ArticleFrontiers in global women's health2025
Multifactorial drivers of engagement in sex work among Ethiopian women: a multinomial logistic regression approach.
Article in Frontiers in global women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Understanding the multifactorial drivers of female sex workers' (FSWs) engagement in Ethiopia is essential for designing effective public health interventions. While economic drivers are often emphasized, the roles of family, social, and geographic contexts remain underexplored. Methods: We analyzed data from a cross-sectional bio-behavioral survey of 6,085 FSWs across 16 Ethiopian urban centers conducted from December 2019 to April 2020. Multinomial logistic regression was used to assess associations between primary motivations for engaging in sex work-categorized as economic, family-related, combined economic-family, or social/behavioral-and socio-demographic, behavioral, and health-related factors. Results: Economic reasons were most common (41.7%), followed by family-related (22.7%), combined economic-family (21.0%), and social/behavioral (14.6%) motivations. Geographic variation was evident: FSWs in Addis Ababa, the capital in central Ethiopia, had lower odds of reporting family-related reasons (AOR = 0.52) than those in Adama, another central commercial city. FSWs in Dessie-Kombolcha, in northeastern Ethiopia, were more likely to report social or behavioral motivations (AOR = 2.02). Age, education, marital status, income, and healthcare access were also significant predictors. Women aged 35-59 were less likely to cite family (AOR = 0.50) or social motivations (AOR = 0.55), while those with secondary education were more likely to report family-related reasons (AOR = 1.54). Limited healthcare access and early initiation into sex work were associated with non-economic drivers. Conclusion: FSWs' engagement is influenced by intersecting economic, familial, and geographic factors. Tailored interventions should consider age, location, and service accessibility. The cross-sectional design limits causal interpretation.
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