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. 2017 Dec 20;10(1):1410048. doi: 10.1080/16549716.2017.1410048

Predictors of stillbirths in Bangladesh: evidence from the 2004–2014 nation-wide household surveys

Tanvir Abir a, Kingsley E Agho a,, Felix A Ogbo a, Garry J Stevens b, Andrew Page a, Milton A Hasnat c, Michael J Dibley d, Camille Raynes-Greenow d
PMCID: PMC5757223  PMID: 29261451

ABSTRACT

Background: Globally, stillbirth remains a significant public health issue, particularly in developing countries such as Bangladesh.

Objective: This study aimed to investigate the potential predictors of stillbirths in Bangladesh over a ten-year period.

Methods: The Bangladesh Demographic and Health Surveys data for the years 2004, 2007, 2011 and 2014 (n = 29,094) were used for the study to investigate the predictors of stillbirths. Stillbirth was examined against a set of community, socio-economic and child characteristics, using a multivariable logistic regression model that adjusted for cluster and sampling variability.

Results: The pooled rate of stillbirth in Bangladesh was 28 in 1000 births (95% CI: 22, 34). Stillbirth rates were higher in rural compared to urban areas in Bangladesh. Mothers who had a secondary or higher level of education (OR = 0.59, 95%CI: 0.43–0.82, P = 0.002) and those with primary education (OR = 0.66, 95%CI: 0.55–0.80, P < 0.001) were less likely to experience stillbirths compared to mothers with no education. Mothers with more than two children were significantly less likely to have stillbirths compared to mothers with one child. Those from poor households reported increased odds of stillbirth compared to those from rich households.

Conclusion: Our analysis indicated that no maternal education, primiparity and poor household were predictors of stillbirths in Bangladesh. A collaborative effort is needed to reduce stillbirth rates among these high-risk groups in Bangladesh, with the socio-economic and health-related Sustainable Development Goals providing a critical vehicle for the co-ordination of this work.

KEYWORDS: Bangladesh, infants, mortality, predictors, stillbirths, under-five

Background

The Global Burden of Disease, Injuries and Risk Factors Study 2015 (GBD 2015) reported that the rate of stillbirth has fallen worldwide by 47% since 1990, and more quickly from the year 2000 [1]. Despite this decline, recent studies have reported that global estimates of stillbirth ranged from 2.1 million [1] to 2.6 million [2] in 2015, and approximately 98% of those fetal deaths occurred in developing countries [1,2]. Variation in global estimates of the stillbirth rate may be due to access to data sources and modelling strategy, as both studies used the standard definition for stillbirth (fetal death after 28 weeks’ gestation).

The United Nations reported that Bangladesh made a significant improvement in reducing under-5 mortality rate during the Millennium Development Goals (MDG) era (between 1990 and 2015) [3]. Despite this achievement, Bangladesh remains a major contributor to stillbirth rates in South Asia [2] with a reported stillbirth rate of 20 per 1000 live births in 2015 [1]. Stillbirths have an enormous impact on mothers, families, health care professionals and the community [4]. Previous studies have quantified the direct [5,6] and indirect [4] financial costs for parents after an experience of stillbirth, however, the psychological and social costs associated with stillbirth have been described as unquantifiable [7].

Based on the health burden associated with stillbirth, there is a renewed focus at the global level on ending preventable stillbirths by 2030 (Sustainable Development Goal, SDG-3.2) [8,9]. Similarly, the Lancet Series on ending preventable stillbirths highlighted the need for policy formulation and ongoing research, particularly improved data collection to support the implementation of evidence-based initiatives [8]. In the context of this global goal, country-specific evidence would be helpful in informing targeted interventions and policy decision-making to reduce stillbirth in Bangladesh.

In Bangladesh, information on risk factors for stillbirths is limited at the national level. Previous studies conducted in rural areas [10,11] and the inner city of Dhaka [12] found that a lack of maternal education, older maternal age (≥35 years), history of alcohol intake and drug abuse were associated with higher rates of stillbirth. The generalisability of these findings to the broader Bangladesh population may be limited, given differences in socio-economic status and geographical regions. The burden of stillbirths can vary within a country, with economically disadvantaged communities having higher rates compared to their economically well-off counterparts [13].

Using a reliable and population-based maternal and child health data source (Bangladesh Demographic and Health Survey, BDHS), we provide nationally representative information on the rate and predictors of stillbirth. Our study aimed to investigate the predictors of stillbirth in Bangladesh, using the BDHS datasets for the period (2004–2014).

Methods

Data sources

Datasets for the years 2004, 2007, 2011 and 2014 from the BDHS were pooled and used for the study. We pooled data across time to increase sample size and statistical power, consistent with previous studies [1416]. The BDHS data were collected by the National Institute of Population Research and Training (NIPORT), with technical support from Measure DHS through the Inner City Fund (ICF) International. A weighted total sample of 29,094 pregnancies over 28 weeks’ gestation for women aged 15–49 years were included in the final analysis (2004: n = 6,395; 2007: n = 5,409; 2011: n = 9,021; and 2014: n = 8,269). The data were weighted to ensure the representativeness of the survey results at the national level.

In the 2011 and 2014 BDHS, a new administrative region called ‘Rangpur’ was created, and when Rangpur was removed from the overall data sets, a total weighted sample of 27,540 pregnancies over 28 weeks’ gestation for women aged 15–49 years was obtained (2004: n = 6,395; 2007: n = 5,409; 2011: n = 8,315; and 2014: n = 7,421). Data with Rangpur (general Bangladesh population) and without Rangpur were reported in this present study to ensure robustness of the analyses. The average response rate for the four surveys was 98%. A detailed description of the survey methodology, sampling procedure and questionnaires used for data collection is provided elsewhere [17].

Outcome variable

The study outcome was stillbirth, defined as death of a fetus of more than or equal to 28 weeks’ gestation, consistent with previous studies [1,2,12]. The outcome was recorded as a binary variable in the datasets, coded as ‘1’ for stillbirth and ‘0’ for no stillbirth.

Study factors

The study factors included community, socio-demographic and child factors. These were selected based on previously published studies and availability of data [1012]. The community factors were place of residence (urban or rural) and geographical region, covering divisions in Bangladesh, namely: Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Sylhet and Rangpur. Socio-demographic factors included number of children ever born, age of mother at the time of the interview, mother’s working status, mother’s marital status, mother’s body mass index (BMI), parents’ level of education, mother’s age at index childbirth, desire for pregnancy, mother’s access to the media (television, radio or newspaper). Child factors comprised gender of the child, previous multiple births, previous death of a sibling and combined birth rank and interval. Based on previous studies [18,19], we combined birth order and interval in the analysis because of the impact of birth order that may be mediated by the birth interval. Household wealth index was constructed by NIPORT and ICF International [17], using the principal components analysis by assigning weights to three household characteristics; namely: type of floor and wall; access to electricity; and six household assets; namely, possession of a radio, television, bicycle, motorcycle, car and fridge. The household wealth index was ranked across the four surveys, where household wealth index was divided into three categories. The bottom 40% of households were arbitrarily classified as poor households, the next 40% as the middle households and the top 20% as rich households [20]. Type of cooking fuels available to household members at the time of survey will be referred to as ‘household air pollution from solid fuel’. Household air pollution from solid fuel were categorised as solid fuels (coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crop, animal dung) and non-solid fuels (electricity, liquefied petroleum gas (LPG), natural gas, biogas, kerosene).

Statistical analysis

Frequency tabulations were first conducted to describe the distributions of data by years of the survey, followed by calculation of the rate of stillbirths, unadjusted odds ratios (OR) and their 95% confidence interval (CI) of all potential predictors.

A three-stage model was performed for the multivariable logistic regression analyses by following a conceptual model that was employed by Chowdhury et al. [21]. In the first modelling stage, community and socio-economic determinants were examined, and only significant variables associated with the study outcome at 5% significance level were retained in model 1. In the second stage, the significant variables in model 1 were added to child demographic factors. In the final stage, media factors and environmental factor were added to significant variables in model 2 to determine factors associated with stillbirth. All analyses were performed in Stata statistical software version 14 (Stata Corp., College Station, TX, USA) that adjusted for sampling weights, intra-cluster variability and sampling design to provide population-based estimates.

Ethics

The study used existing survey datasets that are available online by application, with all identifier information removed. The surveys were approved by the Ethics Committee of the ICF International, USA and the National Research Ethics Committee of Bangladesh Medical Research Council (BMRC), Bangladesh. We obtained approval from Measure DHS to download and use the data for the study.

Results

Characteristics of the study population

The majority of mothers were from the Dhaka administrative region (32.2%), with the smallest group from the Barisal region (5.8%). Half of the mothers belonged to the youngest age group (15–24 years, 50.1%), with 8.8% aged 35–49 years. Mothers with no schooling and those with only primary education were almost equally represented (43.7% and 45.5%, respectively). Approximately 18 out of every 100 households were categorised as wealthy, and 42 out of every 100 households were categorised as poor households. Female and male children were almost equally distributed (Table 1).

Table 1.

Characteristics of the study population in Bangladesh, 2004–2014 (n = 29,094).

  With Rangpur (a) (n = 29,094)
Without Rangpur (n = 27,540)
VARIABLE n n* %* n n* %*
COMMUNITY LEVEL FACTORS
Year of survey
2004 6287 6395 22.0 6287 6395 23.2
2007 5473 5409 18.6 5473 5409 19.6
2011 8986 9021 31.0 7527 8316 30.2
2014 8069 8269 28.4 6714 7420 26.9
Cluster type
Urban 8965 6423 22.1 8242 6212 22.6
Rural 19,850 22,670 77.9 17,759 21,328 77.4
Region
Barisal 3313 1685 5.8 3313 1685 6.1
Chittagong 5876 6472 22.2 5876 6472 23.5
Dhaka 5406 9354 32.2 5406 9354 34.0
Khulna 3296 2605 9.0 3296 2605 9.5
Rajshahi 4124 4609 15.8 4124 4608 16.7
Sylhet 3986 2815 9.7 3986 2815 10.2
Rangpur 2814 1554 5.3      
SOCIOECONOMIC DETERMINANTS
Mother’s Age (years) (n = 29,087)
15–24 14,271 14,576 50.1 13,102 13,920 50.6
25–34 11,890 11,953 41.1 10,571 11,239 40.8
35–49 2634 2558 8.8 2308 2375 8.6
Mother working status (n = 29,090)
Not working 23,132 23,095 79.4 20,648 21,719 78.9
Working 5679 5995 20.6 5351 5818 21.1
Mother BMI (kg/m2) (n = 28,939)
≤18 6329 6360 21.9 5597 5967 21.7
19–25 18,595 19,064 65.5 16,905 18,090 65.7
25+ 3724 3515 12.1 3350 3337 12.1
Maternal marital status
Currently married 28,282 28,572 98.2 25,519 27,041 98.2
Formerly married 533 522 1.8 482 499 1.8
Maternal highest level of education (n = 29,079)
No schooling 12,235 12,712 43.7 10,930 11,969 43.5
Primary 12,775 12,939 44.5 11,524 12,246 44.5
Secondary or more 3785 3428 11.8 3527 3309 12.0
Paternal highest level of education (n = 29,077)
No schooling 13,898 14,440 49.6 12,413 13,588 49.3
Primary 9608 9697 33.3 8690 9178 33.3
Secondary or more 5291 4940 17.0 4880 4756 17.3
Household Wealth Index
Rich 5763 5118 17.6 5123 4860 17.7
Middle 11,526 11,684 40.2 10,568 11,178 40.6
Poor 11,526 12,291 42.3 10,310 11,502 41.8
CHILD DETERMINANTS
Sex (n = 28,685)            
Female 13,861 14,019 48.2 12,547 13,285 48.2
Male 14,538 14,666 50.4 13,137 13,899 50.5
Birth rank and birth interval
2nd/3rd birth rank, more than 2 years interval 10,675 10,935 37.6 9776 10,455 38.0
1st birth rank 9948 9996 34.4 9164 9556 34.7
2nd/3rd birth rank, less than or equal to 2 years interval 1924 1907 6.6 1677 1777 6.5
4th birth rank, more than 2 years interval 5178 5200 17.9 4453 4787 17.4
4th birth rank, less than or equal to 2 years interval 1090 1056 3.6 931 965 3.5
Previous Death of Sibling
No 28,067 28,352 97.5 25,318 26,832 97.4
Yes 748 742 2.6 683 708 2.6
Number of children born (n = 29,011)
1 7990 7999 27.5 7401 7675 27.9
2 8732 8868 30.5 8012 8485 30.8
3 5278 5412 18.6 4727 5110 18.6
4+ 6733 6732 23.1 5795 6196 22.5
Number of children under-five years
1–2 17,873 18,113 62.3 16,467 17,365 63.1
3 or more 10,942 10,981 37.7 9534 10,175 37.0
MEDIA FACTORS      
Watches television every week (n = 29,011)
Yes 16,123 16,080 55.3 14,775 15,421 56.0
No 12,688 13,011 44.7 11,224 12,116 44.0
Listens to radio every week (n = 29,088)
Yes 5158 5385 18.5 5019 5305 19.3
No 23,650 23,703 81.5 20,975 22,229 80.7
Reads newspaper (n = 29,075)
Yes 4501 4115 14.1 4054 3902 14.2
No 24,291 24,960 85.8 21,930 23,621 85.8
ENVIRONMENTAL FACTOR
Type of cooking fuel (n = 26,325)
Solid fuel 2995 2943 10.1 2644 2846 10.3
Non-solid fuel 23,140 23,382 80.4 20,871 22,018 80.0

&Weighted for the sampling probability; n& weighted ‘n’

*percentage did not add up to 100% because of missing values.

(a) Overall Bangladesh population

Rates and predictors of stillbirths

As shown in Figure 1(a) (with Rangpur), the rate of stillbirth was 37 [95% confidence interval (CI): 32, 42] per 1000 births in 2004; 30 (95% CI: 25, 35) per 1000 births in 2007, 26 (95% CI: 23, 29) per 1000 births in 2011 and 21 (95% CI: 18, 25) per 1000 births in 2014. From 2004 to 2014, the overall rate of stillbirth was 28 (95% CI: 22, 34) per 1000 births. These results indicated that stillbirth decreased significantly in 2011 and 2014 compared to 2004, but in 2007 compared to 2011 and 2014, there was no significant decrease in stillbirth rate. In comparison to the population with Rangpur (Figure 1(a)), there was no significant differences in the rate of stillbirth in the population without Rangpur (Figure 1(b)).

Figure 1.

Figure 1.

(a) Rate of stillbirth per 1000 births in Bangladesh (with Rangpur), 2004–2014. (b)Rate of stillbirth per 1000 births in Bangladesh (without Rangpur) 2004–2014.

The analysis showed that the rate of stillbirth was higher among rural mothers, older women, mothers with no schooling and mothers from poor households in Bangladesh (with Rangpur) [Table 2]. The stillbirth rate was significantly higher among households who reported non-solid fuel use and mothers who reported fourth birth order of child with more than 2 years’ birth interval.

Table 2.

Rate and univariate analysis of stillbirth by study factors in Bangladesh, 2004–2014.

  With Rangpur (a)
Without Rangpur
  Rate 95%[CI] Unadjusted odds ratio
Rate 95%[CI] Unadjusted odds ratio
VARIABLE OR (95% CI) OR (95% CI)
COMMUNITY LEVEL FACTORS
Cluster type
Urban 23 [19, 48] 1.00     22 [18, 26] 1.00    
Rural 30 [28, 43] 1.36 1.13 1.65 30 [27, 32] 1.39 1.14 1.69
Region
Barisal 28 [19, 36] 1.00     28 [19, 36] 1.00    
Chittagong 26 [22, 30] 0.93 0.65 1.34 26 [22, 30] 0.93 0.65 1.35
Dhaka 27 [24, 30] 1.01 0.71 1.44 27 [24, 30] 1.02 0.71 1.45
Khulna 24 [18, 30] 0.87 0.57 1.32 24 [18, 30] 0.87 0.57 1.33
Rajshahi 33 [28, 39] 1.26 0.87 1.82 33 [28, 39] 1.26 0.87 1.83
Sylhet 33 [26, 39] 1.26 0.85 1.88 33 [26, 39] 1.27 0.85 1.90
Rangpur 36 [26, 46] 1.37 0.88 2.14        
SOCIOECONOMIC DETERMINANTS
Mother’s Age (years)*
15–24 28 [26, 31] 1.00     28 [25, 31] 1.00    
25–34 28 [25, 31] 0.99 0.85 1.15 28 [25, 31] 0.99 0.85 1.16
35–49 31 [24, 38] 1.09 0.85 1.40 31 [24, 38] 1.11 0.86 1.44
Mother working status
Not working 35 [31, 39] 1.00     28 [26, 30] 1.00    
Working 33 [26, 40] 0.97 0.76 1.25 29 [25, 34] 1.05 0.88 1.25
Mother BMI (kg/m2)*
≤18 30 [25, 34] 1.00     30 [25, 34] 1.00    
19–25 29 [26, 31] 0.97 0.82 1.15 28 [26, 31] 0.95 0.80 1.14
25+ 24 [19, 29] 0.81 0.62 1.06 23 [18, 28] 0.78 0.59 1.03
Maternal marital status
Currently married 28 [26, 30] 1.00     27 [25, 29] 1.00    
Formerly married 63 [41, 85] 2.28 1.56 3.32 59 [37, 81] 2.18 1.46 3.23
Maternal highest level of education*
No schooling 34 [31, 38] 1.00     34 [30, 37] 1.00    
Primary 25 [22, 28] 0.72 0.62 0.84 25 [22, 28] 0.73 0.62 0.85
Secondary or more 20 [15, 25] 0.57 0.44 0.75 20 [15, 25] 0.59 0.45 0.77
Paternal highest level of education*
No schooling 33 [30, 36] 1.00     32 [29, 35] 1.00    
Primary 28 [24, 31] 0.84 0.72 0.99 27 [24, 31] 0.85 0.72 0.99
Secondary or more 17 [13, 21] 0.51 0.40 0.65 17 [13, 20] 0.51 0.40 0.65
Household Wealth Index
Rich 18 [14, 21] 1.00     17[14, 21] 1.00    
Middle 30 [26, 33] 1.71 1.35 2.17 29 [26, 33] 1.74 1.35 2.23
Poor 32 [29, 35] 1.85 1.46 2.35 31 [28, 35] 1.87 1.46 2.40
CHILD DEMOGRAPHICS
Gender*                
Female 14 [12, 16] 1.00     15 [13, 17] 1.00    
Male 13 [12, 16] 0.97 0.79 1.19 14 [12, 17] 0.97 0.80 1.19
Birth rank and birth interval
2nd/3rd birth rank, more than 2 years interval 11 [9,13] 1.00     11 [9,13] 1.00    
1st birth rank 16 [14, 19] 1.47 1.16 1.87 16 [14, 19] 1.48 1.16 1.88
2nd/3rd birth rank, less than or equal to 2 years interval 16 [10, 22] 1.49 0.99 2.23 17 [11, 23] 1.53 1.02 2.29
4th birth rank, more than 2 years interval 102 [93, 111] 9.91 8.05 12.20 98 [89, 107] 9.12 7.38 11.25
4th birth rank, less than or equal to 2 years interval 19 [10, 27] 1.79 1.09 2.92 20 [11, 29] 1.87 1.15 3.06
Previous Death of Sibling
No 28 [26, 30] 1.00     28 [26, 30] 1.00    
Yes 37 [23, 51] 1.31 0.88 1.96 38 [23, 52] 1.36 0.90 2.04
Number of children born*
1 35 [31, 39] 1.00     34 [34, 38] 1.00    
2 21 [18, 24] 0.57 0.47 0.69 21 [17, 24] 0.58 0.48 0.71
3 20 [17, 24] 0.58 0.46 0.73 20 [16, 24] 0.58 0.46 0.74
4+ 25 [21, 29] 0.70 0.57 0.85 25 [21, 29] 0.73 0.59 0.89
Number of children under-five years
1–2 33 [31, 36] 1.00     32 [30, 35] 1.00    
3+ 20 [18, 23] 0.61 0.52 0.71 21 [18, 23] 0.63 0.53 0.74
MEDIA FACTORS
Watches TV every week*
Yes 25 [22, 27] 1.00     24 [22, 27] 1.00    
No 33 [30, 36] 1.36 1.18 1.58 33 [30, 36] 1.38 1.19 1.60
Listens to radio every week*
Yes 31 [26, 36] 1.00     31 [26, 36] 1.00    
No 28 [26, 30] 0.90 0.76 1.08 27 [25, 30] 0.88 0.74 1.06
Read newspaper*
Yes 19 [14, 23] 1.00     18 [14, 22] 1.00    
No 30 [28, 32] 1.61 1.27 2.05 30 [27, 32] 1.63 1.27 2.10
ENVIRONMENTAL FACTOR
Type of cooking fuel*
Solid fuel 18 [13, 23] 1.00     28 [16, 39] 1.00    
Non-solid fuel 31 [28, 33] 1.56 1.18 2.04 37 [33, 41] 1.34 0.82 2.18

*Rates did not add up because of missing values.

Note: 95% confidence intervals (CI) that include 1.00 indicate a non-significant result.

(a) Overall Bangladesh population

Multivariable analyses were performed with and without Rangpur division and showed that there was no substantial statistical difference between inclusion or removal of Rangpur division from the data sets. In this study, we provide interpretation of findings for all regions of Bangladesh (analyses with Rangpur division). In the multivariable analyses, the odds of stillbirth were significantly lower in educated mothers compared to those who had no schooling (Table 3). The risk of stillbirth was significantly higher among mothers from poorer households compared to those from rich households. Mothers with four or more children were significantly less likely to have a stillbirth compared to those who had one child. Mothers who did not read newspapers every week were significantly more likely to experience a stillbirth compared to those who read newspapers every week.

Table 3.

Predictors of stillbirth: adjusted odds ratio (AOR) in Bangladesh, 2004–2014.

  With Rangpur (a)
Without Rangpur
Characteristic AOR (95%CI) P value AOR (95%CI) P value
Year of survey
2004 1.00       1.00      
2007 0.81 0.66 1.00 0.045 0.75 0.61 0.93 0.010
2011 0.54 0.44 0.66 <0.001 0.52 0.42 0.65 <0.001
2014 0.47 0.38 0.59 <0.001 0.41 0.32 0.52 <0.001
Maternal highest level of education
No schooling 1.00       1.00      
Primary 0.66 0.55 0.80 <0.001 0.67 0.55 0.81 <0.001
Secondary or more 0.59 0.43 0.82 0.002 0.63 0.44 0.89 0.008
Household Wealth Index
Rich 1.00       1.00      
Middle 1.30 1.01 1.66 0.040 1.51 1.14 2.01 0.004
Poor 1.47 1.13 1.90 0.004 1.62 1.21 2.16 0.001
Number of children born
1 1.00       1.00      
2 0.56 0.46 0.69 <0.001 0.57 0.46 0.70 <0.001
3 0.49 0.39 0.63 <0.001 0.49 0.38 0.63 <0.001
4+ 0.53 0.43 0.66 <0.001 0.51 0.40 0.65 <0.001
Number of children under-five years
1–2 1.00       1.00      
3 or more 0.74 0.63 0.88 0.001 0.76 0.63 0.91 0.003
Read newspaper
Yes 1.00       1.00      
No 1.34 1.02 1.76 0.037 1.38 1.03 1.86 0.033

Independent variables adjusted for community and socio-economic, child, media and environmental factor.

(a) Overall Bangladesh population

Discussion

The study found that the rates of stillbirth were lower in 2014 compared to 2004. Stillbirth rates were higher in rural areas compared to urban areas in Bangladesh, and low maternal education, poor household, and having one child (primiparity) were significant predictors of stillbirth in Bangladesh. A further stratified analysis (with or without Rangpur division) showed no substantial statistical differences in the results.

The finding that stillbirth declined during the decade 2004–2014 is consistent with previous studies which reported lower rates of stillbirth in Bangladesh between 2009 and 2015 [11,12] and from 1990 to 2015 [1]. The reduction in the rates of stillbirth in Bangladesh has been attributed to a range of maternal and newborn interventions and socioeconomic policies. These include overall economic growth; improved education and social empowerment of women; increased health sector financing and investment; the scale-up of family planning services; and increased access to skilled birth attendants and expansion of the private health sector [22]. The marked improvement in child survival may also be due to the broader influence of programmatic commitments to the MDG’s between 1990 and 2015. Notably, the United Nations reported that Bangladesh was among the few countries worldwide to meet MDG-4 and MDG-5 (reduction of under-5 and maternal mortalities) [3]. While under-5 and maternal mortality rates are not direct measures of stillbirth rate, improvement in appropriate antenatal care, skilled births assistance and newborn care have been described as the core solutions to ending preventable stillbirth [22,23].

Although our study observed no association between maternal age and stillbirth, previous studies from developing countries such as Sudan [24] and Nigeria [25] and developed countries such as Australia [26] and the USA [27] have reported a higher risk of stillbirths in women aged over 35 years. The higher rate of stillbirths among older women may be due to increased risk of congenital anomalies associated with advanced maternal age. In contrast, hospital-based studies conducted in India [28] and Nigeria [29] reported an increased risk of stillbirths in mothers aged less than 20 years. This finding may reflect a lack of education, limited autonomy to make household decisions and poor health-seeking behaviours among teenage women, as reported in Nigeria [30] and India [31]. Nevertheless, a population-based study from Taiwan reported an increased risk of stillbirths in both older (>40 years) and younger mothers (<20 years) [32].

Consistent with previous studies conducted in developing countries, from rural Bangladesh to Uganda [10,11,33,34], this study showed that stillbirth rates were higher among mothers with no formal education compared to educated mothers. A study conducted in Norway indicated that stillbirth rates were higher in Norwegian women with fewer years of education, but not among Pakistani immigrant women in Norway [24]. In addition, our study found that mothers from poor households were more likely to experience stillbirths compared to those from rich households. A link between poverty and higher rates of stillbirth has been documented in developing countries [35,36], and a combination of no formal education associated with low-income family income may act as a major obstacle to timely and appropriate decision to seek early medical care in pregnant women. Our study provides supportive evidence that a lack of maternal education is associated with an increased risk of stillbirth in Bangladesh. This finding will assist public health campaigners advocating for targeted socio-educational initiatives to increase female education in Bangladesh.

In Bangladesh, the proportion of women who give birth at home with assistance from a traditional birth attendant (TBA) remains high [17], highlighting the poor uptake of appropriate perinatal health services such as antenatal care (ANC) and birth assistance from skilled health professionals. Antenatal care is an essential public health intervention and is recommended for all pregnant women worldwide by the WHO, based on evidence underpinning its importance in improving maternal and child health outcomes. However, in rural Bangladesh in particular, a range of factors have been linked with the persistent use of home birthing with TBA’s [37] including; traditional beliefs, poverty, religious fallacy, poor road networks, limited knowledge on the importance of healthcare services and a shortage of skilled health workers. Bangladesh would likely see further substantial improvements in child survival by implementing interventions that increase access to, and use of perinatal services, particularly among mothers in rural settings and other high-risk groups.

This study revealed that the risk of stillbirth was lower in mothers who had more than two children compared to those with one child, consistent with findings from previous studies, which indicated that stillbirth rates were higher among primiparous women [26,38]. In this setting this could be partly attributed to the young age of first-time mothers which is also a known risk for stillbirth, and lower use of health services and knowledge of the importance of timely and routine ANC.

The study findings have policy implications for public health experts, policy decision-makers, health administrators and developmental partners in Bangladesh. The Lancet Series [2,4,8] suggest a roadmap for ending preventable stillbirths. These include stronger independent accountability within countries, the establishment of stillbirth prevention strategic plans, empowerment of women and families, ensuring skilled birth attendance in health facilities, reduction in stigma associated with stillbirths and improvement in bereavement care. Achievement of SDG-3.2 (end preventable deaths of newborns and children under-five years of age by 2030) appears feasible in Bangladesh given the country’s MDGs achievement, however, targeted financial investment and strong political commitment are required. Furthermore, achievement of SDG-3.2 in Bangladesh would require collaborative efforts among various government and non-government agencies at both national and sub-national levels, as well as drawing experiences and capacities from the implementation of the MDGs agenda.

Strengths and limitations

The following limitations should be considered when interpreting the study findings. First, the study used cross-sectional data, and a temporal association between exposure variables and the outcome cannot be determined. Second, the diagnosis of stillbirth was based on self-report, and this is a likely source of recall bias as respondents may incorrectly recall the gestational age they experienced a stillbirth. Third, data on other potential predictors of stillbirths (such as antepartum and intrapartum events, congenital anomalies or maternal drug use) as reported elsewhere [39] were not available. This latter information would have provided an additional contextual understanding of determinants of stillbirths in Bangladesh. Fourth, the study used pooled cross-sectional data, where population characteristics may differ over time. However, we adjusted for period and intra-cluster variability [40]. Additional information on the broader limitations of the DHS data utilisation has been described elsewhere [41].

Despite these limitations, the study has several specific strengths. First, selection bias is unlikely to affect the study findings, given the nationally representative sample and the high response rates that averaged 98%. Second, the BDHS used standardised questionnaires for data collection that provides population-based information on maternal and child health over time, allowing comparability across populations and time. Third, the data were collected by high-quality interviewers, which reduces the potential effect of interviewer bias. Fourth, this study provides country-wide evidence on predictors of stillbirths to health experts who can advocate for interventions to improve child survival and health at the national level in Bangladesh.

Conclusion

Our analysis showed that rates of stillbirth were lower in 2014 compared to 2004 in Bangladesh, and risk factors for stillbirth were low maternal education, primiparity and poor household. These findings highlight the need for collaborative efforts to end poverty, ensure healthy lives for all, promote inclusive and equitable education, and empower women to improve child survival in Bangladesh. Drawing lessons from the implementation of MDGs would help accelerate progress towards achievement of ending preventable stillbirths in Bangladesh by 2030.

Biography

TA and KEA were involved in the conception and designed for this study; TA performed the analysis and contributed to the manuscript draft. FAO interpreted results, drafted the original manuscript and critically revised the final manuscript. KEA, GJS, ANP, MAH, HM, MJD and CRG provided data analysis and interpretation advice, and revised drafts of the manuscript. All authors read and approved the final manuscript.

Responsible Editor Stig Wall, Umeå University, Sweden

Funding Statement

None.

Acknowledgments

The authors are grateful to Measure DHS for providing the datasets for the analysis.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics and consent

The study used existing survey datasets that are available online by application, with all identifier information removed. The surveys were approved by the Ethics Committee of the ICF International, USA and the National Research Ethics Committee of Bangladesh Medical Research Council (BMRC), Bangladesh. We obtained approval from Measure DHS to download and use the data for the study.

Paper context

Globally, stillbirth is a significant public health issue, particularly in developing countries such as Bangladesh. We examined nationally representative data to identify potential predictors of stillbirths in Bangladesh over a ten-year period. Our study found that stillbirth rates were higher in rural areas, and no maternal education, poor household and primiparity were predictors of stillbirths in Bangladesh. There is need for more collaborative action to end preventable deaths and improve child survival in Bangladesh.

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