Reducing healthcare access inequities in West Java through forecasting and location-allocation models

Ensuring equitable access to healthcare services is paramount for the enhancement of societal welfare and the optimisation of public health outcomes. Within the context of West Java Indonesia, accelerated population growth coupled with geographic disparities has resulted in heightened pressure on the existing healthcare infrastructure, thereby rendering numerous communities inadequately served. Although CHCs are already widespread in Indonesia, their distribution remains uneven, especially in growing urban areas. Consequently, there is growing recognition of the need to strategically expand community healthcare centres (CHCs) to improve access across underserved areas. These CHCs are instrumental in providing vital services, encompassing preventive care, maternal and child health, immunisation, dental services, and outpatient care.

This study presents an integrated approach combining population forecasting, spatial demand estimation, and optimisation modelling to support long-term planning of CHCs in Kota Bandung. Kota Bandung was selected as the study area due to its high population density, significant spatial disparities in healthcare access, and strategic importance in regional health planning. Using an ARIMA model, the researchers forecasted population growth through 2045. The year aligns with Indonesia’s national long-term vision, Visi Indonesia 2045. They then estimated daily CHC demand using spatial grid and population density data, applying a standard utilisation rate. Three mixed-integer linear programming (MILP) models were developed to optimize facility locations by maximizing coverage, minimizing travel time, or balancing both. This method allows planners to explore various scenarios based on local needs and constraints.

The forecasting model reveals a consistent escalation in the demand for primary healthcare services within the Bandung region, with distinct growth hotspots identified in presently underserved locales. Optimisation scenarios produced by the MILP models indicate that the strategic positioning of new CHCs can substantially augment service coverage while simultaneously mitigating disparities in healthcare access. Scenario-based simulations were conducted to explore how different planning objectives—such as maximizing coverage or minimizing travel time—impact CHC placement outcomes. The results suggest that the most equitable arrangement of CHCs would enhance accessibility for marginalized populations by more closely aligning resources with geospatial demand.

This integrated modelling framework presents a pragmatic paradigm for long-term health infrastructure planning in contexts characterized by resource limitations. By synthesizing demand forecasting with location-optimisation methodologies, regional health authorities are empowered to make data-informed decisions to enhance access and equity in primary healthcare services. These findings bolster broader initiatives aimed at achieving Universal Health Coverage in Indonesia.