Mapping spatial and socioeconomic disparities in public healthcare accessibility in Patna City

Background Rapid urbanization has intensified spatial inequalities in access to public healthcare in many Indian cities. In Patna City, the concentration of secondary and tertiary healthcare facilities in central areas creates significant accessibility challenges for peripheral and socioeconomically disadvantaged populations. This study evaluates spatial and socioeconomic inequalities in healthcare accessibility and identifies potential sites for future public healthcare facilities using an integrated GIS-based analytical framework. Methods A mixed-methods approach was employed by integrating household survey data from 384 respondents with spatial datasets on healthcare facilities, road networks, land availability, and population distribution. GIS-based buffer analysis, network analysis, Thiessen polygons, and PCA-based weighted overlay analysis were applied to evaluate healthcare accessibility and identify suitable sites for new healthcare facilities. Chi-square and multiple regression analyses were used to examine the influence of socioeconomic characteristics and travel distance on healthcare satisfaction and utilization. Results The analysis revealed that over 62% of municipal wards are underserved, with residents in peripheral areas travelling 8–12 km to reach public hospitals, compared with 2–3 km in central wards. PCA identified population density 29%, underserved service areas 22% and distance from existing hospitals 27% and four priority locations were identified for future healthcare facilities, giving the highest priority to the areas with high population density, underserved service areas and near existing hospitals. The chi-square test showed a significant association between income level and healthcare satisfaction χ² = 15.63, p = 0.004. In multiple regression, travel distance β = −0.472, p < 0.001, health status β = 0.096, p = 0.048, disease severity β = −0.129, p = 0.008), transport mode β = 0.114, p = 0.021, and insurance status β = 0.153, p = 0.002 were significant contributors to the variation in healthcare utilization and income level, age, gender, education, occupation and household structure were not significant. The regression model accounted for 74.2% of the variance in healthcare utilization value is R² = 0.742. Conclusions Integrating GIS-based spatial accessibility analysis with PCA-derived suitability modelling and socioeconomic analysis provides an objective framework for identifying healthcare service gaps and potential sites. The proposed approach supports evidence-based urban health planning and offers a transferable methodology for improving equitable healthcare access in rapidly growing cities with limited healthcare resources.