Bayesian multilevel analysis of multimorbidity among women in Somalia: prevalence, patterns, and determinants

Multimorbidity is a growing public health concern globally, yet remains under-researched in fragile and conflict-affected settings, including Somalia, a country in the Horn of Africa with limited health infrastructure. This study investigates the prevalence, regional patterns, and individual- and contextual-level determinants of multimorbidity among women aged 15–49 years in Somalia. A cross-sectional analytical study design was applied using nationally representative data from the 2020 Somalia Demographic and Health Survey (DHS). The final sample included 8,172 women after excluding cases with missing or incomplete information. Multimorbidity was defined as the presence of ≥ 2 self-reported chronic conditions. Bayesian multilevel logistic regression was employed to account for hierarchical data structure— individuals (Level 1) nested within regions and residence types (Level 2). Random intercept and slope models were compared using Deviance Information Criterion (DIC), with posterior distributions estimated via Markov Chain Monte Carlo (MCMC) simulation. Associations were assessed using odds ratios (ORs) and 95% Bayesian credible intervals (CrIs). The prevalence of single morbidity was 7.2% (95%CI: 6.64%–7.76%), while multimorbidity was 2.1% (95%CI: 1.78%–2.40%), with higher burden among urban residents and certain regions (e.g., Banadir, Bay, Sool). Hypertension, kidney disease, and chronic headache were the most common conditions. Model III (random intercept and slopes) provided the best fit (DIC = 1525.051). Key determinants of higher multimorbidity risk included food insecurity (OR = 2.939, 95%CrI: 2.280–3.775), obesity (OR = 1.601, 95%CrI: 1.175–2.093), older maternal age (OR = 1.047, 95%CrI: 1.026–1.066), lack of internet access (OR = 1.254, 95%CrI: 1.084–1.471), and longer water-fetching time (Up to 30 min, OR = 1.798). Protective factors included rural (OR = 0.658, 95%CrI: 0.464–0.809) and nomadic (OR = 0.349, 95%CrI: 0.250–0.453) residence, non-smoking (OR = 0.641, 95%CrI: 0.434–0.988), lack of agricultural land ownership (OR = 0.563, 95%CrI: 0.484–0.653), and toilet non-sharing (OR = 0.664, 95%CrI: 0.570–0.813). Multimorbidity affects a notable proportion of Somali women, with considerable geographic and socioeconomic variation. The findings underscore the need for regionally tailored, multisectoral interventions targeting modifiable risk factors such as food insecurity, obesity, and access to healthcare infrastructure.

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Journal
Archives of Public Health
Published
2026-03-28
DOI
https://doi.org/10.1186/s13690-026-01905-3
Primary Topic
Chronic Disease Management Strategies
Type
article
Field-Weighted Citation Impact
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article

Bayesian multilevel analysis of multimorbidity among women in Somalia: prevalence, patterns, and determinants

Md. Azizur Rahman, Amir Farah, Jama Mohamed, Saralees Nadarajah et al.
Archives of Public Health
Chronic Disease Management Strategies
article

Bayesian multilevel analysis of multimorbidity among women in Somalia: prevalence, patterns, and determinants

Md. Azizur Rahman, Amir Farah, Jama Mohamed, Saralees Nadarajah, Ahmed Ismail Mohamed
article en

Abstract

Multimorbidity is a growing public health concern globally, yet remains under-researched in fragile and conflict-affected settings, including Somalia, a country in the Horn of Africa with limited health infrastructure. This study investigates the prevalence, regional patterns, and individual- and contextual-level determinants of multimorbidity among women aged 15–49 years in Somalia. A cross-sectional analytical study design was applied using nationally representative data from the 2020 Somalia Demographic and Health Survey (DHS). The final sample included 8,172 women after excluding cases with missing or incomplete information. Multimorbidity was defined as the presence of ≥ 2 self-reported chronic conditions. Bayesian multilevel logistic regression was employed to account for hierarchical data structure— individuals (Level 1) nested within regions and residence types (Level 2). Random intercept and slope models were compared using Deviance Information Criterion (DIC), with posterior distributions estimated via Markov Chain Monte Carlo (MCMC) simulation. Associations were assessed using odds ratios (ORs) and 95% Bayesian credible intervals (CrIs). The prevalence of single morbidity was 7.2% (95%CI: 6.64%–7.76%), while multimorbidity was 2.1% (95%CI: 1.78%–2.40%), with higher burden among urban residents and certain regions (e.g., Banadir, Bay, Sool). Hypertension, kidney disease, and chronic headache were the most common conditions. Model III (random intercept and slopes) provided the best fit (DIC = 1525.051). Key determinants of higher multimorbidity risk included food insecurity (OR = 2.939, 95%CrI: 2.280–3.775), obesity (OR = 1.601, 95%CrI: 1.175–2.093), older maternal age (OR = 1.047, 95%CrI: 1.026–1.066), lack of internet access (OR = 1.254, 95%CrI: 1.084–1.471), and longer water-fetching time (Up to 30 min, OR = 1.798). Protective factors included rural (OR = 0.658, 95%CrI: 0.464–0.809) and nomadic (OR = 0.349, 95%CrI: 0.250–0.453) residence, non-smoking (OR = 0.641, 95%CrI: 0.434–0.988), lack of agricultural land ownership (OR = 0.563, 95%CrI: 0.484–0.653), and toilet non-sharing (OR = 0.664, 95%CrI: 0.570–0.813). Multimorbidity affects a notable proportion of Somali women, with considerable geographic and socioeconomic variation. The findings underscore the need for regionally tailored, multisectoral interventions targeting modifiable risk factors such as food insecurity, obesity, and access to healthcare infrastructure.

Archives of Public Health
Charles Sturt University (AU), University of Hargeisa (SO), University of Manchester (GB), Gollis University (SO)
Openalex Percentile: Top 4%
Chronic Disease Management Strategies
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