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Effect of Maternal Healthcare on the Probability of Child Survival in
Azerbaijan
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Florida National University
HSA-6752 Statistic in Health Care Management: Assignment Week 3
Case Study: Chapters 5 and 6.
Objective: The students will complete a Case study assignments that give the occasion to create and
apply the thoughts learned in this and previous project to examine a real-world scenario. This set-up will
illustrate through example the practical importance and implications of various roles and functions of a
Health Care Administrator in probability and interval Estimates. The investigative trainings will advance
students’ understanding and ability to think critically about basic concepts of probability and introduction
to estimation.
ASSIGNMENT GUIDELINES (10%):
Students will critically measure the readings from Chapters 5 and 6 in your textbook. This
assignment is planned to help you examination, evaluation, and apply the readings and strategies
to your Health Care organization.
You need to read the article (in the additional weekly reading resources localize in the Syllabus
and also in the Lectures link) assigned for week 4 and develop a 3-4 page paper reproducing
your understanding and capability to apply the readings to your Health Care organization. Each
paper must be typewritten with 12-point font and double-spaced with standard margins. Follow
APA format when referring to the selected articles and include a reference page.
EACH PAPER SHOULD INCLUDE THE FOLLOWING:
1. Introduction (25%) Provide a brief synopsis of the meaning (not a description) of each
Chapter and articles you read, in your own words that will apply to the case study presented.
2. Your Critique (50%)
Case Studies
The Effect of Maternal Healthcare on the Probability of Child Survival in Azerbaijan
Nazim Habibov and Lida Fan
1
School of Social Work, University of Windsor, Windsor, ON, Canada N9B 3P4
2
School of Social Work, Lakehead University, Thunder Bay, ON, Canada P7B 5E1
Received 15 February 2015; Revised 23 June 2015; Accepted 23 June 2015; Published 10 July
2015
Academic Editor: Gudlavalleti Venkata Murthy
Copyright © 2014 Nazim Habibov and Lida Fan. This is an open access article distributed under
the Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Abstract
This study assesses the effects of maternal healthcare on child survival by using nonrandomized
data from a cross-sectional survey in Azerbaijan. Using 2SLS and simultaneous equation
bivariate probit models, we estimate the effects of delivering in healthcare facility on probability
of child survival taking into account self-selection into the treatment. For women who delivered
at healthcare facilities, the probability of child survival increases by approximately 18%.
Furthermore, if every woman had the opportunity to deliver in healthcare facility, then the
probability of child survival in Azerbaijan as a whole would have increased by approximately
16%.
1. Introduction
Poor child outcomes are usually associated with underutilization of maternal healthcare [1–3].
Given unusually high mortality rates in countries of Central Asia and Caucasus, poor child
outcomes and maternal healthcare should become important topics for research. Nevertheless,
there are a very few studies on these topics in the region. The available studies can be divided
into two broader groups. The first group explored determinants of child mortality [4, 5]. The
second group explored determinants of maternal healthcare utilization [2, 6–9]. Although these
studies have important contributions, their main limitation is that the most important question on
whether healthcare has an effect on the reduction of child mortality is overlooked. However,
designing and implementing effective health policy require concrete information on the
effectiveness of the existing maternal healthcare.
The contribution of the presented study is that it attempts to fill the gap in the existing literature
by quantifying the direct effect of delivery in healthcare facility on probability of child survival.
The robust evaluation of program effect on population usually involves randomized control trials
(RCT). In many cases, including evaluation of maternal healthcare, conducting a RCT is not
possible from an ethical perspective, withholding vital service, and from technical perspective,
lack of money and time required to conduct a countrywide RCT. To overcome these difficulties,
we assess the effect of healthcare and homecare on child survival by using quasiexperimental
evaluation of nonrandomized data from a cross-sectional survey. In this way, this study
contributes to the recent discussion on appropriate methods for the evaluation of effect of
healthcare programs when RCT is not feasible [10–12].
Azerbaijan, a low-income transitional country on Caucasus, is an interesting setting for
examining the above-mentioned issues for several reasons. First, Azerbaijan has the highest
infant mortality rate and one of the highest proportions of child deliveries outside of healthcare
facilities even compared with other transitional countries in the region [13]. Second, by studying
Azerbaijan, we benefit from recently available 2006 Azerbaijan Demographics and Health
Survey that contains high-quality nationally representative data on the issues of our interest.
Third, there is a current theoretical debate on the actual effectiveness of maternal healthcare in
transitional countries. On the one hand, maternal healthcare is universal, officially free of charge,
fully funded, and operated by the government. It has an extensive network of facilities which is
adequately staffed with qualified personnel. Hence, a fairly strong positive impact on child
survival could be expected and some authors underscore the importance of maternal healthcare
utilization in transitional countries to improve child outcomes. On the other hand, the system is
characterized by chronic underfunding, lack of drugs and supplies, dilapidated facilities, lack of
systematic and effective treatments, and high levels of unofficial out-of-pocket expenditures for
personnel . Hence, no or only weak impact on the child survival could be expected. Therefore, by
focusing on Azerbaijan, a transitional country, this study provides necessary empirical evidence
which will contribute to the current theoretical debate on the effectiveness of maternal healthcare
in transitional countries.
2. Materials and Methods
2.1. Conceptual Framework
We are guided by Mosley and Chen’s framework for studying the determinants of child
survival. According to the framework, socioeconomic determinants at individual (e.g., women’s
education), household (e.g., household income), and community (e.g., healthcare input) levels
affect a total of 14 proximate determinants of mortality which are grouped into several
categories, namely, maternal factors, environmental contamination, nutrient deficiency, and
personal illness control. However, the model has a few limitations for applied research. Some
proximate determinants, for instance, environmental contamination, are notoriously difficult to
define and measure adequately, especially in population based surveys . Furthermore, if a model
includes all socioeconomic and all proximate determinants, then the coefficients on the
socioeconomic variables should not be statistically significant given that the proximate
determinants will pick up all significance by definition . Consequently, we reduced the number
of independent variables to women’s age at birth and education, birth order, low child
birthweight, household wealth, and healthcare input. As a result, we used the reduced set of
independent variables which is similar to previous studies on child survival in the region and
international comparative studies.
2.2. Method
We are interested in estimating effect of treatment, having child delivery at a healthcare facility,
on the outcome, probability of child survival. Thus, we face a problem of self-selection—the
sampled individuals who receive the treatment are different from those who do not receive it in
unobservable ways which are also simultaneously correlated with outcome . To address the selfselection we use simultaneous equation regression that tackles the endogeneity by specifying and
estimating a joint model of the treatment and outcome . Since both treatment and outcome
variable in our case are binomial, we use a simultaneous equation bivariate probit, so-called
biprobit. The model consists of first and main equations. In the first equation, a dummy treatment
variable is regressed on all control variables and one or more instruments. In the main equation, a
dummy outcome variable is regressed on all control variables and the value of the treatment
variable estimated in the first stage. Importantly, the instruments are excluded from the main
equation. This statistical specification is estimated using biprobit command in Stata software
package. After biprobit was estimated, we compute the average treatment effect (ATE) and the
average treatment effect on the treated (ATT) . The value of the ATE indicates the expected
mean effect of the treatment for a woman drawn at random from the population. By contrast, the
value of ATT indicates the expected mean effect of the treatment for a woman who actually
participates in the program and receives treatment. ATT permits us to evaluate the effect on
women who received treatment and who can be considered as a more relevant subpopulation for
the purposes of evaluating effect of a specific program. The full details of biprobit, ATE, and
ATT computations can be found in Greene and Wooldridge .
2.3. Data
This study uses data from the 2006 Azerbaijan Demographic and Health Survey (the AZDHS).
The AZDHS is conducted by the national statistical authority, the State Statistical Committee of
Azerbaijan, with technical assistance of Macro International, USA, and with financial support
from USAID and UNICEF . The AZDHS is a cross-sectional survey of 8,444 women aged 15 to
49 from 7,180 households. Field work was conducted from July to November 2006. The
household gross response rate exceeds 90 percent. The AZDHS gathered information on
demographics, educational level, household wealth, healthcare utilization, and child mortality.
The AZDHS collected information about the outcome of each respondent’s pregnancy for the
period, whether the pregnancy ended in a live birth, a stillbirth, a miscarriage, or an induced
abortion. The survey used the international definition of child mortality, under which any birth in
which a child showed any sign of life such as breathing, beating of the heart, or movement of
voluntary muscles is defined as a live birth. The AZDHS collected information on child
mortality for births in 2001 or later, covering a period of 5 years before the date of the survey
only. Among recorded 13,565 observations, about 92% of children survived between birth and
their fifth birthday and about 8% died. However, our sample is further reduced since the
questions about place of delivery asked only about the most recent birth delivered during the the
last 5 years before the date of the survey. It means that if a women had multiple births during the
last 5 years, the questions about place of delivery was asked only about the latest birth.
Consequently, our final sample consists of 2,285 observations for analysis.
2.4. Outcome and Treatment Variables
The outcome variable of this study is child survival defined as probability to survive during 60
months or 5 years. This variable is binomial; it takes the value of 1 if the child survives 60
months and takes the value of 0 if otherwise. There are two endogenous instrumented variables
of interests which denote treatment and serve to gauge healthcare input. The instrumented
treatment variable is “delivery in a healthcare facility” that takes the value of 1 if the child was
delivered in a healthcare facility and takes the value of 0 if otherwise. The healthcare facility is
defined as a government or private hospital, maternity home, polyclinic, woman’s consultation,
and primary healthcare posts. Overall, from the sample of 2,285 women who answered the
questions about place of delivery in the AZDHS, approximately 79% delivered babies in a
healthcare facility.
2.5. Instrumental Variables
The instrumental variables used to estimate the endogenous treatment variables are taken from a
previous study that used instrumental variables to estimate the effect of prenatal healthcare
utilization on child birthweight in Azerbaijan . There are two instrumental variables—“women
from wealthier households” and “birth order.” The AZDHS contains a variable representing 5
quintiles of household wealth—poorest, poor, middle, richer, and richest. We create a “wealthier
households” dummy variable which denotes women from richest and richer households, and this
variable is used in our model 1 and model 2. Finally, “birth order” is a straightforward
continuous variable denoting number of births.
2.6. Exogenous Variables
The exogenous variables used to explain child survival are taken from the previous studies on the
determinants of child mortality conducted in the countries of the region of Caucasus and Central
Asia . We have two dummy variables representing women’s age: variable “age 20” indicates
women aged 20 or younger at the time of delivery, while variable “age 36” indicates women
aged 36 and older at the time of delivery. Dummy variable “low birthweight” indicates if a
child’s birthweight was 2500 grams or lower. Dummy variable “higher education” indicates
women with bachelor education or higher. Previous studies reported that having delivery at age
<20 and age >35 is associated with higher probability of child mortality. Likewise, previous
studies reported that having low birthweight is associated with higher probability of child
mortality, while having higher educational achievements is associated with lower probability of
child mortality.
2.7. Estimation
We commence with 2SLS model because the tests for overidentifying restrictions and the
adequacy of the instruments are readily available for the 2SLS but not for biprobit . Since the
number of instrumental variables exceeds the number of endogenous variables in our case, the
Hansen statistic is employed to evaluate overidentifying restrictions. If Hansen statistics cannot
reject the null hypothesis, then the selected instrumental variables are exogenous. In addition,
Kleibergen-Paap LM statistic is used to test the adequacy of the instruments. If the test rejects
the null hypothesis, the instruments are adequate to identify the equation. Lastly, we conduct
Durbin-Wu-Hausman test for potential endogeneity. The significance of the test confirms the
presence of endogeneity and suggests that estimation of equations without taking into account
endogeneity will lead to biased results. All the above-described tests have been passed in all
estimated models.
Next we estimate biprobit which is more relevant model due to the binary nature of outcome and
treatment variables. A straightforward Wald test of endogeneity is available in biprobit. If result
of the test is significantly different from zero, then biprobit should be estimated due to the
presence of endogeneity. In all estimated models, the Wald tests have confirmed endogeneity.
After biprobit model estimation, ATE and ATT are computed and reported.
3. Results
The results are reported in Table 1. In the first equation four variables are significant with
predicted directions in 2SLS estimation. Having birth at the age of 20 or earlier and having a
higher value of birth order are associated with lower probability of delivery in a healthcare
facility, while having higher educational achievements and being from a wealthier household are
associated with higher probability of delivery in a healthcare facility. Looking at the main
equation in 2SLS, we can see that having a delivery in the facility improves the chances of child
survival. Results of biprobit estimation are consistent with the results of the 2SLS estimation.
The same four variables are significant in the first equation and with the same direction.
Table 1: The effect of delivery in healthcare facility on probability of child survival.
2SLS model
Bivariate probit model
Std.
Std.
Coef.
Coef.
Err.
Err.
First equation: instrumented variable is delivery in health care facility; instrumental variables
are wealth and birth order
Age 20 or younger
−0.104
0.035
0.003
−0.357
0.116
0.002
Age 36 or older
0.094
0.053
0.076
0.344
0.207
0.098
Low birthweight
0.061
0.045
0.171
0.219
0.179
0.222
Higher education
0.077
0.022
0.000
0.660
0.199
0.001
Wealth
0.198
0.032
0.000
0.891
0.144
0.000
Birth order
−0.073
0.013
0.000
−0.247
0.040
0.000
Constant
0.854
0.038
0.000
1.049
0.135
0.000
Main equation: outcome variable is probability of child survival
Delivery in healthcare facility
0.151
0.063
0.016
0.923
0.347
0.008
Age 20 or younger
0.012
0.016
0.451
0.080
0.154
0.601
Age 36 or older
−0.017
0.031
0.584
−0.136
0.247
0.582
Low birthweight
−0.020
0.019
0.288
−0.174
0.167
0.297
Higher education
−0.032
0.022
0.144
−0.164
0.199
0.410
Constant
0.843
0.051
0.000
0.969
0.319
0.002
Number of observations
2285
(5, 311)
1.31
Prob >
0.000
Number of observations
2285
Log pseudo-likelihood
−1450000000
Wald (11)
126.52
Prob >
0.000
Test of endogeneity
Durbin-Wu-Hausman test and
10.49 (0.001)
(P value)
-statistic and P value
(Rho)
5.647 (0.017)
−0.424
Wald test and P value
4.15 (0.041)
Tests for overidentifying restrictions
Hansen statistic and P value
0.407 (0.686)
Tests for the adequacy of instruments
Kleibergen-Paap LM statistic
42.93 (0.000)
and P value
Effects of treatment
ATE (average effect of treatment)
0.161
ATT (average effect of treatment to
0.184
the treated)
Notes: (1) dependent variable in the first stage is healthcarefacility delivery = 1; otherwise = 0.
Dependent variable in the second stage is child survival = 1; otherwise = 0.
(2) , , and .
(3) Results adjusted to heteroskedasticity and clustering.
(4) Data are rounded up
Source: 2006 Azerbaijan Demographic and Health Survey [17].
4. Discussion and Policy Implications
In this study, we identified and then attempted to fill the important gap in the literature regarding
the effectiveness of maternal healthcare in reducing under-five child mortality in the region of
the Central Asia and the Caucasus. We assessed the effects of delivering in a healthcare facility
on child survival by using a quasiexperimental evaluation based on nonrandomized data from a
cross-sectional survey in Azerbaijan, a low-income country in transition. The empirical evidence
presented in this paper allows for drawing several conclusions.
First, delivering children in healthcare facilities increases the probability of survival. Since
reducing child mortality is raison d’être for maternal healthcare programs, such a funding could
be expected. However, we were able to confirm that the effect of delivering at a healthcare
facility on child survival is statistically significant on the national level. We also quantified the
positive effect of such treatment. For women who delivered at healthcare facilities the
probability of child survival increases by approximately 18%. Furthermore, if every woman in
Azerbaijan had the opportunity to deliver in a healthcare facility, then the probability of child
survival in the country would have increased by approximately 16%. These findings suggest that
utilization of maternal services in transitional countries should be encouraged and promoted in
spite of the limitations and deficiencies in the current maternal healthcare system.
Second, our study d …
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