ArticlePublic health in practice (Oxford, England)2026
Optimizing mobile health clinic placement via geospatial modeling.
Article in Public health in practice (Oxford, England), 2026. 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
Objectives: Mobile health clinics (MHCs) provide flexible, community-based care to underserved populations facing geographic and socioeconomic barriers. Maximizing coverage enables MHCs to reach more individuals, improve preventive and continuous care, and reduce health disparities. However, few strategies exist to guide placement and routing decisions. We present a framework to increase MHC utilization by optimizing service coverage. Study design: This is a retrospective study. Methods: We analyzed MHC deployments for Hepatitis C Virus (HCV) screening and treatment from a local health system in South Carolina. We used a location-allocation model to identify potential MHC placement sites that maximized the number of uninsured residents within a 5-min drive or 10-min walk. Demand was represented by block centroids weighted by the size of the uninsured population. We compared service area population, defined as the size of the target population within driving or walking distance, for model-proposed sites with coverage from previous MHC deployments. We fit negative binomial mixed effects models to evaluate the association between service area population and MHC utilization. Results: Optimized placements can nearly double population coverage, expanding access to uninsured residents within practical travel distances by 90% for driving and 135% for walking-without requiring additional vehicles or resources. This approach also substantially reduces redundant service areas while shortening average travel times. Results show that small geographic shifts can yield significant improvements. In rural regions, greater geographic coverage is significantly associated with higher MHC utilization for HCV screening (drive p = .0037; walk p = .0095). We applied this framework with local health partners to guide real-world MHC deployment in South Carolina. Conclusions: This framework connects spatial analytics to service delivery, offering a replicable, operationally ready tool adaptable to various travel modes, site types, and disease contexts. It supports strategic placement in high-need locations by reducing travel time and service redundancy and ultimately improving health outcomes in medically underserved populations.
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