The revealed and potential accessibility of healthcare services: are they really consistent? — a case study of infectious disease care at the community level in Minhang District, Shanghai, China

Abstract Background As urbanization accelerates, ensuring equitable healthcare accessibility has become a key public health issue. While most research focuses on potential accessibility (PA) based on healthcare facility distribution and population density, revealed accessibility (RA), reflecting actual healthcare use, is less studied due to data constraints. Most RA studies use mobility data rather than directly measuring healthcare-seeking behavior, leaving a gap in comparing PA and RA. This study addresses this gap by analyzing both PA and RA in community-level infectious disease care and exploring the socio-economic and spatial factors that drive discrepancies between them. Methods Using a district of Shanghai as a case study, this research uses the two-step floating catchment area (2SFCA) method combined with hospital-reported disease data to calculate both PA and RA. In addition, we employed the Geodetector model to explore the driving factors that influence the gap between PA and RA. Empirical data are used to examine the impact of three categories on accessibility differences: demographic and socio-economic characteristics, built environment characteristics, and healthcare access indicators. Interactions between these factors are also explored to understand how they shape the spatial distribution of healthcare resources. Results The study reveals significant differences between PA and RA. PA follows a multi-core structure, with high values corresponding to medical resource distribution, while RA is more concentrated in central and peripheral urban areas of the study area. Areas where PA exceeds RA (18.95%) align with the service radius of community hospitals, whereas those where RA exceeds PA (63.51%) are influenced by strong healthcare-seeking capacity or lower healthcare burdens. Key factors driving these differences include access to primary healthcare, district hospital proximity, and public transport density. Interaction effects notably improve the explanatory power of accessibility disparities. Conclusion This study reveals healthcare accessibility disparities at micro level, focusing on the mismatch between PA and RA. By incorporating hospital visit data, it identifies key factors such as healthcare access and built environment. The findings inform policy recommendations to optimize healthcare resource allocation and improve access in urbanization.