Mazumder et al. BMC Pediatrics(2024) 24:572
https://doi.org/10.1186/s12887-024-04979-6

BMC Pediatrics

RESEARCH

Open Access

An analysis of cause-specific under-5 mortality
in Bangladesh using the demographic
and health survey 2011 and 2017–2018
Tapas Mazumder1*, Itismita Mohanty1, Danish Ahmad1,2 and Theo Niyonsenga1
Abstract
Background As the Sustainable Development Goal 3.2.1 deadline (2030) approaches, rapidly reducing under-5
mortality (U5M) gains more prominence. However, initiatives or interventions that aided Bangladesh in achieving
Millennium Development Goal 4 showed varied effectiveness in reducing certain cause-specific U5M. Therefore, thisstudy aimed to examine the predictors of the key cause-specific mortalities.
Methods This cross-sectional study was conducted using the Bangladesh Demographic and Health Survey 2011 and2017-18 data. Cause-specific U5M was examined using multilevel multinomial mixed-effects analyses, and overall/allcause U5M was examined using multilevel mixed-effects analyses. The respective estimates were compared.
Results The cause-specific analysis revealed that pneumonia and prematurity-related U5M were significantly
associated with antenatal care and postnatal care, respectively. However, analysis of overall/all-cause U5M did notreveal any significant association with health services. Twins or multiples had a greater risk of mortality from pretermrelated conditions (adjusted Relative Risk Ratio (aRRR): 38.01, 95% CI: 19.08–75.7, p < .001), birth asphyxia (aRRR: 6.52,
95% CI: 2.51–16.91, p < .001), and possible serious infections (aRRR: 11.12, 95% CI: 4.52–27.36, p < .001) than singletons.
Children born to mothers 18 years or younger also exhibited a greater risk of mortality from these three causes thanchildren born to older mothers. This study also revealed an increase in the predicted risk of prematurity-related
mortality in the 2017-18 survey among children born to mothers 18 years or younger, children born to mothers
without any formal education, twins or multiples and children who did not receive postnatal care.
Conclusions This research provides valuable insights into accelerating U5M reduction; a higher risk of pretermrelated death among twins underscores the importance of careful monitoring of mothers pregnant with twins or
multiples through the continuum of care; elevated risk of death among children who did not receive postnatalcare, or whose mothers did not receive antenatal care stresses the need to strengthen the coverage and qualityof maternal and neonatal health care; furthermore, higher risks of preterm-related deaths among the children of
mothers with low formal education or children born to mothers 18 years or younger highlight the importance ofmore comprehensive initiatives to promote maternal education and prevent adolescent pregnancy.
Keywords Under-5 mortality, Sustainable development goal, Multinomial, Preterm, Pneumonia*Correspondence:
Tapas Mazumder
tapas.mazumder@canberra.edu.au

1

Health Research Institute, Faculty of Health, University of Canberra,
Canberra, ACT 2617, Australia
2
School of Medicine and Psychology, College of Health and Medicine,
Australian National University, Canberra, ACT 2601, Australia© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Mazumder et al. BMC Pediatrics(2024) 24:572

BackgroundAs the Sustainable Development Goal (SDG) 3.2.1 deadline (2030) approaches, reducing the under-5 mortality
rate (U5MR) rapidly to meet the global target has gained
more prominence; indeed, the world observed a remarkable reduction in the U5MR during the Millennium
Development Goal (MDG) period, 2000–2015. However, a 53% reduction in the global U5MR between 1990
and 2015 was still insufficient to meet the global MDG
4 target of a two-thirds reduction. The slow progress inthis period is estimated to have resulted in 14 million
deaths among children under five years that were preventable [1]. If this slow pace of average annual rate ofglobal under-5 mortality (U5M) reduction (based on the
2000–2015 period) continues well into the SDG era, thenthe global achievement of SDG 3.2.1 will be missed, andan additional 68.8 million under-5 (U5) children are estimated to be lost in the SDG era. In contrast, 12.8 million
U5 deaths would be averted if each country could achieve
SDG 3.2.1 [2]. Hence, it is crucial to understand how theapproaches to U5M reduction can be reinforced.
Accelerating U5M reduction can be augmented by generating evidence on the more precise estimation of thecauses of deaths in high burden settings; this approach
will primarily help in the design of targeted and more
effective and contextually relevant interventions compared to interventions developed based on research on
all causes or overall U5M [3]. Specifically, the attributesof different cause-specific U5M are diverse, and the same
predictors can have varying impacts on different causespecific mortalities compared to the overall/all-cause
U5M. For example, institutional delivery may contribute
more to saving preterm newborns than to saving newborns who acquired pneumonia due to unhealthy living
conditions at home. Therefore, a study on cause-specific
U5M would provide better insights by identifying causespecific predictors that may otherwise not be revealed
when overall/all-cause U5M is analysed.
Among the existing studies on the causes of global
U5M, Perin et al. (2022) used Bayesian models incorporating study-level random effects and estimated more
precise evidence on cause-specific deaths [3]. The study
reported that preterm birth complications (17.7%), lower
respiratory infections (13.9%) and intrapartum-related
events (11.6%) were the top three causes of U5M globallyin 2019 and contributed to 43.2% of the total U5M. Furthermore, the proportions of mortality due to preterm
birth complications and intrapartum-related events haveincreased since 2000 [3]. Analysis of the predictors ofcause-specific U5M is available in other region-specificstudies [3].
The broader literature reported that preterm neonatesborn to mothers who did not receive antenatal care
(ANC), were delivered at home, did not receive kangaroo

Page 2 of 25

mother care, were not exclusively breastfed or were
extremely premature at birth, had a higher risk of mortality due to preterm birth complications [4–7]. Studies
elsewhere reported that key underlying risk factors forU5M due to lower respiratory tract infections include
female sex, low birth weight, lack of exclusive breastfeeding, young maternal age, low maternal education, andnot receiving ANC [8, 9]. The key factors associated withintrapartum-related events that cause U5M are maternal age, maternal hypertension, eclampsia, antepartum
haemorrhage, prolonged labour, maternal fever, preterm
birth, and low birth weight [10, 11]. While these global
risk factors help guide interventions, identifying contextually relevant risk factors for U5M in select settings provides guidance to inform health policy and health system
reform.
The trend of overall/all-cause U5M and the distribution of cause-specific U5M in Bangladesh resembled theglobal trend and distribution. However, contrary to theshortfall of global efforts to reach MDG 4, Bangladesh’s
efforts succeeded in achieving MDG 4 [12]; the U5MR
was reduced by 66% from 134/1000 live births during
1993-94 to 46/1000 live births in 2014 [13]. The government’s leadership and collaboration with local and international development partners to form relevant policies,
strategies, and programmes and implement these were
instrumental in reducing U5M and improving other childhealth outcomes. In particular, the initiatives or interventions focused on education, societal and economic
improvements, infrastructural development, and vaccination played a major role in reducing U5M [12, 14, 15].
While these initiatives or interventions focused on U5
mortality, overall, their effect on individual age groups
within the U5 children has remained variable. Moreover, as indicated by mortality rate trends over time, theinterventions were less effective at reducing mortality inthe first 28 days or neonatal period. Consequently, themortality rate of U5 children in the neonatal period, theneonatal mortality rate (NMR), contributed to 67% of thetotal U5M in the 2017–2018 survey period compared to38.8% in the 1993-94 survey period.
Moreover, the proportions of total U5M due to the
top four causes – pneumonia, birth asphyxia, prematurity and possible serious infections – remained thesame in Bangladesh between the 2011 (56.3%) and 201718 (56%) surveys. Furthermore, there was no change inbirth asphyxia-specific mortality rates, and prematurityspecific mortality rates even increased. These two areneonate-specific causes of death. Neonates were also
at a higher risk of death due to possible serious infections than older U5 children; this signifies that initiatives or interventions around health and socioeconomic
sectors that markedly reduced overall U5M during the
MDG period were not as effective at reducing certainMazumder et al. BMC Pediatrics(2024) 24:572

cause-specific mortalities among the neonates. Therefore, the U5M reduction rate became slower over theperiod before potentially stagnating in the 2017-18
period, from a 26% reduction between 2004 and 2007 to
a 2% reduction between 2014 and 2017-18 (from 46/1000
live births to 45/1000 live births) [13].
The inadequacy of the formerly mentioned initiatives
or interventions in reducing NMR, a prime contributor
to U5M, implies that these interventions must be tailoredaccording to the nature of neonatal mortality for a more
effective reduction in U5MR in Bangladesh. Understanding the correlates of these key cause-specific U5M ingreater detail is fundamental to designing more effective
interventions. However, while numerous region-specificstudies have examined the risk factors associated withcause-specific U5M in Bangladesh, nationwide representative studies are rare and limited to investigating overall/all-cause U5M [16–22]. Cause-specific U5M studies
based on nationally representative data may generate newknowledge that could prevent potential slow declines or
stagnancies in the U5MR and avert thousands of childdeaths linked to hindrances to achieving SDG 3.2.1 andthe overall progress of a lower- and middle-income country like Bangladesh.
The knowledge generated from cause-specific studies
will help countries like Bangladesh understand causespecific U5M better and design more appropriate targeted interventions to prevent U5 deaths. The studies
on cause-specific correlates of U5M will provide more
precise and robust evidence and guide effective reduction in the U5MR than studies on overall/all-cause U5M.
Therefore, this study aimed to examine the predictors of
the key cause-specific U5M in Bangladesh using repeatedcross-sectional demographic and health surveys. The
study will enhance the understanding of the nature of
the cause-specific predictors of U5M to achieve a more
effective rate of reduction. This study is novel in its useof cause-specific nationally representative mortality
data from two surveys to investigate the leading causesof U5M and its trend. This study is also unique methodologically and compares the analysis of overall U5M withcause-specific U5M. Above all, the findings of this study
may contribute to the reinforcement of global efforts toachieve SDG 3.2.1, leading to the aversion of preventablechild deaths in millions.

Methods
Study setting, design and data collection

Bangladesh is a country in South Asia, with approximately 165 million people living in its eight divisions and
46% of its population living in urban areas [23]. The current study is based on the secondary data analysis of thenationally representative Bangladesh Demographic and
Health Survey (BDHS) 2011 and 2017-18 [13, 24].

Page 3 of 25

The BDHS surveys applied a two-stage-stratified random sampling method to select nationally representative samples. In the first step, enumeration areas (EAs)or clusters were selected using probability-proportionalto-size sampling using the sampling frame produced by
the Bangladesh Bureau of Statistics. In BDHS 2011, 600
clusters were selected, whereas in BDHS 2017-18, 675
clusters were selected. In both surveys, a sampling frame
was created by carrying out a complete household listing operation in all the selected EAs. In the second step,
30 households, on average, were selected from each ofthese EAs to get statistically reliable estimates of the key
demographic and health variables. These households
were accessed to collect information from 15 to 49-yearold ever-married women on their sociodemographics,
health, pregnancy and children. Verbal autopsies werealso conducted for under 5-year-old children who diedwithin five years of the survey. More information on the
BDHS survey methodology is available elsewhere [13, 24,
25].
To address the research questions of this study, thebirth history, household, community, and verbal autopsy
datasets were merged. Then, the merged data from 2011
and 2017-18 were appended. BDHS 2014 did not conduct
verbal autopsy and therefore, BDHS 2014 data were notincluded in this study.
Sample size

This study presents the findings based on data analysis on two samples – (a) U5 children and (b) three years
or younger children (a sub-sample of the U5 age group)
taken from BDHS 2011 and BDHS 2017-18. The U5 age
group children sample was analysed to study the sociodemographic predictors of cause-specific U5 mortality.
BDHS 2011 and 2017-18 collected information on healthservice utilisation like ANC and postnatal care (PNC)
only for the most recent births three years before eachsurvey. Therefore, children three years or younger wereconsidered as the sample for studying the health servicespecific factors of cause-specific U5 mortality. In the
U5 children sample, the variable specific proportions of
missing values were less than 2% across all sociodemographic variables. In the three-year-old or younger children sample, the variable specific proportion of missing
values were less than 5% across ANC, birth assistantor PNC and other sociodemographic variables. As the
proportions of missing values were less than 5% acrossall variables and the response rates were around 98% in
both surveys, it is unlikely that the estimates obtained
through complete case analyses would be biased [26]. A
sample of 16,703 U5 children was considered for analyses
involving the sociodemographic variables. A subsample
of 9,469 children aged three years or younger (57% of thesample of U5 children) was considered for additional,

Mazumder et al. BMC Pediatrics(2024) 24:572

independent analyses using both sociodemographic
and health service variables. The proportion of mortality among the U5 children was nearly 5%, and the proportion of mortality among children aged three years or
younger was nearly 3%.
Outcome variables

The alive or dead status of U5 children was considered
to create the outcome variables. Based on the evidence
on leading causes of death among U5 children, the specific causes of death for these deceased children wereclassified into six categories, namely, pneumonia, birthasphyxia, possible serious infection, preterm birth,unspecific or undetermined causes and other causesof death [3, 27]. For the analysis of overall/all-cause
U5M, all specific causes were clumped together to create the all-causes binary outcome variable, coded as 1 for“Dead” (1: if deceased due to any of the causes) and as 0
for “Alive” (0: if the child still alive), with “Alive” serving
as the reference category. For cause-specific mortality, amultinomial outcome variable was created (with sevencategories), coded as 0 for “Alive” (the reference category)and 1 to 6 if death occurred due to any of the other six
causes. Tables 1 and 2 show the outcome categories.

Page 4 of 25

Father and household-specific variables

Four variables, father’s education (no education, primary, secondary or higher), hand washing station in
the household (observed, not observed), wealth quintile(poorest, poorer, middle, richer and richest) and cooking fuel (clean, not clean) were included in this domain.
It is important to note that in the Bangladeshi context,
extended families are common, and multiple couples canlive in the same household.
Community and time-specific variables

Three variables were considered – access route to thecommunity (good, poor), division/province of residence(Sylhet, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur,
Barisal, Mymensingh), and survey year (2011, 2017-18).
Health service-specific variables

The number of times the mother accessed/utilised antenatal care services: no utilisation, one to three, and fouror more; birth assistance service utilisation: whether
from a medically trained or non-medically trained professional; and the postnatal care service utilisation status:
at least once or not; were the health service-specific variables included in this study.

Explanatory variables

Statistical analysis

Following the Mosley-Chan framework and recent literature, a range of variables were selected, and their associations with the binary outcome variable (alive or dead)were examined one by one using bivariate analysis [28–35]. The final analyses did not include variables with ap-value ≥ 0.05. However, the sex of the child, the mother’s
participation in decision-making and the type of community access road were included in the final analysis
despite their significance level being more than 5% in thebivariate analysis. These three variables represented three
key hierarchical levels of potential predictors – child,
mother, and community – important for explaining childmortality [28–35].

This study aimed to examine the cause-specific predictors of U5M compared to those of the overall/all-cause
U5M. Therefore, depending on the nature of the outcome variables (cause-specific U5M and overall/all-cause
U5M) and the hierarchical nature of the dataset, twoseparate multilevel regression models were estimatedand compared concerning the observed significant predictors. The multilevel analysis also accounted for theunexplained heterogeneity in the U5M risk. A multilevel
mixed-effects logistic regression analysis was used toinvestigate the predictors of overall/all-cause U5M, anda multilevel multinomial mixed-effects logistic regression
analysis was performed to investigate the predictors of
the key cause-specific (pneumonia, birth asphyxia, possible serious infection, preterm birth, unspecific or undetermined and other causes) U5M. To demonstrate theprecise nature of cause-specific estimates over the other,
this study presents the multilevel mixed-effects logistic
regression first and then presents the multilevel multinomial mixed-effects logistic regression. Both analyses wereconducted independently on the U5 children and theirsubsample (three years or younger children). Therefore,
the estimates presented in this study represent four separate analyses.
To account for the oversampling of the BDHS sample
by divisions, both the weighted and unweighted estimates of the outcome and explanatory variables were
estimated and presented in the descriptive statistics

Child and mother-specific variables

Two child-specific variables, namely, child’s sex (male orfemale) and child’s gestation type (singleton and twin or
multiple), were considered as the potential predictors at
the child level.
The mother-specific variables included mother’s education (no education, primary, secondary and higher), age
at childbirth (18 years or younger, 19 to 24 years, 25 to
30 years, and 31 years or above), body mass index (normal weight: 18.5 kg/m2 to < 25 kg/m2; underweight: <18.5 kg/m2; overweight or obese: ≥ 25 kg/m2), exposure
to television (exposed, not exposed), and participation inhousehold decision making (no participation, some participation or good participation) [36, 37].

Mazumder et al. BMC Pediatrics(2024) 24:572

Page 5 of 25

Table 1 Frequency distribution of sociodemographic predictors of cause-specific mortality among U5 children
N = 16,703
U-5 children (N)Alive
Total deceased
Cause-specific deaths
Death due to other causes
Death due to Pneumonia
Death due to Birth Asphyxia
Death due to Possible Serious Infection
Death Due to Preterm Birth
Unspecific or undetermined
Mother and child-specific predictors
Sex
Male
FemaleGestation type
Singleton
Twin or multiple
Mother’s education
No education
Primary education
Secondary education
Higher education
Mother’s age during childbirth (years)
Mean (SE)18 years or younger
19 years to 24 years
25 years to 30 years
31 years or above
Mother’s Body Mass Index
Normal weight
Underweight
Overweight or obese
Exposure to television
Watched television
Did not watch television
Decision making
No participation of motherSome participation of mother
Good participation of mother
Father and household-specific predictors
Father’s education
No education
Primary education
Secondary education
Higher education
Wealth index
PoorestPoorerMiddleRicherRichestHand washing station

Unweighted sample sizen (%)
16,703
15,879 (95.07)
824 (4.93)

Weighted proportions (%)

225 (1.35)
179 (1.07)
126 (0.75)
87 (0.52)
68 (0.41)
139 (0.83)

1.35
0. 99
0. 72
0. 57
0. 43
0. 84

8660 (51.85)8043 (48.15)

51.78
48.22

16,541 (99.03)
162 (0.97)

99.04
0.96

2199 (13.17)
4979 (29.81)
7488 (44.83)
2037 (12.20)

13.75
29.73
45.41
11.11

23.64 (0.04)
2578 (15.43)
7261 (43.47)
4521 (27.07)
2343 (14.03)

23.54 (0.06)
15.89
43.59
26.86
13.66

9950 (59.57)
3523 (21.09)
3230 (19.34)

60.45
20.72
18.83

10,061 (60.23)
6642 (39.77)

60.43
39.57

3317 (19.86)5062 (30.31)
8324 (49.84)

19.87
31.15
48.99

3625 (21.70)
5325 (31.88)
5102 (30.55)
2651 (15.87)

22.5
32.03
30.72
14.75

3719 (22.27)
3320 (19.88)
3093 (18.52)
3279 (19.63)
3292 (19.71)

22.71
20.5
19.2
19.47
18.13

100
95.09
4.91

Mazumder et al. BMC Pediatrics(2024) 24:572

Page 6 of 25

Table 1 (continued)
N = 16,703
Observed
Not observed or not in the dwelling
Cooking fuel type
Clean fuelSolid fuel or otherCommunity and time-specific predictors
Community access route
Good condition
Poor condition
Division
SylhetChittagong
Dhaka
KhulnaRajshahi
Rangpur
BarisalMymensingh
Survey year2011
2017-18

Unweighted sample sizen (%)
15,173 (90.84)1530 (9.16)

Weighted proportions (%)

2594 (15.53)
14,109 (84.47)

15.48
84.52

12,724 (76.18)
3979 (23.82)

74.31
25.69

2590 (15.51)
3015 (18.05)
2591 (15.51)
1795 (10.75)
1913 (11.45)
2011 (12.04)
1786 (10.69)
1002 (6)

8.05
21.71
28.23
9.08
12.43
10.71
5.52
4.28

8311 (49.76)
8392 (50.24)

49.9
50.1

(see Tables 1 and 2). There was not much variation inweighted and unweighted distribution. A recent study on
three DHSs conducted in Malawi reported no variation
between unweighted and weighted mortality estimates[38]. Studies that applied multilevel analysis also reported
little variation between unweighted and weighted estimates [39, 40]. Therefore, this study refrained from using
weights in the multilevel mixed-effects logistic regression
and multilevel multinomial regression analysis.
This study examined for the presence of multicollinearity among the predictors selected for the analysis using
variance inflation factor (VIF). Based on the existing
literature that used DHS datasets, a VIF value of morethan 10 was considered as an indication of multicollinearity [41–43]. Only the variables that met the criteria(VIF < 10) were included in the final analyses. Please see
Table S1, Table S2.
Multilevel mixed effects logistic regression analysis

Due to the hierarchical nature of the BDHS dataset (e.g.,children nested within mothers, mothers within households, households within communities), children fromthe same household or community could have sharedsimilar mortality risks associated with the same exposure. Therefore, multilevel mixed-effects logistic regression analysis was performed to examine the predictors ofoverall/all-cause U5M. The null models were fitted using
mother, household and community as the nesting levels(random effects) independently to examine the magnitude of variation due to the levels of clustering in the91
9data. Estimates of variance components (coefficients) andtheir standard errors were used to determine the significance of the clustering effects. Based on the significanceof the clustering effects, the null model of the multilevel
mixed-effects logistic regression included only two levels – children (level 1) nested within households (level 2)
as random effects (see Table 3). Model 1 of the multilevel
mixed-effects logistic regression was fitted by adding thechild and mother-specific variables (as fixed effects) tothe null model. In Model 2, father and household-specific variables were added (as fixed effects) to Model (1).
In Model 3, community and time-specific variables wereadded (as fixed effects) to Model (2). Likelihood ratio
tests were performed to determine if the models withadditional variables provided a better fit than the earlier
models with fewer variables at a 5% level of significance.
The Akaike Information Criteria (AIC) and BayesianInformation Criteria (BIC) were also estimated. However, this study determined the goodness of fit based on
the likelihood ratio test (see Table S3 for model-specificestimates).
As the information on health service utilisation was
not available for children aged above 3 years and below 5
years, additional analyses (multilevel mixed-effects logistic regression) could only be performed on children aged
three years or younger (N = 9,469) to examine the effectsof health service utilisation on cause-specific mortality.
The analysis approach was similar to the analysis of U5children. However, only community level random effects
came up as significant. Therefore, the null model of theMazumder et al. BMC Pediatrics(2024) 24:572

Page 7 of 25

Table 2 Frequency distribution of sociodemographic and health-service-specific predictors of mortality among three years or
younger children (a sub-sample of U5 children)
N = 9469
Three years or younger children (N)Alive
Total deceased
Cause-specific deaths
Death due to other causes
Death due to Pneumonia
Death due to Birth Asphyxia
Death due to Possible Serious Infection
Death Due to Preterm Birth
Unspecific or undetermined
Mother and child-specific predictors
Sex
Male
FemaleGestation type
Singleton
Twin or multiple
Mother’s education
No education
Primary education
Secondary education
Higher education
Mother’s age during childbirth (years)
Mean (SE)18 years or younger
19 years to 24 years
25 years to 30 years
31 years or above
Mother’s Body Mass Index
Normal weight
Underweight
Overweight or obese
Exposure to television
Watched television
Did not watch television
Decision making
No participation of motherSome participation of mother
Good participation of mother
Father and household-specific predictors
Father’s education
No education
Primary education
Secondary education
Higher education
Wealth index
PoorestPoorerMiddleRicherRichestHand washing station

Unweighted sample size
n (%)
9469
9190 (97.05)
279 (2.95)

Weighted proportions (%)

66 (0.7)73 (0.77)
41 (0.43)
29 (0.31)
24 (0.25)
46 (0.49)

o.68
0.68
0.43
0.33
0.26
0.41

4896 (51.71)
4573 (48.29)

51.63
48.37

9376 (99.02)
93 (0.98)

99.07
0.93

1071 (11.31)
2699 (28.5)4425 (46.73)
1274 (13.45)

11.77
28.76
47.19
12.29

23.76 (0.06)
1357 (14.33)
4139 (43.71)
2633 (27.81)
1340 (14.15)

23.67 (0.07)
14.71
43.76
27.95
13.59

5710 (60.3)
2139 (22.59)
1620 (17.11)

61.1
22.18
16.72

5809 (61.35)
3660 (38.65)

61.55
38.45

1992 (21.04)
2915 (30.78)4562 (48.18)

21.26
31.32
47.41

1859 (19.63)
3013 (31.82)3020 (31.89)
1577 (16.65)

20.53
31.97
32.02
15.47

2041 (21.55)
1862 (19.66)
1757 (18.56)
1902 (20.09)
1907 (20.14)

21.76
20.28
19.43
19.96
18.56

100
97.2
2.8

Mazumder et al. BMC Pediatrics(2024) 24:572

Page 8 of 25

Table 2 (continued)
N = 9469
Observed
Not observed or not in the dwelling
Cooking fuel type
Clean fuelSolid fuel or otherCommunity and time-specific predictors
Community access route
Good condition
Poor condition
Division
SylhetChittagong
Dhaka
KhulnaRajshahi
Rangpur
BarisalMymensingh
Survey year2011
2017-18
Health service-specific predictors
Antenatal CareNo ANC
1 to 3 ANCs
4 or more ANCs
Birth assistance
Medically trained
Not medically trained
Post natal care
Yes
No

Unweighted sample sizen (%)
8625 (91.09)
844 (8.91)

Weighted proportions (%)

1473 (15.56)
7996 (84.44)

15.45
84.55

7259 (76.66)
2210 (23.34)

74.85
25.15

1388 (14.66)
1715 (18.11)
1449 (15.3)
1053 (11.12)1099 (11.61)
1142 (12.06)
1031 (10.89)
592 (6.25)

7.58
21.91
27.85
9.35
12.5
10.74
5.6
4.47

4643 (49.03)
4826 (50.97)

48.8
51.2

1816 (19.18)
4016 (42.41)
3637 (38.41)

20
43.41
36.59

4187 (44.22)
5282 (55.78)

42.45
57.55

5659 (59.76)
3810 (40.24)

57.98
42.02

multilevel mixed-effects logistic regression on children
three years or younger involved only two levels – children(level 1) nested within community (level 2) (see Table 4).
Models 1, 2 and 3 were fitted as was done to analyse thesample of U5 children. Additionally, Model 4 was run,
where, health service utilisation variables were added toModel 3 (see Table S4 for model-specific estimates). The
estimated results from the multilevel mixed effects logistic regression analyses are reported as odds ratios (ORs)and 95% confidence intervals (CIs).
Equation for multilevel mixed effects logistic regression
analysis

For binary logistic regression, the outcome variable has
two categories (binary outcome data). An individual isclassified in one category with a given probability (sayπ), and in the other category with probability, 1 – π. The
following equation denotes a two-level binary logistic

91.35
8.65

regression model where individual i is nested in household h.
logit(πih) = ln(πih/(1 − πih))= β0 + β1X1ih + β2 X2ih + β3X3ih + . . . +βk Xkih + µh

In the equation above, ln is the natural logarithm, πih is
the probability that child i in household h is dead. β0 is
the intercept term or predicted log odds of child mortality in household h when no predictors have any effect.
X1,X2 …Xk represent the covariates included in the model,
and Xkih is the value of the predictor variable Xk for childi in household h. β1,β2, …βk are the coefficients of the predictors. The random effect or the variability between thehouseholds is µh. The above equation applies to models
that consider children nested in households (analysesof the U5 children sample). The other multilevel binary
logistic regression models that consider children nested
in communities/clusters (analyses of the three years oldRef
1.24 (0.88–1.74)
1.18 (0.81–1.71)
Ref
1.08 (0.79–1.5)
Ref

Ref
1.00 (0.82–1.21)
1.32 (1.07–1.63)**
Ref
1.05 (0.88–1.26)
Ref

0.60 (0.45–0.79)***Ref
0.69 (0.56–0.85)***0.48 (0.38–0.61)***

Mother’s age during childbirth
Below 18 years
19 years to 24 years
25 years to 30 years

31 years or above
Mother’s Body Mass Index
Normal weight
Under weightOver weight or obese
Exposure to television
Watched television
Did not watch television
Decision making
No participation of mother

Ref
Ref
Ref
0.64 (0.44–0.93)* 0.93 (0.6–1.46) 0.65 (0.41–1.02)
0.52 (0.34–0.8)** 0.68 (0.41–1.13) 0.39
(0.22–0.68)**
0.67 (0.41–1.08) 0.89 (0.51–1.54) 0.38 (0.18–0.8)*

0.45 (0.29–0.69)***Higher education

0.44 (0.2–0.97)*

Ref
1.02 (0.65–1.6)
0.78 (0.47–1.29)

1.12 (0.36–3.45)

Ref
1.98 (0.85–4.61)
2.27 (0.94–5.44)

Ref

Ref

Ref

Ref
1.03 (0.63–1.69)

Ref
0.99 (0.59–1.67)
1.23 (0.64–2.35)

Ref
0.71 (0.41–1.23)
0.33(0.16–0.69)**
0.61 (0.29–1.31)

0.82 (0.23–2.96)

Ref
1.04 (0.53–2.04)
1.03 (0.49–2.18)

Ref
11.12
(4.52–27.36)***Ref
1.05 (0.68–1.61)

Ref
0.97 (0.13–7.19)

Ref
0.90 (0.64–1.27)

Unspecific or
undetermined
aRRR (95% CI)

Ref

Ref
0.74 (0.41–1.34)

Ref
0.64 (0.31–1.32)
1.48 (0.78–2.79)

Ref
0.50 (0.27–0.95)*
0.32(0.15–0.69)**
0.37 (0.15–0.91)*

0.30 (0.08–1.16)

Ref

Ref
1.28 (0.86–1.91)

Ref
0.83 (0.54–1.26)
1.47 (0.88–2.45)

0.53 (0.28–1)

Ref
0.69 (0.44–1.08)
0.53 (0.31–0.9)*

0.55 (0.18–1.7)

Ref
Ref
0.58 (0.27–1.23) 0.55 (0.34–0.89)
0.37 (0.16–0.85)* 0.64 (0.38–1.09)

Ref
38.01
(19.08–75.7)***Ref
0.87 (0.53–1.43)

Death due toPreterm BirthaRRR (95% CI)(2024) 24:572

Ref
Ref
1.22 (0.85–1.74) 0.75 (0.48–1.16)

Ref
Ref
1.21 (0.85–1.74) 0.69 (0.41–1.18)
1.11 (0.71–1.73) 1.74 (1.12–2.7)*

Ref
0.68 (0.44–1.05)
0.40(0.24–0.67)**
0.35(0.15–0.83)*

Ref
6.52
(2.51–16.91)***

Ref
0.81 (0.64–1.03)
0.68 (0.52–0.89)**

Ref
0.70 (0.1–5.16)

Mother’s education
No education
Primary education
Secondary education

Ref
1.80 (0.55–5.88)

Ref
5.17 (3.22–8.28)***Ref
Ref
0.97 (0.72–1.31) 0.57
(0.39–0.83)**

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)

Gestation type
Singleton
Twin or multiple

Ref
0.89 (0.68–1.17)

Death due toPneumonia
aRRR (95% CI)

Ref
0.87 (0.74–1)

Death due toother causes
aRRR (95% CI)

Final model/Model 3

Estimates related to predictors of cause-specific U5M

Estimates related to predictorsof overall/all-cause U5M
Null model Final model/
Model 3All causes
togetheraOR (95% CI)
Null modelMultilevel Multinominal Mixed-Effects Logistic Regression

Multilevel Mixed-Effects Logistic Regression

Child and mother-specific predictors
Sex
Male
FemaleN = 16,703

Table 3 Sociodemographic predictors of overall/all-cause mortality and cause-specific mortality among U5 children

Mazumder et al. BMC Pediatrics
Page 9 of 25

0.67 (0.47–0.96)*

Higher education

Cooking fuel type
Clean fuelSolid fuel or otherCommunity and time specificpredictors
Community access route
Good condition
Poor condition
Division

Wealth index
PoorestPoorerMiddleRicherRichestHand washing station
Observed
Not observed or not in the dwelling

Ref
0.91 (0.63–1.33)
0.95 (0.62–1.46)

Ref
0.89 (0.72–1.09)
0.75 (0.59–0.96)*

Ref
1.03 (0.62–1.7)

Ref
1.20 (0.87–1.66)

Ref
0.97 (0.73–1.29)

Ref
1.02 (0.84–1.22)

Ref
0.93 (0.57–1.54)

Ref
2.37 (0.8–7.01)

Ref
2.22
(1.25–3.95)**

Ref
1.11 (0.62–2)
1.03 (0.52–2.06)
0.76 (0.33–1.75)
1.01 (0.35–2.93)

0.59 (0.21–1.66)

Ref
0.93 (0.49–1.78)

Ref
0.96 (0.39–2.32)

Ref
1.16 (0.46–2.91)

Ref
1.09 (0.52–2.28)
0.77 (0.32–1.87)
0.88 (0.36–2.15)
0.77 (0.23–2.52)

0.72 (0.21–2.46)

Ref
Ref
0.80 (0.46–1.38) 0.97 (0.48–1.97)
0.43 (0.21–0.88)* 1.01 (0.45–2.23)

0.62 (0.34–1.16)

0.62 (0.31–1.23)

Death due toPreterm BirthaRRR (95% CI)

Ref
0.80 (0.53–1.21)

Ref
0.59 (0.3–1.14)

Ref
1.49 (0.92–2.43)

Ref
1.37 (0.84–2.25)
1.28 (0.72–2.27)
1.50 (0.81–2.77)
0.51 (0.19–1.35)

Ref
0.86 (0.56–1.33)
0.53(0.31–0.92)*
0.22(0.07–0.65)**

0.85 (0.55–1.32)

0.87 (0.55–1.38)

Unspecific or
undetermined
aRRR (95% CI)(2024) 24:572

Ref
Ref
0.91 (0.63–1.31) 1.31 (0.85–2.01)

Ref
Ref
0.98 (0.55–1.55) 0.91 (0.48–1.74)

Ref
Ref
Ref
1.76 (1.14–2.72)* 1.33 (0.83–2.13) 1.00 (0.5–1.99)

Ref
1.50 (1.18–1.91)**

Ref
0.90 (0.51–1.6)
0.71 (0.37–1.34)
0.91 (0.48–1.73)
0.61 (0.26–1.41)

Ref
0.89 (0.59–1.33)
1.05 (0.67–1.65)
0.80 (0.48–1.35)
1.20 (0.64–2.26)

Ref
0.81 (0.51–1.31)
1.38 (0.85–2.22)
1.35 (0.79–2.31)
1.28 (0.62–2.64)

0.74 (0.35–1.54) 1.12 (0.54–2.36)

Ref
Ref
1.09 (0.73–1.62) 0.68 (0.39–1.18)
0.72 (0.44–1.19) 0.89 (0.5–1.59)

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)
1.33 (0.86–2.06) 0.96
1.66 (0.89–3.13)(0.58–1.158)
1.20 (0.79–1.83) 0.86 (0.53–1.39) 1.29 (0.69–2.39)

Death due toPneumonia
aRRR (95% CI)

Ref
1.00 (0.79–1.25)
1.07 (0.83–1.38)
1.06 (0.8–1.39)
0.94 (0.65–1.35)

0.70 (0.36–1.34)

1.91 (1.23–2.96)

1.12 (0.91–1.38)

Good participation of mother
Father and household-specificpredictors
Father’s education
No education
Primary education
Secondary education

1.81 (1.14–2.86)

1.18 (0.95–1.48)

Death due toother causes
aRRR (95% CI)

Final model/Model 3

Estimates related to predictors of cause-specific U5M

Estimates related to predictorsof overall/all-cause U5M
Null model Final model/
Model 3All causes
togetheraOR (95% CI)
Null modelMultilevel Multinominal Mixed-Effects Logistic Regression

Multilevel Mixed-Effects Logistic Regression

Some participation of motherN = 16,703

Table 3 (continued)

Mazumder et al. BMC Pediatrics
Page 10 of 25

Table 3 (continued)

0.88 (0.23)
0.21-3197.76
9
27.08**
33
6425.52
6680.39NANA
26542.646558.0879381.819435.88NANA

1 (0.22)0.23-4683.91

193
9286.3610776.96

54
114.46***0.87 (0.23)
0.21-4450.18

Ref
2.40(1.74–3.32)***Ref
1.23 (1.03–1.46)*

1 (0.22)0.23-3269.32

Ref
0.70 (0.45–1.09)
0.93 (0.6–1.45)
0.46 (0.25–0.85)*
0.60 (0.35–1.05)
0.49 (0.28–0.88)*
0.81 (0.49–1.34)
0.51 (0.27–0.97)*

Ref
0.77 (0.59–0.99)*
0.88 (0.68–1.15)
0.50 (0.35–0.71)***0.88 (0.66–1.17)
0.70 (0.52–0.95)*
0.89 (0.66–1.19)
0.69 (0.47–1.02)

Death due toother causes
aRRR (95% CI)

Ref
0.86 (0.52–1.43)

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)
Ref
Ref
0.58 (0.3–1.1)
1.12 (0.51–2.45)
0.58 (0.29–1.15) 1.48 (0.67–3.27)
0.49 (0.22–1.08) 0.35 (0.1–1.29)
1.10 (0.58–2.06) 1.62 (0.73–3.6)
0.89 (0.45–1.75) 1.04 (0.43–2.54)
0.85 (0.42–1.7)
1.35 (0.58–3.12)
0.50 (0.19–1.28) 1.22 (0.4–3.7)

Ref
Ref
0.94 (0.66–1.33) 1.30 (0.87–1.95)

Ref
1.16 (0.71–1.88)
0.70 (0.39–1.24)
0.65 (0.33–1.31)
1.22 (0.7–2.11)
0.67 (0.35–1.28)
1.13 (0.64–1.99)
1.12 (0.54–2.34)

Death due toPneumonia
aRRR (95% CI)

Final model/Model 3

Estimates related to predictors of cause-specific U5M

Estimates related to predictorsof overall/all-cause U5M
Null model Final model/
Model 3All causes
togetheraOR (95% CI)
Null modelMultilevel Multinominal Mixed-Effects Logistic Regression

Multilevel Mixed-Effects Logistic Regression

Ref
2.20
(1.23–3.92)**

Ref
0.58 (0.25–1.37)
1.14 (0.52–2.51)
0.83 (0.32–2.14)
0.33 (0.1–1.08)
0.94 (0.38–2.3)
0.46 (0.15–1.47)
0.27 (0.06–1.25)

Death due toPreterm BirthaRRR (95% CI)

Ref
0.49
(0.32–0.75)**

Ref
0.57 (0.32–1.02)
0.91 (0.52–1.57)
0.32 (0.13–0.79)
0.74 (0.39–1.39)
0.65 (0.34–1.27)
0.80 (0.42–1.51)
1.09 (0.44–2.67)

Unspecific or
undetermined
aRRR (95% CI)(2024) 24:572

Notes ***: p < .001, **: p < .01, *: p < .05, SE: Standard Error, CI: Confidence Interval, aOR: Adjusted Odds ratio, aRRR: Adjusted Relative Risk Ratio; Ref: Reference group

Random effects
Household level variance (SE)Intraclass Correlation Coefficient
Log-likelihood
Likelihood ratio test
Degrees of freedom
Test statistic
Information Criteria
Degrees of freedom
Akaike’s Information Criteria
Bayesian Information Criteria

SylhetChittagong
Dhaka
KhulnaRajshahi
Rangpur
BarisalMymensingh
Survey year2011
2017-18

N = 16,703

Mazumder et al. BMC Pediatrics
Page 11 of 25

Mazumder et al. BMC Pediatrics(2024) 24:572

or younger children sample) will be similar – the households (h) will be replaced by communities/clusters (c).
Multilevel multinomial mixed effects analysis

Due to the hierarchical nature of the data and the presence of multinomial outcomes (alive and multiplecause-specific deaths), multilevel multinomial logistic
regression was performed to determine the predictors ofcause-specific deaths. Although the proportions of thecause-specific categories of the outcome variables were
small, they were non-negligible. Also, it was believedthat the multinomial analysis method would create newknowledge over and above the analysis of the outcome
as a binary (alive, dead) variable, a method more often
used on DHS data [30, 34]. Furthermore, this analysis
would generate cause of death-specific knowledge andhelp prevent the causes. Other studies on child mortality conducted in the African region also used a multinomial analysis approach to investigate child mortality.
Although the outcome variables were not cause-specificdeath (age-specific child death instead), these studies still
support using multinomial analysis when the outcome
variable is categorical, and the analysis approach has thepotential to generate more precise and useful knowledge
for reducing U5M [44, 45].
Null models for the multilevel multinomial mixed
effects analyses were estimated using approaches similar
to the multilevel mixed effects logistic regression analyses and included children (level 1) nested within households (level 2) as two levels (see Table 3). The steps to add
sociodemographic variables to fit the models, ascertaining the goodness of fit of these models were the same as
the multilevel mixed effects logistic regression analyses(see Table S5, Table S6, and Table S7 for model-specificestimates).
Additional analyses on children aged three years or
younger (n = 9,469) to investigate the effects of health service utilisation on cause-specific child mortality also followed the same procedure for estimating the null modeland adding the variables to fit the models to the data as
was done in multilevel mixed effects logistic regression
analyses (see Table 4). Additional tables provide information on the Model-specific estimates of variance, likelihood ratio test, AIC and BIC (see Table S8, Table S9,Table S10, and Table S11). The estimated results fromthe multilevel multinomial mixed effects analyses arereported as relative risk ratios (RRRs) and 95% CIs.

Page 12 of 25

logit model, one of the categories is arbitrarily chosen toact as the reference category (or comparator; in general,
the first or final group). The equations for a two-level
multinomial logistic regression model where individual
i is nested in household h are given by the expression
below, leading to D-1 equations (one category being thereference).
ln(πdih)/π0ih )= β0d + β1dX1ih + β2d X2ih + β3dX3ih + . . . +βkdXkih + µh

In the equation above, ln is the natural logarithm, andπdih is the probability that child i in household h is deaddue to dth cause (d being one of the outcome categories).
Taking d = 1 as the reference category (denoted with subscript 0), π0ih is the probability that child i in household hfalls in the reference category (i.e., alive in our study). The
coefficient β0d is the intercept term for dth category when
the predictors’ effect is considered as 0. Since X1,X2 …Xk
represent the covariates included in the model, and Xkih
is the value of the predictor variable Xk for child i inhousehold h, then β1d,β2d, …βkd are the coefficients associated with these predictors. The random effect or the variability between the households is µh as in the multilevel
binary logit model. The above equation applies to models that consider children nested in households (analysesof the U5 children sample). The other multilevel multinomial logistic regression models that consider children
nested in communities/clusters (analyses of the three
years old or younger children sample) will be similar –the households (h) will be replaced by communities/clusters (c).
Predicted probabilities and interaction effects

This study used the interaction effect of time/survey year
to study the moderation effect of time on the key causespecific mortality risks associated with different predictors. Such analyses helped to highlight the change inmortality risks associated with specific predictors (mothers’ age, mother’s education, gestation type and PNC)
between 2011 and 2017-18 surveys. Margins plots werealso generated to illustrate the change in mortality risks
between 2011 and 2017-18 surveys. All the analyses were
performed using Stata 17.0 (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: StataCorp
LLC.).

Results

Equation for multilevel multinomial mixed effects analysis

Sample characteristics

The multinomial logistic regression technique is adequate for an outcome variable that has more than 2 categories (also called multinomial outcome data). Therefore,
an individual is classified in one of the categories (sayD > 2) with a given probability (πd). For the multinomial

Table 1 presents the unweighted and weighted frequency
distribution of the sociodemographic predictors includedin the analysis. However, the descriptions in the results
section in this paper refer to the unweighted proportions
only.

Ref
1.25 (0.67–2.3)
1.91 (1 – 3.63)*
Ref
0.87 (0.48–1.56)

Ref
0.9 (0.66–1.23)
1.95 (1.4–2.73)***Ref
0.87 (0.65–1.18)

Ref
1.15 (0.82–1.63)

Decision making
No participation of motherSome participation of motherRef
1.55 (0.72–3.32)

Ref
0.82 (0.4–1.7)
0.83 (0.38–1.82)
1.03 (0.42–2.54)

Ref
1.24 (0.47–3.22)
1 (0.37–2.76)
0.42 (0.08–2.1)

Ref
1.24 (0.83–1.85)
0.81 (0.52–1.28)
1.61 (1.01–2.6)*

Ref
0.81 (0.55–1.2)
0.69 (0.45–1.05)
0.44 (0.22–0.88)*

Ref
3.94
(0.91–17.04)

Ref
5.97
(3.13–11.41)***

Mother’s age during childbirth
Below 18 years
19 years to 24 years
25 years to 30 years
31 years or above
Mother’s Body Mass Index
Normal weight
Under weightOver weight or obese
Exposure to television
Watched television
Did not watch television

Mother’s education
No education
Primary education
Secondary education
Higher education

Ref
1.21 (0.74–1.97)

Ref
1 (0.78–1.27)

Ref
1.57 (0.81–3.04)

Ref
1.22 (0.69–2.17)

Ref
0.9 (0.51–1.61)
1.64 (0.84–3.22)

Ref
2.22 (0.86–5.73)
1.19 (0.41–3.43)
3.34 (1.2–9.33)*

Ref
0.62 (0.32–1.23)
0.47 (021–1.03)
0.16 (0.04–0.73)*

Ref$1.86e− 08 (-)

Ref
0.93 (0.58–1.48)

Death due toPneumonia
aRRR (95% CI)

Ref
0.93 (0.4–2.14)

Ref
0.39
(0.17–0.89)*

Ref
0.45 (0.17–1.2)
1.68 (0.76–3.71)

Ref
1.72 (0.58–5.08)
0.78 (0.22–2.79)
1.9 (0.53–6.82)

Ref
2.66 (0.72–9.81)
2.28 (0.56–9.29)
2.06(0.32–13.23)

Ref
6.79
(1.5–30.81)*

Ref
0.76 (0.4–1.42)

Ref
1.36 (0.43–4.25)

Ref
0.98 (0.41–2.35)

Ref
1.19 (0.51–2.77)
2.96 (0.95–9.24)

Ref
0.96 (0.34–2.74)
0.31 (0.07–1.34)
1.39 (0.4–4.87)

Ref
0.85 (0.28–2.6)
0.93 (0.26–3.26)
0.37 (0.03–4.85)

Ref$2.89e− 08 (-)

Ref
0.62 (0.29–1.33)

Unspecific or
undetermined
aRRR (95% CI)

Ref
1.39 (0.51–3.78)
1.07 (0.36–3.22)
1.46 (0.45–4.75)

Ref
0.5 (0.22–1.14)
0.48 (0.19–1.24)
0.42 (0.07–2.69)

Ref
2.53 (0.32–20.26)

Ref
Ref
0.35 (0.09–1.36) 0.79 (0.36–1.71)

Ref
Ref
0.86 (0.26–2.84) 0.93 (0.46–1.87)

Ref
Ref
0.37 (0.07–1.89) 0.83 (0.41–1.68)
2.6 (0.96–7.03) 2.31 (0.95–5.62)

Ref
0.99 (0.24–4.1)
0.89 (0.19–4.09)
1.32 (0.26–6.77)

Ref
0.55 (0.13–2.3)
0.46 (0.1–2.03)
1.01 (0.16–6.29)

Ref
73.03 (24.57–
217.03)***Ref
Ref
0.93 (0.39–2.21) 1.56 (0.86–2.84)

Death due toPreterm BirthaRRR (95% CI)

Child and mother-specific predictors
Sex
Male
FemaleGestation type
Singleton
Twin or multiple

Death due toother causes
aRRR (95% CI)

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)

Estimates related to predictors of cause-specific U5M

Estimates related to predictors of overall/all-cause U5M
Null
Final model/
model
Model 4All causes
togetheraOR (95% CI)
Null model Final model/Model 4

Multilevel Multinominal Mixed Effects Logistic Regression

Table 4 Sociodemographic and health service-specific predictors of overall/all-cause mortality and cause-specific mortality among children three years or younger (a sub-sample ofU5 children)
Multilevel Mixed-Effects
Logistic Regression(2024) 24:572

N = 9469

Mazumder et al. BMC Pediatrics
Page 13 of 25

Cooking fuel type
Clean fuelSolid fuel or otherCommunity and time specific predictors
Community access route
Good condition
Poor condition
Division
SylhetChittagong
Dhaka
KhulnaRajshahi

RichestHand washing station
Observed
Not observed or not in the dwelling

Good participation of mother
Father and household-specific predictors
Father’s education
No education
Primary education
Secondary education
Higher education
Wealth index
PoorestPoorerMiddleRicherN = 9469

Table 4 (continued)

Ref
1.81 (0.65–5.03)

Ref
1.5 (0.85–2.64)
Ref
1.14 (0.47–2.75)
1.03 (0.39–2.69)
0.97 (0.34–2.73)
1.22 (0.34–3.72)

Ref
1 (0.63–1.6)

Ref
1.15 (0.85–1.55)
Ref
0.64 (0.41–0.99)*
0.89 (0.57–1.37)
0.61 (0.35–1.05)
0.78 (0.48–1.26)

Ref
1.6 (0.74–3.42)

Ref
1.63 (1.14–2.34)**

Ref
1.39 (0.65–2.96)
1.12 (0.47–2.61)
1.08 (0.42–2.72)

Ref
1.09 (0.75–1.59)
0.91 (0.59–1.4)
1.26 (0.82–1.97)
1.52 (0.5–4.63)

Ref
1.19 (0.55–2.56)
1.5 (0.67–3.37)
0.57 (0.15–2.15)

Ref
1.14 (0.81–1.61)
0.94 (0.63–1.39)
0.6 (0.33–1.1)

1.05 (0.59–1.88)

1.57 (0.76–3.27)

1.07 (0.76–1.49)

Death due toother causes
aRRR (95% CI)

Ref
0.63 (0.23–1.68)
0.65 (0.23–1.83)
0.47 (0.14–1.61)
0.31 (0.8–1.18)

Ref
1.37 (0.66–2.83)

Ref
1.63 (0.51–5.17)

Ref
0.41 (0.1–1.79)

0.62 (0.15–2.51)

Ref
0.7 (0.25–1.96)
0.61 (0.2–1.8)
0.94 (0.32–2.73)

Ref
0.61 (0.27–1.38)
0.3 (0.11–0.83)*
0.34 (0.08–1.33)

Ref
1.71 (0.32–9.11)
0.37 (0.03–4.51)
3.18(0.58–17.36)
2.41 (0.29–20)

Ref
1.24 (0.32–4.84)
0.88 (0.21–3.77)
0.25 (0.03–1.91)

Ref
1.79 (0.82–3.87)

0.45 (0.08–2.47)

Ref
1.26 (0.5–3.17)
1.33 (0.48–3.69)
2.11 (0.75–5.92)

Ref
0.75 (0.34–1.65)
0.76 (0.31–1.88)
0.26 (0.04–1.54)

0.71 (0.34–1.49)

Unspecific or
undetermined
aRRR (95% CI)

Ref
0.4 (0.09–1.67)
1.38 (0.44–4.39)
0.21 (0.02–1.84)
0.85 (0.22–3.26)

Ref
0.85 (0.37–1.97)

Ref
0.79 (0.15–4.14)
1.52 (0.31–7.41)
1.24 (0.2–7.56)
0.77 (0.12–4.91)

Ref
0.72 (0.18–2. 9)

Ref
0.4 (0.15–1.12)
0.65 (0.25–1.67)
0.17 (0.02–1.37)
0.4 (0.11–1.54)

Ref
1.11 (0.56–2.19)

Ref
Ref
Ref
4.62 (0.45–47.58) 0.72 (0.19–2.77) 0.31 (0.11–0.9)*

Ref
Ref
2.67 (1.11–6.41)* 3.71
(0.83–16.65)

0.47 (0.08–2.96)

Ref
0.95 (0.37–2.43)
0.33 (0.08–1.34)
0.35 (0.08–1.58)

Ref
2.5 (0.9–6.93)
1.16 (0.3–4.45)
2.5 (0.42–14.68)

0.59 (0.2–1.75)

Death due toPreterm BirthaRRR (95% CI)(2024) 24:572

Ref
0.52 (0.22–1.22)
0.75 (0.32–1.72)
0.78 (0.29–2.12)
1.08 (0.45–2.56)

Ref
1.1 (0.64–1.9)

Ref
0.84 (0.34–2.07)

Ref
1.74 (0. 9–3.37)

1.75 (0.58–5.24)

Ref
0.99 (0.49–2.03)
1.24 (0.56–2.71)
1.39 (0.59–3.29)

Ref
1.55 (0.81–2.97)
1.09 (0.5–2.39)
1.08 (0.35–3.3)

1.09 (0.56–2.12)

Death due toPneumonia
aRRR (95% CI)

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)
0.74 (0.32–1.69) 2.02 (0.7–5.78)

Estimates related to predictors of cause-specific U5M

Estimates related to predictors of overall/all-cause U5M
Null
Final model/
model
Model 4All causes
togetheraOR (95% CI)
Null model Final model/Model 4

Multilevel Multinominal Mixed Effects Logistic Regression

Multilevel Mixed-Effects
Logistic Regression

Mazumder et al. BMC Pediatrics
Page 14 of 25

Random effects
Community level variance (SE)Intraclass Correlation Coefficient
Log-likelihood
Likelihood ratio test
Degrees of freedom
Test statistic
Information Criteria
Degrees of freedom
4
6.12
37

NA
NA
2

7

217

24
43.62**

Ref
0.74 (0.45–1.24)

Ref
0.88 (0.5–1.58)

0.4 (0.19–0.83)*

Ref
0.4 (0.22–0.73)**Ref
0.7 (0.39–1.26)

1.41 (0.63–3.14)
1.52 (0.5–4.62)

0.77 (0.28–2.1)

Death due toPneumonia
aRRR (95% CI)

Ref
1.8 (0.95–3.41)

Ref
0.63 (0.3–1.32)

0.98 (0.33–2.9)

Ref
1.26 (0.51–3.16)

Ref
0.67 (0.32–1.4)

0.75 (0.24–2.38)
0.47 (0.09–2.42)

Ref
0.76 (0.34–1.73)

Ref
0.86 (0.34–2.21)

0.73 (0.23–2.29)

Ref
0.65 (0.26–1.66)

Ref
0.24 (0.08–0.7)*

0.68 (0.18–2.61)
0.86 (0.09–8.59)

Unspecific or
undetermined
aRRR (95% CI)

Ref
0.27
(0.11–0.63)**

Ref
7.42
(2.42–22.71)**

Ref
0.76 (0.4–1.47)

Ref
Ref
0.29 (0.08–1.12) 1.39 (0.62 − 3.11)

Ref
Ref
3.06
0.48 (0.23–1.03)(0.53–17.72)
2.09 (0.3–14.65) 0.72 (0.3–1.76)

Ref
0.58 (0.18–1.9)

2.97
0.98 (0.33–2.91)(0.51–17.27)
0.52 (0.05–5.87) 1.64 (0.65–4.14)$3.78e− 08 (-)$4.79e− 08 (-)

Death due toPreterm BirthaRRR (95% CI)(2024) 24:572

NANA

0.24 (0.19)
0.07-1554.33

Ref
1.27 (0.77–2.11)

Ref
1.17 (0.91–1.52)

0.26 (0.19)
0.07-1191.85

Ref
1.01 (0.57–1.79)

Ref
0.84 (0.62–1.33)

0.39(0.19)
0.11-1255.67

1.37 (0.56–3.32)

0.7 (0.47–1.04)

0.39 (0.19)
0.11-1734.76

Ref
0.76 (0.55–1.06)

Health service specific predictors
Antenatal CareNo ANC
1 to 3 ANCs

4 or more ANCs
Birth assistance
Medically trained
Not medically trained
Post natal care
Yes
No

Ref
1.32 (0.74–2.35)

Ref
0.71 (0.52–0.96)*

Ref
1.61 (0.75–3.48)

1.73 (0.7–4.3)
0.43 (0.9–2.1)

1.24 (0.79–1.94)
0.62 (0.3–1.29)

BarisalMymensingh
Survey year2011
2017-180.71 (0.22–2.25)

0.76 (0.45–1.29)

Death due toother causes
aRRR (95% CI)

Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)
0.37 (0.09–1.47) 0.54 (0.12–2.44)

Estimates related to predictors of cause-specific U5M

Estimates related to predictors of overall/all-cause U5M
Null
Final model/
model
Model 4All causes
togetheraOR (95% CI)
Null model Final model/Model 4

Multilevel Multinominal Mixed Effects Logistic Regression

Multilevel Mixed-Effects
Logistic Regression

Rangpur

N = 9469

Table 4 (continued)

Mazumder et al. BMC Pediatrics
Page 15 of 25

3542.65
5095.463483.513533.62457.692722.462515.34
2529.65
Akaike’s Information Criteria
Bayesian Information Criteria

Among all the U5 children included in the study, 4.93%
were deceased. Among all the deceased children, the four
leading causes of death were - pneumonia (22%), birthasphyxia (15%), possible serious infection (11%) and preterm birth-related conditions (8%). These four causes
accounted for approximately 56% of the total U5M.
There were more male U5 children (51.85%) thanfemale U5 children (48.15%) included in this study. Twins
were only about 1% of all U5 children. Around 13% of themothers did not have any formal education and the proportion of no formal education was even higher (around22%) among their husbands. More than 15% of the mothers gave birth to the reference child before they became
19 years old. Nearly 40% of the mothers were either
underweight, overweight or obese during the survey.
About one-fourth of the community access road condition was poor.
Table 2 presents the unweighted and weighted frequency distribution of the sociodemographic and healthservice predictors included in the analysis of three years
or younger children, a sub sample of U5 children. However, the analysis presented in this section refers only tothe unweighted frequency distribution of health service
specific predictors.
Approximately one-fifth of the mothers reported thatthey did not receive any ANC during their pregnancy.
More than 50% of the mothers reported that their delivery was not assisted by a medically trained provider, andaround 40% of the mothers reported that their child did
not receive PNC.
Overall/all-cause U5M
Random effects

The analyses of the U5 children sample indicated that
23% of the total variation in child mortality could
be attributed to household-level random effects (seeTable 3). However, an additional analysis of the subsample, i.e., children aged three years or younger indicatedthat 11% of the total variation in child mortality could
be attributed to community-level random effects (seeTable 4).
Predictors

Table 3 presents the estimates related to the sociodemographic predictors of overall U5M and cause-specificU5M. Based on the likelihood ratio tests, only the best-fit
models for both analyses are presented.
Child and mother-specific predictors
Additional files

Death due toPreterm BirthaRRR (95% CI)
Death due to
Death due toBirth Asphyxia Possible SeriaRRR (95% CI) ous Infection
aRRR (95% CI)
Death due toPneumonia
aRRR (95% CI)
Death due toother causes
aRRR (95% CI)

Null model Final model/Model 4

Estimates related to predictors of cause-specific U5M

Estimates related to predictors of overall/all-cause U5M
Null
Final model/
model
Model 4All causes
togetheraOR (95% CI)

Multilevel Multinominal Mixed Effects Logistic Regression
Multilevel Mixed-Effects
Logistic Regression

Table 4 (continued)

N = 9469

Page 16 of 25

Notes ***: p < .001, **: p < .01, *: p < .05, SE: Standard Error, CI: Confidence Interval, aOR: Adjusted Odds Ratio, aRRR: Adjusted Relative Risk Ratio; Ref: Reference group; for estimates related to the sociodemographic predictors,
this paper refers to Table 3 only

(2024) 24:572

Unspecific or
undetermined
aRRR (95% CI)

Mazumder et al. BMC Pediatrics

Gestation type was the only child-specific predictor that was significantly associated with overall U5M;
twins or multiples were associated with more than fivefold (adjusted odds ratio (aOR): 5.17, 95% CI: 3.22–8.28,
p < .001) greater odds of dying than singletons. Mother’sMazumder et al. BMC Pediatrics(2024) 24:572

age at childbirth, mother’s education, and mother’s BMI
were the three mother-specific predictors significantly
associated with child mortality. Compared to motherswith no education, children of mothers with secondaryor higher education had 0.68 times (95% CI: 0.52–0.89,
p < .01) to 0.45 times (95% CI: 0.29–0.69, p < .001) lower
odds of mortality. The U5M risk was 0.69 to 0.48 (p < .001)
times lower among the children of mothers aged 19 years
or older during childbirth than among the children of
mothers 18 years or younger. U5 children whose mothers
were overweight or obese during the survey had a greaterrisk (aOR: 1.32, 95% CI: 1.07–1.63, p < .01) of death than
did those whose mothers had a normal weight during thesurvey.
Father and household-specific predictors

Among the father and household-specific characteristics,
father’s education and existence of a hand washing station in the household were the two predictors associatedwith overall U5M. The mortality risk was 0.75 times (95%
CI: 0.59–0.96, p < .05) lower when fathers had secondary education and 0.67 times (95% CI: 0.47–0.96, p < .05)
lower when fathers had higher education than whenfathers had no education. The risk of U5M was greater(aOR: 1.5, 95% CI: 1.18–1.91, p < .01) when there was nohand washing station in the household than when there
was a hand washing station.
Community and time-specific predictors

Geographically, some provinces (divisions) had a significantly lower U5M risk than the comparison division- Sylhet. Additionally, children from the 2017-18 survey
were 1.23 times (95% CI: 1.03–1.46, p < .05) more likely to
die than children from the 2011 survey.
Health service-specific predictors

Table 4 presents the analysis of sociodemographic andhealth service-specific predictors on the subsample ofU5 children, i.e., children aged three years or younger.
However, this paper refers to Table 4 only for the healthservice specific predictors, and for sociodemographic
predictors, this paper refers to Table 3 only. As presented
in Table 4, none of the health service utilisation-related
predictors were significantly associated with overall/allcause mortality in this age group. However, the directionsof the coefficients point to the mortality risk being lower
when the mother received ANC, and the mortality risk
was higher when the children did not receive any PNC.
Key cause-specific U5M
Random effects

The household and community-level random effects on
cause-specific mortality appeared to be the same as therandom effects of these two levels on overall/all-cause

Page 17 of 25

mortality. Analysis of the U5 children sample showedthat 23% of the total variation in child mortality could
be attributed to household-level random effects (seeTable 3). An additional analysis on the three years or
younger children sample showed that 11% of the total
variation in child mortality could be attributed to community-level random effects (see Table 4).
Predictors

Table 3 presents the estimates related to the sociodemographic predictors of overall U5M and cause-specific U5M. Table 4 presents both sociodemographic andhealth service-specific predictors based on the analysis
on the sub-sample of U5 children, i.e., children three
years or younger. However, this paper refers to Table 4,
only for the estimates related to health-service-specificpredictors.
Based on the likelihood ratio tests, only the best-fit
model for multilevel multinomial logistic regression analyses is presented in Table 4. However, the model 4 of themultilevel mixed effects logistic regression analysis on
three years or younger children is an exception. Model 4
is still presented in Table 4 to advocate for the robustnessof multilevel multinomial logistic regression analysis overmultilevel mixed-effects logistic regression analysis.
Child and mother-specific predictors

The child-specific predictors – sex and gestation typewere significantly associated with cause-specific U5M.
Compared to male children, female children had a lower(adjusted relative risk ratio (aRRR): 0.57, 95% CI: 0.39–
0.83, p < .01) risk of dying from birth asphyxia. Compared to singletons, twins or multiples had a greater riskof death from birth asphyxia (aRRR: 6.52, 95% CI: 2.51–
16.91, p < .001), possible serious infection (aRRR: 11.12,
95% CI: 4.52–27.36, p < .001) and preterm birth related
conditions (aRRR: 38.01, 95% CI: 19.08–75.7, p < .001).
Compared to the children whose mothers had no
education, those whose mothers had secondary (aRRR:
0.4, 95% CI: 0.24–0.67, p < .01) or higher (aRRR: 0.35,
95% CI: 0.15–0.83, p < .05) education had a significantly
lower risk of mortality from pneumonia. Birth asphyxiarelated mortality was significantly lower for children
whose mothers were aged 25 years or older comparedto those whose mothers were aged 18 years or younger
during childbirth. The mortality risk due to possible serious infection was significantly lower among the children
whose mothers were aged between 25 and 30 years during childbirth than those whose mothers were aged 18
years or younger during childbirth. Likewise, mortality
risk due to preterm-related conditions was significantly
higher among the children whose mothers were aged 18
years or younger during childbirth compared to the children whose mothers were older during childbirth.

Mazumder et al. BMC Pediatrics(2024) 24:572

Page 18 of 25

Father and household-specific predictors

Predicted probability of cause-specific mortality

The risk of death from possible serious infection was
lower (aRRR: 0.43, 95% CI: 0.21–0.88, p < .05) among
the children whose fathers had secondary education
than among those whose fathers had no education. The
absence of a hand washing station in the household was
associated with more than two-fold greater (aRRR: 2.22,
95% CI: 1.25–3.95, p < .01) risk of U5M from possible
serious infections than the presence of a hand washingstation.

Figures 1, 2 and 3 (margins plots) are the outputs ofinteraction terms and present the changes over time in
the predicted risks of U5M from preterm-related conditions associated with four key predictors – mother’s
age at childbirth, mother’s education, gestation type and
PNC utilisation. Figures 1, 2 and 3 also illustrate the heterogeneity of mortality risks at different levels of thesepredictors. As it is evident, among some of the groups ofchildren, the mortality risks increased between 2011 and2017-18. For example, Fig. 1a illustrates that while themortality risks increased among all children, the increase
was the highest among the children whose mothers wereaged 18 years or younger during childbirth. Figure 1b
shows that the increase in mortality risk was the highest
among the children whose mothers had no education.
Although the risk of death due to preterm birth-related
conditions remained roughly the same among singletons
between 2011 and 2017-18, the risk increased moderately among twins or multiples (Fig. 2). The prematurityrelated U5M risk moderately decreased when children
received PNC (Fig. 3).

Community and time-specific predictors

None of the community-specific predictors were significantly associated with cause-specific deaths, such as
pneumonia, birth asphyxia, preterm-related conditions,or possible serious infections. However, there was a significant increase (aRRR: 2.2, 95% CI: 1.23–3.92, p < .01) inU5M due to preterm-related conditions during the 201718 survey period compared to the 2011 survey period.
Health service-specific predictors

Table 4 presents the health-service -specific predictorsof all-cause/overall and cause-specific U5M based on theanalysis on children three years or younger, a subsample
of U5 children. Deaths due to pneumonia, and pretermrelated conditions were significantly associated withhealth service-specific predictors. The U5M mortality
risk from pneumonia was 0.4 times lower (95% CI: 0.22–
0.73, p < .01) among children whose mothers had 1–3
ANC visits than those whose mothers had no ANC visit.
The risk was 0.4 times lower (95% CI: 0.19–0.83, p < .05)
when the mother had four or more ANC visits. The riskof U5M from prematurity-related conditions significantly
increased when the child did not receive PNC (aRRR:
7.42, 95% CI: 2.42–22.71, p < .01).

Discussion
This research used nationally representative survey data
to investigate the factors influencing cause-specific U5M
in Bangladesh and highlight the importance of these
insights for achieving a more effective reduction in U5M.
The findings generated significant evidence supporting
the aim of this research.
The analyses of cause-specific U5M have provided
additional and more specific evidence to address U5M
than the analyses of overall/all-cause U5M. For example,
none of the health service-specific predictors demonstrated a significant association with overall/all-cause
U5M in this study. On the contrary, the analysis ofFig. 1 (a) Predicted probability of preterm death by mother’s age at childbirth and by the year of survey (N=16703). (b) Predicted probability of preterm
death by mother’s education level and by the year of survey (N = 16703)

Mazumder et al. BMC Pediatrics(2024) 24:572

Page 19 of 25

Fig. 2 Predicted probability of preterm death by gestation type of the child and by the year of survey (N = 16703)

Fig. 3 Predicted probability of preterm death by PNC utilisation status and by the year of survey (N = 9469)

cause-specific U5M revealed that ANC and PNC reduced
pneumonia, and prematurity-related mortality, respectively. This finding suggests that the effects of predictorsof cause-specific mortality remain masked when they are
analysed as predictors of all-cause U5M, as is commonly
done.

The importance of quality pregnancy, delivery andpostnatal care for ending preventable child deaths haslong been stressed by the World Health Organisation(WHO), the United Nations International Children’s
Emergency Fund and researchers globally [46, 47]. The
findings of this study delve deep into this assertion. Let us
take the example of pneumonia first; our study identified

Mazumder et al. BMC Pediatrics(2024) 24:572

a lower risk of pneumonia-caused mortality among children of mothers who received ANC. As the WHO recommends, immunisation, proper nutrition, and healthyliving conditions can prevent pneumonia among children. ANC contact is a platform where pregnant women
can acquire pneumonia prevention-related knowledge
and act accordingly. Therefore, the inverse relationshipbetween ANC uptake and pneumonia-specific U5M islogically consistent and in line with existing research [48].
On the contrary, a case-control study conducted in Ethiopia found no significant association between childhood
pneumonia and antenatal care utilisation [49]. Second,
our study revealed a lower risk of U5M from prematurity among those who received PNC. Premature birth isassociated with numerous serious health risks for neonates, often leading to death [50–52]. PNC contact witha skilled provider is the opportunity to assess a newborn’s
health, obtain useful advice, apply appropriate interventions, and thus reduce the risk of death from prematurity[53, 54]. While limited studies exist on the association
between preterm or pneumonia as causes of death andmaternal and neonatal health care services, this study has
generated new knowledge. This knowledge can guide thereinforcement of health system interventions for accelerating the U5M reduction rate.
Like health services, sex of child also did not appear to
be a correlate of overall/all-cause U5M. However, analyses of cause-specific mortality data revealed that female
children had a significantly lower risk of birth asphyxiarelated deaths than male children. Studies conductedin the United States and Nepal also reported that maleneonates had a greater risk of developing birth asphyxia[55, 56]. Earlier studies have referred to poor lung maturation associated with male hormones to explain theincreased susceptibility of male infants to respiratory diseases [57, 58]. On the contrary, a study conducted in Brazil reported a higher mortality risk from asphyxia amongfemales, which may be related to the unfair distributionof resources and treatment in the country [59]. Hence,
our findings reinforce the existing evidence. In the context of Bangladesh, these findings can guide neonatal
health policies, with special attention for male children,
to prevent birth asphyxia-related mortality.
Our study also revealed a higher risk of U5M from preterm birth-related conditions, birth asphyxia, and possible serious infections among twins or multiple gestation
infants. Multiple gestation itself is a well-documented
cause of preterm birth [60, 61]. Furthermore, due tounderdeveloped organ functions, preterm babies are
more susceptible to various life-threatening conditions
such as birth asphyxia or serious infections [50–52]. Insummary, multiple gestations often lead to preterm birth,an established risk factor for neonatal death from respiratory problems or other infection-related deaths observed

Page 20 of 25

in Bangladesh and elsewhere. Therefore, interventions
focused on mothers with multiple pregnancies have ahigh potential to reduce preterm birth and associatedchild mortality and morbidity.
Our analysis of the demographic factors also revealed
that being born to a mother aged 18 years or younger was
associated with a higher risk of U5M from birth asphyxia,
possible serious infection, or preterm birth compared to
the risk of children born to older mothers. The influenceof maternal age at childbirth on U5M from birth asphyxia
varies, as reported by different research; a study conducted in Cameroon reported a significantly greater riskof birth asphyxia among neonates of adolescent mothers
than among those of adult mothers, congruent with ourfindings [62]. In contrast, a study conducted in Pakistan
reported that a significantly greater risk was associatedwith maternal age 30 years or older than with maternal
age younger than 30 years. The study conducted in Pakistan might not have considered attributes like physiological maturity, education, and employment that can
influence knowledge and practice among mothers ofdifferent age groups. Had there been more categories ofmaternal age younger than 30 years, such as 19 years or
younger, 20 to 24 years, and 25 to 29 years, the findings ofthe study above would have been more informative [63].
While our study reported a greater risk of U5M frompossible serious infection among the children of mothers
18 years or younger, two studies conducted in Bangladesh
did not find any significant association between neonatal infection and maternal age [64, 65]. Nonetheless, oneof these studies reported that children of mothers aged
25–29 years had a lower risk of infection than children ofyounger mothers [65]. Conversely, a cross-sectional study
conducted in Ethiopia reported that children born tomothers aged between 20 and 34 years had a significantly
greater risk of developing neonatal sepsis than children
born to mothers younger than 20 years [66]. However,like the study in Pakistan [58], this Ethiopian study also
used a wider age range, while the analysis could have provided better insights if the age had been categorised considering the attributes of different maternal age groups.
Hence, further studies are needed to provide better
insights into the relationship between maternal age andneonatal infection.
The association between maternal age and preterm
birth, on the other hand, is less debatable. Most studies have reported a U-shaped relationship, i.e., the riskof preterm birth increases as maternal age becomes
younger or older compared to that in the 20 to 30-year
age group [67, 68]. Our study confirmed this ‘U’ shaped
relationship; moreover, children born to mothers aged
25 to 30 years had the lowest risk of dying from pretermrelated conditions compared to the children of mothers
older or younger. Earlier studies referred to physiologic

Mazumder et al. BMC Pediatrics(2024) 24:572

immaturity as a reason for the higher prevalence of preterm births among younger mothers [69]. In comparison,
the possible influence of hormonal change among older
mothers was stated as a reason for preterm births among
comparatively older mothers [70].
We found a lower risk of U5 deaths from pneumonia
or preterm-related conditions when mothers had formal education than when mothers had no formal education. A recent systematic review and meta-analysis also
reported that mothers’ lower education increased therisk of respiratory infection-related death among U5 children [8]. An Egypt-based study further explained that
educated mothers are possibly more capable of identifying the illness and seeking treatment sooner than mothers with lower education attainment are, which reduces
pneumonia-related deaths [71]. The effect of low maternal education on preterm birth has also been documented in studies representing diverse populations andgeographic locations. For instance, a study conductedin rural Bangladesh reported that women with primaryeducation had a 0.77 times lower risk of preterm delivery than women having less education than primaryeducation [61]. A study conducted on newborns from 12
European countries also highlighted similar findings [72].
Mother’s education is often reported as one of the mostimportant predictors of health [73, 74]. Improved knowledge and awareness, better practices related to nutritionand lifestyle, and better healthcare access stimulated byeducation are some of the potential pathways that lead tolower preterm deaths among mothers with comparatively
higher education levels.
The cause-specific analyses in this study also shed somelight on the possible explanation of the slow decline or
stagnancy of overall U5M between 2014 and 2017-18
BDHS [13]. The analyses of overall U5M data showedthat U5M was 1.23 times greater in 2017-18 than in
2011. However, cause-specific data analyses revealed amore alarming trend; prematurity-related deaths weregreater (2.2 times) in 2017-18 than in 2011. Furthermore,
the cause-specific data analyses revealed that there wasan increase in the predicted risk of prematurity-related
deaths among children whose mothers were aged 18
years or younger during childbirth, children whose mothers were without any formal education, children who
were twins or multiples and children who did not receive
PNC.
Figures 1, 2 and 3 portray the increased risk among
these groups, and these elevated risks indicate that initiatives to prevent U5M from prematurity were notadequately effective among these specific groups andcontributed to the slow decline in U5M between 2014
and 2017-18. For example, a higher risk of mortality among children born to younger mothers and its
increase between 2011 and 2017-18 possibly refers to the

Page 21 of 25

weaknesses in the initiatives to reduce child marriage inthe rural regions of Bangladesh [75]. The weaknesses in
the implementation of these initiatives might have led
to an increase in child marriage where people were lessmotivated in the prevention of early marriage. This risein mortality risk could also have been influenced by theinadequacy of family planning services offered to newly
married couples in rural public health facilities [76].
This increased mortality risk can also be linked tohealth facility readiness. As stated earlier in this paper,
preterm babies are at higher risk of life-threatening complications due to their underdeveloped organ functions
and need quality health care to stay alive and thrive [50–52]. However, several studies highlighted the shortfall in
health facility readiness and quality of postnatal care in
Bangladesh, which explains the increased mortality risk
among preterm babies [77, 78]. Our cause-specific findings also refer to these inadequacies, and these findings
can guide more robust initiatives for an enhanced andsustained reduction in U5MR and avoid possible futurestagnation.
Overall, the findings of our study rationalise the significance of the analysis of cause-specific U5M over overall/
all-cause U5M by providing valuable insights into thecorrelates of cause-specific U5M. Most importantly, thisstudy highlights the key areas of intervention that cancontribute to an accelerated reduction in U5MR by preventing child deaths from preterm-related conditions,birth asphyxia, pneumonia, and possible serious infections, the leading causes of U5M.
From a policy perspective, this study provides evidence of the capacity of maternal and neonatal healthservices in Bangladesh to prevent key cause-specific childdeaths amid concerns regarding the quality of healthservices and preparedness of health facilities. Therefore,
these findings also highlight the scope for Bangladesh toenhance the U5M reduction rate by reinforcing the programmes and policies to improve health facility readinessand quality of health care, which is crucial for achieving
SDG 3.2.1. While a recent study reported that Bangladesh is likely to achieve SDG 3.2.1, our study highlights
the underperforming sectors that can challenge Bangladesh’s achievement of SDG targets [79]. Hence, it ispivotal for Bangladesh and its development partners to
meticulously and continuously monitor the initiatives
implemented for the socioeconomic and health sector’s
improvement and refine these as appropriate to bolster
Bangladesh’s progress towards achieving SDG 3.2.1.
Strengths and limitations

Our study reports the correlates of the key cause-specificU5M using data from two nationally representative surveys (2011 and 2017-18), allowing comparison over sevenyears. Analyses of two surveys provided the scope to use

Mazumder et al. BMC Pediatrics(2024) 24:572

time as a moderating variable to illustrate the change inmortality risk. Our study illustrates an increase in preterm-related mortality risk among certain groups overtime. This explains how the increase contributed to theslow decline in overall U5M between 2014 and 2017-18,
while previous studies commonly reported the predictors of U5M. This research adopted multilevel analysis
and adjusted for the clustering effect or the association
between children belonging to the same household orcommunity. Therefore, the estimates reported in ourstudy are more robust. Our study also compared tworobust approaches to investigating U5M and elucidated
the usefulness of cause-specific mortality analysis over
the overall/all-cause mortality for more effective U5M
reduction.
On the other hand, our study has several limitations.
The cause-specific mortality data may exhibit small
sample sizes of some attributes (small cell sizes), which
may be one of the crucial limitations of our study. The
small sample size constrained our ability to conduct
analyses of U5 children according to the age groups (neonate, post-neonate or child) and other features, as further stratification may lead to cell sizes of less than fiveor even empty cells. Moreover, our ability to investigate
other critical cause-specific deaths, such as congenitalabnormalities, was limited. Furthermore, health servicespecific information like ANC and PNC was available
only for the children born within three years prior to thesurvey. Consequently, we could not study the impact ofhealth services on U5 children older than three years ofage. However, most of the deaths from preterm conditions, birth asphyxia or possible serious infections wereamong the U5 children younger than three years of age.
Another limitation of our study is the cross-sectional
nature of the data (although repeated over time), which
limits our opportunity to ascertain the cause-effect relationships between cause-specific deaths and their correlates. Lastly, the estimates from multilevel analyses are
not based on weighted data since we found little variation
between the weighted and unweighted sample statistics.

Conclusions
This research aimed to understand the correlates ofcause-specific U5M in Bangladesh, generate robust
evidence, and provide evidence-based guidance forcause-specific predictors to guide advanced, effective
interventions for rapid U5M reduction. In our study,cause-specific U5M analyses showed a significant association with both sociodemographic and health service-related predictors, whereas overall/all-cause U5M
analysis showed associations with sociodemographic predictors only. Hence, the cause-specific analysis revealed
valuable insights into effective U5M reduction, which
remained elusive in the analysis of overall U5M.

Page 22 of 25

Given that the mortality risk in Bangladesh remains
high for twins or multiples from causes linked to prematurity, birth asphyxia or possible serious infections,
we advocate for careful monitoring of mothers pregnant
with twins or multiples through the continuum of care.
Our recommendation aligns with the current National
Health Strategy for Maternal Health that mentioned registration of all pregnant women with mobile numbers as
a strategic direction [80]. The Standard Operating Procedure on Maternal Health indicated ultrasonography
as one of the contents of ANC. Ultrasonography during
ANC can identify pregnancies with twins or multiples
and promote a targeted continuum of care [81]. However,
the government and the implementing partners need
to work together to ensure these guidelines are implemented adequately as maternal and neonatal health careis provided across different tiers of health facilities. A
higher risk of preterm-related deaths among the childrenof mothers with low education or children born to mothers 18 years or younger refers to the inadequacies of theexisting initiatives to promote maternal education andprevent adolescent pregnancy. Therefore, this elevatedrisk underscores the importance of adopting more comprehensive initiatives to reduce the U5 mortality rate
more effectively.
The rise in neonatal deaths due to preterm-related conditions over a seven-year period further stresses the needto strengthen the efforts to improve the quality of maternal and neonatal health care. While we know that the
government is committed to improving access to, quality of, and utilisation of antenatal, delivery and postnatal
care in Bangladesh, it is apparent that these efforts havenot been able to deliver the expected outcomes. Further research is needed to understand the gap between
the performance of health service-related initiatives andexpected child health outcomes. Knowledge about thesegaps and the application of this knowledge can further
strengthen Bangladesh’s strategies to achieve SDG 3.2.1.
Abbreviations
AIC	Akaike Information Criterion
ANC	Antenatal CareaOR	Adjusted Odds RatioaRRR	Adjusted Relative Risk Ratio
BDHS	Bangladesh Demographic and Health Survey
BIC	Bayesian Information Criterion
CI	Confidence Interval
EA	Enumeration Area
MDG	Millennium Development Goal
NMR	Neonatal Mortality RateOR	Odds Ratio
PNC	Postnatal Care
RR	Relative Risk
RRR	Relative Risk Ratio
SDG	Sustainable Development Goal
U5	Under-5
U5M	Under-5 Mortality
U5MR	Under-5 Mortality Rate
WHO	World Health Organisation

Mazumder et al. BMC Pediatrics(2024) 24:572

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12887-024-04979-6.
Supplementary Material 1: Table S1: Multicollinearity of the sociodemographic variables included in the analysis of U5 children
Supplementary Material 2: Table S2: Multicollinearity of the sociodemographic and health service-specific variables included in the analysis ofchildren three years or younger (a sub-sample of U5 children)Supplementary Material 3: Table S3: Model-specific estimates of thesociodemographic predictors of overall/all-cause mortality among U5childrenSupplementary Material 4: Table S4: Model-specific estimates of thesociodemographic and health service-specific predictors of overall/allcause mortality among children three years or younger (a sub-sample ofU5 children)Supplementary Material 5: Table S5: Model-specific estimates of the sociodemographic predictors of cause-specific mortality among U5 children– Model 1
Supplementary Material 6: Table S6: Model-specific estimates of the sociodemographic predictors of cause-specific mortality among U5 children– Model 2
Supplementary Material 7: Table S7: Model-specific estimates of the sociodemographic predictors of cause-specific mortality among U5 children– Model 3
Supplementary Material 8: Table S8: Model-specific estimates of thesociodemographic and health service-specific predictors of cause-specificmortality among children three years or younger (a sub-sample of U5children) - Model 1
Supplementary Material 9: Table S9: Model-specific estimates of thesociodemographic and health service-specific predictors of cause-specificmortality among children three years or younger (a sub-sample of U5children) - Model 2
Supplementary Material 10: Table S10: Model-specific estimates of thesociodemographic and health service-specific predictors of cause-specificmortality among children three years or younger (a sub-sample of U5children) - Model 3
Supplementary Material 11: Table S11: Model-specific estimates of thesociodemographic and health service-specific predictors of cause-specificmortality among children three years or younger (a sub-sample of U5children) - Model 4
Acknowledgements
We thank the Demographic and Health Surveys Program for granting accessto the Bangladesh Demographic and Health Survey datasets.
Author contributions
TM, IM, DA, and TN conceptualised the study. TM performed data curation.
TM, IM, DA, and TN conducted the data analysis and visualization. TM, IM,
DA, and TN jointly developed the methodology of the paper. TM developed
the first draft of the manuscript. TM, IM, DA, and TN reviewed and edited themanuscript. All the authors provided final approval of the manuscript for
publication.
Funding
This research did not receive any funding.
Data availability
The datasets analysed during the current study are available in the
Demographic and Health Surveys repository, https://dhsprogram.com/Data/.

Page 23 of 25

Declarations
Ethics approval and consent to participate
The ICF provided approval for accessing the publicly available deidentified
data from BDHS 2011 and 2017-18. Due to the nonsensitive nature of thesesecondary data, the University of Canberra’s Research Ethics Committee
exempted this study from the ethical review process (reference: 11579).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 21 December 2023 / Accepted: 29 July 2024

References
1. You D, Hug L, Ejdemyr S, Idele P, Hogan D, Mathers C, et al. Global, regional,
and national levels and trends in under-5 mortality between 1990 and2015, with scenario-based projections to 2030: a systematic analysis
by the UN Inter-agency Group for Child Mortality Estimation. Lancet.
2015;386(10010):2275–86.
2. Sharrow D, Hug L, You D, Alkema L, Black R, Cousens S, et al. Global, regional,
and national trends in under-5 mortality between 1990 and 2019 withscenario-based projections until 2030: a systematic analysis by the UN
Inter-agency Group for Child Mortality Estimation. Lancet Global Health.
2022;10(2):e195–206.
3. Perin J, Mulick A, Yeung D, Villavicencio F, Lopez G, Strong KL, et al. Global,
regional, and national causes of under-5 mortality in 2000–19: an updated
systematic analysis with implications for the Sustainable Development Goals.
Lancet Child Adolesc Health. 2022;6(2):106–15.
4. Tibaijuka L, Bawakanya SM, Owaraganise A, Kyasimire L, Kumbakumba E,
Boatin AA, et al. Incidence and predictors of preterm neonatal mortality at
Mbarara Regional Referral Hospital in South Western Uganda. PLoS ONE.
2021;16(11):e0259310.
5. Yismaw AE, Gelagay AA, Sisay MM. Survival and predictors among preterm
neonates admitted at University of Gondar comprehensive specialized
hospital neonatal intensive care unit, Northwest Ethiopia. Ital J Pediatr.
2019;45(1):4.
6. Opio C, Malumba R, Kagaayi J, Ajumobi O, Kamya C, Mukose A, et al. Survival
time and its predictors in preterm infants in the post-discharge neonatal
period: a prospective cohort study in Busoga Region, Uganda. Lancet Global
Health. 2020;8:S6.
7. Aynalem YA, Mekonen H, Akalu TY, Gebremichael B, Shiferaw WS. Preterm
neonatal mortality and its predictors in Tikur Anbessa Specialized Hospital,
Addis Ababa, Ethiopia: a retrospective cohort study. Ethiop J Health Sci.
2021;31(1):43–54.
8. Sonego M, Pellegrin MC, Becker G, Lazzerini M. Risk factors for mortality fromacute lower respiratory infections (ALRI) in children under five years of age in
low and middle-income countries: a systematic review and meta-analysis ofobservational studies. PLoS ONE. 2015;10(1):e0116380.
9. Jackson S, Mathews KH, Pulanić D, Falconer R, Rudan I, Campbell H, et al. Risk
factors for severe acute lower respiratory infections in children–a systematic
review and meta-analysis. Croatian Med J. 2013;54(2):110–21.
10. Parker S-J, Kuzniewicz M, Niki H, Wu YW. Antenatal and Intrapartum Risk
factors for hypoxic-ischemic Encephalopathy in a US Birth Cohort. J Pediatr.
2018;203:163–9.
11. Wall SN, Lee ACC, Carlo W, Goldenberg R, Niermeyer S, Darmstadt GL et al.
Reducing Intrapartum-related neonatal deaths in low- and Middle-Income
Countries—what works? Seminars in Perinatology. 2010;34(6):395–407.
12. General Economics Division. Millennium Development Goals - Bangladesh
Progress Report 2015. Bangladesh Planning Commission; 2015 September.
13. National Institute of Population Research Training (NIPORT), and ICF. Bangladesh Demographic and Health Survey 2017–18. Dhaka, Bangladesh andRockville, Maryland, USA: NIPORT, and ICF. 2020.
14. Rubayet S, Shahidullah M, Hossain A, Corbett E, Moran AC, Mannan I, et al.
Newborn survival in Bangladesh: a decade of change and future implications.
Health Policy Plan. 2012;27(Suppl 3):iii40–56.

Mazumder et al. BMC Pediatrics(2024) 24:572

15. PMNCH, World Bank WHO. AHPSR. Success Factors for Women’s andChildren’s Health: Policy and Programme Highlights from Ten Fast-track
Countries. 2014.
16. Imran MIK, Inshafi MUA, Sheikh R, Chowdhury MAB, Uddin MJ. Risk factors for
acute respiratory infection in children younger than five years in Bangladesh.
Public Health. 2019;173:112–9.
17. Nasrin S, Tariqujjaman M, Sultana M, Zaman RA, Ali S, Chisti MJ, et al. Factors
associated with community acquired severe pneumonia among underfive children in Dhaka, Bangladesh: a case control analysis. PLoS ONE.
2022;17(3):e0265871.
18. Rahman MS. Factors associated with neonatal deaths due to birth asphyxia
in Bangladesh: A secondary analysis of Bangladesh Demographic and Health
Survey 2017-18 data. 2023.
19. Mostari S, Chowdhury M, Chowdhury MRK, Monte-Serrat DM, Mohammadnezhad M, Kabir R. Exploring the factors associated with birth asphyxia
among the newborn infants at a rural hospital in Bangladesh. Asian J Res
Nurs Health. 2018;1(1):1–9.
20. Rahman A, Rahman M, Pervin J, Razzaque A, Aktar S, Ahmed JU, et al.
Time trends and sociodemographic determinants of preterm births in
pregnancy cohorts in Matlab, Bangladesh, 1990–2014. BMJ Global Health.
2019;4(4):e001462.
21. Parvin S, BakiBillah AH, Hasan I, Chowdhury AA. Prevalence and Predictorsof Birth Asphyxia Among Neonates in Bangladesh: A Cross-Sectional Study.
2022.
22. Saha S, Hasan M, Kim L, Farrar JL, Hossain B, Islam M, et al. Epidemiology andrisk factors for pneumonia severity and mortality in Bangladeshi children < 5years of age before 10-valent pneumococcal conjugate vaccine introduction.
BMC Public Health. 2016;16:1–12.
23. Bangladesh Bureau of Statistics. Population and housing census. Preliminary
Report. Dhaka, Bangladesh. Statistics and Informatics Division. 2022.
24. National Institute of Population Research Training - NIPORT/Bangladesh,
Mitra and Associates and ICF International. Bangladesh demographic andhealth survey 2011. Dhaka, Bangladesh and Calverton, Maryland, USA:NIPORT, Mitra and Associates, and ICF International. 2013.
25. Banik R, Hossain S, Khudri MM. Regional education and wealth-related
inequalities in malnutrition among women in Bangladesh. Public Health Nutr.
2022;25(6):1639–57.
26. Bennett DA. How can I deal with missing data in my study? Australian and
New Zealand. J Public Health. 2001;25(5):464–9.
27. Rahman AE, Hossain AT, Siddique AB, Jabeen S, Chisti MJ, Dockrell DH et
al. Child mortality in Bangladesh–why, when, where and how? A national
survey-based analysis. J Global Health. 2021;11.
28. Alam N, Van Ginneken JK, Bosch AM. Infant mortality among twins
and triplets in rural Bangladesh in 1975–2002. Tropical Med Int Health.
2007;12(12):1506–14.
29. Das U, Chaplot B, Azamathulla HM. The role of place of delivery in preventing
neonatal and infant mortality rate in India. Geographies. 2021;1(1):47–62.
30. Huda TM, Tahsina T, Arifeen SE, Dibley MJ. The importance of intersectoral
factors in promoting equity-oriented universal health coverage: a multilevel
analysis of social determinants affecting neonatal infant and under-five
mortality in Bangladesh. Global Health Action. 2016;9(1):29741.
31. Huo N, Zhang K, Wang L, Wang L, Lv W, Cheng W, et al. Association ofmaternal body mass index with risk of infant mortality: a dose-response
meta-analysis. Front Pead. 2021;9:650413.
32. Khan GR, Baten A, Azad MAK. Influence of contraceptive use and othersocio-demographic factors on under-five child mortality in Bangladesh:semi-parametric and parametric approaches. Contracept Reprod Med.
2023;8(1):22.
33. Kumar S, Loughnan L, Luyendijk R, Hernandez O, Weinger M, Arnold F, et al.
Handwashing in 51 countries: analysis of Proxy measures of Handwashing
Behavior in multiple Indicator cluster surveys and demographic and healthsurveys, 2010–2013. Am J Trop Med Hyg. 2017;97(2):447–59.
34. Sarkodie AO. Factors influencing under-five mortality in rural-urban Ghana:an applied survival analysis. Soc Sci Med. 2021;284:114185.
35. Sines E, Syed U, Wall S, Worley H. Postnatal care: a critical opportunity to savemothers and newborns. Policy Perspect Newborn Health. 2007;1(7).
36. Amin R, Shah NM, Becker S. Socioeconomic factors differentiating maternaland child health-seeking behavior in rural Bangladesh: a cross-sectional
analysis. Int J Equity Health. 2010;9(1):1–11.
37. World Health Organization. Obesity: preventing and managing the global
epidemic: report of a WHO consultation. 2000.

Page 24 of 25

38. Kaombe TM, Hamuza GA. Impact of ignoring sampling design in theprediction of binary health outcomes through logistic regression: evidence
from Malawi demographic and health survey under-five mortality data;
2000–2016. BMC Public Health. 2023;23(1):1674.
39. Carle AC. Fitting multilevel models in complex survey data with design
weights: recommendations. BMC Med Res Methodol. 2009;9(1):49.
40. Dotse-Gborgbortsi W, Tatem AJ, Alegana V, Utazi CE, Ruktanonchai CW,
Wright J. Spatial inequalities in skilled attendance at birth in Ghana: a multilevel analysis integrating health facility databases with household survey
data. Tropical Med Int Health. 2020;25(9):1044–54.
41. Ahmed MS, Whitfield KC, Yunus FM. Trends and predictors of early initiation, exclusive and continued breast-feeding in Bangladesh (2004–2018):
a multilevel analysis of demographic and health survey data. Br J Nutr.
2022;128(9):1857–67.
42. Belay DG, Kibret AA, Diress M, Gela YY, Sinamaw D, Simegn W, et al. Deworming among preschool age children in sub-saharan Africa: pooled prevalence
and multi-level analysis. Trop Med Health. 2022;50(1):74.
43. Sunuwar DR, Singh DR, Chaudhary NK, Pradhan PMS, Rai P, Tiwari K. Prevalence and factors associated with anemia among women of reproductive
age in seven South and Southeast Asian countries: evidence from nationally
representative surveys. PLoS ONE. 2020;15(8):e0236449.
44. Adeyinka DA, Muhajarine N, Petrucka P, Isaac EW. Inequities in child survival in
Nigerian communities during the sustainable development goal era: insightsfrom analysis of 2016/2017 multiple Indicator Cluster Survey. BMC Public
Health. 2020;20(1):1613.
45. Tesfay N, Tariku R, Zenebe A, Hailu G, Taddese M, Woldeyohannes F. Timingof perinatal death; causes, circumstances, and regional variations among
reviewed deaths in Ethiopia. PLoS ONE. 2023;18(5):e0285465.
46. World Health Organization. UNICEF. Ending preventable newborn deaths
and stillbirths by 2030 2020 [cited 2023 25 October]. https://www.unicef.
org/reports/ending-preventable-newborn-deaths-stillbirths-quality-healthcoverage-2020-2025
47. Mahtab S, Madhi SA, Baillie VL, Els T, Thwala BN, Onyango D, et al. Causes ofdeath identified in neonates enrolled through Child Health and Mortality
Prevention Surveillance (CHAMPS), December 2016–December 2021. PLOS
Global Public Health. 2023;3(3):e0001612.
48. Akbar R, Azam N, Mughal FAR, Rahman MU, Tariq A, Wajahat M, ANTENATAL
CARE AND NEONATAL MORTALITY AND MORBIDITY IN THREE HOSPITALS OF
PUNJAB, PAKISTAN. Pakistan Armed Forces Med J. 2021;71(2):562–66.
49. Yadate O, Yesuf A, Hunduma F, Habtu Y. Determinants of pneumonia among
under-five children in Oromia region, Ethiopia: unmatched case-control
study. Arch Public Health. 2023;81(1):87.
50. Wang ML, Dorer DJ, Fleming MP, Catlin EA. Clinical outcomes of near-term
infants. Pediatrics. 2004;114(2):372–6.
51. Moss TJ, editor. The respiratory consequences of preterm birth. Proceedingsof the Australian Physiological Society; 2005.
52. Collins A, Weitkamp J-H, Wynn JL. Why are preterm newborns atincreased risk of infection? Archives Disease Childhood-Fetal Neonatal Ed.
2018;103(4):F391–4.
53. World Health Organization. WHO recommendations on maternal and newborn care for a positive postnatal experience; 2022.
54. Kleinhout MY, Stevens MM, Osman KA, Adu-Bonsaffoh K, Groenendaal F,
Biza Zepro N et al. Evidence-based interventions to reduce mortality among
preterm and low-birthweight neonates in low-income and middle-income
countries: a systematic review and meta-analysis. BMJ Glob Health. 2021;6(2).
55. Gupta SK, Sarmah BK, Tiwari D, Shakya A, Khatiwada D. Clinical profile ofneonates with perinatal asphyxia in a tertiary care hospital of central Nepal.
JNMA J Nepal Med Assoc. 2014;52(196):1005–9.
56. Mohamed MA, Aly H. Impact of race on male predisposition to birth asphyxia.
J Perinatol. 2014;34(6):449–52.
57. Waldron I. Sex differences in infant and early childhood mortality: major
causes of death and possible biological causes Too young to die genes orgender. 1998 New York United Nations, Department of Economic and Social
Affairs. Population Division.64–83.
58. Abeywardana S, Sullivan EA. Congenital anomalies in Australia 2002–2003.
AIHW National Perinatal Statistics Unit; 2008.
59. Kawakami MD, Sanudo A, Teixeira ML, Andreoni S, de Castro JQ, Waldvogel
B, et al. Neonatal mortality associated with perinatal asphyxia: a populationbased study in a middle-income country. BMC Pregnancy Childbirth.
2021;21(1):1–10.

Mazumder et al. BMC Pediatrics(2024) 24:572

60. Heino A, Gissler M, Hindori-Mohangoo AD, Blondel B, Klungsøyr K, VerdenikI, et al. Variations in multiple birth rates and impact on perinatal outcomes in
Europe. PLoS ONE. 2016;11(3):e0149252.
61. Shah R, Mullany LC, Darmstadt GL, Mannan I, Rahman SM, Talukder RR, et al.
Incidence and risk factors of preterm birth in a rural Bangladeshi cohort. BMC
Pediatr. 2014;14:1–11.
62. Njim T, Agbor VN. Adolescent deliveries in semi-urban Cameroon: prevalence
and adverse neonatal outcomes. BMC Res Notes. 2017;10:1–5.
63. Tabassum F, Rizvi A, Ariff S, Soofi S, Bhutta ZA. Risk factors associated withbirth asphyxia in rural district Matiari, Pakistan: a case control study. Int J Clin
Med. 2014;5(21):1430–41.
64. Chan GJ, Baqui AH, Modak JK, Murillo-Chaves A, Mahmud AA, Boyd TK, et al.
Early‐onset neonatal sepsis in Dhaka, Bangladesh: risk associated with maternal bacterial colonisation and chorioamnionitis. Tropical Med Int Health.
2013;18(9):1057–64.
65. Mitra DK, Mullany LC, Harrison M, Mannan I, Shah R, Begum N, et al. Incidence
and risk factors of neonatal infections in a rural Bangladeshi population: acommunity-based prospective study. J Health Popul Nutr. 2018;37:1–11.
66. Mersha A, Worku T, Shibiru S, Bante A, Molla A, Seifu G et al. Neonatal sepsis
and associated factors among newborns in hospitals of Wolaita Sodo Town,
Southern Ethiopia. Research and reports in neonatology. 2019:1–8.
67. Fuchs F, Monet B, Ducruet T, Chaillet N, Audibert F. Effect of maternal age on the risk of preterm birth: a large cohort study. PLoS ONE.
2018;13(1):e0191002.
68. Hidalgo-Lopezosa P, Jiménez-Ruz A, Carmona-Torres J, Hidalgo-Maestre M,
Rodríguez-Borrego M, López-Soto P. Sociodemographic factors associatedwith preterm birth and low birth weight: a cross-sectional study. Women
Birth. 2019;32(6):e538–43.
69. Butler AS, Behrman RE. Preterm birth: causes, consequences, and prevention.
National academies; 2007.
70. Astolfi P, Zonta LA. Risks of preterm delivery and association with maternalage, birth order, and fetal gender. Hum Reprod. 1999;14(11):2891–4.
71. Azab SFAH, Sherief LM, Saleh SH, Elsaeed WF, Elshafie MA, Abdelsalam
SM. Impact of the socioeconomic status on the severity and outcome ofcommunity-acquired pneumonia among Egyptian children: a cohort study.
Infect Dis Poverty. 2014;3:1–7.

Page 25 of 25

72. Ruiz M, Goldblatt P, Morrison J, Kukla L, Švancara J, Riitta-Järvelin M, et al.
Mother’s education and the risk of preterm and small for gestational age
birth: a DRIVERS meta-analysis of 12 European cohorts. J Epidemiol Community Health. 2015;69(9):826–33.
73. Luo Z-C, Wilkins R, Kramer MS. Effect of neighbourhood income andmaternal education on birth outcomes: a population-based study. CMAJ.
2006;174(10):1415–20.
74. Cantarutti A, Franchi M, Monzio Compagnoni M, Merlino L, Corrao G. Mother’s education and the risk of several neonatal outcomes: an evidence froman Italian population-based study. BMC Pregnancy Childbirth. 2017;17(1):221.
75. Alam GM, Hassan CH, Kamal SMM, Ying Y. CHILD MARRIAGE IN BANGLADESH:
TRENDS AND DETERMINANTS. J Biosoc Sci. 2015;47(1):120–39.
76. Islam A, Biswas T. Health system in Bangladesh: challenges and opportunities.
Am J Health Res. 2014;2(6):366–74.
77. Khan MN, Trisha NI, Rashid MM. Availability and readiness of healthcare facilities and their effects on under-five mortality in Bangladesh: analysis of linked
data. medRxiv. 2022. 2022.06. 22.22276753.
78. Kim ET, Singh K, Weiss W. Maternal postnatal care in Bangladesh: a closer lookat specific content and coverage by different types of providers. J Global
Health Rep. 2019;3.
79. Khan MA, Khan N, Rahman O, Mustagir G, Hossain K, Islam R, et al. Trends
and projections of under-5 mortality in Bangladesh including the effects ofmaternal high-risk fertility behaviours and use of healthcare services. PLoS
ONE. 2021;16(2):e0246210.
80. Ministry of Health and Family Welfare, Government of The People’s Republicof Bangladesh. Bangladesh national strategy for maternal health 2015–2030.
2018.
81. Ministry of Health and Family Welfare, Government of The People’s Republicof Bangladesh. Maternal Health - Standard Operating Procedure Volume – I.

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