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Rachana Patel, PhD (IUSSP 2013) Page 1
IUSSP Conference 2013, Busan, S Korea
Rural Health Facility and Institutional Birth: A study in Composite Index formation
and spatial modeling
Session 208: Spatial approaches to estimation of demographic rates Date: 29-08-2013 at 15:30pm-17:00pm
1. INTRODUCTION
Maternal and child health programs in India have undergone various stages of planning and
intervention for strengthening rural health services in order to facilitate institutional delivery
(MOHFW 2005). It is well established that giving birth under the care and supervision of trained
health-care (especially at health institution) providers promotes child survival and reduces the
risk of maternal mortality (Tsui et al. 1997; WHO 2004a, 2005). Both, child mortality (especially
neonatal mortality) and maternal mortality remain high in India and seven out of every 100
children born in India die before reaching age one (Dyson et al. 2004); and approximately five
out of every 1,000 women who become pregnant die of causes related to pregnancy and
childbirth (MOHFW 2005). Institutional birth has been increasing over the period but still much
below the desired level. DLHS III estimates that national average for institutional births was 47
percent during 2007-08. Yet more than 50 percent of births in India continue to take place at
home, most of them without the assistance of any trained health worker (DLHS 2007-08),
threatening the lives of both mother and child. Also, there is a wide gap in the proportion
between rural and urban. The proportion of births delivered at health institution in urban area
is 71 percent while only 38 percent in rural area. There is clear evidence of high inter-state
variations. The estimates of institutional birth in the weaker States in the north and
central India are very high compared to southern and western region States. The proportion of
births delivered in institution is 68 percent in rural area of the major southern states together
(Maharashtra, Andhra Pradesh, Tamilnadu, Kerala and Karnataka), while only 31 percent in
northern states (DLHS, 2007-08).
Several studies have stressed the importance of access to health services as a factor affecting the
utilization of services (Kumar et al. 1997, Nathan J at. al. 2004, Amy J Kesterton et. al. 2010).
Availability and quality of healthcare services is yet an important aspect for encouraging
healthcare utilization, particularly public health facilities, as evident from the fact that programs
which integrate quality as well as access to services enhance client satisfaction, leads to greater
utilization (Shelton and Davis 1996; Koenig and Khan 1999). Rani et al. (2008) have noted that
Rachana Patel, PhD (IUSSP 2013) Page 2
poor quality of antenatal care is likely to reduce its utilization and as far as ante-natal care is
concerned the quality of service in southern states is superior to than those of northern states in
India. In addition to expanding health-care facilities and infrastructure, India's family welfare
program has been emphasizing outreach programs, including home visits, mobile clinics, and
community-based delivery systems, as mechanisms to increase both the quantity and quality of
services (MOHFW 2005).
Information on the spatial distribution of adequate infrastructure at the public health facility and
service utilization in a district/county is of interest to policymakers and researchers for a number
of reasons. First, it can be used to quantify suspected regional disparities in public infrastructure
standards and identify which areas are falling behind in the process of health improvement even
in the presence of government special health program (NRHM) in all over the focused states.
Second, it facilitates the targeting of programs, such as available and easy access to health center,
and infrastructure aid, whose purpose is to improve utilization from the end users. In many
countries, the main sources of information on spatial patterns of utilization are national/state
household surveys. Geographic objective could be most efficient when the geographic units are
quite small, such as a village or district. The only household information usually available at this
level of disaggregation is national household and health surveys of country. The staggered
economy and huge population demand have had great repercussions on India's health system.
With the exception of few southern regions, and a few urban areas, there is a marked shortage of
equipment and qualified personnel for meeting the need of maternal care. The country had an
estimated 61 allopathic doctors per 1,00,000 population and of the total available doctors 52
percent were from southern states of Andhra Pradesh, Goa, Karnataka, Travancore-Cochin,
Maharashtra and Tamilnadu while MCI Delhi contributed only 5 percent (Medical Council of
India MCI, 2007). The quality of healthcare undoubtedly depends on health facility adequacy for
infrastructure, manpower, equipments, and stock of essential drugs.
Information on the spatial distribution of adequate infrastructure at the health facility,
community education, share of urban population and service utilization in a district/county could
be utilized for the spatial analysis. However, advances in health geography have improved our
understanding of the role played by geographic distribution of health services on access to health
services (Arcury et al. 2005; Luo 2004). Keeping the foregoing discussion in view in this paper
makes an attempt to evaluate adequacy and accessibility of health facilities in north India and
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investigate how it determine the level of institutional delivery. The results of the study shall
provide key policy input for improving the level of institutional delivery and achieving stipulated
UN, Millennium Development Goals of reducing the maternal mortality ratio by three quarters
between 1990 and 2015 (WHO, 2012). First, this study deals with the distribution and inequality
in the public health infrastructure in the districts of EAG states however, apart from the
infrastructure there could be more impediments at district/county/village level hindering the
utilization for maternal care services provided by government health policy. Secondly, spatial
analysis assumes that the relationship between progress and utilization is homogenous and
uniform over space. Overlooking the spatial correlations may or may not bias the model results
depending on the magnitude of such correlations over time. The organization of the paper is as
follows. The next section outlines the health system structure in rural India and recent programs
of the government. This is followed by description of data sources, then a section on
methodology and ends with a section results and discussion.
2. RURAL HEALTH PROGRAMS AND INFRASTRUCTURE
Infrastructure means something that lies below or comes before the structure and is the end result,
or, in some sense, the aim of development and progress. In public health and social-studies,
broadly speaking, ‘infrastructure’ could be seen as all those activities and services whose
contribution to the socio-economic development is not the income generated within the sector
directly but the sustenance and support they provide to the progress and social development in the
society or community. In the view of that Government had launched National Rural Health
Mission (NRHM) in 2005 to strengthen the MCH program for rural area; under decentralization
scheme and Panchayati Raj for the primary health were included under the umbrella. National
Rural Health Mission (NRHM) has provided the opportunities to develop a standard for Sub
Centers (SC), Primary Health Centers (PHC) and Community Health Centers (CHC) in the
country. Under NRHM more emphasis has been given upon the Empowered Action Group (EAG)
states because of their poor health indicators. It is therefore expected that the quality and
standards of care provided by the PHCs in the EAG states will improve and more adequately
satisfy the IPHS standard to reach the level of the non-EAG states in health performance
indicator. The main aim of NRHM is to provide accessible, affordable, accountable, effective and
reliable primary health care, especially to poor and vulnerable sections of the population. It also
aims at bridging the gap in Rural Health Care through creation of a cadre of Auxiliary Nurse
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Midwife (ANM) and Accredited Social Health Activists (ASHA) and improves hospital care,
decentralization of programme to district level to improve intra and inter-sectoral convergence
and effective utilization of resources. The Mission further seeks to build greater ownership of the
programme among the community through involvement of Panchayati Raj Institutions, NGOs and
others to progress more.
The health care infrastructure in rural areas has been developed as a three tier system and is based
on population norms as SC will cover 5,000 population in plain area and 3,000 in hilly area while
PHCs are supposed to cover 30,000 and 20,000 population respectively in plain and hilly area and
CHC will cover in plain and hilly area in 120,000: 80,000 ratio. To strengthen and to improve the
facilities in the existing rural health infrastructure under Reproductive and Child Health
Programme, the Government of India has assisted all the States in improving/ constructing labor
room, operation theatre and providing water/ electricity supply in CHCs/ PHCs etc. so that
essential and emergency obstetric services are improved. These SC, PHC and CHCs are the keys
of MCH program which need to look after in efficient way.
From a programmatic and policy perspective, connecting peoples’ perceptions of health services
and health care delivery system characteristics can contribute to our understanding of utilization
behavior in a more comprehensive manner. Majority of studies included environmental variables
which measured only urban-rural location, or region, which may be imprecise proxies for more
specific measures such as supply of services (Phillips, et al. 1998). Hence, characteristics such as
physician supply and availability of physicians in the community would be important contextual
variables to be considered within the health services utilization model (Andersen, et al. 1996).
Such decisions should be made after analysis and conscious deliberation.
3. DATA SOURCE AND OBJECTIVES
District Level Household Survey 2007-2008 (DLHS 3) data on health facility and village was
used for the purpose. DLHS-3 was a nationally representative survey of households and health
facility at district level. Facility questionnaires were designed to collect information on
manpower, medicines, equipments and infrastructure for all levels of health facilities. These are
Health Sub-Centre (HSC), Primary Health Centre (PHC), Community Health Centre (CHC) and
District Hospitals (DH). Further details of the survey design could be obtained from the country
report (DLHS- III, 2007-08).
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District-level means of the characteristics were obtained from the Census 2011 and inserted into
this equation, generating estimates of rural service utilization for each of the rural districts from
the selected states. The earlier studies have some limitations like it did not generate unbiased
estimates of district-level utilization. Recent DLHS 2007-08 report and some studies utilizing
that data has estimated the maternal health service utilization in urban and rural areas of each of
the 34 states in India. Unlike the earlier utilization-mapping analysis, this study uses household-
level national data, spatial determinants of the utilization and estimates using the spatial
methods. This chapter uses the district-level institutional birth estimates from previous chapter to
investigate the extent to which variables may have an spatial effect on the incidence of
institutional birth in a district. It was decided not to carry out the analysis of geographic
determinants of institutional birth at the village level (PSU) for two reasons. First, the village-
level institutional birth estimates have large standard errors, indicating a large “noise”
component in these estimates. Second, some of the variables may be less accurate at the village
level due to less sample size. Interpolation at the district level is probably more reliable than
interpolation at the village level.
This study is restricted to rural areas of eight socio-economically under developed states in north
India. The analysis is based on 8787 HSCs, 3269 PHCs and 5743 villages from 263 districts of
these states. All facility information was merged with village information. So the sample size was
5687 (villages) for preparation of facility indices at district level. Since, all EAG states comprise
263 districts so the districts are unit of analysis for the spatial analysis. Dependent variable is
institutional births and independent variables are categorized into two groups i.e. intermediate
(external) and direct (internal). All the indicators are computed from the DLHS-3 data and some
of the values like urban percentage, women literacy and SC/ST population were verified with the
census 2001 data. Household, women, village and facility file were used for calculating the
district level data.
Spatial proximity for accessibility studies has traditionally been defined through measures of
Euclidean distance where buffers around health centres and/or villages define travel thresholds
(McLafferty, 1988; Rosero-Bixby, 2004). Hospital choice also depends upon the services
available at the facility. The available studies till now are rarely discussed the correlation/spatial
association of availability of facility infrastructure itself. The present study tries to build on this
gap firstly, in order to examine the infrastructure availability at public health centre and its
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adequacy for the institutional births. More specifically, we prepare the adequacy indices
separately at primary level health facility which are purposefully structured to promote the
utilization. Secondly, spatially weighted regression captures the spatial dependence with the
covariates.
4. METHODOLOGY
4.1 Health Facility adequacy indices:
The research tool employed in the present study is somewhat based on the scale and score
provided by Haddad, Fournier and Potvin (1998) to assess quality of healthcare services after
making adjustment for Indian setting and availability of data on facility information. Since, the
newly revised IPHS (PHC) has considered the services, physical infrastructure, manpower,
equipments and drugs so as to describe minimum assured services and the ideal level services
which the states shall try to achieve. Required infrastructure adequacy for the maternal care was
first aim to access facilities available at Health Sub-Centre (HSC) and Public Health centre
(PHC) using facility survey of DLHS 3, as these HSCs and PHCs are set-up in rural area to
facilitate decentralized government health program (NRHM, 2005) and to meet the maternal
health care need at the gross-root level. In the view to emphasize to take maternal care to the
door step by strengthening SHCs and PHCs, there is every need to scrutinize adequacy of these
facilities to ensure complete utilization. Essential equipments/instruments, manpower and drugs
etc required for birth delivery, was selected with the help of gynecologist in the institute (Dr.
Ambekar, IIPS, Mumbai). Availability of gynecologist, pharmacist, technician, nurses,
equipments for delivery, essential drugs, electricity, functional OT etc. are coded as 1 and 0
otherwise.
On basis of appropriate variables indices of adequacy of health facilities are prepared and
categorized into different sections for the districts. STATA version-10 software was used for
performing non-spatial statistical analysis while spatial analysis was done in GeoDa. The indices
were named as ‘manpower index,’ ‘physical Infrastructure index,’ ‘Essential equipments and
laboratory services index,’ and ‘essential drug index’ at PHC and similar at HSC with addition of
skilled ANM and ANM residing within 5km from the village.
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4.1.1 Primary Health Center (PHC) and Health sub-Center (HSC) level indices: At the
village level PHCs should have proper infrastructure and adequate manpower, equipments and
essential drugs to provide all services related to maternal and child health care including three
major components of pre-natal, delivery and post natal care to the pregnant women. Separate
indices relating to functioning of PHCs and HSCs are outline in the following:
1. Manpower index (MI_P): Availability of trained health professionals at health center is
the primary requirement for institutional birth delivery. Study by López-Cevallos D F et.al.
(2010) provided evidence that density of public health practitioners was positively associated
with health care utilization in rural area. In the view of this composite index for manpower
required for delivery at health facility, was prepared. Manpower, required at health facility for
institutional delivery are medical officer, lady medical officer, staff nurse, pharmacist, lady
health veteran/health assistant, laboratory technician, auxiliary nurse midwife (ANM)/female
health worker, additional staff nurse. However, for the purpose of composite index of manpower
four essential personnel for institutional delivery namely availability of lady medical officer,
health assistant, pharmacist and any ANM are considered.
2. Physical infrastructure index (PII_P): Adequacy of physical infrastructure is crucial in
performing institutional delivery. This makes it important to construct an index of infrastructure
in accessing the implication of adequacy of health facility on maternal care. The items includes
in the construction of infrastructure index are: proper building for PHC, regular water supply,
regular electricity supply, functioning toilet, working phone, Boyler available, at least four bed
for patients, functional labor room, anesthesia, functional OT and communication facility.
3. Essential delivery care equipments/ laboratory services index (ELABI_P): Availability
of selected furniture, instruments, equipments and essential laboratory services required for natal
and delivery care at the health facility is considered for this index.
Selected furniture and instruments are: examination table, delivery table, OT table, bed side
screen, footstep, shadow less lamp light for OT/labor room, Macintosh for labor & OT table,
oxygen trolley with cylinder and flow meter, instrument trolley, sterilization instrument,
instrument cabinet, blood/saline stand, stretcher on trolley, stool for patients, wheel chair,
almirah/cupboard and separate dustbin for biomedical waste.
A number of equipments and kits should be available in the health facility for conducting
delivery. Additionally many storage system and instruments are necessary. Basic equipments
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includes IUD insertion kit, normal delivery kit, equipment for assisted vacuum delivery and
forceps delivery, equipment for new born care and neonatal resuscitation, standard surgical set
(for minor procedures like episiotomy stitching), equipment for manual vacuum aspiration, baby
warmer/incubator and second, cold chain equipment comprises of ILR large/small, deep freezer
large/small, cold box and vaccine carrier. Additional lab requirement for Hb testing, reagent
strips for urine albumin and urine sugar analysis, rapid plasma regain (RPR) test kit for syphills
kit, reagent for peripheral blood smear examination for MP, residual chlorine in drinking water
testing strips, centrifuge, light microscope and binocular microscope.
Laboratory provisions for blood grouping, haemogram (TLC/DLC), diagnosis of RTIs/STDs
(with wet mounting, grams, stain etc.), sputum testing for TB, blood smear examination for
Malaria Parasite, urine (routine culture/sensitivity/microscopy ), rapid tests for pregnancy, rapid
plasma reagin (RPR) test for syphilis are considered in the construction of this index. All these
selected furniture, instruments, equipments, kits, cold storage devices and essential pathological
kits are included in the construction of equipments/lab services index (ELABI_P. Available
items are coded as 1 and 0 otherwise.
4. Essential drug index (EDI_P): Essential drugs, namely availability of antiallergics and
drugs used in anaphylaxis, anti-hypertensive , anti-diabetics, anti-anginal, anti-tubercular, anti-
leprosy, anti-filarials, anti-bacterials, anti-helminthic, anti-protozoal, antidots, solutions
correcting water and electrolyte imbalance and essential obstetric care drugs are considered in
the development of essential drug index.
4.2 Reliability test of Indices: Cronbach’s Alpha (Inter-Item Reliability):
Reliability of the health facility adequacy indices, discussed in the preceding section are tested
by Cronbach’s alpha. Its value ranges between 0 and 1. The closer is the Cronbach’s alpha
coefficient to 1.0 the greater is the internal consistency of the items included in the index. The
size of alpha is determined by both the number of items in the scale and the mean inter-item
correlations.
Based upon the formula α= rk / [1 + (k -1)r] where k is the number of items considered and r is
the mean of the inter-item correlations the size of alpha is determined by both the number of
items in the scale and the mean inter-item correlations. George and Mallery (2003) provide the
following rules of thumb: “_ > 0.9 – Excellent, _ > 0.8 – Good, _ >0 .7 – Acceptable, _ >0 .6 –
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Questionable, _ > 0.5 – Poor, and _ < 0.5 – Unacceptable” (p. 231). While increasing the value
of alpha is partially dependent upon the number of items in the scale, it should be noted that this
has diminishing returns. It should also be noted that an alpha of 0.8 is probably a reasonable
goal. It should also be noted that while a high value for Cronbach’s alpha indicates good internal
consistency of the items in the scale, it does not mean that the scale is uni-dimensional and factor
analysis is a method to determine the dimensionality of a scale.
4.3 Principle component analysis (PCA):
Principle component analysis (PCA) is used to examine the structure of the relationship among
items included in the construction of the above health facility adequacy indices. Prior to running
the factor analysis, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and the
Bartlett’s test of sphericity were performed. An “eigen value greater than 1” criterion was
employed for determining the number of factors. In order to obtain more interpretable results
solution, Varimax rotation was used to rotate the solution. This caused the loadings to be
distributed among the selected factors making it easier to interpret results. STATA version 10
software was used for principle component analysis. Later, PCA scores obtained from the
infrastructures variables at PHC and HSC so that to create 5 adequacy quintiles.
4.4 Spatial autocorrelation:
According to Anseline and Bera (1998), spatial autocorrelation can be loosely defined as the co-
incidence of value similarity with location similarity.
i. Moron’s statistics: The Morons’ scatter plot provides a tool for visual exploration of
spatial autocorrelation (Anseline 1996, 2002). This statistic is used to quantify the degree of
spatial autocorrelation present in the data set across all the districts. Univariate and bivariate
Moran’s I will give the spatial structure in terms of spatial autocorrelation (SAC). Pearson
coefficient measure of SAC is given as
∑
∑
Where zj is standardized variable of interest at location i. wij is weight matrix C = and N is
number of spatial unit. Negative (positive) values indicate negative (positive) spatial
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autocorrelation. Values ranges from -1 (indicating perfect dispersion) to +1 (perfect correlation)
a zero indicates a random spatial pattern (Moron, 1948).
ii. Local Indicators of spatial association (LISA) statistics: Univariate and bi-variate LISA
statistics will be used for the purpose which measures the extent of spatial non-stationarity and
clustering to its neighborhood values.
Where observations zi, zj are in deviations from the mean from ith location to jth location, and the
summation over j is such that only neighboring values j Є Ji are included. For ease of
interpretation, the weights wij may be in row standardized form, though this is not necessary, and
by convention, wii = 0.
4.5 Spatial Weighted Regression Analysis (SWR):
The spatial regression analysis carried out in this study to estimate the institutional delivery as a
function of variables representing socio-economic development, accessibility and adequacy
indices in the districts. As discussed above, we are also interested in examining the geographic
determinants of institutional birth. The dependent variable in this analysis is, itself, an imputed
value, so special care must be taken in interpreting the results, but Elbers, Lanjouw, and Lanjouw
(2004) show that the basic results are essentially the same as they would be with a “true”
measure of institutional birth. Estimation strategy is done with ordinary least-squares (OLS)
model will be estimated with all exogenous variables included; later tests for the two types of
spatial dependence will be performed and lastly, either the spatial error or the spatial lag model
will be used to re-estimate the model using generalized least squares. Spatial weights were
adopted that are proportional to the inverse distance between the geographic centers of the
districts. The spatial lag dependence model can be written as follows:
ε with ε λ ε υ
where yi is the dependent variable for location i,
σ is the spatial autoregressive coefficient,
wij is the spatial weight reflecting the proximity of i and j,
yj is the dependent variable for location j,
,jj
ijii zwzI
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Xi is a row vector of explanatory variables for location i,
β is a column vector of coefficients, and εi is the error term for location i.
The spatial weights matrix w describes the degree of proximity between each pair of spatial
observations. Usually it is a binary variable based on whether the two locations are contiguous or
a continuous variable based on some function of the distance between the two locations. If the
regression analysis is carried out without adjustment for spatial lag dependence, the estimated
coefficients will be biased and inconsistent (Anselin 1988).
The second type of problem is spatial error dependence. When there is spatial error
dependence, ordinary least squares regression coefficients will be unbiased but not efficient (the
standard errors will be larger than they would be if all information were used). This model can be
written as follows:
λ ε u
where yi is the dependent variable for location i,
Xi is a row vector of explanatory variables for location i,
β is a column vector of coefficients,
εi is the error term for location i,
λ is the spatial error autoregressive coefficient,
wij is the spatial weight reflecting the proximity of i and j, and
ui is the uncorrelated portion of the error term for location i.
In this case, using ordinary least squares to estimate the model does not yield biased coefficients,
but the estimates of the coefficient are not efficient and the standard t and F tests will produce
misleading inference (Anselin 1988). In order to test for the presence of spatial autocorrelation,
Moran’s I is frequently used:
I = (x – μ)′W(x – μ)/(x – μ)′(x – μ)
Where x is a column vector of the variable of interest,
μ is the mean of x, and W is the weighting matrix.
This statistic is simply the correlation coefficient between x at one point in space and the
weighted average of the values of x nearby. In order to test whether there is spatial lag
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dependence or spatial error dependence, the Lagrange multiplier is used to test the statistical
significance of the spatial autocorrelation coefficient (λ) in the two models. Anselin (1988)
shows that the model with the larger coefficient (λ) is likely to be the appropriate model.
Whenever spatial error or spatial lag dependence is indicated, special types of generalized least-
squares (GLS) regression models need to be applied. In the case of spatial error dependence, the
spatial error model is appropriate, whereas in the case of spatial lag dependence, the spatial lag
model would be used. The independent variables are listed in Table 1 below.
Table 4.1 Variables descriptionExogenous variables (indirect) Endogenous variables (direct) Percentage of literate women Percentage with lowest quintile adequacy at
PHC (exclude doctors) Percentage urban Percentage with doctor
Percentage SC/ST Percentage women with at least 3 Ante-Natal Care (ANC)
average population covered by SC/PHC Average distance to nearest health centre providing ANC care
Percent women received conditional cash transfer (JSY beneficiary)
Average distance to nearest health centre providing delivery care
Percentage with all weather road connectivity to health centre
Percent lowest quintile population
5. RESULTS AND DISCUSSIONS
5.1 Reliability test of Indices:
Table1 5.1 The first subscale with Cronbach alpha 0.72 included 13 items related to ‘manpower
index (MP_P): adequate availability of health personnel at PHC. The second subscale, ‘physical
Infrastructure index’ (PII_P) with Cronbach alpha 0.79 comprised eighteen items: building for
PHC services, regular water, electricity supply, functional toilet, communication mode
(telephone, vehicle on road), adequacy of beds for patients, clean hospital premises, and proper
disposal of waste etc. The third subscale, ‘Essential delivery care equipments/ laboratory services
index (ELABI_P):’ with Cronbach alpha 0.92, included forty eight variables which include
availability of selected furniture, required instrument, equipments and essential laboratory
services for the delivery care. The fourth subscale ‘essential drug index’ (EDI with Cronbach
alpha 0.80 contained availability of thirteen essential drugs based on record of stock register.
Similarly indices were prepared for HSC, and same method of test scale was performed for
reliability. It had an overall Cronbach’s alpha value of 0.82 that ranged from 0.34 to 0.87. The
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reliability was highest for ‘Physical infrastructure/ equipments index’ (PIEI_HS) (0.87) and
lowest for ‘Manpower index’ (MI_HS)’ i.e.0.34.
Table 5.1: Summary statistics and degree of reliability for adequacy indices
Indices of health facility adequacy Min Max Mean Std. Dev.
Cronbach's alpha(k)
PHC (N=3269)
Manpower index (MI_P) -3.4514 4.2674 5.47E-09 1.7781 0.72 (13)Physical infrastructure index (PII_P) -5.6156 4.1519 3.19E-09 2.0400 0.79(18)
Essential delivery care equipments/ laboratory services index (ELABI_P)
-6.5327 7.7423 -5.78E-09 3.2241 0.92(48)
Essential drug index (EDI_P) -4.6589 3.0733 -1.11E-08 2.0549 0.80(13)
HSC(N=8787) ANM residing in village or within 5km
0.0000 1.0000 0.664732 0.4721 -
Manpower index (MI_HS) -2.6386 4.1639 1.07E-08 1.2236 0.34(7)Physical infrastructure/ equipments index (PIEI_HS)
-3.4392 4.4586 4.90E-09 2.0602 0.87(19)
Essential drug index (EDI_HS) -2.5123 4.0351 -2.15E-08 2.0169 0.76(12)
5.2 Adequacy indices score and distribution by states:
For the selected adequacy indices at PHC and HSC Eigen value was obtained and an “Eigen
value greater than 1” criterion was employed for determining the number of factors. In order to
obtain more interpretable results solution, Varimax rotation was used to rotate the solution. This
caused the loadings to be distributed among the selected factors making it easier to interpret
results. Factor loadings of 0.5 or greater on a factor were regarded as significant. The factor
analysis of the selected items scale on the basis of principal component extraction by using
Varimax rotation converged with iterations. The following figure fig 5.1 (a, b, c, d, e and f)
shows the score plot of Eigen value and probability plot for the selected component based on
PCA factor 1.
a) Adequacy indices:
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Fig 5.1: Scree-Plot and normal plot of the scores of health facility (PHC) adequacy indices: a, b for health personnel; c, d for physical infrastructure. Normal plot of the scores of health facility (PHC) adequacy indices: e for essential equipments and laboratory services; f plot is availability of essential drugs for the maternal care.
.51
1.5
22.
53
Eig
enva
lues
0 5 10 15Number
95% CI Eigenvalues
Scree plot of eigenvalues after pca: health personnel adequacy at PHCHealth personnel score plot at PHC (using PCA) with normal curve
0.1
.2.3
.4D
ens
ity
-4 -2 0 2 4Scores for component 1
01
23
4E
igen
valu
es
0 5 10 15Number
95% CI Eigenvalues
Scree plot of eigenvalues after pca: physical infrastructure adequacy at PHC physical infrastructure score plot at PHC (using PCA) with normal curve
0.1
.2.3
De
nsity
-6 -4 -2 0 2 4Scores for component 1
0.0
5.1
.15
De
nsity
-10 -5 0 5 10Scores for component 1
Essential equipments/lab service score plot at PHC (using PCA) with normal curveEssential drugS score plot at PHC (using PCA) with normal curve
0.1
.2.3
De
nsity
-4 -2 0 2 4Scores for component 1
a b
dc
ef
Rachana Patel, PhD (IUSSP 2013) Page 15
The score provided to health centers were categorized into quintiles and further analysis was
done accordingly to link with its utilization for MCH care. Table 5.2 explains distribution of
health centers in EAG states by the quintiles. Maximum PHCs with lowest health personnel (Q1)
was found in Uttar Pradesh (37%) and Uttaranchal (25%) followed by MP (21%) and maximum
PHCs with highest health personnel adequacy (Q4+Q5) was found in Orissa (60%) and Bihar
(59%). Further, maximum PHCs with least adequate physical infrastructure was found in state
of Orissa (46%) followed b by UP (26%) while the highest equipped (Q1+Q5) states are
Jharkhand (69%) and UTT (59%). Adequacy for required furniture, equipments, instruments for
delivery care and essential laboratory services are found to be least in state of Orissa (37%) and
UP (33%) followed by Bihar (34%) while the highest equipped states are Rajasthan (38%) and
Jharkhand (32%). Least drugs availability of drugs was found in state of Orissa and Bihar while
the highest was found in UTT and Jharkhand.
Surprisingly, Bihar and UP is the state where almost 50 percent ANM residing in village or
within 5km range and highest percentage of ANM (had training in MCH care and birth attendant,
accessible to village) which could motivate to safe delivery if the socially-economic-cultural
environment of those region could not support women for institutional delivery while the least
adequacy (physical infrastructure, essential drugs) for other facility at HSC was found in same
states.
Table 5.2: Percent distribution of EAG states by level (quintile) of selected infrastructure adequacy at PHC
a) Physical Infrastructure index at PHC States Q1 Q2 Q3 Q4 Q5 Total PHC
UTT n 4 11 17 17 30 79 % 5.06 13.92 21.52 21.52 37.97 100 RAJ n 24 87 170 251 145 677 % 3.55 12.85 25.11 37.08 21.42 100 UP n 201 149 164 135 116 765 % 26.27 19.48 21.44 17.65 15.16 100 BH n 103 87 51 58 118 417 % 24.7 20.86 12.23 13.91 28.3 100 JH n 8 18 21 26 76 149 % 5.37 12.08 14.09 17.45 51.01 100 OR n 208 118 60 24 42 452 % 46.02 26.11 13.27 5.31 9.29 100 CHH n 42 81 69 27 25 244 % 17.21 33.2 28.28 11.07 10.25 100 MP n 58 86 100 102 83 429 % 13.52 20.05 23.31 23.78 19.35 100 b) Essential equipments and laboratory services index at PHC
States Q1 Q2 Q3 Q4 Q5 Total PHC UTT n 2 23 34 10 11 80
Rachana Patel, PhD (IUSSP 2013) Page 16
% 2.5 28.75 42.5 12.5 13.75 100RAJ n 9 32 126 253 258 678 % 1.33 4.72 18.58 37.32 38.05 100UP n 256 265 85 48 113 767 % 33.38 34.55 11.08 6.26 14.73 100BH n 157 82 108 96 23 466 % 33.69 17.6 23.18 20.6 4.94 100JH n 6 6 28 62 47 149 % 4.03 4.03 18.79 41.61 31.54 100OR n 168 90 95 48 55 456 % 36.84 19.74 20.83 10.53 12.06 100CHH n 18 60 62 48 56 244 % 7.38 24.59 25.41 19.67 22.95 100MP n 38 96 116 89 90 429 % 8.86 22.38 27.04 20.75 20.98 100
Factor analysis technique was employed to examine the structure of the relationship among
variables representing the adequate infrastructure dimensions of healthcare services in EAG
states. Prior to running the factor analysis, the Kaiser-Meyer-Olkin (KMO) measure of sampling
adequacy and the Bartlett’s test of sphericity were performed. The generated score of KMO was
0.82 and highly significant Bartlett’s test of sphericity supported the appropriateness of using
factor analysis to explore the underlying structure of perceived quality of healthcare services.
5.3 Adequacy Inequality by States:
This, segment of the analysis identified the number of districts with lowest adequacy and
inequality was computed as the relative deviation from the average EAG adequacy. LQ helps to
measure the inequality in the adequacy at PHCs and HSCs and find from the average of overall
EAG estimates. Below average adequacy from the average EAG was calculated as follows:
proportion of lowest (Q1) equipped health centre was observed in the districts and 20 percent
c) Essential drugs index at PHC states Q1 Q2 Q3 Q4 Q5 Total
PHC UTT n 4 12 21 23 20 80 % 5.00 15.00 26.25 28.75 25.00 100 RAJ n 48 125 218 164 123 678 % 7.08 18.44 32.15 24.19 18.14 100 UP n 100 231 149 151 136 767 % 13.04 30.12 19.43 19.69 17.73 100 BH n 149 57 42 90 128 466 % 31.97 12.23 9.01 19.31 27.47 100 JH n 14 18 29 50 38 149 % 9.4 12.08 19.46 33.56 25.5 100 OR n 256 48 49 40 63 456 % 56.14 10.53 10.75 8.77 13.82 100 CHH n 26 54 46 61 57 244 % 10.66 22.13 18.85 25 23.36 100 MP n 58 108 103 79 81 429 % 13.52 25.17 24.01 18.41 18.88 100
Rachana Patel, PhD (IUSSP 2013) Page 17
and more were counted in lowest infrastructure based on assumption of 20 percent cut-off since
average EAG infrastructure quintiles is distributed over equal share of 20 percent in all Q1, Q2,
Q3, Q4 and Q5.
Table 5.3: spatial concentration
Table 5.3 Hence UP and Bihar is the state where maximum number of districts having lowest
adequacy (Q1) of all category of infrastructures at PHCs and HSCs. Followed by Orissa, where
districts with lowest adequacy only at PHCs was observed. Result explored that inequality in the
distribution and pattern of adequacy in the districts. Location quotient for the districts was
calculated as the relative deviation from the median adequacy of EAG. It was observed that most
of the districts from Rajasthan, western UP, Bihar, south western Orissa, Bihar, Jharkhand and
western MP have below average health personnel adequacy at PHC while the quite similar
pattern was found for Physical infrastructure and availability of equipments/laboratory services.
Most of the below adequacy clustered was observed in the districts of eastern UP, Jharkhand,
Bihar and some part of Orissa. while the, very uneven pattern was observed for the adequacy at
HSCs however, some districts of eastern UP and western Orissa have shown below average
adequacy of HSC indices.
5.4 Results from spatial Autocorrelation: Adequacy and Delivery care
a) Moron’s I and LISA: Univariate
Following maps shows LISA cluster and significance map generated from spatial software
GeoDa for all the districts taking institutional births percentage as the georefence values of the
Table 5.3 Number of districts by lowest concentration (Q1) of public health facilities in EAG states states
Facility adequacy at PHC Facility adequacy at HSC Total Districts
Bihar LM LPI LEI LD LS_ANM (<40 %)
ANM_ far (>40%)
LPI_H LD_H N
UTT 6 1 1 2 10 1 5 2 13 Raj 3 2 2 1 31 9 0 0 32 UP 41 32 38 13 34 40 61 51 70 BH 9 26 28 29 16 30 33 35 37 Jh 1 1 2 3 16 3 0 0 22 OR 10 22 14 24 28 0 0 0 30 CHH 10 7 1 2 14 2 0 0 16 MP 15 11 7 11 43 25 3 3 45 Note: LM: Lowest manpower, LPI: Lowest physical infrastructure; LEI: Lowest equipment and instruments; LD: Lowest drugs; LS_ANM: Lowest Skilled ANM, ANM_far: ANM residing far from village; LPI_H: Lowest physical infra at HSC; LD_H: Lowest drugs at HSC.
Rachana Pa
districts.
cluster m
significan
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Correlati
adequacy
Univariat
and HSC
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was foun
required
(I=0.66)
Fig 5.2: Physical and low-l
(1)
(3)
tel, PhD (IUSSP
These maps
map for iden
nce of degre
nce and clus
he Local M
ion matrix an
y indices.
te Moron’s
C. Bivariate
elation with
nd maximum
for materna
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Univariate LISInfrastructure;
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Moron statisti
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I=0.48, p<0.0
I=0.50, p<0.05
ontiguity ba
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(2)
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ments
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5
Rachana Patel, PhD (IUSSP 2013) Page 19
High-high regions shows the positive spatial association with its neighboring values of districts
in institutional births while low-low regions have positive autocorrelation from own and
neighboring low values of institutional births. High-high association are mostly found in the
districts of eastern Rajasthan, weatern MP and some of south-east Orissa. It could be concluded
that about higher inter-district variation and the improved health care utilization for institutional
births in these states. None of the high-high regions are located in UP, Uttaranchal, Chhattisgarh,
Bihar and Jharkhand part where low-low part are observed. Mostly low-low part are observed in
the district of Chhattisgarh, Uttarakhand and UP.
However, very few spatial ouliesr are identified in low-high and high-low regions because of
their inverse association. Low are surrounded by high values and vise-versa. Low-high outliers
are dispersed across total of 13 districts from Orissa, Bihar, Uttaranchal, Rajasthan and MP while
high-low are located in only a distict of Uttaranchal.
Fig 5.3 Moron’s scatter plot and significance map for institutional births in EAG: 1)LISA cluster Map; 2)
Box-plot; 3) LISA Significance map; 4)Moron’s scatter plot
1 2
3 4
Rachana Pa
LISA sig
these ide
are signi
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significan
b) Moro
Bivariate
the select
indices h
adequate
with drug
Result sh
equipmen
districts
said that
between
found fo
institutio
Negative
(1)
tel, PhD (IUSSP
gnificance m
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ificnce at l
nt at 5% l
nce in the di
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ted infrastru
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e essential eq
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howed that t
nts at PHC
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or availabilit
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e spatial auto
P 2013)
map fig 5.3
l clusters and
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istricts of Ra
SA: Bivariat
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the bivariate
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orrelated. Fi
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ty of equip
hile insignifi
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I=-
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ajasthan, MP
te
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aboratory se
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re. Level of
, MP and so
ig 5.3 Bivar
d adequacy i
pments/ lab
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was observe
-0.172, p<0.05
additional i
tliers. Nearly
all the spati
re found si
P, Orissa, Ch
ructure show
s weaker ass
n was obser
ervices at PH
y autocorrela
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ome part fro
riate LISA
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ation was fo
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y all detected
ially correla
ignificantly
hhattisgarh,
wed that spat
sociation wa
rved between
HC (I=0.44)
ated (I=0.71)
y care (insti
and utilizatio
om the Uttar
has confine
nificant pos
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ound with th
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on the sign
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UP and Utta
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while infra
).
itutional birt
on and both
ranchal, ther
d the spatia
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astructure a
he health ma
ntration of f
Pa
nificance lev
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s were foun
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arakhand.
rrelation bet
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nfrastructure
astructure at
ths) and esse
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refore it cou
al autocorrel
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anpower at P
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I=-0.034, p<
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vel of
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PHC.
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<0.05
Rachana Pa
Fig 5.3: bdelivery*phyinfrastructure
5.5 Spati
a) Comp
OLS resi
spatial-la
Spatial la
significan
low value
(3)
1
tel, PhD (IUSSP
bivariate LIysical infrae at PHC; 4) I
ial Regressio
arison of OL
idual and spa
ag model fo
ag-residuals
ntly clustere
es are mostly
Fig 5.4: R
P 2013)
SA map:1)Istructure;3)InInstitutional d
on Model:
LS and Spat
atial- lag-res
r the regres
are highly c
ed in similar
y concentrat
Residual map f
I=-0.1
Institutional nstitutional del*percent lo
tial Lag Mo
sidual map a
ssion predict
correlated (0
states MP, R
ted in Uttara
for institutiona
102, p<0.05
delivery*equdelivery*per
owest concen
del
are shown in
ted and erro
0.6778). The
Rajasthan an
anchal, UP, J
al delivery: 1) O
(4)
2
uipments &rcent lowesntration of equ
n the fig 5.4
or values are
estimated h
nd few distric
Jharkhand an
OLS residual; 2
& lab servit concentra
uipments & la
(1 and 2). A
e generated
high-high res
cts of Orissa
nd some part
2)spatial Lag-r
Pa
ices; 2)Institation of pab services at
After adoptin
for the dist
siduals value
a while low-
t of Chhattis
residual
I=-0.112, p<0
ge 21
tutional physical PHC
ng the
tricts.
es are
with-
sgarh.
0.05
Rachana Patel, PhD (IUSSP 2013) Page 22
Table 5.4: Comparison of OLS and Spatial-lag model (dependent institutional delivery=1) OLS model Spatial lag model
Indep Variables Coefficient (β)
p-value Coefficient (β)
p-value
Direct LOW_ADQ -0.074 0.0124 -0.071 0.0341 DOC_ADQ 0.069 0.0522 0.071 0.3594 3ANC_P 2.079 0.0038 2.960 0.0006 NEAR_ANC -0.113 0.0014 -0.102 0.0251 NEAR_DEL -0.119 0.0158 -0.131 0.0073 VILL_CONNCT 0.134 0.0660 0.111 0.0044 LOW_WI -4.297 0.0000 -3.051 0.0000 Indirect LIT_P 2.132 0.0010 2.782 0.0011 URBAN_P 1.238 0.0001 1.632 0.0332 SCST_P -0.084 0.0461 -0.084 0.0872 POP_COV -0.044 0.4023 -0.311 0.6720 JSY_P 5.0206 0.0000 6.261 0.0000 CONSTANT 22.594 0.0252 19.321 0.0021 N 263 263 Log-likelihood -877.762 -818.402 AIC 1779.52 1662.8
R2 adjusted 0.730647 0.790647 Lag coeff (lamda) 0.588293 Heteroskedasticity test (Breusch-Pagan)
28.72187 0.00250 10.06128 0.0031
Spatial dependence test (likelihood ratio test)
12.034 0.0002
Rachana Patel, PhD (IUSSP 2013) Page 23
5.6 Conclusions:
Inequality measure reveals that UP and Bihar is the state where maximum number of districts
having lowest adequacy of all indices of infrastructures at PHCs and HSCs. Followed by Orissa,
where districts with lowest adequacy only at PHCs was observed. However, districts of Orissa
have better adequacy at HSCs. Inequality in pattern of adequacy was captured very clearly
through the maps which clearly revealed that the most of the districts from Rajasthan, western
UP, Bihar, south western Orissa, Jharkhand and western MP have below average (EAG) of
health personnel adequacy at PHC and quite similar pattern was found for physical infrastructure
and availability of equipments/laboratory services. Though, very uneven pattern was observed
for the facility adequacy at HSCs. On the other hand if we look for the concentration of only
lowest adequate facility at PHCs then most of the districts belongs to eastern UP, Jharkhand,
Bihar and some part of Orissa.
Correlation matrix showed health personnel adequacy index was highly correlated with physical
infrastructure index at PHC. It could be said that availability of physical infrastructure are
supposed to have availability of health personal. Whereas, equipments/laboratory services at
PHC was highly correlated with adequate drugs at PHC and with physical infrastructure at PHC.
This could be easily concluded that adequate availability of one facility at PHC is very much
reflects the availability of other facility. Importantly, physical infrastructure at HSC (r=0.53)
was found to be correlated significantly with physical infrastructure at PHC (r=0.49) and ANM
residing within 5 km (r=0.57) which might be integrated program effect of these services at PHC
and PHC both.
Spatial results (univariate Moron’s and LISA) for infrastructure indices show the significantly
high spatial autocorrelation for adequacy indices at PHC and CHS in districts considering
Rook’s spatial weights to the neighboring districts. Additionally, bivariate results showed the
maximum autocorrelation between physical infrastructure and adequate essential equipments/lab
services at PHC (0.44). Equipments adequacy clustering is found significant in western districts
comprising districts of western Rajasthan, Middle MP and southern UP. Whereas, essential drugs
adequacy are clustered in south-west Orissa, Chhattisgarh, and eastern MP. However, manpower
adequacy is also clustered significantly (high-high) in some districts of Orissa.
Rachana Patel, PhD (IUSSP 2013) Page 24
Spatial dependence for delivery care has captured the better acceptability at some extent to
describe through several tests of spatial diagnosis over dependents (outcome), independents
(covariates) and error term which has come up with the spatially lagged dependent variable term
in the model which estimate are adjusted by spatial autocorrelation. All intermediate and direct
variavles have shown combined effect on the institutional delivery. Some OLS estimates has
shown significant association with the institutional delivery except for average population
covered by PHC while some covariates disappears its influence on independents once spatial-lag
(spatial dependence) parameter incorporated in the model like availability of doctors at PHC,
proportion of SC/ST population and percentage of urban population (p>0.05). Low infrastructure
adequacy at PHC, distant health facility providing ANC or delivery care and proportion of
lowest quintile have significantly (p<0.01) reduced the probability of having institutional
delivery and hypothsesis are rejected. Whereas Receipt of three or more anti-natal visits (ANC),
all weather road connectivity of village to the health center and women literacy have
singnificanlty increase the likelihood of instititonal births. Programamitc efforts of states
government to encourage the institutional birth by providing the incentives to women had
delivered baby in public health facility have singificanlty increase the utilization (p<0.001).
This study improved understanding how women's health-care-seeking behaviour is shaped by the
availability of health services and inform the development of strategies to improve the provision
and use of maternal healthcare at district/county level. As if the barriers to the accessibility of
service are to be effectively reduced any attempt and improve the adequate facility for maternal
care at PHC, to increase maternal care-seeking behavior in rural India will require resources to
be targeted at the most impoverished areas and development of strategies for reaching those not
yet reached.
Rachana Patel, PhD (IUSSP 2013) Page 25
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