# Geospatial variations and predictors of low birth weight in Sub-Saharan Africa: a geospatial modeling using evidence from demographic health survey 2015–2024

## TL;DR

This study maps the geographic patterns and risk factors for low birth weight in sub-Saharan Africa using health survey data from 2015 to 2024.

## Contribution

The study identifies significant hotspots and coldspots of low birth weight and their predictors across sub-Saharan Africa.

## Key findings

- Low birth weight is clustered in sub-Saharan Africa with significant hotspots in countries like Mauritania, Mali, and Nigeria.
- Key predictors include short birth intervals, lack of health facility visits, twin births, no media exposure, and unemployment among women.
- Cold spots with lower risk were found in countries such as Uganda, Kenya, and Rwanda.

## Abstract

Low birth weight, defined as less than 2.5 kg (5.5 lbs) at birth, remains a critical global public health challenge. It significantly increases the risk of neonatal mortality and immediate complications such as sepsis and hypothermia, along with lifelong consequences including childhood disabilities and adult-onset chronic diseases. However, there was a limited study that described the spatial distribution and predictors of low birth weight in sub-Saharan Africa. The study aimed to assess geospatial variations and predictors of low birth weight in sub-Saharan Africa.

A community-based cross-sectional study design based on Demographic and Health Survey (2015–2024) data, comprising a weighted sample of 138,164 women aged 15–49 years with live births among 28 sub-Saharan African countries, was included in the study. Global Moran's I was calculated to determine overall clustering of low birth weight. Statistically significant hot spot and cold spot areas of low birth weight were determined by Getis-Ord G∗ statistics. Ordinary least squares, spatial lag, spatial error, geographically weighted regression, and multiscale geographically weighted regressions were utilized to determine predictors of low birth weight. The best-fitting models were determined by the highest R2 and the lowest corrected Akaike Information Criterion values. Finally, the statistically significant predictors from the final model were displayed on a map.

Low birth weight was clustered (Moran's I 0.23, z-score 50.2, p-value <0.01) in the study area. Significant hotspot areas were depicted in Mauritania, Mali, Senegal, Burkina Faso, Nigeria, Gabon, Angola, Madagascar, South Africa, Lesotho, Malawi, and Ethiopia. Conversely, low-risk cold spots were observed in Uganda, Kenya, Rwanda, Burundi, Tanzania, Zambia, Zimbabwe, Cameroon, and Sierra Leone. Short birth interval, no visit to a health facility in the last year, twin birth, no media exposure, and unemployed women were significant predictors of low birth weight.

There is spatial variation of low birth weight across different regions in sub-Saharan Africa. Significant hotspot and cold spot areas along with significant predictors were identified, which is a priority for policy makers. Targeted maternal health interventions, improved healthcare access, health education using mass media, and economic empowerment for women are recommended to reduce low birth weight.

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