Pooled prevalence and factors of low birth weight among newborns in the top 20 countries with the highest infant mortality: analysis of recent demographic and health surveys
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* Demiss Mulatu Geberu
* Lemlem Daniel Baffa
* Asebe Hagos
* Misganaw Guadie Tiruneh
* Getachew Teshale
* Tesfahun Zemene Tafere
* Kaleb Assegid Demissie
* Melak Jejaw

## Abstract

**Objective** This study aimed to assess the pooled prevalence and factors of low birth weight among newborns in the top 20 countries with the highest infant mortality rates.

**Design** We conducted a community-based cross-sectional analysis using data from Demography and Health Surveys across these countries. The final analysis included a weighted sample of 82 430 live births. We employed binary logistic regression to identify predictors of low birth weight, allowing for the interpretation of results as probabilities. This approach enhances the understanding of event likelihood, which is particularly valuable for policymakers. Statistical significance was determined at a 95% CI with p values <0.05.

**Setting** The focus was on the top 20 countries that report the highest infant mortality.

**Outcome variable** Low birth weight (binary: Yes/No).

**Result** The pooled prevalence of low birth weight among newborns was found to be 13% (95% CI: 11% to 14%), showing notable variation across countries, ranging from 5% in Sierra Leone to 25% in Mauritania. Statistically significant factors included maternal age of 15–19 years (adjusted OR (AOR): 1.38; 95% CI: 1.19 to 1.61), lack of formal education among mothers (AOR: 1.36; 95% CI: 1.26 to 1.46), unemployed mothers (AOR: 1.54; 95% CI: 1.46 to 1.63), divorced mothers (AOR: 1.20; 95% CI: 1.10 to 1.36), absence of antenatal care (ANC) visits (AOR: 1.24; 95% CI: 1.10 to 1.39) and one to three ANC visits (AOR: 1.30; 95% CI: 1.22 to 1.38). Other significant factors included a parity of one to two children (AOR: 1.29; 95% CI: 1.21 to 1.39), twin births (AOR: 6.40; 95% CI: 5.68 to 7.26), and female newborns (AOR: 1.21; 95% CI: 1.15 to 1.28).

**Conclusion and recommendation** The findings indicate that more than 1 out of 10 newborns in these highest infant mortality countries are classified as low birth weight. To mitigate this issue, it is imperative to enhance access to quality healthcare with particular emphasis on ANC and to promote maternal education, especially for younger and less-educated mothers. Increasing the presence of skilled birth attendants and addressing socioeconomic factors, such as women’s unemployment, is crucial. Targeted interventions should support divorced women and address risks associated with maternal age, terminated pregnancies and twin births. Additionally, country-specific strategies that focus on female infants can play a significant role in reducing the prevalence of low birth weight and improving neonatal health outcomes.

*   Child
*   Nutritional support
*   Health
*   Health Services
*   Health policy
*   Epidemiology

### STRENGTHS AND LIMITATIONS OF THIS STUDY

*   This study’s principal strength stems from its utilisation of nationally representative survey data featuring a substantial sample size, which facilitates robust estimations of the prevalence of low birth weight and associated factors.

*   Additionally, the pooled analysis incorporating data from 17 low and middle-income countries with the highest infant mortality rates amplifies the findings’ generalisability across varied contexts.

*   However, the cross-sectional design of the Demographic and Health Survey (DHS) data inherently constrains the establishment of causal relationships between low birth weight and the identified independent variables.

*   Besides, the influence of social desirability and recall biases may lead respondents to under-report negative behaviours, such as smoking, while potentially overstating positive behaviours, including adequate nutritional intake.

*   Moreover, while the DHS datasets provide comprehensive information, they may overlook crucial contextual factors such as access to healthcare services, cultural practices and environmental influences that can significantly affect birth weight outcomes.

## Background

Low birth weight (LBW) remains a critical public health challenge, linked to a variety of health disorders, including cognitive impairment, suboptimal physical and neural development,1 2 hypoglycaemia,3 neonatal asphyxia and fetal distress,4 as well as hypothermia.5 The consequences extended to poor motor and cognitive development,6 7 increased morbidity and mortality in infants and children4 8 9 and long-term issues such as stunting,10 diminished intellectual capacity,11 12 academic challenges and dropout rates,13 elevated blood pressure in young adulthood14 and a higher incidence of adult depression.15 LBW infants face a 20-fold greater risk of mortality compared with their normal weight counterparts.16 17 Furthermore, LBW is a major contributor to global neonatal mortality, particularly in developing nations, accounting for 60–80% of annual neonatal deaths.18 19 In fact, LBW prevalence is disproportionately higher in developing countries,17 with approximately three-fourths of the cases reported in Southern Asia and sub-Saharan Africa during 2015 and 2020.20 21

This pressing public health issue has garnered significant attention from policymakers, evidenced by initiatives aimed at reducing under-five mortality by two-thirds under the Millennium Development Goals (MDGs)22 and targeting neonatal mortality rates below 12 per 1000 live births by the end of 2030 within the Sustainable Development Goals (SDGs) framework.23 Despite this focus, the global incidence of LBW remains alarmingly persistent, with 22.1 million (16.6%) of live births classified as LBW in 2000 and 19.8 million (14.7%) in 2020, an insufficient reduction of merely 1.9%.20 Such data underscore inadequate progress in fully operationalising the MDGs and achieving substantial outcomes during the initial 5 years of SDGs implementation.

Additional literature had reported the pooled prevalence rates of 9% in Latin America and the Caribbean,24 15.9% in developing countries,25 28% in southern Asia,24 17.29% in India,9 14.5% in Bangladesh,26 14% in West and Central Africa as well as 11% in Eastern and Southern Africa,24 9.76–13% in SSA,24 27 16.7% in Zimbabwe28 and 14.1–19.6% in Ethiopia.8 29–31

Numerous studies have elucidated a spectrum of factors contributing to LBW, encompassing maternal health service specifically the number of ANC visits,25 27–30 32–37 the timing of ANC initiation,38 iron supplementation,32 35 37 39 dietary diversity34 38 39 and place of delivery.9 Maternal characteristics such as age,4 8 25 27 30 34 35 40 education,9 25 27 32–34 41 42 marital status,27 32 35 40 occupational status,43 anaemia,28 30 32 39–42 44 parity,9 25 27 45 pregnancy-induced hypertension4 7 28–30 33 38 44 46 and maternal HIV status4 47 also play significant roles. Furthermore, lifestyle factors such as smoking during pregnancy,32 35 interpregnancy interval8 45 and historical pregnancy termination48 contribute to LBW prevalence. Beyond maternal characteristics, household parameters including economic status,9 25 26 35 36 geographical location,25 29 31 35 38 43 49 health insurance access9 and distance to health facilities36 impact LBW rates. Besides, newborn characteristics like sex,9 25 27 29 34 37 41 46 birth type,27 maternal body mass index or mid-upper arm circumference 4 8 25 36 and gestational age8 31 37 40 41 46 further influence outcomes.

Given the substantial public health implications of LBW, this issue has been inadequately assessed, particularly in countries with the highest infant mortality rates. Our study aimed to address this gap, and we intended to provide timely and comprehensive insights necessary for formulating effective prevention strategies. A robust body of evidence is essential for informing stakeholders, including communities, healthcare providers and policymakers, to implement more effective interventions. While previous studies have examined LBW, many were confined to single settings, relied on outdated Demographic and Health Survey (DHS) data, concentrated on SSA countries, or did not specifically target those nations with the highest infant mortality. Therefore, our research endeavours to fill these critical informational voids and establish reliable evidence concerning LBW in high-risk contexts by assessing the pooled prevalence and associated factors of LBW among newborns in the top 20 countries with the highest infant mortality.

## Methods and materials

### Study design, setting and period

This study used secondary data from the most recent DHS, conducted between 2012 and 2023, in the top 20 countries with the highest infant mortality rates. Among the top 20 countries having the highest infant mortality rates, 17 countries were selected for this study, whereas South Sudan and Somalia were excluded due to the unavailability of DHS data, and the Central African Republic was excluded due to outdated (1994/1995) DHS data. The included countries are Afghanistan, Angola, Burkina Faso, Benin, Democratic Republic of the Congo, Côte d'Ivoire, Guinea, Comoros, Liberia, Mali, Mauritania, Mozambique, Nigeria, Niger, Pakistan, Sierra Leone and Chad50 (table 1).

View this table:
[Table 1](http://bmjopen.bmj.com/content/15/4/e098090/T1)

Table 1 
Infant mortality rates and survey year by country

The DHS data used in this study was sourced from the DHS Program’s official database ([http://www.dhsprogram.com](http://www.dhsprogram.com)), accessible publicly on formal approval. These nationally representative surveys, conducted every 5 years in low- and middle-income countries (LMICs), offer comprehensive data on maternal and child health indicators.

### Study population

The study population included newborns of mothers aged 15–49 years who had a live birth within 5 years preceding the survey. The final analysis included a weighted sample of 82 430 newborns with complete birth weight data (figure 1).

![Figure 1](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/15/4/e098090/F1.medium.gif)

[Figure 1](http://bmjopen.bmj.com/content/15/4/e098090/F1)

Figure 1 
Flow chart showing the sampling procedure of DHS data of newborns in countries with highest mortality. DHS, Demographic and Health Survey.

### Sampling design

The DHS employed a two-stage stratified sampling approach to ensure robust representativeness. In the initial stage, enumeration areas (EAs) were selected using probability proportional to size within each country. In the second stage, households within each EA were selected through systematic sampling. All eligible women residing in the selected households were then interviewed. For this study, data from the DHS women’s dataset (KR file) were used, which encompassed comprehensive information on mothers and their children.

### Variables

LBW among newborns was the outcome variable of this study. The independent variables included maternal age (categorised as 15–19, 20–29, 30–39 and 40–49 years), educational attainment (no formal education, primary, secondary, and above) and occupational status (employed or unemployed). Additional factors considered were marital status (single, married, widowed or divorced), place of residence (urban or rural) and socioeconomic status, as measured by the wealth index (poorest, poorer, middle, richer, richest).

ANC factors included the timing of ANC initiation (≤3 months, 4–5 months, 6–7 months, ≥8 months) and the number of ANC visits (no ANC visits, 1–3 visits, ≥4 visits). Delivery-related variables encompassed the place of delivery (home, private health facility or public health facility) and parity (1–2 children, 3–5 children or≥6 children). The preceding birth interval (<24 months or≥24 months), having termination of pregnancy (no, yes) was also examined.

Additional maternal and neonatal characteristics such as iron supplementation during pregnancy (yes or no), type of birth (singleton or twin), sex of the child (male or female) and health insurance coverage (yes or no) were included. Lifestyle and accessibility factors such as smoking status (yes or no) and perceived distance to a health facility (a big problem or not a big problem) were also considered. Finally, countries were classified based on income level (low income or lower-middle income).

### Operational definitions

#### Low birth weight

LBW was defined as a birth weight of less than 2500 g. Newborns with a birth weight below this threshold were classified as having LBW (coded as 1), while those weighing 2500 g or more were categorised as having a normal birth weight (coded as 0).43 51

#### Household wealth index

According to the Guide to DHS Statistics, the wealth index was constructed using principal component analysis based on household assets, including consumer goods and housing characteristics. The index was then divided into five quintiles, representing different levels of economic status. The lowest quintile was classified as ‘poorest’, followed by ‘poorer’ in the second quintile, ‘middle’ in the third, ‘richer’ in the fourth and ‘richest’ in the highest quintile.27 52

#### Distance to health facility

Distance to health facility was categorised based on women’s self-reported difficulty in accessing healthcare when sick. Women who experienced significant challenges due to distance were classified as having ‘a big problem’, while those who did not face such difficulties were categorised as having ‘not a big problem’.52

The *classification* of countries by income level was based on the latest World Bank country income classification.53

### Data processing and statistical analysis

Statistical analysis, data cleaning and recoding were conducted using Stata V.17. The study used DHS data from countries with the highest infant mortality rates. To ensure accurate representation, the individual weight variable for women was denormalised by dividing the standard women’s weight variable by the survey’s sampling fraction. The sampling fraction was determined by dividing the total eligible population by the number of women aged 15–49 years who participated in the survey.

We applied the weighting variable (V005) for survey-specific data analyses as a normalised relative weight. When analysing pooled data, the individual women’s standard weight variable was adjusted by dividing it by the sampling fraction specific to each country. The adjusted weight variable was calculated as Female Adjusted Weight=V005 × (total females aged 15–49 years in the country) / (number of women aged 15–49 years interviewed in the survey). Since DHS weights are designed for the full sample, but not all women had birth weight data, we recalculated the sampling weights specifically for the subsample of women with valid birth weight information. This adjustment ensured that the subsample remained representative of the population in each country while preserving the integrity of the survey’s complex design. The detailed procedure for deriving the final weighted sample size is outlined and visually presented in the accompanying flowchart (figure 1).

Descriptive statistics were presented in terms of frequencies, percentages, medians and IQRs, and were summarised using tables, figures and narrative descriptions. Prior to applying non-parametric statistical methods, we assessed the data distribution using a histogram. The histogram indicated a non-normal distribution, which led to the decision to use non-parametric statistical descriptors. Binary logistic regression was employed for data analysis. Although we initially considered a multilevel analysis model, the low Intracluster Correlation Coefficient in the null model indicated that the assumptions for multilevel analysis were not met, making it unsuitable for our analysis. Therefore, binary logistic regression was chosen as the most appropriate model. This approach is particularly beneficial for binary outcome variables, as it allows for the interpretation of results as probabilities, providing valuable insights into the likelihood of an event occurring, which is useful for policymakers in decision-making. Additionally, binary logistic regression can handle non-linear relationships between the independent variables and the log-odds of the binary outcome. Independent variables with a p value≤0.2 in the bivariate analysis were included in the multivariable model, and variables with a p value≤0.05 were considered statistically significant. Adjusted ORs (AOR) with 95% CIs were used to assess the strength and direction of associations. The variance inflation factor (VIF) test was conducted to check for multicollinearity, and no issues were found, as all variables had VIF values<5, with an average VIF of 1.18. The χ2 assumption test was performed for each independent variable, and those that did not meet the assumptions were excluded from the analysis. The model’s fit was assessed using the Hosmer and Lemeshow goodness-of-fit test, yielding a value of 0.3746, indicating that the model was appropriately fitted.

### Patient and public involvement

Patients and the public were not involved in the design, conduct, reporting or dissemination of this research.

## Results

### Socio-demographic, economic, and health service-related characteristics of respondents

The study included 82 430 newborns, of whom 50.70% were male and 96.08% were singletons. Nearly half (49.77%) were born to mothers aged 20–29 years, and 38.33% of mothers had no formal education. Over one-quarter (28.32%) of newborns were from the wealthiest households, with 51.18% residing in rural areas. Most (65.13%) mothers had>4 ANC visits, and 79.32% delivered in public health facilities. The majority (85.68%) of mothers had no history of pregnancy termination, and 42.90% had three to five children. The largest proportion (16.89%) of participants were from the Democratic Republic of Congo, while the smallest (1.93%) were from Liberia. A majority (71.31%) of respondents were from low-income countries (LICs), based on World Bank income classification (online supplemental material 1).

### Supplementary data

[[bmjopen-2024-098090supp001.pdf]](pending:yes)

### Prevalence of low birth weight among newborns in top 20 countries with highest infant mortality

The pooled prevalence of LBW, estimated using DHS guidelines, was 13% (95% CI: 11% to 14%) across the 17 countries with the highest infant mortality. Prevalence was highest in Mauritania (25%) and lowest in Sierra Leone (5%) (figure 2).

![Figure 2](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/15/4/e098090/F2.medium.gif)

[Figure 2](http://bmjopen.bmj.com/content/15/4/e098090/F2)

Figure 2 
Pooled prevalence of low birth weight of newborns in countries with highest infant mortality. Note: Weights are from ramdom-effects model. DL, DerSimonian and Laird.

### Factors associated with low birth weight of newborns in the top 20 countries with the highest infant mortality

In a bivariable logistic regression, various factors were associated with LBW at p<0.2, including maternal age, education, occupation, marital status, household wealth, ANC visits, delivery location, parity, pregnancy termination history, birth type, child’s sex, distance to health facilities and country income classification.

These variables were included in the multivariable logistic regression to determine the statistical significance association of variables with the dependent variable. After performing multivariable logistic regression analysis, age of women, women educational status, women occupational status, women marital status, number of ANC visits, place of delivery, parity, ever had terminated pregnancy, type of birth (single/twin), sex of child and countries classification by income level were found to be significantly associated with LBW of newborns in top countries with highest infant mortality at p value<0.05.

Newborns of younger mothers (15–19 years) were 1.38 (AOR: 1.38; 95% CI: 1.19 to 1.61) times more likely to have LBW compared with those born to older mothers (40–49 years). Infants of mothers with no formal education were 1.36 (AOR: 1.36; 95% CI: 1.26 to 1.46) times more likely to have LBW as compared with those of mothers with secondary education or above. Infants of mothers with no occupation were 1.54 (AOR: 1.54; 95% CI: 1.46 to 1.63) times more likely to have LBW as compared with those of mothers who had an occupation. Newborns of divorced mothers were 1.20 (AOR: 1.20; 95% CI: 1.10 to 1.36) times more likely to have LBW as compared with those of married mothers. Infants whose mothers had no and one to three ANC visits were 1.24 (AOR: 1.24; 95% CI: 1.10 to 1.39) and 1.30 (AOR: 1.30; 95% CI: 1.22 to 1.38) more likely to have LBW compared with those whose mothers had four or more ANC visits, respectively. Besides, infants delivered at home were 1.10 (AOR: 1.10; 95% CI: 1.00 to 1.22) more likely to have LBW compared with those delivered in public health facilities. Moreover, infants born to mothers with one to two children were 1.29 (AOR: 1.29; 95% CI: 1.21 to 1.39) times more likely to have LBW compared with those born to mothers with three to five children. Furthermore, infants of mothers who had a history of terminated pregnancy were 1.19 (AOR: 1.19; 95% CI: 1.10 to 1.28) times more likely to have LBW compared with those of mothers who had no history of terminated pregnancy.

Twin newborns were 6.40 (AOR: 6.40; 95% CI: 5.68 to 7.26) times more likely to have LBW as compared with those of singletons. Additionally, female newborns were 1.21 (AOR: 1.21; 95% CI: 1.15 to 1.28) times more likely to have LBW as compared with their counterparts. Moreover, infants born in LICs were 20% less likely to have LBW (AOR: 0.80; 95% CI: 0.75 to 0.85) than those in lower-middle-income countries (table 2).

View this table:
[Table 2](http://bmjopen.bmj.com/content/15/4/e098090/T2)

Table 2 
Bivariable and multivariable logistic regression analysis showing factors affecting low birth weight among newborns in the top 20 countries with the highest infant mortality (n=82 430)

Although paternal factors, including education and occupation, were considered in the analysis, they were excluded from the final model due to high proportions of missing data (8.9% and 8.3%, respectively).

## Discussion

This study aimed to estimate the pooled prevalence of LBW and identify its associated factors among the top countries with the highest infant mortality. The findings indicate that 13% (95% CI: 11% to 14%) of neonates were classified as having LBW. This prevalence is higher than reported in previous studies conducted in Colombia (8.7%);54 Addis Ababa, Ethiopia (8.8%);55 the Southern Nations, Nationalities and Peoples’ Region, Ethiopia (8.6%);56 Southwest Ethiopia (10%);57 Northern Ethiopia (8.1%);58 Nigeria (6–8%)40 59 and SSA (9.76%).27 These discrepancies might be attributed to variations in study population and settings. This study specifically focuses on countries with the highest infant mortality rates, where maternal and neonatal health indicators are generally suboptimal, contrasting with the cohorts examined in the previous studies. Additionally, prior studies conducted in urban areas of Addis Ababa and Southern Ethiopia predominantly surveyed populations with better healthcare access and socioeconomic conditions, which likely contributed to a lower prevalence of LBW compared with rural or disadvantaged regions.60 61 The use of nationally representative DHS data in this study highlights the broader burden of LBW among high-risk populations.

Conversely, this study reports a lower pooled prevalence of LBW compared with studies conducted in Amhara, Ethiopia (17.1%);46 rural southeastern Ethiopia (17.88%);62 Senegal (15.7%)63 and Bangladesh (14.27%).64 This variation could be explained by differences in socioeconomic status and access to maternal health services between regions. For instance, rural southeastern Ethiopia has limited access to prenatal care and higher rates of maternal malnutrition, both of which are significant contributors to higher LBW prevalence.65 66 Additionally, in Senegal, only 12.1% of the study population had secondary education or higher, whereas the current study reports over 36%, reflecting better maternal knowledge and health-seeking behaviour, which are associated with improved birth outcomes. Senegal also exhibits high rates of early marriage and teenage pregnancy, which increase LBW risks due to biological immaturity and inadequate prenatal care.67 These factors, combined with limited access to healthcare in rural areas, contribute to the higher LBW prevalence observed in Senegal. Furthermore, differences in study designs, sample sizes and data sources might have contributed to the observed variations.

Maternal age was identified as a significant predictor of LBW. Infants born to younger mothers (aged 15–19 years) had a higher likelihood of LBW compared with those born to older mothers (aged 40–49 years), which is consistent with findings from previous studies.8 25 27 30 34 35 68 This might be due to younger mothers facing socioeconomic challenges such as limited financial resources, poor knowledge about maternal healthcare services and restricted access to quality healthcare, all of which can contribute to inadequate prenatal care and suboptimal nutrition.69 70 These factors increase the risk of pregnancy complications, adversely affecting fetal growth. Younger mothers are also more likely to experience unplanned pregnancies, which may result in insufficient prenatal care and a heightened risk of LBW. In contrast, older mothers often benefit from financial stability, social support and better healthcare access, mitigating LBW risks.71 Additionally, the older mothers might develop ample good experiences from their previous childbearing practices, and their experience could positively affect the self-care practice during their current pregnancy, including dietary diversity, which in turn resulted in a lower risk of occurrence of LBW.9 34

Women’s educational status was another important factor associated with LBW. Infants of mothers with no formal education had a higher likelihood of LBW compared with those of mothers with secondary education or above. This finding aligns with previous studies demonstrating that education is linked to improved maternal and neonatal health outcomes.9 25 32 33 41 42 72 73 Educated women are more likely to possess health literacy, enabling them to adopt healthier behaviours, including proper nutrition, regular ANC, timely diagnosis and treatment of infections, and adherence to medical advice.72 74 Additionally, an educated woman might have greater decision-making autonomy and better access to household resources, which are vital for improved nutrition.75 In contrast, less educated mothers often experience lower living standards and poor maternal nutrition during pregnancy, increasing the risk of intrauterine growth restriction and subsequent LBW.41 This finding underscores the importance of promoting women’s education to reduce LBW prevalence.

Employment status also significantly influenced LBW risk. Infants born to mothers with no occupation were more likely to have LBW as compared with those of mothers with occupations. This finding is in line with previous studies done in developing countries.43 76–78 Employment status affects healthcare access, nutritional status and social support. Employed mothers generally have better financial resources, education and healthcare access, enabling healthier pregnancies and reducing LBW risks.77 79 Conversely, unemployed mothers may face economic hardships, dependence on their spouses and restricted healthcare access, exacerbating LBW risks.76 80 Given that employment status is a determinant of LBW, policies aimed at improving women’s employment opportunities could contribute to better birth outcomes in highest-mortality regions.

The number of ANC visits was strongly associated with LBW prevalence. Infants of mothers who attended four or more ANC visits were less likely to have LBW compared with those whose mothers had fewer or no ANC visits. This finding aligns with previous studies.27–29 32–37 68 81–83 ANC visits provide opportunities for nutritional counselling, micronutrient supplementation and early identification and treatment of pregnancy-related complications, all of which enhance fetal growth and improve birth outcomes.27 84 ANC follow-ups also help to monitor and promote appropriate maternal weight gain, contributing to better neonatal health.35 Encouraging complete ANC attendance is critical in preventing LBW.

Marital status was also another factor significantly associated with LBW. Infants born to divorced mothers exhibited a higher likelihood of LBW compared with those born to married mothers. This finding is in line with the previous studies done in Ethiopia,8 SSA27 and Australia.85 This association might stem from financial instability, restricted healthcare access and elevated stress levels, all of which adversely affect maternal and neonatal outcomes. Conversely, married mothers might benefit from spousal support, shared economic resources, a greater likelihood of maintaining a balanced diet and improved access to prenatal care, collectively reducing the risk of LBW.8 86 Additionally, divorced mothers may experience social stigma and psychological distress, further exacerbating adverse pregnancy outcomes.27 In some cases, women in challenging marital relationships may also encounter intimate partner violence, which can precipitate premature rupture of membranes and compromise neonatal health.87

Place of delivery was another significant factor influencing LBW. Home deliveries were associated with a higher prevalence of LBW compared with facility-based deliveries, consistent with previous studies.9 88 Health facilities offer access to skilled birth attendants, emergency obstetrical care and neonatal interventions, which mitigate LBW risk. Moreover, facility-based deliveries ensure prompt management of complications such as preterm labour and maternal infections.59 89 Furthermore, women who deliver at home often have inadequate ANC attendance, missing opportunities for nutritional counselling and medical interventions that improve birth outcomes.90

Parity was also identified as an important predictor of LBW. Infants born to primiparous mothers (one to two children) were more likely to have LBW compared with those born to multiparous mothers (three to five children), aligning with previous studies conducted.25 27 This could be attributed to physiological stress and maternal inexperience during first pregnancies, which may increase the likelihood of adverse pregnancy outcomes.91 Furthermore, unintended pregnancies, more common among primiparous women, often lead to delayed prenatal care, suboptimal maternal nutrition and heightened psychological stress, all of which contribute to fetal growth restriction.92 However, contrasting evidence from Ghana suggests a higher prevalence of LBW among multiparous women,68 indicating that this association may be context dependent.

A history of pregnancy termination was also significantly associated with LBW. Mothers with a prior history of terminated pregnancies had a higher likelihood of delivering LBW infants compared with their counterparts. This finding is consistent with a previous systematic review and meta-analysis study.48 This may be attributable to underlying health conditions or complications from previous pregnancies that adversely affect fetal development.93 Additionally, cervical trauma from prior terminations could compromise cervical competence, leading to preterm birth and its associated risk of LBW.94 Given these risks, tailored ANC and close monitoring are crucial for women with a history of pregnancy termination.

The type of birth was another influential factor, as twin births were significantly associated with LBW, corroborating findings from prior studies in SSA.27 Twin births were associated with a higher likelihood of LBW compared with singleton births. Multiple gestations often result in intrauterine growth restriction due to increased competition for nutrients and uterine space.95 Moreover, twin pregnancies have a higher propensity for preterm birth,96 further predisposing neonates to LBW. Given the heightened risk associated with multiple pregnancies, enhanced ANC follow-ups and specialised maternal care are essential. Sex differences in birth weight were also observed, with female neonates exhibiting a higher prevalence of LBW. This finding is in line with several previous studies conducted elsewhere9 25 27 29 34 37 41 and might be attributed to biological and sociocultural factors. Male fetuses generally have higher growth rates due to hormonal and genetic differences.25 In addition, cultural preferences for male offspring in certain societies may lead to better maternal care and resource allocation for male pregnancies, whereas maternal stress and gender biases may negatively impact female fetal growth.27

Finally, the economic classification of a country was associated with LBW prevalence. Infants born in LICs exhibited a lower LBW prevalence compared with those born in lower-middle-income countries. This paradoxical finding may be explained by potential under-reporting or inaccuracies in birth weight measurement in LICs due to resource constraints.97 In contrast, LMICs undergoing demographic and economic transitions may face a dual burden of malnutrition and rising non-communicable diseases, including obesity and hypertension, both of which are associated with LBW.98 Furthermore, LMICs might have higher rates of medical interventions, such as early detection of high-risk pregnancies and preterm deliveries, leading to increased reporting of LBW cases.99 Urbanisation in LMICs also introduces environmental stressors, such as pollution and lifestyle changes, that negatively impact maternal and neonatal health outcomes.100

### Strength and limitation of the study

This study’s principal strength stems from its utilisation of nationally representative survey data featuring a substantial sample size, which facilitates robust estimations of the prevalence of LBW and associated factors. Additionally, the pooled analysis incorporating data from 17 LMICs with the highest infant mortality rates amplifies the findings’ generalisability across varied contexts.

However, the cross-sectional design of the DHS data inherently constrains the establishment of causal relationships between LBW and the identified independent variables. Besides, reliance on self-reported maternal health behaviours (eg, smoking, nutritional intake) introduces potential response bias. Additionally, the influence of social desirability and recall biases may lead respondents to under-report negative behaviours, such as smoking, while potentially overstating positive behaviours, including adequate nutritional intake. These biases may compromise the accuracy of factor analysis and limit the identification of true determinants of LBW. To mitigate these limitations in future research, the use of longitudinal study designs should be considered to allow for data collection at multiple time points, improving causal inferences. Moreover, integrating mixed-method approaches could provide a more comprehensive understanding of the research problem. Advanced statistical techniques, such as structural equation modelling, may also be useful in better controlling for confounding variables and improving causal inferences. Furthermore, reliance on primary data sources and document reviews could help address response biases and improve the accuracy of findings.

While the DHS datasets provide extensive information, they may not capture important contextual factors such as access to healthcare services, cultural practices and environmental influences, all of which can significantly impact birth weight outcomes. The absence of such key contextual variables may limit the studies ability to fully elucidate the underlying determinants of LBW. Future studies should aim to incorporate these additional factors to enhance the comprehensiveness and validity of findings in addressing this critical public health issue.

### Conclusion and recommendation

This study highlights a significant burden of LBW in the top 20 countries with the highest infant mortality rates with more than 1 in 10 newborns affected. Key maternal and socioeconomic factors including women education, maternal age, ANC visit, place of delivery, marital status, employment status, type of birth, country income classification, infant sex, history of terminated pregnancies and parity were identified as significant predictors of LBW. To reduce LBW and improve neonatal survival, targeted interventions are essential. Expanding access to high-quality healthcare, particularly comprehensive ANC services, is critical. Strengthening maternal education and ensuring adolescent and unplanned pregnancies receive adequate prenatal care can further mitigate risks. Policies should also focus on improving economic opportunities for women, as financial stability directly impacts maternal nutrition and healthcare access.

Tailored strategies are needed to support high-risk groups, such as divorced mothers, primiparous women and those with multiple pregnancies. Addressing psychosocial stressors, improving nutritional support and promoting skilled birth attendance can help mitigate adverse birth outcomes. Additionally, recognising the role of cultural and environmental factors is crucial in designing country-specific interventions. Ultimately, reducing LBW requires a multidimensional approach, integrating healthcare improvements, social support systems and economic empowerment initiatives. Strengthening healthcare infrastructure and addressing disparities will be vital in ensuring better birth outcomes and improving neonatal survival in high-mortality settings.

Furthermore, special interventions addressing the risks and the complications associated with a history of terminated pregnancies have a pivotal role in reducing LBW. Finally, country-specific initiatives should also be developed to address disparities and ensure that all mothers and infants receive adequate care and support to reduce LBW and improve neonatal health outcomes.

## Data availability statement

Data are available upon reasonable request. Data of this manuscript will be available upon reasonable request from the corresponding author. Additionally, the whole raw DHS data are available online in a public, open access repository ([www.measuredhs.com/data](http://www.measuredhs.com/data)).

## Ethics statements

### Patient consent for publication

Not applicable.

### Ethics approval

We obtained permission from the DHS program to access and use the data available at [http://www.dhsprogram.com](http://www.dhsprogram.com/). The original data collection adhered to established international and national ethical standards, with approval granted by the ICF Macro Institutional Review Board, the Centers for Disease Control and Prevention (CDC) and the relevant Institutional Review Boards (IRBs) in each participating country, in compliance with the US Department of Health and Human Services’ regulations for the protection of human subjects. Written informed consent was obtained from all participants, and legal guardians for minors (under 16 years of age), following the principles outlined in the Declaration of Helsinki. The public-use datasets provided by DHS ensure participant anonymity, as they do not include any personal identifiers or household addresses. This study is non-experimental. Further information regarding the ethical standards and data usage policies of the DHS is available at: [http://goo.gl/ny8T6X](http://goo.gl/ny8T6X).

## Acknowledgments

The authors would like to thank MEASURE DHS for their permission to access the dataset.

## Footnotes

*   Contributors Conceptualisation: DMG. Study design: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. Execution: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. Acquisition of the data: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. Analysis and interpretation: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. Writing: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. Review and editing: DMG, LDB, AH, MGT, GT, TZT, KAD and MJ. All authors have read and approved the final version of the manuscript. DMG is the guarantor of the overall content of this article.

*   Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

*   Competing interests None declared.

*   Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

*   Provenance and peer review Not commissioned; externally peer reviewed.

*   Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

[http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/)
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: [http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/).

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