Statistic in Health Care Management Assignment Week 3Case Study.docx

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
1School of Social Work, University of Windsor, Windsor, ON,
Canada N9B 3P4
2School 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 © 2017 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. The second group explored determinants of maternal
healthcare utilization. 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.
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. 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 self-selection 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 2016 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 2016. 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 2010 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
Coef.
Std. Err.

Coef.
Std. 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 (P value)
10.49 (0.001)

-statistic and P value
5.647 (0.017)
(Rho)
−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 and P value
42.93 (0.000)
Effects of treatment
ATE (average effect of treatment)

0.161
ATT (average effect of treatment to the treated)
0.184
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 demonstrates that the wealth gradient is an
important barrier for utilization and hence influences the child
outcomes. Since maternal healthcare is officially free, the prior
studies explained the effect of wealth gradient by high level of
unofficial out-of-pocket expenditures for healthcare personnel,
supplies, and medication. As a result, the wealthier use
healthcare facilities which the poorer cannot afford. This is in
line with our finding that the wealthier deliver in healthcare
facilities, while the poorer have to deliver outside of healthcare

facilities. while the poorer have to deliver at home. In this
context, one of the promising ways to reduce effect of wealth
gradient to utilization is to introduce the benefits for pregnant
women which could be linked to receipt of targeted social
assistance programs .
Third, our study demonstrates that the risk of not delivering at a
healthcare facility increased for less educated women. Women
with higher education are strongly associated with delivering in
medical settings and hence with higher chances of child
survival. Habibov reported that there is no significant gender
gap in the level of literacy and education in general in
Azerbaijan and concluded that increase in nonacademic
educational activities promoting antenatal care should be a
priority. Habibov and Fan confirmed these conclusions showing
the example of Tajikistan, another transitional country, that
having limited knowledge on matters related to sex is associated
with a lower probability of maternal healthcare utilization. The
authors underlined that significant effect of knowledge about
sex is independent of formal educational level and it persisted
even if formal educational level is controlled for. Effectiveness
of communication campaigns designed to explain the benefits of
maternal healthcare and encourage healthcare utilization is well
documented in developing countries . In addition, intensive
communication campaigns aimed at encouraging healthcare
utilization slowly but steadily became appreciated in some
transitional countries . This positive experience should be
shared across the region.
Finally, the population based nationally representative surveys
such as the Demographic and Health Surveys by Macro
International and Living Standards Measurement Surveys by the
World Bank became an important tool for measuring policy
effect on health outcomes in many transitional and developing
countries . Most of these surveys include modules on healthcare
utilization and childbirth outcome . Having high-quality

microdata to conduct evaluation of healthcare programs is an
effective way to save time, effort, and costs while providing
nationally representative results. From this standpoint, our
results are illustrative to empirical strategies for evaluation of
nonrandomized data from cross-sectional surveys using a
standard statistical software package.
CASE STUDY CHALLENGE
1.
Students should be asked to read the case and discuss all
procedures done and suggest a solution program.
3.
Conclusion (15%)
Briefly summarize your thoughts & conclusion to your critique
of the case study and provide a possible outcome for. The Effect
of Maternal Healthcare on the Probability of Child Survival in
Azerbaijan. How did these articles and Chapters influence your
Maternal Healthcare on the Probability?
Evaluation will be based on how clearly you respond to the
above, in particular:
a) The clarity with which you critique the case study;
b) The depth, scope, and organization of your paper; and,
c) Your conclusions, including a description of the impact of
these Case study on any Health Care Setting
ASSIGNMENT DUE DATE:
The assignment is to be electronically posted no later than noon
on Sunday, September 22, 2019.

Statistic in Health Care Management Assignment Week 3Case Study.docx

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