This study aimed to address the spatial distribution and multilevel analysis of healthcare access barriers among women of reproductive age in Somalia.

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.

The study population consisted of a weighted sample of 5118 women of reproductive age (15–49 years) from the SDHS.

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.

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.

People identified as higher risk by a machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation [FIND-AF]) are at increased risk of cardio-renal-metabolic-pulmonary disease and cardiovascular death. The OPTIMISE-1 randomised controlled trial aims to test the effect of community-based specialist-led identification and management of cardio-renal-metabolic-pulmonary (CRMP) disease and risk factors compared with usual care on the use of therapeutic interventions over a follow-up of 6 months among high FIND-AF risk community-dwelling individuals.

OPTIMISE-1 is a multicentre, pragmatic, prospective, randomised, open-label, blinded-endpoint strategy trial that will recruit 138 participants aged 30 years or older, with a high FIND-AF risk score and previously enrolled in the FIND-AF pilot study (NCT05898165), to be randomised 1:1 to a specialist-led care intervention or usual care. The primary endpoint is a composite of initiation or increase of guideline-directed CRMP therapies. The secondary endpoints are the components of the primary endpoint, time to primary endpoint, diagnosis of new CRMP diseases or risk factors, time to diagnosis of new CRMP diseases or risk factors, initiation or increase of guideline-directed CRMP therapies for participants with recorded CRMP disease, initiation or increase of guideline-directed CRMP therapies for participants with newly diagnosed CRMP disease and change in participant-reported quality of life.

The study has ethical approval (the North East & North Tyneside 2 Research Ethics Committee reference 24/NE/0188). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder’s open access policy.

Clinicaltrials.gov NCT06444711.