Abstract
OBJECTIVE: This study aimed to address the spatial distribution and multilevel analysis of healthcare access barriers among women of reproductive age in Somalia. STUDY SETTING, DESIGN AND ANALYSIS: The study was conducted across Somalia, an East African country facing significant spatial disparities in healthcare access. A cross-sectional study design was employed, using data from the 2020 Somali Demographic and Health Survey (SDHS). The data were analysed using both multilevel logistic regression and spatial analysis. To pinpoint barriers and identify statistically significant spatial clusters, the data were analysed using multilevel logistic regression in Stata V.17 and spatial analysis in R Studio (V.4.4.1), respectively. PARTICIPANTS: The study population consisted of a weighted sample of 5118 women of reproductive age (15-49 years) from the SDHS. RESULTS: Spatial analysis revealed significant regional heterogeneity, with high-prevalence areas concentrated in the northern region of Togdheer and a south-central cluster encompassing Galguduud, Hiiraan and Bakool. Multilevel analysis presented that women in the Bay region had nearly 10 times (AOR: 9.62) the risk of facing healthcare access barriers. While women in the highest quintile of wealth (AOR 0.21), those in higher education (AOR 0.30), those aged 45-49 (AOR 0.49) and not currently working (AOR 0.46) were significantly less likely to report access barriers. CONCLUSION AND RECOMMENDATIONS: Healthcare access barriers in Somalia are driven by a complex interplay of socioeconomic factors, specifically maternal age, education, employment and household wealth, and profound geographical disparities. Access barriers are not uniform but are geographically clustered in the south-central regions (Bay, Bakool, Hiiraan) and Togdheer in the northern region. Policy efforts must prioritise infrastructure investment in these identified high-burden hotspots while simultaneously dismantling systemic inequalities through the expansion of female education and financial protection schemes. This data-driven approach offers a definitive roadmap for decision-makers to equitably allocate resources and ensure that the most vulnerable populations are not left behind.