Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan

Abstract

This study aims to estimate determinants of MM in selected districts of Punjab. Data have been extracted from 196 families from three respective districts. Education, safe water availability, sanitation, health infrastructure, immunization card, family size, residence, household income, and ANC visits are taken as independent variables. Education, family size, poor and middle-income class variables had a positive and significant effect on the MM in DG Khan. This study revealed that education, safe water, and income show positive and significant impact on MM in Chakwal district. While, sanitation variable, area of residence and health infrastructure shown negative and significant impact health. The study found that education, household income and family size had a positive and significant effect on the MM in Sialkot. While, Safe water availability, sanitation, health infrastructure and immunization card have negative and insignificant effect on female health. Government should give more strength to integrated reproductive and newborn child health (IRMNCH) program.

Full text

Global Social Sciences Review (GSSR)
URL:
http://dx.doi.org/10.31703/gssr.2021(VI-I).50
Citation
:
Khan, I. H., Yaseen, M. R., & Anwar, S. (2021). Comparative Analysis of Maternal Mortality in Selected Districts of
Punjab, Pakistan.
Global Social Sciences Review
,
VI
(I), 495-508. https://doi.org/10.31703/gssr.2021(VI-I).50
Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Irfan Hussain Khan *
Muhammad Rizwan Yaseen †
Sofia Anwar ‡
This study aims to estimate determinants of MM in selected
districts of Punjab. Data have been extracted from 196 families
from three respective districts. Education, safe water availability, sanitation,
health infrastructure, immunization card, family size, residence, household
income, and ANC visits are taken as independent variables. Education, family size,
poor and middle income class variables had a positive and significant effect on
the MM in DG Khan. This study revealed that education, safe water and income
show positive and significant impact on MM in Chakwal district. While, sanitation
variable, area of residence and health infrastructure shown negative and
significant impact health. The study found that education, household income and
family size had a positive and significant effect on the MM in Sialkot. While, Safe
water availability, sanitation, health infrastructure and immunization card have
negative and insignificant effect on female health. Government should give more
strength to integrated reproductive and new born child health (IRMNCH)
program.
•
DOI:
10.31703/gssr.2021(VI-I).50
•
Vol.
VI
, No.
I (Winter 2021)
•
Pages:
495 ‒ 508
•
p- ISSN:
2520-0348
•
e-ISSN:
2616-793X
•
ISSN-L:
2520-0348
Key Words:
Maternal Mortality, Health Status, Punjab
Introduction
The health of a nation's people is inextricably linked to
its development. In addition, it is widely recognized as
an essential predictor of a country's economic success
(Sengupta, 2016). Health is more than just the
deficiency of disease or weakness; it also refers to a
state of complete physical, mental, and social well-
being (Kuhn and Rieger, 2017). "The Lancet (2009)"
defined health as the body's capacity to adapt to new
challenges. After the Alma-Ata Declaration was signed
in 1978, the slogan "Health for All" became a signature
motto (Birn and Krementsov, 2018). Thus, one of the
Millennium Development Goals (MDGs) was
developed to increase people's health. In 2015, the
United Nations set 17 life-changing goals for the global
economy's welfare (Judd, 2020). SDG 3 is one of 17
goals that strive to ensure everybody's health and well-
being, comprising a strong commitment to eliminating
AIDS, TB, malaria, and other infectious diseases by
2030. Pakistan is a signatory to the SDGs-2030 and must
accomplish Goal 3: "Well-being for all at all ages" (Aziz
et al
., 2021). Unfortunately, Pakistan is amongst the
South Asian countries confronting the highest maternal
mortality (MM). MM consider as reflection of health
status of any economy. However, Figure 1 identifies
Pakistan as the nation of South Asia that has the highest
maternal death rate than SDGs.
Figure 1:
Maternal Mortality comparison in Asia
Source: Writer’s own constructed on the data from WDI (2017)
*
PhD Scholar, Department of Economics, Government College University Faisalabad, Punjab, Pakistan.
Email: Irfansial007@hotmail.com
†
Associate Professor, Department of Economics, Government College University Faisalabad, Punjab, Pakistan.
‡
Dean & Chairperson, Department of Economics, Government College University Faisalabad, Punjab, Pakistan.
Abstract

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
496
Global Social Sciences Review (GSSR)
In contrast, Pakistan has a total population of
199.1 million people, ranking sixth globally, with
a fertility rate of 3.1, a dependency ratio of 58,
and an aging index of 12.82 (Government of
Pakistan, 2016-2017). “Pregnancy is special, let’s
keep it safe” was the slogan for World Health Day
in 1998. Unfortunately, many women have
terrible health and sometimes die during their
pregnancies. MM refers to the death of pregnant
women between the first day of pregnancy and
40 days following birth (WHO, 2019). It is
measured as the number of maternal deaths per
1 lac pregnant women who are still living. Figure
2 explains the MM number of Punjab as
compared to Pakistan.
Figure 2:
Maternal mortality numbers of Pakistan and Punjab
Source: Author’s own calculation based on the data from NIPS (2019)
In 1990, MM rates in Punjab and Pakistan
were compared. But in 2020, MM numbers in
Punjab are still higher than in Pakistan. So, Punjab
has the highest MM numbers in Pakistan.
Maternal health outcomes are more related to
pregnancy and child birth outcomes, including
miscarriage, prenatal abortion, stillbirth, and
maternal death. MM remains the main cause of
death among women in developing countries
(Aziz
et al.
, 2020). Maternal health is
unidirectional and bidirectional related to the
health of an economy (Centers for Disease
Control, 1999; Ki-Moon, 2010,). So it is
concluded that MM is the fundamental problems
in Punjab, Pakistan, which should be addressed.
So this study aims to investigate factors of MM in
selected districts of Punjab, Pakistan. Existing
study filled the research gap by estimating
important socio-economic factors influencing
MM at the regional level.
Material and methods
The random sampling technique has been used
for selecting 3 districts in Punjab, DG Khan,
Chakwal, and Sialkot. A short description of
these three districts is given below:
DG Khan
In the southwest of Pakistan, the DG Khan region
is located. There are two major cities, DG Khan
and Taunsa. According to the MICS survey, DG
Khan having 54 IM numbers, and 60 deaths per
1000 alive birth, which is well short of SDGs
standards. So, because of large numbers in MM,
DG Khan is taken into this study.
Chakwal
The boundary between Chakwal districts
comprises Rawalpindi and Attock's northern
regions, in the east Jhelum, in the southern areas
Khushab, and the west Mianwali. While Chakwal
has a maternal death rate of 276 per 100,000
people (department of CEO, DHA, Chakwal).
Sialkot
Sialkot is a 354-square-kilometer areas district
that stretches from the Ravi Valley in the
southeast to the Chenab River in the northwest.
According to the MICS report, Sialkot has an IM
of 55, whereas Punjab has 60 deaths per 1000 live
births (department of CEO, DHA, Sialkot).

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
497
Data Collection and Sources
The data was collected by questionnaires. The
questionnaire that was supposed to capture the
data was field-tested. The questionnaire was
updated, completed, and processed in light of
the pre-testing results. The required data was
then collected by interviewing 196 household
respondents where maternal death have been
occurred in one year (1 January, 2018 to 31
december, 2018. We examined the following
sources of data information for acquiring
observed information for study:
1) Monthly basis health data (DHIS). 2)
Tehsil-level municipal administration, 3)
Maternal and newborn child integrated
program (IRMNCH) data. 4) Union council
level (Local Government Center) data
Public, private clinics from three districts.
Econometric Model
The following model is used in this study to find
determinants of maternal mortality:
!!!!
MM=
f
(EduM, SW, SN, HIF, IC, FS, HI, ANC,
AR)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(1)
Model Specification and Data
The model for determinants of health status in
the Punjab district has the following econometric
specification and functional form.
𝑀𝑀 = 𝑓
(
𝐸𝐷𝑈𝑀𝑤,𝑆𝑊,𝑆𝑁,𝐻𝐼𝐹,𝐼𝐶,𝐹𝑆,𝐴𝑅,𝐻𝐼
)
4444444(2)
MM=
𝛽!
+
𝛽!𝐸𝐷𝑈𝑀𝑤 + 𝛽"𝑆𝑊 + 𝛽#𝑆𝑁 + 𝛽$𝐻𝐼𝐹 +
𝛽%𝐼𝐶 + 𝛽&𝐹𝑆 + 𝛽'𝐴𝑅 + 𝛽(𝐻𝐼 + 𝜇
(3)
Table 1.
Variables and its Description
Table 1 summarizes the descriptions of all variables utilized in the study.
Variables
Abbreviation
Description of Variables
Dependent Variables
Maternal Mortality
MM
=1 for non-occurrence of maternal death during maternal
life
= 0 for occurrence of maternal death during maternal life
Independent Variables
Female education
EDUM
=1 Primary education
=2 Secondary education
=3 Higher education
Safe Water
SW
=1 if safe water is available
=0 if safe water is not available
Sanitation
SN
=1 if washroom facility is available
=0 if washroom facility is not available
Health
Infrastructure
HIF
=1 if there is any health facility available near household
=0 If no health facility is not available near household
Immunization card
IC
=1 if immunization card is available
=0 if immunization card is not available
Family size
FS
Total numbers of family members of the household.
Residence
AR
=1 if household is situated in urban area
=0 if household is situated in rural area
Household Income
HI
=1 Poor income class
=2 Middle income class
=3 Higher income class
Antenatal Care
ANC
=1 if maternal women perform at least 2 ANC visits per
month.
=0 If maternal women perform less 2 ANC visits per month
Methodology: Logistic Regression
For describing health status, an existing study
uses logistic and multinomial techniques. The
influence of socioeconomic factors of household
variables on health status is determined using
logistic regression. In addition, multivariate
analysis is being used, and the general function is
as follows.
𝑌𝑖 = !𝑓
(
𝑋!
)
𝑖 = (1,2,3 … … 𝑛)!!!!!!!!!!!!!!!!!!!!(4)
Where:

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
498
Global Social Sciences Review (GSSR)
Y
i
describes the Health Status
X
i
describes different independent variables
When dependent variable is in binary category
while independent variables is in binary form, or
continuous. (Starkweather and Moske, 2011).
Equation 5 explains the logistic equation from
simple linear regression, where “Y” is considered
as dependent variable.
𝑌𝑖 = ! 𝛼)+ 𝛼!𝑋!* + µ!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(5)
Where:
Yi denotes the dependent variable,
𝑎𝑜
and
𝑎1
are
used as intercept and slope, while Xi represents
independent variables.
𝑌
*= 𝛼)+ 𝛼!𝑋!* + 𝛼"𝑋"* + ⋯ + 𝛼+𝑋+* + 𝜀*
(6)
Logistic regression is similar to the ordinary least
square (OLS). For example, equation 7 explains
that if there is only one independent variable X1,
we can construct the probability of “Y”.
P(Y) =
!!
!,-!"#$%#&'&()
(7)
For equation 7, P(Y) describes the occurrence of
“Y” based on natural logarithm explained by “ ”
Logistic regression and linear regression have
many similarities, due to binary or categorical
form of nature, we cannot apply the linear
regression (Mohammadi
et al
., 2014). In logistic
regression as the dependent variables is
categorical or binary form, the condition of
linearity cannot apply. So we can transforms the
non-linear form by taking log of the equation.
Results and Discussion
Descriptive Analysis
Table 2 presents an estimate of the association
between several socio-economic variables and
MM in three Punjab districts chosen for study.
Table 2.
Distribution of Maternal Outcome by District wise
District
Maternal Mortality
Maternal Alive
Total
DG Khan
42
42
84
Chakwal
30
30
60
Sialkot
26
26
52
Region of Residence and Maternal Mortality
Maternal mortality is influenced by the area or
region in which a woman lives. As previously
stated, there is a lower likelihood of MM in urban
areas. More health-care facilities, such as
hospitals, 24-hour delivery systems, and other
emergency health services, are available in urban
areas. Therefore, MM and residence are
expected to be highly correlated.
Table 3.
Distribution of Maternal Outcome by Region of Residence
Region
Maternal Mortality
Maternal Alive
Total
Rural
66
(81.48)
[67.35]
32
(27.83)
[32.65]
98
(50.00)
[100]
Urban
15
(18.52)
[15.31]
83
(72.17)
[84.69]
98
(50.00)
[100]
Total
81
(100)
[41.33]
115
(100)
[58.67]
196
(100)
[100]
Source: Author’s calculations based on Survey
Table 3 divides the responses of the
maternal women is divided by area of residence.
For example, the table describes that MM in rural
areas is 81.48% while this MM in urban areas is
18.52%. A big difference in MM between urban
and rural areas reveals that the MM rate in rural
areas is very high compared to urban areas.
Family size and Maternal Mortality
The size of the household also has an impact on
e

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
499
MM. The higher the family size, the greater the
MM chances. Table 4 shows the relationship
between MM outcome and family size. When
family size lies from (1-4), 21.57 % of MM women
involve out of the total maternal outcome. On the
other hand, the family size (9-12) person causes
increases the MM up to 60.87%. Moreover, family
member size (13-16) boosts the MM up to
78.18%.
Table 4.
Distribution of Maternal Outcome by family Size
Size of HH
Maternal Mortality
Maternal Alive
Total
1-4
11
(11.22)
[21.57]
40
(40.82)
[78.43]
51
(26.02)
[100]
5-8
16
(16.33)
[36.36]
28
(28.57)
[63.64]
44
(22.45)
[100]
9-12
28
(37.66)
[60.87]
18
(17.5)
[39.13]
46
(15.92)
[100]
13-16
43
(3.94)
[78.18
12
(2.10)
[21.82]
55
(1.70)
[100]
Total
98
(65.21)
[50.00]
98
(89.38)
[100.00]
196
(66.09)
[100.00]
Source: Author own calculations constructed through Survey
Education and Maternal Mortality
Table 5 reveals that education is the most vital
determinant influencing the MM in all aspects. An
illiterate woman is more likely to face the MM.
Education considering as a source of self-
development, is closely linked with MM. Higher
the education level, less probability of MM
prevails.
Table 5.
Distribution of Maternal Outcome by Maternal Education
Maternal Education
Maternal Outcome
Total
Maternal Mortality
Maternal Alive
Primary Education
41
(45.56)
[64.06]
23
(21.70)
[35.94]
64
(32.65)
[100]
Secondary Education
29
(32.22)
[45.31]
35
(33.02)
[54.69]
64
(32.65)
[100]
High Education
20
(41.01)
[29.41]
48
(35.85)
[70.59]
68
(15.92)
[100]
Total
90
(118.79)
[45.92]
106
(90.57)
[117.78]
196
(81.23)
[100.00]
Source: Author own calculations constructed through Survey
The relationship between maternal education
and MM is explained in Table 5. An primary
educated woman is more likely to face MM 64.06
% higher than higher educated women. The
likelihood of MM decreases as one's educational
level rises. Educated women can better care for
their health, thus reducing the MM.

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
500
Global Social Sciences Review (GSSR)
Empirical Analysis
Table 6 explains the results of logit model for measuring the effects of determinants of MM in DG
Khan.
Table 6.
Determinants of Maternal Mortality in DG Khan
Dependent Variable: Maternal Mortality
Independent Variables
Coef.
S.E.
Wald
Df
Sig.
Exp(B)
[Overall Education]
[Primary=1.00]
[Secondary Class=2.00]
2.994
3.604
1.268
1.295
7.821
5.575
7.741
2
1
1
.020
.018
.005
19.973
36.757
Safe Water (SW)
-2.029
0.815
6.194
1
0.013
0.131
Sanitation (SN)
-0.040
1.003
0.002
1
0.968
0.961
Health Infrastructure (HIF)
-0.322
0.234
1.894
1
0.169
0.725
Immunization card (IC)
-1.576
1.071
2.164
1
0.141
0.207
Family Size (FS)
1.137
0.403
7.962
1
0.005
3.117
Area of residence (AR)
-3.415
1.919
3.169
1
0.075
0.033
Overall Income
[Poor Class=1.00]
[Middle Class=2.00]
2.380
1.104
1.185
1.025
4.173
4.032
1.160
2
1
1
0.124
0.045
0.282
10.809
3.017
Antenatal Care (ANC)
-0.810
0.397
4.176
1
0.041
0.207
Constant
0.179
3.854
0.002
1
0.963
1.196
A Variable(s) entered on step 1: Femaledu, HHincome, residence, safewater, imcard, fs,
Healthinfrastructure, sanitation.
Source: Author own calculation, using SPSS version 23
Table 7 describes the Logistic Regression
analysis for the determinants of MM in DG Khan
district. Primary educated women have more
chances of having MM as compared to higher
educated women. Women with no education
have a higher risk of MM, whereas women with
more education have a lower risk of MM.
Increasing women's schooling years lowered the
risk of several maternal health problems during
pregnancy/birth by up to 29%. Raising women's
education appears to reduce short birth intervals
and unplanned pregnancies. On the other hands,
women having low education more chances of
maternal mortality. Primary level women have
less knowledge regarding their health care and
ANC care. It is possibly due to changes in
women's cognitive skills, economic resources,
and independence. So there is less likelihood of
MM (Karlsen
et al
., 2011).
Secondary educated women have more
chances of having MM as compared to higher
educated women. Education and maternal
health have positive relationship among them. As
education level increase it causes positive
impact on maternal and decrease in mortality
(Thaddeus and Maine, 1991; Shen and
Williamson, 1999).
Women having better safe water facility have
less chances of having MM as compared to
women without safe water facility. Safe water
facility have positive impact on maternal health.
But on the other hands, WHO report on water,
sanitation and hygiene also endorse that water
quality is poor and toxic. It can influence
maternal women’s health negatively. Poor water
is highly linked with the MM (Golding
et al
., 1989;
Benova
et al
., 2014; WHO, WASH, 2015).
As household having good sanitation facility
have less chances of having MM as compared to
household without sanitation facility. If a
bathroom facility is available, it is connected
with a lower risk of MM. The researchers (Cheng
et al.,
2012) also endorsed that sanitation facility
availability increases the maternal women’s
survival rate.
Households having health infrastructure
have less chances of MM as compared to people
have no access to health infrastructure. A good
health infrastructure availability like; medicine
and health care services and road distance can
bring down the MM rates. Contrarily, decision-
making delays in health care treatment provision
and deficient health facilities result in a high MM

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
501
numbers (Khan and Pradhan, 2013; Hanson
et al.
,
2015).
Immunization includes TT vaccination
during pregnancy, if women have TT vaccination
during pregnancy have less chances of maternal
mortality as compared with women have partial
or no vaccination of immunization. Therefore,
increases in women’s vaccination may decrease
the likelihood of death occurrence. WHO also
endorse that if immunization increases in the
pregnancy period, there will be less chances of
MM numbers (Singh
et al
., 2012; Giles
et al
.,
2018).
As family size increase, households have
more chances of having MM as compared to
women have small family size. Therefore, if there
is an increase in the number of children, decline
the resource allocated to mothers and affects
their general health outcomes. If women
pregnant again and again more chance of
mortality (Wu and Li, 2012; World Health
Organization, 2019).
As female belong from urban area having
less chances of MM as compared to women who
belong from rural areas. While, urban areas have
additional facilities than rural areas in DG Khan,
so fewer maternal death chances are less than in
rural areas. In urban areas, women are educated
have more knowledge and awareness regarding
ANC and medical care. Women living in rural
areas have been considered related to
inadequate ANC facilities linked to living in the
urban area (Naseem
et al
., 2017; Hanif
et al.
,
2021).
Female from poor economic status have
more chances of MM as compare to rich female.
The poor have more chances of maternal
mortality as compared to rich. Thus, income
surges the lesser chances of MM; on the other
side, reducing annual income and expenditure
can increase maternal death. Our results are
matched with the study (Wang
et al
., 2003).
Middle class female also show more
chances of MM as compare to high income
women. The positive value which shows that
middle income women have more chances of
maternal mortality as compared to the rich
(Jeong
et al
., 2020).
As ANC visit increase, women have less
chances of death as compared to women have
less numbers of ANC during pregnancy. If
women have seven ANC visits according to WHO
standards, there is less chance of MM. ANC is
considered as the best therapy for maternal
women as well as for an upcoming child. Noh
et
al.
(2019) and Kaaya
et al
. (2021) also support the
results of this study.
The model summary is given as follows:
Table 7.
Model Summary for DG Khan District for MM
-2 Log likelihood
52.154
Pseudo R square
0.535
a Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.
Table 8 describes the results of logit model for
calculating the effects of socio-economic
variables on maternal mortality in Chakwal
district.
Table 8.
Determinants of Maternal Mortality in Chakwal
Dependent Variable: Maternal Mortality
Independent Variables
Coef.
S.E.
Wald
Df
Sig.
Exp(B)
[Overall Education]
[Primary=1.00]
[Secondary Class=2.00]
1.420
2.598
1.263
1.225
4.672
1.264
4.498
2
1
1
0.097
0.261
0.034
4.138
13.441
Safe Water (SW)
2.885
1.079
7.152
1
0.007
17.906
Sanitation (SN)
-2.663
1.416
3.537
1
0.060
0.070
Health Infrastructure (HIF)
-0.710
0.317
5.030
1
0.025
0.492
Immunization card (IC)
-0.640
0.837
0.585
1
0.444
0.527
Family Size (FS)
0.157
0.274
0.328
1
0.567
1.170
Area of residence (AR)
-7.194
2.782
6.685
1
0.010
0.001
Overall Income
[Poor Income=1.00]
3.849
-0.719
1.563
1.013
8.095
6.062
2
1
0.017
0.014
46.931
0.487

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
502
Global Social Sciences Review (GSSR)
Dependent Variable: Maternal Mortality
Independent Variables
Coef.
S.E.
Wald
Df
Sig.
Exp(B)
[Middle Income=2.00]
0.503
1
0.478
Antenatal Care (ANC)
-0.339
0.360
0.886
1
0.347
0.713
Constant
8.313
4.095
4.121
1
0.042
4076.191
a Variable(s) entered on step 1: Femaledu, HHincome, residence, safewater, imcard, fs,
Healthinfrastructure, sanitation.
Source: Author calculation, using the SPSS version 23.
Table 9 describes the Logistic Regression
analysis for the determinants of MM in the
Chakwal district. Primary educated women have
more chances of having MM as compared to
higher educated women in Chakwal (McAlister
and Baskett, 2006; Karlsen
et al.,
2011) also
indicates and endorse that, increase in the level
of education reduces the MM risk.
Secondary educated women have more
chances of having MM as compared to higher
educated women. The positive value of
coefficient describes that they have 2.598 more
chances of MM as compared with higher
education female (McAlister and Baskett, 2006).
Women having better safe water facility have
less chances of having MM as compared to
women without safe water facility. Unsafe water
is associated with maternal health; it causes
several water-borne diseases that ultimately
cause maternal death (Gould, 2010; Cheng
et al
.,
2012).
People having efficient sanitation facility
have less chances of having MM as compared to
people without sanitation facility. WASH
interventions may further increase the health and
well-being of women. An increase in the water
and sanitation facility makes chance less of
maternal death, which is endorsed by (Benova
et
al
., 2014; Komarulzaman
et al.
, 2017)
Households having access to health
infrastructure have less chances of MM as
compared to people have no access to health
infrastructure. Thus, increases in the health
infrastructure, health services, ANC facilities
show a positive association with maternal health.
In addition, (Gao and Kelley, 2019;
Phommachanh
et al.,
2019) also support the
results of this study.
Immunization includes TT vaccination
during pregnancy, if women have TT vaccination
during pregnancy have less chances of maternal
mortality as compared with non-availability of
immunization card. An increase in the
immunization vaccine during pregnancy causes a
positive impact on maternal women.
Furthermore, TT vaccine is very helpful during
pregnancy, which WHO has suggested in many
countries (Pan American Health Organization,
2017).
As family size increase, households have
more chances of having MM as compared to
women have small family size. Furthermore,
women having good family relationships have
more chances to use maternal health care, deliver
in a health facility, more chances of survival of
maternal women (Allendorf, 2010).
As female belong from urban area having
less chances of MM as compared to women who
belong from rural areas. Thus, there is less
likelihood of MM in urban areas where health
facilities are higher as compared to rural areas.
Kozhimannil
et al
. (2020) also support this
study’s results.
Female from poor economic status have
more chances of MM as compare to rich female.
The poor have more chances of maternal
mortality as compared to rich. Thus, high-income
inflows lead towards the availability of high
educational facilities and more health facilities
for a household. The researchers (Jeong
et al
.,
2020) also endorsed that high household income
plays its part in reducing the MM.
Middle class female show less chances of
MM as compare to high income women. The
positive value which shows that middle income
women have more chances of maternal mortality
as compared to the rich in Chakwal district
(Jeong
et al
., 2020).
As ANC visit increase, women have less
chances of death as compared to women have
less numbers of ANC visit during pregnancy. As
increases in the ANC visit improves the maternal
health and reduces maternal death risk. ANC is
acting as physical therapy during the pregnancy
period. Our results are parallel with the study
(Das, 2017).
The model summary is given as follows:

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
503
Table 9.
Model Summary for Chakwal district for MM
-2 Log likelihood
40.871
Pseudo R square
0.506
a Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.
The table 10 tells results of logit model for calculating determinants of the MM in Sialkot
Table 10.
Determinants of Maternal Mortality in Sialkot
Dependent Variable: Maternal Mortality
Independent Variables
Coef.
S.E.
Wald
Df
Sig.
Exp(B)
[Overall Education]
[Primary=1.00]
[Secondary Class=2.00]
4.308
4.300
1.921
1.872
6.001
5.029
5.276
2
1
1
0.050
0.025
0.022
74.274
73.667
Safe Water (SW)
-0.105
1.281
0.007
1
0.935
0.900
Sanitation (SN)
-2.195
1.216
3.257
1
0.071
0.111
Health Infrastructure (HIF)
-0.260
0.224
1.343
1
0.246
0.771
Immunization card (IC)
-2.531
1.791
1.998
1
0.158
0.080
Family Size (FS)
0.910
0.688
1.747
1
0.186
2.483
Area of residence (AR)
-1.364
1.855
0.540
1
0.462
0.256
Overall Income
[Poor Income=1.00]
[Middle Income=2.00]
5.603
5.414
2.356
2.642
5.811
5.657
4.199
2
1
1
0.055
0.17
0.040
271.287
224.632
Antenatal Care (ANC)
-2.139
0.855
6.257
1
0.012
0.118
Constant
0.235
5.038
0.002
1
0.963
1.265
a Variable(s) entered on step 1: Femaledu, HHincome, residence, safewater, imcard, fs,
Healthinfrastructure, sanitation.
Source: Author own calculation, using the SPSS version 23.
Table 11 describes the Logistic Regression
analysis for the factors of MM in the Sialkot
district. Primary educated women have more
chances of having MM as compared to higher
educated women in Sialkot district. There is a
clear link between maternal health and women's
education. In comparison to illiterate women,
educated women might seek better health. In
addition, the number of years spent in education
instigates the probability that maternal women
will have a better chance of survival as compared
to maternal mortality (Karlsen
et al.,
2011).
Secondary educated women have also more
chances of having MM as compared to higher
educated women. Low education level are linked
with higher maternal mortality (Karlsen
et al
.,
2011).
Household having better safe water facility
have less chances of having MM as compared to
people without safe water facility The
researchers (Semmelweis, 1983; Karlsen
et al
.,
2011; Benova
et al.,
2014) also supported that
educated maternal women can better care for
themselves, their diet, and nutrition level. Thus,
educated maternal women may have fewer
chances of MM.
People having better sanitation facility have
less chances of occurrence MM as compared to
people without sanitation facility. The findings
show that improving sanitary facilities has a
positive impact on maternal women. The data
reported in (WHO, UNICEF, 2012) reports also
support that, increase in sanitation facilities
result in decreased MM (Tomasz, 2009; Campbell
et a
l., 2015).
Households having health infrastructure
have less chances of non-occurrence of MM as
compared to people have no access to health
infrastructure. Health infrastructure is a broad
term that encompasses health-related services,
medicine availability, and the presence of
medical personnel. Therefore, increases in the
health structure, skill birth attendance, and
health professionals can reduce the risk of MM
(Nesbitt
et al.
, 2016; McGuire
et al
., 2021).
Immunization includes TT vaccination
during pregnancy, if women have TT vaccination
during pregnancy have less chances of

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
504
Global Social Sciences Review (GSSR)
occurrence of maternal mortality as compared
with women have partial or no vaccination of
immunization. Vaccines may keep women
healthy and active, and as immunization rates
rise, their risk of death falls. Similarly, TT
immunization is one of the tried-and-true
methods for eradicating maternal and neonatal
tetanus during pregnancy (Mamoro and Hanfore,
2018).
As family size increase, households have
more chances of having MM as compared to
women having small family size. Women having
large family size have more chance of MM
(Allendorf, 2010; Bucher-Koenen
et al
., 2020).
As female belong from urban area having
less chances of occurrence of MM as compared
to women who belong to rural areas.
Furthermore, Midhet
et al
. (1998) and
Kozhimannil
et al
. (2014) also endorsed that
compared to urban areas, where rural areas have
more chances of MM owing to a lack of facilities
such as low-grade clinics.
Female from poor economic condition have
more chances of MM as compare to rich female.
The poor have more chances of maternal
mortality as compared to rich. One risk factor for
MM is a woman's socioeconomic status. Low
levels of income have a negative influence on
maternal health. Mother and Mother, (2012) also
supported that maternal women having lower
household income confront more occurrence of
MM than the higher-income women.
Middle income class females also have more
chances of MM as compared to high income
women. Income have positive and significant
impact on the health of maternal women (Mother
and Mother, 2012).
As ANC visit increases, women have less
chances of occurrence of maternal death as
compared to women having less numbers of
ANC visit during pregnancy. Thus, ANC
positively affects maternal health since it
improves mother health and decreases maternal
mortality. ANC refers to the specific medical
therapy care that a pregnant woman receives
from skilled healthcare providers to sustain a
healthy pregnancy (Das, 2017; Ogu and
Alegbeleye, 2018).
The model summary is given as follows:
Table 11.
Model Summary for Sialkot district in MM
-2 Log likelihood
27.214
Pseudo R square
0.578
a Estimation terminated at iteration number 7 because parameter estimates changed by less than .001
Conclusion
Maternal mortality is used as a health status
indicator in existing study. Variables like female
education, safe water availability, sanitation,
health infrastructure, immunization card, family
size, residence, household income, and ANC
visit have influence on health status. The study
found that education, family size, poor and
middle income class variables had a positive and
significant effect on the MM in DG Khan.
However, safe water, sanitation facility,
immunization card, area of residence, health
infrastructure and ANC visit had a negative and
insignificant effect on health status.
This study revealed that primary educated
female category and family size can cause
positive and insignificantly impact the health
status, but middle educated female, safe water
and poor income categories show positive and
significant impact. While, sanitation, area of
residence and health infrastructure show
negative and significant impact health.
Furthermore, immunization card, middle income
and ANC visit show negative and insignificant
impact on health in Chakwal.
This study found that Sialkot is the highest
income category district. The study found that
education, household income and family size
had a positive and significant effect on the MM in
Sialkot. Safe water availability, sanitation, health
infrastructure and immunization card have
negative and insignificant effect on female
health.
Policy and Suggestions
The study recommends various policy and
recommendation suggestions for future
perspectives based on the findings.

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
505
• In many rural regions of each district, the
mobile clinic should be launched since
DG Khan has large rural areas with poor
conditions for maternal mortality.
• There should be free mobile health care
for each child and maternal woman in all
districts.
• Refresher training programs should be
conducted for nurses, lady doctors, and
paramedical staff for technological up-
gradation of the safe delivery system.
• Maternal women should be provided with
a free supply of folic acid pills.
• Government should develop a dedicated
policy on reducing infant and maternal
mortality with all other connected
departments.
• Government should seek digital software
to record new pregnancies and newborn
infants.
• Government should initiate women's
education awareness initiatives.
• Women should be empowered to respect
their fundamental human rights, including
access to health care services
• Local government and project planners
should take initiatives to provide movable
toilet blocks built on more stable areas
where there are more feasible options for
the treatment of waste and sanitation.
• Community involvement in selecting and
designing the water and sanitation
facilities.
• Encourage water treatment at the point of
use.
• Promote a sanitation package in each
region for vulnerable households.
• Promote hygiene education for maternal
women, especially in each district.
• Promote water and sanitation social
marketing strategies in each district
• In the backward district households,
promote patient-friendly pit latrines.
• Encourage subsidies to sanitation
platforms in low-income districts for
vulnerable households.
• Encourage the utilization of locally
available materials for the construction of
sanitation and hygiene facilities.
• Government should provide micro fiancé
loaning to newly young couple that will
further helpful for upcoming children.
• Government should launch new RO plant
for safe drinking water.
• Government should give more strength to
integrated reproductive and new born
child health (IRMNCH) program that is
total related with the maternal health.

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
506
Global Social Sciences Review (GSSR)
References
Allendorf, K. (2010). The Quality of Family
Relationships and Use of Maternal Health-
care Services in India.
Studies in Family
Planning
,
41
(4), 263–276.
https://doi.org/10.1111/j.1728-
4465.2010.00252.x
Asim, M., & Nawaz, Y. (2018). Child Malnutrition
in Pakistan: Evidence from Literature.
Children
,
5
(5), 60.
https://doi.org/10.3390/children5050060.
Aziz, F., Tahir, F., & Qureshi, N. A. (2021).
Millennium development goals (MDGs-
2000-2015) to sustainable development
goals (SDGs-2030): a chronological
landscape of public sector health care
segment of Pakistan.
Journal of the Pakistan
Medical Association
,
71
(2), 1–17.
https://doi.org/10.47391/jpma.394.
Benova, L., Cumming, O., & Campbell, O. M. R.
(2014). Systematic review and meta-
analysis: association between water and
sanitation environment and maternal
mortality.
Tropical Medicine & International
Health
,
19
(4), 368–387.
https://doi.org/10.1111/tmi.12275.
Birn, A. E., & Krementsov, N. (2018). ‘Socialising’
primary care? The Soviet Union, WHO and
the 1978 Alma-Ata Conference.
BMJ Global
Health
,
3
(Suppl 3), e000992.
https://doi.org/10.1136/bmjgh-2018-
000992.
Bove, F., Shim, Y., & Zeitz, P. (2002). Drinking
water contaminants and adverse pregnancy
outcomes: a review.
Environmental Health
Perspectives
,
110
(suppl 1), 61–74.
https://doi.org/10.1289/ehp.02110s161.
Bucher-Koenen, T., Farbmacher, H., Guber, R., &
Vikström, J. (2020). Double Trouble: The
Burden of Child-rearing and Working on
Maternal Mortality.
Demography
,
57
(2),
559–576. https://doi.org/10.1007/s13524-
020-00868-6
Campbell, O. M. R., Benova, L., Gon, G., Afsana,
K., & Cumming, O. (2015). Getting the basic
rights – the role of water, sanitation and
hygiene in maternal and reproductive
health: a conceptual framework.
Tropical
Medicine & International Health
,
20
(3),
252–267. https://doi.org/10.1111/tmi.12439.
Centers for Disease Control and Prevention
(CDC. (1999). Ten great public health
achievements--United States, 1900-
1999. MMWR.
Morbidity and mortality
weekly report, 48(12), 241-243.
Cheng, J. J., Schuster-Wallace, C. J., Watt, S.,
Newbold, B. K., & Mente, A. (2012). An
ecological quantification of the
relationships between water, sanitation and
infant, child, and maternal mortality.
Environmental Health
,
11
(1), 1–8.
https://doi.org/10.1186/1476-069x-11-4.
Das, A. (2017). Does antenatal care reduce
maternal mortality?
Mediscope
,
4
(1), 1–3.
https://doi.org/10.3329/mediscope.v4i1.34
372.
Gao, X., & Kelley, D. W. (2019). Understanding
how distance to facility and quality of care
affect maternal health service utilization in
Kenya and Haiti: A comparative geographic
information system study.
Geospatial
Health
,
14
(1).
https://doi.org/10.4081/gh.2019.690.
Giles, M. L., Krishnaswamy, S., & Wallace, E. M.
(2018). Maternal immunisation: What have
been the gains? Where are the gaps? What
does the future hold?
F1000Research
,
7
,
1733.
https://doi.org/10.12688/f1000research.154
75.1
Golding, J., Ashley, D., McCaw-Binns, A.,
Keeling, J. W., & Shenton, T. (1989).
Maternal Mortality in Jamaica:
Socioeconomic Factors.
Acta Obstetricia et
Gynecologica Scandinavica
,
68
(7), 581–
587.
https://doi.org/10.3109/0001634890901327
4.
Gould, I. M. (2010). Alexander Gordon,
puerperal sepsis, and modern theories of
infection control—Semmelweis in
perspective.
The Lancet Infectious
Diseases
,
10
(4), 275–278.
https://doi.org/10.1016/s1473-
3099(09)70304-4.
Hanif, M., Khalid, S., Rasul, A., & Mahmood, K.
(2021). Maternal Mortality in Rural Areas of
Pakistan: Challenges and Prospects.
Rural
Health [Working Title]
. Published.
https://doi.org/10.5772/intechopen.96934.
Hanson, C., Cox, J., Mbaruku, G., Manzi, F.,
Gabrysch, S., Schellenberg, D., Tanner, M.,
Ronsmans, C., & Schellenberg, J. (2015).
Maternal mortality and distance to facility-
based obstetric care in rural southern

Comparative Analysis of Maternal Mortality in Selected Districts of Punjab, Pakistan
Vol. VI, No. I (Winter 2021)
507
Tanzania: a secondary analysis of cross-
sectional census data in 226 000
households.
The Lancet Global Health
,
3
(7),
e387–e395. https://doi.org/10.1016/s2214-
109x(15)00048-0.
Jeong, W., Jang, S. I., Park, E. C., & Nam, J. Y.
(2020). The Effect of Socioeconomic Status
on All-Cause Maternal Mortality: A
Nationwide Population-Based Cohort
Study.
International Journal of
Environmental Research and Public Health
,
17
(12), 4606.
https://doi.org/10.3390/ijerph17124606.
Judd, A. (2020). Sustainable development goal
3: ensure healthy lives and promote well-
being for all at all ages.
Kaaya, E. S., Ko, J., & Luhanga, E. (2021).
Maternal knowledge-seeking behavior
among pregnant women in Tanzania.
Women’s Health
,
17
.
https://doi.org/10.1177/1745506521103844
2.
Karlsen, S., Say, L., Souza, J. P., Hogue, C. J.,
Calles, D. L., Gülmezoglu, A. M., & Raine, R.
(2011). The relationship between maternal
education and mortality among women
giving birth in health care institutions:
Analysis of the cross sectional WHO Global
Survey on Maternal and Perinatal Health.
BMC Public Health
,
11
(1), 1–10.
https://doi.org/10.1186/1471-2458-11-606
Khan, N., & Pradhan, M. R. (2013). Identifying
Factors Associated with Maternal Deaths in
Jharkhand, India: A Verbal Autopsy Study.
Journal of Health, Population and Nutrition
,
31
(2), 262–271.
https://doi.org/10.3329/jhpn.v31i2.16391.
Ki-Moon, B. (2010).
Global strategy for women’s
and children’s health
. New York: United
Nations.
Komarulzaman, A., Smits, J., & de Jong, E.
(2017). Clean water, sanitation and
diarrhoea in Indonesia: Effects of
household and community factors.
Global
Public Health
,
12
(9), 1141–1155.
https://doi.org/10.1080/17441692.2015.112
7985.
Kozhimannil, K. B., Interrante, J. D., Tofte, A. N.,
& Admon, L. K. (2020). Severe Maternal
Morbidity and Mortality Among Indigenous
Women in the United States.
Obstetrics &
Gynecology
,
135
(2), 294–300.
https://doi.org/10.1097/aog.0000000000003
647.
Kühn, S., & Rieger, U. M. (2017). Health is a state
of complete physical, mental and social
well-being and not merely absence of
disease or infirmity.
Surgery for Obesity and
Related Diseases
,
13
(5), 887.
https://doi.org/10.1016/j.soard.2017.01.046.
Lancet. (2009).
The Lancet Publisher
.
Liang, J., Dai, L., Zhu, J., Li, X., Zeng, W., Wang,
H., Li, Q., Li, M., Zhou, R., & Wang, Y. (2011).
Preventable maternal mortality:
Geographic/rural-urban differences and
associated factors from the population-
based maternal mortality surveillance
system in China.
BMC Public Health
,
11
(1),
1–9. https://doi.org/10.1186/1471-2458-11-
243.
Mamoro, M. D., & Hanfore, L. K. (2018). Tetanus
Toxoid Immunization Status and
Associated Factors among Mothers in
Damboya Woreda, Kembata Tembaro
Zone, SNNP, Ethiopia.
Journal of Nutrition
and Metabolism
,
2018
(6), 1–9.
https://doi.org/10.1155/2018/2839579.
McAlister, C., & Baskett, T. F. (2006). Female
Education and Maternal Mortality: A
Worldwide Survey.
Journal of Obstetrics
and Gynaecology Canada
,
28
(11), 983–990.
https://doi.org/10.1016/s1701-
2163(16)32294-0.
McGuire, F., Kreif, N., & Smith, P. C. (2021). The
effect of distance on maternal institutional
delivery choice: Evidence from Malawi.
Health Economics
,
30
(9), 2144–2167.
https://doi.org/10.1002/hec.4368.
Midhet, F., Becker, S., & Berendes, H. W. (1998).
Contextual determinants of maternal
mortality in rural Pakistan.
Social Science &
Medicine
,
46
(12), 1587–1598.
https://doi.org/10.1016/s0277-
9536(97)10137-x.
Mohammadi, F., Bavaghar, M. P., & Shabanian,
N. (2014). Forest Fire Risk Zone Modeling
Using Logistic Regression and GIS: An
Iranian Case Study.
Small-Scale Forestry
,
13
(1), 117–125.
https://doi.org/10.1007/s11842-013-9244-4.
Naseem, S., Khattak, U. K., Ghazanfar, H., &
Irfan, A. (2017). Maternal health status in
terms of utilisation of antenatal, natal and
postnatal services in a Periurban setting of
Islamabad: A community based survey.
J
Pak Med Assoc, 67
(8), 1186-1191.
National Institute of Population Studies (NIPS)
[Pakistan] and ICF. (2019). 2017-18 Pakistan

Irfan Hussain Khan, Muhammad Rizwan Yaseen and Sofia Anwar
508
Global Social Sciences Review (GSSR)
Demographic and Health Survey Key
Findings. Islamabad, Pakistan, and
Rockville, Maryland, USA: NIPS and ICF.
Nesbitt, R. C., Lohela, T. J., Soremekun, S., Vesel,
L., Manu, A., Okyere, E., Grundy, C.,
Amenga-Etego, S., Owusu-Agyei, S.,
Kirkwood, B. R., & Gabrysch, S. (2016). The
influence of distance and quality of care on
place of delivery in rural Ghana.
Scientific
Reports
,
6
(1), 1–8.
https://doi.org/10.1038/srep30291.
Noh, J. W., Kim, Y. M., Lee, L. J., Akram, N.,
Shahid, F., Kwon, Y. D., & Stekelenburg, J.
(2019). Factors associated with the use of
antenatal care in Sindh province, Pakistan:
A population-based study.
PLOS ONE
,
14
(4), e0213987.
https://doi.org/10.1371/journal.pone.02139
87.
Ogu, N. R., & Alegbeleye, J. O. (2018).
Improving maternal health:Women’s
attitude to antenatal care utilization is
crucial.
Clinical Obstetrics, Gynecology and
Reproductive Medicine
,
4
(1), 1–3.
https://doi.org/10.15761/cogrm.1000209.
Pan American Health Organization. (2017, July
5).
Maternal and Neonatal Immunization
Field Guide for Latin America and the
Caribbean
.
https://iris.paho.org/handle/10665.2/34150.
Phommachanh, S., Essink, D. R., Jansen, M.,
Broerse, J. E. W., Wright, P., & Mayxay, M.
(2019). Improvement of Quality of
Antenatal Care (ANC) Service Provision at
the Public Health Facilities in Lao PDR:
Perspective and Experiences of Supply and
Demand Sides.
BMC Pregnancy and
Childbirth
,
19
(1), 1–13.
https://doi.org/10.1186/s12884-019-2345-0.
Semmelweis, I. (1983).
The Etiology, Concept
and Prophylaxis of Childbed Fever
. The
University of Wisconsin Press, Madison, WI.
Sengupta, K. (2016).
Determinants of health
status in India
. New Delhi: Springer India.
Shen, C., & Williamson, J. B. (1999). Maternal
mortality, women’s status, and economic
dependency in less developed countries: a
cross-national analysis.
Social Science &
Medicine
,
49
(2), 197–214.
https://doi.org/10.1016/s0277-
9536(99)00112-4.
Singh, A., Pallikadavath, S., Ogollah, R., &
Stones, W. (2012). Maternal Tetanus Toxoid
Vaccination and Neonatal Mortality in Rural
North India.
PLoS ONE
,
7
(11), e48891.
https://doi.org/10.1371/journal.pone.00488
91.
Sommer, J. M., Shandra, J. M., Restivo, M., &
Coburn, C. (2015). Water, Sanitation, and
Health in Sub-Saharan Africa: A Cross-
national Analysis of Maternal and Neo-natal
Mortality.
Human Ecology Review
,
22
(1),
129–152.
https://doi.org/10.22459/her.22.01.2015.08.
Thaddeus, S., & Maine, D. (1991). Too far to
walk: maternal mortality in
context.
Newsletter (Women's Global
Network on Reproductive Rights)
, (36), 22-
24.
Tomasz, N. (2009). The statement of Polish
Gynaecologic Society experts concerning
drinking water consumption in women in
reproductive age, pregnancy and breast
feeding.
Ginekologia polska
,
80
(7), 538-
547.
Wang, L. (2003). Determinants of children
mortality in LDCs: empirical findings from
demographic and health surveys.
Health
Policy
,
65
(3), 277–299.
https://doi.org/10.1016/s0168-
8510(03)00039-3.
World Health Organization Report. (2015). Wash
Report, 2015.
World Health Organization. (2019). More
women and children survive today than
ever before–UN report.
WHO: Geneva,
Switzerland
.
Wu, X., & Li, L. (2012). Family size and maternal
health: evidence from the One-Child policy
in China.
Journal of Population Economics
,
25
(4), 1341–1364.
https://doi.org/10.1007/s00148-011-0361-0.