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Prevalence and associated factors of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa: a multilevel analysis of the recent demographic and health survey

Maternal Health, Neonatology and Perinatology volume 11, Article number: 10 (2025) Cite this article

Abstract

Background

Stillbirth is one of the biggest adverse pregnancy outcomes in countries with low and middle incomes. If current trends continue, 15.9 million babies will be stillborn; nearly half of these (7.7 million, or 48%) will occur in sub-Saharan Africa. Although stillbirth is one of the health care indicators, its prevalence and determinates are not well studied in low- and middle-income countries (LMIC). Therefore, this study aims to assess the prevalence and associated factors of stillbirth among people at extreme ages of reproductive life in Sub-Saharan Africa.

Methods

Data from the most recent Demographic and Health Surveys, which covered 23 Sub-Saharan African countries from 2015 to 2022, were used for secondary data analysis. The study used a total of 76,451 women. STATA 14 was used to analyze the data. The associated factors of stillbirth were determined using a multilevel mixed-effects logistic retrogression model. Significant factors associated with stillbirth were declared significant at p- value < 0.05.

Results

The prevalence of stillbirth in Sub-Saharan Africa was 6.18% (95% CI: 6.01, 6.35). Higher odds of stillbirth were observed among women at advanced age (35–49 years) (AOR = 3.72, 95% CI: 2.57, 5.41), those who consumed alcohol during pregnancy (AOR = 1.58, 95% CI: 1.24, 2.00), and those who underwent cesarean section delivery (AOR = 1.23, 95% CI: 1.11, 1.37). Additionally, rural residence (AOR = 1.11, 95% CI: 1.01, 1.23), high community levels of illiteracy (AOR = 1.19, 95% CI: 1.07, 1.32), and residing in South sub-Saharan Africa (AOR = 1.19, 95% CI: 1.03, 1.38) were positively associated with stillbirth.

Conclusions

This study concludes that stillbirth among women at extreme ages of reproductive life is high compared to the UNICEF 2022 report. The study identified that both individual and community-level variables were associated factors of stillbirth. Therefore, the ministries of health in Sub-Saharan African countries should give attention to those women at the extreme ages of reproductive life and to women from rural areas while designing policies and strategies targeting reducing stillbirth rates.

Background

WHO defines stillbirth as a dead fetus weighing at least 1000 g at birth, after 28 complete weeks of gestation, or when it reaches a length of at least 35 cm from the crown to the heel [1, 2]. Of all the adverse pregnancy outcomes, stillbirths have the highest incidence and are more prevalent in low- and middle-income countries (LMIC) [3]. Globally, an estimated 2.6 million stillbirths occur annually, and countries with low to middle incomes account for 98% of the total stillbirths [4, 5]. Approximately one million babies are stillborn in Africa every year, with at least 300,000 of those deaths occurring during childbirth. Of these, 3 in 4 stillbirths occurred in sub-Saharan Africa [6, 7]. In 2019, stillbirth rates showed significant regional variation, ranging from 22.8 stillbirths per 1,000 live births in West and Central Africa to 2.9 in Western Europe [8]. The highest rates of stillbirths in 2019 were seen in South Asia, Eastern and Southern Africa, and West and Central Africa. It is believed that in these areas, the intrapartum phase accounts for around half of all stillbirths [9]. Global stillbirth has decreased dramatically, from 24.9 per 1000 live births in 2000 to 18.9 per 1000 live births in 2015, with an annual rate of reduction of 2%; however, this rate of reduction has fallen behind maternal, neonatal, and child mortality, especially in Sub-Saharan Africa [5, 10, 11].

Maternal infections like syphilis, HIV, and malaria; chronic diseases like diabetes and hypertension; fetal development limitations; and pregnancy and childbirth-related problems are the main causes of stillbirth. In high-income nations, congenital anomalies account for less than 10% of all stillbirths reported worldwide [12, 13]. Although stillbirth has a huge global burden and poses psychological costs, especially to women and their families, such as maternal depression, financial consequences, and economic repercussions, as well as stigma and taboo, it is largely absent from global indicators of health, which underestimates its extent of public health importance [9]. Furthermore, despite being included in the Every Newborn Action Plan (ENAP) coordinated by UNICEF and WHO, stillbirths are not specifically addressed in the Sustainable Development Goals agenda [14, 15]. As a result the majority of countries, especially those in Sub-Saharan Africa, do not disclose stillbirths in their vital statistics, hence the problem continues to be overlooked and underreported [16, 17].

The studies carried out in different parts of the world revealed that maternal age [18, 19], maternal education [20], wealth index [21], preceding birth interval [22, 23], mode of delivery [24, 25], antenatal care visits [26, 27], and place of delivery [28] had higher odds of stillbirth. The Every Newborn Action Plan (ENAP), which was approved by the World Health Assembly in 2014, sets a global goal of 12 or fewer third-trimester stillbirths per 1,000 live births worldwide by 2030. In 2021, 139 countries, mainly high- and upper-middle-income countries, had met this target, but 56 countries will not meet the ENAP target by 2030 if further efforts are not made. If current trends continue, 15.9 million babies will be stillborn; nearly half of these (7.7 million, or 48%) will occur in sub-Saharan Africa [2].

As far as our review of the literature and knowledge goes, no study has been done on stillbirths among women at extreme ages of reproductive life in Sub-Saharan Africa using the large sample from DHS data, despite the fact that Sub-Saharan African countries share a significant portion of the global stillbirth burden. Thus, the current study used a multilevel mixed effect analysis of the most recent Demographic and Health Survey to investigate the prevalence and associated factors of stillbirth among women in Sub-Saharan Africa who are at extreme ages of reproductive life.

Methods

Study setting

The African countries south of the Sahara Desert are collectively referred to as Sub-Saharan Africa. At the broadest point of the continent, the African Transition Zone crosses the southern border of the Sahara Desert. Sub-Saharan Africa includes many of the countries that make up the African Transition Zone. The sub-Saharan region consists of four large and diverse regions, including Eastern Africa, Central Africa, Western Africa, and Southern Africa. The sub-Saharan Africa region constitutes an area of 9.4 million square miles [29]. In 2021, the total population of Sub-Saharan Africa amounted to approximately 1.18 billion inhabitants [30] and over 1.9 billion people are predicted to live in Sub-Saharan Africa by 2030 [31]. This study was conducted based on the recent DHS survey data from twenty three sub-Saharan African countries such as Angola, Burkina Faso, Benin, Burundi, Cameron, Ethiopia, Gabon, Gambia, Guinea, Kenya, Liberia, Mali, Malawi, Mozambique, Nigeria, Rwanda, Senegal, Sera lion, Tanzania, Uganda, South Africa, Zambia, and Zimbabwe.

Study design and period

A multilevel, community-based, cross-sectional study with mixed effects was carried out. A multilevel mixed effect analysis has been conducted using DHS data from 23 sub-Saharan African countries that were surveyed between 2015 and 2022. As a component of the global Demographic and Health Survey, the Demographic and Health Survey (DHS) is a five-year national study that deploys organized, pretested, and validated tools. Seven years of DHS data, beginning in 2015, were collected in order to obtain a representative sample of recent Demographic and Health Survey data from every region of sub-Saharan African countries. The surveys have huge sample sizes, are population-based, and are nationally representative of all countries.

Population and eligibility criteria

Women at extreme ages of reproductive life who are 15–19 years old and 35–49 years old in Sub-Saharan African countries were the source population. The study population was all women at extreme ages of reproductive life in the selected enumeration areas, which were included in the analysis. The selection of countries was guided by the availability of recent and standardized DHS datasets conducted within the last 10 years at the time of data extraction and analysis. This approach ensured that the data used was not only up-to-date but also methodologically consistent and comparable across countries.

Data source and sampling procedure

In order to investigate stillbirth and determinates among women at extreme ages of reproductive life, the DHS surveys from 23 sub-Saharan countries were appended together. Each country’s survey involves various datasets, such as information on key health indicators like mortality, disease, family planning services, fertility, and maternal and child health services. The With a stratified two-stage cluster design, the Demographic and Health Survey first generates the enumeration areas and then creates a sample of households from each enumeration area in the second stage. For this study, we used the individual record dataset (IR file) to extract the dependent and independent variables for each country, and then we appended the data using STATA version 14. Pregnancy losses occurring after seven completed months of gestation from the individual record (IR) dataset were recoded to generate the outcome variable stillbirth. A binary logistic regression model was used to determine the factors associated with stillbirth. Associated factors of stillbirth were reported in terms of an adjusted odds ratio (AOR) with a significance level of 95%. In the bivariable analysis, at 95% confidence intervals, a p-value of < 0.25 was considered a candidate for the multivariable analysis of the data. All variables with p values < 0.05 were considered statistically significant in multivariable logistic regression. A total weighted sample of 76,451 women at extreme ages of reproductive life in Sub-Saharan Africa was included in the study (Table 1).

Table 1 Sample size for prevalence and associated factors of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa, 2024 (N = 76,451)
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Study variable

Dependent variables

Women were asked to report any pregnancy losses that had happened in the past five years. Every pregnancy that did not result in a live birth had its duration reported individually. Stillbirths were defined as pregnancy losses that occurred after seven complete months of gestation. The frequency of stillbirths among mothers of reproductive age (15–49 years) was used as the study’s response variable. Yi, a random variable with two possible values coded as 1 and 0, served as the response variable for the ith mother. As a result, the response variable for the eighth mother, Yi, was assessed as a dichotomous variable, with potential values of Yi = 1 if the mother had experienced stillbirth and Yi = 0 if the mother had had a live birth [20, 32].

Independent variables

Since DHS data are hierarchical, independent variables from two sources (variables at the individual and community levels) were taken into consideration for this analysis. The individual-level independent variables were: Sex of household head (male, female), Maternal age [15,16,17,18,19, 35,36,37,38,39,40,41,42,43,44,45,46,47,48,49], maternal educational status (no formal education, primary, secondary, higher), Husband educational status (no formal education, primary, secondary, higher), marital status of the mother (unmarried, married, ever married), pregnancy complications (no, yes), number of ANC visits (no visit, 1–3, ≥ 4), Total children ever born (≤ 3, 4–6, 7–9, > 9), household wealth index (poor, middle, rich), distance to health facility (big problem, not big problem), household media exposure (no, yes), smoking (no, yes), drinking alcohol (no, yes), iron supplementation during pregnancy (no, yes), foliate supplementation during pregnancy (no, yes), taking drugs during pregnancy (no, yes), previous mode of delivery (vaginal, caesarean section), type of current pregnancy (single, multiple), history of pregnancy termination (no, yes). The community-level variables were place of residence (urban or rural), community-level women’s illiteracy (high, low), community-level poverty (high, low), community-level media exposure (low, high), and community-level ANC utilization (low, high), country category (Central SSA, East SSA, West SSA, South SSA) (Fig. 1).

Fig. 1
figure 1

Conceptual framework for factors associated with stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa

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Operational definition

Wealth index

The Wealth Index in the Demographic and Health Surveys (DHS) is a tool designed to evaluate a household’s relative economic position based on information about household assets, services, and amenities. It is derived from the survey’s existing data to analyze different societal indicators, including health, nutrition, education, and population, in relation to economic standing [33].

Community-level women illiteracy

The proportion of women with at least primary education is calculated based on respondents’ educational attainment. The individual education levels of women were cross-tabulated by cluster number (v001) and then classified according to the national mean. Communities where the percentage of women with at least primary education was equal to or greater than 50% of the national mean were categorized as having low community-level illiteracy, while communities where the percentage was less than 50% of the national mean were classified as having high community-level illiteracy [34].

Community-level poverty

The proportion of women in the rich and middle-class categories is factored into the calculation. After cross-tabulating the individual-level combined wealth index with the cluster number (v001), the results were categorized based on the national mean value of the wealth index: low community-level poverty refers to communities with a wealth index equal to or greater than 50% of the national mean, while high community-level poverty refers to communities with a wealth index less than 50% of the national mean [35].

Community-level media exposure

The calculation incorporates the proportion of individuals in the community with access to media, including television, radio, and magazines/newspapers. By cross-tabulating the individual-level media access data with the cluster number (v001), the results are categorized according to the national average of media exposure. Communities with low community-level media exposure are those where the proportion of individuals with media access is equal to or less than 50% of the national average, while communities with high community-level media exposure have a proportion greater than 50% of the national average [36].

Community-level ANC utilization

Community-level ANC utilization is defined based on the proportion of women in a community who access antenatal care (ANC) services. After cross-tabulating individual-level ANC utilization data with the cluster number (v001), the communities were categorized into two groups based on the national mean ANC utilization rate: low community-level ANC utilization refers to communities where the proportion of women accessing ANC services is equal to or less than 50% of the national mean, while high community-level ANC utilization refers to communities where the proportion of women accessing ANC services is greater than 50% of the national mean [37].

Data processing and statistical analysis

The data were extracted from recent DHS data sets and cleaned, recorded, and analyzed with STATA version 14 Statistical Software. Prior to conducting any statistical analysis, the data were weighted using the sampling weight, primary sampling unit, and stratum in order to restore the survey’s representativeness and account for the sampling design when computing standard errors to produce accurate statistical estimates. We used the weighting variable (v005) as a relative weight normalized to make the analysis survey-specific, while for the pooled data, we denormalized extreme ages of reproductive life women’s individual standard weight variable by dividing the women’s individual standard weight by the sampling fraction of each country: (women adjusted weight = V005× (total women aged 15–19 and 35–49 years in the country at the time of the survey)/ (number of women aged 15–19 and 35–49 years in the survey).

The assumptions of standard logistic regression model such as independence observations and equal variance are violated due to the hierarchical nature of the DHS data. For instance mothers nested within a cluster, and we assume that study subjects in the same cluster may share similar characteristics to participants in another cluster violates the independence observations and equal variance assumptions between clusters of the classical logistic regression model. This suggests that using an appropriate model to take into account between-cluster factors is necessary. Given this, multilevel mixed-effects logistic regression was used to determine the factors that associated with stillbirth. Multilevel mixed effect logistic regression uses four models: the null model (outcome variable only), model I (only individual level variables), model II (only community level variables), and model III (both individual and community level variables). The null model, which lacks independent variables, was employed to examine the variation in stillbirth rates within the cluster. The statistical regression was conducted to see the relationships between the outcome variable and the factors at the individual and community levels. Finally, the association between the community- and individual-level variables and the outcome variable was fitted simultaneously in the final model (Model III).

Random effects

Random effects or measures of variation such as Likelihood Ratio test (LR), Intra-class Correlation Coefficient (ICC), and Median Odds Ratio (MOR) were computed to measure the variation of stillbirth across clusters. Taking clusters as a random variable, the ICC quantifies the degree of heterogeneity of stillbirth between clusters (the proportion of the total observed variation in stillbirth that is attributable to between cluster variations) [38] is computed as; ICC=\(\:\frac{VC}{VC+3.29}\times\:100\%\). When people are randomly selected from two clusters, the median odds ratio between the cluster with a high likelihood of stillbirth and the cluster with a lower risk is what’s known as the Median Odds Ratio (MOR). It quantifies the variation or heterogeneity in stillbirth between clusters in terms of odds ratio [39]; MOR= 𝑒 0.95√VC.

Moreover, the PCV demonstrates the variation in the stillbirth explained by associated factors and computed as; PCV=\(\:\:\frac{Vnull-Vc}{Vnull}\times\:100\%\); where Vnull = variance of the null model and VC = cluster level variance [40]. The fixed effects were used to estimate the association between the likelihood of stillbirth, individual and community level independent variables. The strength was presented using adjusted odds ratio (AOR) and 95% confidence intervals with a p-value of < 0.05. Deviance = -2 (log likelihood ratio) was used to compare the models due to the nested nature of the data; the model with the lowest deviance and the highest log likelihood ratio was chosen as the best-fit model. The variables used in the models were verified for multi-collinearity by measuring the generalized variance inflation factor (GVIF).

Result

Socio-demographic and obstetric characteristics of study participants

A total of 76,451 women were included in the analysis. About 32,788 (43.97%) of women at extreme age groups had no formal education. More than half (68.90%) of women are living in rural areas of Sub-Saharan Africa, and about 52.81% of women living in a community with a high poverty (Table 2).

Table 2 Socio-demographic and obstetric characteristics of women at extreme ages of reproductive life in Sub-Saharan Africa, 2024 (N = 76,451)
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Prevalence of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa

The overall prevalence of stillbirth in the 23 Sub-Saharan African countries was 6.18% (95% CI: 6.01, 6.35). The urban and rural prevalence of stillbirth in Ethiopia was found to be 1.94% and 4.24%, respectively (Fig. 2). Stillbirth was significantly varied across the regions of Sub-Saharan Africa. Subsequently, the lowest stillbirth was observed in southern Sub-Saharan Africa (9.06%), while the highest was seen in eastern Sub-Saharan Africa (44.36%) (Fig. 3).

Fig. 2
figure 2

Urban and rural prevalence of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa

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Fig. 3
figure 3

Regional prevalence of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa

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Random effect and model fitness

The null model revealed that about 4.44% of the variation in stillbirth rates was due to differences between communities, indicating that community-level factors contribute to this variation. Additionally, we observed that an individual in a community with a higher stillbirth risk has 1.45 times higher odds of experiencing a stillbirth compared to someone in a lower-risk community. In Model II, community-level factors explained 4.27% of the variance, and in Model III, both individual and community factors accounted for 23.21% of the variation in stillbirths. The final model (Model III) had the best fit, with the lowest deviance and highest log-likelihood ratio (Table 3).

Table 3 Model comparison and random effect analysis for stillbirth among women at extreme ages of reproductive life in Sub-saharan Africa, 2024 (N = 76,451)
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Factors associated with stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa

In multivariable multilevel logistic regression analysis, where both the individual and community level factors were fitted simultaneously, maternal age, consuming alcohol during pregnancy, mode of delivery, rural residence, community level women illiteracy, and region (central sub-Saharan Africa) were significantly associated with stillbirth at a p-value of < 0.05 and 95% confidence interval.

Women at the extremes of reproductive life had 3.72 times higher odds of stillbirth among those aged 35–49 compared to those under the age of 20 (AOR = 3.72, 95% CI: 2.57, 5.41). Women who drink alcohol at extreme stages of their reproductive lives had 1.58 times higher odds of stillbirth than women who do not drink (AOR = 1.58, 95% CI: 1.24, 2.00). The odds of stillbirth were 1.23 times higher among women at extreme ages of reproductive life who experienced cesarean section delivery as compared to women who had previous vaginal delivery (AOR = 1.23, 95% CI: 1.11, 1.37). The odds of stillbirth were 1.11 times higher among women at extreme ages of reproductive life in rural residences compared to women living in urban areas (AOR = 1.11, 95% CI: 1.01, 1.23). Women who live in a high community level of illiteracy had 1.19 times higher odds of stillbirth compared to women who live in a low community level of illiteracy (AOR = 1.19, 95% CI: 1.07, 1.32). Compared to women from west sub-Saharan Africa, women at extreme reproductive ages in south sub-Saharan Africa had 1.19 times higher odds of stillbirth (AOR = 1.19, 95% CI: 1.03, 1.38) (Table 4).

Multicollinearity has been tested using the generalized variance inflation factor (GVIF). The mean GVIF value was found to be 2.54, indicating that multicollinearity is not a concern in our models.

Table 4 Multivariable multilevel logistic regression analysis of individual-level and community level factors associated with stillbirth among women at extreme ages of reproductive life in Sub-saharan Africa, 2024 (N = 76,451)
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Discussion

This study aimed to assess the prevalence and associated factors of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa using the recent Demography and Health Survey. The prevalence of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa was found to be 6.18% (95% CI: 6.01, 6.35). The prevalence of stillbirth in Sub-Saharan Africa was higher than the findings conducted in Ethiopia, 3.68% [41], 2.5% [42], Nigeria, 3.96% [43], 4.03% [44], Pakistan, 5.75% [45], Cambodia, 3.81% [46], Sub-Saharan Africa, 2.3% [47], East Africa, 0.86% [20], multi-country study from the Global Network, 2.82% [48], Brazil, 1.5% [49], and in the 20 countries of Latin America, 0.81% [50]. On the other hand the prevalence of stillbirth in this study was lower than the findings conducted in Ethiopia, 8.7% [3], 8% [51], 14.5% [51], Nigeria, 12.2% [43]. The observed variability in stillbirth prevalence across different studies can be attributed to several factors, including disparities in healthcare access, reporting practices, and cultural factors across settings. Sub-Saharan Africa faces significant disparities in maternal healthcare, including low rates of skilled birth attendance, limited cesarean section availability, and inadequate management of maternal infections and complications during labor and delivery [52]. Additionally, reporting practices and definitions of stillbirth may vary, with some studies relying on facility-based data while others include community-reported outcomes [53]. Cultural factors, such as stigma around reporting pregnancy outcomes, may also contribute to the observed differences [52].

In multivariable multilevel logistic regression analysis, maternal age, consuming alcohol during pregnancy, mode of delivery, rural residence, community level women illiteracy, and region (central sub-Saharan Africa) were significantly associated with stillbirth.

In this study, women at the extremes of reproductive life had 3.72 times higher odds of stillbirth among those aged 35–49 compared to those under the age of 20. This finding is in line with the studies conducted in Ethiopia [54], Ghana [55], Cameroon [56], and a systematic review of Sub-Saharan Africa [57]. The possible explanation could be that with advancing years, a mother’s chances of developing health problems and having abnormal chromosomes increase, which may potentially harm the fetus. Accordingly, stillbirths in women 35 years of age and older may cause chromosomal or congenital abnormalities [57]. In addition, an increased risk of stillbirth may result from advanced maternal age. This is a result of the placenta’s potential decline in function in older women, which deprives the fetus of oxygen and nutrients. There may also be increased stress on the placenta’s cells, which makes them less able to recover from injuries and raises the risk of stillbirth [58]. Moreover, the elevated risk can be partly attributed to the higher prevalence of comorbidities such as hypertension and diabetes among older mothers. Hypertension, including preeclampsia, is associated with impaired placental perfusion, increasing the risk of fetal hypoxia and stillbirth [59, 60]. Similarly, diabetes, particularly when poorly controlled, is linked to fetal macrosomia, placental insufficiency, and congenital abnormalities, all of which heighten the likelihood of stillbirth [61, 62].

Women who drink alcohol at extreme stages of their reproductive lives had 1.58 times higher odds of stillbirth compared to women nondrinking women. This was consistent with studies reported in Ethiopia [63], Mozambique [64], Tanzania [65], and the USA [66]. This could be because drinking alcohol during pregnancy can harm the developing fetus because alcohol may get into the baby’s bloodstream from the mother and damage the baby’s cells because the fetal liver is still developing and breaks down alcohol more slowly, which contributes to stillbirth [67]. Culturally and socioeconomically, alcohol consumption during pregnancy may be influenced by various factors, including a lack of awareness about its risks, societal norms surrounding alcohol use, and limited access to healthcare education and services. For instance, in certain settings, alcohol consumption might be more prevalent among women facing economic hardships or social stressors, which can contribute to poor health behaviors. Moreover, alcohol use may act as a proxy for other risk factors, such as inadequate antenatal care, malnutrition, or underlying mental health issues, all of which independently increase the risk of adverse birth outcomes [68].

The odds of stillbirth were 1.23 times higher among women at extreme ages of reproductive life that experienced cesarean section delivery as compared to women who had previous vaginal delivery. This finding is consistent with previous studies in Gambia [69], Nigeria [70], and Ireland [71]. The explanation could be that women with a history of previous cesarean deliveries are more likely to experience antepartum hemorrhage, have a poor obstetric history, or have medical or surgical complications that limit fetal development and ultimately result in stillbirth.

However, it is important to note that this association may not directly implicate the mode of delivery as a causal factor. Instead, it likely reflects underlying maternal or fetal complications necessitating cesarean delivery, such as antepartum hemorrhage, a history of poor obstetric outcomes, or medical and surgical conditions that compromise fetal development. These complications may increase the risk of stillbirth independent of the delivery method [72].

The odds of stillbirth were 1.11 times higher among women at extreme ages of reproductive life in rural residences compared to women living in urban areas. This was consistent with studies conducted in Ethiopia [73], Nigeria [70], south Africa [74], Ghana [75], and Nepal [76]. This may be because of differences in mothers’ patterns of seeking medical attention as well as in the accessibility and availability of healthcare services. Compared to rural residents, women in urban settings had better health-seeking behaviors [20].

Women who live in a high community level of illiteracy had 1.19 times higher odds of stillbirth compared to women who live in a low community level of illiteracy. It is in line with study findings in Zimbabwe [77], Nepal [76], India [78], and Canada [79]. This is due to the fact that illiteracy at the community level can influence healthcare access and utilization through several pathways. Low literacy levels hinder women’s ability to understand health information, recognize the importance of preventive measures, and navigate healthcare systems effectively. For instance, women in illiterate communities may face challenges in interpreting information about the risks associated with stillbirth, such as smoking, alcohol consumption, inadequate nutrition, and delayed ANC visits [80]. Furthermore, low-literacy communities are often characterized by limited health literacy, which reduces adherence to ANC recommendations, including timely ANC visits and the uptake of essential maternal services. This is compounded by cultural and social norms in low-literacy settings that may deprioritize maternal health or perpetuate misinformation about pregnancy care [81, 82]. Health-seeking behaviors in such settings may also be influenced by reduced autonomy among women, as education often correlates with increased agency in seeking timely care [83]. Logistical barriers, such as difficulty reading transportation schedules or healthcare facility instructions, further exacerbate access issues [84].

Compared to women from west sub-Saharan Africa, women at extreme reproductive ages in south sub-Saharan Africa had 1.19 times higher odds of stillbirth. This might be related to the disparities in the availability of health facilities and wealth quintiles.

The study’s strength was the utilization of recently conducted large-sample national demography and health surveys from 23 sub-Saharan African countries. Another strength of this study was the use of mixed multilevel logistic regression to determine two-level factors (individual and community-level factors), which could not be done using classical logistic regression. However, the study was limited in its ability to include other variables that might have been associated with the outcome variables, such as maternal psychological factors, gestational diabetes, gestational hypertension, and other chronic diseases, due to the lack of these important variables in the DHS dataset. The exclusion of these factors may have resulted in an incomplete understanding of the full range of influences on stillbirth, potentially underestimating or misrepresenting key associations. Additionally, the study used the WHO definition of stillbirth (after seven complete months of gestation); however, variations in gestational age reporting across countries and potential misclassification of stillbirths in the DHS datasets may have influenced the validity of the prevalence estimates. Finally, the cross-sectional nature of the data limits our ability to establish causality or determine the temporal sequence between stillbirth and its associated factors, as the study design captures associations at a single point in time rather than tracking changes over the course of pregnancy.

Conclusions and recommendations

This study concludes that stillbirth among women at extreme ages of reproductive life is high. The study identified that both individual and community-level variables were associated factors of stillbirth. Therefore, the Ministries of health in Sub-Saharan Africa should implement specialized antenatal care for women at extreme reproductive ages, including targeted screenings for age-related complications such as hypertension and gestational diabetes. To reduce alcohol consumption during pregnancy, we recommend adapting successful strategies from other regions, such as community-based awareness campaigns and support programs. Additionally, to improve rural healthcare access, we suggest expanding mobile clinics, training community health workers, and exploring telemedicine solutions to bridge the gap in resource-constrained settings.

Data availability

The most recent data from the Demographic and Health Survey were used in this study, and it is publicly available online at (https://www.dhsprogram.com).

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Acknowledgements

We are grateful to the DHS programmes for letting us use the relevant EDHS data in this study.

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Authors and Affiliations

  1. Department of Medical Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

    Alebachew Ferede Zegeye

  2. Department of Surgical Nursing, College of Medicine and health sciences, University of Gondar, Gondar, Ethiopia

    Enyew Getaneh Mekonen

  3. Department of Pediatric and Child Health Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

    Tadesse Tarik Tamir, Berhan Tekeba, Tewodros Getaneh Alemu, Mohammed Seid Ali & Almaz Tefera Gonete

  4. Department of clinical Midwifery, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

    Alemneh Tadesse Kassie

  5. School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

    Mulugeta Wassie

  6. Department of Emergency and Critical care Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

    Belayneh Shetie Workneh

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  1. Alebachew Ferede Zegeye
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Contributions

Alebachew Ferede Zegeye: involved in designing the study, data extraction, data analysis, interpretation, report and manuscript writing. Enyew Getaneh Mekonen: involved in data analysis, interpretation, and manuscript writing. Tadesse Tarik Tamir: involved in interpretation, report and manuscript writing. Berhan Tekeba: involved in data extraction, and manuscript writing. Tewodros Getaneh Alemu: involved in data extraction and result interpretation. Muhamed Seid Ali: involved in data curation, formal analysis, methodology. Almaz Tefera Gonete: involved in software, supervision, data validation. Alemneh Tadesse Kassie: involved in conceptualization, validation, writing original draft. Mulugeta Wassie: involved in software, methodology, validation, visualization. Belayneh Shetie Workneh: involved in review and editing, validation, writing original draft.

Corresponding author

Correspondence to Alebachew Ferede Zegeye.

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Ethical approval and consent to participate

Since this study is merely a secondary analysis of the DHS data, ethical approval is not needed. We enrolled with the DHS web archive, requested the dataset for our study, and were granted permission to view and download the data files. As per the DHS study, all participant data were anonymized at the time of survey data collection. Anyone can get more information on DHS data and ethical standards by visiting http://www.dhsprogram.com.

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Zegeye, A.F., Mekonen, E.G., Tamir, T.T. et al. Prevalence and associated factors of stillbirth among women at extreme ages of reproductive life in Sub-Saharan Africa: a multilevel analysis of the recent demographic and health survey. matern health, neonatol and perinatol 11, 10 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40748-025-00205-y

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Keywords

Maternal Health, Neonatology and Perinatology

ISSN: 2054-958X

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