Optimizing mobile health clinic placement via geospatial modeling.

Area of Science:

• Public Health
• Spatial Analysis
• Health Services Research

Background:

• Mobile health clinics (MHCs) are crucial for delivering care to underserved populations facing access barriers.
• Effective placement and routing strategies are needed to maximize MHC reach and impact.
• Current strategies often fall short in optimizing coverage for vulnerable communities.

Purpose of the Study:

• To develop and evaluate a framework for optimizing MHC placement and routing to increase service coverage.
• To maximize the number of underserved individuals reached by MHCs within practical travel times.
• To improve the utilization of MHCs for preventive and continuous care services.

Main Methods:

• A retrospective study analyzing MHC deployments for Hepatitis C Virus (HCV) screening and treatment.
• Utilized a location-allocation model to identify optimal MHC sites based on proximity to uninsured populations.
• Employed negative binomial mixed effects models to assess the relationship between service area population and MHC utilization.

Main Results:

• Optimized MHC placements can nearly double population coverage, increasing access for uninsured residents by 90% (driving) and 135% (walking).
• The framework reduces redundant service areas and average travel times without additional resources.
• Greater geographic coverage in rural areas was significantly associated with higher MHC utilization for HCV screening.

Conclusions:

• A spatial analytics framework can significantly enhance MHC service delivery and coverage.
• This adaptable tool supports strategic placement in high-need areas, reducing travel time and service redundancy.
• The framework has the potential to improve health outcomes for medically underserved populations.