Abstract
BACKGROUND: Estimates from high-income countries suggest that approximately 40% of dementia cases may be attributable to modifiable risk factors across the life course. However, most evidence informing these estimates originates from high-income settings, and population-level estimates from sub-Saharan Africa remain limited. We aimed to estimate population attributable fractions (PAFs) for modifiable dementia risk factors in the Democratic Republic of the Congo (DRC). METHODS: We conducted a cross-sectional analysis of community-dwelling adults aged 65 years and older enrolled in the Étude du Vieillissement Cognitif et de Démence en République Démocratique du Congo (EVCD-RDC). Prevalence estimates of dementia and associated exposures were derived from prior epidemiological studies in this population. Odds ratios were estimated using logistic regression, and population attributable fractions were calculated by integrating exposure prevalence with effect size estimates. To account for correlations between exposures, communality weights were applied when estimating combined PAFs across risk factors. FINDINGS: Combined modifiable risk factors were estimated to account for 37.3% (95% CI 14.3-55.6) of dementia cases in this sample. Poverty had the largest weighted PAF (18.4%, 95% CI 13.3-22.8), followed by low educational attainment (11.3%, 95% CI 7.3-15.3) and depression (5.8%, 95% CI 2.8-8.6). Additional contributors included traumatic events (5.4%), war exposure (2.1%), diabetes (1.3%), and hypertension (1.1%). A hypothetical 15% proportional reduction in these risk factors was estimated to reduce dementia prevalence by 6.4% (95% CI 2.1-10.8), corresponding to approximately 10 700 cases prevented in the DRC by 2025. INTERPRETATION: Modifiable risk factors account for a substantial proportion of dementia burden in the DRC, with structural determinants such as poverty and education contributing the largest fractions. Dementia prevention strategies in low- and middle-income countries may therefore require broader public health approaches that address socioeconomic and structural determinants alongside conventional clinical risk factors. FUNDING: National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR002378).