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What is the Strength of Health Related Ties between New Mothers, Self Help Groups and
the Health System in Uttar Pradesh, India? Social Network Analysis Report
May 2014
Jenny Ruducha1 James Potter1 Robin Lemaire2 Divya Hariharan3 Deborah Maine1
Center for Global Health and Development Boston University School of Public Health
Boston, Massachusetts, USA
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Acknowledgements We would like to thank Dr. Dileep Mavalankar, Danish Ahmad, Anurag Chaturvedi, and Parveen Kumar at PHFI for supporting the project in countless ways: from reviewing the concept note, providing valuable comments and enabling the project to move forward administratively and logistically. We are also grateful to Sampath Kumar and PS Mohanan of RGMVP for supporting the Social Network Study by generously providing us with their time and feedback during different stages of the project. Their support enabled other RGMVP staff to became part of our core team to review the questionnaire, participate in the pre-‐testing, interviewer training and in the preparation of the report. We also acknowledge and thank the team members at the Lucknow, Banda, and Varanasi CRDCs who have provided ground-‐level support of the data collection process and many other local RGMVP staff and volunteers we met along the way, who provided assistance whenever possible. ME Khan, at the Population Council, offered his time and observations about field based experiences while conducting the BCM Project’s baseline study that assisted our team in improving the survey approach and field management. At Boston University, we recognize the contribution of Ariel Falconer our program manager who supports us administratively and keeps us on track through our weekly meetings. We are also grateful to Deborah Maine for her leadership and engagement in many discussions during the development and implementation of this project. We would also like to thank our survey team for their hard work and dedication to the data collection: Supervisor Shailendra Tripathi and interviewers Anju Jaiswal, Lavi Triveni, Kaleem Ahmed, and Divya Gupta. Lastly, we are indebted to all the mothers, SHG members, health providers and key community members who offered their time and provided us with the information to build an understanding of their networks.
1 Boston University Center for Global Health and Development (BU CGHD) 2 Virginia Technical University and BU CGHD 3 Rajiv Gandhi Mahila Vikas Pariyojana
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Table of Contents ACRONYMS…………………………………………………………………………………………………………………6 EXECUTIVE SUMMARY………………………………………………………………………………………………..8 1. INTRODUCTION ................................................................................................... 13 1.1. Background of Behavior Change Management Project ........................................ 13 1.2. What is Social Network Analysis? ........................................................................ 14 1.3. Research questions ............................................................................................. 14 1.4. Structure of the report ........................................................................................ 15 2. METHODOLOGY .................................................................................................. 16 2.1. Sample selection ................................................................................................. 16 2.2. Instrument design and pre-‐testing ....................................................................... 17 2.3. Interviewer selection and training ....................................................................... 18 2.4. Survey implementation ....................................................................................... 18 3. DESCRIPTIVE ANALYSIS ........................................................................................ 19 3.1. Response rates and sampling methodology – Recently Delivered Women ........... 19 3.2. Basic background information of RDW respondents ............................................ 20 3.3. Maternal and newborn health outcomes among RDW respondents .................... 22 3.4. Response rates and sampling methodology – SHG structures, health workers and key community members ........................................................................................... 23 3.5. Characteristics of SHG structures, health workers and key community members 24 4. RECENTLY DELIVERED WOMEN ............................................................................ 27 4.1. Introduction ........................................................................................................ 27 4.2. What are the most important maternal and newborn health advice networks for RDWs? ....................................................................................................................... 29 4.3. How are RDWs connected to village and block level health providers and other key community members in receiving government schemes and services? ................ 35 4.4. What is associated with the practice of key healthy behaviors? .......................... 37 4.5. Advice Networks Summary and Program Implications ......................................... 41 5. GP-‐BLOCK SHG STRUCTURES, HEALTH WORKERS AND KEY COMMUNITY MEMBERS ........................................................................................................... 45 5.1. Introduction ........................................................................................................ 45 5.2. What are the information networks of relationships within different SHG levels and across AAAs, block health structures and key community players? ...................... 46 5.3. How are services coordinated between the different groups in the network? ..... 49 5.4. What are the groups that discuss and coordinate family planning supplies and other health products? ............................................................................................... 51 5.5. Overview of measures of key SHG and health system linkages ............................ 53
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5.6. Summary and implications: SHG Structures and Gram Panchayat (GP) health system linkages .......................................................................................................... 57 Tables 3. DESCRIPTIVE ANALYSIS Table 3.1: Respondent list in each GP by location of residence.................................. 20 Table 3.2: Actual Interviews conducted with respect to location type (column percentages) .......................................................................... 20 Table 3.3: RDW Respondent Background Information by SHG Connection Status (column percentages within each category)........................... 21 Table 3.4: RDW Respondent Health Behaviors and Outcomes by SHG Connection Status (column percentages within each category) .....................................................22 Table 3.5: Response Rates for GP and Block Interviews by District............................. 24 Table 3.6: Comparison of Respondent Background Information by Caste and Education.................................................................................................................. 25 Table 3.7: Respondent with Friends or Neighbors who are SHG Members by “Affiliation” .............................................................................................................. 26 Table 3.8: VHNSC Existence by Job Affiliation............................................................ 27 Table 3.9: RKS Existence by Job Affiliation................................................................. 27 4. RECENTLY DELIVERED WOMEN Table 4.1: RDW Advice Network Measures................................................................34 Table 4.2: RDW Service Networks: Density, Total Ties and Average Degree Centrality.................................................................................................................. 37 Table 4.3: Logit regression identifying predictors of three health behaviors among RDW respondents..................................................................................................... 38 5. GP-‐BLOCK SHG STRUCTURES, HEALTH WORKERS AND KEY COMMUNITY MEMBERS Table 5.1: Density and Centralization of Whole GPs................................................... 53 Table 5.2 Block Level Health System Linkages............................................................ 57 Figures 3. DESCRIPTIVE ANALYSIS Figure 3.1: Uttar Pradesh Map with Study Districts Highlighted................................ 19 Figure 3.2: Delivery Location of RDW Respondents.................................................... 23 4. RECENTLY DELIVERED WOMEN Figure 4.1: Personal Advice Networks in Banda.........................................................30 Figure 4.2: SHG Advice Network in Hardoi.................................................................31 Figure 4.3: SHG Advice Network in Mirzapur.............................................................31
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Figure 4.4: SHG Advice Network in Banda................................................................. 32 Figure 4.5: Health Worker and Village Advice Networks in Banda............................. 32 Figure 4.6: Block Advice Network in Mirzapur.......................................................... 33 Figure 4.7: Advice Consideration in Making MNH Decisions in Hardoi....................... 35 Figure 4.8: Hardoi RDW Village Community Services Network.................................. 36 Figure 4.9: Hardoi: RDW Block Services Network..................................................... 36 Figure 4.10: Mirzapur RDW Block Services Network..................................................37 5. GP-‐BLOCK SHG STRUCTURES, HEALTH WORKERS AND KEY COMMUNITY
MEMBERS Figure 5.1: Unconfirmed: Banda-‐GP2/Block: Information Exchange Network about Health Programs and Services..........................................................................47 Figure 5.2: Confirmed Banda-‐GP2/Block: Information Exchange Network about Health Programs and Services..........................................................................48 Figure 5.3: Confirmed Hardoi-‐GP3/Block: Information Exchange Network about Health Programs and Services..........................................................................49 Figure 5.4: Hardoi-‐GP 4/Block: GP-‐Block Health Services Coordination and Referrals Network......................................................................................................50 Figure 5.5: Mirzapur-‐GP 1: GP-‐Block Health Services Coordination & Referrals Network.................................................................................................................... 50 Figure 5.6: Banda-‐GP5: GP-‐Block Health Services Coordination and Referrals Network.................................................................................................................... 51 Figure 5.7: Hardoi-‐GP3/Block: Health Supplies Network...........................................52 Figure 5.8: Mirzapur-‐GP3: GP-‐Block Health Supplies Network...................................52 Figure 5.9: Banda-‐GP2: GP-‐Block Health Supplies Network...................................... 53 Figure 5.10: Number of SHG-‐RGMVP Relationship Dyads Across Information Exchange, Health Services and Health Supplies Networks by District......................... 56 Figure 5.11: Number of SHG-‐RGMVP Relationship Dyads Across Information Exchange, Health Services and Health Supplies Networks by District......................... 56 Bibliography……………………………………………………………………………………………………… 61 Appendices Appendix I: Acronyms Used in Plot Construction....................................................... 62 Appendix II: Survey Instrument – Recently Delivered Women................................... 65 Appendix III: Survey Instrument – SHG Structures, Health Workers and Key Community Members .................................................................................................................. 74 Appendix IV: Guide to the Plots................................................................................ 81 Appendix V: Multivariate Analysis Variable Specifications ........................................ 82 Appendix VI: Complete Tables with Network Results ................................................ 84
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Acronyms AAA ASHA, ANM & AWW ANM Auxiliary Nurse Midwife ASHA Accredited Social Health Activist AWW Anganwadi Worker BCM Behavior Change Management BO Block Organization BPL Below Poverty Line BU Boston University CAPI Computer Assisted Personal Input CDPO Child Development Project Officer CGHD Center for Global Health and Development CHC Community Health Center CHT Community Health Trainer CRDC Community Resource Development Centre CSPro Census and Survey Processing System CV Community Volunteer (RGMVP) FO Field Officer (RGMVP) GP Gram Panchayat ICDS Integrated Child Development Services ISC Internal Social Capital (RGMVP) JSY Janani Suraksha Yojana MNH Maternal & Newborn Health OBC Other Backwards Caste PC Population Council PHC Primary Health Center PHFI Public Health Foundation of India QAP Quadratic Assignment Procedure QCA Qualitative Comparative Analysis RDW Recently Delivered Woman RKS Rogi Kalyan Samiti RGMVP Rajiv Gandhi Mahila Vikas Pariyojana RMNCH Reproductive, Maternal, Newborn & Child Health RPM Regional Program Manager (RGMVP) SC Scheduled Caste SHG Self Help Group SNA Social Network Analysis SS Swasthya Sakhi ST Scheduled Tribe TBA Traditional Birth Attendant UP Uttar Pradesh
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VHSNC Village Health, Sanitation & Nutrition Committee VO Village Organization
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Executive Summary Background The goal of the Uttar Pradesh Behavior Change Management Project (UP BCM) is to reduce maternal and neonatal mortality by increasing the adoption of evidence-‐based high-‐impact family health behaviors and increasing access to the local health system. The primary method of achieving these goals is through a federation of Self Help Groups (SHGs) established by the Rajiv Gandhi Mahila Vikas Pariyojana (RGMVP). The federation of SHGs, along with Village Organizations (VOs) and Block Organizations (BOs), is capable of providing a platform for innovative and scalable methods of disseminating these health messages and increasing health system access for the entire community. One of the important advantages of this strategy is that the federated SHG structure serves as an entry point into different levels of community and government organizations , from Blocks down to individual purwas (hamlets) within Gram Panchayats (GPs). The project can presumably take advantage of the strength and depth of these structures to more efficiently disseminate health messages and to increase the interactions of the community with local and Block level health workers as well as other key stakeholders. Moreover, a well designed strategy for message dissemination and health linkage promotion could be taken up by communities themselves, providing the potential for changes introduced by the project to last well beyond the duration of the project itself. The Social Network Analysis Study The advantage of this community-‐based approach, described above, relies on a keen understanding of the community’s existing structure of relationships, both its strengths and weaknesses. Network Analysis is a methodology designed to understand how people and organizations interact with each other as well as the strength of these connections. This Social Network Analysis was primarily designed to look at two types of networks: 1. The networks of Recently Delivered Women (RDW): We measured the strength of
connections that RDWs maintained with family, SHG structures, as well as key stakeholders at the GP and Block level, including the sources of the advice they receive. This is important for the UP BCM project because message dissemination in the real world never takes place in isolation, and understanding the existing, and often competing, sources of information can play a crucial role in developing successful strategies for influencing what can sometimes be very tenacious behavior trends.
2. The networks of SHG Structures, Health Workers and Community: We measured the strength of connections that all of the key health-‐related stakeholders
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maintained at the GP and Block levels. This is important for the UP BCM project because knowing who talks with whom, who works with whom, and how often people interact can all play a role in the design of a community-‐based project strategy. This understanding is also especially important for the design of strategies to increase the number of linkages between SHG structures and the health system, as well as to strengthen these connections. The Social Network Analysis results can help guide decisions about the connections that should be the focus of the project strategy.
Methodology The survey instruments for the Social Network Study were designed based on a Concept Note developed by the Boston University (BU) team. Drafts of the survey instrument were pre-‐tested, finalized, and converted to CAPI format for digital data collection on mini-‐laptop computers. A team of five local staff was hired to conduct data collection, beginning with a three-‐day training and a field test. Data collection lasted six weeks and was supervised by staff from both BU and Public Health Foundation of India (PHFI). The data collection covered 3 Learning Phase Blocks within 3 different Districts: Banda, Hardoi, and Mirzapur. Data collection occurred between November 2013 and January 2014, timing which was designed intentionally to allow the study to serve as a baseline of existing networks in the community. As scale-‐up begins and the UP BCM project expands its scope, the results from this study may be used as a guide as to what might be expected in new Blocks and is intended to serve as a baseline for a follow-‐up network study that will assess how well the UP BCM project has succeeded in increasing the strength and diversity of these networks. Major Findings and Implications The major findings from the network study, based on both qualitative and quantitative analyses of the collected data are summarized below. The findings are presented in two tables, one each for the RDW and the SHG Structure, Health Worker and Community sections. Listed next to each finding are some suggested implications for the UP BCM project. All findings discussed in the tables below are discussed in greater detail in the relevant sections of the full report. RDW Findings and Implications The following table summarizes the findings and implications from the RDW section of the report, which focuses on the advice networks and the sources of health services for RDWs surveyed as part of the study:
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Findings Implications Families are sources of advice for all women
Develop program strategies to target specific family members of pregnant women and women of reproductive age for promoting and diffusing key BCM messages as they are the main channels of advice provision
Almost all RDWs receive advice from AAA regardless of SHG membership or SHG presence in the area
The tasks of the SHGs and VOs can be refocused to promoting and monitoring the equitable expansion of entitlements and services that are to be delivered by AAAs, which could be a major vehicle for the expanded use of services
The VO structure and the Swasthya Sakhi do not currently appear to be strong sources of advice
Develop clear roles for how VO members and Swasthya Sakhis will deliver health messages to target women and key groups of influence in the community
The strength of connections with SHGs was associated with lower levels of practicing key health behaviors
There is a need to understand and influence the beliefs of SHG members who are mostly older women beyond the child bearing age before they can spread new key behaviors that they themselves may not have practiced
Only some RDWs received advice from block level health sources
Strategies should be developed to help increase the quality of interactions at block level facilities, including increasing awareness of entitlements and the delivery process at a government facility
Most RDWs admire a variety of sources for good health advice, both within the HH and in the village community, but many rely more heavily on household sources
Strategies should be developed to reach out to RDWs to expand their exposure to non-‐household sources of evidence-‐based advice that can improve MNH
Decisions are often made based on the advice of multiple people; the husband and “Doctor” are frequent sources of advice
The important role of the husband and “Doctor” as a source of advice for health-‐related decisions should be considered and strategies should be developed to increase awareness among husbands and unqualified health providers in the community about health messages being delivered
SHG Structure, Health Worker and Community Findings and Implications The following table summarizes the findings and implications from the networks of SHG structures, health workers, and other community stakeholders. This section focuses on the network structures of different types of relationships between SHGs, health providers and other community members, specifically mutual health information sharing, health service coordination, and health supply information and coordination:
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Findings Implications RGMVP has built a well-‐connected network of SHGs, VOs and BOs that are exchanging information and discussing health services and health products amongst themselves
RGMVP can use its functional SHG platform to improve the quality of the interactions and meeting processes between different sets of SHG levels by defining specific outcomes to be achieved and ways to monitor progress through self-‐assessment and data analysis feedback loops.
SHG connections with the health system are limited, especially at the VO and BO levels
Identify opportunities to increase linkages from VOs and BOs to the health system, especially through government defined accountability structures that have built-‐in sustainability, such as the VHSNC meetings at the GP level and RKS at the block level. The purpose of the linkage must be well defined: such as promoting the equitable distribution of entitlements and development of community accountability processes for AAAs and block level facilities.
SHG members are often more connected to the GP health system than SHG Swasthya Sakhis
Increase awareness of UP BCM activities among key stakeholders in the community, including the identification of Swasthya Sakhis and their role in the community. Build on the existing relationships between the SHG member and the ASHA to expand the scope of the conversation with the SHG Swasthya Sakhi and VO Swasthya Sakhi to address broader community level RMNCH issues.
Most networks are organized into two clusters (SHG/RGMVP side and Government health services side, often including key others)
Identify and promote linkages between SHGs/RGMVP and government health services that can contribute to UP BCM program goals. It is important to identify and leverage the “bridging” members that have developed trust and confidence in both clusters to hasten program diffusion.
ASHA or ANM is a common connector creating a bridge among SHGs and health providers
These linkages are “low-‐hanging fruit” and should be leveraged more explicitly so that they exist in as many program areas as possible
Health services coordination and emergency referrals are the weakest in the system
The potential for SHG members and RGMVP staff to play a greater role in health services coordination should be discussed among partners
PRI, RMP and to a lesser extent, GP Drug Shop, are connected to the government health system especially for health supplies
As the UP BCM project increases local awareness of its activities, these key stakeholders should be included in sensitization activities, including village meetings etc.
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Conclusion This Social Network Analysis, under the UP BCM project has provided the project with a large collection of evidence and tools to take forward in developing new strategies, and refining existing ones, to further enhance the efficacy of project goals. This analysis provides insights into the ways in which new mothers in UP currently interact with their families and communities in receiving information and services related to health, a resource that can be used to enhance the efficacy of UP BCM project messaging strategies. The analysis also provides insights into the existing structures of health information exchange and coordination among RGMVP’s SHG platform, health workers and other key stakeholders in the community. These insights are a resource that can be utilized during the design and refinement of strategies to increase linkages among SHG structures, the greater community, and the local health system.
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1. Introduction
1.1. Background of Behavior Change Management Project
Despite proven family health interventions for reducing neonatal mortality and improving maternal and child health, there is a dearth of community-‐based demand-‐side approaches that can take these interventions to scale, thereby ensuring a rapid and sustainable impact on family health outcomes. The self-‐help group (SHG) model is a promising institutionalized and demand-‐side social platform for scaling up family health interventions. The Rajiv Gandhi Mahila Vikas Pariyojana (RGMVP) has developed a rapidly expanding federated SHG model that can serve as an operating system on which family health interventions can be layered. The project aims at reducing Maternal and Neonatal Mortality within selected blocks of Uttar Pradesh through proven health interventions and linkages with the health services and supply system. It is being implemented in 100 RGMVP program blocks, where Block Level Institutions have been formed. In order to disseminate these health messages, an Maternal and Newborn Health (MNH) package of interventions have been developed; training in the learning phase blocks have been undertaken and health linkage interventions are being developed. The project aims are expected to be achieved through the following objectives: (1) To increase adoption of evidence-‐based high-‐impact family health behaviors through self-‐help groups in 60 blocks to reach the poor; (2) To increase access to local health system and health services through actions of Village Organizations (VOs) and Block Organizations (BOs) in 60 blocks. They will interact with health services as well as empower the community for use of those services; (3) To scale up evidence-‐based high impact health behaviors and strategies to improve access to health services for the poor in 100 new blocks. A tested Behavior Change Management (BCM) model and activities to improve access to local health services will be delivered through a robust dissemination strategy; and (4) To document and disseminate the tested approaches and implementation strategies for the BCM model’s wider expansion and application. Social interactions and knowledge exchange form an important basis of Self-‐Help Group Federations. The project implementation depends on pre-‐established women’s networks, which are responsible for delivering the health messages. These social networks form important building blocks for disseminating information and creating linkages for accessing resources and services. In an attempt to understand these networks, an empirical research study was conducted by Boston University to understand the existing pattern of relationships between the recently delivery women, their household members, the SHG networks, the local self-‐governing body (Panchayati Raj Institution) and the health service providers at the village and the block levels.
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1.2. What is Social Network Analysis? In recent years, there has been an effort to examine how social and organizational networks impact health outcomes and health systems.1 Many studies examining the impact of networks on health outcomes and systems employ network analysis. Network Analysis is a methodology developed to study how individuals, communities, organizations, and other entities connect and interact with one another.2 The focus of network analysis is the relationship between agents (people and organizations among others) and how the pattern of relationships can be used to understand system processes and performance. The mapping and measurement of formal and informal relationships can improve understanding of what facilitates or impedes knowledge flows i.e., who knows whom, and who shares what information with whom by what communication media. Because these relationships are not usually readily discernible, social network analysis may be described as a “social relationship x-‐ray”. To analyze the social network characteristics, a range of quantitative measures is used to describe relationships at different levels. These relationships are depicted visually through plots or graphs. This provides a dynamic understanding of how people are related to each other within a system, such as a village and block.
1.3. Research questions The primary purpose of Social Network Analysis (SNA) is to address the second objective of the UP BCM Project: “To increase access to the local health system and services through actions of VOs and BOs in 60 blocks in conjunction with BCM model.” The study undertaken has tried to document the levels (frequency, content and quality) of
1 For a review of these efforts, see Varda, D., Shoup, J.A., and Miller, S. 2012. A systematic review of collaboration and 2 Valente, T.W., Coronges, KA, Stevens, GD, and Cousineau, MR. 2008. Collaboration and competition in a children’s health initiative coalition: A network analysis. Evaluation and Program Planning, 31:392-‐402.
Box 1.1 -‐ Network Definition of Terms Node or Individual Level Ties: Degree centrality is calculated by simply counting the number of connections between individuals. Based solely on direct connections, it is a measure of communication activity and the assumption is that more connections are better than fewer connections. The higher the degree centrality, the less the reliance on intermediaries to access information or resources. Betweeness centrality measures the extent to which individuals fall between pairs of other individuals on the shortest paths connecting them. It represents potential mediation or flow of information or resources between people in the network when direct connections do not exist. Box 1.2 – Whole Network Level Ties:
Centralization is an expression of how tightly the network structure is organized around its most central actor(s). It is calculated based on the distribution of degree centrality scores of all the individuals. Values of centralization can best be understood in comparison to 100% centralization in which actors are connected only to one central actor and thus, must pass through that one actor to connect with any other network actors.
Density is defined as the sum of the ties that exist divided by the total number of possible ties. The density of a network may give us insights into such phenomena as the speed at which information diffuses among the nodes and the extent to which actors have high levels of social capital and/or social constraint.
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relationships with the health providers at the village and block levels. Additionally, there has also been an attempt to identify key transmitters of newborn and maternal health advice to mothers or Recently Delivered Women (RDW). This information can potentially help the project to target and further build upon the most effective communication channels. The Social Network baseline results can be used to improve strategies to strengthen linkages with the health system and other important stakeholders that influence access to health and social resources. A follow-‐up network survey at the end of the project will be used to evaluate the project’s intervention strategies including the effectiveness of the delivery channels and linkages to improve the advice and health services interactions and health outcomes. The research questions that the Network Analysis has tried to address are listed below. The second and third question will be answered in the end-‐line survey after one year of project implementation:
1) What are the existing communication channels and linkages of health workers and other key stakeholders with recently delivered women and SHG structures at the purwa, larger village, Gram Panchayat, and Block levels at the start of the Learning Phase?
2) What is the effectiveness of the UP BCM project’s intervention delivery channels in improving interactions and linkages between the different groups during the Learning Phase?
3) How are the potential strengthening of interactions or relationships in networks associated with improved behavioral outcomes and access to health services at the end of the Learning Phase?
1.4. Structure of the report The report has been divided into 5 sections. Following the introduction in Chapter 1, the second chapter on methodology explains the sample selection process, the instrument design and pre-‐testing phase, the interviewer selection and training process, survey implementation and how the analysis was undertaken. This section is crucial to understand the basics of Social Network Analysis and how the study was conducted in the three selected Learning Phase program districts of the UP BCM project. Chapter 3 presents a descriptive analysis of the data collected during the field survey. This includes the response rates and basic background information about the respondents, as well as a description of some relevant health behaviors for the RDWs included in the study. Chapter 4 presents the main results from RDW surveys, including network plots of information and advice networks among RDWs, as well as a multivariate analysis to identify predictors of health behaviors relevant to the UP BCM program. The end of the
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chapter includes a summary of the findings from the chapter, along with a discussion of the various implications for the UP BCM program. The 5th and final chapter presents the main results from the surveys of SHG structures, Health Workers, and Key Community Stakeholders. Plots are presented to describe the network structures of health information sharing, health services coordination, as well as health supplies information among respondents in each GP. This chapter also includes a summary of results from this section of the study, as well as a discussion of the various implications for the UP BCM project.
2. Methodology
2.1. Sample selection The baseline Social Network Analysis was designed to identify existing networks in UP BCM program areas. At the time of data collection, the UP BCM program had begun implementation in 10 designated GPs within each of 10 Learning Blocks. After consultation with various program staff, the Learning Phase blocks in Hardoi, Mirzapur, and Banda were chosen based on the expectation that network measures would be different in these areas, due to regional socioeconomic and cultural differences, as well as the different experiences and challenges faced by RGMVP in SHG formation across the three blocks. RGMVP has been functioning for at least two years before the start of data collection in all three blocks, and the UP BCM project had begun trainings about two months before the start of data collection. Within each Block, 6 out of 10 UP BCM Learning Phase GPs were chosen for data collection, for a total of 18 GPs. During the selection of GPs, data was provided by RGMVP about the existence and number of purwas (hamlets) in each GP, as well as the number of SHGs and their approximate level of functioning, when available. On the basis of this information, in each of the three learning blocks, 2 GPs were chosen that had only a main village, with no purwas, and 4 GPs were chosen with purwas that had functioning SHGs. In Banda, 3 GPs of both types were chosen, because there were fewer Learning Phase GPs with purwas available. Within each category, GPs were chosen in order to sample both GPs with strong SHG functionality and GPs where SHG networks appeared weaker, according to available information. The goal was to maximize the diversity of sampled networks within each block. The selection of RDW respondents was designed in consultation with UP BCM program partners to capture some of the important aspects that were expected to influence network outcomes. Specifically, RDW respondents were sampled on the basis of SHG membership as well as location of residence. RDWs were sampled from three locations in each GP: 1) the main village, 2) one purwa that has active SHGs and 3) one purwa that
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has no currently active SHGs. In each location with an SHG, one SHG member RDW and one non-‐member RDW were interviewed, and in the purwa with no active SHGs, only one RDW was sampled, for a total of 5 RDWs per GP. The selection of other community respondents was designed to capture the variety of stakeholders involved in health at the village and block level, including the RGMVP federated SHG structure, government health workers, as well as private health providers and other community members. The full list of respondents, which is available in Appendix I, was designed and finalized through consultations with UP BCM project partners.
2.2. Instrument design and pre-‐testing The two survey instruments administered during data collection, one for RDW respondents (Appendix II) and one for other key stakeholders (Appendix III), captured demographic and background information about each respondent, including education, age, and caste. The majority of the survey instrument was dedicated to collecting network information about each respondent. The basic methodology that informed the network measures section was based on a validated survey design introduced by Provan et. al.3 Respondents are asked about a variety of possible connections, and for each connection, several follow-‐up questions are asked about the qualities and strength of that connection. These follow-‐up categories were designed in collaboration with UP BCM partners in order to capture aspects of village and block level connections that would be relevant for program implementation. RDW respondents were also asked about several health behaviors related to the UP BCM program, so that demographic and network measures could be related to actual health behaviors among these respondents. The health behaviors questions were designed in consultation with the results of the Population Council’s Baseline Survey Report, which identified the prevalence of different behaviors related to the UP BCM program. The survey instrument was pre-‐tested on several respondents with the help of RGMVP colleagues in a GP in the Raebarelli District of Uttar Pradesh. After pre-‐testing, the survey instrument was refined and edited as needed for both length and content. The final questionnaire was then implemented as an electronic CAPI (computer assisted personal interviewing) program using the freely available CSPro (version 5.0) software. This software was tested both during the interview training and during the first day of field-‐testing to address any software issues before the start of data collection.
3 Provan, Keith G., Veazie, Mark. A., Staten, Lisa K., and Teufel-‐Shone, N.I. 2005. The use of network analysis for strengthening community partnerships in health and human services. Public Administration Review 65: 603-‐613
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2.3. Interviewer selection and training Four interviewers and one supervisor were selected to execute the data collection, all of whom were from Uttar Pradesh and had several years of experience with health-‐related survey interviewing and/or supervision. Training took place over the course of four days. The first day was devoted to background information about the UP BCM project and the Social Network Analysis project, as well as survey ethics and a discussion about field logistics. The second day was devoted to learning the questionnaire in depth, and an introduction was provided to the CAPI software and laptop protocols. The third day continued laptop and CAPI software orientation, and the fourth day was devoted to a field test, which took place in a GP in the Lucknow District of Uttar Pradesh.
2.4. Survey implementation Data collection was carried out for approximately 6 weeks, from November 2013 through January 2014. The survey team spent two weeks in each of the 3 survey Blocks. The BU Research Fellow accompanied the survey team, providing technical support and monitoring data collection quality. A representative from the PHFI management team was also present to monitor survey progress and assist in logistic issues for about one third of the data collection period. When possible, the team would arrive in the afternoon before the start of data collection so that the supervisor could meet with local RGMVP staff for orientation. The RGMVP Regional Program Manager (RPM) from the local CRDC arranged meetings with the RGMVP Field Officer (FO) in each survey Block. The FO, or another RGMVP volunteer, assisted in arranging Block-‐level and GP-‐level introductions as necessary. The survey team identified RDW respondents independently whenever possible, and the assistance of RGMVP volunteers was enlisted when the survey team could not find respondents meeting the inclusion criteria. Inevitably, some respondents held multiple roles within the context of the survey design, e.g. a respondent may have been both the VO Swasthya Sakhi and a BO Member. In such cases, a second respondent was identified whenever possible so that a unique set of data could be collected for each community role. During the last several days of data collection in each Block, at least 10% of interviews in each GP were repeated as “quality checks”, in most cases by a different interviewer, in order to provide internal feedback on interviewer performance to the field supervision team, as well as to ensure the quality of the final output. During this process, only the network portions of the surveys were repeated. In the final data, the quality check network data are reflected in all cases, except when there was a data entry error during the quality check itself.
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3. Descriptive Analysis
3.1. Response rates and sampling methodology – Recently Delivered Women
The study covered one block in each of three UP BCM Learning Phase districts: Ahirori in Hardoi District, Majhwa in Mirzapur District, and Tindwari in Banda District. These districts represented distinct regions that would provide information on geographic diversity within UP. In each block, six Gram Panchayats (GPs) were covered, and according to the study design, five recently delivered women (RDW) were to be interviewed in each GP. Target response rates for RDW were met in each GP: Ahihiri, Hardoi -‐ 31 RDW; Majhawa, -‐ 30 RDW; and Tindwari – 31 RDW. Figure 3.1: Uttar Pradesh Map with Study Districts Highlighted
Many GPs in Uttar Pradesh consist of a main village, along with other, often smaller residential clusters called “Purwas” located within several kilometers of the main village. These residential clusters are sometimes areas where lower income or otherwise marginalized communities settle, and RGMVP has established SHGs in many of these purwa areas. In both Hardoi and Mirzapur, four GPs with Purwas were included, and
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two GPs with only a main village were included. In Banda, due to a lower number of villages with Purwas, three GPs with only main villages were included in the study. The following respondent list was used for GPs, based on whether there were purwas or only a main village: Table 3.1: Respondent list in each GP by location of residence
GP with Purwas GP with only Main Village
Main Village 2 (1 SHG Member and 1 Non-‐Member) 5 (2 SHG Members and 3 Non-‐Members)
SHG Purwa 2 (1 SHG Member and 1 Non-‐Member) N/A
Non-‐SHG Purwa 1 (1 Non-‐Member) N/A
Total 5 5
According to the above respondent list, the following table shows actual interviews conducted by residence type in each block: Table 3.2: Actual Interviews conducted with respect to location type (column percentages)
Hardoi (%) Mirzapur (%) Banda (%) Total (%)
Main Village 19 (61) 18 (60) 22 (71) 59 (64) SHG Purwa 8 (26) 8 (27) 6 (19) 22 (24)
Non-‐SHG Purwa 4 (13) 4 (13) 3 (10) 11 (12) Total 31 30 31 92
Note that in Banda, 3 surveyed GPs out of 6 had only a main village, compared to only 2 such villages in Hardoi and Mirzapur. One measure of interest for this study is the level of connection to SHGs in the village that each respondent maintained. We sought out both members and non-‐members for interviews, but some non-‐members were connected either through household members or through friends and neighbors to SHGs. Out of 92 total respondents, 37 (40%) were SHG members, 23 (25%) had an indirect connection through a household member or friend, and 32 (35%) had no connection to SHG members. Among the 37 RDW who are themselves connected to SHGs, the average time of connection was 1.6 years (median = 1.9, range: 1 month to 3.2 years).
3.2. Basic background information of RDW respondents Respondents ranged in age from 18-‐40 years old (mean = 25.4, median = 25). More than half of respondents had no formal education, and on average, respondents with no connections to SHGs were more educated than those who were members of SHGs.
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Among those respondents who were not SHG members themselves, some had indirect connections through friends, neighbors, or relatives. Of those respondents, only 3 were connected through a household member. The table below shows a summary of background information collected about respondents with respect to SHG connection status: Table 3.3: RDW Respondent Background Information by SHG Connection Status (column percentages within each category) Characteristic No Connection
(%) N = 32
Friend, Neighbor, or HH (%)
N = 23
Self Member (%)
N = 37
Total (%)
Any Education (%)� 16 (50) 14 (61) 12 (32) 42 (46) SC (%) 17 (53) 13 (57) 28 (76) 58 (63) Mean Age (median) 25 (25) 25 (25) 25.8 (25) 25.4 (25) Household Type Nuclear Joint Extended
17 (53) 11 (34) 4 (13)
11 (48) 10 (43)
2 (9)
18 (49) 9 (24)
10 (27)
46 (50) 30 (33) 16 (17)
Able to go to meetings * Yes, alone Yes, but with someone I don’t go to meetings
1 (3) 0 (0)
31 (97)
0 (0) 1 (4)
22 (96)
15 (41) 14 (38) 8 (22)
16 (17) 15 (16) 61 (66)
Owns phone or phone in family * No Phone Has phone but never discusses health Has phone and discusses health
10 (31) 5 (16)
17 (53)
0 (0) 12 (52)
11 (48)
7 (19) 16 (43)
14 (38)
17 (18) 33 (36)
42 (46)
Agree that most people in village are ready to help in an emergency *
17 (53) 19 (82) 32 (86) 68 (74)
� χ2 pr < 0.10 * χ2 pr < 0.05 ** χ2 pr < 0.01 Among all respondents, 88 (96%) were Hindu, while the other 4 (4%) were Muslim. Nine (10%) of respondents were General Caste, while the rest were divided between Scheduled Castes (SC) (63%) and Other Backwards Castes (OBC) (27%). No respondents identified themselves as having a Scheduled Tribes (ST) affiliation. Although the targeted groups for RGMVP’s programs include ST populations, their numbers are very low in the selected study areas.
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3.3. Maternal and newborn health outcomes among RDW respondents
RDW respondents had between 1 and 7 children. Twenty-‐one percent had just had their first child, 28% had their second child, and 14% had over 3 children. Among respondents who had more than one child, the average gap between their youngest two children was 2.8 years (median 2.7 years, range 1-‐7 years). It is recommended that families maintain a gap of at least 3 years between pregnancies. Twenty-‐eight respondents (40%) of respondents with at least two children had maintained a gap a gap of at least 3 years between pregnancies. Below is a table summarizing some of the basic health outcome data collected from all respondents: Table 3.4: RDW Respondent Health Behaviors and Outcomes by SHG Connection Status (column percentages within each category) No Connection
(%) N = 32
Friend or Neighbor (%)
N = 23
Self-‐Connected (%)
N = 37
Total (%)
Institutional Delivery (%) 22 (69) 17 (74) 29 (78) 68 (74) Fed Colostrum * 13 (41) 19 (83) 26 (70) 58 (63)
Breastfeeding (within first hour after delivery)
16 (50) 12 (52) 15 (41) 43 (47)
First feeding was breast milk
21(66) 18 (78) 28 (76) 67 (73)
3-‐Year Gap Between Pregnancies (among respondents with more than one child)
8 (33) 5 (28) 15 (52) 28 (39)
Wanted Last Pregnancy? Yes, wanted it to happen Yes, wanted but after some time No, didn't want at all
29 (90)
2 (6)
1 (3)
14 (61)
6 (26)
3 (13)
27 (73)
5 (14)
5 (14)
70 (76)
13 (14)
9 (10) � χ2 pr < 0.10 * χ2 pr < 0.05 ** χ2 pr < 0.01 Of all RDW respondents, 26% gave birth at home, while the remaining 74% gave birth at a public or private health facility. Of the 56 respondents who gave birth at a public health facility, 95% received a conditional cash transfer through Janani Suraksha Yojana (JSY), a government scheme providing cash payments to women who give birth in approved facilities. None of the respondents having delivered in a private institution received any payment through JSY.
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Respondents were asked if they wanted their most recent pregnancy, and if so, whether they had wanted to wait longer before pregnancy. Ten percent of respondents had not wanted their most recent pregnancy, while 14% had wanted the pregnancy, but had wanted to wait longer before getting pregnant. Of those that had wanted to wait longer before getting pregnant, all had a gap between their youngest two children of less than 3 years. Of those that hadn’t wanted another pregnancy, all had at least 3 children (up to 6). It is possible that institutional delivery rates might vary according to residential location, so that respondents located in purwas would have institutional deliveries less often. However, there was no statistically significant variation in institutional delivery rates by residential location between main villages and both SHG and Non-‐SHG Purwas.
A majority (73%) of respondents first fed breast milk to their child after birth, and there appears to be no significant difference between feeding behavior among different levels of connection to SHGs. Sixty-‐three percent of all RDW respondents fed their child the colostrum, and among those respondents, 70% also breastfed first after birth, but the timing of first breastfeeding varied. Almost half of respondents reported starting breastfeeding of their
most recent child within an hour after delivery, and most respondents reported having started breastfeeding within the first day after delivery.
3.4. Response rates and sampling methodology – SHG structures, health workers and key community members
The second component of this Social Network Analysis focused on a variety of key stakeholders that might plausibly be involved in health information, coordination, and supplies. Stakeholders at the Gram Panchayat (GP) level as well as at the Block level were included in the respondent list for this study. These respondents included many of the formal health sector workers such as PHC staff members, ASHA workers and ANM workers, but also include other stakeholders that may be involved with health in various capacities, such as RMP doctors, Panchayati Raj members, Drug Shop Owners and Traditional Birth Attendants. The full respondent list is available in Appendix I. Each GP-‐level respondent was interviewed about the health workers in their respective GPs,
Figure 3.2: Delivery Location of RDW Respondents
26%$
14%$
23%$
23%$
13%$
1%$
Delivery(Loca-on((N(=(92)(
At$Home$
District$Hospital$
CHC$
PHC$
Private$Hospital$
Other$
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while each Block-‐level respondent was interviewed about a maximum of two GPs under their jurisdiction out of the six GPs included in our study from each Block. In each block, block-‐level respondents were asked about the same two GPs so that in the analysis, two GPs would have a confirmed network including block-‐level respondents. The two GPs were chosen on the basis of maximum variability with respect to distance from Block-‐level health resources and the reported robustness of RGMVP’s SHG platform. The response rates by Block for this section of the study are reported in the table below: Table 3.5: Response Rates for GP and Block Interviews by District Ahirori, Hardoi Majhwa,
Mirzapur Tindwari, Banda
Interview Level GP Block GP Block GP Block Total Desired Respondents 90 18 90 18 90 18 324 Actual Respondents 83 20 88 16 81 17 305 Interview Response Rate (%) 92 110* 98 88 90 94 94 * Due to non-‐overlapping coverage of our study GPs among several block level respondents (e.g. ANM Supervisor), more interviews than expected in the survey design were required
One GP level respondent in the survey was a qualified doctor (i.e. with a degree) who is based at the village level or otherwise works in village areas. This type of respondent could not be found in a majority of villages. In all other cases where a respondent is not included, this was because they could not be located or were otherwise absent during the survey exercise.
3.5. Characteristics of SHG structures, health workers and key community members
The descriptive analysis focusing on SHG structures, health workers and key community members, is presented in this sub-‐section. We present some background information on caste and education status of respondents, as well as SHG affiliations and knowledge of local VHSNC and RKS meetings. Job Affiliations: The respondents for this section of the analysis were divided into four broad categories corresponding roughly to their affiliation with respect to the study:
1. SHG Structure (SHG Members, SHG & VO Swasthya Sakhis, Other VO & BO Members)
2. RGMVP Staff (CHT, CV, FO, ISC) 3. Government Health & Nutrition (ASHA, ANM, AWW, PHC Doctor & Nurse, CHC
Doctor and Nurse, ICDS Supervisor, ANM Supervisor, CDPO) 4. Other (PRI Member, Religious Leader, RMP, Qualified Doctor, Drug Shop Owner,
Private Health Facility)
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These categories are used in descriptive summaries in this section, as well as in network plots reported in subsequent sections, in order to summarize the diverse set of respondents in a meaningful way for the purposes of providing feedback to the UP BCM project. Caste Distribution: In the following descriptive summaries, the caste categories “Scheduled Tribe (ST)” and “Other” were omitted for ease of visualization, because there was only one respondent in each category. The respondent choosing “Other” is a CHC employee who identified as Christian in response to the caste question and the respondent identifying as ST is a religious leader. The distribution of caste among health worker respondents by position is shown below. In general, the SHG platform members are most frequently SC, although there are OBC and General Caste members in most positions. Block level positions are more frequently OBC or General Caste, and all ANM supervisors interviewed were General Caste. The full distribution is shown below: Table 3.6: Comparison of Respondent Background Information by Caste and Education (row percentages within caste and education measures) SC
(%) OBC (%)
Gen %)
No Education
(%)
Some Educatio
n (%)
Total (%)
Affiliation SHG Structure RGMVP Staff Gov Health Other
83 (69) 14 (61) 28 (37) 47 (49)
25 (21) 7 (30)
16 (21) 23 (24)
11 (9) 2 (9)
32 (42) 26 (27)
69 (58) 3 (13) 1 (1)
27 (28)
50 (42) 20 (87) 76 (98) 70 (72)
119 23 76 96
Location Purwa GP Block
23 (68)
123 (56) 26 (43)
8 (24)
49 (22) 14 (23)
3 (9)
47 (21) 21 (34)
22 (65) 69 (31) 9 (15)
12 (35)
151 (69) 53 (85)
34
220 62
Caste SC OBC Gen
74 (43) 23 (32)
2 (3)
98 (57) 48 (67) 69 (97)
172 71 71
TOTAL 172 (55) 71 (23) 71 (23) 100 (32) 216 (68) 316 Two attributes of our respondents were developed for visualization of network plots, “Location” and “Affiliation”. The table above shows the caste distribution of respondents according to their location attributes. “Location” contains the categories (1) Purwa/Village, (2) GP, and (3) Block, corresponding to the most appropriate level at which their health activities most frequently take place. Purwa/Village and GP-‐level respondents were more frequently SC. Block-‐level respondents were more evenly distributed across caste categories.
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The second attribute is “Affiliation”, corresponding to what were determined to be operationally meaningful categories of work. The categories are (1) SHG Structure, including SHG, VO, and BO (2) RGMVP Staff (3) Government Health and Nutrition Workers and (4) Other. Again, SHG structure respondents, and to a lesser extent RGMVP staff, tend more heavily towards SC caste, while other categories were more evenly distributed. Education Distribution: Education also varies to some degree by position of health worker respondents, although to a lesser extent compared to caste. In order to make it easier to make a clear comparison, the collected education data is summarized into two categories: (1) No Education and (2) Some Education. In general, SHG structure members were less likely to be educated, while the majority of other respondents were more likely to be educated, with the notable exceptions of Traditional Birth Attendants and Religious Leaders. This data can be summarized according to the “Affiliation” attribute, where it can be seen more clearly that education only shows real variation within the SHG structure. The majority of the “No Education” entries for “Other” affiliation come from the Traditional Birth Attendant and Religious Leader, as discussed above. Respondents in purwas and villages (SHG Members and SHG Swasthya Sakhis) were more likely to be uneducated than respondents at the GP or Block level. Block level respondents were more likely to be educated than GP level respondents. Almost all General Caste respondents have received at least some education, while some SC and OBC respondents have received no education. Connection to SHGs: Of all respondents, 106 (out of 316) were connected to an SHG. All except 5 of these SHG members were part of SHG structures or RGMVP staff, but connections to SHGs through friends and neighbors extend beyond RGMVP and the SHG structure. The table below shows the percentage of respondents in each job affiliation category indicating that they have a friend or neighbor outside of their household who is connected to an SHG. Thirty percent of government health and nutrition workers and 60% of respondents in the “Other” category know someone in an SHG. Table 3.7: Respondent with Friends or Neighbors who are SHG Members by “Affiliation” (row percentages)
Affiliation No (%) Yes (%) Don't Know (%) Total
SHG Structure 16 (13) 103 (87) 0 (0) 119 RGMVP Staff 4 (17) 18 (78) 1 (4) 23 Gov. Health 45 (58) 23 (30) 9 (12) 77 Other 33 (34) 58 (60) 6 (6) 97
Total 95 (31) 202 (64) 16 (5) 316
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Village Health, Sanitation & Nutrition Committee and Rogi Kalyan Samiti Meetings: All respondents whose work takes place primarily in a single GP were asked whether they were aware of a Village Health, Sanitation & Nutrition Committee (VHSNC) in their GP. A majority of respondents didn’t know if there was a VHNSC in their GP, and a majority of the remainder indicated that there was no such committee in their GP. Table 3.8: VHSNC Existence by Job Affiliation (row percentages) Affiliation No (%) Yes (%) Don't Know (%) Total
SHG Structure 34 (33) 9 (9) 60 (58) 103 RGMVP Staff* 2 (20) 5 (50) 3 (30) 10 Gov. Health 14 (26) 14 (26) 25 (47) 53 Other 36 (40) 14 (15) 41 (45) 91
Total 86 (33) 42 (16) 129 (50) 257 *Note: The RGMVP response rate for this question is low because only respondents with responsibilities confined to a single GP were asked about VHSCs, and almost all RGMVP staff interviewed has responsibilities including more than one GP.
Respondents whose position covers more than one GP, or whose position is primarily at the Block level, were asked about participation in the local Rogi Kalyan Samiti (RKS) at either a PHC or CHC. Table 3.9: RKS Existence by Job Affiliation (row percentages) RKS Knowledge No (%) Yes (%) Don't Know (%) Total
SHG Structure 6 (46) 1 (8) 6 (46) 13 RGMVP Staff 5 (38) 2 (15) 6 (46) 13 Gov Health 6 (25) 14 (58) 4 (17) 24 Other 2 (50) 1 (25) 1 (25) 4
Total 19 (35) 18 (33) 17 (31) 54
These responses suggest that for both the VHSC at the GP level and the RKS at the Block level, RGMVP should consider how to increase participation in these existing institutions that are engaging in health issues at local levels.
4. Recently Delivered Women
4.1. Introduction Recently delivered women (RDW), including the pregnancy period, form a key target group for the delivery of health interventions in the form of evidenced based messages that if transformed into a practicing behaviors can lead to improve health outcomes of
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the mother and the newborn. Relationships form the basis of interactions and influence and provide insight into the dynamics of how health decisions are made and how government services are accessed. Interactions with health providers and key community members are further instrumental in providing a mechanism not only to receive health advice, but also to obtain needed government services. This RDW section explores these two networks, advice on maternal and newborn health and receiving help in obtaining government services. A visualization of selected plots that represent commonly found relationship patterns will be presented. These plots utilize the degree centrality measure, a count of the number of ties present, to help convey where the highest and lowest number of ties are. Additional network measures of density and centralization, provide information on the broader structure of the entire network and will also be included in table formats. Density measures the percentage of actual relationships in comparison to all potential relationships in a network. For example, if there were 10 people in a village then there would be a total of 90 possible relationships [10 x (10-‐1)] and the density percentage would indicate the percent of those possible 90 ties that are present. The centralization measure captures to what extent the network is dispersed or centered around a key individual or group. A completely centralized network would appear as a spoke and wheel with one person in the middle and other people only connected to that person but not to each other. Multivariate analyses will explore the associations between key RDW and network characteristics and outcomes. Lastly, a summary of key findings and implications for the UP Community Mobilization Project will be presented. This study asked RDWs about whether they had a relationship with specific people in four groups: 1) family and friends, 2) SHGs and their organizational structures at the GP level, 3) health providers and other key individuals in their village communities, and 4) health providers and others at the block level. They were then asked about the types of interactions that they engaged in over the last year, the frequency of the interaction and the place of interaction. The types of questions posed varied by group to allow for a more appropriate focus on the ways that different groups were expected to interact. For the more personal networks (family and SHGs), RDWs were asked the following questions: Have you attended social gatherings (like eating meals together, celebrating weddings, Diwali, mela, Rakhi)?; Have you gotten a loan or have given a loan?; and Have you received advice on maternal & newborn health topics? RDW respondents were also asked about how frequently they interacted in the last year to discuss topics related to maternal and newborn health and how much trust did they have in the advice provided? For the SHG and Village Organization group of relationships, they were also asked about the most common method of interaction that included physical locations as well as phone calls. For the list of potential types of people that RDWs knew at the village community and block levels, the advice question remained and a second question was added: Have you received help in obtaining government services (such as family planning supplies, JSY, or BPL card)? For the village
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level relationships, the frequency and place of interaction as well as trust questions remained. For the Block level relationships, the same questions as the SHG and Village Organization questions were asked except the place of interaction, was not included as most instances of having an exchange would occur at the respective block level facilities. This report will focus on the results of a subset of these questions that are most relevant for the BCM Project. This section on RDWs will begin with presenting advice networks across all groups of relationships followed by the relationships that were important in receiving help to obtain government services. The information is displayed in plots: RDW nodes are assigned a color based on their relationship to an SHG and a shape based on their location in the GP (See below). For more detailed presentation see Appendix IV. Box 4.1: Guide to Plots by Location (Shape) and SHG Affiliation (Color)
The gray squares represent the connections with the types of relationships that RDWs have such as husband, mother-‐in-‐law, RMP, SHG member and/or Swasthya Sakhi to name a few. The size of the gray square is based on the number of connections that were counted by the RDWs in each block. Squares are larger when more RDWs identified that person as someone with whom they were connected. A full description of the acronyms used in creation of the plots is listed in Appendix I.
4.2. What are the most important maternal and newborn health advice networks for RDWs?
Personal Advice Networks: Knowing where new mothers or recently delivered women (RDWs) received advice about maternal and newborn health is important in understanding the sources of influence of key behaviors to improve health. Figure 4.1 presents the family and friends or personal advice network of RDWs in Banda. The network is very dense as all RDWs, regardless of where they live or how connected they are to an SHG, consistently receive advice from their family and friends. This pattern of relationships was consistent across all the 3 districts. Interactions were
30
frequent, often at least once a week or once a month and there was often a high level of trust. Figure 4.1: Personal Advice Networks in Banda
SHG Advice Networks: The advice received by RDWs from the SHGs across the three districts of Hardoi, Mirzapur and Banda is confined to women who are either members themselves (green nodes), have a HH member in the SHG (dark green) or a friend or neighbor belong to an SHG (blue). All the non-‐SHG connected women (in orange) are not getting any advice at all from the SHG structures even though some of them live in a purwa that has an existing SHG (designated by orange inverse triangle). In Hardoi, most of the advice is centered around the GHH or the SHG household which has 8 incoming ties, followed by receiving advice from a friend or neighbor who is a member of an SHG or GnHH or has a connection to the SHG but not through the household. The SHG Swasthya Sakhi (GSS) has 4 incoming ties. However it should be noted that there are many isolates, including no interactions that provided maternal and newborn health advice for 5 (out of 24) RDWs with a connection to an SHG. Also no Village Organization (VO) members were identified as providing advice to RDWs.
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H
M
ML
FL
MH
PH
Fr
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Figure 4.2: SHG Advice Network in Hardoi
Figure 4.3: SHG Advice Network in Mirzapur
The patterns in Mirzapur and Banda as displayed in Figures 4.3 and 4.4 were more similar to each other, with almost all RDW members of SHGs receiving some advice from different SHG members including the VO. The main source of the advice was the friend or neighbor’s SHG but not the women’s own SHG household. In Banda, 4 out of 7 RDWs who knew someone outside of their own household that was a member of an SHG still did not receive any maternal and newborn health advice. The GSS were linked in but
RDWa_HA1
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GnHH
VOM
VOB
VOSS
RG
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were not a very extensive source of advice, especially in Banda. Overall the VO network including the VO Swasthya Sakhi (VOSS) were not important players in the advice provided to RDWs. Figure 4.4: SHG Advice Network in Banda
Figure 4.5: Health Worker and Village Advice Networks in Banda:
Health Workers and Village Community Advice Networks: Across all three districts, the ANMs, ASHAs and to a lesser extent the Anganwadi workers (AAAs) played a major role in providing maternal and newborn health advice to RDWs in the last one year, including in the pregnancy and post-‐partum periods. As illustrated in Figure 4.5, the ASHA followed by the ANM claim the center of the advice network and all RDWs, that is whether they are connected (green and blue nodes) or are not connected (orange
RDWa_BT1
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nodes) to the SHG are still connected to the AAAs for their maternal and newborn health advice. It is also observed that RDWs who are SHG members themselves (green nodes) are also receiving advice from the RMP and the drug seller/shop owner of the village (DS). There is minimal advice provision by the religious leader (RL) and the traditional birth attendant (TBA) but whenever they are connected, it is with the RDWs who are SHG members. Block Level Advice Networks: The majority of RDWs had an institutional delivery (74%) and had contact with health providers at PHCs, CHCs, or their respective District Hospitals. However, many RDWs did not identify any health providers at the block or district hospital that provided advice on maternal and newborn health (ranging from 20% to 50%). Hardoi had the least connectivity with respect to getting advice from health providers at the PHC and CHC facilities (not shown in plot). The District Hospital staff provided advice mostly to RDWs that were not connected to SHGs. Banda and Mirzapur had fewer isolates, more connectivity with the CHC and PHC and minimal interaction with the District Hospital. As illustrated in Figure 4.6, RDWs connected to SHGs were relying on PHC level advice, which may be related to their choice of birth facility while non-‐SHG connected RDWs were receiving advice from the CHC level providers. Also, a group of mainly SHG-‐connected RDWs had multiple connections with a private facility. Figure 4.6: Block Advice Network in Mirzapur
Summary Network Measures: Density, Total Ties and Average Degree Centrality: Density is a measure that describes the overall network. It measures how interconnected the network is compared to the number of possible connections. It is calculated by dividing the total number of actual ties by the total number of possible ties. For networks, it is often important to have a higher rather than lower density. However, the literature shows that an overly dense network may not be as effective
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because it requires a lot of time and resources to build and maintain these relationships and decision-‐making becomes more difficult.4 The number of total ties provides a numeric value to the number of advice providers within each category that is used in computing density. Average degree centrality establishes how many connections an RDW has for each category of people that she knows from the household, SHG group, Village community and block. The average degree centrality values reveal how many different individuals, on average, provided MNH advice to RDWs. As displayed in Table 4.1, personal advice networks are the most dense, with an average of 5 connections per RDW in Mirzapur and Banda, and 3-‐4 connections, on average in Hardoi. Across the different categories of connections, the SHG advice networks were the least dense, even in comparison to the block level, despite the geographical distance from respondents’ residences. Across the 3 districts, RDWs in Mirzapur had the highest density and average number of people providing advice, except for the block level health category, where Banda had the highest density and average number of connections, at 2.3 contacts. Hardoi, overall had the least dense networks and lowest number of connections on average for all categories. . SHG Maternal and Newborn Health Decision Network: Apart from the standard tables of listed relationship types, RDW respondents were also asked to freely list up to five people whose advice they take into consideration when making decisions about maternal and newborn health. For the three districts combined, the leading influencer of health decisions was the husband, as 68% of RDWs had listed. The second most important source of influencing health decisions was a doctor (both qualified and unqualified), nurse or health facility provider (50%). The AAAs (but most prominently ASHAs and ANMs) were close behind at 46%. Lastly, only 4% of RDWs listed an SHG member or RGMVP staff member as an influencer of MNH decisions.
Figure 4.7 depicts these results for Hardoi illustrating the general pattern of the husband being the leading influence on MNH decisions.
4 Provan K. 2001. Do networks really work? A framework for evaluating public-‐sector organizational networks. Public Administration Review, 61(4):414-‐423.
Density (%) Total Ties Avg. Degree Centrality Hardoi Banda Mirzapur Hardoi Banda Mirzapur Hardoi Banda Mirzapur Personal 50.7 72.8 75.7 110 158 159 3.548 5.10 5.300 SHG 9.2 13.8 19.5 20 30 41 0.645 0.968 1.367 Village 30.6 33.5 39.6 76 83 95 2.452 2.677 3.167 Block 10.0 25.4 21.9 28 71 59 0.903 2.290 1.967
Table 4.1: RDW Advice Network Measures
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Figure 4.7: Advice Consideration in Making MNH Decisions in Hardoi
4.3. How are RDWs connected to village and block level health providers and other key community members in receiving government schemes and services?
Recently delivered women were asked to identify village and block level health providers that helped them in obtaining government services such as family planning supplies, Janani Surakshya Yojana (JSY) funds for institutional delivery, or a Below Poverty Level Card (BPL). About two-‐thirds of the women in Hardoi identified someone in the GP that assisted them in obtaining these entitlements. The most common sources of help were the ASHA, ANM and AWW. All types of women reported receiving assistance: whether or not they resided in a purwa with or without an SHG or whether they were an SHG member or not. When examining the assistance received at the block level in Hardoi, the picture is reversed, in that only a third of RDWs identified a block level provider who has assisted them even though the majority of the women had a contact with providers during their institutional delivery. Of those that indicated they received services from a Block level agency, the RDWs without an SHG connection identified the District Hospital as their only source of assistance at the Block level, while all but one of the SHG connected women identified both the District Hospital as well as CHC or PHC providers.
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Figure 4.8: Hardoi RDW Village Community Services Network
Figure 4.9: Hardoi: RDW Block Services Network
In Mirzapur (see Figure 4.10), another structure is evident as the PHC and CHC providers do not have any of the same RDWs linked to their services and form two separate clusters. The RDWs connected with SHGs do not exhibit any more connectivity to the block health system compared to non-‐SHG connected women and many of them are equally left out of receiving this type of assistance from Block level providers.
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Figure 4.10: Mirzapur RDW Block Services Network
Summary SHG Service Network Measures: Density, Total Ties and Average Degree Centrality: RDW respondents were asked whether they received help in obtaining government services such as: family planning supplies, JSY, or a BPL Card from Gram Panchayat and block level health service providers and other key individuals in the community. In general, more interactions in this category take place at the GP level, averaging 2-‐3 connections for each respondent. At the block level, the average number of connections was 1-‐2 per respondent. Banda consistently had the most dense service connections between RDWs and service providers, reporting the highest number of ties and yielding the highest average degree centrality at 4 for the both the GP and block level providers. Hardoi and Mirzapur had comparable measures at the GP level, but Mirzapur had a higher number of connections than Hardoi at the Block level. Table 4.2: RDW Service Networks: Density, Total Ties and Average Degree Centrality Density (%) Total Ties Avg. Degree Centrality Hardoi Banda Mirzapur Hardoi Banda Mirzapur Hardoi Banda Mirzapur Village 20.6 26.6 19.2 51 66 46 1.645 2.677 1.533 Block 9.0 25.4 15.9 25 52 43 0.806 1.677 1.433
4.4. What is associated with the practice of key healthy behaviors? One of the goals of this Social Network Analysis was to obtain a better understanding of social and information networks among RDWs. In this section, statistical modeling is used to understand in more detail how different factors affect health behaviors among RDWs interviewed during the study. Three behaviors were chosen as the outcome variables in the analysis: immediate breastfeeding, institutional delivery, and a three-‐year gap between the two most recent deliveries. The independent variables include basic socioeconomic status markers, such as caste and education, as well as geographic
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indicators and SHG connection status. In addition to these basic indicators, a number of variables were also included that relate to advice and health decision making among RDWs. Each RDW was asked to name people whose advice she would be likely to listen to when making decisions about maternal and child health. According to these responses, several variables were designed to estimate how characteristics of these “decision networks” might be related with health behaviors of interest to the UP BCM project. Sources of this type of advice were categorized into three groups: Personal (Mother, Father, Husband etc.), AAA’s (ANM, ASHA and Anganwadi Worker), and Other Health Providers (Local Doctor, RMP, etc.). These groups made up the majority of responses among respondents. The independent variables were used to separately predict the three chosen dichotomous outcome variables through logistic models. The “linktest” function from the STATA 11.0 software package was used to test for specification errors in the three models, but none were found. Results of these logistic models are presented in Table 4.3 below. Table 4.3: Logit regression identifying predictors of three health behaviors among RDW respondents Outcome Variable Immediate
Breastfeeding Institutional Delivery
3 Year Birth Spacing
Odds Ratio
SE Odds Ratio
SE Odds Ratio
SE
Age 0.98 0.07 1.13 0.09 1.13 0.10
SHG Connection Strength 0.53� 0.17 0.92 0.33 1.58 0.60
Education 1.75 0.68 2.45 1.32 3.13* 1.37 Caste (SC = 1) 2.74 1.73 3.66 2.86 4.47* 3.27
District 2 Dummy 8.46* 7.18 18.11* 16.15 0.66 0.53 District 3 Dummy 4.37* 3.25 29.37* 28.99 0.26 0.23
Lives in Purwa? 0.12* 0.09 0.80 0.57 0.95 0.69 Decision – Personal 0.18* 0.16 5.82� 5.59 0.19� 0.19
Decision – AAA’s 0.32 0.37 6.80 9.87 0.07� 0.10
Decision – Other HP 0.36 0.36 4.11 4.86 0.07* 0.09 Decision – Personal & Other HP 6.74 8.28 1.58 2.20 11.94� 15.73
Decision – Personal & AAA’s 70.74* 101.72 0.17 0.26 6.02 9.00 Decision – AAA’s & Other HP 0.13 0.18 0.20 0.30 19.17� 28.97
p > χ2 < 0.001 0.001 0.03
N 92 92 71 � p < 0.10 * p < 0.05 ** p < 0.01
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For the reader’s reference, information about the detailed construction of each variable included in the model, as well as information about the distributions of each variable among included respondents, can be found in Appendix V. Logistic Model Results: Comparing the statistically significant predictors within the socioeconomic and background variables, there is no clear pattern among the three models. Education is only a significant positive predictor of birth spacing behavior, but is in all cases at least a weakly positive predictor. The same is true of Scheduled Caste affiliation, which at least weakly predicted the three health behaviors in the above models, and in one case was a significant predictor. Living in a purwa predicted a lower probability of immediate breastfeeding, but did not significantly predict the other two outcomes. In both the immediate breastfeeding and institutional delivery models, the district-‐wise dummy variables captured a large amount of variation, in large part due to the lower average values for these two health outcomes among respondents in Hardoi, the base case in the dummy variable construction. Across the three models, many of the six variables related to whose advice respondents take when making decisions were significant, but each model shows a different pattern among these variables. In the immediate breastfeeding model, respondents who only took advice from their personal networks had a lower probability of immediate breastfeeding. Those who took advice from both personal and AAA networks had a higher probability of immediate breastfeeding compared to those who took advice from only one of those two groups. The birth spacing model showed strong predictive value among most of the decision-‐related variables. The model results show that those with only one type of advice source have a lower probability of having a 3 year gap between their two youngest children. In comparison, those with both personal and other health providers as advice sources, as well as those with both AAA’s and other health providers as advice sources, have a higher probability of a 3-‐year gap. Having both personal and AAA sources of advice was also a positive predictor, but was not statistically significant. The institutional delivery model provided generally lower predictive value in these decision-‐related variables, due partially to the strong district-‐wise variation in this outcome. Diverse Sources of Health Advice and Implications: One of the working hypotheses during the design of this model was that more diverse sources of health advice would lead to better health outcomes. The results suggest that respondents who have more diverse sources of advice when they make decisions have better health outcomes. However, simply having more sources of advice does not guarantee better health outcomes. The results suggest that for different types of health outcomes, different
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groupings of advice sources were the most positive predictors. For example, having personal and AAA sources of advice for decision making had the most positive predictive value for immediate breastfeeding, while having other health providers in combination with either AAAs or personal sources had the most positive predictive value for birth spacing. Generalizing these results, there appears to be a positive value in having diverse sources of advice outside the family when it comes to health decision-‐making. Some health behaviors are more amenable to change than others from certain types of advice sources. The reasons for these differences likely to point to the many differences between these health behaviors and how these decisions might to be made. As an example, one important difference might be the time window of the decision, where immediate breastfeeding is an acute decision taken soon after delivery, while birth spacing is a long-‐term decision. Different sources of advice may also be more or less influential in certain realms of decision-‐making. The ASHA worker in a village, for example, has certain areas of expertise where she may be very influential, such as institutional delivery, but may be less influential in other realms such as immediate breastfeeding. For the UP BCM project, an important implication of these results is that a higher exposure of women not just to more information sources, but more diverse information sources, appears to be an important area for strategic focus. There are many diverse and competing sources of health information available and the UP BCM project should target not just pregnant women for health messaging but also the key people that influence and enable her decision making and practice of maternal and newborn health behaviors. The Swasthya Sakhi can be part of this strategy, by functioning as one of these sources of advice, but the results from the Social Network Study show that she won’t be, by any means, the only source of advice that women are receiving. Increasing the consistency of messages by the diversity of these advice sources, and focusing on some of the most influential sources, may lead to better health outcomes. Degree of Connection to SHGs: One other interesting result was the uncertain effect of different levels of SHG connection among respondents. The statistically significant negative result of having an SHG connection on immediate breastfeeding practice brings up an important question about the target intervention groups in the project, mostly young women, and the reality of SHG membership, where many SHG members are older women in the communities. One hypothesis to explain why, after controlling for other basic socioeconomic status markers, SHG membership has negative predictive value on this health outcome, might be that young women in SHGs may in fact be exposed to incorrect information from other women in the group. Unless her alternative advice networks are strong enough, she might be influenced to some degree by the reinforcement of poor health advice and the culture of maintaining old patterns of practices.
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Limitations: One limitation of these regression models is that the sample size, while more than adequate for the network results discussed in other sections, is on the small side for regression analysis. However, the sample still appears adequate to address some of the hypotheses about the potential role of advice and decision-‐making among RDWs in their health behaviors and health outcomes.
4.5. Advice Networks Summary and Program Implications A major objective of the UP BCM program is to spread important information about maternal and child health to as many people as possible through innovative messaging techniques, including through the medium of RGMVP’s SHG network. This Social Network Analysis was designed to understand in greater detail the actual information networks that currently exist in villages within the UP BCM Learning Phase areas, with the goal of informing targeted strategies for message dissemination. The following are some of the major findings and implications from the analysis of RDW advice networks. Finding 1: Families are sources of advice for all women Implication: Develop program strategies to target specific family members of pregnant women and women of reproductive age for promoting and diffusing key BCM messages as they are the main channels of advice provision All respondents talk about maternal and newborn health with their families, including parents, in-‐laws, and husbands. These advice networks already appear to be robust, and on average there are high levels of trust in the information provided by these sources. The UP BCM program should evaluate its strategy for message dissemination to consider how it can take advantage of these existing advice sources. While women will be provided with high quality messages from UP BCM program sources, if these messages conflict with existing advice sources within the family, then there may be substantial pressure to act on the advice from within the family when it comes time to make important decisions related to maternal and child health. Currently the BCM Project is only targeting pregnant women in its messaging and would be more effective if the main sources of influence were also included. The messages would remain the same but the strategies would be different for husbands as opposed to mothers-‐in-‐law. Finding 2: Almost all RDWs receive advice from AAA regardless of SHG membership or SHG presence in the area Implication: Assuming AAA connections with RDWs are strong, the tasks of the SHGs and VOs can be refocused to promoting and monitoring the equitable expansion of entitlements and services that are to be delivered by AAAs, which could be a major vehicle for expanding the use of health services. Respondents were sought from a variety of different programmatically relevant locations: main villages (all with SHGs), purwas with SHGs, and purwas without any SHGs. One hypothesis is that even without existing health interventions, the very
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presence of SHGs, as well as the SHG membership, could increase the social integration of individuals within the GP, and could serve to increase connections to local resources, including the AAA workers. This concept is of particular interest because it would dictate a certain type of approach to non-‐RGMVP resources within the village. The findings from the Social Network Analysis suggest that there appears to be no correlation between residence in these three types of locations and connections to local AAAs, meaning that the existence of SHGs by itself does not appear to increase these connections. These results also suggest that those living in purwas, where there are often larger clusters of marginalized groups targeted by RGMVP, are not at a disadvantage in terms of connections with AAA workers in the village. There also appeared to be no correlation between SHG membership and connections with AAA workers. In general, a majority of respondents, regardless of SHG membership status or location, were connected with AAA workers, especially with the ASHA and Anganwadi Worker. These results have important implications for strategy design within the UP BCM program, because it appears that there is no need to explicitly strengthen connections with AAA workers among SHG members and the rest of the village. These connections appear to already be quite robust, and the program strategy can focus instead on taking advantage of these existing connections, strengthening them and reinforcing messages already being delivered by these sources. Finding 3: The VO structure and the Swasthya Sakhi do not currently appear to be strong sources of advice Implication: Develop clear roles for how VO members and Swasthya Sakhis will deliver health messages to target women and key groups of influence in the community Even among SHG members, the VO structure was not a strong source of advice, even including the VO Swasthya Sakhi. SHG-‐level Swasthya Sakhis were also very rarely listed as sources of health advice by respondents. Within the SHG structure, individual SHG members were much more often cited as advice sources, compared to Swasthya Sakhis and the VO structure. This result can be partially attributed to the recent introduction of the UP BCM health program within the study areas at the time of the survey. The UP BCM project had only begun implementation about two months prior to data collection. As a result, in some cases the Swasthya Sakhis were only recently identified in survey areas, and while VO structures had been well established, they were not likely to be sources of health advice prior to the beginning of the UP BCM project. However, These findings suggest that the UP BCM should put a strong focus on identifying a clear strategy for the Swasthya Sakhis and implementing this strategy at the ground level. During data collection, the survey team often had trouble identifying Swasthya Sakhis for interview, in part because some SHG members were not sure whether or not they were a Swasthya Sakhi. This may in part be a function of RGMVP’s desire to continually shift responsibility among SHG members to avoid the appearance of dominance or privilege within the SHG, but it also appears to be an impediment to developing a strong sense of identity among Swasthya Sakhis, which would be important for these women
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to feel confident in performing their roles. These issues should be clarified among partners in order to facilitate a more effective implementation of the Swashtya Sakhi model, at both the SHG and VO levels. Finding 4: In Mirzapur, SHG members were also sources of advice for RDWs. In other districts, SHG advice networks were not as strong Implication: Investigate the reasons why SHG members are more successful in health communication with the wider community in some areas rather than others. Learn from high performing areas and focus resources on strengthening the community outreach activities in developing areas As discussed above, the SHG structure in general does not appear to be a strong source of health advice for women, but this does not appear to be true in Mirzapur, where among SHG members, the SHG structure is a robust source of health advice. The most common sources of advice during the study period were actually other SHG members, as opposed to Swasthya Sakhis at the SHG or VO level. While almost all SHG members surveyed in Mirzapur received health advice from SHG members, respondents who were not SHG members themselves, but knew an SHG member either through their household or in their neighborhood, also had received health advice from SHG members. This suggests that the first element of a diffusion strategy beyond SHGs, namely spreading health messages to friends and relatives, appears to be already functional in Majhwa Block of Mirzapur. There may be several reasons for these regional differences. Of course, the socioeconomic context varies greatly across districts in Uttar Pradesh. Additionally, RGMVP’s management structure is largely decentralized across the different CRDCs (essentially regional management offices) in the project, and so it would be useful for RGMVP to evaluate whether any better performing strategies in one area could be implemented in other program areas. Finding 5: The strength of connections with SHGs was associated with lower levels of practicing of some key health behaviors Implication: There is a need to understand and influence the beliefs and practices of SHG members who are mostly older women beyond the childbearing age before they can spread new key behaviors that they themselves may not have practiced The strength of connections with SHGs was associated with lower levels of practicing of some key health behaviors, a result that suggests a more comprehensive approach to message delivery within SHGs. While the main targets of the UP BCM project are women of childbearing age, many SHGs members are older than this target age range. As results from other sections of this study indicate, women receive advice from a wide variety of sources. These older women may very well be sources of advice for RDWs, and the messages of the UP BCM project may conflict with their long-‐held beliefs and
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practices. Messaging strategies should consider how to address SHGs as a whole, given the challenges of addressing groups with members from different generations. Finding 6: Only some RDWs received advice from block level health sources (unrelated to SHG membership) Implication: Strategies should be developed to help increase the quality of interactions at block level facilities, including but not limited to increasing awareness of entitlements and the delivery process at a government facility Some respondents received health advice from block level health sources, such as PHC and CHC staff, but there was no pattern related to this advice and SHG membership. Given that the institutional delivery rates are high in the sample, there might be an expectation that a majority of respondents would at least receive some health advice from these block level sources. The fact that these women do not believe that they have received any advice from block level sources is in line with anecdotes from respondents that other than the delivery itself and, there was very little interaction with health staff at the location of the delivery. The UP BCM program might want to consider developing strategies to increase the quality of these interactions, including providing women with information on their entitlements proximal to the time of delivery, and what they should be expecting at the PHC or CHC for delivery. Finding 7: Most RDWs admire a variety of sources for good health advice, both within the HH and in the village community, but many rely more heavily on household sources Implication: Strategies should be developed to reach out to RDWs to expand their exposure to non-‐household sources of evidence-‐based health advice Respondents were asked whom they admire for providing high quality health advice. Overall, respondents admire a variety of sources, including personal sources like family and friends, as well as local public and private health service providers. While the sample is too small to make strong conclusions about the differences between SHG members and non-‐members, it appears that SHG members tend to have slightly more diverse sources of advice outside of the family. While this is a positive sign, it is not clear from the collected data whether SHG membership has led to more diverse sources of advice, or whether those who join SHGs are also those who are more likely to already have more diverse advice networks. Either way, increasing the diversity of the admired sources of advice among non-‐members will be a challenge for the UP BCM project. It may not be as simple as creating new SHGs if those women who have less diverse advice sources are also those who are not interested in joining SHGs. The diffusion strategy in the UP BCM program will need to consider the challenges of reaching out to community members who may be less amenable to outside advice sources. Finding 8: Decisions are often taken based on the advice of multiple people, and the husband and “Doctor” are a frequent source of this advice
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Implication: The important role of the husband and “Doctor” as a source of advice for health-‐related decisions should be considered, and strategies should be developed to increase awareness among husbands and unqualified health providers in the community about health messages being delivered through government sources and through the UP BCM project Respondents often consider the advice of multiple people when making decisions about maternal and child health, and many respondents consider the advice of sources outside the household, particularly the ASHA and to a lesser extent the Anganwadi Worker. The fact that outside many respondents consider outside advice sources bodes well for the UP BCM program strategy of health messaging to women in program GPs. An environment in which outside advice was not well received would be a much more challenging environment for this type of project. One particular result worth considering is the prominence of the husband as a source of advice for decision making about maternal and newborn health. The husband was the most frequently cited advice source in this context in all three districts, even more so than the mother-‐in-‐law, who is frequently thought to be a key influencer in household decision-‐making. The UP BCM program should take note of the importance of the husband in health decision-‐making by designing strategies to include the husband in the process of health messaging. This might take the form of door-‐to-‐door discussions with men in the village, or through other village-‐level meetings that could expose husbands to the messaging that women are receiving separately through SHG meetings or through other aspects of the diffusion strategy. The goal would be to have the husband accept and encourage the practice of a few key behaviors that can improve the health of his family. Any resistance that takes place within the household itself might weaken the health messages being disseminated through the UP BCM project.
5. GP-‐Block SHG Structures, Health Workers and Key Community Members
5.1. Introduction The relationships within different levels of SHG structures and RGMVP and their linkages with the government health system are a major mechanism to improve access to entitlements and health services. Respondents were asked about three types of linkages with each other: 1) exchange of information about new and existing programs and services; 2) coordination of health services including emergency referrals; and 3) discussion or coordination on the supply of family planning and other health products. Additionally the frequency of interaction within the last year was ascertained. Respondents included: GP level SHG and VO members; the village health workers, mainly the AAAs, as well as traditional practitioners and community political and
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religious leaders; Block Level organizations comprising of VO representatives; and block and district level health structures. In the development of network measures, unconfirmed or non-‐reciprocal acknowledgement of a specific type of relationship is an indication of a weak tie. When criteria of reciprocity are applied, commonly about half of the relationships are dropped. In this section, we will present examples of unconfirmed and confirmed whole networks. Unlike the RDW questionnaire, these respondents from both the GP and Block levels were asked about their relationships with each other. In each of the three blocks, block level respondents were asked about their relationships with people in two out of the six GPs, as the interviews at the block level would be unreasonably lengthy. Therefore, in each block four GPs only have confirmed plots at the GP level for each of the three types of linkages, while two GPs have both GP level and Block level plots, which we call “whole network plots”. This section will contain only the whole network plots to provide a more comprehensive view of the ties within the GP level as well as with block level health providers. The section is further divided into the three questions that are oriented around learning about the current status of relationships in each of the network domains: 1) What are the information networks of relationships within different SHG levels and across AAAs, block health structures and key community players? 2) How are services coordinated between the different groups in the network? and 3) What are the groups that discuss and coordinate family planning supplies and other health products? The next subsection provides a summary of key linkages across all the three types of networks with a focus on the important relationship dyads. After a discussion of the networks in general, statistical tests comparing how similar the networks were across districts will be presented. The section then concludes with an overall summary with key findings and implications for the Uttar Pradesh BCM Project.
5.2. What are the information networks of relationships within different SHG levels and across AAAs, block health structures and key community players?
Information exchange is the gateway to the development of other types of relationships that require additional engagement and intensity. Information sharing networks should be typically denser and less centralized in comparison to other types of relationships because sharing information requires less coordination and information can freely flow along different paths.5 Sharing information may only be one-‐sided, if one agency shares information with another but that relationship is not reciprocated. Usually when looking at these unconfirmed information-‐sharing relationships there are many connections, as 5 Borgatti, Stephen P. 2005. Centrality and network flow. Social Networks, 27(1): 55-‐71.
47
noted in Banda in Figure 5.1. Even though there are many information-‐sharing relationships in the plot, we can clearly see clustering in terms of who is sharing information with whom. The “nodes” representing people interviewed are clustered in groups by color representing their affiliation, either SHG structure in green, RGMVP in dark blue, government health in red and others in light blue. Each group has people who are central in facilitating connections and people who are on the periphery, except that the unqualified health workers, private facility and PRI appear at the edge of the network and not as immersed in the information-‐sharing network as the other groups. The BO Office Bearer operates as a bridge between the SHG levels and the government health system and thus, stands out as having the largest node or betweeness centrality. When this network gets confirmed, around half of the ties get dropped, but the structure of the network remains fairly the same (see Figure 5.2). It still remains fairly dense for a confirmed relationship network and one of the strongest examples in the study. The BO Office Bearer (BOB) maintains the most important position and connects the two separate clusters with each other. However, with fewer ties overall, additional bridging roles become important to maintaining the links between the different clusters. These bridging roles are carried out by the RGMVP trainer (RGT) and the AWW who connect different sets of players in the two networks. The health system is positioned on one side and SHG/RGMVP remains on the other, which is a common pattern throughout all plots. Figure 5.1: Unconfirmed: Banda-‐GP2/Block: Information Exchange Network about Health Programs and Services
G1SS
G2SS
GMVOM
VOB
VOSS
VOHG
VOPRASHA
ANM
AWW
TBA
RMP
PRIDS
RL
Dr
BOB
BOR
BOHG
BOPR
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PHN
PHS
CHM CHN
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PF
ANMs
ICDSs
BPRI
BDO
CDPO
DHM
DHOB
DHN
RKS
RGF
RGT
RGV
RG
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Box 5.1: Guide to Plots by Location (Shape) and Affiliation (Color)
On the SHG/RGMVP side: a) BO is taking a central role; b) VOs and BOs are connected; and c) the SHG SS is connected with the VO SS. On the health system side, the health functionaries are fairly interconnected with the (AAA’s), maintaining a close structural closeness to SHGs but also connected to providers at the PHC. However, the ASHA and ANM are not connected to any SHG structures with the AWW forming a conduit to information exchange between the two. The CHC remains completely isolated from their own government system as well as the SHG/RGMVP structures. Not all anticipated respondents could be located during survey exercises. Actual respondents have a thick border around their shape in confirmed plots, and by definition, only actual respondents can have connections in a confirmed plot. Figure 5.2: Confirmed Banda-‐GP2/Block: Information Exchange Network about Health Programs and Services
G1SS
G2SS
GM
VOM
VOB
VOSS
VOHG
VOPR
ASHA
ANM
AWW
TBA
RMP
PRI
DS
RL
Dr
BOB
BOR
BOHG
BOPR
PHM
PHN
PHSCHM
CHN
CHS
PF
ANMs
ICDSs
BPRI
BDO
CDPO
DHM
DHOB
DHN
RKS
RGF
RGT
RGV
RG
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Figure 5.3: Confirmed Hardoi-‐GP3/Block: Information Exchange Network about Health Programs and Services
The structural patterns of the networks that were evident in Banda are replicated in Hardoi. Figure 5.3 displays two separate sides aligned by homogenous categories. There is a main bridge connecting the two clusters, through the ANM, that can potentially threaten the integrity of the communication between the two sides. However, the ASHA and AWW are also reinforcing a smaller sub-‐set of linkages thereby reducing the threat to information exchange between the two sides. In the SHG and RGMVP cluster, RGMVP plays a central role in linking the VO and BO together. The ASHA appears to be well connected to the SHG and the VO as well as the PHC and CHC and is well positioned to further facilitate the exchange of information about programs and services between the two clusters.
5.3. How are services coordinated between the different groups in the network?
The coordination of services and referrals requires a higher level of interaction and a system for working together. There is a wider range of experiences across the blocks and GP’s as compared to the information exchange networks. The figure below is an example of a fairly functional network in terms of being integrated by homogenous clusters connected via the AWW who is in the position to act as a conduit between the health system and SHG structures at all levels. RGMVP is again at the center of the SHG cluster but there is also more interconnectedness and decentralization. The SHG member is connected both to the ASHA and SHG Swasthya Sakhi, expanding her chances for getting assistance.
G1SS
G2SS
GM
VOM
VOB
VOSS
VOHG
VOPR
ASHA
ANM
AWW
TBA
RMP
PRI
DSRL
Dr
BOB
BOR BOHG
BOPR
PHM
PHN
PHS
CHMCHN
CHSPF
ANMs
ICDSs
BPRIBDO
CDPO
DHMDHOBDHNRKS
RGF
RGT
RGV
RG
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Figure 5.4: Hardoi-‐GP 4/Block: GP-‐Block Health Services Coordination and Referrals Network
In contrast to the Hardoi GP, the Mirzapur network in Figure 5.5 is hanging on by a string, resembling a kite structure. If any one persons drops out, everything collapses. The AAAs however are interconnected and linked to the SHG structures by the SHG member. The VO Office Bearer at the tail, has a larger node as she is connecting the VO Member (VOM) and SHG SS (G1SS) into the sparse network. Figure 5.5: Mirzapur-‐GP 1: GP-‐Block Health Services Coordination & Referrals Network
G1SS
G2SS
GM
VOM
VOB
VOSS
VOHG
VOPR
ASHA
ANM
AWW
TBA
RMP
PRI
DS
RL Dr
BOB
BORBOHG
BOPR
PHMPHNPHS
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CHN
CHSPF
ANMs
ICDSs
BPRIBDO
CDPO
DHMDHOBDHNRKS
RGF
RGT
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VOM
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VOHG
VOPR
ASHA
ANMAWW
TBA
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The third type of health services and referral network in Banda is illustrated in Figure 5.6. The network is not functioning, as the two disjointed clusters are not connecting with each other at all. RGMVP, on the SHG side, is very centralized and surrounded by a periphery of members who are not well connected to each other which limits coordination and referrals for health services. There is an over-‐reliance on one individual to do everything. On the health side, the ASHA links in the ANM and AWW as well as the SHG member who is not connected even within her own SHG structure. Figure 5.6: Banda-‐GP5: GP-‐Block Health Services Coordination and Referrals Network
5.4. What are the groups that discuss and coordinate family planning supplies and other health products?
The availability of health supplies and contraceptives are an essential component of health services delivery. For the first time, the Hardoi GP3 Health Supplies Network has the ASHA on the RGMVP/SHG side with the ANM serving a bridging role with the health providers at the block level (see Figure 5.7). The RMP is on the periphery but connected to RGMVP staff and the VO member, while the PRI and TBA are tied into the government health cluster. Figure 5.8 depicts Mirzapur with two distinct interconnected supply networks. They are dense within their respective clusters with large broker roles played by the Pradhan (PRI) and Swasthya Sakhi. RGMVP is at the center of the SHG cluster, but SHG and BO have more roles in creating linkages with the health system. The SHG member again has departed from her SHG friends and is closer to the health network with the ANM and also PRI. She is however linked to SHG Swasthya Sakhi.
G1SS
G2SS
GM
VOM
VOB
VOSS
VOHG
VOPR
ASHA
ANM
AWW
TBA
RMP
PRI
DS
RL
Dr
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BOPR
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PHS
CHM
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CHS
PF
ANMs
ICDSs
BPRI
BDO
CDPO
DHM
DHOB
DHN
RKS
RGF
RGT
RGVRG
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Figure 5.7: Hardoi-‐GP3/Block: Health Supplies Network
Figure 5.8: Mirzapur-‐GP3: GP-‐Block Health Supplies Network
The network structure remains consistent in Banda. However, the bridging roles change as the AWW gains prominence along with the BO Member. The ASHA is close to the edge of this network and not interacting with any SHG structures. The CHC and Private facility (PF) are isolates and not part of any network.
G1SS
G2SS
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VOM
VOB
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VOPR
ASHA
ANM
AWW
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PRI
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BPRIBDO
CDPO
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ICDSs
BPRI
BDOCDPO
DHM
DHOB
DHN
RKS
RGF
RGT
RGVRG
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Figure 5.9: Banda-‐GP2: GP-‐Block Health Supplies Network
5.5. Overview of measures of key SHG and health system linkages Overall, there seems to be some key differences in the various networks structures, as discussed above. To summarize these differences before delving further into a discussion of the presence and absence of key linkages, Table 5.1 provides the network level measures for the whole GP networks. As block level confirmed relationships were limited to two whole GP-‐Block networks, the total number of whole GPs was 6. Details comparing these 6 GPs are provided below in Table 5.1. The table provides the specific values for the unconfirmed and confirmed density, confirmed centralization, and average degree centrality for each type of network for each of the whole GPs. Table 5.1: Density and Centralization of Whole GPs
Information Sharing Networks District GP Unconfirmed
Density (%) Confirmed Density (%)
Confirmed Centralization (%)
Avg. Degree
Centrality Hardoi 3 37.9 14.2 15.0 4.293 4 39.5 14.7 29.4 4.293 Banda 2 38.9 13.3 13.6 1.889 5 36.3 15.4 08.4 4.195 Mirzapur 1 33.8 15.9 09.9 4.488 3 34.8 16.3 09.5 4.780
Services Coordination and Referrals Networks
G1SS
G2SS
GM
VOM
VOB
VOSS
VOHG
VOPR
ASHA ANM
AWW
TBA
RMP
PRI
DS
RL
Dr
BOB
BOR
BOHG
BOPR
PHM
PHNPHS
CHM
CHN
CHS
PF
ANMs
ICDSs
BPRI
BDO
CDPO
DHM
DHOB
DHN
RKS
RGF
RGT
RGV
RG
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Hardoi 3 29.8 09.2 13.4 2.780 4 33.8 12.0 13.4 3.512 Banda 2 16.6 06.4 05.5 1.805 5 23.8 07.3 06.6 2.000 Mirzapur 1 12.6 02.4 03.0 0.683 3 13.7 02.8 08.3 0.829
Health Supplies Networks Hardoi 3 37.0 14.4 12.7 4.341 4 37.8 14.0 15.2 4.098 Banda 2 34.1 13.3 12.2 3.756 5 36.0 14.8 09.2 4.049 Mirzapur 1 30.9 14.5 10.4 4.098 3 31.7 15.0 10.0 4.390 One of the important points about Table 5.1 is the comparison of density values across the different types of networks. For all the networks, when the ties are confirmed the density falls to around 50% lower. As noted already in the discussion, this difference is common when confirming relationships, but more noteworthy is the variation in density across the three types of networks. The information sharing and health supplies networks had similar density values across the 6 GPs, but the density scores were much lower for the Services Coordination networks. This is consistent with the discussion of the plots above in which we saw that the Services Coordination networks were more fragmented than the Information Sharing and Supply networks. The variation in density values within the same network type but across districts was greater, without much of a consistent pattern when comparing across the districts. Turning to centralization now, the range in how centralized the various district networks were by relationship type are 8.4%-‐29.4% for Information Sharing, 3%-‐13.4% for Service Coordination, and 9.2%-‐15.2% for Supplies. The range in centralization for information sharing is much greater than density; however, the centralization pattern is similar to that as found with density. The information sharing and supplies networks are centralized to about the same extent and tend to be more centralized than the service coordination networks. This again supports the discussion up to this point that the service coordination networks were more fragmented and lacked a central figure acting as bridge between clusters. Having an important central figure acting as bridge leads to a higher centralized network than one that is fragmented. In comparing across districts, the only pattern that emerges is that the Hardoi networks tend to be more highly centralized than the Banda or Mirzapur networks. Since we saw that the Hardoi networks tend to have a more cohesive structure, this is consistent with what the plots illustrated.
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The final column in Table 5.1 provides the average number of ties for each of the networks. For the most part, most network actors had an average of 4 ties for the information sharing and supplies networks. Consistent with the discussion thus far, this is higher than the average number of service coordination ties, for which the average ranged from less than <1 to 3. Quadratic Assignment Procedure: To statistically test how similar the different types of networks were across districts, we examined how correlated the networks are to one another using the Quadratic Assignment Procedure (QAP). Overall, the similarity between the networks across the three districts is highly significant for information sharing and supplies. (See Appendix VI for complete results and a more detailed discussion). This means that the similarity of these networks is higher than would occur at random. However, the service networks were not significantly similar to the information sharing and supplies networks. This suggests that there is a difference between the structure of the service networks and the information sharing and supplies networks, as was shown with the plots and the Table 5.1 above. Since the service networks were more fragmented than the information sharing and supplies networks, it would require greater effort to build up the coordination structure and function. The patterns that emerge from Table 5.1 are representative overall of all 18 GPs. The specific density, centralization, and average degree values for all GPs are provided in Appendix VI. These values are based on the village networks and exclude all BO actors. In addition, there was a great deal of variation across the GPs within districts, for example meaning that not all of the GP information sharing networks within each district were similar to one another. Whereas this information is important to consider in strategies for enhancing the connectivity within each GP, these details are not discussed here; however, the QAP results comparing GPs are included in Appendix VI, which provides details about the extent of similarity across GPs within districts. Summary of Key Dyadic Relationships at GP Level: The discussion above, however, gives us only a general view of the relationships and does not provide information about key relationships. In an effort to understand the current status of key sets of relationships that are particularly relevant in the development of strategies for the UP CMP Project, a summary matrix of all the plots was developed. There were 6 GPs in each district with three types of relationships for a total of 18 plots per district. Figure 5.10 displays the SHG and RGMVP relationship dyads by district. As noted in most of the plots, RGMVP and different SHG structures display strength and interconnectedness creating a platform for potentially effective exchange of information and services. The relationships that are still maturing are between the SHG Swasthya Sakhi and the VO Swasthya Sakhi, ranging from 8 to 12 ties out of 18.
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Figure 5.10: Number of SHG-‐RGMVP Relationship Dyads Across Information Exchange, Health Services and Health Supplies Networks by District (N=18 within each District and type of relationship)
Figure 5.11: Number of SHG-‐RGMVP Relationship Dyads Across Information Exchange, Health Services and Health Supplies Networks by District (N=18 within each District and type of relationship)
In assessing the overall ties between different players in the SHG structures and the government health providers at the GP level (AAAs), there are many gaps. The linkage between the SHG and ASHA in Hardoi is present in half of the networks (9/18) and represents the maximum achieved, as all other relationships are fewer. The VO and
0
5
10
15
20
G1SS-‐VOSS RG-‐SHG/VO/BO SHG-‐VO
No.
SHG Levels-‐RGMVP Relaqonship Dyads
Hardoi Mirzapur Banda
0 1 2 3 4 5 6 7 8 9
10
SHG-‐ASHA VO-‐ASHA VO-‐ANM RGMVP-‐AAAs
No.
SHG/RGMVP-‐GP Health Relaqonship Dyads
Hardoi Mirzapur Banda
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ANM and RGMVP and AAAs are the least connected with only one to three relationships in Mirzapur and Banda. The VO and ASHA relationships, which could be a gateway to accessing more health services and other resources, are also very limited with no linkages at all in Banda. Summary of Key Relationships at Block Level: The relationship with Block level providers are key to accessing higher levels of services for women needing institutional delivery, care for limited illnesses as well as life saving treatments or appropriate and timely referrals. The overall levels of connectivity are quite low with the BO having the greatest number of ties (6 out of 18 possible connections among the 6 included GPs), followed by the RGMVP and VO at four linkages each. The SHG had no direct ties with the Block level providers.
5.6. Summary and implications: SHG Structures and Gram Panchayat (GP) health system linkages
Finding 1: RGMVP has built a well-‐connected network of SHGs, VOs and BOs that are exchanging information and discussing health services and health products amongst themselves. However, the SHG and VO Swasthya Sakhis are the least involved. Implication: RGMVP can use its functional SHG platform to improve the quality of the interactions and meeting processes between different sets of SHG levels by defining specific outcomes to be achieved, and ways to monitor progress through self-‐assessment and data analysis feedback loops. The results show that RGMVP staff and volunteers at the GP and Block level play an important role in information coordination among the SHGs, VOs and BOs. During the design of strategies interfacing with different aspects of the health system, especially for strategies involving the VO and BO, it will be important to keep in mind the role that RGMVP staff and volunteers currently play in coordination between the different levels of the SHG federated structure. There is an opportunity to further develop the capacity of different SHG structures to accelerate the performance of tasks to improve RHMCH. Leadership, facilitation and organizational skills can be developed in addition to specific tools for conducting better meetings that have agendas, participatory engagement through small groups exercises, collective identification of roadblocks and action items, with a commitment for follow-‐through and measurable accountability standards. Finding 2: SHG connections with the health system are limited, especially at the VO and BO levels.
SHG-‐Block Health Linkages (At least one relationship)
No. of ties
BO-‐Block Health 6 VO-‐Block Health 4 SHG-‐Block Health 0 RGMVP-‐Block Health 4
Table 5.2: Block Level Health System Linkages (N=18)
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Implication: Identify opportunities to increase linkages from VOs and BOs to the health system, especially through government defined accountability structures that have built-‐in sustainability: VHSNC meetings at the GP level and RKS at the block level. The purpose of the linkage must be well defined: such as promoting the equitable distribution of entitlements and development of community accountability processes for AAAs and block level health facilities. In the survey areas, there were overall very few connections directly between the VO and key stakeholders in the local public and private health systems. As the center of all SHG activity at the GP level, strategies should be considered to determine how, if at all, the VO can take advantage of its positioning to collaborate directly with the local health system. While there are many avenues through which the VO can engage with the local health system, one promising opportunity would be to become involved in local VHSC activities, which are organized on a GP-‐level basis. Our results in Section 3 above demonstrate that engagement with the VHSNC among SHG members is currently low, even if the committee exists in the GP. Many SHG and VO members were not informed or aware about the existence of the VHSNC. Finding 3: SHG members are often more connected to the GP health system than SHG Swasthya Sakhis. Implication: Increase awareness of UP BCM activities among key stakeholders in the community, including the identification of Swasthya Sakhis and their role in the community. Build on the existing relationships between the SHG member and the ASHA to expand the scope of the conversation with the SHG Swasthya Sakhi and VO Swasthya Sakhi to address broader community level RMNCH issues. As of the time of data collection, SHG members had more connections with the local health system than the SHG and VO Swasthya Sakhis. This finding was also consistent with the RDW interviews. This result can be partially explained by the relatively recent introduction of the UP BCM intervention in study GPs, about two months before data collection. The Swasthya Sakhi is currently tasked with message dissemination within the SHG and the local community, as well as with engaging in joint home visits with the ASHA. Beyond these responsibilities, whether the Swasthya Sakhi should be engaging with other health-‐related stakeholders is an important strategy question for UP BCM partners to consider. This type of engagement does not appear to be happening yet, but there are opportunities for Swasthya Sakhis to engage with certain types of local stakeholders who appear to be important to the local health system, such as RMP doctors and PRI members. Finding 4: Most networks are organized into two clusters (SHG/RGMVP side and Government health services, often including key others) connected by one or more members. Implication: Identify and promote linkages between SHGs/RGMVP and government health services that can contribute to UP BCM program goals. It is important to identify
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and leverage the “bridging” members that have developed trust and confidence in both clusters to hasten program diffusion. The networks connecting SHG structures with RGMVP staff and volunteers are generally strong, and the networks connecting government health service providers with each other and with other key stakeholders are also generally strong, but the ties across these two network clusters are generally weak, with very few respondents connected directly with both groups. One of the goals of the UP BCM project is to increase these linkages between the SHG structures and the health system, and so project partners should discuss which connections would be the most feasible and the most effective in establishing meaningful linkages. As of now, very few such connections exist, making it all the more important to discuss strategies to increase these connections. Finding 5: ASHA or ANM is a common connector creating a bridge among SHGs and health providers, although this varies across the different GPs. Implication: These linkages are “low-‐hanging fruit” and should be leveraged more explicitly so that they exist in as many program areas as possible The ASHA and ANM are most often the strongest connections between the SHG structure and rest of the health system and other key stakeholders. Since these results are based on confirmed connections, it appears that the ANM and ASHA workers are most likely among health workers to be aware of SHG members and the SHG structures, although this varies among GPs. As important stakeholders within the village level health structure, it is a positive step that these workers are aware of SHG activity in their coverage areas, but it is also important to create these ties in villages where they do not yet exist. ASHA workers often live in areas where SHG meetings take place, so it is logistically possible to make the ASHAs aware of SHG health-‐related activities, but developing a strategy for engagement with ANMs will require some consideration, given that they are often only present in GPs during formal activities related to their roles as health workers. The ANM engagement is best suited for both the VO and BO levels. Specific content areas of interaction could include: health supplies, use of misoprostol in home deliveries, communication and referral in case of emergencies, conducting regular VHND and promoting and participating in community governance structures (VHNSC and RKS). Finding 6: Health services coordination and emergency referrals are the weakest in the system. Implication: The potential for SHG members and RGMVP staff to play a greater role in health services coordination should be discussed among partners Health services coordination and emergency referrals are the weakest of the three network measures included in the survey, indicating that information sharing is more common than the actual coordination of work activities. While SHG members are taught about how to identify health emergencies and health issues that require medical
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attention, there are no explicit roles laid out for other members of the SHG structure, such as the VO and BO. UP BCM program partners should discuss whether this type of strategy would be feasible and helpful to the program goals. Finding 7: PRI, RMP and to a lesser extent, GP Drug Shop, are connected to the government health system especially for health supplies at GP and block levels. Implication: As the UP BCM project increases local awareness of its activities, these key stakeholders should be included in sensitization activities, including village meetings etc. Several community members who are not often identified as key stakeholders in health contexts appear to play a role, especially with respect to health supplies at the GP and block level. These members include PRI members, RMP doctors, and to a lesser extent, GP-‐level Drug Shops. These results suggest that the UP BCM program should identify ways to engage with these community members, who may not be part of the formal health system, but may play an important role in health issues, especially with respect to health supplies, at the village level.
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BIBLIOGRAPHY Borgatti, Stephen P. 2005. Centrality and network flow. Social Networks, 27(1): 55-‐71. Borgatti, S. P., Everett, M.G., and Freeman, L.C. 2002. Ucinet for windows: Software for
social network analysis. Harvard: Analytic Technologies. Friedman, S.R. and Aral, S. 2001. Social networks, risk-‐potential networks, health, and
disease. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 78:411-‐418.
Hanneman, Robert A. and Riddle, Mark. 2005. Introduction to social network
methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/ )
Provan, K. 2001. Do networks really work? A framework for evaluating public-‐sector
Organizational Networks. Public Administration Review, 61(4):414-‐423. Provan, Keith G., Veazie, Mark. A., Staten, Lisa K., and Teufel-‐Shone, N.I. 2005. The use
of network analysis for strengthening community partnerships in health and human services. Public Administration Review, 65: 603-‐613.
Valente, T.W., Coronges, K.A., Stevens, G.D., and Cousineau, M.R. 2008. Collaboration
and competition in a children’s health initiative coalition: A network analysis. Evaluation and Program Planning, 31:392-‐402.
Varda, D., Shoup, J.A., and Miller, S. 2012. A systematic review of collaboration and
network research in the Public Affairs literature: Implications for public health practice and research. American Journal of Public Health, 102: 564—571
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APPENDICES Appendix I: Acronyms used in Plot Construction RDW Code Guide RDW respondents were coded to reflect both the Block and District of the respondent, according to the table below, as well as the GP of the respondent. They all begin with “RDW”, followed by an underscore, and then a District/Block Code. In each Block, GPs were assigned a number from 1 through 6, and this GP code is placed after the District & Block code. District/Block Code District, Block BT Banda, Tindwari HA Hardoi, Ahirori MM Mirzapur, Majhwa For respondents from purwas, an extra number was appended to their end of their code, with 1 representing a respondent from a purwa with SHGs, and 2 representing a respondent from a purwa with no SHGs. For respondents from the main village in their GP, this last number is omitted. In cases where multiple respondents existed in one location, respondents have unique numbers after “RDW” in order to ensure that all respondents have a unique code. For example:
• RDW1_HA2 and RDW2_HA2 are two respondents from the main village of GP #2 in Ahirori Block, Hardoi.
• RDW2_BT51 is a respondent living in a purwa with SHGs from GP #5 of Tindwari Block, Banda.
• RDW_MM32 is a respondent living in a purwa without SHGs from GP#3 of Majhwa Block, Mirzapur.
OTHER Code Guide For all plots in the “SHG structures, health workers, and key community stakeholders” Category, plot codes were constructed using a code for the respondent type, the district and block of the respondent, followed by a number (1-‐6), which identify the GP for each GP-‐level respondent. The following table contains the position codes for all respondents in the “SHG structures, health workers, and key community stakeholders” category:
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Position Code Respondent G1SS SHG Swasthya Sakhi G2SS Other SHG Swasthya Sakhi GM SHG Member not in HH VOM1 VO Permanent Member VOB VO Office Bearer VOSS VO Swasthya Sakhi VOHG VO Health and Gender Committee Member VOPR VO Poverty Reduction Committee Member ASHA ASHA ANM ANM AWW Anganwadi Worker TBA Traditional Birth Attendant RMP GP Doctor (no degree) e.g. RMP, Jhola Chaap PRI Pradhan or Panchayat Member DS Chemist, Pharmacist, etc. RL Religious Leader Dr GP Level Degree Doctor BOB BO Office Bearers BOR BO Representative BOHG BO Health and Gender Committee Member BOPR BO Poverty Reduction Committee Member PHM PHC MOIC PHN PHC Nurse PHS PHC (Any) Staff CHM CHC MOIC CHN CHC Nurse CHS CHC (Any) PF Block Level Private Health Facility ANMs ANM Supervisor ICDSs ICDS Supervisor PRI Block PRI BDO BDO (Block Development Officer) CDPO CDPO (Child Development Program Officer) DHM District Hospital Medical Officer DHOB District Hospital OBGYN/Lady Doctor DHN District Hospital Nurse RKS Rogi Kalyan Samiti Member RGF RGMVP FO RGT RGMVP Trainer
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RGV RGMVP CV RG RGMVP (ISC) The following table contains the district/block codes for all respondents in the “SHG structures, health workers, and key community stakeholders” category: District/Block Code District, Block BT Banda, Tindwari HA Hardoi, Ahirori MM Mirzapur, Majhwa The full code was constructed as follows: Position_District/Block A number corresponding to the respondent’s GP appended to the end of all respondents at the GP level. A couple of examples:
• ASHA_BT2 would be the ASHA interviewed in GP #2 of Tindwari Block, Banda. • VOB_MM1 would be the VO office bearer interviewed in GP#1 of Majhwa Block,
Mirzapur. • CHN_HA would be CHC Nurse interviewed in Ahirori Block, Hardoi. There is no
GP code because this respondent has a position at the Block level.
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Appendix II: Survey Instrument – Recently Delivered Women UP BCM Project: Recently Delivered Woman’s Social Networks Questionnaire
Namaste. My name is -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐. On behalf of Boston University, we are conducting a survey with the aim of learning how better to improve the health of women and their children. We are particularly interested in how information about how health topics are shared among people in the village and within the health system. In that context, we are interested in learning more about the types of people that villagers communicate with about health and other topics, and how health workers communicate amongst each other. As part of this study, I would like to take some time to ask you some questions related to these topics, including how you get advice about maternal and child health and what kinds of people you get this advice from. This will take about 45 minutes of your time. We would very much appreciate your participation, as this discussion with you will be very useful in helping to understand these topics better.
Before beginning the interview, I will read you a consent form. After the consent form, you will have the chance to choose whether you would like to continue to participate in this interview or not. May I read you the consent form?
[INTERVIEWER: Read the consent form]
[INTERVIEWER: After receiving positive consent from interviewee and answering any questions
a. UNIQUE ID: ____ ____ ____ b. District Name _____________________ District ID: ___ c. Block Name ______________________ Block ID: ___ d. Gram Panchayat Name_____________ GP ID: ___ ___ e. Village Name______________________ Village ID: ____ f. Purwa Name (if lives in Purwa)____________ Purwa ID: ____ ____ g. Respondent Type (Job or position, e.g. RDW, SHG member, ASHA) ________________ l. Interviewer(s): ______________________ m. Date of Interview: ___________________ n. Supervisor Reviewed:_________________ o. Date of Review: ____________________
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the interviewee may have]
May I begin now?
Q no.
Question
1
How old are you?
Age in Completed Years:________
2
Gender:
1=Male 2=Female
4 What is your highest level of completed education?
0 = No School (GO to Q 5; otherwise SKIP to Q6) 1 = If less than college, list grade completed: ___ ___ 2 = Some college 3 = Graduate 4 = Post-‐Graduate
5 Can you read or write or both? 1 = Read 2 = Write 3 = Both read and write 4 = Neither
6 What is your religion? 1 = Hindu 2 = Muslim 3 = Sikh 4 = Christian 5 = Other
7 What is your caste? 1 = Scheduled Caste 2 = Scheduled Tribe 3 = Other Backward Caste 4 = General Caste 5 = Other response ______________
8 What type of household do you live in for most of the year?
1 = Nuclear 2 = Joint 3 = Extended
9 Where do you live? 1 = In-‐laws Home 2 = Maternal Home 3 = With own husband/children only 4 = Other
10 How many years have you lived in this purwa or village?
___ ___ Years (If less than 1 year, write 01)
11 How many times have you traveled out of the village in the last year?
___ ___ Number of times
12 Are you or someone in your HH connected to an SHG in this village?
0 = Not connected to SHG 1 = Someone in the family is part of SHG 2 = SHG member herself
13 (If connected to SHG) How much time have you been connected to an SHG, in years and months?
____ years and _____ months (if less than 1 month, write 1 month)
14 Do you have a friend or neighbor, outside of your HH, that is connected to an SHG?
0 = No friend or neighbor connected to SHG 1 = At least one friend or neighbor connected to SHG
15 Are you able to go to meetings in your village? 1 = Alone
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Questions related to Pregnancy and Childbirth: Q no.
Question
1
How many children do you have?
(Numerical response)
2
How old are your children, starting with the youngest?
1. ___Years___ ___Months 2 ___ ____Years ____ ___Months 3. ___ ____Years ____ ___Months 4. ___ ____Years ____ ___Months 5. ___ ____Years ____ ___Months
4 During your last pregnancy, did you want to get pregnant, or did you feel that you wanted to wait for some time, or did you not want to get pregnant at all?
1 = Yes, wanted it to happen 2 = No, wanted it to happen, but after some time 3 = No, didn’t want it at all
5 Where was your youngest child born? 1 = At home 2 = District hospital 3 = CHC 4 = PHC 5 = Sub-‐centre 6 = Private Hospital 7 = Other
6 (If Response 2-‐7 for last question) Did you receive JSY (for this most recent birth)?
0 = No 1 = Yes 2 = Don’t know about JSY
7 What was the first thing that you fed your youngest child after birth?
1 = Breastmilk 2 = Milk (Goat, cow or other) 3 = Water or sweet water 4 = Other
8 Did you feed the colostrum to your youngest child?
0 = No 1 = Yes 2 = Don’t know
2 = With someone else only 3 = Not at all 4 = Not applicable / I don’t go to meetings
16 Do you have your own mobile phone? 0 = No 1 = Yes
17 If no, is there mobile phone in your family? 0 = No 1 = Yes
18 How often do you talk on the mobile phone? 1 = All of the time 2 = Most of the time 3 = Sometimes 4 = Never
19 How often do you use a mobile phone to discuss health issues
1 = All of the time 2 = Most of the time 3 = Sometimes 4 = Never
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9 After birth, when did you first breastfeed your youngest child?
1 = In the first hour after birth 2 = Within a day 3 = Within 3 days 4 = After 3 days 5 = Never
Table 1: Family and Friends Network First, we are going to talk about your family and friends, and how you are connected to them. I’m going to ask you about what kind of advice and other activities you engaged with your family and friends over the last year: (COLUMNS 3, 4, & 5)
(1) Person
(2) Relation-‐
ship
(3) Have you attended social gatherings (like eating meals together, celebrating weddings, Diwali, mela, Rakhi)?
(4) Have you gotten a loan or given a loan?
(5) Have you received advice on maternal & newborn health topics?
(6) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year to discuss topics related to maternal & newborn health?
(7) How much trust do you have in the advice given by this person?
RESPONSE Categories
0=No; 1=Yes
0=No; 1=Yes
0=No; 1=Yes
0=No; 1=Yes
0 = None 1 = Once or twice 2 = Three or four times 3 = At least once a month 4 = At least once a week
0 = None 1 = Very little 2 = Some 3 = A lot
1. Husband
2. Mother 3. Mother-‐in-‐law
4. Father-‐in-‐law
5. Other Maternal Relative
6. Other Husband’s Relatives
7. Friend(s)/Neighbors
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Table 2: Self Help Group and Village Organization (VO) Network For this section, I’m going to ask about your relationships with some members of the Self Help Group and Village Organization and RGMVP staff. (COLUMN 1 & 2) Now I’m going to ask you about what kind of advice and other activities you engaged in with them over the last year: (COLUMNS 3, 4, 5)
ONLY for COLUMN 7: Place or method of Interaction (the most common one, if there are many) 0= Nowhere; 1=SHG mtg; 2=VO mtg; 3=at home; 4=VHSC; 5=VHND; 6=AWC; 7=PHC; 8=CHC; 9=District Hospital; 10=Private Hospital; 11=Sub-‐Centre; 12=On the Phone; 13=Other_____(specify)
(1) Person
(2) Do you have a relation-‐ship with ______?
(3 ) Have you attended social gatherings (like eating meals together, celebrating weddings, Diwali, mela, Rakhi)?
(4) Have you gotten a loan or have given a loan?
(5) Have you received advice on maternal & newborn health topics?
(6) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year to discuss topics related to maternal & newborn health?
(7) What is the most common place or method of inter-‐action?
(8) How much trust do you have in the advice given by this
person?
RESPONSE CATEGORY
0=No; 1=yes 0=No; 1=Yes
0=No; 1=Yes
0=No; 1=Yes
0 = None 1 = Once or twice 2 = Three or four times 3 = At least once a month 4 = At least once a week
SEE ABOVE
0 = None 1 = Very little 2 = Some 3 = A lot
1. SHG HH member of RDW__________
2. SHG Swasthya Sakhi
3. SHG member BUT not part of
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HH 4. SHG_Permanent member to VO
5. VO Office Bearer (any)
6. VO Swasthya Sakhi
7. RGMVP Staff (FO, CV, MIS, other)
Table 3: Village-‐Community Network For this section, I’m going to ask about some other relationships within the village. (COLUMN 1 & 2) Now I’m going to ask you about what kind of activities you engaged with some other members of the village over the last year. (COLUMNS 3 & 4)
ONLY for COLUMN 6: Place or method of Interaction (the most common one, if there are many) 0= Nowhere; 1=SHG mtg; 2=VO mtg; 3=at home; 4=VHSC; 5=VHND; 6=AWC; 7=PHC; 8=CHC; 9=District Hospital; 10=Private Hospital; 11=Sub-‐Centre; 12=On the Phone; 13=Other_____(specify)
(1) Person
(2) Do you have a relation-‐ship with ______?
(3) Have you
received advice on maternal & newborn health,
and/or information on where to seek or get referred for emergency care?
(4) Have you received help in obtaining government services (Family Planning Supplies, JSY, BPL Card)?
(5) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year?
(6) What is the most common place or method of interaction?
(7) How much trust do you have in the information this person provides?
RESPONSE CATEGORY
0=No; 1=yes
0=No; 1=Yes
0=No; 1=Yes
0 = None 1 = Once or twice 2 = Three to four times 3 = At least once a month 4 = At least once a week
SEE ABOVE
0 = None 1 = Very little 2 = Some 3 = A lot
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1. ASHA 2. ANM 3. AWW 4. Dai/TBA 5. RMP, Bengali, Jhola Chap or local doctor 6. Pradhan or other Panchayat Member 7. Chemist /Pharmacist/Medicine Seller 8. Your Religious leader (Pandit, Maulvi, Imam, etc.) Table 4: Block & District Level Network – Block Organization (BO) & Health Providers For this last section, I will ask about your relationships with the following people or organizations at the block and district levels. (COLUMNS 1 & 2) I’m going to ask you about what kind of advice and other activities you engaged with them over the last year. (COLUMNS 3 & 4)
(1) Person
(2) Do you have a relation-‐ship with _____?
(3) Have you received advice on maternal & newborn health, and/or information on where to seek or get referred for emergency care?
(4) Have you received help in obtaining government
services (Family Planning Supplies, JSY, BPL Card)?
(5) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year?
(6) How much trust do you have in the information this person provides?
RESPONSE CATEGORY
0=No; 1=Yes
0=No; 1=Yes 0=No; 1=Yes 0 = None 1 = Once or twice 2 = Three or four times 3 = At least once a month 4 = At least once a week
0 = None 1= Very little 2 = Some 3 = A lot
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Block & District Health Structure 1. PHC-‐Medical Officer 2. PHC-‐Nurse 3. PHC – Any (if MOIC/Nurse not known or unsure) 4. CHC-‐Medical Officer 5. CHC-‐Nurse 6. CHC – Any (if MOIC/Nurse not known or unsure) 7.Block Private Health facility 8. District Private Health Facility 9. District Hospital Now I would like to ask you some general questions about trust and solidarity in your community: Ques. #
Do you agree, sort of agree, or not agree at all with the following statements?
Strongly Agree
Somewhat Agree
Neutral Somewhat Disagree
Strongly Disagree
1 Most people in this village can be trusted.
2 In this village, people can be trusted in matters of money.
3 Most of the people in this village are ready to help if there is an urgent issue, such as a health emergency.
4 One has to be careful in this village, or else someone can take advantage of them.
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We would like to know whom you most admire or look up to in providing good advice about newborn health and mother’s pregnancy and related problems or emergencies. Please list up to five such people.(Show card with categories of network types) 1. _________________________ 2. _________________________ 3. _________________________ 4. _________________________ 5. _________________________
There may be many reasons why you may or may not be able to take in the best advice. So, whose advice are you mostly likely to listen to when you make decisions about how to take care of your baby or when you have a health problem related to having a baby? (Show card with categories of network types) 1. _________________________ 2. _________________________ 3. _________________________ 4. _________________________ 5. _________________________
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Appendix III: Survey Instrument – SHG Structures, Health Workers and Key Community Members
SNA: UP BCM Project: SHG, VO, BO and GP Community and Social Network Questionnaire
Respondents Social Networks Questionnaire Q no.
Question
1
How old are you?
Age in Completed Years:________
2
Gender:
1=Male 2=Female
4 What is your highest level of completed education?(circle)
0 = No School (GO to Q 5; otherwise SKIP to Q6) 1 = If less than college, list grade completed: ___ ___ 2 = Some college 3 = Graduate 4 = Post-‐Graduate
5 Can you read or write or both? 1 = Read 2 = Write 3 = Both read and write
6 How long have you worked in your current job?
___ ___ Months ___ ___ Years (If less than 1 month, write in 1 month)
7 What is your caste? 1 = Scheduled Caste 2 = Scheduled Tribe 3 = Other Backward Caste 4 = General Caste
a. UNIQUE ID: ____ ____ ____ b. District Name _____________________ District ID: ___ c. Block Name ______________________ Block ID: ___ d. Gram Panchayat Name_____________ GP ID: ___ ___ e. Village Name______________________ Village ID: ____ f. Purwa Name (if lives in Purwa)____________ Purwa ID: ____ ____ j. Interviewer(s): ______________________ k. Date of Interview: _____________ l. Supervisor Reviewed:__________________ m. Date of Review: ________________
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5 = Other response ______________ 8 What is your marital status? 1= Married
2= Separated/Divorced 3= Widowed 4= Never Married 5= Other (SPECIFY) ____________ 6= Refused
9 (Only ask if ASHA or AWW) What purwas or village do you cover as part of your work?
1. ________________ 2. ________________ 3. ________________ 4. ________________ 5. ________________
10 (Only ask if ANM) What GPs do you cover as part of your work?
1. ________________ 2. ________________ 3. ________________ 4. ________________ 5. ________________
11 Is there a Self Help Group (SHG) where you live or work?
0 = No 1 = Yes 2 = Don’t Know
12 Are you or someone in your HH connected to an SHG?
0 = Not connected to SHG 1 = Someone in the family is part of SHG 2 = SHG member herself 3 = Don’t know
13 (If connected to SHG) How much time have you been connected to an SHG, in years and months?
____ years and _____ months (if less than 1 month, write 1 month)
14 Do you have a friend or neighbor, outside of your HH, that is connected to an SHG?
0 = No friend or neighbor connected to SHG 1 = At least one friend or neighbor connected to SHG
15. Is there is a Village Health and Sanitation Committee (VHSC) in this village? (ONLY ASK FOR VILLAGE LEVEL RESPONDENTS)
0 = No 1 = Yes 2 = Don’t know if there is a VHSC
16. (If yes to Q13) Do you participate in the VHSC meetings? (ONLY ASK FOR VILLAGE LEVEL RESPONDENTS)
0 = No 1 = Yes
17. Is there a Rogi Kaliyan Samitii (RKS) in the PHC? (ONLY ASK FOR BLOCK & DISTRICT LEVEL RESPONDENTS)
0 = No 1 = Yes 2 = Don’t Know if there is an RKS
18. Is there a Rogi Kaliyan Samitii (RKS) in the CHC? (ONLY ASK FOR BLOCK & DISTRICT LEVEL RESPONDENTS)
1 = Yes 2 = No 2 = Don’t Know if there is an RKS
19. (If yes to Q17 or 18) Do you participate in RKS meetings? (ONLY ASK FOR BLOCK & DISTRICT LEVEL RESPONDENTS)
0 = No 1 = Yes
20. Do you have a cell phone? 0 = No 1 = Yes
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21. How often do you use a cell phone to discuss health-‐related issues?
1= All of the time 2= Most of the time 3= Sometimes 4= Never
Table 1: SHG, Village Organization (VO) Network I’m going to ask about members of the Self Help Group & Village Organization. Now I will ask about the different ways you have communicated and coordinated with each other in the last year. (COLUMNS 3, 4, & 5)
(1) Person
(2) Do you have a relation-‐ship with ____?
(3) Have you exchanged information about new and existing health programs & services?
(4) Have you coordinated health services, including emergency referrals?
(5) Have you discussed or coordinated with [person] on the supply of family planning & other health products?
(6) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year?
RESPONSE CATEGORY
0=No; 1=Yes; 2=Myself
0=No; 1=Yes
0=No; 1=Yes 0=No; 1=Yes 0 = None 1 = Once or twice 2 = Three or four times 3 = At least once a month 4 = At least once a week
1. Swasthya Sakhi from an SHG 2. Swasthya Sakhi from a different SHG 3. SHG Member not in HH 4. Permanent member to VO from an SHG 5. Permanent member to VO from a different SHG 6. VO Office Bearers 7. VO Swasthya Sakhi 8 VO Health & Gender Com 9. VO Poverty
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Reduction Com
Table 2: Village-‐Community Network Do you have a relationship with the following people or organizations in your community? (0=No; 1=Yes; 2=myself) Now I will ask about the different ways you have communicated and coordinated with each other in the last year. (COLUMNS 3, 4, & 5)
(1) Person
(2) Do you have a relation-‐ship with ____?
(3) Have you exchanged information about new and existing health programs & services?
(4) Have you coordinated health services, including emergency referrals?
(5) Have you discussed or coordinated with [person] on the supply of family planning & other health products?
(6) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year?
RESPONSE CATEGORY
0=No; 1=Yes; 2=Myself
0=No; 1=Yes
0=No; 1=Yes
0=No; 1=Yes 0 = None 1 = Once or twice 2 = Three to four times 3 = At least once a month 4 = At least once a week
1. ASHA 2. ANM 3. AWW 4. Dai/TBA 5. RMP, Bengali, Jhola Chap or local doctor 6. Pradhan or Panchayat Representative 7. Chemist/Pharmacist/Medicine Seller 8. Religious Leader (Pandit, Maulvi, Imam, etc.) 9. GP level Doctor with degree
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Table 3: Block & District Level Network – Block Organization (BO) & Health Providers Do you have a relationship with the following people or organizations in your community? (0=No; 1=Yes; 2=myself) Now I will ask about the different ways you have communicated and coordinated with each other in the last year. (COLUMNS 3, 4, & 5)
(1) Person
(2) Do you have a relation-‐ship with ____?
(3) Have you exchanged information about new and existing health programs & services?
(4) Have you coordinated health services, including emergency referrals?
(5) Have you discussed or coordinated with [person] on the supply of family planning & other health products?
(7) How frequently have you interacted with (INSERT NAME OF PERSON) in the last year?
RESPONSE CATEGORIES
0=No; 1=Yes; 2=Myself
0=No; 1=Yes
0=No; 1=Yes 0=No; 1=Yes 0 = None 1 = Once or twice 2 = Three to four times 3 = At least once a month 4 = At least once a week
Block Organization 1. BO Office Bearers 2. BO representative from the VO 3. Member of Health & Gender Com 4. Member of Poverty Reduction Com Block & District Health Structure 5. PHC-‐MO/MOIC 6. PHC-‐Nurse 7. PHC – Any (if MOIC/Nurse not known or unsure) 8. CHC-‐MO/MOIC 9. CHC-‐Nurse
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10. CHC – Any (if MOIC/Nurse not known or unsure) 11.Block Private Health facility 12. ANM Supervisor (LHV) 13. ICDS Supervisor 14. Block PRI 15. BDO (Block Dev Officer) 16. Child development Project Officer (CDPO) 17. District Hospital -‐Medical Officer (title?) 18. District Hospital – Maternity Ward OBGYN (“lady doctor”) 19. District Hospital – Maternity Ward Nurse 20. Rogi Kalyan Samiti (Any member) RGMVP 21. RGMVP Field Officer 22. RGMVP Trainer 23. RGMVP CV 24. RGMVP (any block level staff) Now I would like to ask you some general questions about trust and solidarity in your community: Ques. #
Do you agree, sort of agree, or not agree at all with the following statements?
Strongly Agree
Somewhat Agree
Neutral Somewhat Disagree
Strongly Disagree
1 Most people in this
80
village can be trusted. 2 In this village, people
can be trusted in matters of money.
3 Most of the people in this village are ready to help if there is an urgent issue, such as a health emergency.
4 One has to be careful in this village, or else someone can take advantage of them.
We would like to know whom you most admire or look up to in providing good information about newborn health and mother’s pregnancy and solving related problems or emergencies. Please list up to five such people.(Show list from above categories)
1. ____________________________________ 2. ____________________________________ 3. ____________________________________ 4. ____________________________________ 5. _________________________________
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Appendix IV: Guide to the Plots I. Plot Guide to Recently Delivered Women 1. Locations and Shape 2. SHG Affiliation
II. SHG Structures, Health Workers and Community 1. Locations and Shape 2. Node Color Affiliations
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Appendix V: Multivariate Analysis Variable Specifications The specifications for all variables included in the multivariate logistic model are provided in the table below. The results of these analyses can be found in Section 4.4. Variable Specification
Age Age of respondent in years
SHG Connection Strength 0 = no connection to SHGs
1 = Friend, Neighbor or Household member connected to SHG
2 = Self connected to SHG Education
0 = No education
1 = Some education Caste (SC = 1)
This is a dummy variable equal to 1 if Caste provided is Scheduled Caste
District 2 Dummy Dummy Variable for Banda
District 3 Dummy Dummy Variable for Mirzapur
Lives in Purwa? 0 = Lives in main village of GP
1 = Lives in a purwa of GP
Note: The following variables all refer to an open ended question which was asked of all respondents: “whose advice are you mostly likely to listen to when you make decisions about how to take care of your baby or when you have a health problem related to having a baby?” Each respondent was allowed to list up to five people in response to the question. Decision – Personal
0 = No personal connections listed
1 = At least one personal connection listed Decision – AAA’s
0 = None of the AAA workers listed
1 = At least one of the AAA workers listed Decision – Other HP
Note: “Other Health Provider” refers to any health provider, government or private, other than the AAA workers
0 = No other health providers Listed
1 = At least one other health provider listed Decision – Personal & Other HP 0 = If a respondent did not list both personal and one other health
provider
1 = If a respondent listed both personal and one other health provider
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Decision – Personal & AAA’s 0 = If a respondent did not list both personal and one AAA worker
1 = If a respondent listed both personal and one AAA worker Decision – AAA’s & Other HP 0 = If a respondent did not list both a AAA worker and one other
health provider
1 = If a respondent listed both a AAA worker and one other health provider
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Appendix VI: Complete Tables with Network Analysis Results I. RDW Networks
The centrality scores for all the individuals with whom the RDWs interact are given below. Table A-‐1 provides the centrality scores for Advice and Table A-‐2 for Services. These values denote the number of RDWs who indicated they receive advice or services from that particular individual or agency. The higher the value, the more important that person/agency is in the advice or service network. For advice, in Hardoi more RDWs indicated their mothers than any other individual/agency, in Banda both Friend/Neighbor and ASHA, and Friend/Neighbor in Mirzapur. For Services, more RDWs indicated the ASHA than in other individual/agency in all three districts.
Table A-‐1: RDW Advice -‐ Centrality Scores of Recently Delivered Women Hardoi Banda Mirzapur Personal Husband 17 24 22 Mother 21 25 20 Mother-‐in-‐law 16 24 24 Father-‐in-‐law 7 9 15 Maternal 15 21 22 Paternal 17 27 27 Friend/Neighbor 17 28 29 SHG GHH 8 8 9 GSS 4 1 5 GnHH 7 15 15 VOM 0 2 6 VOB 0 1 2 VOSS 0 0 1 RG 1 3 3 Village ASHA 14 28 25 ANM 13 24 19 AWW 13 11 21 TBA 4 2 6 RMP 2 8 8 PRI 1 0 1 DS 2 7 6 RL 2 3 9 Block PHM 1 13 7 PHN 1 13 7 PHS 1 12 6 CHM 4 6 11 CHN 4 7 11 CHS 4 6 9
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PF 3 5 7 DH 10 5 1 Table A-‐2: RDW Service Column Centrality Scores Hardoi Banda Mirzapur Village ASHA 14 25 17 ANM 13 20 13 AWW 13 10 8 TBA 4 0 2 RMP 2 4 2 PRI 1 2 1 DS 2 5 1 RL 2 0 2 Block PHM 2 11 6 PHN 0 11 6 PHS 0 8 5 CHM 4 4 9 CHN 4 6 9 CHS 3 6 7 PF 3 1 1 DH 9 0 0 II. SHG, Health Workers and Key Community GP Networks – Density and
Centralization The unconfirmed density, confirmed density, centralization, and average degree centrality scores are provided below in Table A-‐3 for all 18 GPs. These values are based only on the GP networks and exclude Block level organizations so that the networks are the same size and can be compared with one another. The higher the density values, the more connections there are in that particular network. The higher the centralization, the more that particular network is reliant on key nodes connecting everyone together. A higher average degree centrality indicates a higher number of connections that network actors have on average. These values vary across all the networks, suggesting differences in the structure of the different GP networks; but in general, the information sharing and supplies networks tend to be denser and more centralized than the services networks.
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Table A-‐3: GP Only Density and Centralization
Information Sharing District GP Unconfirmed
Density Confirmed Density
Confirmed Centralization
Avg. Degree Centrality
Hardoi 1 28.5 12.5 0.213 1.778 2 36.6 14.9 0.191 2.111 3 37.9 24.3 0.176 3.667 4 39.5 23.5 0.294 3.556 5 27.3 12.5 0.201 1.778 6 37.6 25.1 0.246 3.556 Banda 1 28.8 13.3 0.136 1.889 2 38.9 19.3 0.228 2.556 3 32.2 17.3 0.246 2.800 4 28.7 12.6 0.221 1.667 5 36.3 22.7 0.956 3.222 6 25.7 10.2 0.235 1.444 Mirzapur 1 33.8 22.8 0.151 3.444 2 34.1 21.5 0.542 3.263 3 34.8 18.0 0.162 2.556 4 30.4 19.8 0.161 3.200 5 30.3 14.0 0.191 2.111 6 27.2 15.3 0.114 2.316
Service Coordination and Referrals District GP Unconfirmed
Density Confirmed Density
Confirmed Centralization
Avg. Degree Centrality
Hardoi 1 25.3 11.0 0.228 1.556 2 29.0 11.0 0.162 1.556 3 29.8 16.9 0.360 2.556 4 33.8 19.1 0.272 2.889 5 22.0 06.3 0.132 0.889 6 31.3 11.8 0.143 1.667 Banda 1 22.5 11.0 0.810 1.556 2 27.2 05.9 0.147 0.778 3 19.1 10.5 0.310 1.700 4 18.9 04.2 0.162 0.556 5 23.8 07.1 0.500 1.000 6 15.8 06.3 0.140 0.88 Mirzapur 1 12.6 05.1 0.404 0.778 2 16.6 06.9 0.245 1.053 3 13.7 05.5 0.147 0.778 4 14.1 05.6 0.091 0.900 5 10.0 02.9 0.103 0.444 6 07.2 02.4 0.110 0.333
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Supplies District GP Unconfirmed
Density Confirmed Density
Confirmed Centralization
Avg. Degree Centrality
Hardoi 1 26.2 14.1 0.331 2.000 2 31.6 13.3 0.206 1.889 3 37.0 24.3 0.176 3.667 4 37.8 20.6 0.324 3.111 5 28.2 12.5 0.201 1.778 6 35.1 20.4 0.169 2.889 Banda 1 0.24.7 0.11.0 0.147 1.556 2 0.34.1 0.14.3 0.206 1.889 3 0.26.2 0.13.0 0.228 2.100 4 0.21.1 0.10.9 0.169 1.444 5 0.36.0 0.22.7 0.112 3.222 6 0.19.7 0.07.1 0.199 1.000 Mirzapur 1 30.9 19.9 0.165 3.000 2 28.9 21.5 0.418 3.263 3 31.7 17.3 0.235 2.444 4 31.3 20.4 0.167 3.300 5 33.0 16.9 0.162 2.556 6 22.7 09.4 0.176 1.333 III. Correlations of Network Structures in Whole Networks -‐ Quadratic Assignment
Procedure (QAP) To test how similar the networks were across districts, we examined how correlated the networks are to one another. Applying statistical and inferential tools to network data requires some modification because network data abuses the assumptions that most statistical tools require. The modification required is to compare the results against a random distribution – i.e. compare the level of association between two networks against what we would expect to occur with random networks of the same size and shape. The procedure we used to statistically compare the networks to one another is Quadratic Assignment Procedure (QAP) in Ucinet.6 Since the relationships examined here are binary, a relationship exists or not, the appropriate measure of association to use is the Simple Matching Coefficient.7 The tables include the simple matching coefficients as well as the level of significance.
6 Borgatti, S. P., Everett, M.G., & Freeman, L.C. 2002. Ucinet for windows: Software for social network analysis. Harvard: Analytic Technologies. 7 Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside ( published in digital form at http://faculty.ucr.edu/~hanneman/ )
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Interpretation of the tables is as follows. If we look at the association between both Hardoi whole information sharing networks (GP3 & GP4), we see an observed simple matching value of 0.8767. This means that if there is a relationship between two organizations in one network (Hardoi GP3) then there is an 87.67% chance that that relationship will also exist in the other network (Hardoi GP4). This high percentage seems to indicate association, but we have to consider the density of the two networks and whether that likelihood would be the same as what we would expect at random. In the case here, that level of likelihood is higher than what would occur at random and thus, the association between the two networks is significantly high (at the p<0.001 level in this instance). Looking at all three tables, what we see is that the similarity between the networks across the three districts is highly significant for information sharing and supplies. This means that the similarity of these networks is higher than would occur at random. What is interesting, however, is that the same is not true for all of the services networks. Many of the service networks are significantly similar, but the Mirzapur networks are not significantly similar to all of the others. Specifically, Mirzapur GP1 is only marginally similar to Hardoi GP 3 (p<0.10) and not significantly similar to any other network except the other Mirzapur network (GP 3). That Mirzapur network (GP3) is in turn significantly similar to the Hardoi networks, but only marginally (p<0.10) similar to Banda GP 2 and not significant in comparison to Banda GP 5. Though the simple matching coefficients are quite high when comparing these Mirzapur networks with the other networks, the point is that given the density of the networks, this observed matching measure hardly differs from a random result. Thus, the similarity between the Mirzapur service networks and the other service networks is no different than what we could expect if we randomly compared any two networks of the same size and shape. Also, the similarity of the Banda service networks with the Hardoi service networks are not at as highly a level of significance as we see with the other relationships (information sharing and supplies), suggesting the structure of the service networks differ more so than is the case for these other structures. Thus, this provides further evidence for our findings from examining the network plots that the structure of the service networks is more different across the three districts than is the case with the information sharing and supplies networks. Table A-‐4: Whole GP Information Sharing Confirmed-‐ QAP Correlations (Simple Matching Coefficient) Hardoi_GP3 Hardoi_GP4 Banda_GP2 Banda_GP4 Mirzapur_GP1 1 Hardoi_GP3 1 2 Hardoi_GP4 0.8767*** 1 3 Banda_GP2 0.8319*** 0.8286*** 1 4 Banda_GP5 0.8509*** 0.8554*** 0.9111*** 1 5 Mirzapur_GP1 0.8741*** 0.8741*** 0.8407*** 0.8537*** 1 6 Mirzapur_GP3 0.8767*** 0.8707*** 0.8517*** 0.867*** 0.9063***
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Table A-‐5: Whole GP Services Confirmed-‐ QAP Correlations (Simple Matching Coefficient) Hardoi_GP3 Hardoi_GP4 Banda_GP2 Banda_GP4 Mirzapur_GP1 1 Hardoi_GP3 1 2 Hardoi_GP4 0.9100*** 1 3 Banda_GP2 0.8897** 0.8652** 1 4 Banda_GP5 0.9009*** 0.8875*** 0.9454*** 1 5 Mirzapur_GP1 0.9052† 0.8784 0.9222 0.9296 1 6 Mirzapur_GP3 0.9092** 0.8931** 0.9224† 0.9259 0.9652** Table A-‐6: Whole GP Supplies Confirmed-‐ QAP Correlations (Simple Matching Coefficient) Hardoi_GP3 Hardoi_GP4 Banda_GP2 Banda_GP4 Mirzapur_GP1 1 Hardoi_GP3 1 2 Hardoi_GP4 0.8808*** 1 3 Banda_GP2 0.8388*** 0.8402*** 1 4 Banda_GP5 0.8500*** 0.8661*** 0.9213*** 1 5 Mirzapur_GP1 0.8879*** 0.8741*** 0.8685*** 0.8704*** 1 6 Mirzapur_GP3 0.8792*** 0.8698*** 0.8836*** 0.8902*** 0.9295***
IV. Within District QAP Analysis (Simple Matching Coefficient) In addition to examining the correlation among the whole GP networks, we also examined the correlation of the GPs within districts. The tables below provide the results for these comparisons. There is variation in the significance of the similarity among these different networks, which means that it cannot be assumed that all the GP networks within each district are highly similar to one another. Table A-‐7: Hardoi Information Sharing – QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.851** GP 3 0.7569* 0.8039*** GP 4 0.7373 0.7765** 0.7426** GP 5 0.8745** 0.898*** 0.8118*** 0.8157*** GP 6 0.7569* 0.7098 0.702* 0.6588 0.7176 Table A-‐8: Banda Information Sharing – QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8151*** GP 3 0.8667*** 0.8277*** GP 4 0.8655** 0.8487*** 0.9118*** GP 5 0.8431*** 0.8697*** 0.8588*** 0.8613*** GP 6 0.8667** 0.7773† 0.8196** 0.8445* 0.8039*
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Table A-‐9: Mirzapur Information Sharing -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.7882*** GP 3 0.7882** 0.7647** GP 4 0.8275*** 0.8353*** 0.7804** GP 5 0.8382*** 0.8*** 0.8157** 0.8471*** GP 6 0.8275*** 0.8431*** 0.8118** 0.8745*** 0.8941*** Table A-‐10: Hardoi Service -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8902*** GP 3 0.8078* 0.8078* GP 4 0.8000* 0.8000* 0.8309*** GP 5 0.8902** 0.8745* 0.8235† 0.8314** GP 6 0.8588* 0.8196 0.7922† 0.7922* 0.8588 Table A-‐11: Banda Service -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8866* GP 3 0.8667** 0.8950* GP 4 0.8613 0.9160 0.8950* GP 5 0.8902* 0.9370** 0.9216*** 0.9118 GP 6 0.8980** 0.9202** 0.9137*** 0.9034 0.9373*** Table A-‐12: Mirzapur Service -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8824 GP 3 0.9137 0.8980† GP 4 0.9216* 0.8902† 0.9294** GP 5 0.9338 0.9059 0.9451* 0.9373** GP 6 0.9373 0.9294* 0.9373† 0.9451** 0.9608† Table A-‐13: Hardoi Supplies – QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8353** GP 3 0.7451* 0.8078*** GP 4 0.7686* 0.8235*** 0.8015*** GP 5 0.8588*** 0.8980*** 0.8078*** 0.8314*** GP 6 0.8039** 0.7725* 0.8314*** 0.7373* 0.8196***
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Table A-‐14: Banda Supplies -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8529** GP 3 0.8588*** 0.8866*** GP 4 0.8866** 0.8824*** 0.9202*** GP 5 0.8510*** 0.8529*** 0.8588*** 0.8782*** GP 6 0.9216*** 0.8445* 0.8353† 0.9118*** 0.8196** Table A-‐15: Mirzapur Supplies -‐ QAP Correlations GP1 GP2 GP3 GP4 GP5 GP 1 GP 2 0.8314*** GP 3 0.8549*** 0.8275*** GP 4 0.7922*** 0.7882*** 0.7961*** GP 5 0.8235*** 0.7686** 0.8471*** 0.8000*** GP 6 0.8863*** 0.8275*** 0.9059*** 0.8431*** 0.8941***