Abstract
BACKGROUND: In Sub-Saharan Africa (SSA), several women and children are significantly affected by female genital mutilation (FGM), but there is limited studies on the prevalence and determinants of FGM due to a combination of factors, including cultural-political barriers, funding constraints, and ethical concerns. This study aims to address this gap by examining the current prevalence and determinants of FGM in ten at-risk countries in SSA. METHODS: This cross-sectional study utilised Demographic and Health Survey (DHS) data of 129,182 women aged 15-49 years from ten at-risk countries. The included countries are Guinea, Mali, Sierra Leone, Burkina Faso, Gambia, Mauritania, Ethiopia, Nigeria, Chad, and Liberia. Data were sample-weighted and our dependent variable was mutilation status whereas women's socio-demographic characteristics constituted the independent variables. Multivariable modified Poisson regression analyses were performed, with a p-value threshold of < 0.05 used to determine statistical significance. RESULTS: The pooled prevalence of FGM across the ten Sub-Saharan African (SSA) countries was 53.5% (95% CI: 52.3-54.7). Older women demonstrated a higher likelihood of having undergone mutilation, with adjusted prevalence ratios (APRs) increasing from 2.69 (95% CI: 2.66-2.75) among women aged 20-24 to 2.86 (95% CI: 2.80-2.92) among those aged 45-49. Women with no formal education were significantly more likely to have undergone mutilation compared to those with secondary education or higher (APR = 2.53, 95% CI: 2.51-2.56). Rural residency was strongly associated with a higher prevalence of FGM (APR = 2.59, 95% CI: 2.56-2.61), as was unemployment compared to active employment (APR = 2.56, 95% CI: 2.54-2.58). Furthermore, women from poorer households were more likely to have undergone mutilation compared to those from wealthier households (APR = 2.66, 95% CI: 2.62-2.69). CONCLUSION: The overall FGM prevalence of 53.5% indicates a significant problem. Urgent interventions are needed and should consider significant factors including older age, lower education level, rural residency, women unemployment and poverty.