
==== Front
BMC Pediatr
BMC Pediatr
BMC Pediatrics
1471-2431
BioMed Central London

39251961
4979
10.1186/s12887-024-04979-6
Research
An analysis of cause-specific under-5 mortality in Bangladesh using the demographic and health survey 2011 and 2017–2018
Mazumder Tapas tapas.mazumder@canberra.edu.au

1
Mohanty Itismita 1
Ahmad Danish 12
Niyonsenga Theo 1
1 grid.1039.b 0000 0004 0385 7472 Health Research Institute, Faculty of Health, University of Canberra, Canberra, ACT 2617 Australia
2 grid.1001.0 0000 0001 2180 7477 School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT 2601 Australia
9 9 2024
9 9 2024
2024
24 57221 12 2023
29 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ 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 the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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/.
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, this study 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 and 2017-18 data. Cause-specific U5M was examined using multilevel multinomial mixed-effects analyses, and overall/all-cause 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 not reveal any significant association with health services. Twins or multiples had a greater risk of mortality from preterm-related 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 than children 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 preterm-related 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 postnatal care, or whose mothers did not receive antenatal care stresses the need to strengthen the coverage and quality of 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 of more comprehensive initiatives to promote maternal education and prevent adolescent pregnancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-024-04979-6.

Keywords

Under-5 mortality
Sustainable development goal
Multinomial
Preterm
Pneumonia
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

As 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 in this 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 of global under-5 mortality (U5M) reduction (based on the 2000–2015 period) continues well into the SDG era, then the global achievement of SDG 3.2.1 will be missed, and an 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 the approaches to U5M reduction can be reinforced.

Accelerating U5M reduction can be augmented by generating evidence on the more precise estimation of the causes 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 attributes of different cause-specific U5M are diverse, and the same predictors can have varying impacts on different cause-specific 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 cause-specific 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 globally in 2019 and contributed to 43.2% of the total U5M. Furthermore, the proportions of mortality due to preterm birth complications and intrapartum-related events have increased since 2000 [3]. Analysis of the predictors of cause-specific U5M is available in other region-specific studies [3].

The broader literature reported that preterm neonates born to mothers who did not receive antenatal care (ANC), were delivered at home, did not receive kangaroo 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 for U5M due to lower respiratory tract infections include female sex, low birth weight, lack of exclusive breastfeeding, young maternal age, low maternal education, and not receiving ANC [8, 9]. The key factors associated with intrapartum-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 the global trend and distribution. However, contrary to the shortfall 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 child health 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, the interventions were less effective at reducing mortality in the first 28 days or neonatal period. Consequently, the mortality rate of U5 children in the neonatal period, the neonatal mortality rate (NMR), contributed to 67% of the total U5M in the 2017–2018 survey period compared to 38.8% 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 the same in Bangladesh between the 2011 (56.3%) and 2017-18 (56%) surveys. Furthermore, there was no change in birth asphyxia-specific mortality rates, and prematurity-specific mortality rates even increased. These two are neonate-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 cause-specific mortalities among the neonates. Therefore, the U5M reduction rate became slower over the period 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 tailored according to the nature of neonatal mortality for a more effective reduction in U5MR in Bangladesh. Understanding the correlates of these key cause-specific U5M in greater detail is fundamental to designing more effective interventions. However, while numerous region-specific studies have examined the risk factors associated with cause-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 new knowledge that could prevent potential slow declines or stagnancies in the U5MR and avert thousands of child deaths linked to hindrances to achieving SDG 3.2.1 and the overall progress of a lower- and middle-income country like Bangladesh.

The knowledge generated from cause-specific studies will help countries like Bangladesh understand cause-specific 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 repeated cross-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 use of cause-specific nationally representative mortality data from two surveys to investigate the leading causes of U5M and its trend. This study is also unique methodologically and compares the analysis of overall U5M with cause-specific U5M. Above all, the findings of this study may contribute to the reinforcement of global efforts to achieve SDG 3.2.1, leading to the aversion of preventable child 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 the nationally representative Bangladesh Demographic and Health Survey (BDHS) 2011 and 2017-18 [13, 24].

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-proportional-to-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 of these EAs to get statistically reliable estimates of the key demographic and health variables. These households were accessed to collect information from 15 to 49-year-old ever-married women on their sociodemographics, health, pregnancy and children. Verbal autopsies were also conducted for under 5-year-old children who died within 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, the birth 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 not included 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 health service utilisation like ANC and postnatal care (PNC) only for the most recent births three years before each survey. Therefore, children three years or younger were considered as the sample for studying the health service-specific 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 assistant or PNC and other sociodemographic variables. As the proportions of missing values were less than 5% across all 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 the sample of U5 children) was considered for additional, 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 were classified into six categories, namely, pneumonia, birth asphyxia, possible serious infection, preterm birth, unspecific or undetermined causes and other causes of 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, a multinomial outcome variable was created (with seven categories), 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.

Table 1 Frequency distribution of sociodemographic predictors of cause-specific mortality among U5 children

N = 16,703	Unweighted sample size
n (%)	Weighted proportions (%)	
U-5 children (N)	16,703	100	
Alive	15,879 (95.07)	95.09	
Total deceased	824 (4.93)	4.91	
Cause-specific deaths			
Death due to other causes	225 (1.35)	1.35	
Death due to Pneumonia	179 (1.07)	0. 99	
Death due to Birth Asphyxia	126 (0.75)	0. 72	
Death due to Possible Serious Infection	87 (0.52)	0. 57	
Death Due to Preterm Birth	68 (0.41)	0. 43	
Unspecific or undetermined	139 (0.83)	0. 84	
Mother and child-specific predictors			
Sex			
Male	8660 (51.85)	51.78	
Female	8043 (48.15)	48.22	
Gestation type			
Singleton	16,541 (99.03)	99.04	
Twin or multiple	162 (0.97)	0.96	
Mother’s education			
No education	2199 (13.17)	13.75	
Primary education	4979 (29.81)	29.73	
Secondary education	7488 (44.83)	45.41	
Higher education	2037 (12.20)	11.11	
Mother’s age during childbirth (years)			
Mean (SE)	23.64 (0.04)	23.54 (0.06)	
18 years or younger	2578 (15.43)	15.89	
19 years to 24 years	7261 (43.47)	43.59	
25 years to 30 years	4521 (27.07)	26.86	
31 years or above	2343 (14.03)	13.66	
Mother’s Body Mass Index			
Normal weight	9950 (59.57)	60.45	
Underweight	3523 (21.09)	20.72	
Overweight or obese	3230 (19.34)	18.83	
Exposure to television			
Watched television	10,061 (60.23)	60.43	
Did not watch television	6642 (39.77)	39.57	
Decision making			
No participation of mother	3317 (19.86)	19.87	
Some participation of mother	5062 (30.31)	31.15	
Good participation of mother	8324 (49.84)	48.99	
Father and household-specific predictors			
Father’s education			
No education	3625 (21.70)	22.5	
Primary education	5325 (31.88)	32.03	
Secondary education	5102 (30.55)	30.72	
Higher education	2651 (15.87)	14.75	
Wealth index			
Poorest	3719 (22.27)	22.71	
Poorer	3320 (19.88)	20.5	
Middle	3093 (18.52)	19.2	
Richer	3279 (19.63)	19.47	
Richest	3292 (19.71)	18.13	
Hand washing station			
Observed	15,173 (90.84)	91	
Not observed or not in the dwelling	1530 (9.16)	9	
Cooking fuel type			
Clean fuel	2594 (15.53)	15.48	
Solid fuel or other	14,109 (84.47)	84.52	
Community and time-specific predictors			
Community access route			
Good condition	12,724 (76.18)	74.31	
Poor condition	3979 (23.82)	25.69	
Division			
Sylhet	2590 (15.51)	8.05	
Chittagong	3015 (18.05)	21.71	
Dhaka	2591 (15.51)	28.23	
Khulna	1795 (10.75)	9.08	
Rajshahi	1913 (11.45)	12.43	
Rangpur	2011 (12.04)	10.71	
Barisal	1786 (10.69)	5.52	
Mymensingh	1002 (6)	4.28	
Survey year			
2011	8311 (49.76)	49.9	
2017-18	8392 (50.24)	50.1	

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	Unweighted sample size
n (%)	Weighted proportions (%)	
Three years or younger children (N)	9469	100	
Alive	9190 (97.05)	97.2	
Total deceased	279 (2.95)	2.8	
Cause-specific deaths			
Death due to other causes	66 (0.7)	o.68	
Death due to Pneumonia	73 (0.77)	0.68	
Death due to Birth Asphyxia	41 (0.43)	0.43	
Death due to Possible Serious Infection	29 (0.31)	0.33	
Death Due to Preterm Birth	24 (0.25)	0.26	
Unspecific or undetermined	46 (0.49)	0.41	
Mother and child-specific predictors			
Sex			
Male	4896 (51.71)	51.63	
Female	4573 (48.29)	48.37	
Gestation type			
Singleton	9376 (99.02)	99.07	
Twin or multiple	93 (0.98)	0.93	
Mother’s education			
No education	1071 (11.31)	11.77	
Primary education	2699 (28.5)	28.76	
Secondary education	4425 (46.73)	47.19	
Higher education	1274 (13.45)	12.29	
Mother’s age during childbirth (years)			
Mean (SE)	23.76 (0.06)	23.67 (0.07)	
18 years or younger	1357 (14.33)	14.71	
19 years to 24 years	4139 (43.71)	43.76	
25 years to 30 years	2633 (27.81)	27.95	
31 years or above	1340 (14.15)	13.59	
Mother’s Body Mass Index			
Normal weight	5710 (60.3)	61.1	
Underweight	2139 (22.59)	22.18	
Overweight or obese	1620 (17.11)	16.72	
Exposure to television			
Watched television	5809 (61.35)	61.55	
Did not watch television	3660 (38.65)	38.45	
Decision making			
No participation of mother	1992 (21.04)	21.26	
Some participation of mother	2915 (30.78)	31.32	
Good participation of mother	4562 (48.18)	47.41	
Father and household-specific predictors			
Father’s education			
No education	1859 (19.63)	20.53	
Primary education	3013 (31.82)	31.97	
Secondary education	3020 (31.89)	32.02	
Higher education	1577 (16.65)	15.47	
Wealth index			
Poorest	2041 (21.55)	21.76	
Poorer	1862 (19.66)	20.28	
Middle	1757 (18.56)	19.43	
Richer	1902 (20.09)	19.96	
Richest	1907 (20.14)	18.56	
Hand washing station			
Observed	8625 (91.09)	91.35	
Not observed or not in the dwelling	844 (8.91)	8.65	
Cooking fuel type			
Clean fuel	1473 (15.56)	15.45	
Solid fuel or other	7996 (84.44)	84.55	
Community and time-specific predictors			
Community access route			
Good condition	7259 (76.66)	74.85	
Poor condition	2210 (23.34)	25.15	
Division			
Sylhet	1388 (14.66)	7.58	
Chittagong	1715 (18.11)	21.91	
Dhaka	1449 (15.3)	27.85	
Khulna	1053 (11.12)	9.35	
Rajshahi	1099 (11.61)	12.5	
Rangpur	1142 (12.06)	10.74	
Barisal	1031 (10.89)	5.6	
Mymensingh	592 (6.25)	4.47	
Survey year			
2011	4643 (49.03)	48.8	
2017-18	4826 (50.97)	51.2	
Health service-specific predictors			
Antenatal Care			
No ANC	1816 (19.18)	20	
1 to 3 ANCs	4016 (42.41)	43.41	
4 or more ANCs	3637 (38.41)	36.59	
Birth assistance			
Medically trained	4187 (44.22)	42.45	
Not medically trained	5282 (55.78)	57.55	
Post natal care			
Yes	5659 (59.76)	57.98	
No	3810 (40.24)	42.02	

Explanatory variables

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 a p-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 the bivariate analysis. These three variables represented three key hierarchical levels of potential predictors – child, mother, and community – important for explaining child mortality [28–35].

Child and mother-specific variables

Two child-specific variables, namely, child’s sex (male or female) 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 in household decision making (no participation, some participation or good participation) [36, 37].

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 can live in the same household.

Community and time-specific variables

Three variables were considered – access route to the community (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 four or 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.

Statistical analysis

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, two separate multilevel regression models were estimated and compared concerning the observed significant predictors. The multilevel analysis also accounted for the unexplained heterogeneity in the U5M risk. A multilevel mixed-effects logistic regression analysis was used to investigate the predictors of overall/all-cause U5M, and a 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 the precise 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 were conducted independently on the U5 children and their subsample (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 (see Tables 1 and 2). There was not much variation in weighted 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 more than 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 from the same household or community could have shared similar mortality risks associated with the same exposure. Therefore, multilevel mixed-effects logistic regression analysis was performed to examine the predictors of overall/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 the data. Estimates of variance components (coefficients) and their standard errors were used to determine the significance of the clustering effects. Based on the significance of 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 the child and mother-specific variables (as fixed effects) to the 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 were added (as fixed effects) to Model (2). Likelihood ratio tests were performed to determine if the models with additional variables provided a better fit than the earlier models with fewer variables at a 5% level of significance. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were also estimated. However, this study determined the goodness of fit based on the likelihood ratio test (see Table S3 for model-specific estimates).

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

N = 16,703	Multilevel Mixed-Effects Logistic Regression	Multilevel Multinominal Mixed-Effects Logistic Regression	
Estimates related to predictors of overall/all-cause U5M	Estimates related to predictors of cause-specific U5M	
Null model	Final model/
Model 3	Null model	Final model/Model 3	
		All causes together
aOR (95% CI)		Death due to other causes
aRRR (95% CI)	Death due to Pneumonia
aRRR (95% CI)	Death due to Birth Asphyxia
aRRR (95% CI)	Death due to Possible Serious Infection
aRRR (95% CI)	Death due to Preterm Birth
aRRR (95% CI)	Unspecific or undetermined
aRRR (95% CI)	
Child and mother-specific predictors										
Sex										
Male		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Female		0.87 (0.74–1)		0.89 (0.68–1.17)	0.97 (0.72–1.31)	0.57 (0.39–0.83)**	1.05 (0.68–1.61)	0.87 (0.53–1.43)	0.90 (0.64–1.27)	
Gestation type										
Singleton		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Twin or multiple		5.17 (3.22–8.28)***		1.80 (0.55–5.88)	0.70 (0.1–5.16)	6.52 (2.51–16.91)***	11.12 (4.52–27.36)***	38.01 (19.08–75.7)***	0.97 (0.13–7.19)	
Mother’s education										
No education		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Primary education		0.81 (0.64–1.03)		1.02 (0.65–1.6)	0.68 (0.44–1.05)	1.98 (0.85–4.61)	1.04 (0.53–2.04)	0.58 (0.27–1.23)	0.55 (0.34–0.89)	
Secondary education		0.68 (0.52–0.89)**		0.78 (0.47–1.29)	0.40 (0.24–0.67)**	2.27 (0.94–5.44)	1.03 (0.49–2.18)	0.37 (0.16–0.85)*	0.64 (0.38–1.09)	
Higher education		0.45 (0.29–0.69)***		0.44 (0.2–0.97)*	0.35 (0.15–0.83)*	1.12 (0.36–3.45)	0.82 (0.23–2.96)	0.30 (0.08–1.16)	0.55 (0.18–1.7)	
Mother’s age during childbirth										
Below 18 years		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
19 years to 24 years		0.69 (0.56–0.85)***		0.64 (0.44–0.93)*	0.93 (0.6–1.46)	0.65 (0.41–1.02)	0.71 (0.41–1.23)	0.50 (0.27–0.95)*	0.69 (0.44–1.08)	
25 years to 30 years		0.48 (0.38–0.61)***		0.52 (0.34–0.8)**	0.68 (0.41–1.13)	0.39 (0.22–0.68)**	0.33 (0.16–0.69)**	0.32 (0.15–0.69)**	0.53 (0.31–0.9)*	
31 years or above		0.60 (0.45–0.79)***		0.67 (0.41–1.08)	0.89 (0.51–1.54)	0.38 (0.18–0.8)*	0.61 (0.29–1.31)	0.37 (0.15–0.91)*	0.53 (0.28–1)	
Mother’s Body Mass Index										
Normal weight		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Under weight		1.00 (0.82–1.21)		1.24 (0.88–1.74)	1.21 (0.85–1.74)	0.69 (0.41–1.18)	0.99 (0.59–1.67)	0.64 (0.31–1.32)	0.83 (0.54–1.26)	
Over weight or obese		1.32 (1.07–1.63)**		1.18 (0.81–1.71)	1.11 (0.71–1.73)	1.74 (1.12–2.7)*	1.23 (0.64–2.35)	1.48 (0.78–2.79)	1.47 (0.88–2.45)	
Exposure to television										
Watched television		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Did not watch television		1.05 (0.88–1.26)		1.08 (0.79–1.5)	1.22 (0.85–1.74)	0.75 (0.48–1.16)	1.03 (0.63–1.69)	0.74 (0.41–1.34)	1.28 (0.86–1.91)	
Decision making										
No participation of mother		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Some participation of mother		1.18 (0.95–1.48)		1.81 (1.14–2.86)	1.33 (0.86–2.06)	0.96 (0.58–1.158)	1.66 (0.89–3.13)	0.62 (0.31–1.23)	0.87 (0.55–1.38)	
Good participation of mother		1.12 (0.91–1.38)		1.91 (1.23–2.96)	1.20 (0.79–1.83)	0.86 (0.53–1.39)	1.29 (0.69–2.39)	0.62 (0.34–1.16)	0.85 (0.55–1.32)	
Father and household-specific predictors										
Father’s education										
No education		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Primary education		0.89 (0.72–1.09)		0.91 (0.63–1.33)	1.09 (0.73–1.62)	0.68 (0.39–1.18)	0.80 (0.46–1.38)	0.97 (0.48–1.97)	0.86 (0.56–1.33)	
Secondary education		0.75 (0.59–0.96)*		0.95 (0.62–1.46)	0.72 (0.44–1.19)	0.89 (0.5–1.59)	0.43 (0.21–0.88)*	1.01 (0.45–2.23)	0.53 (0.31–0.92)*	
Higher education		0.67 (0.47–0.96)*		0.70 (0.36–1.34)	0.74 (0.35–1.54)	1.12 (0.54–2.36)	0.59 (0.21–1.66)	0.72 (0.21–2.46)	0.22 (0.07–0.65)**	
Wealth index										
Poorest		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Poorer		1.00 (0.79–1.25)		0.89 (0.59–1.33)	0.81 (0.51–1.31)	0.90 (0.51–1.6)	1.11 (0.62–2)	1.09 (0.52–2.28)	1.37 (0.84–2.25)	
Middle		1.07 (0.83–1.38)		1.05 (0.67–1.65)	1.38 (0.85–2.22)	0.71 (0.37–1.34)	1.03 (0.52–2.06)	0.77 (0.32–1.87)	1.28 (0.72–2.27)	
Richer		1.06 (0.8–1.39)		0.80 (0.48–1.35)	1.35 (0.79–2.31)	0.91 (0.48–1.73)	0.76 (0.33–1.75)	0.88 (0.36–2.15)	1.50 (0.81–2.77)	
Richest		0.94 (0.65–1.35)		1.20 (0.64–2.26)	1.28 (0.62–2.64)	0.61 (0.26–1.41)	1.01 (0.35–2.93)	0.77 (0.23–2.52)	0.51 (0.19–1.35)	
Hand washing station										
Observed		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Not observed or not in the dwelling		1.50 (1.18–1.91)**		1.76 (1.14–2.72)*	1.33 (0.83–2.13)	1.00 (0.5–1.99)	2.22 (1.25–3.95)**	1.16 (0.46–2.91)	1.49 (0.92–2.43)	
Cooking fuel type										
Clean fuel		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Solid fuel or other		0.97 (0.73–1.29)		1.03 (0.62–1.7)	0.98 (0.55–1.55)	0.91 (0.48–1.74)	2.37 (0.8–7.01)	0.96 (0.39–2.32)	0.59 (0.3–1.14)	
Community and time specific predictors										
Community access route										
Good condition		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Poor condition		1.02 (0.84–1.22)		1.20 (0.87–1.66)	0.91 (0.63–1.31)	1.31 (0.85–2.01)	0.93 (0.57–1.54)	0.93 (0.49–1.78)	0.80 (0.53–1.21)	
Division										
Sylhet		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Chittagong		0.77 (0.59–0.99)*		0.70 (0.45–1.09)	1.16 (0.71–1.88)	0.58 (0.3–1.1)	1.12 (0.51–2.45)	0.58 (0.25–1.37)	0.57 (0.32–1.02)	
Dhaka		0.88 (0.68–1.15)		0.93 (0.6–1.45)	0.70 (0.39–1.24)	0.58 (0.29–1.15)	1.48 (0.67–3.27)	1.14 (0.52–2.51)	0.91 (0.52–1.57)	
Khulna		0.50 (0.35–0.71)***		0.46 (0.25–0.85)*	0.65 (0.33–1.31)	0.49 (0.22–1.08)	0.35 (0.1–1.29)	0.83 (0.32–2.14)	0.32 (0.13–0.79)	
Rajshahi		0.88 (0.66–1.17)		0.60 (0.35–1.05)	1.22 (0.7–2.11)	1.10 (0.58–2.06)	1.62 (0.73–3.6)	0.33 (0.1–1.08)	0.74 (0.39–1.39)	
Rangpur		0.70 (0.52–0.95)*		0.49 (0.28–0.88)*	0.67 (0.35–1.28)	0.89 (0.45–1.75)	1.04 (0.43–2.54)	0.94 (0.38–2.3)	0.65 (0.34–1.27)	
Barisal		0.89 (0.66–1.19)		0.81 (0.49–1.34)	1.13 (0.64–1.99)	0.85 (0.42–1.7)	1.35 (0.58–3.12)	0.46 (0.15–1.47)	0.80 (0.42–1.51)	
Mymensingh		0.69 (0.47–1.02)		0.51 (0.27–0.97)*	1.12 (0.54–2.34)	0.50 (0.19–1.28)	1.22 (0.4–3.7)	0.27 (0.06–1.25)	1.09 (0.44–2.67)	
Survey year										
2011		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
2017-18		1.23 (1.03–1.46)*		2.40 (1.74–3.32)***	0.94 (0.66–1.33)	1.30 (0.87–1.95)	0.86 (0.52–1.43)	2.20 (1.23–3.92)**	0.49 (0.32–0.75)**	
Random effects										
Household level variance (SE)	1 (0.22)	0.88 (0.23)	1 (0.22)	0.87 (0.23)	
Intraclass Correlation Coefficient	0.23	0.21	0.23	0.21	
Log-likelihood	-3269.32	-3197.76	-4683.91	-4450.18	
Likelihood ratio test					
Degrees of freedom	NA	9	NA	54	
Test statistic	NA	27.08**	NA	114.46***	
Information Criteria					
Degrees of freedom	2	33	7	193	
Akaike’s Information Criteria	6542.64	6425.52	9381.81	9286.36	
Bayesian Information Criteria	6558.08	6680.39	9435.88	10776.96	
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

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 effects of health service utilisation on cause-specific mortality. The analysis approach was similar to the analysis of U5 children. However, only community level random effects came up as significant. Therefore, the null model of the 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 the sample of U5 children. Additionally, Model 4 was run, where, health service utilisation variables were added to Model 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).

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 of U5 children)

N = 9469	Multilevel Mixed-Effects Logistic Regression	Multilevel Multinominal Mixed Effects Logistic Regression	
Estimates related to predictors of overall/all-cause U5M	Estimates related to predictors of cause-specific U5M	
Null model	Final model/
Model 4	Null model	Final model/Model 4	
		All causes together
aOR (95% CI)		Death due to other causes
aRRR (95% CI)	Death due to Pneumonia
aRRR (95% CI)	Death due to Birth Asphyxia
aRRR (95% CI)	Death due to Possible Serious Infection
aRRR (95% CI)	Death due to Preterm Birth
aRRR (95% CI)	Unspecific or undetermined
aRRR (95% CI)	
Child and mother-specific predictors										
Sex										
Male		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Female		1 (0.78–1.27)		1.21 (0.74–1.97)	0.93 (0.58–1.48)	0.76 (0.4–1.42)	0.62 (0.29–1.33)	0.93 (0.39–2.21)	1.56 (0.86–2.84)	
Gestation type										
Singleton		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Twin or multiple		5.97 (3.13–11.41)***		3.94 (0.91–17.04)	$1.86e− 08 (-)	6.79 (1.5–30.81)*	$2.89e− 08 (-)	73.03 (24.57–217.03)***	2.53 (0.32–20.26)	
Mother’s education										
No education		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Primary education		0.81 (0.55–1.2)		1.24 (0.47–3.22)	0.62 (0.32–1.23)	2.66 (0.72–9.81)	0.85 (0.28–2.6)	0.55 (0.13–2.3)	0.5 (0.22–1.14)	
Secondary education		0.69 (0.45–1.05)		1 (0.37–2.76)	0.47 (021–1.03)	2.28 (0.56–9.29)	0.93 (0.26–3.26)	0.46 (0.1–2.03)	0.48 (0.19–1.24)	
Higher education		0.44 (0.22–0.88)*		0.42 (0.08–2.1)	0.16 (0.04–0.73)*	2.06 (0.32–13.23)	0.37 (0.03–4.85)	1.01 (0.16–6.29)	0.42 (0.07–2.69)	
Mother’s age during childbirth										
Below 18 years		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
19 years to 24 years		1.24 (0.83–1.85)		0.82 (0.4–1.7)	2.22 (0.86–5.73)	1.72 (0.58–5.08)	0.96 (0.34–2.74)	0.99 (0.24–4.1)	1.39 (0.51–3.78)	
25 years to 30 years		0.81 (0.52–1.28)		0.83 (0.38–1.82)	1.19 (0.41–3.43)	0.78 (0.22–2.79)	0.31 (0.07–1.34)	0.89 (0.19–4.09)	1.07 (0.36–3.22)	
31 years or above		1.61 (1.01–2.6)*		1.03 (0.42–2.54)	3.34 (1.2–9.33)*	1.9 (0.53–6.82)	1.39 (0.4–4.87)	1.32 (0.26–6.77)	1.46 (0.45–4.75)	
Mother’s Body Mass Index										
Normal weight		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Under weight		0.9 (0.66–1.23)		1.25 (0.67–2.3)	0.9 (0.51–1.61)	0.45 (0.17–1.2)	1.19 (0.51–2.77)	0.37 (0.07–1.89)	0.83 (0.41–1.68)	
Over weight or obese		1.95 (1.4–2.73)***		1.91 (1 – 3.63)*	1.64 (0.84–3.22)	1.68 (0.76–3.71)	2.96 (0.95–9.24)	2.6 (0.96–7.03)	2.31 (0.95–5.62)	
Exposure to television										
Watched television		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Did not watch television		0.87 (0.65–1.18)		0.87 (0.48–1.56)	1.22 (0.69–2.17)	0.39 (0.17–0.89)*	0.98 (0.41–2.35)	0.86 (0.26–2.84)	0.93 (0.46–1.87)	
Decision making										
No participation of mother		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Some participation of mother		1.15 (0.82–1.63)		1.55 (0.72–3.32)	1.57 (0.81–3.04)	0.93 (0.4–2.14)	1.36 (0.43–4.25)	0.35 (0.09–1.36)	0.79 (0.36–1.71)	
Good participation of mother		1.07 (0.76–1.49)		1.57 (0.76–3.27)	1.09 (0.56–2.12)	0.74 (0.32–1.69)	2.02 (0.7–5.78)	0.59 (0.2–1.75)	0.71 (0.34–1.49)	
Father and household-specific predictors										
Father’s education										
No education		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Primary education		1.14 (0.81–1.61)		1.19 (0.55–2.56)	1.55 (0.81–2.97)	0.61 (0.27–1.38)	2.5 (0.9–6.93)	1.24 (0.32–4.84)	0.75 (0.34–1.65)	
Secondary education		0.94 (0.63–1.39)		1.5 (0.67–3.37)	1.09 (0.5–2.39)	0.3 (0.11–0.83)*	1.16 (0.3–4.45)	0.88 (0.21–3.77)	0.76 (0.31–1.88)	
Higher education		0.6 (0.33–1.1)		0.57 (0.15–2.15)	1.08 (0.35–3.3)	0.34 (0.08–1.33)	2.5 (0.42–14.68)	0.25 (0.03–1.91)	0.26 (0.04–1.54)	
Wealth index										
Poorest		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Poorer		1.09 (0.75–1.59)		1.39 (0.65–2.96)	0.99 (0.49–2.03)	0.7 (0.25–1.96)	0.95 (0.37–2.43)	1.71 (0.32–9.11)	1.26 (0.5–3.17)	
Middle		0.91 (0.59–1.4)		1.12 (0.47–2.61)	1.24 (0.56–2.71)	0.61 (0.2–1.8)	0.33 (0.08–1.34)	0.37 (0.03–4.51)	1.33 (0.48–3.69)	
Richer		1.26 (0.82–1.97)		1.08 (0.42–2.72)	1.39 (0.59–3.29)	0.94 (0.32–2.73)	0.35 (0.08–1.58)	3.18 (0.58–17.36)	2.11 (0.75–5.92)	
Richest		1.05 (0.59–1.88)		1.52 (0.5–4.63)	1.75 (0.58–5.24)	0.62 (0.15–2.51)	0.47 (0.08–2.96)	2.41 (0.29–20)	0.45 (0.08–2.47)	
Hand washing station										
Observed		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Not observed or not in the dwelling		1.63 (1.14–2.34)**		1.6 (0.74–3.42)	1.74 (0. 9–3.37)	0.41 (0.1–1.79)	2.67 (1.11–6.41)*	3.71 (0.83–16.65)	1.79 (0.82–3.87)	
Cooking fuel type										
Clean fuel		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Solid fuel or other		1 (0.63–1.6)		1.81 (0.65–5.03)	0.84 (0.34–2.07)	1.63 (0.51–5.17)	4.62 (0.45–47.58)	0.72 (0.19–2.77)	0.31 (0.11–0.9)*	
Community and time specific predictors										
Community access route										
Good condition		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Poor condition		1.15 (0.85–1.55)		1.5 (0.85–2.64)	1.1 (0.64–1.9)	1.37 (0.66–2.83)	0.85 (0.37–1.97)	0.72 (0.18–2. 9)	1.11 (0.56–2.19)	
Division										
Sylhet		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Chittagong		0.64 (0.41–0.99)*		1.14 (0.47–2.75)	0.52 (0.22–1.22)	0.63 (0.23–1.68)	0.4 (0.09–1.67)	0.79 (0.15–4.14)	0.4 (0.15–1.12)	
Dhaka		0.89 (0.57–1.37)		1.03 (0.39–2.69)	0.75 (0.32–1.72)	0.65 (0.23–1.83)	1.38 (0.44–4.39)	1.52 (0.31–7.41)	0.65 (0.25–1.67)	
Khulna		0.61 (0.35–1.05)		0.97 (0.34–2.73)	0.78 (0.29–2.12)	0.47 (0.14–1.61)	0.21 (0.02–1.84)	1.24 (0.2–7.56)	0.17 (0.02–1.37)	
Rajshahi		0.78 (0.48–1.26)		1.22 (0.34–3.72)	1.08 (0.45–2.56)	0.31 (0.8–1.18)	0.85 (0.22–3.26)	0.77 (0.12–4.91)	0.4 (0.11–1.54)	
Rangpur		0.76 (0.45–1.29)		0.71 (0.22–2.25)	0.77 (0.28–2.1)	0.37 (0.09–1.47)	0.54 (0.12–2.44)	2.97 (0.51–17.27)	0.98 (0.33–2.91)	
Barisal		1.24 (0.79–1.94)		1.73 (0.7–4.3)	1.41 (0.63–3.14)	0.75 (0.24–2.38)	0.68 (0.18–2.61)	0.52 (0.05–5.87)	1.64 (0.65–4.14)	
Mymensingh		0.62 (0.3–1.29)		0.43 (0.9–2.1)	1.52 (0.5–4.62)	0.47 (0.09–2.42)	0.86 (0.09–8.59)	$3.78e− 08 (-)	$4.79e− 08 (-)	
Survey year										
2011		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
2017-18		0.71 (0.52–0.96)*		1.32 (0.74–2.35)	0.7 (0.39–1.26)	0.67 (0.32–1.4)	0.24 (0.08–0.7)*	0.58 (0.18–1.9)	0.27 (0.11–0.63)**	
Health service specific predictors										
Antenatal Care										
No ANC		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
1 to 3 ANCs		0.76 (0.55–1.06)		1.61 (0.75–3.48)	0.4 (0.22–0.73)**	1.26 (0.51–3.16)	0.65 (0.26–1.66)	3.06 (0.53–17.72)	0.48 (0.23–1.03)	
4 or more ANCs		0.7 (0.47–1.04)		1.37 (0.56–3.32)	0.4 (0.19–0.83)*	0.98 (0.33–2.9)	0.73 (0.23–2.29)	2.09 (0.3–14.65)	0.72 (0.3–1.76)	
Birth assistance										
Medically trained		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
Not medically trained		0.84 (0.62–1.33)		1.01 (0.57–1.79)	0.88 (0.5–1.58)	0.63 (0.3–1.32)	0.86 (0.34–2.21)	0.29 (0.08–1.12)	1.39 (0.62 − 3.11)	
Post natal care										
Yes		Ref		Ref	Ref	Ref	Ref	Ref	Ref	
No		1.17 (0.91–1.52)		1.27 (0.77–2.11)	0.74 (0.45–1.24)	1.8 (0.95–3.41)	0.76 (0.34–1.73)	7.42 (2.42–22.71)**	0.76 (0.4–1.47)	
Random effects										
Community level variance (SE)	0.39(0.19)	0.26 (0.19)	0.39 (0.19)	0.24 (0.19)	
Intraclass Correlation Coefficient	0.11	0.07	0.11	0.07	
Log-likelihood	-1255.67	-1191.85	-1734.76	-1554.33	
Likelihood ratio test					
Degrees of freedom	NA	4	NA	24	
Test statistic	NA	6.12	NA	43.62**	
Information Criteria					
Degrees of freedom	2	37	7	217	
Akaike’s Information Criteria	2515.34	2457.69	3483.51	3542.65	
Bayesian Information Criteria	2529.65	2722.46	3533.6	5095.46	
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

Additional files

Equation for multilevel mixed effects logistic regression analysis

For binary logistic regression, the outcome variable has two categories (binary outcome data). An individual is classified in one category with a given probability (say π), and in the other category with probability, 1 – π. The following equation denotes a two-level binary logistic regression model where individual i is nested in household h.

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eqalign{& {\rm{logit}}({\pi _{ih}}){\rm{ }} = {\rm{ }}ln{({\pi _{ih}}/(1{\rm{ }} - {\pi _{ih}}))_{}} \cr & = {\beta _0} + {\rm{ }}{\beta _1}{X_{1ih}} + {\rm{ }}{\beta _2}{X_{2ih}} + {\beta _3}{X_{3ih}} + \ldots {\rm{ }}{ + _{}}{\beta _k}{X_{kih}} + {\rm{ }}{\mu _h} \cr}$$\end{document}

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 child i in household h. β1,β2, …βk are the coefficients of the predictors. The random effect or the variability between the households is µh. The above equation applies to models that consider children nested in households (analyses of the U5 children sample). The other multilevel binary 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).

Multilevel multinomial mixed effects analysis

Due to the hierarchical nature of the data and the presence of multinomial outcomes (alive and multiple cause-specific deaths), multilevel multinomial logistic regression was performed to determine the predictors of cause-specific deaths. Although the proportions of the cause-specific categories of the outcome variables were small, they were non-negligible. Also, it was believed that the multinomial analysis method would create new knowledge 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 and help 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-specific death (age-specific child death instead), these studies still support using multinomial analysis when the outcome variable is categorical, and the analysis approach has the potential 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-specific estimates).

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 model and 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 from the multilevel multinomial mixed effects analyses are reported as relative risk ratios (RRRs) and 95% CIs.

Equation for multilevel multinomial mixed effects analysis

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 (say D > 2) with a given probability (πd). For the multinomial logit model, one of the categories is arbitrarily chosen to act 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 the reference).

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eqalign{& ln({\pi _{dih}})/{\pi _{0ih}}){\rm{ }} \cr & = {\beta _{0d}} + {\rm{ }}{\beta _{1d}}{X_{1ih}} + {\rm{ }}{\beta _{2d}}{X_{2ih}} + {\beta _{3d}}{X_{3ih}} + \ldots {\rm{ }}{ + _{}}{\beta _{kd}}{X_{kih}} + {\rm{ }}{\mu _h} \cr}$$\end{document}

In the equation above, ln is the natural logarithm, and πdih is the probability that child i in household h is dead due 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 h falls 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 in household 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 (analyses of 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 cause-specific mortality risks associated with different predictors. Such analyses helped to highlight the change in mortality risks associated with specific predictors (mothers’ age, mother’s education, gestation type and PNC) between 2011 and 2017-18 surveys. Margins plots were also 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

Sample characteristics

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

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%), birth asphyxia (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%) than female U5 children (48.15%) included in this study. Twins were only about 1% of all U5 children. Around 13% of the mothers did not have any formal education and the proportion of no formal education was even higher (around 22%) 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 health service 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 to the unweighted frequency distribution of health service specific predictors.

Approximately one-fifth of the mothers reported that they 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, and around 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 (see Table 3). However, an additional analysis of the subsample, i.e., children aged three years or younger indicated 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. Based on the likelihood ratio tests, only the best-fit models for both analyses are presented.

Child and mother-specific predictors

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 age at childbirth, mother’s education, and mother’s BMI were the three mother-specific predictors significantly associated with child mortality. Compared to mothers with no education, children of mothers with secondary or 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 greater risk (aOR: 1.32, 95% CI: 1.07–1.63, p < .01) of death than did those whose mothers had a normal weight during the survey.

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 associated with 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 when fathers had no education. The risk of U5M was greater (aOR: 1.5, 95% CI: 1.18–1.91, p < .01) when there was no hand 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 and health service-specific predictors on the subsample of U5 children, i.e., children aged three years or younger. However, this paper refers to Table 4 only for the health service 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/all-cause mortality in this age group. However, the directions of 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 the random effects of these two levels on overall/all-cause mortality. Analysis of the U5 children sample showed that 23% of the total variation in child mortality could be attributed to household-level random effects (see Table 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 and health 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-specific predictors.

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 the multilevel 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 robustness of multilevel multinomial logistic regression analysis over multilevel mixed-effects logistic regression analysis.

Child and mother-specific predictors

The child-specific predictors – sex and gestation type were 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 risk of 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 asphyxia-related mortality was significantly lower for children whose mothers were aged 25 years or older compared to 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.

Father and household-specific predictors

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 washing station.

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) in U5M due to preterm-related conditions during the 2017-18 survey period compared to the 2011 survey period.

Health service-specific predictors

Table 4 presents the health-service -specific predictors of all-cause/overall and cause-specific U5M based on the analysis on children three years or younger, a subsample of U5 children. Deaths due to pneumonia, and preterm-related conditions were significantly associated with health 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 risk of 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).

Predicted probability of cause-specific mortality

Figures 1, 2 and 3 (margins plots) are the outputs of interaction 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 these predictors. As it is evident, among some of the groups of children, the mortality risks increased between 2011 and 2017-18. For example, Fig. 1a illustrates that while the mortality risks increased among all children, the increase was the highest among the children whose mothers were aged 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 prematurity-related U5M risk moderately decreased when children received PNC (Fig. 3).

Fig. 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)

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)

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 of cause-specific U5M revealed that ANC and PNC reduced pneumonia, and prematurity-related mortality, respectively. This finding suggests that the effects of predictors of 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 and postnatal care for ending preventable child deaths has long 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 a lower risk of pneumonia-caused mortality among children of mothers who received ANC. As the WHO recommends, immunisation, proper nutrition, and healthy living 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 relationship between ANC uptake and pneumonia-specific U5M is logically 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 is associated with numerous serious health risks for neonates, often leading to death [50–52]. PNC contact with a 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 and maternal and neonatal health care services, this study has generated new knowledge. This knowledge can guide the reinforcement 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 asphyxia-related deaths than male children. Studies conducted in the United States and Nepal also reported that male neonates had a greater risk of developing birth asphyxia [55, 56]. Earlier studies have referred to poor lung maturation associated with male hormones to explain the increased susceptibility of male infants to respiratory diseases [57, 58]. On the contrary, a study conducted in Brazil reported a higher mortality risk from asphyxia among females, which may be related to the unfair distribution of 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 to underdeveloped organ functions, preterm babies are more susceptible to various life-threatening conditions such as birth asphyxia or serious infections [50–52]. In summary, multiple gestations often lead to preterm birth, an established risk factor for neonatal death from respiratory problems or other infection-related deaths observed in Bangladesh and elsewhere. Therefore, interventions focused on mothers with multiple pregnancies have a high potential to reduce preterm birth and associated child 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 influence of maternal age at childbirth on U5M from birth asphyxia varies, as reported by different research; a study conducted in Cameroon reported a significantly greater risk of birth asphyxia among neonates of adolescent mothers than among those of adult mothers, congruent with our findings [62]. In contrast, a study conducted in Pakistan reported that a significantly greater risk was associated with 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 of different age groups. Had there been more categories of maternal age younger than 30 years, such as 19 years or younger, 20 to 24 years, and 25 to 29 years, the findings of the study above would have been more informative [63].

While our study reported a greater risk of U5M from possible 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, one of these studies reported that children of mothers aged 25–29 years had a lower risk of infection than children of younger mothers [65]. Conversely, a cross-sectional study conducted in Ethiopia reported that children born to mothers 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 and neonatal 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 risk of 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 preterm-related conditions compared to the children of mothers older or younger. Earlier studies referred to physiologic 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 the risk 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 and geographic locations. For instance, a study conducted in rural Bangladesh reported that women with primary education had a 0.77 times lower risk of preterm delivery than women having less education than primary education [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 most important predictors of health [73, 74]. Improved knowledge and awareness, better practices related to nutrition and lifestyle, and better healthcare access stimulated by education are some of the potential pathways that lead to lower preterm deaths among mothers with comparatively higher education levels.

The cause-specific analyses in this study also shed some light 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 showed that U5M was 1.23 times greater in 2017-18 than in 2011. However, cause-specific data analyses revealed a more alarming trend; prematurity-related deaths were greater (2.2 times) in 2017-18 than in 2011. Furthermore, the cause-specific data analyses revealed that there was an 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 not adequately effective among these specific groups and contributed 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 weaknesses in the initiatives to reduce child marriage in the 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 less motivated in the prevention of early marriage. This rise in mortality risk could also have been influenced by the inadequacy of family planning services offered to newly married couples in rural public health facilities [76].

This increased mortality risk can also be linked to health 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 and sustained reduction in U5MR and avoid possible future stagnation.

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 the correlates of cause-specific U5M. Most importantly, this study highlights the key areas of intervention that can contribute 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 health services in Bangladesh to prevent key cause-specific child deaths amid concerns regarding the quality of health services and preparedness of health facilities. Therefore, these findings also highlight the scope for Bangladesh to enhance the U5M reduction rate by reinforcing the programmes and policies to improve health facility readiness and 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 is pivotal 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-specific U5M using data from two nationally representative surveys (2011 and 2017-18), allowing comparison over seven years. Analyses of two surveys provided the scope to use time as a moderating variable to illustrate the change in mortality risk. Our study illustrates an increase in preterm-related mortality risk among certain groups over time. This explains how the increase contributed to the slow 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 or community. Therefore, the estimates reported in our study are more robust. Our study also compared two robust 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 five or even empty cells. Moreover, our ability to investigate other critical cause-specific deaths, such as congenital abnormalities, was limited. Furthermore, health service-specific information like ANC and PNC was available only for the children born within three years prior to the survey. Consequently, we could not study the impact of health services on U5 children older than three years of age. However, most of the deaths from preterm conditions, birth asphyxia or possible serious infections were among 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 of cause-specific U5M in Bangladesh, generate robust evidence, and provide evidence-based guidance for cause-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.

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 care is provided across different tiers of health facilities. A higher risk of preterm-related deaths among the children of mothers with low education or children born to mothers 18 years or younger refers to the inadequacies of the existing initiatives to promote maternal education and prevent adolescent pregnancy. Therefore, this elevated risk 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 need to 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 have not been able to deliver the expected outcomes. Further research is needed to understand the gap between the performance of health service-related initiatives and expected child health outcomes. Knowledge about these gaps and the application of this knowledge can further strengthen Bangladesh’s strategies to achieve SDG 3.2.1.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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 of children three years or younger (a sub-sample of U5 children)

Supplementary Material 3: Table S3: Model-specific estimates of the sociodemographic predictors of overall/all-cause mortality among U5 children

Supplementary Material 4: Table S4: Model-specific estimates of the sociodemographic and health service-specific predictors of overall/all-cause mortality among children three years or younger (a sub-sample of U5 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 the sociodemographic and health service-specific predictors of cause-specific mortality among children three years or younger (a sub-sample of U5 children) - Model 1

Supplementary Material 9: Table S9: Model-specific estimates of the sociodemographic and health service-specific predictors of cause-specific mortality among children three years or younger (a sub-sample of U5 children) - Model 2

Supplementary Material 10: Table S10: Model-specific estimates of the sociodemographic and health service-specific predictors of cause-specific mortality among children three years or younger (a sub-sample of U5 children) - Model 3

Supplementary Material 11: Table S11: Model-specific estimates of the sociodemographic and health service-specific predictors of cause-specific mortality among children three years or younger (a sub-sample of U5 children) - Model 4

Abbreviations

AIC Akaike Information Criterion

ANC Antenatal Care

aOR Adjusted Odds Ratio

aRRR 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 Rate

OR 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

Acknowledgements

We thank the Demographic and Health Surveys Program for granting access to 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 the manuscript. 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/.

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 these secondary 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.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. You D Hug L Ejdemyr S Idele P Hogan D Mathers C Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, 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 10.1016/S0140-6736(15)00120-8 26361942
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 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. Lancet. 2015;386(10010):2275–86.26361942 10.1016/S0140-6736(15)00120-8
2. Sharrow D Hug L You D Alkema L Black R Cousens S Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-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 10.1016/S2214-109X(21)00515-5 35063111
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 with scenario-based projections until 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. Lancet Global Health. 2022;10(2):e195–206.35063111 10.1016/S2214-109X(21)00515-5
3. Perin J Mulick A Yeung D Villavicencio F Lopez G Strong KL 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 10.1016/S2352-4642(21)00311-4 34800370
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):106–15.34800370 10.1016/S2352-4642(21)00311-4
4. Tibaijuka L Bawakanya SM Owaraganise A Kyasimire L Kumbakumba E Boatin AA Incidence and predictors of preterm neonatal mortality at Mbarara Regional Referral Hospital in South Western Uganda PLoS ONE 2021 16 11 e0259310 10.1371/journal.pone.0259310 34727140
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):e0259310.34727140 10.1371/journal.pone.0259310
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 10.1186/s13052-018-0597-3 30616641
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):4.30616641 10.1186/s13052-018-0597-3
6. Opio C Malumba R Kagaayi J Ajumobi O Kamya C Mukose A 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 10.1016/S2214-109X(20)30147-9
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:S6.10.1016/S2214-109X(20)30147-9
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 34158751
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):43–54.34158751
8. Sonego M Pellegrin MC Becker G Lazzerini M Risk factors for mortality from acute lower respiratory infections (ALRI) in children under five years of age in low and middle-income countries: a systematic review and meta-analysis of observational studies PLoS ONE 2015 10 1 e0116380 10.1371/journal.pone.0116380 25635911
Sonego M, Pellegrin MC, Becker G, Lazzerini M. Risk factors for mortality from acute lower respiratory infections (ALRI) in children under five years of age in low and middle-income countries: a systematic review and meta-analysis of observational studies. PLoS ONE. 2015;10(1):e0116380.25635911 10.1371/journal.pone.0116380
9. Jackson S Mathews KH Pulanić D Falconer R Rudan I Campbell H Risk factors for severe acute lower respiratory infections in children–a systematic review and meta-analysis Croatian Med J 2013 54 2 110 21 10.3325/cmj.2013.54.110
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):110–21.10.3325/cmj.2013.54.110
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 10.1016/j.jpeds.2018.08.028 30270166
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;203:163–9.30270166 10.1016/j.jpeds.2018.08.028
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 - NIPORTICF Bangladesh Demographic and Health Survey 2017-18 2020 Dhaka, Bangladesh, and Rockville, Maryland NIPORT and ICF
National Institute of Population Research Training (NIPORT), and ICF. Bangladesh Demographic and Health Survey 2017–18. Dhaka, Bangladesh and Rockville, Maryland, USA: NIPORT, and ICF. 2020.
14. Rubayet S Shahidullah M Hossain A Corbett E Moran AC Mannan I Newborn survival in Bangladesh: a decade of change and future implications Health Policy Plan 2012 27 Suppl 3 iii40 56 10.1093/heapol/czs044 22692415
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):iii40–56.22692415 10.1093/heapol/czs044
15. PMNCH, World Bank WHO. AHPSR. Success Factors for Women’s and Children’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 10.1016/j.puhe.2019.05.011 31271965
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;173:112–9.31271965 10.1016/j.puhe.2019.05.011
17. Nasrin S Tariqujjaman M Sultana M Zaman RA Ali S Chisti MJ Factors associated with community acquired severe pneumonia among under five children in Dhaka, Bangladesh: a case control analysis PLoS ONE 2022 17 3 e0265871 10.1371/journal.pone.0265871 35320317
Nasrin S, Tariqujjaman M, Sultana M, Zaman RA, Ali S, Chisti MJ, et al. Factors associated with community acquired severe pneumonia among under five children in Dhaka, Bangladesh: a case control analysis. PLoS ONE. 2022;17(3):e0265871.35320317 10.1371/journal.pone.0265871
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
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 Time trends and sociodemographic determinants of preterm births in pregnancy cohorts in Matlab, Bangladesh, 1990–2014 BMJ Global Health 2019 4 4 e001462 10.1136/bmjgh-2019-001462 31423346
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):e001462.31423346 10.1136/bmjgh-2019-001462
21. Parvin S, BakiBillah AH, Hasan I, Chowdhury AA. Prevalence and Predictors of Birth Asphyxia Among Neonates in Bangladesh: A Cross-Sectional Study. 2022.
22. Saha S Hasan M Kim L Farrar JL Hossain B Islam M Epidemiology and risk factors for pneumonia severity and mortality in Bangladeshi children < 5 years of age before 10-valent pneumococcal conjugate vaccine introduction BMC Public Health 2016 16 1 12 10.1186/s12889-016-3897-9 26728978
Saha S, Hasan M, Kim L, Farrar JL, Hossain B, Islam M, et al. Epidemiology and risk factors for pneumonia severity and mortality in Bangladeshi children < 5 years of age before 10-valent pneumococcal conjugate vaccine introduction. BMC Public Health. 2016;16:1–12.26728978 10.1186/s12889-016-3897-9
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 and health 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 10.1017/S1368980021003840 34482847
Banik R, Hossain S, Khudri MM. Regional education and wealth-related inequalities in malnutrition among women in Bangladesh. Public Health Nutr. 2022;25(6):1639–57.34482847 10.1017/S1368980021003840
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
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 10.1111/j.1365-3156.2007.01959.x
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):1506–14.10.1111/j.1365-3156.2007.01959.x
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 10.3390/geographies1010004
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):47–62.10.3390/geographies1010004
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 10.3402/gha.v9.29741 26880153
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):29741.26880153 10.3402/gha.v9.29741
31. Huo N Zhang K Wang L Wang L Lv W Cheng W Association of maternal body mass index with risk of infant mortality: a dose-response meta-analysis Front Pead 2021 9 650413 10.3389/fped.2021.650413
Huo N, Zhang K, Wang L, Wang L, Lv W, Cheng W, et al. Association of maternal body mass index with risk of infant mortality: a dose-response meta-analysis. Front Pead. 2021;9:650413.10.3389/fped.2021.650413
32. Khan GR Baten A Azad MAK Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches Contracept Reprod Med 2023 8 1 22 10.1186/s40834-023-00217-z 36864535
Khan GR, Baten A, Azad MAK. Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches. Contracept Reprod Med. 2023;8(1):22.36864535 10.1186/s40834-023-00217-z
33. Kumar S Loughnan L Luyendijk R Hernandez O Weinger M Arnold F Handwashing in 51 countries: analysis of Proxy measures of Handwashing Behavior in multiple Indicator cluster surveys and demographic and health surveys, 2010–2013 Am J Trop Med Hyg 2017 97 2 447 59 10.4269/ajtmh.16-0445 28722572
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 health surveys, 2010–2013. Am J Trop Med Hyg. 2017;97(2):447–59.28722572 10.4269/ajtmh.16-0445
34. Sarkodie AO Factors influencing under-five mortality in rural-urban Ghana: an applied survival analysis Soc Sci Med 2021 284 114185 10.1016/j.socscimed.2021.114185 34293678
Sarkodie AO. Factors influencing under-five mortality in rural-urban Ghana: an applied survival analysis. Soc Sci Med. 2021;284:114185.34293678 10.1016/j.socscimed.2021.114185
35. Sines E, Syed U, Wall S, Worley H. Postnatal care: a critical opportunity to save mothers and newborns. Policy Perspect Newborn Health. 2007;1(7).
36. Amin R Shah NM Becker S Socioeconomic factors differentiating maternal and child health-seeking behavior in rural Bangladesh: a cross-sectional analysis Int J Equity Health 2010 9 1 1 11 10.1186/1475-9276-9-9 20148118
Amin R, Shah NM, Becker S. Socioeconomic factors differentiating maternal and child health-seeking behavior in rural Bangladesh: a cross-sectional analysis. Int J Equity Health. 2010;9(1):1–11.20148118 10.1186/1475-9276-9-9
37. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. 2000.
38. Kaombe TM Hamuza GA Impact of ignoring sampling design in the prediction 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 10.1186/s12889-023-16544-4 37653375
Kaombe TM, Hamuza GA. Impact of ignoring sampling design in the prediction 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):1674.37653375 10.1186/s12889-023-16544-4
39. Carle AC Fitting multilevel models in complex survey data with design weights: recommendations BMC Med Res Methodol 2009 9 1 49 10.1186/1471-2288-9-49 19602263
Carle AC. Fitting multilevel models in complex survey data with design weights: recommendations. BMC Med Res Methodol. 2009;9(1):49.19602263 10.1186/1471-2288-9-49
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 10.1111/tmi.13460
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):1044–54.10.1111/tmi.13460
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 10.1017/S0007114521004761 34924064
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):1857–67.34924064 10.1017/S0007114521004761
42. Belay DG Kibret AA Diress M Gela YY Sinamaw D Simegn W Deworming among preschool age children in sub-saharan Africa: pooled prevalence and multi-level analysis Trop Med Health 2022 50 1 74 10.1186/s41182-022-00465-w 36209125
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):74.36209125 10.1186/s41182-022-00465-w
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 10.1371/journal.pone.0236449 32790764
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):e0236449.32790764 10.1371/journal.pone.0236449
44. Adeyinka DA Muhajarine N Petrucka P Isaac EW Inequities in child survival in Nigerian communities during the sustainable development goal era: insights from analysis of 2016/2017 multiple Indicator Cluster Survey BMC Public Health 2020 20 1 1613 10.1186/s12889-020-09672-8 33109141
Adeyinka DA, Muhajarine N, Petrucka P, Isaac EW. Inequities in child survival in Nigerian communities during the sustainable development goal era: insights from analysis of 2016/2017 multiple Indicator Cluster Survey. BMC Public Health. 2020;20(1):1613.33109141 10.1186/s12889-020-09672-8
45. Tesfay N Tariku R Zenebe A Hailu G Taddese M Woldeyohannes F Timing of perinatal death; causes, circumstances, and regional variations among reviewed deaths in Ethiopia PLoS ONE 2023 18 5 e0285465 10.1371/journal.pone.0285465 37159458
Tesfay N, Tariku R, Zenebe A, Hailu G, Taddese M, Woldeyohannes F. Timing of perinatal death; causes, circumstances, and regional variations among reviewed deaths in Ethiopia. PLoS ONE. 2023;18(5):e0285465.37159458 10.1371/journal.pone.0285465
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-health-coverage-2020-2025
47. Mahtab S Madhi SA Baillie VL Els T Thwala BN Onyango D Causes of death identified in neonates enrolled through Child Health and Mortality Prevention Surveillance (CHAMPS), December 2016–December 2021 PLOS Global Public Health 2023 3 3 e0001612 10.1371/journal.pgph.0001612 36963040
Mahtab S, Madhi SA, Baillie VL, Els T, Thwala BN, Onyango D, et al. Causes of death identified in neonates enrolled through Child Health and Mortality Prevention Surveillance (CHAMPS), December 2016–December 2021. PLOS Global Public Health. 2023;3(3):e0001612.36963040 10.1371/journal.pgph.0001612
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 10.51253/pafmj.v71i2.5156
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):562–66.10.51253/pafmj.v71i2.5156
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 10.1186/s13690-023-01103-5 37165410
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):87.37165410 10.1186/s13690-023-01103-5
50. Wang ML Dorer DJ Fleming MP Catlin EA Clinical outcomes of near-term infants Pediatrics 2004 114 2 372 6 10.1542/peds.114.2.372 15286219
Wang ML, Dorer DJ, Fleming MP, Catlin EA. Clinical outcomes of near-term infants. Pediatrics. 2004;114(2):372–6.15286219 10.1542/peds.114.2.372
51. Moss TJ, editor. The respiratory consequences of preterm birth. Proceedings of the Australian Physiological Society; 2005.
52. Collins A Weitkamp J-H Wynn JL Why are preterm newborns at increased risk of infection? Archives Disease Childhood-Fetal Neonatal Ed 2018 103 4 F391 4 10.1136/archdischild-2017-313595
Collins A, Weitkamp J-H, Wynn JL. Why are preterm newborns at increased risk of infection? Archives Disease Childhood-Fetal Neonatal Ed. 2018;103(4):F391–4.10.1136/archdischild-2017-313595
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 of neonates with perinatal asphyxia in a tertiary care hospital of central Nepal JNMA J Nepal Med Assoc 2014 52 196 1005 9 10.31729/jnma.2802 26982900
Gupta SK, Sarmah BK, Tiwari D, Shakya A, Khatiwada D. Clinical profile of neonates with perinatal asphyxia in a tertiary care hospital of central Nepal. JNMA J Nepal Med Assoc. 2014;52(196):1005–9.26982900 10.31729/jnma.2802
56. Mohamed MA Aly H Impact of race on male predisposition to birth asphyxia J Perinatol 2014 34 6 449 52 10.1038/jp.2014.27 24577433
Mohamed MA, Aly H. Impact of race on male predisposition to birth asphyxia. J Perinatol. 2014;34(6):449–52.24577433 10.1038/jp.2014.27
57. Waldron I. Sex differences in infant and early childhood mortality: major causes of death and possible biological causes Too young to die genes or gender. 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 Neonatal mortality associated with perinatal asphyxia: a population-based study in a middle-income country BMC Pregnancy Childbirth 2021 21 1 1 10 10.1186/s12884-021-03652-5 33388035
Kawakami MD, Sanudo A, Teixeira ML, Andreoni S, de Castro JQ, Waldvogel B, et al. Neonatal mortality associated with perinatal asphyxia: a population-based study in a middle-income country. BMC Pregnancy Childbirth. 2021;21(1):1–10.33388035 10.1186/s12884-021-03652-5
60. Heino A Gissler M Hindori-Mohangoo AD Blondel B Klungsøyr K Verdenik I Variations in multiple birth rates and impact on perinatal outcomes in Europe PLoS ONE 2016 11 3 e0149252 10.1371/journal.pone.0149252 26930069
Heino A, Gissler M, Hindori-Mohangoo AD, Blondel B, Klungsøyr K, Verdenik I, et al. Variations in multiple birth rates and impact on perinatal outcomes in Europe. PLoS ONE. 2016;11(3):e0149252.26930069 10.1371/journal.pone.0149252
61. Shah R Mullany LC Darmstadt GL Mannan I Rahman SM Talukder RR Incidence and risk factors of preterm birth in a rural Bangladeshi cohort BMC Pediatr 2014 14 1 11 10.1186/1471-2431-14-112 24387002
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;14:1–11.24387002 10.1186/1471-2431-14-112
62. Njim T Agbor VN Adolescent deliveries in semi-urban Cameroon: prevalence and adverse neonatal outcomes BMC Res Notes 2017 10 1 5 10.1186/s13104-017-2555-3 28057050
Njim T, Agbor VN. Adolescent deliveries in semi-urban Cameroon: prevalence and adverse neonatal outcomes. BMC Res Notes. 2017;10:1–5.28057050 10.1186/s13104-017-2555-3
63. Tabassum F Rizvi A Ariff S Soofi S Bhutta ZA Risk factors associated with birth asphyxia in rural district Matiari, Pakistan: a case control study Int J Clin Med 2014 5 21 1430 41 10.4236/ijcm.2014.521181
Tabassum F, Rizvi A, Ariff S, Soofi S, Bhutta ZA. Risk factors associated with birth asphyxia in rural district Matiari, Pakistan: a case control study. Int J Clin Med. 2014;5(21):1430–41.10.4236/ijcm.2014.521181
64. Chan GJ Baqui AH Modak JK Murillo-Chaves A Mahmud AA Boyd TK Early‐onset neonatal sepsis in Dhaka, Bangladesh: risk associated with maternal bacterial colonisation and chorioamnionitis Tropical Med Int Health 2013 18 9 1057 64 10.1111/tmi.12150
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):1057–64.10.1111/tmi.12150
65. Mitra DK Mullany LC Harrison M Mannan I Shah R Begum N Incidence and risk factors of neonatal infections in a rural Bangladeshi population: a community-based prospective study J Health Popul Nutr 2018 37 1 11 10.1186/s41043-018-0136-2 29304840
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: a community-based prospective study. J Health Popul Nutr. 2018;37:1–11.29304840 10.1186/s41043-018-0136-2
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 10.1371/journal.pone.0191002 29385154
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):e0191002.29385154 10.1371/journal.pone.0191002
68. Hidalgo-Lopezosa P Jiménez-Ruz A Carmona-Torres J Hidalgo-Maestre M Rodríguez-Borrego M López-Soto P Sociodemographic factors associated with preterm birth and low birth weight: a cross-sectional study Women Birth 2019 32 6 e538 43 10.1016/j.wombi.2019.03.014 30979615
Hidalgo-Lopezosa P, Jiménez-Ruz A, Carmona-Torres J, Hidalgo-Maestre M, Rodríguez-Borrego M, López-Soto P. Sociodemographic factors associated with preterm birth and low birth weight: a cross-sectional study. Women Birth. 2019;32(6):e538–43.30979615 10.1016/j.wombi.2019.03.014
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 maternal age, birth order, and fetal gender Hum Reprod 1999 14 11 2891 4 10.1093/humrep/14.11.2891 10548643
Astolfi P, Zonta LA. Risks of preterm delivery and association with maternal age, birth order, and fetal gender. Hum Reprod. 1999;14(11):2891–4.10548643 10.1093/humrep/14.11.2891
71. Azab SFAH Sherief LM Saleh SH Elsaeed WF Elshafie MA Abdelsalam SM Impact of the socioeconomic status on the severity and outcome of community-acquired pneumonia among Egyptian children: a cohort study Infect Dis Poverty 2014 3 1 7 10.1186/2049-9957-3-14 24401663
Azab SFAH, Sherief LM, Saleh SH, Elsaeed WF, Elshafie MA, Abdelsalam SM. Impact of the socioeconomic status on the severity and outcome of community-acquired pneumonia among Egyptian children: a cohort study. Infect Dis Poverty. 2014;3:1–7.24401663 10.1186/2049-9957-3-14
72. Ruiz M Goldblatt P Morrison J Kukla L Švancara J Riitta-Järvelin M 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 10.1136/jech-2014-205387 25911693
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):826–33.25911693 10.1136/jech-2014-205387
73. Luo Z-C Wilkins R Kramer MS Effect of neighbourhood income and maternal education on birth outcomes: a population-based study CMAJ 2006 174 10 1415 20 10.1503/cmaj.051096 16682708
Luo Z-C, Wilkins R, Kramer MS. Effect of neighbourhood income and maternal education on birth outcomes: a population-based study. CMAJ. 2006;174(10):1415–20.16682708 10.1503/cmaj.051096
74. Cantarutti A Franchi M Monzio Compagnoni M Merlino L Corrao G Mother’s education and the risk of several neonatal outcomes: an evidence from an Italian population-based study BMC Pregnancy Childbirth 2017 17 1 221 10.1186/s12884-017-1418-1 28701151
Cantarutti A, Franchi M, Monzio Compagnoni M, Merlino L, Corrao G. Mother’s education and the risk of several neonatal outcomes: an evidence from an Italian population-based study. BMC Pregnancy Childbirth. 2017;17(1):221.28701151 10.1186/s12884-017-1418-1
75. Alam GM Hassan CH Kamal SMM Ying Y CHILD MARRIAGE IN BANGLADESH: TRENDS AND DETERMINANTS J Biosoc Sci 2015 47 1 120 39 10.1017/S0021932013000746 24480489
Alam GM, Hassan CH, Kamal SMM, Ying Y. CHILD MARRIAGE IN BANGLADESH: TRENDS AND DETERMINANTS. J Biosoc Sci. 2015;47(1):120–39.24480489 10.1017/S0021932013000746
76. Islam A Biswas T Health system in Bangladesh: challenges and opportunities Am J Health Res 2014 2 6 366 74 10.11648/j.ajhr.20140206.18
Islam A, Biswas T. Health system in Bangladesh: challenges and opportunities. Am J Health Res. 2014;2(6):366–74.10.11648/j.ajhr.20140206.18
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 look at 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 Trends and projections of under-5 mortality in Bangladesh including the effects of maternal high-risk fertility behaviours and use of healthcare services PLoS ONE 2021 16 2 e0246210 10.1371/journal.pone.0246210 33539476
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 of maternal high-risk fertility behaviours and use of healthcare services. PLoS ONE. 2021;16(2):e0246210.33539476 10.1371/journal.pone.0246210
80. Ministry of Health and Family Welfare, Government of The People’s Republic of Bangladesh. Bangladesh national strategy for maternal health 2015–2030. 2018.
81. Ministry of Health and Family Welfare, Government of The People’s Republic of Bangladesh. Maternal Health - Standard Operating Procedure Volume – I.
