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. 2025 Jul 17;25(1):2488.
doi: 10.1186/s12889-025-23683-3.

Decomposing socioeconomic and educational inequalities in infant mortality in Sub-Saharan Africa: DHS insights linked to countdown to 2030 and agenda 2063

Rahel Mulatie Anteneh  1 , Zekaryas Ewnetu Gashu  2 , Lemlem Daniel Baffa  3 , Desalegn Anmut Bitew  4 , Chalachew Yenew  5 , Kaleb Assegid Demissie  6 , Bewuketu Terefe  7 , Melak Jejaw  6 , Alebachew Ferede Zegeye  8 , Misganaw Guadie Tiruneh  6 , Tadesse Tarik Tamir  9 , Tadele Biresaw Belachew  6 , Wubshet Debebe Negash  6   10 , Elsa Awoke Fentie  4   11 , Desale Bihonegn Asmamaw  4   12
Affiliations

Decomposing socioeconomic and educational inequalities in infant mortality in Sub-Saharan Africa: DHS insights linked to countdown to 2030 and agenda 2063

Rahel Mulatie Anteneh et al. BMC Public Health. .

Abstract

Introduction: Infant mortality, defined as the death of a baby within its first year of life, remains a critical public health issue, particularly in Sub-Saharan Africa (SSA). Health equity is a fundamental right, yet significant disparities persist, with poorer populations experiencing disproportionately higher mortality rates. This study aims to assess whether these inequalities in infant mortality continue despite governmental efforts and the pursuit of Sustainable Development Goals (SDGs) in SSA countries.

Methods: We analyzed a comprehensive dataset comprising 30,109 weighted samples from Demographic and Health Surveys across 14 SSA countries. Data cleaning was performed using Microsoft Excel, and statistical analyses were conducted with STATA Version 17 software. To evaluate wealth-related inequalities in infant mortality, we employed the concentration index and curve, alongside Wag staff decomposition analysis. A p-value of < 0.05 was considered statistically significant.

Result: The pooled estimate of the infant mortality rate in Sub-Saharan Africa was found to be 34.14% (95% CI: 28.74, 39.54%), with substantial heterogeneity (I² = 83.6%), indicates wide variation across the country. Rates varied significantly across countries, ranging from 18.75% in Gabon to 52.8% in Côte d'Ivoire. The weighted concentration index for infant mortality based on women's education was - 0.0072 (95% CI; -0.00134, -0.0012), while the wealth index concentration index was - 0.0093 (95% CI: -0.00154, -0.0032), both with p-values < 0.0001.

Conclusion and recommendation: Our analysis reveals that wealth-related disparities in infant mortality disproportionately impact poorer populations in Sub-Saharan Africa, with children of mothers who possess lower socioeconomic status and less education facing higher risks. Factors contributing to these disparities include family size, media exposure, and birth order. The persistence of social injustice is largely due to the inadequate response to these health inequalities. Policymakers must prioritize equitable access to healthcare and consider the socioeconomic distribution of infant mortality in their strategies. To combat these disparities and improve infant survival rates, it is vital to enhance child-rearing practices through targeted health policies, promote equitable economic development, engage communities in health initiatives, and establish monitoring systems. By focusing on these strategies, we can make significant progress toward reducing infant mortality and addressing the underlying social injustices affecting vulnerable populations in line with the goals of Countdown to 2030 and 2063.

Keywords: Infant mortality; Sub-Saharan Africa; Sustainable development goals; Wealth inequality.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approval and informed consent were not required for this specific study since it was a secondary data analysis of publicly available survey data from the measures DHS program. We requested DHS program data archivist, and permission be granted to download and use for this study from www.measuredhs.com . All the procedures were carried out in the relevant regulations and the declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Sub group Analysis of IMR by countries income level (A) and by sub regions (B) in SSA
Fig. 2
Fig. 2
Concentration Curve of IMR of SSA countries based on wealth index (A) and women’s education (B)
See this image and copyright information in PMC

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