensuring that health workers are available at health facilities).

The specific social and communal features of Nagaland present a unique setting to study

these dynamics. Nagaland is a state in northeast India that is different from other states in many

ways. Nagaland is home to two million people. Most people in the state are associated with one

of sixteen recognized tribes. Each tribe has its own customs and traditions, and many villages

are homogeneous by tribe. In the mid nineteenth-century, American missionaries spread

Christianity throughout the state, so today, most people in Nagaland share a common religion.

The Constitution of India protects the religious and social practices of the Nagas, and enables

them to be run by customary law. As such, a Council of elected men runs each village. The

Village Council has authority to make decisions about a range of civil activities, from decisions

about criminal justice to ownership and transfer of land. Furthermore, in 2002, the state initiated

a cross-sectoral policy (in the health, education, forestry, water and sanitation, roads and power)

to leverage community social capital by incorporating community members into the management

of government services.

Recent data from Nagaland underscores the importance of social and environmental

determinants of health throughout the state. As compared to the rest of India, Nagaland ranks

less favorably in many health service delivery outcomes. In 2015-16, Nagaland had among the

lowest rates of immunization, institutional deliveries and pre and postnatal care in the country

(71).8 Yet, Nagaland ranks better in certain health outcomes, including infant and under-five

8 The proportion of pregnant women who had an institutional delivery was 32.8% (national average: 78.9%); the

proportion of women who received post natal care form a health professional was 22.3% (national average: 62.4%),

and the proportion of fully immunized children aged 12-23 months was 35.7% (national average: 62.0%).

42

mortality (71).9 This indicates that beyond accessibility of health services, social and

environmental determinants likely play a large role in health outcomes in Nagaland. Our study

seeks to understand whether one critical determinant of health is social capital.

3.3 Methods

3.3.1 Study population and household survey

We used data from a cross-sectional household survey conducted in April and May 2015

by the Department of Health and Family Welfare, Nagaland through the World Bank‘s Nagaland

Health Project. The survey employed a multi-stage cluster sampling approach. First, 101 health

facilities were purposively selected, and then a village from within the catchment area of the

health facility was randomly selected. Next, 15 households within each village were selected to

complete the household questionnaire using a random walk technique. An additional nine

villages were included in the pilot phase of the survey, increasing the total number of

communities in the survey from 101 to 110. The target sample size was 1,650 households, and

the survey achieved a 99% response rate (1642 households). A trained enumerator collected data

from the head of the household in the local dialect using a structured questionnaire that included

questions on social capital, health seeking behavior and health expenditures of household

members, water and sanitation facilities available to each household, and other socio-economic

indicators.

In this study, we considered community social capital to be confined to a village.

However, we recognize that social ties in Nagaland are complex. People have connections with

their family, their clan (group of inter-related families), their Khel (group of multiple clans), their

9 Nagaland had an infant and under-five mortality rate of 29 deaths and 37 deaths per 1000 live births respectively,

as compared to the national average of 41 deaths and 50 deaths per 1000 live births.

43

village and their tribe (47). Most villages in Nagaland have multiple clans and two to three

Khels (47). The data in our study did not sufficiently capture the social capital of a clan, Khel or

tribe. Furthermore, we recognize the limitation of our data to capture community social capital

using data reported from a small number of individuals in each village.

Ethical clearance for the quantitative data collection was gained from the Institutional

Review Board of the Public Health Foundation of India, and the Institutional Review Board of

Johns Hopkins Bloomberg School of Public Health for secondary data analysis.

3.3.2 Validation of social capital scale

The nine social capital items we used in this study were based on items from the

Shortened Adapted Social Capital Assessment Tool (SASCAT) developed by researchers at the

Young Lives Project (134). The SASCAT was first validated in 2006 in Peru and Vietnam. The

authors convened a group of experts and determined that the SASCAT had good face and

content validity. They also conducted single level exploratory factor analysis to examine the

construct validity of the scale. They found that three factors emerged: 1) group

membership/social support, 2) citizenship and 3) cognitive social capital. The first two factors fit

under the umbrella of structural social capital. Lastly, they used cognitive validation to examine

whether respondents interpreted the questions in the same way that the researchers intended.

The authors provided a set of recommendations for adaptations of the SASCAT, which were

made to the version applied in Nagaland. A summary of the SASCAT and modified SASCAT is

presented in Annex 3. Shortly after data collection was completed in Nagaland for this study,

another cognitive validation of the SASCAT was conducted in Bangladesh (135).

As presented in Table 3.3, we hypothesized that the items in the modified SASCAT

measured the structural and cognitive components of social capital. The structural social capital

44

indicators included two items (1-2) that assessed formal and informal networks. These items

were aligned with Bourdieu‘s theory that social capital is a product of an individual‘s social

relationships, and the resources that they can obtain through these relationships. The three

remaining structural social capital items (3-5) assessed citizenship, which aimed to capture the

extent to which people were willing to intervene for the benefit of their community. For the

cognitive social capital items, three items (6-8) assessed interpersonal and generalized trust,

which was based on relationships between people and within the community more broadly. The

last item (9) assessed social cohesion, which facilitated the sharing of resources and working

together to improve access to resources. All items were worded so that an individual (as

opposed to the community) was the referent. All social capital items were coded so that a larger

value indicated higher social capital.

45

Table 3.3: Coding of nine survey items to measure structural and cognitive social capital

Question Coding

Str

uct

ura

l

1. a. In the last 12 months have you been a member of any

of the following types of groups in [NAME OF

VILLAGE]?

Bonding/bridging groups

Religious Group (for example regularly attending

church)

Students' Union

Traders' Association

Professional Association

Sports group

Cultural or Arts Group

Agricultural Group

Linking groups

Village council

Village Union

Women's Village Union

Village Education

Village Health Committee

Political Group

Non Governmental Organization

b. If respondent is a member of a group ask: In the last 12

months, did you receive any support (emotional,

economic, or other kinds) from [NAME OF GROUP]?

0 = No group

membership/did not

receive support from

community groups

1 = Receive support from

bonding/bridging or

linking groups

2 = Receive support from

bonding/bridging and

linking groups

2. In the last 12 months, have you received any support

(emotional, financial, or other kinds) from any of the

following:

Bonding/bridging individuals

Family

Neighbors

Friends who are not neighbors

Religious leaders

Linking individuals

Community leaders

Politicians

Government officials

Charitable organizations/NGOs

0 = Do not receive

support from individuals

1 = Receive support from

bonding/bridging or

linking individuals

2 = Receive support from

bonding/bridging and

linking individuals

3. In the last 12 months, have you joined together with

other community members to address a problem or

common issue?

0 = No

1 = Yes

46

Question Coding

4. In the last 12 months, have you talked with a local

authority or governmental organization about problems

in [NAME OF VILLAGE]?

0 = No

1 = Yes

5. Did you vote in the last state or national election?

0 = No

1 = Yes

Cogn

itiv

e

6. In general, do you trust your neighbors? 0 = None

1 = Some

2 = All

7. In general, do you trust leaders of [NAME OF

VILLAGE]?

0 = None

1 = Some

2 = All

8. In general, do you trust strangers in [NAME OF

VILLAGE]?

0 = None

1 = Some

2 = All

9. Do you feel as though you are really a part of [NAME

OF VILLAGE]?

0 = No

1 = Yes

As a point of comparison for our analysis, we first conducted single level CFA on our

entire sample of 1642 households, which has been a common approach to validate social capital

scales (136-149). We used confirmatory factor analysis rather than exploratory factor analysis

because we had a preconceived theory for the components of social capital we were measuring,

and our goal was to determine whether our data fit our theory. Figure 3.1 presents a path

diagram for single level CFA. In single level CFA, items ( ) tap into a social capital construct

( ), which is represented by the equation:

= (1)

Where is a x 1 vector of observed responses on p items, is a x 1 vector of factor loadings

relating each item to the underlying latent construct , and is a x 1 vector of error

terms for the observed items.

47

Figure 3.1: Path diagram of single level measurement model

When running ML-CFA, the total correlation matrix for the observed items is

decomposed into a pooled within-group and between-group matrix (119,150). Hence, as

illustrated in Figure 3.2, each observed scale item is separated into within and between

components. The within-group component (level 1, representing relationships within

communities) is represented by:

, (2)

where is a vector of observed responses for each item for each individual ( in village ( ),

represents a vector of village ‘s random intercepts (average response for each item), is a

vector of factor loadings relating the latent variable at the individual level to each

observed item , and is the residual for individual ( in village ( ).

Following common path notation, circles represent latent variables, squares represent

observed variables, straight one-headed arrows represent direction of influence between the

latent trait and observed items, and short one-headed arrows represent measurement error

of the latent trait. This diagram models the association ( between a latent construct ( and each observed indicator ( .

48

The between-group component (level 2, representing relationships between communities)

is represented by:

where is the overall expectation (grand mean) for each observed item, is a vector of factor

loadings relating the latent variable at the between level to each village ‘s random

intercept ( , and is the residual for village

Combining the models, each observed item ( ) is represented by:

(4)

Figure 3.2: Path diagram of multilevel measurement model

This path diagram illustrates the relationship ( between an individual level latent construct ( ) and each

observed item ( . The red dots at the within level represent the village random intercepts for each item. At the

between level, random intercepts are represented as ovals. The diagram presents the relationship ( between

the community level latent construct ( ) and each random intercept ( . At the within and between levels, the

error terms are represented as a short-one headed arrow. This is considered measurement error of the latent

construct at each level.

49

To assess the construct validity of our social capital indicators at an individual and

community levels, we applied multilevel confirmatory factor analysis (ML-CFA) based on

Muthen‘s approach (119) using Mplus version 7 (151).

First, we examined the between group variance within the data by calculating the intra-

class correlation (ICC) for every item in the scale. The ICC determined the proportion of

variance that was explained at the community level. If a substantial proportion of the total

variance is explained by variation between communities, a multilevel analysis is justified. Hox

recommended that in general cases, ICC values of 0.05, 0.10 and 0.15 were considered small,

medium and high, respectively (152). In an assessment of work conducted to analyze

neighborhoods and health, Diez-Roux found that the variance in outcomes between

neighborhoods is often well under 10% (153). However, Diez-Roux explained that while an ICC

below 10% may seem low, in fact, the variation between neighborhoods could still characterize

―important and policy-relevant effects of neighborhood characteristics on health‖ (153).

Second, we estimated the within and between group structure of the data using a

polychoric correlation matrix since the items were ordinal (154). Polychoric correlations assume

that the underlying constructs measured by an ordinal item are continuous and normally

distributed. We used the within group correlation matrix to examine the correlation of our items

within communities (119). We used the between group correlation matrix to examine the

correlation of our items between communities. For each matrix, we compared the direction and

magnitude of association among the nine items in the scale.

Next, we ran the ML-CFA on our entire sample. To assess model fit, we examined the

model chi-square test, normed comparative fit index (CFI), Tucker-Lewis index (TLI), root mean

square error of approximation (RMSEA) and standardized root mean square residual (SRMR).

50

We used the following guidelines to assess the fit of our model: a non-significant chi-square test,

CFI and TLI above 0.95, RMSEA below 0.07 and the SRMR is below 0.08 (155). The chi-

square test is sensitive to sample size, so it is often not an adequate measure of model fit for

larger samples (156).

We then ran a series of different models. First, we tested the hypothesis that there was

one social capital factor at both the individual and community levels. Next, we hypothesized that

there were two factors at both the individual and community levels to represent the structural and

cognitive dimensions of social capital. For these models, we also tested cross-level measurement

invariance by constraining factor loadings to be equal across levels and specifying that there was

no residual for items at the community level (157). This model, also called a Hierarchical Latent

Variable Model, imposed strict constraints so that the factor structure was estimated at the within

level only, and then imposed at the between level (158). If cross level measurement invariance

holds, then the factor structure and relationship between each scale items and its associated latent

construct would be the same at each level. In other words, the scale items were tapping into the

same latent construct at each level. If cross level invariance did not hold, then the relationship

between the items and the underlying latent construct would not be the same at each level. This

would indicate that the items were tapping into different constructs at the individual and

community levels. We compared each model using a Satorra-Bentler scaled chi-square

difference test, which better estimates chi-square for non-normal data.10

10

Reference 153 describes calculations for the Satorra-Bentler scaled test chi-square difference test. To run the test,

each model had to be re-run in WLSM rather than WLSMV to obtain the scaling correction factor that could better

calculate chi-square under non normality.

51

For each model, we used Weighted Leased Square Mean and Variance adjusted estimator

(WLSMV), which was specifically designed for ordinal data (151,159).11

The WLSMV

estimator generates probit coefficients. A probit regression coefficient reports a change in z-

score for a one-unit change in the predictor. The models were estimated by fixing the factor

variance to 1.0.

3.3.3 Application of validated scale to the study of social capital and health

After validating our measure of social capital at an individual and community level, we

assessed the relationship between social capital and self-rated health. We ascertained data on

self-rated health from the head of each of the 1642 households interviewed. The respondent was

asked: overall, how would you describe your health these days? The response options were very

good, good, fair and poor. For the purpose of this analysis, the outcome variable was

dichotomized (poor/fair versus good/very good). In 2013, Kawachi et al. found that the most

consistent evidence between social capital and physical health had been reported for a one-item

measure of self-rated health that was dichotomized during analysis (129).

We first ran a model that only examined the relationship between social capital and self-

rated health. Next, we controlled for several individual and community level covariates. At the

individual level, we controlled for gender, age, education (less than primary school, primary

school and more), occupation (agriculture or non-agriculture), marital status (not married,

married but Gauna12

not performed, and married) and household assets. These covariates were

commonly included in other social capital and health studies (83). At the community level, we

controlled for average village education level (proportion of respondents in a village with

11

According to Muthén and Muthén: a WLSMV estimator uses weighted least square parameter estimates using a

diagonal weight matrix with standard errors and mean and variance adjusted chi-square test statistic that use a full

weight matrix. 12

A husband and wife may not live together after formal marriage. Oftentimes, a woman will return to her parent‘s

home after her wedding ceremony. A husband and wife may only cohabitate after the Gauna ceremony is performed.

52

primary school education or more), average household assets, and geographic region of the state

(urban district, rural district, remote and rural district).13

Since we included individual and

aggregated community average measures for education and household assets, the individual level

measures were group mean centered. After examining both models, we examined the robustness

of our results by running a stratified analysis by gender.

We constructed a household asset index due to the difficulties of collecting accurate data

on household income or household expenditures. To construct the asset index, we examined

descriptive statistics of various indicators of asset ownership that distinguished the poor from the

very poor, or the wealthy from the very wealthy. Consistent with other asset indices, our final

index included variables related to durable assets, access to utilities and infrastructure, and

housing characteristics (160,161). Unlike social capital, we constructed an index rather than a

scale. We assumed that all items in a scale had the same underlying cause. However, for items

in an index, their underlying cause may differ. When items in an index are considered all

together, they determine the level of a construct (104).

To run our models, we used multilevel structural equation modeling (ML-SEM), rather than

multilevel regression analysis (ML-regression), the more common approach to study social

capital and self-rated health. Both methods are similar in that they model variation in self-rated

health within and between communities, allow for the analysis of individual and community

level explanatory variables within the same framework, and correct for inaccurate standard errors

that result when cluster sampling is used. ML-SEM is an extension of ML-CFA. It allowed us to

examine the relationship between the latent social capital variables, which we validated at the

individual and community levels, with other observed variables. The advantage of ML-SEM

13

Urban districts included Dimapur, Kohima and Mokokchung. Rural districts included Peren, Phek, Wokha and

Zunheboto. Remote and rural districts included Kiphire, Longleng, Mon and Tuensang.

53

over multilevel regression is that it controls for measurement error of social capital (162).14

A

comparison of the model specification of ML-SEM with multilevel regression is presented in

Annex 2, along with details about our exploratory data analysis in Annex 4 and results of the

multilevel regression analysis in Annex 5. We also estimated our ML-SEM models using MPlus

version 7 with a WLSMV estimator.

3.4 Results

3.4.1 Description of sample

Table 3.4 presents the characteristics of the 1642 individuals who provided data in the

household survey, disaggregated by self-rated health. The majority of respondents were female,

with an average age of 39 years and had primary school level of education or more. The

majority of people in the sample were married, but their Gauna had not been performed. The

sample was split between those who worked in the agriculture sector as compared to the non-

agriculture sector. At the community level, most people in a given village had primary school

level of education or more. The villages were split almost equally among urban, rural and

remote and rural districts of the state. When disaggregated by self-rated health, there was a

statistically significant difference among people who were younger, had more education, who

were not married or married (but did not have a Gauna performed) and had greater household

assets – these individuals reported having better health. Likewise, people living in communities

with more educated people, higher average asset scores and in urban districts were more likely to

report good or very good health.

14

As previously mentioned, factor analysis identifies commonality between items in a scale, which is the variation

caused by the latent construct, as well as uniqueness. Uniqueness is variation in the indicator that is not caused by

the latent variable, and it is considered measurement error of the latent variable. ML-SEM extends upon ML-CFA,

so once we confirm the structure of social capital at an individual and community level, we model relationships with

other observed variables while controlling for measurement error (or the variation in each indicator that is not

caused by social capital) at each level.

54

Table 3.4: Characteristics of study participants by self-rated health

Characteristic

Total

N or Mean

Self-rated health

P-

value Poor/Fair

(%) or

Mean(SD)

(N=724)

Good/Very

Good

(%) or

Mean(SD)

(N=918)

Individual Characteristics

Sex

Male 737 46.13 53.87 0.11

Female 877 42.19 57.81

Age 39.38 (15.47) 42.15(16.30) 37.22(14.45) <0.001

Education

Less than primary school* 449 52.23 46.77

<0.001 Primary school and more 1,151 40.49 59.51

Marital Status

Not married* 317 38.80 61.20

<0.01 Married, Gauna not performed 791 41.72 58.28

Married 491 51.12 48.88

Household occupation

Non-Agriculture*

824 42.96 57.04 0.37

Agriculture 815 45.15 54.85

Household Assets 0.00(2.07) -0.19(2.03) 0.15(2.09) <0.01

Community Characteristics Average education level

** 0.72 (0.16) 0.71(0.17) 0.73(0.16) 0.01

Average household assets 0.00(1.68) -0.30(1.62) 0.24(1.68) <0.001

Geographic region

Urban district* 510 31.37 68.63

<0.001 Rural district 621 39.77 60.23

Remote and rural district 511 62.04 37.96 *Reference category

**Proportion of respondents in a given village who had primary school education or more

We use chi-square tests of independence to examine differences by self-rated health for categorical variables and t-

tests to examine differences by self-rated health for continuous variables; SD is standard deviation

Table 3.5 presents the descriptive statistics for each of the nine social capital items. We

present the grand mean for each item. We also disaggregated each social capital item by self-

rated health. We found that there was a statistically significant difference among people with

more individual support, who joined with others in the community to address common problems,

who voted in the last state or national election, who trusted strangers and who thought that the

55

majority of people in their village got along with each other – these individuals reported having

worse self-rated health.

Table 3.5: Descriptive statistics for social capital items by self-rated health

Indicator Total

(N)

(N=1642)

Self-rated health P-

value Poor/

Fair

(%)

(N=724)

Good/

Very Good

(%)

(N=918)

1. Group support: In the last 12 months, received support

(emotional, financial, or other kinds) from:

Nobody 1,255 42.71 57.29

0.11 Bonding/bridging or linking groups 343 48.10 51.90

Bonding/bridging and linking groups 44 52.27 47.73

2. Individual support: In the last 12 months, received support

(emotional, economic, or other kinds) from:

Nobody 312 29.17 70.83

<0.001 Bonding/bridging or linking individuals 929 48.87 51.13

Bonding/bridging and linking individuals 401 44.64 55.36

3. Join: In the last 12 months, joined together with

other community members to address a

problem or common issue

No 1,233 41.85 58.15 <0.01

Yes 405 50.37 49.63

4. Authorities: In the last 12 months, talked with a local

authority or governmental organization

about problems in village

No 1,436 43.80 56.20 0.74

Yes 202 45.05 54.95

5. Vote: Voted in the last state or national election

No 177 33.33 66.67 <0.01

Yes 1,461 45.24 54.76

6. Trust neighbors: In general, trust all neighbors in village

None 61 37.70 62.30

0.35 Some 331 41.69 58.31

All 1,236 44.90 55.10

56

Indicator Total

(N)

(N=1642)

Self-rated health P-

value Poor/

Fair

(%)

(N=724)

Good/

Very Good

(%)

(N=918)

7. Trust leaders: In general, trust all leaders in village

None 143 35.66 64.34

0.11 Some 421 45.37 54.63

All 1,068 44.48 55.52

8. Trust strangers: In general, trust all strangers in village

None 516 38.95 61.05 0.01

Some 692 47.69 51.31

All 428 44.16 55.84

9. Belong: Feel as though really a part of village

No 40 32.50 67.50 0.14

Yes 1,593 44.19 55.81 We use chi-square tests of independence to examine differences by self-rated health

3.4.2 Scale validation

We first validated our nine-item social capital scale at the individual level (level one, or

within level). Table 3.6 presents the model fits statistics for a one factor and a two-factor

measurement model. The one factor model did not sufficiently fit the data, whereas the two-

factor model was a better fit. We compared the models using a chi-square difference test, and

found that the one factor model was significantly worse and should not be retained (chi-square:

244.34, p<0.001). We present the standardized factor loadings for the two-factor model in Figure

3.3. The factor loadings for vote and trust in strangers were poor (0.37 and 0.33 respectively),

but all factor loadings were statistically significant. The structural and cognitive factors had a

low correlation of 0.27.

57

Table 3.6: Model fit statistics for single level social capital measurement models

Model x2 Df CFI TLI RMSEA

1 factor model 686.90 27 0.72 0.63 0.63

2 factor model 163.77 26 0.94 0.92 0.06

Figure 3.3: Path diagram of single level social capital measurement model

Rather than assuming that the same relationships held at the community level, we

examined the factor structure at an individual and community level. Table 3.7 presents the ICC

for each item, a measure of the proportion of variation that is due to differences between

communities. The ICCs for the items in our scale ranged from a low of 0.06 to a high of 0.19.

According to the standard established by Hox (152), the ICCs were high enough to warrant a

multilevel analysis.

Standardized coefficients; *p < = .05 **p < = .01 ***p < = .001

58

Table 3.7: Interclass correlation coefficient for social capital items

Indicator ICC

1. Group support 0.06

2. Individual support 0.08

3. Join 0.09

4. Authorities 0.10

5. Vote 0.11

6. Trust neighbors 0.08

7. Trust leaders 0.09

8. Trust strangers 0.18

9. Belong 0.19

Next, we examined the item correlations within and between communities to examine

whether they were similar. Table 3.8 presents the polychoric correlations for individuals within

communities below the diagonal, and the polychoric correlation between communities above the

diagonal. For the structural social capital items (1-5), item five (vote) weakly correlated with all

other structural social capital indicators at the individual level, with correlations ranging from

0.05 to 0.33. At the community level, the correlations between item five (vote) remained low

with the other citizenship items (join and authorities).

The cognitive social capital items (6-9) were weakly correlated with the structural social

capital indicators at the individual level, with correlations ranging from -0.04 to 0.36. As

expected, they were more strongly correlated with the other cognitive social capital indicators,

with correlations ranging from 0.10 to 0.76. At the community level, some of the results were

unexpected. The social cohesion item belong (item 9) was weakly correlated with items 6-8 (the

trust variables), and instead more strongly correlated with the structural social capital items.

59

Table 3.8: Polychoric correlation of social capital items within and between communities

Item 1 2 3 4 5 6 7 8 9

1 Group support 1.00 0.99 0.59 0.32 0.68 0.23 0.12 -0.21 0.03

2 Individual support 0.39 1.00 0.63 0.46 0.50 0.24 0.48 -0.20 0.69

3 Join 0.33 0.30 1.00 0.99 0.26 0.31 0.20 0.31 0.62

4 Authorities 0.32 0.26 0.73 1.00 0.13 0.29 0.31 0.36 0.50

5 Vote 0.15 0.05 0.33 0.26 1.00 0.43 0.28 0.32 0.58

6 Trust neighbors 0.16 0.21 0.09 0.15 0.17 1.00 0.54 0.61 -0.01

7 Trust leaders 0.10 0.18 0.05 0.02 0.07 0.70 1.000 0.08 0.17

8 Trust strangers -0.04 0.05 0.13 0.02 0.11 0.31 0.25 1.00 -0.26

9 Belong 0.36 0.24 0.12 0.14 0.08 0.43 0.63 0.10 1.00 Individual level polychoric correlations on lower left triangle and community level polychoric correlations on upper

right triangle

Next, we ran the ML-CFA. Table 3.9 presents the fit statistics for the 1 factor within: 1

factor between (1:1) and 2 factors within: 2 factors between (2:2) multilevel models. Similar to

what we found when we ran the single level analysis, a one-factor solution at the individual and

community level did not fit the data well. Moving from a one-factor model to a two-factor model

at each level improved the model fit indices, with the exception of the SRMR-between. We

found that the 1:1 model was significantly worse than the 2:2 model and should not be retained

(Satorra-Bentler Scaled Chi-Square: 448.60, p<0.001).

We then examined cross level measurement invariance for our 2:2 multilevel model by

comparing a 2:2 model where factor loadings were freely estimated at each level to one where

the factor structure and loadings were the same at each level. We found that there was a

significant difference in the models when we constrained the factor loadings (Satorra-Bentler

Scaled Chi-Square: 47.87, p<0.001). This indicated that restricting the loadings to be equal

across each level worsened the model. Hence, the items in our model may have a different

relationship with the underlying latent variable at the individual level as compared to the

community level. To further justify the need for a multilevel model, we also compared the 2:2

multilevel model to a nested model where all level two factor loadings were constrained to zero.

60

We found that the nested model had a significantly worse fit (Satorra-Bentler Scaled Chi-Square:

120.60, p<0.001), thereby justifying the need for a multilevel model.

Table 3.9: Model fit statistics for multilevel social capital measurement models

Model x2 Df CFI TLI RMSEA

SRMR

Within Between

1 factor within: 1 factor between 657.92 54 0.73 0.63 0.08 0.18 0.21

2 factors within: 2 factors between 176.24 52 0.94 0.92 0.04 0.08 0.21

Table 3.10 presents the standardized factor loadings for the 2:2 multilevel model. As we

expected from our assessment of the correlation matrix and from our single level analysis, item 5

(vote) had a lower factor loading on the structural social capital factor as compared to other

structural social capital indicators. All loadings of the structural social capital items were

approximately the same or stronger at the between level with the exception of ―talk with

authorities.‖ For the cognitive social capital variables, the loadings at the community level were

all lower than at the individual level. Similar to the single level analysis, one cognitive social

capital variable, item 8 (trust strangers), had low loadings at both levels.

In the 2:2 multilevel model, the correlation of the factors at the individual level was 0.24,

whereas it was 0.91 at the community level. The high correlation between the factors at the

community level likely explained why there was a very small difference in the SRMR-between

value for the 1:1 multilevel model (0.21) and the 2:2 multilevel model (0.21), and indicated that

all items may load on a single factor at the community level. We therefore ran an additional

model with two factors at the individual level to represent the structural and cognitive

dimensions, and one overall social capital factor at the community level. The results of this

model are also presented in Table 3.10, and are depicted in the path diagram in Figure 3.4. The

model fit statistics and factor loadings of the 2:1 model were similar to the 2:2 model (Model fit

statistics: x2: 174.54, df: 53, CFI: 0.95, TLI: 0.93, RMSEA: 0.04, SRMR-within: 0.08; SRMR-

61

between: 0.21). We compared the two models, and found that the nested 2:1 model was not

significantly worse than the 2:2 model, and could be retained (Satorra-Bentler Scaled Chi Square

= 0.04, p-value=0.85).

Lastly, in all multilevel models, the factor loading on item 8 (trust in strangers) was low:

at the within level the factor loading was 0.33 (Standard error (SE): 0.03), and at the between

level the factor loading was 0.08 (SE: 0.15). This indicator had high measurement error,

particularly at the between level. We therefore removed this item from our final 2:1 model in our

analysis of social capital and self-rated health.

Table 3.10: Standardized factor loadings for single and multilevel social capital measurement

models

Item

2 factor

single level

model

2 factors within: 2

factors between

multilevel model

2 factors within: 1

factor between

multilevel model

Factor loadings

Group support 0.51***

(0.04)

0.50***

(0.04)

0.74***

(0.16)

0.50***

(0.04)

0.74***

(0.16)

Individual support 0.48***

(0.03)

0.44***

(0.03)

0.91***

(0.09)

0.44***

(0.05)

0.90***

(0.09)

Join with community 0.84***

(0.04)

0.82***

(0.03)

0.79***

(0.13)

0.82***

(0.03)

0.79***

(0.13)

Talk with authorities 0.82***

(0.04)

0.83***

(0.04)

0.64***

(0.17)

0.83***

(0.04)

0.63***

(0.17)

Vote 0.37***

(0.05)

0.35***

(0.06)

0.61***

(0.18)

0.35***

(0.06)

0.61***

(0.18)

Trust neighbors 0.88***

(0.04)

0.86***

(0.04)

0.44*

(0.19)

0.86***

(0.04)

0.40**

(0.15)

Trust leaders 0.78***

(0.04)

0.81***

(0.04)

0.46*

(0.20)

0.81***

(0.04)

0.43**

(0.15)

Trust strangers 0.33***

(0.03)

0.33***

(0.03)

0.10

(0.16)

0.33***

(0.03)

0.08

(0.15)

Belong 0.64***

(0.06)

0.68***

(0.06)

0.64*

(0.28)

0.68***

(0.06)

0.61**

(0.21)

Factor 1 with Factor 2 0.27***

0.24***

0.91 0.24***

(0.04) (0.05) (0.33) (0.05)

N 1642 1642 110 1642 110 Standardized factor loadings (standard error); *p < = .05 **p < = .01 ***p < = .001

62

Figure 3.4: Path diagram of multilevel social capital measurement model

We found that at the community level, a single social capital factor captured variation between communities. At

the individual level, the structural and cognitive factors captured variation of people within villages.

Standardized factor loadings; *p < = .05 **p < = .01 ***p < = .001

63

3.4.3 Analysis of social capital and self-rated health

More than half of the respondents in the sample (56%) reported that they had good or

very good health. Self-rated health had an ICC of 0.20, indicating that a large proportion of

variation in self-rated health could be explained by community characteristics. Model 1 in Table

3.11 presents our analysis of individual and community level social capital and self-rated health

(model fit statistics: x2: 168.50, df: 52, CFI: 0.94, TLI: 0.92, RMSEA: 0.04, SRMR Within: 0.08;

SRMR Between: 0.17). Within communities, individual structural and cognitive social capital

did not have a statistically significantly association with self-rated health (Estimate (Est.): -0.01,

Standard Error (SE): 0.05 and Est.: -0.04, SE: 0.04 respectively). Between communities, we

found that community social capital had a negative and statistically significant association with

self-rated health (Est. -0.43, SE: 0.08).15

We present the path diagram for our final model with covariates in Model 2 of Figure 3.6

(model fit statistics: x2: 395.79, df: 140, CFI: 0.86, TLI: 0.83, RMSEA: 0.03, SRMR-within:

0.09; SRMR-between: 0.29). When controlling for sex, age, education level, marital status,

occupation, household assets, average education level, average household assets and geographic

region, we still found that there was no statistically significant relationship between individual

structural social capital or individual cognitive social capital and self-rated health (Est: -0.05, SE:

0.05 and Est.: -0.01, SE: 0.04 respectively). We found that community social capital still had a

statistically significant and negative association with self-rated health (Est: -0.26, SE: 0.06). We

also found that women (Est: -0.20, SE: 0.09), people who were older (Est: -0.02, SE: 0.00), and

who lived in rural and remote districts (Est: -0.85, SE: 0.16) were significantly associated with

15

The non-standardized coefficient is interpreted as follows: For every one-unit increase in community social

capital, the self-rated health z-score decreased by 0.42, controlling for all other variables in the model. The

standardized coefficient is interpreted as follows: For every one-standard deviation increase in community social

capital, the self-rated health z-score in a given village decreased by 0.83, controlling for all other variables in the

model.

64

worse self-rated health. However, those who were wealthier were significantly associated with

better self-rated health (Est: 0.07, SE: 0.02).

Figure 3.5: Path diagram of relationship between social capital and self-rated health with

covariates

Unstandardized (standardized) coefficients; *p < = .05 **p < = .01 ***p < = .001

65

Table 3.11: Multilevel structural equation model results for relationship between social capital

and self-rated health

Covariate Model 1 Model 2

Est. SE Est. SE

Individual level

Structural social capital -0.01

(-0.01)

0.05 -0.05

(-0.04)

0.05

Cognitive social capital -0.04

(-0.04)

0.04 -0.01

(-0.01)

0.04

Sex (ref: male)

Female -0.20*

(-0.19)

0.09

Age -0.02***

(-0.29)

0.00

Education 0.07

(0.03)

0.09

Marital status (ref: not married)

Married, Gauna not performed 0.10

(0.09)

0.10

Married -0.15

(-0.14)

0.12

Occupation (ref: non-agriculture)

Agriculture -0.02

(-0.02)

0.07

Household assets 0.07***

(0.13)

0.02

Community level

Social capital -0.43***

(-0.87)

0.08 -0.26***

(-0.48)

0.06

Average education -0.19

(-0.10)

0.48

Average household assets 0.05

(0.16)

0.05

Geographic region (ref: urban district)

Remote district -0.24

(-0.44)

0.16

Remote/rural district -0.85***

(-1.54)

0.16

N (individual) 1642 1592

N (community) 110 110 Unstandardized (standardized) probit coefficients; *p < = .05 **p < = .01 ***p < = .001

66

In Annex 5, we compare the results of our structural equation model to the results of a

multilevel regression analysis using a scale score for social capital. Both models came to the

same conclusion – that community social capital was negatively and significantly associated with

self-rated health in a given community. We also examined the robustness of our results when

stratified by sex, as presented in Annex 6. We found that men and women provided statistically

different responses to the nine social capital items, with the exception of trust in leaders.

However, when we ran our analysis on the male and female samples separately, we continued to

find that community social capital was negatively and significantly associated with self-rated

health, whereas individual structural and cognitive social capital did not have a significant

relationship with self-rated health.

3.5 Discussion

This paper aimed to validate a nine-item social capital scale at two levels to match the

conceptualization of social capital as a multilevel construct – in other words, a construct that has

meaning for both individuals and communities. It then applied the validated scale to assess the

classic relationship between individual and community level social capital and self-rated health

in Nagaland, India.

3.5.1 Scale validation

We began our analysis by conducting a single level CFA. This approach is commonly

used to examine the construct validity of latent variables, such as social capital. The results of

our single level factor analysis demonstrated that our nine-item scale was a valid measure of

social capital for individuals. We determined that a two-factor model, one factor for structural

social capital and a second factor for cognitive social capital, adequately fit our data. One

structural social capital indicator, vote, and one cognitive social capital indicator, trust in

67

strangers, had lower loadings as compared to the other indicators. While the majority of social

capital researchers would take the results of the single level CFA and construct a community

level measure using a mean score, we used an alternative approach.

We first determined that the ICCs for each of our indicators were above 0.05, indicating

that a substantial proportion of variation in our data were based on differences between

communities, rather than within communities, warranting a multilevel analysis. We also

examined the correlation structure of our data within as compared to between communities, and

determined that there were different relationships among certain indicators when we examined

them at two different levels. These findings suggested that the indicators might have a different

relationship with the underlying latent construct at each level, and potentially even a different

factor structure.

Our use of ML-CFA revealed that the factor loadings for our social capital indicators were

not the same when we examined their relationship within and between communities. We also

found that our nine indicators had a different factor structure at each level. Within communities,

the model with the best fit had two factors – structural social capital and cognitive social capital

– which was what we found in our single level CFA. However, between communities, the

indicators all loaded onto one social capital construct. These findings suggest that the items were

tapping into different latent constructs at each level. This finding is consistent with current

thinking about social capital, where individual social capital is considered the resources made

available to people through their relationships. This is distinct from community social capital,

which is conceptualized as the social cohesion within a community that facilitates coordinated

action for mutual benefit.

68

The two items that performed poorly in the single level analysis, vote and trust in

strangers, also performed poorly at the individual level when we applied the ML-CFA.

However, the vote indicator had a higher loading at the between level, whereas the trust in

strangers indicator had an even lower loading at the between level. Hence, we determined that

voting was a relevant indicator to leave in the analysis, as it taped into community level social

capital. However, trust in strangers should be removed, as a large portion of its variance was

unique at each level, and not common with the other indicators in our scale.

Overall, our results from the ML-CFA suggest that the common approach of conducting

single level factor analysis to confirm the structure of a social capital scale, and then aggregating

the data to construct community level measures might not be the best approach to develop

community level social capital variables. The factor loadings and factor structure of community

level social capital was different from those at the individual level. Our findings corroborate

those of Dunn et al., who came to the same conclusion based on their multilevel factor analysis

of collective efficiency, a related construct to social capital (158).

3.5.2 Relationship between social capital and self-rated health

When we applied our validated scale to measure social capital and self-rated health at two

different levels, we found surprising results. First, individual structural and cognitive social

capital did not have a statistically significant association with self-rated health. We also found

that higher community social capital was associated with worse self-rated health in a given

community.

The results from our study were different from the findings of Rawal‘s study, which was

the only other study of social capital and self-rated health in India (28). This study, which took

place in an urban location in Chandigarh (as compared to our study, which was in a much more

69

rural region), focused on the relationship between social capital and self-rated health among

people over the age of 60. The authors assessed social capital at the individual level only and

found that it did not have a statistically significant relationship with self-rated physical health,

but it did have a positive and significant association with self-rated mental health.

The findings in our study were contrary to most social capital theory, which suggests that

higher social capital has positive implications for health. Our findings should be interpreted with

the recognition that our analysis was conducted using cross-sectional data. Within a SEM

framework, we hypothesized the direction of the relationship between social capital and self-

rated health. In our analysis, and consistent with other research in this area, we expected that the

direction of influence was from individual and community social capital to health. However,

with cross sectional data, it is possible that there was reverse causation – poor health could be

driving higher levels of community social capital. The direction of this relationship could be

plausible in Nagaland, as people may be especially reliant on one another during times of illness

and particularly when barriers to seeking care are high. In essence, people might rely solely or

primarily on their community members when they are ill, rather than formal care providers. This

community dependence and common experience among people with poor health could foster

social capital.

To interpret our findings, we also considered reasons why there could be a negative

relationship between social capital and health. As discussed earlier, Portes proposed four

potential downsides of social capital (11). In communities with high social capital, certain people

could be excluded from gaining access to community benefits, specific members could be under

more stress to support others, people could be reluctant to seek help for personal health issues for

70

a fear of lack of privacy, or behaviors and beliefs reinforced by the wider community could be

damaging to health.

A recent systematic review conducted by Villalonga-Olives and Kawachi specifically

examined the ―dark side‖ of social capital and its implication for health (44). In addition to

Portes‘ four categories, the authors proposed an additional downside of social capital: social

contagion of unhealthy behavior (when influential people spread damaging health behaviors). In

their review, Villalonga-Olives and Kawachi identified 44 studies that found a negative

relationship between social capital and health. The majority of these studies were cross sectional

(34 studies), conducted in the United States or Japan and written after 2008. The presence of this

large body of research reinforces the idea that social capital might not always positive – it could

have downsides. However, since we use cross-sectional, we cannot reach a conclusion about the

―dark side‖ of social capital in our study.

3.5.3 Study limitations

The results of our study should be considered alongside its limitations. In relation to our

measure of social capital, Stapleton et al. indicated that when modeling group level constructs

using individual level data, it is not possible to statistically evaluate whether the variation at the

community level is spurious, or in fact a true cluster level construct (163). Since there is a both

literature and consensus that social capital operates as both an individual and community level

attribute, we believe that there is theoretical underpinning to explain our results. However, there

is a chance that community level variation in our study was spurious.

Second, due to time and budget constraints, the nine social capital items were not forward

or back translated, and did not undergo more rigorous cognitive validation within Nagaland

before the survey was implemented. As a result, people responding to the questionnaire could

71

have different interpretations of some of the more abstract concepts covered (i.e.: trust). A more

rigorous approach to validating the social capital items using in-depth interviews could help

ensure that study participants interpret the items appropriately. Story et al. and DeSilva et al.

have both published studies with similar social capital questions, and conducted in-depth

interviews in the setting where the scale was to be applied (135,164). Using a similar approach

would be beneficial in Nagaland, or any other country setting where a social capital scale is

applied.

Finally, our study did not calculate a measure of reliability for our social capital scale.

Geldhof, Preacher and Zyphur provide recommendations for calculating reliability estimates of

multilevel scales (165). However, their reliability measures are for continuous data that tap into a

uni-dimensional construct. Geldhof and colleagues highlighted that more research is needed to

examine reliability estimates of binary and ordinal data. While there are recent developments in

examining multilevel reliability using multilevel item response theory (including multilevel item

response functions and multilevel information curves), these applications have examined uni-

dimensional scales only (166). More research is needed to apply these techniques to examine

multi-dimensional scales, such as our social capital scale.

3.5.4 Implications for future research

Our study has several implications for future research. To improve measurement of social

capital using individual level data, our study underscored the importance of clearly defining

social capital and specifying the level at which it is being studied. Since we could not statistically

test whether the community level relationships from our ML-CFA was in fact due to a true

community level construct or spurious clustering, more research is needed to compare

community level measures constructed with individual level data with social capital measures

72

ascertained at the community level (i.e. voter turn out rates, or features that the researcher

directly observes (167)).

Our application of ML-SEM is a new approach to examine the association between social

capital and self-rated health. More research is needed to compare the advantages and

disadvantages of employing multilevel regression analysis and ML-SEM, and to understand

when the results of these analyses converge, and when they diverge.

One of the criticisms of the body of social capital and health research is that there is

limited evidence to support the pathways through which social capital is associated with health

(37,39). In our study, we proposed potential reasons why social capital could have a negative

association with self-rated health, but we did not assess these pathways with our data. ML-SEM

may be an appropriate technique to model these complex pathways in the future. ML-SEM has

the added advantage of taking into account measurement error of social capital to generate more

accurate estimates of the relationship between social capital and health. In addition, it enables

researchers to explore complex relationships between predictors, including mediating variables

and reciprocal effects. Moving forward and with existing research, ML-SEM could be a useful

approach to confront existing challenges with measuring social capital and studying its complex

relationship with health.

3.6 Conclusions

This paper proposed a new way to examine social capital and health. Our application of

ML-CFA helps to bridge existing gaps between how social capital is conceptualized and how it

is measured. If social capital is conceptualized as a multilevel construct, then ML-CFA enables

researchers to validate a scale at multiple levels. When we applied our validated measure of

social capital to examine its association with self-rated health, we determined that there was a

73

negative association between community level social capital and self-rated health in a given

community. This finding suggests that the effects of social capital may not always be positive

and that more research is required to understand the reason behind this negative association.

Moving forward, ML-SEM could be used as a method to deepen our understanding of the

complex pathways that link social capital to health.

74

4. Communitization of health centers in Nagaland, India: have

health facility committees been implemented as planned?

(paper 2)

Abstract

In 2002, the Government of Nagaland, India began implementation of the

Communitisation of Public Institutions and Services Act to improve the quality of government

health services. The Act aimed to leverage social capital in Naga villages by establishing health

facility committees at government health centers that included community representatives. The

committees were responsible for taking ownership and managing health centers, promoting

disease prevention, and encouraging traditional medicines and practitioners. In this study, we

used in-depth interviews with 61 committee members and health workers, and survey data from

97 health facilities, 179 health workers and 1446 households, to assess four implementation

outcomes of Communitization of Health Centers: fidelity, acceptability, appropriateness and

feasibility. We determined that there were some gaps between how the committees were

envisioned and how they operated in practice. While most respondents accepted the concept of

Communitization and thought that it was appropriate for the social context in Nagaland, they felt

that certain design components of the Act, such as relying on community donations to

supplement gaps in government funding and withholding salaries of absent staff, were

challenging to implement. Many respondents felt that the health facility committees have not

reached their full potential. These results suggest that additional investment and specific

adjustments to the program‘s design could help these committees have a strong and positive

impact on health service delivery in the future.

75

4.1 Introduction

Health policymakers increasingly recognize that people are more likely to use health

services if they take part in how they are delivered (168-171). While community participation in

health can take many forms, a common approach is to establish health facility committees.

McCoy et al. defined health facility committees as, ―Any formally constituted structure with

community representation that has an explicit link to a health facility and whose primary purpose

is to enable CPH [community participation in health] with the aims of improving health service

provision and health outcomes‖ (172). With many countries in Africa (173-180), East Asia

(181), South Asia (182-184), and South America (185,186) creating health facility committees,

there is still limited evidence on how implementation – the act of carrying an intent into effect –

has occurred and could be improved in the future (172,179). One way to determine whether

implementation of the health facility committees has been successful is to assess implementation

outcomes (187,188). Implementation outcomes precede service delivery and patient satisfaction

outcomes in that a policy, program or intervention will not have its desired effect unless it is

implemented well (189). An assessment of implementation outcomes is important to determine

whether the success or failure of an initiative is due to its design, or the way in which it was put

into effect. This study examines four implementation outcomes (fidelity, acceptability,

appropriateness and feasibility) of the health facility committees that were established at

government clinics in Nagaland, India through the state‘s 2002 Communitisation of Public

Institutions and Services Act.

76

4.2 Background

Health service delivery in Nagaland is a challenge. The majority of health services in

Nagaland are delivered through the public sector. Across India, chronic underfunding for public

sector health services has contributed to poor infrastructure and unavailability of medicines

(69,190). At the turn of the millennium, the Government of Nagaland recognized many of these

limitations and acknowledged that the quality of health services in the public sector needed

improvement. At this time, utilization of health services was low, with 60% of women in

Nagaland reporting that they needed to visit a health facility, but did not (191).

To confront these service delivery challenges, the state government initiated the

Communitization Act to improve the quality and utilization of existing public services (73). The

Communitization Act has been implemented in various sectors. In the health sector, the Act

established health facility committees that include community representatives into committees

that manage services at Sub Centers (SCs), Primary Health Centers (PHCs) and Community

Health Centers (CHCs) alongside health workers.16

When a village does not have a health

facility, a Village Health Committee is established to spread information about health promotion

and disease prevention.17

The health facility committees have three main functions: to take

ownership and management of health centers, to promote preventive health through education

and action, and to encourage traditional medicines and its practitioners (73).

The committees in Nagaland are part of a larger initiative throughout India, and globally,

to decentralize health service delivery to communities. Notably, the Alma Ata Declaration of

16

Government services are delivered in India through a network of SC, PHCs, CHCs and district hospitals. Chapter

two provides more details about the services provided at these facilities. 17

As mentioned in the background section, within Nagaland, health committees associated with CHCs and PHCs

are called Health Center Management Committees (HCMCs). The committees associated with SCs or those that are

not affiliated with any facility are called Village Health Committees. For this paper, we refer to the committees

associated with SCs, PHCs and CHCs as health facility committees.

77

1978 called on countries to strengthen the role of communities in the delivery of primary health

care (192). Health facility committees serve a variety of purposes to achieve the goals of Alma

Ata – ensuring that health services meet local expectations, drawing on resources from within the

community to supplement those provided by the government, adding a layer of accountability to

providers and linking the community to the health system (172).

Multiple studies have demonstrated that health facility committees have a positive impact

on service delivery. Health facility committees have been linked with greater immunization rates,

vitamin A coverage, institutional births, community heath knowledge, facility revenues, staffing

levels and use of antenatal care and family planning (174,193,194). However, the achievements

of health facility committees differ in each setting. In some instances, health committees were

not functioning well (175,195-197). These findings indicate that merely giving people the

opportunity to take action and improve their health services does not lead to success (198).

A consistent finding when examining the performance of health facility committees is the

importance of context (172,199-201). While health facility committees have had positive

impacts, findings from one country about how and why committees were effective (or not

effective) may not be applicable in another setting. Even results from studies of health facility

committees in different parts of India – which highlight that some committees have limited

awareness of their role and were not routinely meeting – may not be applicable to the health

facility committees Nagaland (202-205). The committees in Nagaland were established prior to

those in the rest of India, and follow a state specific policy. The government in Nagaland

envisioned that the health committees would leverage the social capital in their communities to

increase financial resources, hold health providers accountable and promote health. Contrary to

the other states in India, the majority of people in Nagaland are affiliated with a tribe and live in

78

villages that are governed by community level institutions that have been protected through the

Indian constitution. Since governance in Nagaland is already highly decentralized, devolving

health facility management could be more effective than in other parts of the country.

This study builds upon existing research on health committees in Nagaland that has mixed

results – some assessments concluded that the health committees improved the reach of health

services to rural areas and were routinely functioning (74,78,80), whereas others found that

funding and human resources shortages hampered the initiative (79). Despite these findings,

today there is still a recognized need to improve the way that the health committees are

implemented so they can have a greater impact on the delivery of public sector services (81,82).

Our study confronts this need directly.

We first examine the fidelity of the intervention by comparing how the committees operate

in practice (thirteen years after they were first established), to the original policy documents,

which are available in the Department of Health and Family Welfare‘s Handbook on

Communitisation of Health Centres (73). We then examine three contextual factors – the

acceptability, appropriateness and feasibility of the intervention – to understand why there are (or

are not) gaps in fidelity. This analysis helps determine whether changes are needed to the design

of the Act or the way it has been put into effect. Our study contributes to a growing body of

global evidence that studies how contextual factors of the health system and society more

broadly influence health committee implementation and effectiveness (199).

4.3 Methods

We used multiple methods to assess the implementation outcomes of the health facility

committees in Nagaland. Our data came from two sources: a survey from health facility

managers, health workers and community members; and in-depth interviews with people

79

responsible for establishing and running the health facility committees. Data collection and

analysis took place in a series of three steps. The first step was quantitative data collection,

followed by qualitative data collection, and finally data analysis.

4.3.1 Quantitative data collection

The quantitative data were collected through the Government of Nagaland‘s baseline

health survey during April and May 2015, as part of the World Bank‘s Nagaland Health Project.

A health facility questionnaire and health worker questionnaire were completed at 101 project

health facilities throughout the state to better understand the structural and technical quality of

health services. All CHCs (21) and PHCs (55) targeted by the project were included in the

sample. Of the 90 Sub Centers targeted by the project, two were randomly selected from each of

the eleven districts in Nagaland, with an additional three SCs included during the pilot stage of

the survey. In total, 25 SCs were included in the sample. The Medical Officer or head of the

facility was the main respondent of the health facility questionnaire, drawing on information

from the facility‘s pharmacist, lab technician and records officer. The health facility

questionnaire had a response rate of 96% (97 facilities, including 23 SCs, 53 PHCs and 21

CHCs). From each health facility, two health workers, including the officer in charge and the

next most senior staff member, were selected to complete the health worker questionnaire. The

health worker questionnaire had a response rate of 89% (179 health workers, including 42

Medical Officers, 5 AYUSH doctors, 50 Nurse/midwives, 55 Auxiliary Nurse Midwives, 23

pharmacists and four other types of providers).

A household questionnaire collected data on community members‘ perceptions of

committee functioning, health seeking behaviors and socio-economic status. A multi-stage

cluster sampling approach was used to select households for the survey. From the same 101

80

health facilities selected for the health facility and health worker questionnaires, a village from

within the catchment area was randomly selected, and then 15 households were selected to

complete the questionnaire using a random walk technique. The response rate associated with

the 97 health facilities that provided data for the health facility questionnaire was 99% (1446

households representing 7285 individuals).

Ethical clearance for the quantitative data collection was gained from the Institutional

Review Board of the Public Health Foundation of India, and the Institutional Review Board of

the Johns Hopkins Bloomberg School of Public Health for secondary data analysis.

4.3.2 Qualitative data collection

We selected sites for qualitative data collection to achieve maximum variation in

experiences. Hence, we selected committees that differed in their level of care (SC, PHC, CHC),

geographic region (urban vs. rural) and level of activity of the committee (high vs. low). At each

level of care, we first selected a committee with the highest number of meetings in an urban

district (Dimapur, Kohima and Mokokchung) and a rural district (Kiphire, Longleng, Peren,

Phek, Wokha and Zunheboto) as reported by the head of the health facility in the health facility

survey.18

Within the same district, we selected a committee with the lowest number of meetings

or with an ―unknown‖ number of meetings – thus from each district there was one high and one

low activity committee. On average across our sample, the committees had three meetings

during the year preceding the survey (standard deviation 2.6 meetings). We used this measure

because having regular meetings is a necessary, but not sufficient indicator of committee

functioning. Figure 4.1 illustrates how twelve health facilities were selected for this study.

18

We did not consider committees in Mon and Tuensang due to the poor road conditions in the rainy season during

which data collection took place.

81

Figure 4.1: Process to select health facilities for qualitative data collection

At each facility, we aimed to interview five respondents: the chairman of the committee,

a health workers serving on the committee, a community member serving on the committee, and

two health workers not serving on the committee. The exact composition of people interviewed

varied by facility. Table 4.1 summarizes the interviews conducted at each level of the health

system.

Level of care Region Activity

Sub Center

Urban

4 meetings

2 meetings

Rural 5 meetings

2 meetings

Primary Health Center

Urban 12 meetings

3 meetings

Rural 15 meetings

Unknown

Community Health Center

Urban 4 meetings

Unknown

Rural 3 meetings

Unknown

82

Table 4.1: Summary of in-depth interview participants

Level Role of respondent Number of respondents

Male Female Total

SC Committee chair 3 0 3

Health workers serving on committee 3 2 5

Community member serving on committee 3 0 3

Health worker not serving on committee 1 6 7

Total 10 8 18

PHC Committee chair 4 0 4

Health workers serving on committee 5 2 7

Community member serving on committee 3 1 4

Health worker not serving on committee 0 4 4

Total 12 7 19

CHC Committee chair 2 1 3

Health workers serving on committee 2 2 4

Community member serving on committee 3 0 3

Health worker not serving on committee 2 4 6

Total 9 7 24

Total number of interviews 61

A trained interviewer conducted each in-depth interview in Nagamese or English using a

semi-structured questionnaire. During each session, the interviewer took hand written notes in

English, and the data collection team met after each session to discuss findings and write notes

from the daily session. Based on permission from the respondent, the data collectors recorded

each session. Recordings were not used in nine interviews, in which case, the research team

relied on hand written notes only. Upon completion of data collection, the data collectors

translated and transcribed all recordings. The qualitative data collection received ethical

clearance (exemption) from the Institutional Review Board of the Johns Hopkins Bloomberg

School of Public Health.

83

4.3.3 Data analysis

Conceptual framework

We used the qualitative and quantitative data to inform how the committees had been

implemented according to an adaptation of Proctor et al.‘s implementation outcomes framework.

Proctor and colleagues proposed the use of eight implementation outcomes (acceptability,

adoption, appropriateness, feasibility, fidelity, implementation cost, penetration and

sustainability) to assess how an intervention was carried into effect (189). Peters et al. adapted

the definitions of Proctor‘s eight implementation outcomes to make them more applicable to the

analysis of health policies, programs and interventions (187,188). Proctor suggested that the

implementation outcomes are inter-related, but that the relevance of each outcome and its

relationship with the other outcomes is context specific. For the purpose of our analysis, we

proposed that the implementation outcomes work together in a system as illustrated in Figure

4.2.

Figure 4.2: Implementation outcome framework

84

At the core, we proposed that implementation of a policy, program or intervention is

about moving from adoption to penetration, and from penetration to sustainability. Adoption is

defined as ―the intention, initial decision, or action to try or employ a new intervention‖ (187).

Penetration moves beyond adoption to include the degree to which a policy, program or

intervention is integrated into a service setting and its subsystems (187,189). For our study, this

means that health facility committees were not just established but they also took action to

improve the quality and responsiveness of health services. Sustainability follows penetration and

is defined as the ―extent to which an intervention is maintained or instiutionalised in a given

setting‖ (187). For this study, existing evidence supports that the health facility committees were

adopted, that they penetrated the health system and have since been sustained – a 2009 impact

assessment conducted by the Government of Nagaland found that 100% of SCs, 73% of PHCs

and 100% of CHCs had a committee in place (74).

The starting point of our analysis was therefore the fidelity of the Communitization Act

in the health sector, which can only be assessed once the health committees have penetrated or

been sustained within a system. Fidelity is defined as the ―degree to which an intervention was

implemented as it was prescribed in the original protocol or as it was intended by the program

developers‖ (189).

Surrounding adoption, penetration, sustainability and fidelity in Figure 4.2, we have the

remaining implementation outcomes. We proposed that acceptability, appropriateness and

feasibility are contextual factors that influence whether an initiative moves from adoption to

sustainability in a manner that is consistent with how it was designed. Hence, in this study

where the health committees have been operating for 13 years, we focused on how the contextual

85

factors influenced the consistency between how the facilities were operating in practice and how

they were designed (i.e. their fidelity).

Acceptability is defined as the ―perception among stakeholders (for example, consumers,

providers, managers, policy makers) that an intervention is agreeable‖ (187). For our study, we

first assessed whether the committee members had a sufficient understanding of the purpose of

the health facility committees and the broader Communitization Act. Once we determined how

committee members understood their role, we assessed the extent to which they thought it was

agreeable.

Appropriateness is defined as the ―perceived fit or relevance of the intervention in a

particular setting or for a particular target audience or problem‖ (187). We assessed whether

committee members thought that the health facility committees and Communitization Act were

relevant and fit the wider social and economic context in Nagaland.

Feasibility is the ―extent to which an intervention can be carried out in a particular setting

or organisation‖ (187). For our study, we examined the extent to which the health facility

committees were facilitated or hindered by the broader health system constraints in Nagaland.

Application of conceptual framework to data analysis

The quantitative and qualitative data analyses were done iteratively. We deductively

analyzed our qualitative data using a thematic framework method according to the

implementation outcomes (206,207). We first developed codes for each implementation outcome

and applied them to all transcripts. We then reviewed all data from a given facility and wrote a

memo describing our overall impression of how that health facility was functioning, including

key findings across the implementation outcomes. After developing a similar memo for each

health facility, we compared and contrasted memos across facilities at the same level of the

86

health system (i.e. SC, PHC, CHC) and then across each level. This approach helped us identify

common themes and significant outliers across the 12 facilities. Once all data were coded, we

identified relevant quantitative indicators from the survey data to complement our findings and

triangulate our results. We then calculated descriptive statistics for each indicator (means,

standard deviations). The quantitative data is presented in the text, as well as in Annex 7.

4.4 Results

4.4.1 Fidelity

The Communitization Act specified the responsibilities for the health facility committees

and government, which are summarized in Table 4.2. The health facility survey found that 98%

of the health facilities in our sample had a committee. With regards to whether the committees

had been implemented as planned, a CHC health worker explained, ―What happens at the policy

level, theoretically, is very good. Then it comes to facility level. Some dilution. Then it trickles

down to another level at the district level. Again some dilution is there. Then from the district

level it comes to village level. By the time it goes down to village level, the quality decreases‖

(CHC committee health worker, male). Consistent with this perspective, other respondents

shared inconsistencies in how the committees were implemented as compared to how they were

envisioned in the original Act.

87

Table 4.2: Role of health facility committee and government under Communitization of Health

Centers Act

Health system

function

Health Facility Committee Role Government Role

Health

workforce Check staff attendance and

implement ‗no work no pay‘

rule

Supervise, direct, guide and

support the health center staff

Train and deploy health workers to

facilities

Health financing Manage health facility

finances

Mobilize funds from private

sources

Provide annual grants for

purchasing of medicines, salaries of

health workers and other recurring

expenditures

Health

information Record and maintain all vital

statistics

Collect and aggregate vital statistics

Medicines,

vaccines,

technologies

Procure annual requirement of

medicines from any retail

store according to government

list

Provide list of required medicines

Governance Assess village health needs

and develop/execute annual

plan for the facility

Construct District Coordination

Committee (DCC) in each district

to monitor and provide technical

support to committees

Build capacity of committee

members

Service Delivery Make repairs to the facility

infrastructure

Arrange transport for

emergency cases and referrals

Establish a center for health

promotion or develop

indigenous health care system

Approve and promote development

of indigenous health care system

with involvement from the

committees

Source: (73)

Committee composition

The composition of the committees varied depending on whether they were affiliated

with a SC, PHC or CHC. All committees were required to have at least one female member, the

most senior health workers at the facility and a representative from the church. Committees

associated with SCs should also include the village Anganwadi worker, Dai and Accredited

88

Social Health Activist (ASHA). Alternatively, committees associated with PHCs and CHCs

should include the chairmen of the Village Health Committees from the communities covered

within the catchment area of the facility. As Table 4.3 indicates, the committees were often

missing key personnel. Most notably, 28% of SCs and 16% of PHCs and CHCs did not include

the senior most health worker of the facility, and only one third of the PHCs and CHCs included

Village Health Committee Chairmen from other villages.

Table 4.3: Recommended and actual composition of health facility committees

Facility Type

SC (n=23) PHC/CHC (n=74)

Senior most health worker 72.7% Senior most health worker 83.8%

Female member 91.3% Female member 82.4%

Church representative 63.6% Church representative 62.8%

Member of Village Council 86.4% Chairman of Village Health

Committees from other villages

33.8%

Accredited Social Health Activist 50.0%

Village Anganwadi worker 36.4%

Village Dai

13.6%

*Chairman elected by Village Council *Chairman Elected from among all of the

VHC chairmen at the first meeting

Source: (73)

Health workforce

Checking staff attendance and implementing „no work no pay‟ rule: The household

survey revealed that health worker absenteeism was a challenge at government facilities – among

households that were aware of where the nearest government health facility was located, 49%

reported that the doctor was never or sometimes present during working hours. This figure was

15% for nurses.

In the qualitative interviews, some respondents corroborated this finding and reported

that health worker absenteeism was a problem at their facility, with one committee member

89

calling absenteeism ―the most pressing problem in the facility‖ (CHC committee member, male).

Despite this finding, many interview respondents shared that they did not implement the No

Work No Pay rule. Oftentimes respondents at the same facility were not consistent – some

stated that they implemented the rule, whereas others said that they did not.

Supervising, directing, guiding and supporting health center staff: The health worker

survey revealed that the majority of providers were content with the management of their facility

and the support they received from supervisors and managers: 87% were satisfied or very

satisfied with their supervisor support, whereas this figure was 69% in relation to facility

management.

Interview respondents reported that they mostly gave verbal encouragement to providers.

Certain facilities took a different approach to support heath workers. At one PHC, the committee

explained to the community the constraints that health workers faced. In doing so, the committee

helped confront misconceptions about why the facility was unable to provide medications and

certain services. The providers at this facility found that this support reduced blame on health

workers who were sometimes unable to treat patients due to structural challenges at their facility.

At a different PHC, the committee uniquely worked to make the schedule more manageable for

providers by assigning people to 24-hour shifts for three or four consecutive days, which

decreased absenteeism. Furthermore, the committee assigned tasks to each provider to manage

during their downtime to make their work more engaging.

Training and deploying health workers to facilities: Results from the health facility

survey indicated that all SCs had at least one auxiliary nurse midwife (ANM), and nearly all

PHCs (98%) had an ANM and general nurse midwife. The majority of PHCs (88%) had a

90

medical officer, whereas all CHCs had at least one senior medical officer and medical officer.

In addition, 73% of providers reported that they received their salary on time.

The interview respondents explained that positions at their facility were vacant. At one

PHC, a doctor was posted to the facility, but had left on study leave for three years and had not

been replaced. A committee member described that people did not come to the facility because

there was no doctor. The committee members were discouraged because of staff vacancies, with

one member explaining, ―We the staff as well as the patients suffer‖ (PHC committee health

worker, female).

Committee members also corroborated the survey finding that health workers often did

not receive their salary on time. The committees were responsible for disbursing salaries, but

funding for salaries came from the government. According to a PHC health worker, ―People are

looking for a monthly salary not a quarterly salary, and it‘s been like this since I came for the

past four to five years. So instead of encouraging us, the department is discouraging us‖ (PHC

committee health worker, male).

Health finance

Managing health facility finances: The health facility survey found that 51% of

committees approved the annual facility budget and 66% reviewed facility expenditures.

Furthermore, only 44% of the facilities maintained a book of accounts to record funds received

from the government, community or other private sources. In many instances, interview

respondents explained that one or two committee members controlled facility funds and others

were unaware of how much money was available or how it was spent. A community member

described, ―The Senior Medical Officer and the Chairman deals with all the funds. I have no idea

91

how much funds and how many times the government funds come‖ (PHC committee member,

male).

Mobilizing funds from private sources: The health facility survey indicated that 8% of

facilities collected financial donations from the community, and 10% collected in-kind

contributions from the community. Interview respondents described that in-kind contributions

ranged from providing the land or wood to build the health center, time to make the facility or

community cleaner, furniture for the facility or refreshments for staff.

Providing annual grants for medicines, salaries and other recurring expenditures: The

health facility survey revealed that there were delays in funding from various government

sources. Among the facilities that maintained a book of accounts (n=43), 49% reported that they

did not receive funds from the District Health Department or Department of Health and Family

Welfare in 2014, whereas this figure was 30% from the Central Ministry of Health/National

Health Programs.

Interview respondents confirmed that funds for the initiative were often limited and could

be delayed. Several committees did not meet if they did not have funds to implement activities.

Funding constraints contributed to a sense of discouragement among committee members. One

SC committee member shared, ―We usually face financial problems a lot, like not getting funds

in time. Sometimes getting only half or even less, so all this makes it impossible for us to work

smoothly so that‘s when I feel discouraged‖ (SC committee member, male).

Health information

Recording and maintaining all vital statistics: The health facility survey found that all

but one facility maintained a health management information system report, and the interviewers

visually confirmed the report in 89% of cases. However, in the health worker survey, 20% of

92

providers reported that the health management data had been discussed during a committee

meeting. In the interviews, some respondents described that they examined their data to

understand community health trends. A PHC health worker described, ―We have our monthly

data. So if delivery is less or immunization is less, then like, we try to see why the rates are going

down‖ (PHC committee health worker, male).

Medicines, vaccines and technologies

Procuring annual requirement of medicines from any retail store according to

government list: The health facility survey found low availability of drugs at all facilities.

Among five essential drugs,19

none of the SCs surveyed had all five available on the day of the

survey. Similarly, only 6% of PHCs and 5% of CHC had all five drugs available. The health

worker survey indicated that 81% of providers were dissatisfied or very dissatisfied with the

availability of drugs, supplies and equipment at their facility.

Many interview respondents confirmed that the unavailability of medicines was one of

the largest challenges at their facility, and that funds provided from the government were not

sufficient to procure the medicines needed by the community. For example, a CHC committee

member explained, ―Most of the funds are being used in buying medicines. Since our hospital is

big these medicines bought for one month gets over within one week‖ (CHC committee member,

male).

The committees had a variety of different ways to cope with the limited funds to procure

medicines. One respondent explained that their facility relied on medicines from their District

Hospital, as well as contributions from the Village Council to procure medicines. At a different

19

Paracetamol, Chloroquine Phosphate, Zinc Sulphate, Oral Rehydration Salt and Tetanus Toxoid vaccine

93

facility, a respondent shared that the committee increased out of pocket payments for patients so

that they could purchase medicines.

While some committees established coping mechanisms to deal with the shortages of

drugs, other committees felt more helpless about the situation. A SC Chairman described that the

committee was demotivated when they did not have drugs. Their only option was to write the

name of the drug required, and send it with the patient to purchase at a private pharmacy. At a

different SC, when asked about the lack of medicines at their facility, a health worker shared,

―We become helpless though we want to help them‖ (SC committee health worker, male).

Governance

Assess village health needs and developing/executing annual plan for the facility: The

health facility survey found that 11% of facilities reported mobilizing the community to use the

health facility during the prior six months.

The interview respondents did not describe a formal process to reach out and assess

community health needs. While some committees used their meetings to discuss village health

needs and to develop a strategy for the health facility, some expressed fatigue with developing a

plan because they did not have the resources to make improvements. A SC health worker

described, ―The thing is that only if the government gives anything or funds, then we can take

decisions, but since there‘s nothing to discuss and nothing to decide and divide, we got no point

of decision making‖ (SC committee health worker, male).

Building capacity of committee members: The health facility survey indicated that

government authorities engaged some facilities to provide instruction: 20% of facilities received

health-related instruction and 16% received administrative instruction from higher authorities in

the month prior to the survey during a supervision visit. Most interview respondents shared that

94

they had not received Communitization-specific training. Some participants were able to

describe trainings they attended, like traveling to model facilities to study how committees were

operating and to learn from their experiences. These respondents described the trainings as

effective, but they had not taken place recently.

Monitoring, reviewing and improving health facility committees: The health facility

survey revealed that 41% of facilities received at least one visit from the Chief Medical Officer,

and 47% received one or more visits from the Department of Health and Family Welfare within

the prior 6 months. Many interview respondents shared that they did not receive supervisory

visits from the government. Also, respondents said that when they reached out to the government

about specific issues, such as lacking a blood bank, creating an orthopedic or eye clinic,

obtaining new equipment, or upgrading their facility, they often did not hear back.

Service delivery

Making repairs to the facility infrastructure: The health facility survey revealed that 14%

of facility committees provided new supplies or equipment to the facility, 11% provided new

infrastructure, and 19% made repairs to the facility during the 6 months preceding the survey.

Interview respondents explained that they used their position on the committee to reach

out to higher authorities and lobby for equipment or improved infrastructure. Other committee

members described that they had completed road and fence construction, improved the water

supply for the facility and cleaned the facility compound. In rare instances, respondents said that

they were responsible for constructing components of the facility.

Arranging transport for emergency cases and referrals: Transportation served a major

barrier to accessing health services in Nagaland due to poor road conditions and lack of public

transport. The health facility survey revealed the remote nature of many of the facilities in

95

Nagaland. Facilities were on average six kilometers away from a paved road (standard deviation:

13 kilometers), and 41 kilometers away from a District Hospital (standard deviation: 24

kilometers). When health facility managers described the main activities implemented by their

committee, very few (3%) reported that they arranged transport for health workers to make home

visits.

The interview respondents corroborated that geographic accessibility was a barrier to

care. For example, a SC health worker explained, ―The road condition is so bad that even when

the patients are taken to [urban center in Nagaland], some die on the road before reaching the

hospital, and on rainy days, we cannot travel at all‖ (SC committee health worker, male).

Many patients could not afford to pay for transportation to reach the facility, so instead,

providers reported that they walked to deliver antenatal care to pregnant women and

immunization to children, provide nutritional days and obtain medicines. Respondents at certain

facilities shared that the poor condition of the ambulance was a source of frustration. A CHC

committee member explained, ―At times, there is an emergency case at night and we are unable

to provide ambulance to the patient because the ambulance is also not in good condition. So

these things are frustrating when the blame comes upon the committee‖ (CHC committee

member, male).

Establishing a center for health promotion or developing indigenous health care system:

The health facility survey found that Village Health and Nutrition Days (VHND) were the most

common activity implemented by the health committees. When asked what actions the

committee had taken for the facility in the last six months, 26% of health facility managers

reported that the committee organized a VHND. However, the health facility managers were

also asked about the number of VHNDs the facility held during the past six months. On average,

96

the facilities had 8 VHNDs during this period (standard deviation = 11), and only 10% of

facilities reported that they did not hold a VHND.

Interview respondents described that both communicable and non-communicable

diseases affected people in Nagaland, explaining that poor water quality and changes in climate

contributed to many cases of malaria, diarrhea and typhoid. Respondents believed that poor food

habits led to an increase in diabetes and hypertension, and the availability of opioids led to an

increase in drug addiction. Overwhelmingly, the respondents focused on their ability to provide

curative services during the interviews. In certain instances, the committees did not function

because they did not have medicines. However, some respondents highlighted the potential

impact of holding VHNDs. An SC health worker shared, ―There is a lot of changes in the village

and this is all because of the VHND. The village is cleaner, and the mothers and the children are

healthier because of the regular antenatal care, prenatal check-ups and immunization. There is no

stagnant water around the homes of the villagers‖ (SC health worker, female).

4.4.2 Contextual factors

The prior section revealed that nearly all health facilities had a committee. However,

there were some gaps between how committees were envisioned in the Communitization Act and

how they were operating in practice. In this section, we present contextual factors that may

explain these findings.

Acceptability

The health worker survey captured perceptions about the health committees 15 years after

initial implementation, and found that 72% of providers thought the committee helped improve

service delivery at their facility. However, for frontline workers to accept the committees, they

should have a sufficient understanding of their purpose.

97

Many interview respondents described Communitization as a partnership between the

community and the government where the community took an active role in the delivery of

health services. However, others understood Communitization more narrowly. Some respondents

described the initiative as supervision, where the committee‘s role was to check staff, monitor

their performance and ensure that drugs were available. Certain committee members described

their role even more narrowly, explaining that they were responsible for cooking and preparing

gifts for visitors of the facility.

When the committees and Communitization was first introduced, respondents described

having resistance to the concept. A CHC health worker explained that since people in Nagaland

received many social services, people were accustomed to the government being the sole

provider of these services. As a result, it was a ―major hurdle to overcome the myth that the

government should be the sole provider‖ (CHC committee health worker, male). In addition,

respondents described resistance from village governing authorities. The idea of creating new

committees for health and other sectors to oversee development threatened to dilute the powers

of the Village Development Board, which was the main entity to receive government funds for

village development prior to the Communitization Act. Alternatively, respondents described that

some people were in favor of the initiative for this very reason – there was an opportunity for

new groups to receive funds directly from the government. As the same respondent explained,

there was initially a lot of ―fanfare‖ for the initiative ―because people thought they would get

funds.‖

Despite some initial resistance, respondents described Communitization as a good

concept. They thought that the initiative could ensure greater transparency in how funds were

used, help health workers feel closer to the community, and enable community members to

98

communicate their grievances with higher authorities. As a PHC health worker explained, ―Any

development program, it belongs to the community…definitely Communitzation is much better.

No other scheme will work better‖ (PHC committee health worker, male).

Although some community members recognized that the health committees were not

functioning well, they did not recommend overhauling the initiative. Respondents were

interested in finding ways to make it work better, or as a CHC health worker explained, ―The

wheel is already there, let's not try to reinvent it, but how to get it moving? That is the question‖

(CHC committee health worker, male).

Appropriateness

Two themes emerged to assess the appropriateness of the health facility committees: the

appropriateness given the social and economic context in Nagaland.

Social context: The household survey confirmed that specific components of social

capital in Naga villages were high. Nearly all respondents reported that they felt as though they

belonged in their village (97%). Furthermore, respondents were very trusting of neighbors and

leaders in their villages, but are less trusting of strangers (75% trusted all neighbors and 64%

trusted all leaders, versus 27% who trusted all strangers). Respondents also actively voted in

state and national elections (89%), were involved in community groups (45%), and received

emotional or economic support from family, friends and leaders within their community (80%).

More surprisingly, however, was that a smaller percentage of people reported that they joined

together with community members to address a problem or common issue, or talked with a local

authority or governmental organization about a problem in their village (24% and 12%

respectively).

99

Interview respondents confirmed that since communities in Nagaland were small and

people knew each other intimately, they had high levels of social cohesion. As one PHC health

worker described, Communitization ―is perfectly fitted to this Naga culture…because this Naga

community is very, very much a socialized society. Individuality is not much, community

concern is very important‖ (PHC committee health worker, male). This respondent continued to

describe the context in Nagaland, where people could rely on others within their community for

support, providing an example of how community members often come together to build each

other homes. He explained the same community mentality would make the committees effective:

―If it is a very sincerely taken… every activity and every performance should be focused as for

community.‖

Committee members expressed that they were motivated to serve in the best interest of

their village where they were from and had deep ties. A PHC committee member explained that

the physician serving on their committee was motivated because ―he is also from this same

village, born and brought up here. So from what I have observed is that he also wants to bring

changes during his time and leave a mark before his tenure ends‖ (PHC committee member,

male).

Furthermore, respondents described that community representatives were in the best

position to understand the needs of the village, which was relevant since some health workers

were not from the community they served and were not familiar with the village customs. A

PHC health worker described, ―Public participation is very important for us because I am here

but I am not a local citizen of this area… if it works, then we can know what is the real problem

and what they [the community] want‖ (PHC committee health worker, male).

100

Since the Communitization Act was designed to leverage social capital, the government

intended for the Act to be implemented from the bottom up. A CHC health worker described

that this design was appropriate, explaining, ―To sustain this mission of the Communitization, it

has to come from within, not from outside‖ (CHC committee health worker, male). To ensure

that the initiative came from within, communities could elect to participate in the initiative.

However, in practice, respondents said many committees were established based on a

government directive. The same respondent described, the bottom up approach was never

realized in practice: ―Without much research [Communitization] came down from top to bottom.

While we wanted things to go from bottom to top.‖ This participant believed that implementation

of the Act was not appropriate from the very beginning, stating, ―Started from 2001 or 2000 it

was a dead concept.‖

Respondents described that village politics permeated committee implementation and

often played a role in who served on the committees. A CHC, a health worker shared, ―As per

my observation, selection is done based on relations, clans and not through proper criteria…

according to me the system of selection of the member is not appropriate. Because of this the

talented people are not selected‖ (CHC health worker, male).

Some respondents shared that they used their personal connections to get medicines or

upgrade facility infrastructure. A PHC health worker explained, ―I pursued [upgrading the

facility infrastructure] with the department, and frankly speaking, that time the medical minister

was my friend. So I went personally to his house to file, and like this, built the quarter‖ (PHC

committee health worker, male).

Economic context: The household survey provided information about the economic

context in Nagaland. The highest level of education for 45% of households was completion of

101

primary school. Furthermore, 51% of households relied on agriculture as their main occupation.

The level of education and type of occupation were not evenly distributed across the villages –

some committees had less access to educated members or members with government jobs.

Interview respondents described the economic situation throughout the state. Farmers

who were serving on the committees expressed that they had difficulties traveling to committee

meetings because they had to sacrifice time in the field, and incur travel expenses (committee

members do not receive compensation for their time). Many farmers simply could not cover

these expenses, and did not show up for meetings. A CHC committee member explained, ―It

becomes frustrating when they don‘t turn up for meetings‖ (CHC committee member, male).

Several respondents also expressed that the committees could be more effective if

members were educated. Respondents described that educated members could better understand

how the health facility operated and could develop better ideas to confront village needs. One

respondent described that her committee would run better if they had educated members or

members from the administration. She described: ―All the four members from the community

are farmers and to do anything official we cannot do it on our own‖ (CHC health worker,

female).

Feasibility

Three main health systems challenges emerged that impacted the feasibility of the

Communitization Act: the availability of funds, human resources and facility infrastructure and

equipment.

Funding: The health facility survey revealed that it was not feasible to collect funds for

health from existing community governing bodies. Among the facilities that maintained a book

of accounts (n=43), none of the facilities recorded funding from the Village Council in 2014.

102

In the interviews, respondents at nearly every committee discussed implementation

challenges due to funding constraints. As described in the Fidelity section, funding was often

delayed. The respondents shared two reasons why they did not collect donations from the

community. First, many committees felt that it was the government, and not the community‘s

role, to fund health services. A SC health worker explained, ―We should be the ones helping

people‖ (SC committee health worker, male). Secondly, the majority of people in the villages

could not donate to the facility. The same committee member shared: ―Most of [the community]

is poor, so donation is not possible. All are farmers…The Village Development Board alone

can‘t help us.‖

Human resources: The health worker survey indicated that most providers live 18

minutes from their place of work (standard deviation = 23 minutes)20

and 76% reported that were

satisfied or very satisfied with the amount of their salary.

Committee members explained that it was difficult to motivate or sanction health workers

in light of the larger health system constraints, primarily because housing for providers was

insufficient and salaries were often delayed. This prevented the committees from implementing

the No Work No Pay rule, as one committee health worker explained that rather than cut pay ―we

try to compromise as much as possible and work on mutual understanding‖ (SC committee

health worker, male). At a PHC, a committee member explained that if they cut salaries, they

would ultimately feel too guilty to spend the recovered funds, so when providers were absent

―we just warn them repeatedly‖ (PHC committee health worker, male).

Interview respondents also shared that working on the committee was difficult due to

human resource constraints. Health workers explained that making time to work on the

committee in addition to their routine work was challenging, with one ANM at a SC disclosing,

20

This could be 18 minutes walking, driving or taking public transport.

103

―Sometimes I tend to neglect my duties as an ANM because of too much work as the secretary of

the committee‖ (SC committee health worker, female).

Infrastructure and equipment: As Table 4.5 presents, the health facility survey revealed

that the health committees were operating at facilities that lacked of infrastructure and

equipment. The condition of repair of the facilities and cleanliness was also low. Infrastructure

availability, condition of repair and cleanliness was poorer at SC as compared to the other

facilities.

Table 4.5: Availability and condition of infrastructure and equipment at government health

facilities

Composite Scores Total

(N=97) SC

(N=23)

PHC

(N=53)

CHC

(N=21)

p-value

Infrastructure Availability1

(Out of 100))

Mean 56.4 23.32 63.12 75.8 <0.001

S.D. 26.4 15.2 20.9 13.6

General Equipment

Availability2

(Out of 100)

Mean 55.8 40.8 56.4 70.9 <0.001

SD 16.8 17.2 12.8 10.8

Condition of Repair3 Mean 59.6 62.8 59.6 56.1 0.64

(Out of 100) S.D. 23.5 21.7 25.4 20.6

Cleanliness4 Mean 44.7 30.4 49.4 48.4 0.03

(Out of 100) S.D. 29.7 25.0 29.8 30.7 1Composite score for infrastructure availability included 11 items: waiting rooms for patients, separate rooms for patient examination, labor room,

ward/separate room with beds, separate room for drug storage, laboratory, vaccine storage and cold chain, refrigerator, staff quarters for any staff, water supply and a waste disposal pit. 2 Composite score for general equipment included 18 items: children‘s weighing scale, height measuring scale, measuring tape, adult weighing

scale, blood pressure instrument, thermometer, stethoscope, fetoscope, ooscope, vision chart, IV Stand, sterilization equipment, x-ray machine, ultrasound machine, binocular microscope, sharps container ("safety box"), environmental disinfectant (e.g., chlorine, alcohol), alcohol-based

hand rub. 3Composite scores for condition of repair included nine items: windows and doors, interior walls, interior wall paint, floor, condition of outside wall, condition of outside wall paint, ceiling, furniture in waiting room and examination room. If no repairs were required, each item was scored

as ―2‖, if some repairs were required as ―1‖ and if complete renovation was needed as ―0‖. 4Composite score for cleanliness assess whether specific parts of the facility, namely the waiting room, examination room, labor room, in-patient wards, toilets and area surrounding the building, were clean or not.

104

The interview respondents shared that while the committees were responsible for

updating facility infrastructure, many were unable to do so because the condition of the facility

was irreparable or it was not available in the first place. In worst-case scenarios, respondents

described that the entire building needed construction, lacked the ability to house patients, and

did not have consistent electricity or water supply. Committee members stressed the importance

of working with the government to improve infrastructure – it was not feasible for the committee

to upkeep infrastructure and equipment if it was not available.

In several instances, committee members described the building as irreparable. In these

situations, health workers often had to turn patients away. At one PHC, a committee member

explained how health workers become discouraged when they could not treat all patients. She

provided an example, sharing, ―When two women are to be delivered at the same time, we pick

one after the other since we have only one ambulance and when we go to pick the other one, they

get angry with us and that‘s how we sometimes get disappointed‖ (PHC committee member,

female).

4.5 Discussion

Overall, respondents expressed that health facility committees had not yet reached their

full potential. While nearly all facilities in our study had a committee thirteen years after the

Communitization Act passed, there was low fidelity between how the committees were described

in the original Act and how they functioned in practice. Most notably, some committees were

missing key representatives from the community and not implementing the No Work No Pay

principle, even though absenteeism was a challenge at many facilities. Furthermore, many

committees did not review facility expenditures or collect donations from the community.

Oftentimes, facility infrastructure was in poor condition. A key question from these findings is

105

whether the low fidelity is due to the design of the Act, or the way in which it was put into

effect?

4.5.1 Design of the Act

We identified several strengths in how the health facility committees were designed.

Respondents overwhelmingly accepted the concept of Communitization, which was the premise

for the health facility committees. Most respondents thought that Communitization was a good

idea for the context in Nagaland. Even at facilities where the committees were not working well,

people were still in favor of the initiative and thought that adjustments were worthwhile to

strengthen the committees.

The Act has a strong focus on disease prevention and health promotion. This was a

strong design feature because several respondents described that the most pressing health

concerns in their village were related to poor hygiene, sanitation and diet, or due to high alcohol

and drug use. These findings corroborate with statewide trends in the disease profile – Nagaland

is moving along the epidemiologic transition, but the prevalence of communicable diseases still

remains high, and alcohol and tobacco use in the state is higher than national averages (208).

Health promotion and disease prevention initiatives are cost effective strategies to reduce

diseases associated with these risk factors (209,210). More than any other initiative, respondents

described the positive impact that health promotion initiatives, like VHNDs, and awareness

campaigns, had on their community. Hence, the health committees have potential to make a real

impact on the state‘s most pressing health conditions through health promotion.

The health committees were designed to leverage social capital in Naga communities.

The idea was that given the opportunity through the health committee, people with common

interests in their community would work together collectively to make improvements to their

106

health center. We found that social capital was working as expected in some communities, but

not all. For certain facilities, the committee connected health works to their patients and

mobilized people to take action for the health center. At other facilities, the committees were

interested in serving their community by improving health service delivery, but became

demotivated by the lack of infrastructure, funding and human resources available to them. In

other instances, social capital could be exclusive – specific groups within the community looked

out for their own interests and controlled who served in positions of leadership. In these

communities, we observed a darker side of social capital where people were motivated to serve

the interests of a specific group. Overall, social capital did not always work as envisioned, or at

times, it was not enough to overcome the systems level constraints to deliver health services.

While there have not been other studies that examine social capital and health facility

committees, other researchers have found that wider social and political dynamics that exist in a

community will also play out on the committee, which was what we observed in Nagaland

(183,185,211-214).

Our analysis identified some weaknesses in how the committees were designed. While

the committees were responsible for covering the gap between the funds provided by the

government, respondents expressed that raising additional revenue from within the community

was not feasible given the larger statewide economic constraints. The literature outside of

Nagaland has an array of recommendations in regards to ways to increase donations for the

health facility. In Nepal, Bishai found that when health committees were more representative of

their community and included people from lower castes, the facility received more donations

(182). In Nigeria, Abimbola‘s study of health facility committees found the opposite (215). The

authors concluded that including more high-income and high status committee members was

107

critical when the committees lacked funding and government support because these individuals

could invest in the intervention with their personal funds and influence others within their

network to support the facility. In Nagaland, we sometimes saw this trend, where people with

connections could use their time and influence to obtain more resources for the facility.

One of the greatest responsibilities of the health facility committees was to deduct staff

salaries through the No Work No Pay principle. This is a unique mandate in Nagaland as

compared to other states in India. Globally, there is no positive or negative evidence to support

the impact of salary deductions on absent staff. In Nagaland, providers at the same facility often

gave conflicting accounts about whether the principle had been implemented. This may have

been because some respondents considered threatening to take away a salary was sufficient to be

considered actual implementation of the policy. Alternatively, there was social desirability bias

where respondents wanted the committees to appear more active than they were in reality.

Overall, when providers explained why they did not use the principle, it was because they were

considerate of the challenges that providers already faced in their work, and did not want to add

additional constraints. These accounts suggest that the No Work No Pay principle may not be

the most effective way to reduce staff absenteeism and motivate staff. Elsewhere in India, Peters

et al. found that job content and work environment were more important characteristics of the

―ideal job,‖ above having a good income (216). Likewise, Purohit and Bandyopadhyay found

that above an adequate salary, the top ranked motivating factors for government doctors were job

security, interesting work and respect and recognition (217).

4.5.2 Implementation of the Act

A separate issue from the design of the health facility committees is the way in which

they were put into effect. We identified certain strengths in how the committees were

108

implemented. Most notably was the initial sensitization of community members. While there

was some variation in the understanding of Communitization, the initial efforts to educate

community members about the Act and to establish committees across the state were largely

successful, as at the time of this study (thirteen years after initial implementation), committees

were aware of their mandate. This finding confirms what another analysis of Communitization

in Nagaland found (74).

A second implementation strength was that the health facility committees reported that

they were routinely collecting service delivery data to report to the government. However, only

some respondents indicated that the committees used the information for their own planning

purposes. Most respondents described a more unofficial process to understand community needs

and develop a plan for the health facility. There is an opportunity to improve the health

committee‘s responsiveness by training members on how to use this data for facility planning.

In addition to these two strengths, we found certain weaknesses in implementation.

Despite the strong emphasis on health promotion and disease prevention in their design, the

respondents focused strongly on their ability to improve curative services. Furthermore, none of

the committees described initiatives they took to link health services to traditional providers.

This finding is similar to that of a 2014 study conducted on the health committees in Nagaland

(79). In our quantitative data, we also found a discrepancy in how many VHNDs the committee

reported as compared to what the facility reported. This indicates that the health facilities might

be holding VHNDs without involvement of the committee. While other studies found that rifts

between health workers and community members serving on the committees could lead to

difficulties working together (218,219), this was not a major theme in our analysis.

109

Inadequate funding was a key constraint to implementation that emerged in nearly every

interview, and which was verified by our quantitative data. However, less than half of the

facilities maintained a book of accounts to document the funds they had received during the year

preceding the survey. Numerous studies have found that funding, either from the government or

a non-governmental organization, was a critical input to ensure that health committees

functioned and had an impact (193,201,219,220). A common finding across many settings,

including Nagaland and other states in India, was that when funding was not available, the

committees did not feel that there was a point to meet or plan activities for the facility

(79,201,219). We also found this trend in our study.

A main role of the health facility committees was to motivate and support health workers,

whereas the government was responsible for training and deploying health workers. While most

committee members described that they primarily gave verbal encouragement to providers, more

could be done to motivate health workers. Some committees were able to dispel myths within the

community about the quality of services being delivered, and make the community more aware

of the constraints that the providers faced. These committees were able to defend providers who

were sometimes blamed for constraints that were out of their hands. In Nagaland, as has been

found elsewhere, the committee‘s ability to support providers was a powerful way to motivate

them (215).

Finally, the government was responsible for building capacity of community members

and supervising the committees, yet at many facilities, the committees were detached from the

government. In these circumstances, respondents underscored the importance of working in a

partnership with the government to make the committees more effective. The Communitization

Act stressed for the committees to work with the government in a partnership. Other studies

110

have also documented that the strength of this partnership strongly influences the functioning

and performance of the committees (201,215).

4.5.3 Study limitations

The limitations of this study are worth noting. First, the facilities were not randomly

sampled and thus survey results may not be generalizable to other health facilities in Nagaland.

Second, our quantitative and qualitative data sources sometimes had discrepancies (the

quantitative data would show that the health committees were less active than some of the

interview respondents suggested), as did some interview respondents at the same facility. There

may have been social desirability bias, where certain interview respondents felt pressured to

make the committees seem more active than they were in reality. Third, the qualitative data

incorporated perspectives from the frontline workers who were responsible for implementing the

Communitizaton Act, namely, community members serving on the committee and health

workers. Perspectives from community members served by the health facility and policymakers

at the state level could provide additional insight into how the health facility committees were

implemented, but were not included in this study.

4.6 Conclusion

After more than a decade of implementation, the health facility committees established

through the Communitization Act in Nagaland have been adopted, but some were not

functioning as they were envisioned in the original policy. Before the health facility committees

can have an impact on the quality and use of government health services, certain elements of the

Act may need to be adapted. In addition, the committees need to work in partnership with the

government to ensure that they can fulfill their role within the context of Nagaland‘s health

system.

111

5. Does social capital influence the functioning of health facility

committees? A quantitative analysis in Nagaland, India

(paper 3)

Abstract

Social capital has been proposed as a key ingredient to achieve a wide set of development

objectives: it has been described as the ‗missing link‘ to generate economic growth, and is

recognized by the World Health Organization as a factor that shapes population health outcomes.

In 2002, the Government of Nagaland initiated a cross-sectorial policy that leveraged social

capital to improve the delivery of government services. In the health sector, the policy

established committees that incorporated community members into the management of

government clinics, alongside health workers. The idea behind Communitization was that given

the opportunity, communities with high social capital would take action to improve their health

services. Our analysis used cross sectional data collected in 2015 to examine the association

between social capital and health committee functioning in Nagaland, India. We found that there

was no statistically significant association between social capital and health committee

functioning. When we examined other determinants of health committee functioning, we found

that committees that included more women, and received government funding and supervision

visits were associated with better functioning. The results of our analysis suggest that social

capital may not be enough to make health committees function: committees should be

empowered with financial resources and have meaningful engagement with the government to

take action to improve their health services.

112

5.1 Introduction

Health facility committees have been established in many countries to improve the quality

of health service delivery. Health committees serve a variety of purposes. They provide a forum

for community members to discuss service delivery challenges, connect the community to formal

health system actors, lobby government officials, provide back-up support through co-financing

and co-management, and oversee the day to day operations of the facility (215). A deep body of

literature examines the factors that make these committees successful (171,172,199). However,

within the existing body of literature, there has not been a focus on the role of social capital in

making health committees function more effectively. The primary objective of this study is to

determine whether communities in Nagaland, India with higher social capital are associated with

better functioning health facility committees. The secondary objective is to determine whether

other features of the community, health committee and facility are associated with better

functioning committees.

Multiple studies within India have examined the functioning and effectiveness of Village

Health Sanitation and Nutrition Committees (VHSNCs) (219,221-225) and Rogi Kalyan Samities

(Patient Welfare Committees) (202-205), which are health committees that were implemented

nationwide under the National Health Mission. The VHSNCs are village committees responsible

for promoting health and nutrition in their community, whereas the Rogi Kalyan Samities are

affiliated with health clinics and play a role in managing primary health services. Distinct from

elsewhere in India, the health committees in Nagaland were established under a statewide policy

before the National Health Mission was in place. This is the first paper in Nagaland and India to

quantitatively examine the factors associated with health committee functioning, and to

investigate the role of social capital in committee functioning.

113

5.2 Background

5.2.1 Community participation and health committees

Community participation in health – defined as ―the process by which individuals and

families assume responsibility for their own health and welfare and for those of the community,

and develop the capacity to contribute to their and the community‘s development‖ – has a long

history within and outside the heath sector (226). The importance of community participation

stems from a reaction to traditional economic development approaches, which relied heavily on

top down and external expertise to design, implement and evaluate programs that targeted the

most disadvantaged populations. As global initiatives shifted to adopt a more holistic

understanding of the complex political, social and cultural aspects of poverty, so did the

approaches to alleviate it. Starting in the 1960 and 1970s, there was greater emphasis on

involving communities in the design and implementation of programs from which they would

benefit (212,227).

The global commitment to apply a people centered approach to improve population

health outcomes followed a similar trajectory – after years of top down programs, the importance

of engaging beneficiaries of health interventions became more apparent to achieve success. The

importance of communities in designing and delivering programs that aimed to improve their

health was solidified in the Alma Ata declaration, which explicitly stated, ―People have the right

and duty to participate individually and collectively in the planning and implementation of their

health care‖ (192). As Arnstein noted in her landmark paper on community participation, ―The

idea of citizen participation is a little like eating spinach: no one is against it in principle because

it is good for you‖ (228).

114

The widespread support that Arnstein alluded to in her 1969 article has continued until

today, as evidence by continued support of Declaration of Alma Ata in 2018 at its 40th

anniversary (229). The rationale for community participation in health is, first and foremost,

based on the belief that communities have the right to be involved in health initiatives that target

them (168). Second, is that the expertise needed to make changes to population health does not

lie solely in the hands of medical or public health professionals. Rather, communities have

unique knowledge of their needs, values, preferences and lifestyle, which are necessary to make

health services more responsive to local expectations (230,231). Community participation can

also increase accountability of health providers and use of government funds by making

providers directly answerable to the people they serve (198,227,230,231). Furthermore,

community participation can generate more awareness of health problems, which could change

behavior, inform the appropriate use of health services and promote disease prevention

(198,231,232).

Community participation can be achieved through many different mechanisms.

Countries have established health boards, cooperatives, committees, coalitions, forums and focus

groups to elicit greater participation from health program beneficiaries (198). Evidence from

these experiences demonstrates that there have been many challenges to achieve community

participation in health: merely giving community members an opportunity to participate in their

health programs does not lead to success. Oftentimes, there is resistance among those in

leadership positions to redistribute power to communities, or there may not be a genuine

commitment of health workers to include community members in new roles (212,228).

Furthermore, the community may not have the knowledge base to assume a new leadership role,

115

or the ability to organize an accountable and representative group of citizens in face of social

distrust (226,228,232,233).

The establishment of health facility committees is a common approach to incorporate

community members into the management and delivery of health services. Three systematic

reviews have examined the factors that facilitate and hinder health committee performance

(171,172,199). In all three reviews, the authors discussed the importance of the community that

the committee serves. For example, McCoy et al. found that the political, economic and cultural

features of the community influenced the performance of health facility committees (172).

Molyneux et al. described that community structures and socio-cultural norms could influence

the functioning and impact of community accountability mechanisms more broadly (171).

Lastly, George et al. described that health committee effectiveness could be a product of

community contextual factors, such as awareness, trust, resources and social inequalities (199).

While these three studies all highlighted the role of the community in health committee

performance, they did not explicitly describe the potential role of social capital.

5.2.2 The role of social capital in health committee functioning

Like community participation in health, the concept of social capital has also gained

widespread attention among public health practitioners. Social capital has many definitions (17).

However, the ‗communitarian‘ school of thought made popular by Robert Putnam is particularly

relevant to community participation and health committees (8,83). Putnam defined social capital

as ―features of social organizations such as networks, norms and social trust that facilitate

coordination and cooperation for mutual benefit‖ (9). In essence, Putnam proposed that

relationships among people in a community were a resource that enabled them to work together

towards collective goals.

116

Despite immense interest in both the relationship between social capital and health, and

the role of health committees in improving health and health service delivery, researchers have

not yet widely applied social capital theory to study health committees. Social capital is relevant

to this area of research because it captures a community‘s ability to work together. A

community with a high level of trust, common norms and dense networks may be more equipped

to overcomes some of the common pitfalls of community participation. Specifically,

communities with high social capital may be better able to organize a committee that works in

the broader interests of the community, mobilize existing knowledge and resources to benefit

service delivery, and increase the accountability of healthcare providers and government

resources. While social capital could be a precursor to health committee functioning, it is also

possible that the health committees build (or diminish) social capital within a community.

The existing literature on community participation in health has already tapped into some

of the concepts that were popularized by Putnam and social capital theory. Brancht discussed

the factors that inhibit or facilitate community participation, noting that the degree of

homogeneity or heterogeneity of the community, the degree of internal control and existing

involvement in community life were key factors (226). Hurlbert discussed the role of social trust

in making people more willing to cooperate with one another and work towards a common

solution, stating, ―A clear link exists between trust and the levels of citizen involvement‖ (233).

In her review of community participation in health programs, one of Rifkin‘s four main findings

was that ―people have individual and collective resources (time, money, materials and energy) to

contribute to activities for health improvements in their community‖ (168).

Among the studies that examined health committee performance, researchers again

discussed the importance of concepts closely related to social capital. In South Africa, Gilson et

117

al. concluded that trust among committee members, service providers and policymakers was a

key consideration to improve health committee performance (218). In Nepal, Gurung et al. found

that a ―sense of volunteerism and team spirit‖ among committee members was critical to health

facility committees (234). The focus on community, the resources and the trust embedded within

them suggests that the role social capital in health committees should be explored in greater

depth.

5.2.3 Social capital and health committees in Nagaland

Health in India is decentralized to the state level, so each state has the power to adopt

specific elements of the Federal government‘s programs. The health committees in Nagaland

therefore differ from those implemented elsewhere in India (the VHSNCs and Rogi Kalyan

Samities) because they were implemented through the state‘s Communitisation of Public

Institutions and Services Act in 2002. The Communitization Act is a cross-sectoral policy that

incorporates community members into the management of government services. In the health

sector, community members develop village health committees to promote health and spread

awareness about disease prevention. The policy also established health facility committees,

which include both community members and health workers, at Sub Centers (SCs), Primary

Health Centers (PHCs) and Community Health Centers (CHCs). These facilities, along with

District Hospitals and a network of community health workers, deliver government health

services to communities throughout India.

The Communitization Act aims to leverage social capital in Naga communities. In other

words, policymakers in Nagaland envisioned that social capital was a pre-cursor to make the

health committees function effectively and have an impact on health services. They

hypothesized that specific features of the state, such as the strong tribal and Naga identity,

118

traditional governance structures and remote nature of villages, resulted in high social capital.

Hence, the premise for the Communitization Act was to incorporate community members into

the management of government services because they would have a vested interest in making

services work for their community, and the ability to work together efficiently to achieve results

of mutual interest.

In 2010, R.S. Pandey, the Chief Secretary for the Government of Nagaland from 2000-

2004 when the Communitization Act was passed, wrote a book that provides an in-depth

description of the role of social capital in the policy. Pandey described ten mantras of social

capital, which are summarized in Table 5.1. He suggested that understanding these mantras was

necessary to leverage social capital in Nagaland to produce value, and to achieve the underlying

goal of the Communitization Act. This goal was to ―leverage the funds, the expertise and the

regulatory powers of government with the social capital of the user community and combine the

best of the public and the private sector systems‖ (72).

119

Table 5.1: Pandey's ten mantras of social capital

Mantra Description Relevance to Communitization of

Health Services

1. Commonality of

interests is the raison

d‟etre of social capital

Common interest binds the community

together and leads to activation of social

capital for promotion of common interest

―User community‖ of

government health services has

commonality of interests

2. Commonality of

interest and

cooperation in a group

extends to a ―radius of

trust‖

―Radius of trust‖ is a circle of people

among whom cooperative norms operate

Trust cover a whole village or only

members of a particular clan

Groups may also generate ―radius of

distrust‖ of outsiders

Having only one committee in a

village with many factions may

not produce desired results.

Committees may be more

effective in certain areas.

3. Heterogeneous groups

will have less social

capital than

homogeneous groups

Cooperation may be difficult among the

rich and the poor, the young and the old,

different castes

Villages in Nagaland are marked

by homogeneity, whereas urban

areas are more heterogeneous

4. How social capital is

used is dependent on

value system and skills

of the group

Effectiveness of social capital depends

on a group‘s value system and skills of

its members and leaders

Continual capacity building of

committee members is key to

success

5. Social capital is useful

in a variety of ways Social capital can increase productivity

by reducing cost of doing business,

facilitating coordination and cooperation,

promoting information flows and

reciprocity, and promoting contribution

by members in tangible and non-tangible

ways

Social capital can increase

resources available to support

public sector delivery

6. Social capital also has

a downside Factors such as religion, ethnicity, socio-

economic status (which are the basis of

social capital) can also cause social

divides and promote narrow pursuit of

interests

Committees have rotating

membership to avoid rent-

seeking behavior among

members

7. Quantity and quality

of social capital

increases with use

Collective action that benefits society

activates, renews or creates new social

networks, thereby enhancing social

capital

Communitization enables regular

and frequent meetings among

members, thereby renewing

social bonds

8. Need or adversity

triggers utilization of

social capital

Where need is more, collective action or

in other words, activation of social

capital is more likely

The poor state of public delivery

systems may trigger reform

through communitization

9. Investment and use of

social capital is

facilitated through an

agent

Without the agent, social capital may lie

dormant

Both the stock of the social

capital and a strong agent (health

committee) are required to

produce positive results

10. Investment in social

capital‘s is not

sufficient for

achieving desired

results

Social capital is not a substitute for

financial capital, political capital or

intellectual capital

Health committees cannot solve

all challenges alone – health

system and government support

is required

Source: (72)

120

While health committees have been established in countries throughout the world, the

health committees in Nagaland are unique in their rationale to leverage social capital. Outside of

Nagaland, there has been minimal discussion of the role of social capital in making health

committees function. This study will fill the current research gap.

5.3 Methods

5.3.1 Conceptual framework

In this study, we examined health committees that are associated with SCs, PHCs and

CHCs. McCoy et al. developed a conceptual framework of the performance determinants of

committees affiliated with a health facility (172). As presented in Figure 5.1, their framework

was divided into features of the health facility, health committee and community. They also

included process factors (features related to how community participation is achieved) and

contextual factors (those related to the health system and society more broadly). We adapted this

framework to our study of health facility committees in Nagaland.

121

Figure 5.1: Conceptual framework for determinants of health facility committee performance

Source: Adapted from (172)

As previously discussed, other studies found that features of the community influenced

health committee performance. Political, economic and social divisions that existed in

communities at large also existed on the committees. For example, other researchers have found

that political elites appointed their own supporters to serve in positions of power, and that large

segments of the population faced economic barriers to participating on the committees (235,236).

In their analysis of VHSNCs in Uttar Pradesh, India, Kumar et al. found that local residents and

committee members knew the caste and social status of all other health workers and committee

members. Some higher caste members did not participate in meetings when lower caste members

were also involved (219). Scott et al. had similar findings in their analysis of VHSNCs in rural

north India, as people from different castes did not sit together during meetings (201). In our

Process Factors

Community mobilization

Facilitation and support

Features of Health Facility

•Resources (human, financial)

•Staff skills, attitudes and perceptions towards community participation

Features of Committee

•Composition

•Capability of members (management skills, leadership, confidence)

•Clarity on roles, function and authority

Features of Community

•Socio-economic environmment

•Socio-political environment

•Social capital

Social Health system

Contextual Factors

122

study, in addition to the social capital of the community, we examined the socio-economic

environment of the community.

Other studies found that features of the committee, including its composition and

representativeness, influenced its performance. Committees that represented the communities

they served by including women and members from minority groups ensured that the needs of all

people were represented (181,235,237). More representative committees may also help secure

resources for the health facility. For example, Bishai et al. found that in Nepal, health

committees that included more people from lower level castes were able to raise more funds for

the facility from within the community (182). Other studies have also found that the skills and

capabilities of committee members were important components (180,197,238). In Bangladesh,

Mahmud found that beyond management and leadership skills, committee members needed

confidence because they made suggestions and decisions alongside health workers who were

well-educated and respected members of the community (236). Along the same lines, multiple

researchers identified that committee members should have clear responsibilities so that conflicts

did not arise regarding the roles of the health workers as compared to community members

(197,211,237,239). In this study, we focused on the composition of the committee as a

determinant of its performance.

McCoy et al. also found that health facilities often functioned ―synergistically‖ with the

health facility. Multiple studies concluded that well-staffed, funded and equipped health facilities

that had providers who were supportive of community participation had better committee

performance (180,201,218,219). Conflicts sometimes arose between health workers and

community members serving on a committee or among heath workers that had different levels of

authority (218,219). Some researchers found that younger providers with less training who were

123

already responsive to their community were more welcoming of community participation in

health facility management, and therefore facilitated committee functioning (180,194). In this

study, we focused on the financial resources and government support available to the health

facility.

McCoy et al. noted that process factors could help the committees achieve better results.

These process factors included efforts to strengthen civil society, sensitize communities to

incorporate women or minorities in positions of leadership, interventions to mobilize different

segments of the community and routine follow up with the committees, rather than one-time

training (197,223,234,240,241). We did not study process factors in the scope of this study due

to unavailability of data.

McCoy also discussed features of the health system and society more broadly that could

influence the health facility committees. Health system features included the legislative and

regulatory environment in which the committees were functioning, or the attitudes of health

system leaders (for example, district level authorities) towards community participation in

health. In terms of the wider society, beliefs, customs and power relations could also influence

the functioning of the committees (180,197,235). We also did not examine these contextual

features in the scope of this study due to unavailability of data.

5.3.2 Data sources

We used cross-sectional data from health facility and household surveys conducted by the

Government of Nagaland in 2015 as a part of the World Bank Funded Nagaland Health Project.

One hundred and one health facilities targeted by the project were purposively selected to

complete the health facility questionnaire, including 21 CHCs, 55 PHCs and 25 SCs. The survey

124

achieved a 96% response rate (21 CHCs, 53 PHCs, 23 SCs). The Medical Officer or head of the

health facility provided responses to the questionnaire.

For the household survey, a village was randomly selected from the catchment area of each

of the 101 facilities that was selected to complete the facility survey. Then, within each selected

village, 15 households were selected using a random walk technique. The head of the household

provided responses to the questionnaire, which asked questions about health seeking behavior,

social capital and socioeconomic indicators. The household survey had a 99% response rate. For

this analysis, we used data from the households matched to the 97 health facilities that provided

information for the health facility survey.

Ethical clearance for the quantitative data collection was gained from the Institutional

Review Board of the Public Health Foundation of India, and the Institutional Review Board of

Johns Hopkins Bloomberg School of Public Health for secondary data analysis.

5.3.3 Measures

Health committee functioning

To measure health committee functioning, we developed an index that incorporated

multiple indicators. Our index allowed us to comprehensively assess whether the committees

were performing their roles across all domains of the health system, as outlined in

Communitization Act for health centers (available in the Handbook of Communitisation of

Health Centres) (73). The total score for our health committee functioning index ranged from 0-

12.

We measured health committee functioning as an index rather than a scale. With an

index, we would not expect that the values of the 12 items arose for a common cause (104).

Instead, the value for the 12 items would determine the level of health committee functioning. In

125

addition, we anticipated that the index was multi-dimensional (i.e. it measured functionality

across all domains of the health system, and not just one domain), and so we did not expect that

the indicators would be highly correlated. We present the correlation matrix for the health

committee functioning items in Annex 8.

Table 5.2 presents the roles of the health committee as outlined in the Communitization

Act. Based on the roles of the health committees, we assigned indicators to cover each health

system component. For basic functioning, we included one variable of committee activity,

which was if the committee met during the past 12 months (item 1).

For the health workforce, the committees were responsible for motivating staff and for

implementing the ‗no work, no pay principle,‘ where the committee could deduct salaries from

absent staff. To implement this principle, the committee needed to review and approve staff

salaries. We therefore assessed whether the committee had approved staff salaries within the 6

months preceding the survey (item 2).

For health financing, the health committees were responsible for deciding how facility

funds should be used,21

managing expenditures and raising funds form within the community.

We included three indicators. First, was whether the committee had approved the annual budget

for the facility (item 3). If the committee approved the facility budget, it implied that they had a

plan for how funds should be spent during the upcoming year. The second variable was whether

the committee reviewed expenditures (item 4). This item demonstrated that after approving the

budget, the committee tracked expenditures against how they planned to spend their budget. The

final item was whether the committee raised funds from the community within the past 6 months

21

The National Health Mission has regulations about how certain funds can be used. However, the committees also

have untied funds that they can use more liberally to improve health service delivery.

126

(item 5). This item demonstrated that the committee was able to collect financial resources from

within the community to supplement government funding.

The committees were responsible for supervising and ensuring that health center staff

recorded and maintained vital statistics of patients, and using this information to plan committee

activities. We therefore determined whether the facility maintained a health management

information system report (item 6). The survey enumerator visually confirmed whether the

facility maintained the report.

The committees were also responsible for procuring medicines. We included two items

for this role. First, was whether or not the facility maintained a drug stock register (item 7). If

the facility maintained a drug stock register, at a minimum, the committee would be aware of and

tracking the availability of medicines at the facility. The second indicator was whether the

committee had provided drugs for the facility in the past 6 months (item 8). We included both

indicators because the ability to provide medicines was dependent upon whether the committee

received government funding, which was outside of the committee‘s control.

One of the core design features of the health committees was to decentralize governance

and incorporate community members into service delivery to make services more responsive to

village needs and to better mobilize the community. To assess this objective, we included a

variable for whether the committee mobilized the community to use health services in the past 6

months (item 9).

The health committees played a role in the delivery of health services. They were

responsible for maintaining facility infrastructure and making repairs when needed. Over a six-

month period, all facilities would require at least some basic maintenance. Hence, we assessed

whether the committee had made any repairs or improvements to the health facility during this

127

timeframe (item 10). These repairs could include making general repairs, providing new

supplies, equipment or infrastructure, constructing toilets or improving water quality or supply,

improving electricity or improving security at the facility. The committees were also responsible

for drawing on in-kind contributions from the community to improve patient care. We included

a variable for whether the committee raised in-kind contributions during the past six months

(item 11). Lastly, the committees played a role in implementing Village Health and Nutrition

Days (VHNDs). Across India, VHNDs are events were community members can receive basic

health education and preventive services (242). VHNDs should occur on a monthly basis, and the

committee should help to organize the VHND. We therefore included an item for whether the

committee organized a VHND during the past 6 months (item 12).

128

Table 5.2: Variables included in health committee functioning index

Health system

function

Health Facility Committee Role Item %

(n)

General Hold routine meetings 1. Committee held

meeting during past 12

months

77.32

(75)

Health

workforce Supervise, direct, guide and support

the health center staff

Improve staff accommodation

Check staff attendance and

implement ‗no work no pay‘ rule

2. Committee approved

staff salaries in past 6

months

8.89

(8)

Health

financing Manage health facility finances

Mobilize funds from private sources

3. Committee approved

annual budget for

facility

50.52

(49)

4. Committee reviewed

expenditures

(monthly, quarterly or

annually)

65.98

(64)

5. Committee raised

funds from community

in past 6 months

8.25

(8)

Health

information Record and maintain all vital

statistics

6. Facility maintains

health management

information system

report

88.66

(86)

Medicines,

vaccines,

technologies

Procure annual requirement of

medicines from any retail store

according to government list

7. Facility maintains

drug stock register

78.35

(76)

8. Committee provided

drugs in past 6 months

23.91

(22)

Governance Assess village health needs

Mobilize community

Develop/execute annual plan for the

facility

9. Committee mobilized

community to use

health services in past

6 months

11.11

(10)

Service

Delivery Make repairs to the facility

infrastructure and take measures to

prevent damage or misuse of health

facility infrastructure

Organize Village Health and

Nutrition Day (VHND)

Arrange transport for emergency

cases and referrals

At own expense, establish a center

for health promotion or develop

indigenous health care system

10. Committee

maintained/ repaired/

improved

infrastructure in past 6

months

32.99

(32)

11. Committee raised in-

kind contributions for

health facility in past 6

months

9.89

(9)

12. Committee organized

VHND in past 6

months

25.56

(23)

129

Measure of social capital

We used a validated scale of community social capital that was constructed using data

reported by individuals in the household survey. Our social capital scale used five items that

measured structural social capital, which is defined as the formal structures that enable people to

develop social ties and build networks (91,134). The scale also used three items that measured

cognitive social capital, which is defined as the quality and nature of social interaction (91,134).

Chapter 3 describes the details about each of the items and multilevel validation of the scale.

In contrast to health committee functioning, which we measured using an index, we

viewed social capital as a reflective indicator and measured it as a scale (104). Hence, unlike

health committee functioning, where the value of each indicator determined the level of health

committee functioning, we believed that the values of the items in our scale were caused by an

underlying latent construct, social capital. As a result, the indicators in our scale should be

highly correlated since they had the same underlying cause.

Determinants of health committee functioning

We included two variables that captured the composition of the health committee. To

ensure that the committee was representative of the community, we included an indicator of the

number of different stakeholders serving on the committee.22

The number of different

stakeholders on the committee ranged from 0 to 12. A higher score indicated that the committee

was more diverse. We also included a variable for the number of women on the committee.

22

Stakeholders include: 1) head of the health facility, 2) skilled health facility staff, 3) non-skilled health facility

staff, 4) village council representative, 5) committee member from another village, 6) ASHA, 7) AHSA coordinator,

8) Block Program Manager, 9) representative from the church, 10) representative from a Women‘s group, 11)

Anganwadi Worker, 12) trained DAI.

130

Our health facility variables covered the human and financial resources available to the

committees. More specifically, we included the type of health facility (SC or PHC/CHC),23

whether funds were received from the government for the financial year of 2014 and recorded in

the facility‘s book of accounts (yes/no), and the number of supervision visits made by the

Department of Health and Family Welfare in the past six months.

Lastly, our community variables assessed the economic and educational levels within the

community. To measure the economic level of each community, we developed a household

asset index, and took the average score for the community. Consistent with other asset indices,

our final index included variables related to durable assets, access to utilities and infrastructure,

and housing characteristics (160,161). To generate a measure of the education level within a

village, we determined the highest level of education within a given household. The highest level

of education ranged between a score of 1 to 8.24

We then generated a village average education

score for the community.

We also considered other community and facility variables.25

However, our exploratory data

analysis revealed that these variables were not independently associated with and did not explain

a large proportion of variation in the health committee functioning index. Furthermore, when

added into our model, they did not change the association between social capital and health

23

The Communitization Act distinguishes health committees that serve at SCs from those that serve at PHCs and

CHCs. The former are called Village Health Committees (VHC), whereas the latter are called Health Center

Management Committees (HCMC). The VHCs associated with SCs and HCMCs have the same mandate and roles

in the Communitization Act. However, they are working in different contexts. The VHCs are closer to the

community and manage the delivery of basic health services, whereas the HCMCs cover a larger population and

manage more advanced levels of care. 24

The education score for each household could range from 1-8. 1 = did not complete primary education; 2 =

completed primary school/grade 5; 3 = completed high school/grade 10; 4 = completed higher secondary

school/grade 12; 5 = completed undergraduate degree; 6 = completed undergraduate certificate/degree; 7 =

completed postgraduate degree; 8 = completed postgraduate certificate/diploma. 25

Other variables considered: 1) number of doctors at the facility, 2) number of nurses at the facility, 3) distance

from the facility to the nearest paved road, 4) size of facility catchment population.

131

committee functioning. To achieve a more parsimonious model, these variables were not

included in our final model.

5.3.4 Statistical analysis

Our statistical analysis consisted of two parts. The first part was a manifest analysis,

where we used an observed measure of social capital – the average social capital scale score of

the 15 individuals in each village. We conducted the manifest analysis first to facilitate

exploratory data analysis and linear regression diagnostics. The second part of the analysis was a

latent analysis that used multilevel structural equation modeling (ML-SEM). ML-SEM was

advantageous over regression analysis because it controlled for measurement error of social

capital (162). In addition, it allowed us to develop a measurement model for community social

capital (the portion of the model linking observed variables to underlying latent variables) that

was based on how each of the nine observed items varied between communities (119,158,162).

The results section presents the findings form the latent analysis only, whereas the results of the

manifest analysis are presented in Annex 9.

Manifest Analysis

We first conducted exploratory data analysis of our main variables of interest. We

examined the distribution of each continuous variable using a histogram to identify extreme

values. For the continuous variables in our model, we examined their correlation with the health

committee functioning index, and observed the linear relationship with the health committee

functioning index using scatter plots with best-fitted lines. For the binary variables in our model,

we examined the distribution of the health committee functioning index over each response

option using a box plot. For all variables in our model, we examined the bivariate relationship

with the health committee functioning index.

132

We next ran a linear regression analysis using ordinary least squares.26

Our first model

included only our manifest measure of community social capital. Our second model included

other community determinants of health committee functioning, whereas our third model also

incorporated committee determinants, and our final model also included facility determinants.

We examined our model assumptions using a series of regression diagnostics (243). We

first checked for multicoliniearity of our explanatory variables by examining their variance

inflation factors. We then examined any influential data points by exploring outliers, and points

with high leverage and influence. We also examined the Adjusted Variable Plots for each

regression coefficient to visualize any influential points. We then removed the influential data

points from our analysis and re-ran our models to determine whether the results remained

consistent. To assess the assumption of normality of error terms, we examined a Kernel Density

Plot and used a Shapiro-Wilk test to assess the hypothesis that the distribution was normal.

Finally, to examine homoscedasticity, we used a Breusch-Pagan test, which tested the null

hypothesis test that the variance of the residuals was homogeneous. We ran the analysis of the

manifest model in Stata version 13.1.

Latent Analysis

Figure 5.2 presents the path diagram of the ML-SEM we used in our analysis to assess

the relationship between social capital and the health committee functioning index. Structural

equation models include a measurement portion and a structural portion. The measurement

portion of the model describes the relationship between the eight observed indicators of social

26

Note: when we ran our manifest analysis, we developed community averages for three variables (community

social capital, average asset score in a community and average education in a community) and then ran a single level

model for the sample of 97 health committees (level 2 in our multilevel structural equation model).

133

capital and the latent construct of social capital, whereas the structural portion of the model

describes the relationship between latent social capital and health committee functioning.

In our analysis, the measurement model was partitioned into within and between

relationships. The ―within level‖ described associations among people within villages. The

―between level‖ described relationships between communities. The between portion of the

measurement model was most relevant to this study, as it established our community level

measure of social capital. The structural model then examined the relationship between

community social capital and the health committee functioning index, which was a community

level measure. Hence, our structural model was at the between, or community, level only.

134

Figure 5.2: Path diagram of association between social capital and health committee functioning

index

Path notation: We used common path notation, where circles represent latent variables, squares represent observed

variables, straight one-headed arrows represent direction of influence between the latent trait and observed items, and

short one-headed arrows represent measurement error of the latent trait.

Measurement model: At the within level, we modeled the relationship ( between individual structural and cognitive

social capital (

) and each observed item ( . The red dots at the within level represents the village

random intercepts for each indicator. At the between level, random intercepts are represented as ovals. We modeled

the relationship ( between community social capital ( ) and each random intercept ( .

Structural model: We were interested in studying a structural relationship at the between level only. In our model, the

linear regression coefficient BB related community social capital ( ) to the health committee functioning index (yj).

135

We built our latent model in a similar fashion to the manifest model. We first examined

the association between social capital and health committee functioning. We then included the

other community variables, followed by the committee variables and finally the health facility

variables.

Since our observed social capital items were categorical, we estimated this model using a

Weighted Least Squares Means and Variance adjusted (WLSMV) estimator (244).27

The

WLSMV estimator generated probit coefficients in the measurement portion of the model since

our social capital items were ordinal. However, it generated linear regression coefficients in the

structural portion of the model since we considered our health committee functioning index to be

a continuous variable. Our latent analysis was conducted in MPlus version 7 (151).

In each model, we examined the R-squared value, which described the proportion of variance

in the health committee functioning index that was explained by the variables in our model. To

assess the fit of our model, we assessed the model chi-square value, normed comparative fit

index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and

standardized root mean square residual (SRMR). We used the following guidelines to assess the

fit of our model: CFI and TLI above 0.95, RMSEA below 0.07 and the SRMR below 0.08 (155).

27

According to Muthén and Muthén: a WLSMV estimator users weighted least square parameter estimates using a

diagonal weight matrix with standard errors and mean and variance adjusted chi-square test statistic that use a full

weight matrix.

136

5.4 Results

Table 5.3 summarizes the descriptive statistics for the health committee functioning index

and the other determinants of health committee functioning. On a scale that ranged from 0 to 12,

the average health committee functioning score was 4.73 (Standard deviation (SD): 2.26).

The social capital items revealed that few people in the sample received support (financial,

emotional or other) from boding, bridging and linking groups (2.49%), whereas nearly a quarter

received support (financial, emotional or other) from bonding, bridging or linking individuals

(24.14%). Approximately one quarter (23.58%) of the sample joined with others in their

community to address a common problem, whereas less people (11.65%) spoke with a village

authority or government official about a problem in their village within the past year. A large

proportion of people voted in the last state or national election (89.46%). Most people reported

having full trust in their neighbors (75.54%) and village leaders (64.14%), and felt as though

they belonged in their village (97.43%).

In terms of the community variables, the average asset score was 0.17 (SD: 1.65), whereas

the average education score was 3.11 (SD: 0.55). In essences, the average education level for the

head of a household in a given village was the completion of high school. For the committee

composition variables, we found that the committees had an average of 1.50 women (SD: 1.41)

and 4.65 different community representatives (SD: 2.50). For the facility variables, the majority

of the committees in our sample served at PHC or CHCs (76.29%). Among the 97 facilities,

34.02% reported that they had received government funding in the past year, and had received an

average of 0.77 supervision visits from the Department of Health and Family Welfare (SD:

1.06).

137

Table 5.3: Summary of health committee functioning determinants

Indicators Mean (SD)/Percentage (n)

Health Committee Functioning Index 4.73 (2.26)

Social Capital Items*

Group support

No group support 76.97 (1,113)

Bonding/bridging or linking groups 20.54 (297)

Bonding/bridging and linking groups 2.49 (36)

Individual support

No individual support 19.85 (287)

Bonding/bridging or linking individuals 56.02 (810)

Bonding/bridging and linking individuals 24.14 (349)

Joined with community to address common issue

No 76.42 (1,102)

Yes 23.58 (340)

Talked with authority/governmental about problems

No 88.35 (1,274)

Yes 11.65 (168)

Voted in the last state/national election

No 10.54 (152)

Yes 89.46 (1,290)

Trust neighbors in village

None 3.90 (56)

Some 20.56 (295)

All 75.54 (1,084)

Trust leaders in village

None 9.12 (131)

Some 26.74 (384)

All 64.14 (921)

Feel as though part of village

No 2.57 (37)

Yes 97.43 (1,400)

Community Variables

Average asset score 0.17 (1.65)

Average education score 3.11 (0.55)

Health committee Variables

Number of women on committee 1.50 (1.41)

Number of community group on committee 4.65 (2.50)

Health Facility Variables

Type of facility

SC 23.71 (23)

PHC/CHC 76.29 (74)

Received government funds 34.02 (33)

Number of DHFW supervision visits 0.77 (1.06) *We did not present the social capital factor score and distribution in this table because Mplus does not currently

have the ability to export factor scores for multilevel analyses estimated with a WLSMV estimator (151).

138

Table 5.4 presents the association between the explanatory variables and the health

committee functioning index. Model 1 presents the bivariate relationship between social capital

and health committee functioning. This model revealed that social capital had a negative and

non-statistically significant association with health committee functioning (Estimate (Est.): -

0.20; Standard Error (SE): 0.31). When the community indicators were added in model 2, the

relationship between social capital and health committee functioning remained approximately the

same. When the committee indicators were added in model 3, we found that social capital had a

slightly positive, but not statistically significant association with health committee functioning

(Est.: 0.05; SE: 0.26). We also found that the number of women serving on the committee was

positively and significantly associated with health committee functioning (Est.: 0.56; SE: 0.21).

When we added the health facility indicators, the relationship between social capital and health

committee functioning strengthened, but remained non-significant (Est.: 0.14 SE: 0.24).28

In the

final model, the number of women serving on the committee remained positively and

significantly associated with health committee functioning (Est.: 0.49; SE: 0.24). In addition,

facilities that received government funds and DHFW supervisions visits were associated with

significantly better functioning health facility committees (Est.: 1.00; SE: 0.47; and Est.: 0.51;

SE: 0.24 respectively).

Our models also revealed that social capital alone explained a small proportion of the

variation in health committee functioning (R2

= 0.01 in Model 1). A large proportion of variation

in the health committee functioning index remained unexplained in our final model (R2

= 0.24 in

28

The non-standardized coefficient is interpreted as follows: For every one-unit increase in community social

capital, the health committee functioning index increases by 0.14 units, controlling for all other variables in the

model. The standardized coefficient is interpreted as follows: For every one-standard deviation increase in social

capital, the health committee functioning index increases by 0.06 standard deviations, controlling for all other

variables in the model.

139

Model 4). Our final model had a chi-squared value of 169.14 (103 degrees of freedom and p-

value <0.001), an RMSEA of 0.02, CFI of 0.96, TLI of 0.95, SRMR within of 0.09 and SRMR

between of 0.26.

Table 5.4: Structural equation model results for relationship between community social capital

and health committee functioning index

Indicator Model 1 Model 2 Model 3 Model 4

Est. SE Est. SE Est. SE Est. SE

Community social

capital

-0.20

(-0.09)

0.31 -0.18

(-0.08)

0.32 0.05

(0.02)

0.26 0.14

(0.06)

0.24

Community Indicators

Average asset score

0.21

(0.16)

0.16 0.14

(0.11)

0.16 -0.03

(-0.03)

0.18

Average education

score

-0.53

(-0.13)

0.48 -0.52

(-0.12)

0.46 -0.25

(-0.06)

0.45

Committee Indicators

Number of women on

committee

0.56**

(0.35)

0.21 0.49*

(0.31)

0.24

Number of community

groups on committee

0.04

(0.04)

0.11 0.00

0.00

0.12

Facility Indicators

Facility type (Ref: SC)

PHC/CHC

-0.07

(-0.03)

0.51

Govt. funds (Ref: No)

Yes

1.00*

(0.44)

0.47

Number DHFW

supervision visits

0.51*

(0.24)

0.24

R2

0.01 0.02 0.15 0.24

Number of observations 1446 1446 1446 1446 Standardized linear regression coefficients are presented in parenthesis; *p < = .05 **p < = .01 ***p < = .001;

Est. = Estimate; SE = Standard Error

140

Figure 3 presents the path diagram for the structural equation model. The path diagram

includes both the measurement model of social capital, and the structural model that examined

the relationship between social capital and health committee functioning (Table 5.4, model 4).

Figure 5.3: Path diagram of relationship between social capital and health committee

functioning index with covariates

Unstandardized (standardized) coefficient; *p < = .05 **p < = .01 ***p < = .001

141

We present our findings for the manifest model, which used a community average score for

social capital, in Annex 9. In the manifest model, we came to similar conclusions. We used the

manifest model to explore outliers and influential data points in our model, and to conduct

regression diagnostic tests. The results of the regression diagnostic tests, which are presented in

Annex 10, revealed that when outliers and influential points were removed from the model, there

were no major changes in our findings. We found that our model met the assumptions of linear

regression: the predictors were linearly associated with our outcome, the explanatory variables

were not highly correlated with one another, and the residuals were normally distributed and

homoscedastic. In Annex 10, we also examined whether the relationship between social capital

and the health committee functioning index differed according to health facility type (being a SC

versus PHC/CHC). However, we found that there was no statistically significant difference.

5.5 Discussion

The purpose of the paper was to determine whether social capital was associated with

health committee functioning, and in addition, to examine whether other characteristics of the

committee, health facility and community were associated with health committee functioning.

Our main finding was that social capital had a positive but not statistically significant association

with health facility committee functioning. We found that facilities that received government

funds and more government supervision visits were associated with significantly higher health

committee functioning scores. In addition, there was a positive association between the number

of women serving on a committee and the health committee functioning index.

142

5.5.1 Social capital findings

Our findings were surprising given the design of the Communitization Act, which

intended to leverage social capital within communities to improve service delivery. As Pandey

discussed in his ten mantras, the health committees were designed to mobilize people around a

common interest, namely, the improvement of health services in their community. He anticipated

that the adversity that the community faced in the quality of their health services would trigger

the use of social capital, which would be facilitated through the health committee. However,

our results suggest that social capital may not be enough to make the committees function. Our

findings were therefore in alignment with Pandey‘s tenth mantra, that investment in social capital

alone might not be sufficient for achieving desired results.

5.5.2 Community features

Beyond social capital, features of the community were not significantly associated with

health committee functioning and the magnitude of the association for community assets and

education level was small. This implies that wealthier and more educated communities were not

associated with better functioning committees as compared to communities that were poorer and

less educated.

There may be no association between community wealth and health committee

functioning because people living in poorer communities may not have the time or resources

available to improve services at their government facility. These communities may have a more

difficult time making financial or in-kind donations to the facility, or taking the time away from

their work to make repairs to the facility. On the other hand, people in wealthier communities

may be less inclined to support the committee because they may have fewer vested interests in

143

the delivery of public sector services, as they may be more likely to seek services in the private

sector.

While the overall education level of the community was not associated with health

committee functioning, it could be that the education level of the actual committee members was

a more important determinant. The committees in Nagaland do not have any requirements for

the level of education of their committee members (73). While communities with more educated

people overall may also have more educated committee members, this may not always be the

case. Further investigation is required to develop a more concrete conclusion about the role of

education in health committee functioning.

An important consideration in interpreting our community level measures is that they

were based on a community average generated from data reported from 15 individuals from one

village within the catchment area of the health facility. Because we have a small number of

individuals sampled from each village, the average may be unreliable (245).

5.5.3 Committee features

Committees with a higher number of women had a significantly higher level of health

committee functioning. We did not find a strong or a significant association between the number

of community groups represented on the committee and its level of functionality. The

Communitization Act was and continues to be a unique policy in the state because it specifies

that women should serve in a formal leadership position. This contrasts other governance

structures in Nagaland, such as the Village Council, which consist of only men. According to

Amer‘s recent analysis of the political status of women in Nagaland, ―The participation and

representation of women in politics is invisible‖ (246).

144

Women may bring unique attributes that help improve the way that the committee is

working, and they may use their role on the health committee to push the boundaries of

traditional gender roles within Nagaland. Scott et al. conducted an analysis of how gender and

power roles play out in VHNSCs in an unspecified Northern and remote region of India (247).

The authors found that in a community where it is unacceptable for women to speak publically in

front of men, their role on the VHNSC pushed female community health workers to serve in

leadership positions and enabled them to work for increased access to education for girls. While

the context in Nagaland is different from other states in India, it may also be possible that the

health committees have enabled women to push their current role within the community, and

make a meaningful impact. However, since our study is cross sectional, we cannot rule out the

potential for reverse causation. The Handbook on Communitisation specifies that each

committee should include at least one female representative (73). Hence, committees that were

functioning better overall may have been following the mandate more closely, and including

women.

5.5.4 Health facility features

Our study found that there was not a strong or statistically significant association between

health facility type and health committee functioning. However, our analysis suggests that top

down government support is important to the functioning of health committees. The

Communitization Act called for the community to work in a partnership with the government to

deliver health services, and our analysis underscored the importance of government funding and

supervision. These findings were similar those of other researchers. Abimbola et al. in Nigeria

concluded that ―to function optimally, community health committees require national

government or non-government organization mentoring and support…and they also require

145

financial support to subsidise their operation costs (215).‖ In Kenya, Goodman et al. found that

funding was critical to health facility committee success, as they attributed committee meetings,

planning and their general sense of purpose to a new pilot scheme that provided direct facility

financing (239). In an analysis of VHSNCs in Northwestern India, Singh et al. found that

supervision of the committees was non-existent, and concluded that it was a potential reason why

committees in this District did not meet the National Health Mission‘s guidelines (222). Also in

India, Srivastava‘s analysis of VHSNCs concluded that they needed more monitoring and formal

links with the wider health system (224).

5.5.5 Study limitations

The results of this study should be taken into consideration alongside its limitations. A

main limitation in this study was related to our community level measures. We made the

assumption that variables collected from one community were representative of the entire

catchment area. At higher-level facilities, such as CHCs that covered a larger geographic area,

this assumption may not hold. This assumption may be problematic for our social capital

measure in areas of the state where the catchment area of a health facility was not homogeneous

by tribe or religion (i.e. for health centers that are close to Nagaland‘s boarders with other states

in India).

Second, our model used cross sectional data. Our conclusions were therefore limited to

associations between variables, and not causation. While the path diagrams presented in this

analysis showed one-way directionality between the explanatory variables and health committee

functioning, we could not conclude with our data that the explanatory variables led to, or were

the cause of, health committee functioning.

146

A third limitation was that this study used data form 97 purposively sampled health

facilities in Nagaland. The facilities were selected to be part of a new initiative to improve

health service delivery throughout the state. To be selected as a part of the project, the facilities

had to meet a basic level of functionality. While the sample included all CHCs throughout the

state, it included a purposively selected sample of PHCs and SCs. Hence, this study was only

representative of the 97 facilities in the sample, and it did not necessarily capture health

committee functioning at the worst performing facilities.

A fourth limitation was that our study was missing certain explanatory variables that

would be beneficial to further understand the factors associated with health committee

functioning. The R-squared value of our final model was 0.24, indicating that a large portion of

variation in our outcome was unexplained by variables in our model. Most notably, we were

missing indicators on health worker attitudes towards the health facility committees, details

about the capabilities of the specific committee members (including their level of confidence,

education and management capabilities), the committee member‘s clarity of their roles and

responsibilities, details about the political environment in each respective village, and

information about process factors, such as the amount of training that each committee received.

Without these variables, our model was subject to omitted variable bias. In addition, our

government funding variable was based on whether a facility received and recorded funding in

their book of accounts. This variable could be under-reported if facilities received, but do not

make record of their funding.

Lastly, some while the measures of model fit (CFI, TLI, RMSEA) met established cut off

points for model fit, these guidelines were established for single level structural equation models.

More work is needed to develop appropriate indices of model fit for multilevel models (162).

147

5.6 Conclusions

The Communitization Act was uniquely designed to leverage social capital in Naga

villages to improve the quality and responsiveness of government services by establishing

committees that include community representatives. However, our study found that social

capital was not significantly associated with health committee functioning at SCs, PHCs and

CHCs. Instead, it was the support the committees received from the government, through the

provision of funding and supervision visits, as well as the inclusion of women, which was

associated with better functioning committees. Our findings suggest that beyond social capital,

health facility committees should have meaningful empowerment and a lasting partnership with

the government to take control of and make improvements to their health services.

148

6. Conclusion

6.1 Summary of findings

The theme of this dissertation was the relationship between social capital and health. The

main findings of this dissertation challenged some of the conventional expectations about the

relationship between social capital and self-rated health, and the role of social capital in the

functioning of health facility committees. The main findings in this dissertation were the

following:

Our nine-item scale of social capital had a different factor structure at the individual

and community levels. This factor structure indicated that the scale items had different

relationships at each level, and that the interpretation of the constructs at each level could differ.

For individuals, we proposed that social capital be measured by a cognitive social capital factor

and structural social capital factor, whereas for communities we proposed that it be measured by

one social capital factor.

Our community measure of social capital had a negative and statistically significant

association with self-rated health in a given community. This suggests that social capital may

have a downside: certain groups within the community could be excluded from the benefits of

social capital; people in communities with high social capital could experience greater stress and

pressure to supply resources and support to others in their network; or there could be restrictions

on freedom or downward leveling norms that encourage people to act like others in the group,

even if it does not lead to greater health benefits. Alternatively, since our study was cross-

sectional, our findings could mean that people living in communities where self-rated health is

worse overall could come together to support one another, and this could in turn build

community social capital.

149

The health facility committees established through the Communitization Act were in

place, but there was variation in how closely the committees followed original policy

documents. While some committees included many representatives, met routinely and engaged

the community, others were missing key community members, not monitoring expenditures, or

implementing the No Work No Pay principle. Health workers and committee members

overwhelmingly accepted the concept of Communitization and thought that it was an appropriate

fit for the social context in Nagaland. However, wider health systems challenges (particularly

related to funding, infrastructure and human resource gaps at higher level facilities) made certain

components of the policy less feasible. The act was designed to leverage social capital in Naga

villages. At some facilities, social capital appeared to operate as expected, and drove community

members to mobilize for the benefit of the facility. However, at other facilities, the committees

felt discouraged by the systems level constraints were inactive.

Community social capital had a positive, but not statistically significant, association

with our index of health committee functioning. We recognize that our community social

capital measure may not be representative of the entire catchment area of a health facility so

more research is still required to examine this relationship. We also found that health committees

that included more women, received government funding and supervision visits were associated

with higher functionality. These findings suggest that the committees should work with the

government in a partnership to take action to improve their health facility. They also suggest that

the presence of women on these committees plays an important role in improving how they

function.

150

6.2 Contributions to existing research

Many researchers have examined the relationship between social capital and health. This

study builds off those and introduces several new elements to this knowledge base:

This was the first study to examine social capital and health in Nagaland and only the

second study to examine social capital and self-rated health in India. The results of this

application of social capital in Nagaland challenged some of the findings to date and uncovered

meaningful observations. The existing literature on social capital and health research in India did

not have consistent findings. Not all studies used the same measure of social capital, or

measured the construct at the individual and community levels. This has made comparisons

across studies difficult. Interestingly enough, as observed in Nagaland, the association between

social capital and health was not always positive elsewhere in India.

This was among the first studies to examine the relationship between social capital and

health facility committees. A large body of researcher has examined health committees

globally, and these studies have touched upon closely related concepts to social capital like the

importance of trust within a community. Since one of the main functions of social capital is for

people in a community to mobilize and undertake collective action, the role of social capital in

achieving community participation in health may be instrumental.

151

This was among the first social capital studies to use multilevel factor analysis (ML-FA) and

multi-level structural equation modeling (ML-SEM). To date, ML-FA has primarily been

applied in methodological journals, although its application is growing in certain fields

(education, organizational management, psychology). ML-FA allows researchers to validate a

scale at two different levels, controls for measurement error of the latent construct, and model

more complex relationships (both direct and indirect effects) between latent constructs and other

observed variables through ML-SEM.

6.3 Policy implications

The results from our first paper were unexpected. Whereas most social capital theory

supports that communities with higher social capital are associated with better health our study

found a negative association. Recently, there has been more interest and attention to studies that

have found a negative relationship, as a negative association calls into question how we should

expect higher levels of social capital to influence health. More research is needed to explore the

nature of this relationship in Nagaland before concrete steps can be taken. Furthermore, our

study examines association, and not causation. We therefore cannot assume that community

social capital is the cause of worse health, or vice versa.

While acknowledging these limitations, it is still interesting to consider the policy

implications if social capital in Nagaland does in fact have a downside. In this case, it is

important to determine whether social capital in communities is excluding certain groups of

people and if health interventions can specifically target these groups. Likewise, if social capital

is placing high stress on certain groups within a community who take on responsibility to support

others, then interventions are needed to alleviate this stress and to develop mechanisms that pool

resources across the entire community for those who need them. It is also important to

152

understand whether there is social contagion or downward leveling norms within communities.

This means that there could be certain health behaviors or habits that are detrimental to health

and could be widely accepted or even encouraged. These behaviors could be in relation to

actions like drinking, smoking or dietary habits, or perceptions about vaccinations or the use of

certain health services. A greater understanding of commonly accepted and promoted health

behaviors could help design a program that targets the issue directly.

Alternatively, it is also interesting to consider policy options if social capital is working

in a different direction where people with worse health come together to support one another,

and this builds community social capital. One of the proposed advantages of communities with

high social capital is that information can rapidly spread through the tight and close networks.

Where true, there may be an opportunity to leverage these tight networks by spreading accurate

information about prevention and care for the main health conditions affecting these

communities.

Findings from chapters four (paper two) and five (paper three) suggest that the health

facility committees in Nagaland would benefit form more engagement with the government. In

chapter four (paper two), nearly all respondents discussed delays in government funding as a

barrier to their work and were interested in having more training opportunities. Chapter five

(paper three) also suggests that engagement with the government through funding and

supervision were important factors for committee functioning. Beyond influencing the

functioning of the committees, increasing the number of supervision visits to the facilities could

provide an opportunity for government officials to hear about challenges that the committees

face. In many communities, there was tension between the community and the health facility

when the health facility could not provide needed services, as well as tension between the

153

committee and the government when the government could not provide funding. The ability to

overcome these tensions likely requires better engagement on all sides. Increasing the face-to-

face time between the committees and government maybe a first step to improve engagement,

for each side to better understand service delivery constraints and to strategize ways to overcome

them together.

Chapter four (paper two) also indicated that health promotion initiatives run by the

committees could have a positive impact on community health, and that some committees also

had a positive impact on health worker motivation. However, these positive findings were not

common across all committees. With additional training, the health committees may benefit

from a re-orientation to a focus on health promotion and disease prevention. Furthermore,

specific training on non-financial approaches to motivate health workers – through management

style, increased recognition and appreciation from managers and the community, and

improvements to physical working conditions – could be beneficial.

Lastly, chapter five (paper three) suggests that women may have a unique role in making

the health committees function. Empowering more women to serve on the committees could

have an impact on what the committees can achieve for their community. As has been

demonstrated in many countries, empowering women in positions of leadership has widespread

and positive implications for community health and economic development.

6.4 Areas for further research

As we discussed in chapter three (paper one), there has been an increasing trend to use a

multilevel definition of social capital and to use multilevel methods to study its relationship with

self-rated health. More attention is still needed to explore how different researchers have defined

social capital as a multilevel construct and to assess whether their measure of social capital

154

aligns with their definition. To draw attention to this topic, a systematic review that focuses on

measurement of social capital as a multilevel construct would be advantageous. As a part of this

review, attention is needed to determine 1) the multilevel definition of social capital, 2) the

precise items used to measure the construct, 3) the approach to validate the measures and 4) a

summary of study results. This study would call attention to how closely (or not) existing

multilevel measures of social capital are to the definitions provided.

More research is still needed within and outside of Nagaland to understand the pathways

through which social capital influences health. While there are multiple hypotheses about how

the two are linked, they have not been confirmed empirically. ML-SEM is a potentially useful

method to study these pathways because it is designed to accommodate direct and indirect

effects, as well as reciprocal effects. For example, on an individual level, with SEM we could

explore whether social capital is related to health through increase access to information and

health services and reduced levels of stress. At a community level, we could examine whether

social capital is linked to health by influencing behaviors or through collective efficiency.

This dissertation served as a starting point to examine the role of social capital in

community participation in health through health facility committees. There are four different

ways to examine the relationship between social capital and health committee performance (22).

First, as was the case in our study, an intervention could be designed to leverage social capital.

Hence, social capital was a predictor of health committee functioning. A second approach is to

study whether social capital is a channel, or mediating variable, through which the health

committees have an impact on health. A third approach is to examine whether social capital is a

segmenting, or moderating variable, that predicts the success (or the failure) of the health

committees on health. Lastly is to examine whether well-functioning heath committees build

155

new forms of social capital. Examining social capital from these four different angles would

help policymakers better understand how social capital can be translated into an intervention that

has broader health impacts – a area of research that is still understudied, yet is imperative to the

utility and importance of this concept.

156

Annex 1: Summary of social capital research in India

Author Population Topic Methods Objective Findings Ref.

Bhuiyan, 2011 Dhesian Khana,

Doaba region of

Pubjab

Economic

development

Case study To explore the role of

social capital in

community

development

―The emergence of adversarial

relationships due to material interests

created by postcolonial development

activities had eroded social capital.

But the positive intervention of

external forces such as block

development officials, community-

building activities of the local youth

(organized and trained through their

club), and the overseas financial

support from remittances helped in the

reemergence of social capital, which,

in turn, contributed to the overall

development of the village (pg 538).‖

(248)

Bhattacharya, 2001 Rural West Bengal General Case study To redefine social

capital to fit the Indian

context

For social capital to be relevant in

India, it is necessary to ―redefine

social capital (a) by focusing on its

stratified character in a hierarchical

society of classes and segments and

(b) by refuting the claim that social

capital in civic community is

unconditionally good for democracy

(pg. 673).‖

(34)

Blomkvist &

Swain, 2001

Orissa Governance &

Democracy –

protest

mobilization

Case study To examine the role of

social capital for

environmental protest

movements to succeed

―The success or failure of the protests

cannot be simply explained by social

capital alone…many other intervening

variables may impact the outcome of

such collective action: leadership, the

responsiveness of the political system,

external support to the cause of the

movement. However, social capital

can probably play the most

consequential role in providing space

for the fringe groups to coordinate

(249)

157

Author Population Topic Methods Objective Findings Ref.

among themselves under a larger

umbrella of mobilization and also help

to sustain it for longer periods of time

(pg. 641).‖

Das, 2005 Orissa Governance &

Democracy -

Relations between

rural poor and

state

Mixed (In-depth

interviews and

cross sectional

survey)

To determine the extent

to which there are

relations of trust and

cooperation between

officials and poor rural

people, and to explain

the observed level of

trust.

―The beneficial interaction of state

officials is often limited to local

politicians and proprietary classes

(private contractors, rich landowners).

Their relations with the less well-off

in rural society, or those who belong

to the wage laboring class, are

typically characterized by a lack of

trust and therefore by lack of mutual

cooperation (pg. 75).‖

(250)

DeGroot &

Tadepally, 2008

Andra Pradesh Environment –

irrigation

Cross sectional

survey

To determine factors

that facilitate a local

NGO to facilitate

community action for

restoration of an

irrigation system

―(Pre-existing) collective social

capital, as measured through five

simple indicators, strongly correlates

with success of the NGO strategy (pg.

519).‖

(251)

DeSilva &

Harpham, 2005

Andhra Pradesh

(as well as Peru,

Ethiopia and

Vietnam)

Health – nutrition Cross sectional

survey

To determine whether

social capital is

associated with

nutritional outcomes

―Children whose mothers are involved

in some citizenship activities have

lower height-for-age z -scores in

Andhra Pradesh (pg. 349).‖

(24)

DeSilva, Huttly,

Harpham &

Kenward, 2007

Andhra Pradesh

(as well as Peru,

Ethiopia and

Vietnam)

Health – mental

health

Cross sectional

survey

To determine whether

social capital is

associated with mental

health outcomes

―No associations are seen in Vietnam

or Andhra Pradesh (pg. 12).‖

(23)

Meinzen-Dick &

Raju, 2002

Rajasthan and

Karnataka

Environment –

irrigation

committees

Cross sectional

survey

To identify factors

affecting organizational

and collective action

among water users in

major canal irrigation

systems

―Size of the command area and

distance to market play a larger role,

along with leadership and social

capital (indicated by influential

persons, college graduates, and

number of temples, but not other

economic cooperatives in the village)

(pg. 662).‖

(252)

Feigenberg, Field,

Pande, Rigol &

Sarkar, 2014

Kolkata Microfinance Randomized

experiment

To examine the

association between

group meetings and

―Social capital gains associated with

more frequent meetings continue to

accrue across multiple lending cycles.

(253)

158

Author Population Topic Methods Objective Findings Ref.

social capital in a

classic microfinance

model

However, these effects are reduced

when group members differ in their

borrowing history. In addition, clients

who start with low levels of

empowerment report higher social

capital gains when matched with

similar clients (pg. 932).‖

Heller, 1996 Kerala General N/A To determine the types

of social capital in

Kerala

―The ―synergy‖ of state and class

mobilization in Kerala has produced

two forms of social capital. The first

underwrote the provision of

redistributive goods, the second

facilitated class coordination (pg.

1066).‖

(254)

Isham and

Kahkonen, 2002

Karnataka and

Maharashtra

Water and

Sanitation -

committees

Mixed (In-depth

interviews and

cross sectional

survey)

To determine the

factors under which a

community based

approach will succeed.

―In communities with high levels of

social capital—in particular, with

active community groups and

associations—design participation is

more likely to be high and monitoring

mechanisms are more likely to be in

place. In those communities,

households are accustomed to working

together, and social ties deter free-

riding (pg. 684).‖

(255)

Jayal, 2001 Uttaranchal Governance &

Democracy

Case study of two

villages

To explore the link

between democracy

and social capital

―Social capital cannot be understood

outside of its particular cultural,

ideological and institutional contexts

or indeed independently of the nature

of social segmentedness, whether

along caste, class or any other lines

(pg. 655).‖

(31)

Joshi & Aoki,

2014

Tamil Nadu Disaster recovery Cross sectional

survey

To determine the role

of social capital in the

implementation of

recovery policies in

areas affected by

disaster

―The style of each community prior to

the disaster and the presence of a

strong village leader are both crucial

for the successful implementation of a

recovery program…social capital

significantly affects successful policy

implementation, which will lead

people to utilize government resources

(256)

159

Author Population Topic Methods Objective Findings Ref.

for disaster recovery (pg. 100).‖

Krishna, 2007 Rajasthan General Longitudinal

analysis

To determine how

social capital is formed

and how it changes.

―Factors such as faith in government

institutions, relative modernization,

relative need, and social stratification

do not help explain these changes.

Organizations promoted by outsiders

have also not helped. Social capital is

socially generated through the internal

efforts of community groups.

Villagers‘ self-initiated organizations

and local leadership have helped grow

social capital, along with locally

formulated rules and lower economic

inequality in the initial period (Pg.

941).‖

(257)

Krishna, 2004 Madhya Pradesh

and Rajasthan

Economic

development

Cross sectional

survey

To determine if social

capital improves

development outcomes.

―Villages that have high levels of

social capital also have significantly

higher development performance.

However, the utility of social capital is

enhanced considerably when this

resource is utilized strategically… in

addition to high social capital, agency

capacity matters. Villages that do not

have capable agents achieve much

lower development success even when

they have high levels of social capital

(pg. 293).‖

(258)

Lahiri-Dutt and

Samanta, 2006

Burdwan, West

Bengal

Microfinance -

women‘s groups

In-depth

interviews and

focus group

discussions

To determine the

reasons for the failure

of micro-financing self-

help groups

―A primary reason for the poor

performance of DWCRA is that

women are ‗targets‘ of action and their

voices are rarely heard. The problem

with ‗self-help‘ schemes for women is

the way they problematise the ‗self‘,

mostly in the mould of the

bureaucrat‘s own image, rather than

the women for whom these schemes

are made (pg. 292).‖

(259)

160

Author Population Topic Methods Objective Findings Ref.

Levien, 2015 Rajasthan Land ownership Ethnographic case

study

To demonstrate the

limitations of the

collective social capital

thesis and advance the

individual social capital

thesis

―Inequalities in individual social

capital, rooted in the agrarian class

structure, enabled a section of farmers

to capture substantial rents as brokers

of the dramatic real estate speculation

that the SEZ generated…use of

individual social capital came at the

expense of fellow villagers, violated

collective norms, undermined trust,

and removed any possibility for

collective action around shared

grievances toward the project

(pg.78).‖

(260)

Lise, 2000 Haryana, Uttar

Pradesh and Bihar

Forest

management

Cross sectional

survey

To determine the

conditions most

conducive for people‘s

participation in forest

management.

―When the condition of the forest is

good and/or when people are

dependent on the forest, participation

goes up. Low average levels of

education in the family and high levels

of education of the respondent

enhance participation. Greater

involvement of women in the

community stimulates participation

(pg. 390).‖

(261)

Mayer, 2001 15 largest Indian

states

Economic

development

Ecological study To apply the central

methodology of Robert

Putnam‘s work to the

study of Indian states

―The least civic states in India – where

traditional hierarchical dominance is

strong and active citizenship is

weakest – are those where infant

mortality is elevated, life expectancies

are relatively short and too few girls

learn to read (pg. 691).‖

(262)

Mohapatra, 2001 Orissa General Case study To explore the

associations that build

trust among people,

increase the

predictability of their

behavior and promote

―Collective memory and its role in

conflict resolution in the village is

crucial in creating or destroying trust.

Memory, among other things, holds

the key for the continuation of social

connectedness or engagements (pg.

(263)

161

Author Population Topic Methods Objective Findings Ref.

collective action. 655).‖

Morris, 1998 17 states Economic

development

Cross sectional

survey

To determine if states

with larger

endowments of social

capital have been more

successful at reducing

poverty.

―Those states which were initially

well endowed with social capital, were

also more successful at reducing

poverty (pg. 16).‖

(264)

Pai, 2001 Uttar Pradesh Governance &

Democracy

Case study To determine the role

of social capital in the

functioning of

democratic institutions

in segmented societies

―Segmentation arising out of

caste/class divisions is a significant

contextual variable in determining the

development of trust, social capital

between groups and democratic

functioning (pg. 645).‖

(30)

Sekhar Orissa Fisheries

management

Cross sectional

survey

To determine the role

of social capital in

fisheries management

―Bonding and bridging social capital

keeps the fishers together in times of

resource scarcity, checks violations of

community rules and sanctions, and

strengthens the community fisheries

management. In contrast, linking

social capital in Chilika appears to be

weak, as is evident from the lack of

trust in external agencies, seeking the

help of formal institutions for legal

support, and increasing conflicts.

Trust and cooperation among fishers

is crucial in helping to build the social

capital.‖

(265)

Serra, 2001 16 Indian states Measurement of

social capital

Cross sectional

survey

To analyze the

feasibility and the

validity of measuring

social capital at the

state level and to

identify its role in

explaining the

differential

The study ―identifies three types of

problems in the application of this

methodology to interstate analysis:

finding appropriate measures for

social capital; locating alternative

indicators valid for interstate analysis;

and interpreting the statistical

association between social capital and

(32)

162

Author Population Topic Methods Objective Findings Ref.

performance

state performance (pg. 693).‖

Sivaram, Zelaya,

Srikrishnan Latkin,

Solomon &

Celentano, 2009

Chennai, Tamil

Nadu

Health - HIV

stigma

Nested study in

randomized

control trial

To explore the

associations between

social capital and

stigma among men and

women who are patrons

of wine shops or

community-based

alcohol outlets in

Chennai

―Reports of social capital indicators

were associated with reduced fear of

transmission of HIV/AIDS, lower

levels of feelings of shame, blame and

judgment, lower levels of personal

support and perceived community

support for discriminatory actions

against PLHA [people living with

HIV/AIDS] (pg. 233).‖

(25)

Story, 2014 India – nationally

representative

Health – health

service utilization

Cross sectional

survey

To examine the

association between

social capital and the

utilization of antenatal

care, professional

delivery care and

childhood

immunizations

―Social capital operated at the

community level in association with

all three care-seeking behaviors;

however, the results differed based on

the type of health care utilized.

Specifically, components of social

capital that led to heterogeneous

bridging ties were positively

associated with all three types of

health care use, whereas components

of social capital that led to strong

bonding ties were negatively

associated with the use of preventive

care, but positively associated with

professional delivery care (pg. 73).‖

(26)

Story & Carpiano,

2017

India – nationally

representative

Health – child

nutrition

Cross sectional

survey

To examine the

association between 1)

socio-economic status

and social capital, and

2) social capital and

child malnutrition.

―Greater household wealth is

associated with each social capital

form and amplifies the extent that

linking ties to medical and educational

institutions, and within-village

bridging organizations are associated

with lower odds of child underweight

(pg. 112).‖

(27)

Deininger & Liu,

2009

Andra Pradesh Governance &

Democracy -

Cross sectional

survey

To assess the economic

and social impacts of

There are ―positive impacts on

empowerment and nutritional intake in

(266)

163

Author Population Topic Methods Objective Findings Ref.

Formation of self-

help groups

the formation of self-

help groups

program areas overall and

heterogeneity of impacts between

members of pre-existing and newly

formed groups, as well as non-

participants. Female social and

economic empowerment in program

areas increased irrespective of

participation status, suggesting

positive externalities (pg. 1).‖

Vikram, 2018 India – nationally

representative

Health – child

nutrition

Cross sectional

survey

To examine the

association between

social capital and child

nutrition, and the

mediating role of

development.

―Household based bridging social

capital, expressed as connections with

development base organizations, is

positively associated with child

nutrition. Bonding social capital,

expressed as ties with cast and

religious based organizations, has the

opposite impact. At the village level,

contextual measures of social capital

are associated with nutritional status

of children, but their influence is

conditional on local development (pg.

42).‖

(29)

Widmalm, 2005 Kerala and

Madhya Pradesh

General Cross sectional

survey

To examine the role of

bonding social capital

―High levels of bonding trust cannot

only facilitate political cooperation.

They may also work as a shield

against public sector employees who

attempt to exploit citizens in a corrupt

or clientelistic manner (pg. 75).‖

(267)

164

Annex 2: Comparison of multilevel regression modeling and multilevel structural equation

modeling

Single level regression Single level SEM Path diagrams

Notation

where:

is a continuous outcome

is a scale score for items x1 – x4

is the regression coefficient linking and (change in y for a one unit

change in )

is the intercept (value of when is zero – not shown in path diagram)

is the residual and is

Measurement Model

[

] [

] [

] [ ] [

]

where:

x1 – x4 are observed scale items

is a latent construct

are factor loadings relating the observed scale items to the latent

construct

are intercepts (not shown in path diagram)

are measurement error of the latent construct

Structural model

where:

165

Single level regression Single level SEM is a continuous outcome

is a latent construct

is the regression coefficient linking and (change in y for a unit change

in )

is the intercept (value of when is zero - not shown in path diagram)

is the residual and is

Mplus syntax

Variable:

Names are

x1 x2 x3 x4 x_score y community_id;

Missing are all (-9999);

Usevariables = x_scre y;

Model:

y on x_score;

Variable:

Names are

x1 x2 x3 x4 x_score y community_id;

Missing are all (-9999);

Usevariables = x1 x2 x3 x4 y;

Model:

f1 by x1;

f1 by x2;

f1 by x3;

f1 by x4;

y on f1;

166

Multilevel regression Multilevel SEM Path diagrams

Notation

Level 1 model: Variation of individuals (i) within communities (j)

( )

where:

is a continuous outcome for individual (i) in community (j)

( ) is a group mean centered scale score for individual i

is a regression coefficient linking ( ) to

is the intercept of village j (the random intercept) or community j‘s

estimated mean response for

is an residual for individual ( in community ( ), or the individual

Measurement model:

Level 1 model: Variation of individuals (i) within communities (j)

[

] [

] [

] [ ] [

]

where:

is a vector of observed items for each individual ( in community ( )

is the latent construct at the individual level

is a vector of factor loadings relating to each observed item ( )

167

Multilevel regression Multilevel SEM deviation from community j‘s estimated mean response for

Level 2 model: Variation between communities (j)

where:

is the intercept of village j (the random intercept) or community j‘s

estimated mean response for

is a mean scale score for community j

is a regression coefficient linking to

is the overall expectation of (estimate of the grand mean of )

is the residual for community (the village deviation from the estimated

grand mean of )

Combined model:

( )

A note about group mean centering:

Data collected from individuals ( nested within communities has two

sources of variation: between and within group. An individual‘s deviation from

the group mean is their contribution towards within group variation (

We group mean center the individual level variable to isolate variation that is

within communities from that which is between communities. The average value

in a group ( contributes to between group variation.

is a vector of community ‘s estimated mean responses for each item

(random intercepts)

is the residual for individual ( in community ( ), or the measurement

error of the individual level latent construct

Level 2 model: Variation between communities (j)

[

] [

] [

] [ ] [

]

where:

is a vector of community ‘s estimated mean responses for each item

(random intercepts)

is the latent construct at the between level is a vector of factor loadings relating to

is the overall expectation for each (the estimate of the grand mean)

is the residual for community , or the measurement error of the

community level latent construct

Combined model:

[

] [

] [

] [ ] [

] [ ] [

] [

]

Structural model: Level 1 model: Variation of individuals (i) within communities (j)

Level 2 model: Variation between communities (j)

Combined model:

where all variables are the same as in the multilevel regression model, with

the exception of and which are now latent variables.

168

Multilevel regression Multilevel SEM Mplus syntax

Variable:

Names are

x1 x2 x3 x4 x_score y community_id;

Missing are all (-9999);

Usevariables = x_scre y x_mean;

Cluster = community_id;

Within = x_score;

Between = x_mean;

Define:

CENTER x_score (GROUPMEAN);

x_mean = CLUSTER_MEAN(x_score);

Analysis:

Type = twolevel;

Model:

%within%

y on x_score;

%between%

y on x_mean;

Variable:

Names are

x1 x2 x3 x4 x_score y community_id;

Missing are all (-9999);

Usevariables = x1 x2 x3 x4 y;

Cluster = community_id;

Within = ;

Between = ;

Analysis:

Type = twolevel;

Model:

%within%

f1_w by x1;

f1_w by x2;

f1_w by x3;

f1_w by x4;

y on f1_w;

%between%

f1_b by x1;

f1_b by x2;

f1_b by x3;

f1_b by x4;

y on f1_b;

169

Annex 3: Comparison of original SASCAT and modified SASCAT

The table below presents the indicators included in DeSilva et al.‘s Shortened Adapted

Social Capital Assessment Tool (SASCAT) (24). It then explains the changes made to the

SASCAT based on their validation study (164). Words that are underlined indicate portions of

the SASCAT that were removed in the modified SASCAT. Words that are in italics are those

that were added to the modified SASCAT.

Description of change/rationale SASCAT Modified SASCAT

Removed ―active.‖ Change made

because ―active member‖ was

intended to identify people who

participate in groups rather than

play a passive role, but it was not

well understood by respondents.

―Active member‖ was often

interpreted as having an official role

within the group (ie: being the

group treasurer).

Added list of context specific

groups.

1. In the last 12 months have you

been an active member of any of the

following types of groups in your

community?

Work related/trade union

Community association/co-

op

Women's group

Political group

Religious group

Credit/funeral group

Sports group

1. In the last 12 months have you

been a member of any of the

following types of groups in [NAME

OF VILLAGE]?

Village council

Religious Group (for

example regularly attending

church)

Students' Union

Village Union

Women's Village Union

Non Governmental

Organization

Traders' Association

Village Education

Village Health Committee

Professional Association

Sports group

Political Group

Cultural or Arts Group

Agricultural Group

Other

Removed emotional help, economic

help and assistance in helping know

or do things to include support more

broadly. Enumerator instructed to

provide additional examples if

respondent inquired. Change made

because question aims to identify

respondent‘s sense of social support

more broadly.

2. If respondent is a member of a

group ask: In the last 12 months, did

you receive from the group any

emotional help, economic help, or

assistance in helping you know or do

things?

2. If respondent is a member of a

group ask: In the last 12 months, did

you receive any support (emotional,

economic, or other kinds) from

[NAME OF GROUP]?

170

Description of change/rationale SASCAT Modified SASCAT

Same as above 3. In the last 12 months, have you

received any help or support from

any of the following, this can be

emotional help, economic help, or

assistance in helping you know or do

things?

Family

Neighbors

Friends who are not

neighbors

Community leaders

Politicians

Government officials/civil

servants

Charitable

organizations/NGOs

Religious leaders

Other

3. In the last 12 months, have you

received any support (emotional,

financial, or other kinds) from any of

the following:

Family

Neighbors

Friends who are not

neighbors

Community leaders

Politicians

Government officials

Charitable

organizations/NGOs

Religious leaders

Other

No changes made 4. In the last 12 months, have you

joined together with other

community members to address a

problem or common issue?

Yes

No

4. In the last 12 months, have you

joined together with other

community members to address a

problem or common issue?

Yes

No

No changes made 5. In the last 12 months, have you

talked with a local authority or

governmental organization about

problems in this community?

Yes

No

5. In the last 12 months, have you

talked with a local authority or

governmental organization about

problems in [NAME OF VILLAGE]?

Yes

No

Added question on author‘s

discretion to capture political

participation.

6. Did you vote in the last state or

national election?

Yes

No

Split question into three parts to ask

about trust in neighbors, leaders and

strangers separately. Change made

because respondents felt that they

could not speak about the majority

of people within their community,

and only about people they know or

their leaders.

Also added option for respondent to

answer Yes, all; Yes, some; or No.

Change made so to provide

respondents with greater flexibility

in their response.

6. In general, can the majority of

people in this community be trusted?

Yes

No

7. In general, do you trust your

neighbors?

Yes, all

Yes, some

No

8. In general, do you trust leaders of

[NAME OF VILLAGE]?

Yes, all

Yes, some

No

9. In general, do you trust strangers

in [NAME OF VILLAGE]?

Yes, all

Yes, some

No

171

Description of change/rationale SASCAT Modified SASCAT

This indicator was not included in

our analysis. The indicator included

a don‟t know response option,

which cannot be used to provide a

rating of social capital. This

indicator also measures community

social capital only. In future

versions of this instrument, the

referent should be changed to the

individual (rather than community),

and the response options should be

changed to be consistent with

questions 7-9:

Do you generally get along with

people in [NAME OF VILLAGE]?

Yes, all

Yes, some

No

7. Do the majority of people in this

community generally get along with

each other?

Yes

No

10. Do the majority of people in

[NAME OF VILLAGE] generally get

along with each other?

Yes

No

Don‟t know

No changes made 8. Do you feel as though you are

really a part of this community?

Yes

No

11. Do you feel as though you are

really a part of [NAME OF

VILLAGE]?

Yes

No

Question removed. Change made

because majority of respondents did

not understand what ―try to take

advantage of‖ meant.

9. Do you think that the majority of

people in this community would try

to take advantage of you if they got

the chance?

Yes

No

172

Annex 4: Exploratory data analysis for relationship between social

capital and self-rated health

MPlus does not have the capacity to easily explore data when running multilevel

structural equation modeling. Hence, the exploratory data analysis for this paper was conducted

using STATA and ―manifest‖ measure of social capital. For individuals, this was a score that

ranged from 0-12 and was then group-mean centered to isolate variation within villages. For

communities, we created an average for each of the 110 villages in our sample based on the

individual social capital scores for the 15 people in each village. This mean score enabled us to

examine variation between villages.

We first examined the proportion of variation in self-rated health that was explained by

differences between communities (the ICC), which was 20%. We examined the relationship

between self-rated health and each continuous variable in our model using a logit transformed

lowess curve. We then used t-tests to examine the equality of means for people who reported

poor/fair health and people who reported good/very good health. For categorical variables, we

conducted chi square tests to examine whether the distribution of our independent variable was

similar or different according to self-rated health.

Next, we also isolated within and between effects for self-rated health by group mean

centering the value reported by an individual, and then also creating a village average. This

allowed us to visualize within and between effects. We examined two-way scatter plots between

group-mean centered self-rated health and the group mean centered (individual) social capital

variables (examination of within effects). We also explored two-way scatter plots between

average self-rated health in a village and average (community) social capital in a village

(examination of between effects).

173

Figure A4.1 presents the lowess curve of self-rated health and community social capital,

whereas Figure A4.2 presents the two-way scatter plot of average self-rated health in a village

and average social capital in a village (along with a best-fitted line and lowess curve). Consistent

with the findings in our analysis, there is a negative association between self-rated health and

community social capital.

Figure A4.1: Logit transformed lowess curve of self-rated health and community social capital

Figure A4.2: Scatterplot of average community self-rated health scores and community social

capital

*Best fitted line is presented red, lowess curve is presented in green

174

Annex 5: Results of multilevel regression analysis examining

association between social capital and self-rated health

Table A5.1 presents the models that used a latent and manifest measure social capital.

The manifest measure of individual structural and cognitive social capital are group mean

centered scale scores between 0-7 and 0-5 respectively. The manifest community social capital

score is an average scale score for the 15 individuals in each village.

Table A5.1: Association between social capital and self-rated health using multilevel regression

Covariate Model 2 - latent Model 2 - manifest

Est. SE. Est. SE

Individual level

Structural social capital -0.05

(-0.04)

0.05 -0.03

(-0.04)

0.03

Cognitive social capital -0.01

(-0.01)

0.04 0.00

(0.00)

0.02

Sex (ref: male)

Female -0.20*

(-0.19)

0.09 -0.21*

(-0.20)

0.09

Age -0.02***

(-0.29)

0.00 -0.02***

(-0.28)

0.00

Education 0.07

(0.03)

0.09 0.09

(0.03)

0.09

Marital status (ref: not married)

Married, Gauna not performed 0.10

(0.09)

0.10 0.09

(0.08)

0.10

Married -0.15

(-0.14)

0.12 -0.14

(-0.13)

0.12

Occupation (ref: non-agriculture)

Agriculture -0.02

(-0.02)

0.07 -0.02

(-0.02)

0.07

Household assets 0.07***

(0.13)

0.02 0.07***

(0.13)

0.02

Community level

Social capital -0.26***

(-0.48)

0.06 -0.25***

(-0.33)

0.08

Average education -0.19

(-0.10)

0.48 -0.15

(-0.05)

0.45

Average household assets 0.05

(0.16)

0.05 0.04

(0.13)

0.04

175

Covariate Model 2 - latent Model 2 - manifest

Est. SE. Est. SE

Geographic region (ref: Urban district)

Rural district -0.24

(-0.44)

0.16 -0.24

(-0.44)

0.14

Remote/rural district -0.85***

(-1.54)

0.16 -0.75***

(-1.36)

0.16

N (individual) 1592 1592

N (community) 110 110 Standardized (unstandardized) probit coefficients; *p < = .05 **p < = .01 ***p < = .001

176

Annex 6: Results of multilevel regression analysis examining

association between social capital and self-rated health stratified by

sex

Table A6.1 presents the nine social capital items disaggregated by sex. Women and men

provide statistically different responses for all nine social capital items with the exception of trust

in leaders. Most notably, women are less likely to join with others in the community to address a

problem or common issue in their community, speak with authorities about a common issue in

their community.

Table A6.1: Self-rated health and social capital items disaggregated by sex

Indicator Total

(N)

(N=1614)

Sex P-

value Male

(%)

(45.66%)

Female

(%)

(54.34%)

0. Self rated health

Poor/Fair 710 46.13 42.19 0.11

Good/Very good 904 53.87 57.81

1. Group support: In the last 12 months, received support

(emotional, financial, or other kinds) from:

Nobody 1234 71.51 80.62

<0.001 Bonding/bridging or linking groups 337 25.92 16.65

Bonding/bridging and linking groups 43 2.58 2.74

2. Individual support: In the last 12 months, received support

(emotional, economic, or other kinds) from:

Nobody 304 17.50 19.95

0.01 Bonding/bridging or linking individuals 913 54.41 58.38

Bonding/bridging and linking individuals 397 28.09 21.66

3. Join: In the last 12 months, joined together with

other community members to address a

problem or common issue

No 1215 67.03 82.53 <0.001

Yes 395 32.97 17.47

177

Indicator Total

(N)

(N=1614)

Sex P-

value Male

(%)

(45.66%)

Female

(%)

(54.34%)

4. Authorities: In the last 12 months, talked with a local

authority or governmental organization

about problems in village

No 1410 79.70 94.18 <0.001

Yes 200 20.30 5.82

5. Vote: Voted in the last state or national election

No 173 6.40 14.38 <0.001

Yes 1437 93.60 85.62

6. Trust neighbors: In general, trust all neighbors in village

None 60 3.01 4.37

0.04 Some 327 18.33 22.21

All 1213 78.66 73.42

7. Trust leaders: In general, trust all leaders in village

None 140 7.67 9.61

0.39 Some 419 26.44 25.86

All 1045 65.89 64.53

8. Trust strangers: In general, trust all strangers in village

None 510 28.75 34.21 0.01

Some 679 46.19 38.90

All 419 25.07 26.89

9. Belong: Feel as though really a part of village

No 40 0.96 3.78 <0.001

Yes 1566 99.04 96.22

We examined the bivariate association between each of the 9 social capital items and

their relationship with self-rated health stratified by sex. For many of the indicators, the direction

and magnitude of the association between social capital and self-rated health is similar.

However, women who received more group support and joined with others within the

community to address a common problem were less likely to report good or very good health.

Men who trusted their neighbors were less likely to report good or very good health.

178

Table A6.2: Bivariate association between social capital items and self-rated health,

disaggregated by sex

Social capital item Male

Est. (SE) Female

Est. (SE)

1. Group support -0.05 (0.08) -0.22 (0.07)**

2. Individual support -0.09 (0.10) -0.01 (0.09)

3. Join 0.04 (0.11) -0.26 (0.12)*

4. Authorities 0.04 (0.13) 0.25 (0.40)

5. Vote -0.31 (0.21) -0.26 (0.20)

6. Trust neighbors -0.21 (0.10)* 0.05 (0.08)

7. Trust leaders -0.09 (0.08) -0.03 (0.07)

8. Trust strangers -0.08 (0.07) -0.05 (0.06)

9. Belong 0.39 (0.49) -0.36 (0.26) Probit coefficient (standard error); *p<0.05 **p<0.01 ***p<0.001

When we examined the single level factor structure of the nine social capital items, a

two-factor model fit the data for both men and women. However, as Table A6.3 presents, the

magnitude of the factor loadings differed when comparing men and women.

Table A6.3: Model fit statistics for single level social capital scales, disaggregated by sex

Model x2 Df CFI TLI RMSEA

Male 58.63 19 0.96 0.94 0.53

Female 105.44 26 0.93 0.90 0.60

179

Table A6.4 Standardized factor loadings for nine social capital items, disaggregated by sex

Total Male Female

Factor loadings

Group support 0.51***

(0.04)

0.45***

(0.05)

0.59***

(0.06)

Individual support 0.48***

(0.03)

0.45***

(0.05)

0.54***

(0.06)

Join with community 0.84***

(0.04)

0.88***

(0.05)

0.71***

(0.06)

Talk with authorities 0.82***

(0.04)

0.83***

(0.05)

0.71***

(0.07)

Vote 0.37***

(0.05)

0.32***

(0.09)

0.33***

(0.07)

Trust neighbors 0.88***

(0.04)

0.99***

(0.09)

0.80***

(0.05)

Trust leaders 0.78***

(0.04)

0.69***

(0.07)

0.86***

(0.05)

Trust strangers 0.33***

(0.03)

0.35***

(0.06)

0.30***

(0.04)

Belong 0.64***

(0.06)

-

-

0.68***

(0.07)

Factor 1 with Factor 2 0.27***

0.19**

0.32***

(0.04) (0.07) (0.05)

N 1642 738 878

Standardized factor loading (standard error); *p<0.05 **p<0.01 ***p<0.001

Note: there was no variation in the ―Belong‖ item for men, so it was dropped from the analysis

Due to the smaller sample size in each group, it was difficult to run a multilevel

confirmatory factor analysis and multilevel structural equation model in MPlus for men and

women separately. We therefore examined the relationships between social capital and self-

rated health using a manifest measure (scale score) for social capital. We calculated a group-

mean centered individual structural and cognitive scale score, as well as an average social capital

score for both men and women. We present the final model of the paper for the entire sample,

and disaggregated by sex in Table A6.5. As the table illustrates, the relationships between social

capital and health for men and women are similar.

180

Table A6.5: Association between social capital and self-rated disaggregated by sex

Covariate Total Men Women

Est. SE Est. SE Est. SE

Individual level

Structural social capital -0.03

(-0.04)

0.03 0.01

(0.01)

0.04 -0.05

(-0.05)

0.04

Cognitive social capital 0.00

(0.00)

0.02 -0.04

(-0.04)

0.04 0.04

(0.06)

0.04

Sex (ref: male)

Female -0.21*

(-0.20)

0.09

Age -0.02***

(-0.28)

0.00 -0.02***

(-0.27)

0.00 -0.02***

(-0.25)

0.00

Education 0.09

(0.03)

0.09 0.10

(0.04)

0.13 0.11

(0.04)

0.13

Marital status (ref: not

married)

Married, Gauna not

performed

0.09

(0.08)

0.10 -0.09

(-0.08)

0.16 0.27

(0.25)

0.14

Married -0.14

(-0.13)

0.12 -0.11

(-0.10)

0.18 -0.14

(-0.13)

0.17

Occupation (ref: non-

agriculture)

Agriculture -0.02

(-0.02)

0.07 0.06

(0.06)

0.12 -0.04

(-0.04)

0.10

Household assets 0.07***

(0.13)

0.02 0.07*

(0.13)

0.03 0.07***

(0.14)

0.03

Community level

Social capital -0.25***

(-0.33)

0.08 -0.18*

(-0.29)

0.08 -0.21***

(-0.37)

0.08

Average education -0.15

(-0.05)

0.45 0.08

(0.02)

0.56 -0.39

(-0.12)

0.45

Average household assets 0.04

(0.13)

0.04 0.03

(0.10)

0.05 0.09

(0.27)

0.05

Geographic region (ref: urban

district)

Remote -0.24

(-0.44)

0.14 -0.31

(-0.55)

0.20 -0.16

(-0.31)

0.18

Remote/rural -0.75***

(-1.36)

0.16 -0.92***

(-1.63)

0.21 -0.61**

(-1.18)

0.20

N (individual) 1592 722 870

N (community) 110 110 110 Unstandardized coefficient (standardized coefficient); *p<0.05 **p<0.01 ***p<0.001

181

Figure A6.1 presents the statistically significant relationship between community social

capital and average self-rated health within a community for the entire sample, and for men and

women separately.

Figure A6.1: Scatterplot of average community self-rated health scores and community social

capital

Total Male Female

Lastly, we ran a model with an interaction term between social capital and self-rated

health using our entire sample and a manifest measure of social capital. We found that there

was no statistically significant difference in the relationship between social capital and self-rated

health between men and women.

Table A6.6: Association between social capital and self-rated health, with interaction between

social capital and sex

Covariate Estimate Standard

Error

Individual level

Sex (ref: male)

Female -0.38

(-0.36)

0.82

Structural social capital -0.01

(-0.01)

0.04

Structural social capital x sex -0.06

(-0.04)

0.06

Cognitive social capital -0.05

(-0.07)

0.04

182

Covariate Estimate Standard

Error

Cognitive social capital x sex 0.09 0.06

(0.09)

Age -0.02***

(-0.28)

0.00

Education 0.09

(0.04)

0.10

Marital status (ref: Not married)

Married, Gauna not performed 0.10

(0.09)

0.10

Married -0.14

(-0.13)

0.12

Occupation (ref: non-agriculture)

Agriculture -0.02

(-0.02)

0.08

Household assets 0.07***

(0.13)

0.02

Community level

Social capital -0.27*

(-0.35)

0.11

Social capital x sex 0.03

(0.08)

0.12

Average education -0.15

(-0.04)

0.46

Average household assets 0.05

(0.14)

0.04

Geographic region (Urban district)

Remote district -0.24

(-0.44)

0.14

Remote/rural district -0.75***

(-1.34)

0.16

N (individual) 1592

N (community) 110 Unstandardized coefficient (standardized coefficient); *p<0.05 **p<0.01 ***p<0.001

183

Annex 7: Summary of quantitative data according to

implementation outcome

Table A7.1: Indicators presented in Paper 2 to describe fidelity of the Communitization Act

according to health system function

Indicator N/Mean %/SD Source

Hea

lth

Work

forc

e

Nurse is present at this facility during working hours* HH

Never 16 1.2%

Sometimes 186 14.2%

Always 872 66.7%

Don‘t know 234 17.9%

Doctor is present at this facility during working hours* HH

Never 168 13.1%

Sometimes 459 35.9%

Always 379 29.6%

Don‘t know 273 21.3%

HW satisfaction with supervisor support HW

Very unsatisfied 6 3.4%

Unsatisfied 16 8.9%

Satisfied 137 76.5%

Very satisfied 18 10.1%

Don‘t know 2 1.1%

HW satisfaction with management of health facility HW

Very unsatisfied 15 8.4%

Unsatisfied 41 22.9%

Satisfied 118 65.9%

Very satisfied 5 2.8%

Number of months during past year HW salary not received HW

0 months 131 73.2%

>0 months 48 26.8%

Hea

lth

Fin

an

ce

Committee approved annual budget of facility 49 50.5% HF

Committee reviewed expenditures of facility

(monthly, quarterly or annually)

64 66.0% HF

Facility maintained book of accounts 43 44.3% HF

Committee raised funds from community in past 6

months

8 8.3% HF

Committee raised in-kind contributions for health

facility in past 6 months

9 9.9% HF

Info

rma

tion

Facility maintained health management information

system (HMIS) report

86 88.7% HF

HMIS report was discussed during a HCMC/VHC

meeting during past 3 months

37 20.7% HW

184

Indicator N/Mean %/SD Source M

edic

ines

Drugs available on day of survey HF

Paracetamol (tablet) 58 59.8%

Chloroquine Phosphate 32 33.0%

Zinc sulphate 17 17.5%

Oral rehydration salt 54 55.7%

Tetanus toxoid 59 60.8%

HW satisfaction with availability of drugs, supplies and equipment HW

Very unsatisfied 43 24.0%

Unsatisfied 102 57.0%

Satisfied 30 16.8%

Very satisfied 4 2.2%

Gover

nan

ce

Committee mobilized community to use health

services in past 6 months

10 11.11% HF

Facility received health related training in past

month (during supervision visit)

19 19.6% HF

Facility received administrative related training in

past month (during supervision visit)

15 15.5% HF

Facility received Department of Health and Family

Welfare supervision visit in past 6 months

46 47.4% HF

Facility received Chief Medical Officer supervision

visit in past 6 months

40 41.2% HF

Ser

vic

e D

eliv

ery

Committee provide new supplies/equipment for

facility in past 6 months

13 14.3% HF

Committee provide new infrastructure for facility in

past 6 months

10 11.0% HF

Committee made repairs to facility for facility in

past 6 months

17 18.7% HF

Distance from facility to pucca road 5.6 13.2 HF

Distance from facility to District Hospital 40.9 23.9 HF

Committee organized VHND in past 6 months 23 25.6% HF

Number of VHNDs held by facility in past 6 months 8.1 11.0 HF

Acc

e

pta

bil

ity

HW thinks HCMC/VHC helps to improve service

delivery

129 72.1% HW

Ap

pro

pri

ate

nes

s

HW serving on committee who trust other members

on the HCMC/VHC to work in the best interest of

the facility***

84 90.3% HW

Highest level of education within household HH

Did not complete primary education 108 7.5%

Complete primary school 527 36.5%

Completed high school 339 23.5%

Completed higher secondary school 211 14.6%

Completed undergraduate degree 137 9.5%

Completed undergraduate certificate 71 4.9%

185

Indicator N/Mean %/SD Source

Completed postgraduate degree 44 3.0%

Completed postgraduate certificate 8 0.6%

Occupation HH

Non-agriculture 707 48.9%

Agriculture 739 51.1%

Fea

sib

ilit

y

Received funds from Village Council in 2014** 0 0.0% HF

Time for HW to travel from residence to the facility 18.0 23.4 HW

HW satisfaction with amount of salary HW

Very unsatisfied 11 6.2%

Unsatisfied 32 17.9%

Satisfied 119 66.5%

Very satisfied 17 9.5% HF = Health Facility Survey (N=97)

HH = Household Survey (N = 1642)

HW = Health Worker Survey (N=179)

*Conditioned on households who knew where government health facility was located (N=1440)

**Conditioned on health facilities that maintained a book of accounts (N = 43)

***Among health workers serving on a committee (N=93)

186

Annex 8: Descriptive statistics for health committee functioning

index

Table A8.1: Tetrachoric correlation of health committee functioning index

Items 1 2 3 4 5 6 7 8 9 10 11 12

1. Meeting 1.00

2. Approve salary -0.09 1.00

3. Approve budget 0.50 0.50 1.00

4. Monitor expenditure 0.67 0.08 0.84 1.00

5. Raise funds 0.17 0.20 -0.10 0.32 1.00

6. HMIS 0.34 -0.41 -0.23 0.16 1.00 1.00

7. Drug register 0.08 0.13 0.15 0.35 0.13 0.22 1.00

8. Provide drugs 0.36 -0.15 0.36 0.41 0.32 -0.01 0.49 1.00

9. Mobilize community 0.26 0.61 0.58 0.41 0.12 -0.52 -0.03 0.01 1.00

10. Repair

infrastructure/equipment 0.31 -0.07 0.43 0.44 0.50 -0.01 0.14 0.84 0.47 1.00

11. Raise in-kind 0.26 0.12 0.07 0.41 0.12 1.00 -0.03 0.37 0.32 0.33 1.00

12. VHND 0.13 0.62 0.59 0.46 0.07 -0.32 0.21 0.34 0.60 0.50 0.32 1.00

We considered the 12 items to be an index of health committee functioning. However, if

we consider them to be a scale (arising from one common latent variable), then we would also

consider the reliability and construct validity of the items. As a scale, the 12 items have fair

reliability. Items 5, 6 and 11 had lower factor loadings. However, we kept these items in the

analysis to represent the full range of committee functions, as described in the Communitization

Act.

KR-20 measure of reliability for 12 items: 0.67

187

Table A8.2: Confirmatory factor analysis of 12 health committee functioning items

Item Estimate Standard Error

1. Meeting 0.56*** 0.11

2. Approve salary 0.52*** 0.13

3. Approve budget 0.73*** 0.10

4. Monitor expenditure 0.90*** 0.09

5. Raise funds 0.31* 0.15

6. HMIS -0.02 0.17

7. Drug register 0.28* 0.14

8. Provide drugs 0.76*** 0.09

9. Mobilize community 0.68*** 0.10

10. Repair infrastructure/equipment 0.79*** 0.09

11. Raise in-kind 0.38 0.21

12. VHND 0.69*** 0.11 Standardized factor loadings; *p<0.05 **p<0.01 ***p<0.001

188

Annex 9: Results of linear regression analysis examining association

between social capital and health committee functioning

Table A6.1 presents the final models that used a manifest measure of community social capital

(average scale score between 0-12 for the 15 individuals in each village). We also present the

final latent model, which was presented in the main body of paper 3, as a point of comparison.

As the table demonstrates, the results in both models were similar.

Table A9.1: Final models using manifest social capital

Indicator Manifest Latent

Model 1 Model 2 Model 3 Model 4 Model 4

Est. SE Est. SE Est. SE Est. SE Est. SE

Community

social capital -0.37 0.33 -0.42 0.23 -0.20 0.33 -0.12 0.32 0.14 0.24

Community Indicators Avg. asset

score 0.21 0.18 0.14 0.17 -0.03 0.18 -0.03 0.18

Avg. education

score -0.65 0.55 -0.58 0.53 -0.29 0.52 -0.25 0.45

Committee Indicators Number of

women on

committee

0.54**

0.18 0.48* 0.19 0.49

* 0.24

Number of

community

groups on

committee

0.04 0.10 0.00 0.10 0.00 0.12

Health Facility Indicators Facility type

(Ref: Sub

Center)

PHC/CHC -0.11 0.55 -0.07 0.51

Received

government

funds (Ref:

No)

Yes 0.99* 0.45 1.00* 0.47

Number of

DHFW

supervision

visits

0.51* 0.24 0.51* 0.24

R2 0.01 0.03 0.16 0.24 0.24

Number of

observations

97 97 97 97

1446

Unstandardized coefficients presented; *p < = .05 **p < = .01 ***p < = .001

189

Annex 10: Exploratory data analysis and linear regression model

diagnostics for association between social capital and health

committee functioning

The exploratory data analysis and linear regression diagnostics presented below were run

in STATA, using a manifest measure of social capital (average scale score between 0-12 for the

15 individuals in each village). The diagnostics were run on the final model of the analysis

between social capital, community, committee and health facility characteristics and the health

committee functioning index.

Exploratory Data Analysis

Figure A10.1: Distribution of health committee functioning index (HCF)

Figure A10.2: Distribution of community social capital scores (SC)

190

Figure A10.3: Scatterplot between health committee functioning index (HCF) and community

social capital scores (SC)

Multicoliniarity

We examined variance inflation factors (vif) of each indicator to ensure that none of the

explanatory indicators were linearly related to one another. The vifs ranged from 1.03 to 2.06 – a

vif above 10 would indicate potential issues with multicolineairy.

Influential data

We examined outliers (observations with large residuals), as well as the leverage (an

observation with an extreme value on a given predictor) and influence (a product of being an

outlier and having high leverage) of each observation. Observations that were outliers, or that

had high leverage or influence could change the results of the analysis substantially if they were

removed. We examined these influential points in more detail to understand how they

influenced the results of the analysis.

To examine outliers, we produced a stem and leaf plot of the studentized residuals. We

focused on the observations that had residuals +/- 2 standard deviations. We determined that

facilities with the IDs of 26, 40 and 67 had the largest residuals respectively.

191

Table A10.1: Stem and leaf plot of studentized residuals -1** | 89,87

-1** | 79,67,64,63 -1** | 45

-1** | 31,29

-1** | 17,16,14,08,08,07,06,03 -0** | 96,93,92,87,84,81,81,80

-0** | 75,72,70,63

-0** | 58,56,51,49,49,43 -0** | 37,37,32,31,27,23

-0** | 13,11,10,09,08,02,02,01

0** | 02,03,04,06,07,10,12,13,17,17,18,19 0** | 20,25,25,30,30,30,30

0** | 40,44,48,52,58

0** | 75,79,79 0** | 88,91,92,92

1** | 02,04,08,14,15

1** | 28,28,34 1** | 40,46

1** | 66,67,78

1** | 84 2** |

2** | 32

2** | 2** | 70

2** | 94

Next, we predicted the leverage of each data point. We identified observations that had a

value greater than (2k+2)/n, where k was the number of explanatory variables in the model (8)

and n was the number of observations (97). We found that facilities with the IDs of 53, 87 and

15 had a leverage value greater than 0.186.

Table A10.2: Stem and leaf plot of observation leverage

plot in units of .001 3* | 25779

4* | 112589

5* | 1225556899 6* | 113347888

7* | 22357799

8* | 12446788 9* | 3457889

10* | 2466779

11* | 12 12* | 126

13* | 116

14* | 89 15* | 13

16* | 148

17* | 89 18* | 38

19* | 8

20* | 21* |

22* | 9

192

In the graph below, we plotted the residuals and the leverage of each observation. The

observations that had both high residuals and leverage (15, 51) are those that needed further

examination.

Figure A10.4: Plot of leverage versus normalized residuals squared to assess influential data

points

193

Finally, we examined the Adjusted Variable Plots (AVplots) for each regression

coefficient. AVplots graph the relationship between a single predictor and the outcome variable

after it was adjusted for the other covariates in the model. The slope of the line in the plot is

equal to the regression coefficient. The plots illustrated outliers and influential points.

Figure A10.5: Adjusted Variable Plot

When we removed the variables with high residuals, high leverage and high influence

(table B.3), we found that the results of our model did not change substantially.

194

Table A10.3: Association between health committee functioning index and social capital,

controlling for features of the committee, community and facility

Indicator Full Observations

with high

residuals

removed

Observations

with high

leverage

removed

Observations

with high

influence

removed

Community social capital -0.12 -0.03 -0.24 0.03

Community Indicators

Average asset score -0.03 -0.13 -0.10 -0.01

Average education score -0.29 0.01 -0.13 -0.32

Committee Indicators

No. women on committee 0.48* 0.58

** 0.54

** 0.50

**

No. community groups 0.00 0.01 0.08 0.06

Health Facility Indicators

PHC/CHC (ref: PHC) -0.11 0.55 -0.13 -0.38

Received government funds (ref: no) 0.99* 1.27

** 1.14

* 1.23

**

No. DHFW supervision visits 0.51* 0.36 0.56

* 0.44

*p < = .05 **p < = .01 ***p < = .001

Normality of residuals

An assumption of linear regression is that the error terms are normally distributed. We

tested this assumption using a Kernel Density plot. The Kernel density estimate was closely

overlaid with the normal density plot, indicating that the residuals were normally distributed.

Figure A10.6: Kernel Density plot of final linear regression model

195

We further test this assumption we used a Shapiro-Wilk test, which tested they

hypothesis that the distribution was normal. We obtained a p-value of 0.17, so we failed to reject

ouR hypothesis that the residuals were normally distributed.

Homoscedasticity of Residuals

Another assumption of linear regression is homogeneity of variance of the residuals. We

tested this using Breusch-Pagan‘s test, which tested the null hypothesis that the variance of the

residuals was homogeneous. With a chi square value of 0.16 and a p-value of 0.69, we failed to

reject the null hypothesis. We concluded that the variance of the residuals was homogeneous.

Linearity

Linear regression also assumes that each of the covariates is linearly related to the

outcome. To test this assumption, we examined scatterplots of each predictor and the outcome of

interest (not presented here). To examine linearity when there were multiple covariates in a

model is more complex. One way to examine this assumption when conducting multiple

regression was to plot the standardized residuals against each covariate in the model. The plot

should produce a non-linear pattern. When we examined these plots, we did not identify any

linear patterns.

Analysis of results stratified by health facility type

In Nagaland, the government distinguished health committees that were serving in

villages and SCs from those that were serving at PHCs or CHC. We therefore examined our

results stratified by health facility type. The boxplot below illustrates the distribution of the

health committee functioning index by SC versus PHC and CHC.

196

Figure A10.7: Distribution of health committee functioning index by health facility type

The scatterplots below illustrate the relationship between social capital and health

committee functioning, disaggregated by health facility type (SC versus PHC/CHC).

Figure A10.8: Scatterplot of health committee functioning index versus social capital score,

disaggregated by health facility type

Sub-Centers PHC/CHCs

Table A7.4 presents the association between social capital and health committee

functioning stratified by health facility type. The association was stronger for PHC/CHCs, yet

neither were statistically significant.

Table A10.4: Association between health committee functioning index and community social

capital, disaggregated by health facility type

Indicator Sub Centers PHC/CHC

Est. SE Est. SE

Community social capital -0.11 0.60 -0.48 0.40 *p < = .05 **p < = .01 ***p < = .001

197

Finally, table A7.5 presents model 4 (see results of our paper) with an interaction term

added between health facility type and social capital. The non-significant interaction term

indicated that the relationship between social capital and health committee functioning did not

differ according to facility type.

Table A10.5: Association between health committee functioning index and social capital, with

interaction between social capital and health facility type

Indicator Estimate Standard

Error

Community social capital -0.08 0.65

Facility type (Reference: Sub Center)

PHC/CHC 0.27 5.1

Interaction term (SC*PHC/CHC) -0.05 0.75

Community Indicators

Average asset score -0.03 0.19

Average education score -0.28 0.54

Committee Indicators

Number of women serving on committee 0.48* 0.19

Number of community groups on committee 0.00 0.10

Health Facility Indicators

Received government funds (Reference: No)

Yes 0.99* 0.45

Number of DHFW supervision visits 0.51* 0.24

*p < = .05 **p < = .01 ***p < = .001

We conducted the same analysis to examine whether the relationship between social

capital and health committee functioning differed by whether or not the facility received funding.

We also found that it did not (results not presented here).

198

Annex 11: Mplus code for final models

The Mplus code for our final model in paper 1 is as follows:

Title:

Final model, paper 1

Data:

File is srh_10_24_17.dta.dat ;

Variable:

Names are

q_7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_08 q_7_09

asset_score_c asset_avg health occ female age married married_g educ educ_avg

tribe rural rural_remote village_code;

Missing are all (-9999) ;

Usevariables = 7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_09

asset_score_c asset_avg health occ female age married married_g educ educ_avg

tribe rural rural_remote;

Categorical = 7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_09

health;

Cluster = village_code;

Within = asset_score_c occ female age married married_g educ;

Between = educ_avg asset_avg tribe rural rural_remote;

Analysis:

Type = twolevel;

Estimator = wlsmv;

Model:

%within%

sc1 by q_7_01*;

sc1 by q_7_02;

sc1 by q_7_03;

sc1 by q_7_04;

sc1 by q_7_05;

sc1 @1;

sc2 by q_7_06*;

sc2 by q_7_07;

sc2 by q_7_09;

sc2@1;

health on sc1 sc2 asset_score_c occ female age married married_g educ;

%between%

sc3 by q_7_01*;

sc3 by q_7_02;

sc3 by q_7_03;

sc3 by q_7_04;

199

sc3 by q_7_05;

sc3 by q_7_06;

sc3 by q_7_07;

sc3 by q_7_09;

health on sc3 asset_avg educ_avg tribe rural rural_remote;

Output:

standardized;

The MPlus code for the final model in paper 3 is as follows:

Title:

Final model, paper 2

Data:

File is hcf_1_29_18.dta.dat ;

Variable:

Names are

q_7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_08 q_7_09

c1 asset_avg edu_index q4_4 q4_5tot hf_typ gov_fundb q3_6 village_code;

Missing are all (-9999);

Usevariables = q_7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_08 q_7_09

asset_avg edu_index q4_4 q4_5tot hf_typ gov_fundb q3_6;

Categorical = q_7_01 q_7_02 q_7_03 q_7_04 q_7_05 q_7_06 q_7_07 q_7_08 q_7_09;

Cluster = village_code;

Within = ;

Between = c1 asset_avg edu_index q4_4 q4_5tot hf_typ gov_fundb q3_6;

Analysis:

Type = twolevel;

Estimator = wlsmv;

Model:

%within%

sc1 by q_7_01*;

sc1 by q_7_02;

sc1 by q_7_03;

sc1 by q_7_04;

sc1 by q_7_05;

sc1 @1;

sc2 by q_7_06*;

sc2 by q_7_07;

sc2 by q_7_09;

sc2@1;

%between%

sc3 by q_7_01*;

200

sc3 by q_7_02;

sc3 by q_7_03;

sc3 by q_7_04;

sc3 by q_7_05;

sc3 by q_7_06;

sc3 by q_7_07;

sc3 by q_7_09;

sc3@1;

q_7_01@0;

c1 on sc3 asset_avg edu_index q4_4 q4_5tot hf_typ gov_fundb q3_6

Output:

standardized;

201

References

(1) Hanifan LJ. The Rural School Community Center. Ann Am Acad Pol Soc Sci 1916;67:130-138.

(2) Jacobs J. The Death and Life of Great American Cities. New York: Random House; 1961.

(3) Loury G. A Dynamic Theory of Racial Income Differences. In: Wallace PA, Le Mund A, editors.

Women, Minorities an Employment Discrimination Lexington, MA: Lexington Books; 1977.

(4) Bourdieu P. The forms of capital. In: Richardson J, editor. Handbook of Theory and Research for the

Sociology of Education New Yrok: Greenwood; 1986. p. 241-258.

(5) Coleman JS. Social capital in the creation of human capital. Am. J. Sociol. 1988;94:S95-121.

(6) Coleman J. Foundations of Social Theory. Cambridge; MA: The Belknap Press of Harvard University

Press; 1990.

(7) Schlicht E. Koordinationskosten und Social Kapital. In: Schlieper U, Schmidtchen D, editors.

Economics: University of Munich, Department of Economics; 1993.

(8) Putnam R. Making Democracy Work: Civic Traditions in Modern Italy. Princeton: Princeton

Universtiy Press; 1993.

(9) Putnam R. Bowling Alone: America‘s Declining Social Capital. Journal of Democracy 1995;6(1):65-

78.

(10) Putnam RD. Bowling Alone: The Collapse and Revival of American Community. New York, NY:

Simon and Schuster Paperbacks; 2000.

(11) Portes A. Social Capital: Its Origins and Applications in Modern Sociology. Annu. Rev. Sociol.

1998;24:1-24.

(12) Portes A. The Two Meanings of Social Capital. Sociological Forum 2000;15(1):1-12.

(13) Portes A, Landolt P. The downside of social capital. The American Prospect 1996;7(26).

(14) Clinton WJ. January 24, 1995: State of the Union Address. 1995; Available at:

https://millercenter.org/the-presidency/presidential-speeches/january-24-1995-state-union-address.

Accessed February 1, 2018.

(15) Isham J, Kelly T, Ramaswamy S. Social Capital and Well-Being in Developing Countries: An

Introduction. In: Isham J, Kelly T, Ramaswamy S, editors. Social Capital and Economic Development:

Well-being in Developing Countries Northampton: Edward Elgar Publishing Limited; 2002.

202

(16) Moore S, Haines V, Hawe P, Shiell A. Lost in translation: a genealogy of the "social capital" concept

in public health. J Epidemiol Community Health 2006 Aug;60(8):729-734.

(17) Moore S, Kawachi I. Twenty years of social capital and health research: a glossary. J Epidemiol

Community Health 2017 May;71(5):513-517.

(18) Grootaert C. Social Capital: The Missing Link? Washington, DC: World Bank; 1998.

(19) Kawachi I, Kennedy BP, Lochner K, Prothrow-Stith D. Social capital, income inequality, and

mortality. Am J Public Health 1997 Sep;87(9):1491-1498.

(20) Scheffler RM, Brown TT. Social capital, economics, and health: new evidence. Health Econ Policy

Law 2008 Oct;3(Pt 4):321-331.

(21) Kim D, Subramanian SV, Kawachi I. Social Capital and Physical Health: A Systematic Review of

the Literature. In: Kawachi I, Subramanian SV, Kim D, editors. Social Capital and Health New York,

NY: Springer Science; 2008.

(22) Kawachi I, Berkman L. Social capital, social cohesion and health. In: Berkman, L.F., Kawachi, I.,

Glymour, M.M., editor. Social Epidemiology. 2nd ed. New York, NY: Oxford University Press; 2014. p.

290.

(23) De Silva MJ, Harpham T. Maternal social capital and child nutritional status in four developing

countries. Health Place 2007 Jun;13(2):341-355.

(24) De Silva MJ, Huttly SR, Harpham T, Kenward MG. Social capital and mental health: a comparative

analysis of four low income countries. Soc Sci Med 2007 Jan;64(1):5-20.

(25) Sivaram S, Zelaya C, Srikrishnan AK, Latkin C, Go VF, Solomon S, et al. Associations between

social capital and HIV stigma in Chennai, India: considerations for prevention intervention design. AIDS

Educ Prev 2009 Jun;21(3):233-250.

(26) Story WT. Social capital and the utilization of maternal and child health services in India: a

multilevel analysis. Health Place 2014 Jul;28:73-84.

(27) Story WT, Carpiano RM. Household social capital and socioeconomic inequalities in child

undernutrition in rural India. Soc Sci Med 2017 May;181:112-121.

(28) Rawal I, Manmeet Kaur, Poonam Khanna, PVM Lakshmi, Navkiran Kaur. Association between

Social Capital and Self-rated Health of Older People in Chandigarh, India. Indian Journal of Community

Health 2017;9(2).

(29) Vikram K. Social capital and child nutrition in India: The moderating role of development. Health

Place 2018 03/01;50:42-51.

(30) Pai S. Social Capital, Panchayats and Grass Roots Democracy: Politics of Dalit Assertion in Uttar

Pradesh. Economic and Political Weekly 2001;36(8):645-654.

203

(31) Jayal NG. Democracy and Social Capital in Central Himalaya: Tale of Two Villages. Economic and

Political Weekly 2001;36(8):655-664.

(32) Serra R. Social Capital: Meaningful and Measurable at the State Level? Economic and Political

Weekly 2001;36(8):693-704.

(33) Galab S, Piush Antony, Ian Wilson, Nicola Jones, Anne McCoy, D. Sree Rama Raju, et al. Exploring

linkages between maternal social capital and children‘s nutritional status in Andhra Pradesh.

2006;Working Paper No. 32.

(34) Bhattacharya D. 'Civic Community' and Its Margins: School Teachers in Rural West Bengal.

Economic and Political Weekly 2001;36(8):673-683.

(35) De Silva MJ, McKenzie K, Harpham T, Huttly SR. Social capital and mental illness: a systematic

review. J Epidemiol Community Health 2005 Aug;59(8):619-627.

(36) Almedom A, Glandon D. Social Capital and Mental Health: An Updated Interdisciplinary Review of

Primary Evidence. In: Kawachi I, Subramanian SV, Kim D, editors. Social Capital and Health New York,

NY: Springer Science.

(37) Derose KP, Varda DM. Social capital and health care access: a systematic review. Med Care Res

Rev 2009 Jun;66(3):272-306.

(38) Lindstrom M. Social Capital and Health-Related Behaviors. In: Kawachi I, Subramanian SV, Kim D,

editors. Social Capital and Health New York, NY: Springer Science and Business Media; 2008.

(39) Uphoff EP, Pickett KE, Cabieses B, Small N, Wright J. A systematic review of the relationships

between social capital and socioeconomic inequalities in health: a contribution to understanding the

psychosocial pathway of health inequalities. Int J Equity Health 2013 Jul 19;12:54-9276-12-54.

(40) Vyncke V, De Clercq B, Stevens V, Costongs C, Barbareschi G, Jonsson SH, et al. Does

neighbourhood social capital aid in levelling the social gradient in the health and well-being of children

and adolescents? A literature review. BMC Public Health 2013 Jan 23;13:65-2458-13-65.

(41) Agampodi TC, Agampodi SB, Glozier N, Siribaddana S. Measurement of social capital in relation to

health in low and middle income countries (LMIC): a systematic review. Soc Sci Med 2015 Mar;128:95-

104.

(42) Poortinga W. Social capital: an individual or collective resource for health? Social Science and

Medicine 2006;62(2):929-302.

(43) Story WT. Social capital and health in the least developed countries: a critical review of the literature

and implications for a future research agenda. Glob Public Health 2013;8(9):983-999.

(44) Villalonga-Olives E, Kawachi I. The dark side of social capital: A systematic review of the negative

health effects of social capital. Soc Sci Med 2017 10/21.

204

(45) Wakefield SE, Poland B. Family, friend or foe? Critical reflections on the relevance and role of

social capital in health promotion and community development. Soc Sci Med 2005 Jun;60(12):2819-

2832.

(46) Census Organisation of India. Nagaland Population Census Data 2011. 2011; Available at:

http://www.census2011.co.in/census/state/nagaland.html. Accessed April 1, 2016, 2016.

(47) Department of Planning & Coordination Government of Nagaland. Nagaland State Development

Report. New Delhi: Department of Planning & Coordination Government of Nagaland; 2004.

(48) Pruett GE. Christianity, history, and culture in Nagaland. Contributions to Indian Sociology 1974

01/01; 2017/12;8(1):51-65.

(49) Guha R. Tribal Trouble. In: Guha R, editor. India After Gandhi: The History of the World's Largest

Democracy; 2007. p. 261.

(50) Zagefka H, Jamir L. Conflict, fear and social identity in Nagaland. Asian Journal of Social

Psychology 2015 03;18(1):43-51.

(51) Shimray RA. Decentralization from Below: A Case Study of Nagaland, India. International Journal

of Scientific and Research Publications 2014;4(3).

(52) Siddiqui S, Shilpa Chohan. Legal Response to Institutionalizing Participatory Land Use Planning in

Nagaland. 2015.

(53) Bhore Committee. Report of the Health Survey and Development Committee. Delhi: Manager of

Publications; 1946.

(54) Government of India Ministry of Health and Family Welfare. National Health Policy. 1983;

Available at: http://www.communityhealth.in/~commun26/wiki/images/6/64/Nhp_1983.pdf. Accessed

2018, February 1.

(55) Government of India Ministry of Health and Family Welfare. National Health Policy. 2002;

Available at: http://apps.who.int/medicinedocs/documents/s18023en/s18023en.pdf. Accessed February 1,

2018.

(56) Ministry of Health and Family Welfare, Government of India. National Health Mission. 2016;

Available at: http://nrhm.gov.in/. Accessed February 28, 2016.

(57) Dhingra B, Dutta AK. National Rural Health Mission. The Indian Journal of Pediatrics 2011

12/01;78(12):1520-1526.

(58) Gupta I, Bhatia M. The Indian Healthcare System. 2016; Available at:

http://international.commonwealthfund.org/countries/india/. Accessed February 5, 2018.

(59) Ministry of Health and Family Welfare, Government of India. Indian Public Health Standards. 2012;

Available at: http://nrhm.gov.in/nhm/nrhm/guidelines/indian-public-health-standards.html.

205

(60) Das J, Aakash Mohpal. Socioeconomic Status and Quality of Care in Rural India: New Evidence

from Provider and Household Surveys. Health Affairs 2016;5(10):1764-1773.

(61) Das, Jishnu, Alaka Holla, Veena Das, Manoj Mohanan, Diana Tabak, Brian Chan. In Urban and

Rural India, a Standardized Patient Study Showed Low Levels of Provider Training and Huge Quality

Gaps. Health Affairs 2012;31(12):2774-2784.

(62) Mohanan, Manoj, Katherine Hay, Nachiket Mor. Quality of Health Care in India: Challenges,

Priorities, and the Road Ahead. Health Affairs 2016;35(10):1753-1758.

(63) Das J, Hammer J. Location, location, location: residence, wealth, and the quality of medical care in

Delhi, India. Health Aff (Millwood) 2007 May-Jun;26(3):w338-51.

(64) Rao KD, Sundararaman T, Bhatnagar A, Gupta G, Kokho P, Jain K. Which doctor for primary health

care? Quality of care and non-physician clinicians in India. Soc Sci Med 2013 May;84:30-34.

(65) National Health Systems Resource Centre. National Health Accounts Estimates for India (2014-15).

Delhi: Ministry of Health and Family Welfare, Government of India; 2017.

(66) Jakovljevic M, Potapchik E, Popovich L, Barik D, Getzen TE. Evolving Health Expenditure

Landscape of the BRICS Nations and Projections to 2025. Health Econ 2017 Jul;26(7):844-852.

(67) Indian Council of Medical Research, Public Health Foundation of India, and Institute for Health

Metrics and Evaluation. India: Health of the Nation's States - The India State-level Disease Burden

Initiative. New Delhi: ICMR, PHFI, and IHME; 2017.

(68) Department of Health and Family Welfare. Overview: Network of Health Centres in Nagaland. 2010.

(69) Ministry of Health and Family Welfare, Government of India. District Level Household and Facility

Survey 4: State Fact Sheet Nagaland. Mumbai: International Institute for Population Sciences; 2015.

(70) Government of Nagaland. State Programme Implementation Plan 2008-09. 2008.

(71) International Institute for Population Sciences. National Family Health Survey - 2015-16. Mumbai:

Government of India, Ministry of Health and Family Welfare; 2016.

(72) Pandey RS. Communitisation: The Third Way of Governance. New Delhi: Concept Publishing

Company; 2010.

(73) Government of Nagaland Health and Family Welfare Department. Handbook on Communitisation of

Health Centres. Kohima: Government of Nagaland; 2005.

(74) Department of Health and Family Welfare. Communitisation and Health Care Delivery: An Impact

Evaluation Study of Communitisation in the Health Sector. Kohima: Department of Evaluation,

Government of Nagaland; 2009.

(75) Ministry of Health and Family Welfare, Government of India. Operational Guidelines for Financial

Management. 2012.

206

(76) Ministry of Health and Family Welfare, Government of India. Constitution of Rogi Kalyan Samities:

Guidelines for constitution of Rogi Kalyan Samiti / Hospital Management society. 2013; Available at:

http://nhm.gov.in/nhm/nrhm/guidelines/nrhm-guidelines/constitution-of-rogi-kalyan-samities.html.

Accessed November 6, 2017.

(77) Department of Planning and Coordination. Communitisation of Health: the Nagaland Experience.

Kohima: Government of Nagaland; 2011.

(78) Broome NP. Communitisation of Public Services in Nagaland: A step towards creating alternative

model of delivering public services? Pune, Maharashtra: Kalpavriksh.; 2014.

(79) Oxford Policy Management. Design of program to support communitisation in the health and

nutrition sectors in Nagaland. New Delhi: Oxford Policy Management; 2014.

(80) Tushi A, Kaur P. Process Evaluation of Communitisation Programme in Public Sector Health

Facilities, Mokokchung District, Nagaland, 2015. Indian J Community Med 2017 Apr-Jun;42(2):81-87.

(81) Nagaland: Communitization policy requires effective implementation. Morung Express April 23,

2016.

(82) ‗Nagaland Communitization Policy becoming languid; needs revival‘. Morung Express 2016 June

11.

(83) Kawachi I, Subramanian SV, Kim D. Social Capital and Health. New York, NY: Springer Science

and Business Media, LLC; 2008.

(84) Bjornskov C, Sonderskov KM. Is Social Capital a Good Concept? Soc Indicators Res

2013;114(3):1225-1242.

(85) Fine B. Theories of Social Capital: Researchers behaving badly. New York, NY: Pluto Press; 2010.

(86) Arrow KJ. Observations on Social Capital. In: Partha D, Serageldin I, editors. Social Capital. A

Multifaceted Perspective Washington, DC: World Bank; 1999. p. 3-5.

(87) Solow RM. Notes on Social Capital and Economic Performance. In: Dasgupta P, Serageldin I,

editors. Social Capital: A Multifaceted Perspective Washington, DC: World Bank; 1999. p. 6.

(88) Farr J. Social Capital: A Conceptual History. Political Theory 2004;32(1):6-33.

(89) Villalonga-Olives E, Kawachi I. The Measurement of Social Capital. Gaceta Sanitaria 2015;29(1).

(90) Bain K, Hicks N. Building social capital and reaching out to excluded groups: the challenge of

partnerships. 1998.

(91) Harpham T, Grant E, Thomas E. Measuring social capital within health surveys: key issues. Health

Policy Plan 2002 Mar;17(1):106-111.

(92) Gitell R, Vidal A. Community organizing: building social capital as a development strategy.

Newbury Park: SAGE Publications; 1998.

207

(93) Harpham T. The Measurement of Community Social Capital through Surveys. In: Kawachi I,

Subramanian SV, Kim D, editors. Social Capital and Health New York: Springer; 2008. p. 51.

(94) Engbers TA, Thompson MF, Slaper TF. Theory and Measurement in Social Capital Research. Soc

Indicators Res 2016:1-22.

(95) Scrivens K, Smith C. Four Interpretations of Social Capital: an agenda for measurement.

2013;Working Paper no 55.

(96) Engbers TA, Thompson MF, Slaper TF. Theory and Measurement in Social Capital Research. Soc

Indicators Res 2016:1-22.

(97) Baum FE, Ziersch AM. Social capital. J Epidemiol Community Health 2003 05/01;57(5):320.

(98) Campos-Matos I, Subramanian SV, Kawachi I. The 'dark side' of social capital: trust and self-rated

health in European countries. Eur J Public Health 2016 Feb;26(1):90-95.

(99) Gregson S, Mushati P, Grusin H, Nhamo M, Schumacher C, Skovdal M, et al. Social capital and

women's reduced vulnerability to HIV infection in rural Zimbabwe. Popul Dev Rev 2011;37(2):333-359.

(100) Aida J, Kondo K, Kawachi I, Subramanian SV, Ichida Y, Hirai H, et al. Does social capital affect

the incidence of functional disability in older Japanese? A prospective population-based cohort study. J

Epidemiol Community Health 2013 Jan;67(1):42-47.

(101) Giordano GN, Merlo J, Ohlsson H, Rosvall M, Lindstrom M. Testing the association between

social capital and health over time: a family-based design. BMC Public Health 2013 Jul 17;13:665-2458-

13-665.

(102) Imamura H, Hamano T, Michikawa T, Takeda-Imai F, Nakamura T, Takebayashi T, et al.

Relationships of Community and Individual Level Social Capital with Activities of Daily Living and

Death by Gender. Int J Environ Res Public Health 2016 Aug 29;13(9):10.3390/ijerph13090860.

(103) Inaba Y, Wada Y, Ichida Y, Nishikawa M. Which part of community social capital is related to life

satisfaction and self-rated health? A multilevel analysis based on a nationwide mail survey in Japan. Soc

Sci Med 2015 Oct;142:169-182.

(104) DeVeillis R. Scale Development: Theory and Applications. Chapel Hill: Sage Publications; 2003.

(105) Salehi A, Harris N, Marzban M, Coyne E. Confirmatory Factor Analysis of Scales Measuring

Trust, and Control-Self-efficacy of Young Iranian Women. Soc Indicators Res 2015;124(3):1033-1047.

(106) Yip W, Subramanian SV, Mitchell AD, Lee DT, Wang J, Kawachi I. Does social capital enhance

health and well-being? Evidence from rural China. Soc Sci Med 2007 Jan;64(1):35-49.

(107) Kim D, Kawachi I. A multilevel analysis of key forms of community- and individual-level social

capital as predictors of self-rated health in the United States. J Urban Health 2006 Sep;83(5):813-826.

(108) Han S, Kim H, Lee HS. A multilevel analysis of social capital and self-reported health: evidence

from Seoul, South Korea. Int J Equity Health 2012 Jan 26;11:3-9276-11-3.

208

(109) Kouvonen A, Oksanen T, Vahtera J, Stafford M, Wilkinson R, Schneider J, et al. Low workplace

social capital as a predictor of depression: the Finnish Public Sector Study. Am J Epidemiol 2008 May

15;167(10):1143-1151.

(110) Mackenbach JD, Lakerveld J, van Lenthe FJ, Kawachi I, McKee M, Rutter H, et al. Neighbourhood

social capital: measurement issues and associations with health outcomes. Obes Rev 2016 Jan;17 Suppl

1:96-107.

(111) De Clercq B, Vyncke V, Hublet A, Elgar FJ, Ravens-Sieberer U, Currie C, et al. Social capital and

social inequality in adolescents' health in 601 Flemish communities: a multilevel analysis. Soc Sci Med

2012 Jan;74(2):202-210.

(112) De Clercq B, Pfoertner TK, Elgar FJ, Hublet A, Maes L. Social capital and adolescent smoking in

schools and communities: a cross-classified multilevel analysis. Soc Sci Med 2014 Oct;119:81-87.

(113) Elgar FJ, Davis CG, Wohl MJ, Trites SJ, Zelenski JM, Martin MS. Social capital, health and life

satisfaction in 50 countries. Health Place 2011 Sep;17(5):1044-1053.

(114) Dean L, Subramanian SV, Williams DR, Armstrong K, Charles CZ, Kawachi I. The role of social

capital in African-American women's use of mammography. Soc Sci Med 2014 Mar;104:148-156.

(115) Dean LT, Subramanian SV, Williams DR, Armstrong K, Zubrinsky Charles C, Kawachi I. Getting

Black Men to Undergo Prostate Cancer Screening: The Role of Social Capital. Am J Mens Health 2015

Sep;9(5):385-396.

(116) D'Haenensa E, Van Dammea J, Onghenab P. Multilevel exploratory factor analysis: illustrating its

surplus value in educational effectiveness research. School Effectiveness and School Improvement: An

International Journal of Research, Policy and Practice 2010;21(2):209-235.

(117) Diez Roux AV. A glossary for multilevel analysis. J Epidemiol Community Health 2002

Aug;56(8):588-594.

(118) Puntscher S, Hauser C, Walde J, Tappeiner G. Measuring Social Capital with Aggregated

Indicators: A Case of Ecological Fallacy? Soc Indicators Res 2016;125(2):431-449.

(119) Munthen BO. Multilevel Covariance Structure Analysis. Sociological Methods and Research

1994;22(3):376-398.

(120) Boonen T, Pinxten M, Van Damme J, Onghena P. Should schools be optimistic? An investigation

of the association between academic optimism of schools and student achievement in primary education.

Educational Research and Evaluation 2014 01/02;20(1):3-24.

(121) Dunn EC, Masyn KE, Jones SM, Subramanian SV, Koenen KC. Measuring psychosocial

environments using individual responses: an application of multilevel factor analysis to examining

students in schools. Prev Sci 2015 Jul;16(5):718-733.

(122) Li F, Duncan TE, Duncan SC, Harmer P, Acock A. Latent Variable Modeling of Multilevel

Intrinsic Motivation Data. Measurement in Physical Education and Exercise Science 1997

12/01;1(4):223-244.

209

(123) Toland MD, De Ayala RJ. A Multilevel Factor Analysis of Students' Evaluations of Teaching.

Educational and Psychological Measurement 2005 04/01; 2017/02;65(2):272-296.

(124) Dedrick RF, Greenbaum PE. Multilevel Confirmatory Factor Analysis of a Scale Measuring

Interagency Collaboration of Children's Mental Health Agencies. J Emot Behav Disord 2011

Mar;19(1):27-40.

(125) Dyera NG, Hangesa PJ, Hallb RJ. Applying multilevel confirmatory factor analysis techniques to

the study of leadership. The Leadership Quarterly 2005;16(1):149–167.

(126) Mathisen GE, Torsheim T, Einarsen S. The team-level model of climate for innovation: A two-level

confirmatory factor analysis. J Occup Organ Psychol 2006;79(1):23-35.

(127) Allen KL, Byrne SM, McLean NJ. The dual-pathway and cognitive-behavioural models of binge

eating: prospective evaluation and comparison. Eur Child Adolesc Psychiatry 2012 Jan;21(1):51-62.

(128) Bakker AB, Sanz-Vergel A, Rodriguez-Munoz A, Oerlemans WGM. The state version of the

recovery experience questionnaire: A multilevel confirmatory factor analysis. European Journal of Work

and Organizational Psychology 2015 05/04;24(3):350-359.

(129) Kawachi I, Takao S, Subramanian SV. Global Perspectives on Social Capital and Health. New

York; NY: Springer; 2013.

(130) Kawachi I, Berkman LF. Social ties and mental health. J Urban Health 2001 Sep;78(3):458-467.

(131) Aye M, Champagne F, Contandriopoulos AP. Economic role of solidarity and social capital in

accessing modern health care services in the Ivory Coast. Soc Sci Med 2002 Dec;55(11):1929-1946.

(132) Thiede M. Information and access to health care: is there a role for trust? Soc Sci Med 2005

Oct;61(7):1452-1462.

(133) Mohnen SM, Volker B, Flap H, Groenewegen PP. Health-related behavior as a mechanism behind

the relationship between neighborhood social capital and individual health - a multilevel analysis. BMC

Public Health 2012 02/10;12(1):116.

(134) Desilva MJ, Harpham T, Tuan T, Bartolini R, Penny ME, Huttly SR. Psychometric and cognitive

validation of a social capital measurement tool in Peru and Vietnam. Social Science and Medicine

2006;62.

(135) Story WT, Taleb F, Ahasan M, Ali NA. Validating the Measurement of Social Capital in

Bangladesh: A Cognitive Approach. Qual Health Res 2015:1-14.

(136) Narayan D, Cassidy MF. A Dimensional Approach to Measuring Social Capital: Development and

Validation of a Social Capital Inventory. Current Sociology 2001 03/01; 2018/02;49(2):59-102.

(137) Elgar FJ, Davis CG, Wohl MJ, Trites SJ, Zelenski JM, Martin MS. Social capital, health and life

satisfaction in 50 countries. Health Place 2011 Sep;17(5):1044-1053.

210

(138) Smith JW, Anderson DH, Moore RL. Social Capital, Place Meanings, and Perceived Resilience to

Climate Change. Rural Sociol 2012;77(3):380-407.

(139) Chen X, Stanton B, Gong J, Fang X, Li X. Personal Social Capital Scale: an instrument for health

and behavioral research. Health Educ Res 2009 04/01;24(2):306-317.

(140) Chen D, Yang T. The pathways from perceived discrimination to self-rated health: An investigation

of the roles of distrust, social capital, and health behaviors. Social Science & Medicine 2014 March

2014;104:64-73.

(141) Ziersch AM, Baum F, Darmawan IGN, Kavanagh AM, Bentley RJ. Social capital and health in

rural and urban communities in South Australia. Aust N Z J Public Health 2009;33(1):7-16.

(142) Phongsavan P, Chey T, Bauman A, Brooks R, Silove D. Social capital, socio-economic status and

psychological distress among Australian adults. Soc Sci Med 2006 Nov;63(10):2546-2561.

(143) Pollack CE, von dem Knesebeck O. Social capital and health among the aged: comparisons

between the United States and Germany. Health Place 2004 Dec;10(4):383-391.

(144) Son J, Lin N. Social capital and civic action: A network-based approach. Soc Sci Res 2008

03/01;37(1):330-349.

(145) Dorsey S, Forehand R. The Relation of Social Capital to Child Psychosocial Adjustment

Difficulties: The Role of Positive Parenting and Neighborhood Dangerousness. Journal of

Psychopathology and Behavioral Assessment 2003 03/01;25(1):11-23.

(146) Brisson D, Usher CL. Conceptualizing and Measuring Bonding Social Capital in Low-Income

Neighborhoods. Journal of Social Service Research 2007 08/22;34(1):1-11.

(147) Valencia-Garcia D, Simoni JM, AlegrÃa M, Takeuchi DT. Social Capital, Acculturation, Mental

Health, and Perceived Access to Services among Mexican American Women. J Consult Clin Psychol

2012 Apr;80(2):177-185.

(148) Boyce WF, Davies D, Gallupe O, Shelley D. Adolescent risk taking, neighborhood social capital,

and health. J Adolesc Health 2008 Sep;43(3):246-252.

(149) Stafford M, De Silva M, Stansfeld S, Marmot M. Neighbourhood social capital and common mental

disorder: testing the link in a general population sample. Health Place 2008 Sep;14(3):394-405.

(150) Zyphur, M. J., Kaplan, S. A., & Christian, M. S. Assumptions of cross-level measurement and

structural invariance in the analysis of multilevel data: Problems and solutions. . Group Dynamics:

Theory, Research, and Practice 2008;12(2):127.

(151) Muthen LK, Muthen BO. Mplus User's Guide. 7th ed. Los Angeles, CA: Muthen & Muthen; 1998-

2012.

(152) Hox JJ. Multilevel Analysis: Techniques and Applications. 2nd ed. New York: Routledge, Taylor

and Francis Group; 2010.

211

(153) Diez-Roux A. Neighborhoods and health: where are we and were do we go from here? Rev

Epidemiol Sante Publique 2007 02;55(1):13-21.

(154) Asparouhov T, Muthen B. Computationally Efficient Estimation of Multilevel High-Dimensional

Latent Variable Models. 2007.

(155) Hooper D, Coughlan J, Mullen M:. Structural Equation Modeling: Guidelines for determining

model fit. Electronic Journal of Business Research Methods 2008;6(1):53-60.

(156) Iacobucci D. Structural equations modeling: Fit Indices, sample size, and advanced topics. Journal

of Consumer Psychology 2010 01/01;20(1):90-98.

(157) Mehta PD, Neale MC. People are variables too: multilevel structural equations modeling. Psychol

Methods 2005 Sep;10(3):259-284.

(158) Dunn EC, Masyn KE, Johnston WR, Subramanian SV. Modeling contextual effects using

individual-level data and without aggregation: an illustration of multilevel factor analysis (MLFA) with

collective efficacy. Popul Health Metr 2015 May 10;13:12-015-0045-1. eCollection 2015.

(159) Li C. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and

diagonally weighted least squares. Behavior Research Methods 2016 09/01;48(3):936-949.

(160) The DHS Program. Wealth Index. 2016; Available at: https://dhsprogram.com/topics/wealth-

index/Wealth-Index-Construction.cfm. Accessed November 1, 2017.

(161) Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal

components analysis. Health Policy Plan 2006 Nov;21(6):459-468.

(162) Heck RH, Thomas Scott. An Introduction to Multilevel Modeling Techniques: MLM and SEM

Approaches Using Mplus. 3rd ed. New York: Routledge Taylor and Francis Group; 2015.

(163) Stapleton LM, Yang JS, Hancock GR. Construct Meaning in Multilevel Settings. Journal of

Educational and Behavioral Statistics 2016 10/01; 2017/02;41(5):481-520.

(164) De Silva MJ, Harpham T, Tuan T, Bartolini R, Penny ME, Huttly SR. Psychometric and cognitive

validation of a social capital measurement tool in Peru and Vietnam. Soc Sci Med 2006 Feb;62(4):941-

953.

(165) Geldhof GJ, Preacher KJ, Zyphur MJ. Reliability estimation in a multilevel confirmatory factor

analysis framework. Psychol Methods 2014 Mar;19(1):72-91.

(166) Sulis I, Toland MD. Introduction to Multilevel Item Response Theory Analysis. The Journal of

Early Adolescence 2017 01/01; 2017/02;37(1):85-128.

(167) Lochner K, Kawachi I, Kennedy BP. Social capital: a guide to its measurement. Health Place 1999

Dec;5(4):259-270.

(168) Rifkin SB. Lessons from community participation in health programmes: a review of the post

Alma-Ata experience. Int Health 2009 Sep;1(1):31-36.

212

(169) Declaration of Alma-Ata. ; 1978.

(170) Cornwall A, Lucas H, Pasteur K. Accountability through participation: developing workable

partnership models in the health sector. IDS Bulletin 2000;31:53-7.

(171) Molyneux S, Atela M, Angwenyi V, Goodman C. Community accountability at peripheral health

facilities: a review of the empirical literature and development of a conceptual framework. Health Policy

Plan 2012 Oct;27(7):541-554.

(172) McCoy DC, Hall JA, Ridge M. A systematic review of the literature for evidence on health facility

committees in low- and middle-income countries. Health Policy Plan 2012 Sep;27(6):449-466.

(173) Boulle T, Makhamandela N, Goremucheche R, Loewenson R. Promoting partnerships between

communities and frontline health workers: strengthening community health committees in South Africa.

2008.

(174) Bjorkman M, Svensson J. Power to the People: Evidence from a Randomized Field Experiment on

Community-Based Monitoring in Uganda. The Quarterly Journal of Economics 2009 05/01;124(2):735-

769.

(175) Mubyazi GM, Mushi A, Kamugisha M, Massaga J, Mdira KY, Segeja M, et al. Community views

on health sector reform and their participation in health priority setting: case of Lushoto and Muheza

districts, Tanzania. J Public Health (Oxf) 2007 Jun;29(2):147-156.

(176) Ngulube T, Mdhluli L, Gondwe K, Njobvu C. Governance, participatory mechanisms and

structures in Zambia's health system: an assessment of the impact of Health Center Committees (HCCs)

on equity in health and health care. Harare, Zimbabwe: EQUINET.

(177) Uzochukwu BS, Akpala CO, Onwujekwe OE. How do health workers and community members

perceive and practice community participation in the Bamako Initiative programme in Nigeria? A case

study of Oji River local government area. Soc Sci Med 2004 Jul;59(1):157-162.

(178) Meuwissen LE. Problems of cost recovery implementation in district health care: a case study from

Niger. Health Policy Plan 2002 Sep;17(3):304-313.

(179) Goodman C, Opwora A, Kabare M, Molyneux S. Health facility committees and facility

management - exploring the nature and depth of their roles in Coast Province, Kenya. BMC Health

Services Research 2011;11(1):1-12.

(180) Loewenson R, Rusike I, Zulu M. The impact of health centre committees on health outcomes in

Zimbabwe: paper presented to Forum 9. Mumbai: Global Forum for Health Research; 2005.

(181) Ramiro LS, Castillo FA, Tan-Torres T, Torres CE, Tayag JG, Talampas RG, et al. Community

participation in local health boards in a decentralized setting: cases from the Philippines. Health Policy

Plan 2001 Dec;16 Suppl 2:61-69.

(182) Bishai D, Niessen LW, Shrestha M. Local governance and community financing of primary care:

evidence from Nepal. Health Policy Plan 2002 Jun;17(2):202-206.

213

(183) Israr SM, Islam A. Good governance and sustainability: a case study from Pakistan. Int J Health

Plann Manage 2006 Oct-Dec;21(4):313-325.

(184) Shukla A, Scott K, Kakde D. Community monitoring of rural health services in Maharashtra. Econ.

Political Weekly ;30:78-85.

(185) Iwami M, Petchey R. A CLAS act? Community-based organizations, health service decentralization

and primary care development in Peru. Local Committees for Health Administration. J Public Health Med

2002 Dec;24(4):246-251.

(186) Cornwall A, Shankland A. Engaging citizens: Lessons from building Brazil‘s national health

system. Social Science and Medicine 2008;66:2173-2184.

(187) Peters DH, Adam T, Alonge O, Agyepong IA, Tran N. Implementation research: what it is and how

to do it. BMJ 2013(347).

(188) Peters DH, Tran NT, Adam T. Implementation Research in Health: A Practical Guide. 2013.

(189) Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for

Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda.

Adm Policy Ment Health 2011;38:65-76.

(190) Bali AS, Ramesh M. Health care reforms in India: getting it wrong. Public Policy and

Administration 2015;30(3-4):300-19.

(191) International Institute for Population Sciences, Ministry of Health and Family Welfare and

TALEEM Research Foundation. Reproductive and Child Health District Level Household Survey 2:

Nagaland 2002-2004. 2004.

(192) Declaration of Alma Ata. Alma Ata, USSR: World Health Organization; 1978.

(193) Lowensen R, Rusike I, Zulu M. Assessing the impact of Health Centre Committees on health

system performance and health resource allocation. Discussion paper 18 ed. Zimbabwe: Equinet; 2004.

(194) Sohani S. Health care access of the very poor in Kenya. Meeting the health related needs of the very

poor, DFID Workshop 14-15 February: Workshop Paper 11. Kenya: Aga Khan Health Service; 2005.

(195) Meuwissen LE. Problems of cost recovery implementation in district health care: a case study from

Niger. Health Policy Plan 2002;17.

(196) Mubyazi G, Hutton G. Rhetoric and Reality of Community Participation in Health Planning,

Resource Allocation and Service Delivery: a Review of the Reviews, Primary Publications and Grey

Literature. Rwanda Journal of Health Sciences 2012;1(1).

(197) Zakus JD. Resource dependency and community participation in primary health care. Soc Sci Med

1998 Feb-Mar;46(4-5):475-494.

(198) Frankish CJ, Kwan B, Ratner PA, Higgins JW, Larsen C. Challenges of citizen participation in

regional health authorities. Soc Sci Med 2002 May;54(10):1471-1480.

214

(199) George A, Scott K, Garimella S, Mondal S, Ved R, Sheikh K. Anchoring contextual analysis in

health policy and systems research: A narrative review of contextual factors influencing health

committees in low and middle income countries. Soc Sci Med 2015 May;133:159-167.

(200) Molyneux S, Atela M, Angwenyi V, Goodman C. Community accountability at peripheral health

facilities: a review of the empirical literature and development of a conceptual framework. Health Policy

Plan 2012:1-14.

(201) Scott K, George AS, Harvey SA, Mondal S, Patel G, Ved R, et al. Beyond form and functioning:

Understanding how contextual factors influence village health committees in northern India. PLOS ONE

2017 08/24;12(8):e0182982.

(202) Shrivastava S, Bobhate P. Implementation of Rogi Kalyan Samiti (RKS) at Primary Health Centre

Durvesh (2009-2010). TAF Prev Med Bull 2012;11(3):307-314.

(203) Chavan PS. Study of constitution and working of Rogi Kalyan Samiti in various states: A review

based on the survey of Common Review Mission of MHFW. International Journal of Biomedical

Research 2016;7(6):311-323.

(204) Panda B, Zodpey SP, Thakur HP. Local self governance in health - a study of it's functioning in

Odisha, India. BMC Health Serv Res 2016;16(Suppl 6):15-27.

(205) Tripathi G, Singh S. The Factors Affecting the Functioning of Rogi Kalyan Samiti (RKS) in

District Hospital in Central Madhya Pradesh: Observational Study. Indian Journal of Applied Research

2016;6(6):195-199.

(206) Ritchie J, Spence L. Qualitative data analysis for applied policy research. In: Bryman A, Burgess

RG, editors. Analyzing qualitative data London: Routledge; 1994. p. 173-194.

(207) Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the

analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013 Sep

18;13:117-2288-13-117.

(208) International Institute for Population Sciences (IIPS). National Family Health Survey (NFHS-4),

2015-16: Nagaland. 2016.

(209) Prüss-Üstün A, Bos R, Gore F, Bartram J. Safe water, better health: costs, benefits and

sustainability of interventions to protect and promote health. Geneva: World Health Organization; 2008.

(210) World Health Organization. Reducing risks, promoting healthy life. Geneva: World Health

Organization; 2002.

(211) La Forgia GM. Fifteen years of community organization for health in Panama: An assessment of

current progress and problems. Soc Sci Med 1985 01/01;21(1):55-65.

(212) Oakley P, Kahssay HM. Community involvement in health development: an overview. In: Kahssay

HM, Peter Oakley, editors. Community involvement in health development: a review of the concept and

practice Geneva: WHO; 1999.

215

(213) Kapiriri L, Norheim OF, Heggenhougen K. Public participation in health planning and priority

setting at the district level in Uganda. Health Policy Plan 2003 Jun;18(2):205-213.

(214) Kaseje DC, Sempebwa EK. An integrated rural health project in Saradidi, Kenya. Soc Sci Med

1989;28.

(215) Abimbola S, Molemodile SK, Okonkwo OA, Negin J, Jan S, Martiniuk AL. 'The government

cannot do it all alone': realist analysis of the minutes of community health committee meetings in Nigeria.

Health Policy Plan 2016 Apr;31(3):332-345.

(216) Peters D,H., Chakraborty,Subrata, Mahapatra,Prasanta, Steinhardt,Laura. Job satisfaction and

motivation of health workers in public and private sectors: cross-sectional analysis from two Indian states.

Human Resources for Health 2010;8(27).

(217) Purohit B, Bandyopadhyay T. Beyond job security and money: driving factors of motivation for

government doctors in India. Hum Resour Health 2014;12:12-4491-12-12.

(218) Gilson L, Erasmus E. Trust and accountability in health service delivery in South Africa.

Johannesburg: Centre for Health Policy, University of Witwatersrand; 2006.

(219) Kumar V, Jayanta Mishra A. Healthcare under the Panchayati Raj Institutions (PRIs) in a

decentralised health system. Leadersh Health Serv (Bradf Engl) 2016 May 3;29(2):151-167.

(220) Cleary SM, Molyneux S, Gilson L. Resources, attitudes and culture: an understanding of the factors

that influence the functioning of accountability mechanisms in primary health care settings. BMC Health

Services Research 2013 08/16;13(1):320.

(221) Malviya A, Dixit S, Bhagwat AK, Bansal SB, Khatri AK. Assessment of Functioning of Village

Health and Sanitation Committees (VHSCs) of Indore District. Online J Health Allied Scs 2013;12(4):1.

(222) Singh CM, Jain PK, Nair KS, Kumar P, Dhar N, Nandan D. Assessment of utilization of untied

fund provided under the national rural health mission in Uttar Pradesh. Indian J Public Health 2009 Jul-

Sep;53(3):137-142.

(223) Nongdrenkhomba HN, Prasad BM, Shome BK, Baishya AC. Community-based health committee

initiatives in India: a descriptive analysis of village health sanitation and nutrition committee model.

BMC Proc 2012;6(Suppl 5):O29-6561-6-S5-O29.

(224) Srivastava A, Gope R, Nair N, Rath S, Rath S, Sinha R, et al. Are village health sanitation and

nutrition committees fulfilling their roles for decentralised health planning and action? A mixed methods

study from rural eastern India. BMC Public Health 2015;16:10.1186/s12889-016-2699-4.

(225) Nandan D. Rapid Appraisal of Functioning of Village Health and Sanitation Committees (VHSCs)

under NRHM in Orissa. New Delhi: National Institute of Health and Family Welfare; 2008.

(226) Bracht N, Tsourso A. Principles and strategies of effective community participation. Health Promot

Internation 1990 01/01;5(3):199-208.

216

(227) Murthy RK, Klugman B. Service accountability and community participation in the context of

health sector reforms in Asia: implications for sexual and reproductive health services. Health Policy Plan

2004 Oct;19 Suppl 1:i78-i86.

(228) Arnstein SR. A Ladder Of Citizen Participation. J Am Inst Plann 1969 07/01;35(4):216-224.

(229) G2H2 Geneva Global Health Hub. G2H2 Task Group ―40 Years of Alma-Ata‖. 2016; Available at:

http://g2h2.org/engage/g2h2-working-groups/aa40/. Accessed January 30, 2018.

(230) Charles C, DeMaio S. Lay participation in health care decision making: a conceptual framework. J

Health Polit Policy Law 1993 Winter;18(4):881-904.

(231) Marston C, Renedo A, McGowan CR, Portela A. Effects of community participation on improving

uptake of skilled care for maternal and newborn health: a systematic review. PLoS One

2013;8(2):e55012.

(232) Rifkin SB. Examining the links between community participation and health outcomes: a review of

the literature. Health Policy Plan 2014;29.

(233) Hurlbert M, Gupta J. The split ladder of participation: A diagnostic, strategic, and evaluation tool to

assess when participation is necessary. Environ Sci & Policy 2015 06/01;50:100-113.

(234) Gurung G TS. Fostering good governance at peripheral public health facilities: an experience from

Nepal. Rural and Remote Health 2013;2042.

(235) Kapiriri L, Norheim OF, Heggenhougen K. Public participation in health planning and priority

setting at the district level in Uganda. Health Policy Plan 2003 Jun;18(2):205-213.

(236) Mahmud S. Citizen Participation in the Health Sector in Rural Bangladesh: Perceptions and Reality.

ICDS Bulletin 2004;35(2).

(237) Meier BM, Pardue C, London L. Implementing community participation through legislative reform:

a study of the policy framework for community participation in the Western Cape province of South

Africa. BMC International Health and Human Rights 2012 08/25;12(1):15.

(238) Chung P, Grogan CM, Mosley JE. Residents' perceptions of effective community representation in

local health decision-making. Soc Sci Med 2012 05/01;74(10):1652-1659.

(239) Goodman C, Opwora A, Kabare M, Molyneux S. Health facility committees and facility

management - exploring the nature and depth of their roles in Coast Province, Kenya. BMC Health Serv

Res 2011 Sep 22;11:229-6963-11-229.

(240) Atkinson JA, Fitzgerald L, Toaliu H, Taleo G, Tynan A, Whittaker M, et al. Community

participation for malaria elimination in Tafea Province, Vanuatu: Part I. Maintaining motivation for

prevention practices in the context of disappearing disease. Malar J 2010;9.

(241) Iwami M, Petchey R. A CLAS act? Community-based organizations, health service decentralization

and primary care development in Peru. Local Committees for Health Administration. J Public Health Med

2002;24.

217

(242) Ministry of Health and Family Welfare, Government of India. Village Health Nutrition Day. 2013;

Available at: http://www.nhm.gov.in/communitisation/village-health-nutrition-day.html. Accessed

December 5, 2017.

(243) UCLA Institute for Digital Research and Education. Regression with STATA Chapter 2 -

Regression Diagnostics. 2017; Available at: https://stats.idre.ucla.edu/stata/webbooks/reg/chapter2/stata-

webbooksregressionwith-statachapter-2-regression-diagnostics/. Accessed February 20, 2018.

(244) Muthen M, Muthen L, Asparouhov T. Estimator choices with categorical outcomes. 2015;

Available at: https://www.statmodel.com/download/EstimatorChoices.pdf. Accessed 2018, February 1.

(245) Ludtke O, Marsh HW, Robitzsch A, Trautwein U, Asparouhov T, Muthen B. The multilevel latent

covariate model: a new, more reliable approach to group-level effects in contextual studies. Psychol

Methods 2008 Sep;13(3):203-229.

(246) Amer M. Political Status of Women in Nagaland. Journal of Business Management and Social

Sciences Research 2013;2(4).

(247) Scott K, George AS, Harvey SA, Mondal S, Patel G, Sheikh K. Negotiating power relations, gender

equality, and collective agency: are village health committees transformative social spaces in northern

India? International Journal for Equity in Health 2017 09/15;16(1):84.

(248) Shahjahan HB. Social Capital and Community Development: An Analysis of Two Cases from India

and Bangladesh. Journal of Asian and African Studies 2011 12/01; 2018/02;46(6):533-545.

(249) Blomkvist H, Swain A. Investigating Democracy and Social Capital in India. Economic and

Political Weekly 2001;36(8):639-643.

(250) Das R. Rural Society, the State and Social Capital in Eastern India: A Critical Investigation. The

Journal of Peasant Studies 2005 01/01;32(1):48-87.

(251) De Groot WT, Tadepally H. Community action for environmental restoration: a case study on

collective social capital in India. Environ Dev Sustainability 2008 08/01;10(4):519-536.

(252) Meinzen-Dick R, Raju KV, Gulati A. What Affects Organization and Collective Action for

Managing Resources? Evidence from Canal Irrigation Systems in India. World Dev 2002

04/01;30(4):649-666.

(253) Feigenberg B, Field E, Pande R, Rigol N, Sarkar S. Do group dynamics influence social capital

gains among microfinance clients? Evidence from a randomized experiment in urban India. Journal of

Policy Analysis and Management 2014;33(4):932-949.

(254) Heller P. Social capital as a product of class mobilization and state intervention: Industrial workers

in Kerala, India. World Development 1996 June 1996;24(6):1055-1071.

(255) Isham J, Kähkönen S. Institutional Determinants of the Impact of Community‐Based Water

Services: Evidence from Sri Lanka and India. Economic Development and Cultural Change 2002 04/01;

2018/02;50(3):667-691.

218

(256) Joshi A, Aoki M. The role of social capital and public policy in disaster recovery: A case study of

Tamil Nadu State, India. International Journal of Disaster Risk Reduction 2014 March 2014;7:100-108.

(257) Krishna A. How Does Social Capital Grow? A Seven-Year Study of Villages in India. Journal of

Politics 2007;69(4):941-956.

(258) Krishna A. Understanding, measuring and utilizing social capital: clarifying concepts and

presenting a field application from India. Agricultural Systems 2004 December 2004;82(3):291-305.

(259) Lahiri-Dutt K, Samanta G. Constructing Social Capital: Self-Help Groups and Rural Women‘s

Development in India. Geographical Research 2006;44(3).

(260) Levien M. Social Capital as Obstacle to Development: Brokering Land, Norms, and Trust in Rural

India. World Development 2015 October 2015;74:77-92.

(261) Lise W. Factors influencing people‘s participation in forest management in India. Ecological

Economics 2000 September 2000;34(3):379-392.

(262) Mayer P. Human Development and Civic Community in India: Making Democracy Perform.

Economic and Political Weekly 2001;36(8):684-692.

(263) Mohapatra BN. Social Connectedness and Fragility of Social Capital: View from an Orissa Village.

Economic and Political Weekly 2001;36(8):665-672.

(264) Morris M. Social capital and poverty in India. Brighton: Institute of Development Studies; 1998.

(265) Sekhar NU. Social Capital and Fisheries Management: The Case of Chilika Lake in India. Environ

Manage 2007 04/01;39(4):497-505.

(266) Deininger K, Liu Y. Economic and Social Impacts of Self-Help Groups in India. 2009;Policy

Research Working Paper.

(267) Widmalm S. The utility of bonding social capital. Journal of Civil Society 2005 05/01;1(1):75-95.

219

Curriculum Vitae – Avril Kaplan

EDUCATION

Johns Hopkins University, Bloomberg School of Public Health

Doctor of Philosophy: International Health Department, 2014 – 2018.

Georgetown University, School of Nursing and Health Studies Master of Science: Health Systems Administration, 2011.

Georgetown University, Edmond A. Walsh School of Foreign Service Bachelor of Science in Foreign Service: Science, Technology and International Affairs, 2009.

RESEARCH EXPERIENCE

Abt Associates Inc., Bethesda, MD

International Health Division, Consultant

May 2017 – current

Leading a study to determine the magnitude of outmigration of nurses and physicians

trained in Haiti and the factors associated with intent to migrate

Integrated Child Development Services Conditional Cash Transfer (CCT) Study, Uttar

Pradesh, India

Johns Hopkins University, Student Researcher

May 2017 – current

Designing Discrete Choice Experiment to determine optimal attributes for a CCT

program that aims to improve nutritional and health outcomes for mothers and children

under 3.

Diabetes Networking Tool Project, Baltimore, MD

Johns Hopkins University, Student Researcher

January 2016 – June 2016

Coordinated community forums that aim to understand how people in Southwest

Baltimore use their social networks to improve management of Type 2 Diabetes.

Collected data through the community forums aimed to inform the development of a

mobile phone application.

World Bank Group, Washington, DC

Health, Nutrition, and Population Division, Short Term Consultant

April 2015 – December 2015

Conducted qualitative study on the role of social capital in utilization of publically

provided health services in Nagaland, India.

220

WORK EXPERIENCE

Abt Associates Inc., Bethesda, Maryland

International Health Division, Intern (2011), Analyst (2011-2013), Senior Analyst (2013-

2014)

January 2011 – July 2014

Collaborated with Ministry of Health in Haiti to design accreditation system for private

nursing schools.

Examined feasibility of low-income countries to transition health care workers away

from PEPFAR support to alternative and sustainable sources of funding.

Provided technical support for resource tracking activities, including development of the

Health Resource Tracker and National Health Accounts (NHA) estimation in Rwanda.

understand spending on health promotion and disease prevention.

Assessed availability, quality and relevance of a core set of national health system

performance indicators in Bangladesh, Ethiopia, Peru, Vietnam and Zambia.

Pan American Health Organization, Washington, DC

Health Systems and Services Unit, Intern

June 2010 – August 2010

Conducted literature review and analysis on future constraints- specifically, finances,

human resources, and service delivery- for healthcare delivery in the Central and South

America Regions.

World Health Organization, Geneva, Switzerland

Patient Safety Department, Intern

June 2009 – August 2009

Performed background research and designed set of indicators to assess impact of the

Department‘s research grants.

TEACHING EXPERIENCE

Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

Teaching Assistant

Health Financing in Low and Middle Income Countries, 2016 Term 3

Health Economics III, 2016 Term 4

Health Systems in Low and Middle Income Countries, 2015 Term 2

Foundations of International Health, 2015 Term 1 & Term 2, 2017 Term 1

OTHER

French (Professional)

Country experience in Haiti, India, Rwanda, Tanzania, United States

Stata, Mplus, NVivo, Excel, CSPro

PERSONAL DETAILS

Born July 3, 1987 in Edmonton, Alberta

Citizen of Canada, US Permanent Resident