Spatiotemporal evolution and spatial heterogeneity of driving factors of gonorrhea incidence in Mainland China before and after the COVID-19 pandemic

Gonorrhea remains a major challenge to global public health systems. The Coronavirus Disease 2019 (COVID-19) pandemic has profoundly disrupted the epidemic patterns and surveillance systems of sexually transmitted infections. However, its specific impact on the long-term spatiotemporal patterns and driving mechanisms of reported gonorrhea incidence in mainland China remains unclear. This study aims to analyze the spatiotemporal evolution of reported gonorrhea incidence in mainland China before and after the COVID-19 outbreak and to explore the spatial heterogeneity and dynamic changes of socioeconomic influencing factors. Annual gonorrhea incidence data from 31 provincial-level administrative regions in the mainland of China were divided into pre-pandemic and post-pandemic periods. Spatial autocorrelation analysis was employed to reveal spatial aggregation patterns. Ordinary Least Squares (OLS) regression and Multiscale Geographically Weighted Regression (MGWR) models were utilized to investigate the impacts of five socioeconomic factors on incidence across economic, medical resource, and social structure dimensions. Gonorrhea incidence in mainland China exhibited significant spatiotemporal heterogeneity. Temporally, incidence peaked in 2017, declined sharply in 2020, rebounded briefly in 2021, fell to its lowest point in 2022, and rebounded again in 2023. Spatially, a stable north–south disparity intensified after the pandemic, with Global Moran’s I rising from 0.2218 in 2015 to 0.3338 in 2023. The MGWR model significantly outperformed OLS, with adjusted R 2 increasing from 0.252 to 0.875 pre-pandemic and from 0.301 to 0.855 post-pandemic. Regression analysis revealed strong spatial nonstationarity. Per capita gross domestic product (GDP), health technicians, and urbanization rate all exhibited systematic global–local divergence. For per capita GDP, positive global coefficients reflected superior surveillance capacity in wealthier regions, whereas negative local coefficients suggested reduced transmission through strengthened infrastructure. For health technicians and urbanization rate, negative global estimates masked positive local coefficients. Medical institutions remained protective across both scales. Population density displayed genuine spatial nonstationarity, with opposing local effects that cancelled out globally. After the pandemic, health workforce effects tended toward homogenization across broad regions, whereas population density effects became more localized and context-dependent. COVID-19 temporarily suppressed reported cases but solidified regional disparities. Extensive low-reporting regions may mask the true disease burden. When spatial stationarity is incorrectly assumed, OLS misspecification may produce global–local reversals, causing global averages to misrepresent local realities. Future prevention and control should move beyond uniform national policies and adopt region-specific precision interventions, including active screening in southeastern hotspots and improved healthcare access and reporting quality in northwestern cold spots, to optimize resource allocation in the post-pandemic era.