Abstract
Ambient PM(2).(5) exposure has been associated with an increased risk of acute respiratory infections (ARIs). However, the magnitude and direction of this association may vary across geographical settings, both within and between countries, underscoring the need for context-specific investigation. We combined Demographic and Health Surveys (DHS) data from 1998 to 2022 across 34 sub-Saharan African (SSA) countries with satellite-derived geospatial PM(2.5) data from the V5.GL.02 product (ACAG). The DHS GPS coordinates allowed us to extract PM(2.5) exposure data within a 2 km buffer for urban areas and 5 km buffer for rural areas to help account for geographic coordinate displacement. This study used Bayesian multilevel spatial logistic regression model implemented in the Integrated Nested Laplace Approximations (INLA) framework for the analysis. Of the 129,769 children included in this study, 22.7% experienced ARI in SSA. Higher ambient PM(2).(5) exposure was consistently associated with increased odds of ARI in SSA. In the country level analysis, postnatal exposure showed a clear positive association with ARI (AOR = 1.14; 95% CrI: 1.07-1.21), while prenatal exposure showed no meaningful relationship. In the Sub-national level analysis, the strength of the association increased: prenatal exposure indicated a modest elevated risk but remained statistically non-significant, and postnatal exposure remained the strongest predictor (AOR = 1.22; 95% CrI: 1.07-1.38). Geographical differences were evident, with East and Central Africa demonstrating the most pronounced associations across both modelling frameworks. The study findings underscore the need for tailored country-specific interventions for reducing the burden of childhood ARIs in SSA attributable to air pollution. Spatial variations in air pollution health effects need to be taken into consideration whenever policymakers are developing and implementing air pollution control policies.