{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T18:14:22Z","timestamp":1768241662282,"version":"3.49.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Gonorrhea represents a major global public health challenge, demonstrating significant spatiotemporal heterogeneity across China. Therefore, spatiotemporal analytical approaches are required to better understand gonorrhea incidence patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Employing an innovative methodological framework that combines spatiotemporal scan statistics with Geographically and Temporally Weighted Regression (GTWR), this study systematically examines gonorrhea incidence dynamics across mainland China from 2003 to 2022, with particular focus on exploring the impacts of healthcare resources, economic development, education levels, and demographic structure on gonorrhea incidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our comprehensive analysis reveals three key findings: First, while national incidence rates declined significantly from 20.64 cases per 100,000 population in 2004 to 5.99 in 2022, the rate of decrease slowed markedly after 2013, suggesting a stabilization phase in prevention effectiveness. Second, spatial analysis identified distinct epidemiological patterns, with persistent high-risk clusters in the southeastern region contrasting with emerging hotspots in western provinces, reflecting China\u2019s uneven regional development. Third, our GTWR modeling uncovered complex driver dynamics: healthcare resources showed temporally weakening effects, economic development shifted from protective to risk-enhancing effects, education maintained stable protective effects, and demographic influences exhibited significant spatial variation \u2013 particularly the increasing association between male predominance and higher incidence in southern provinces.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings enhance gonorrhea surveillance precision and inform targeted regional prevention approaches, while providing a scientific basis for optimizing public health resource allocation.<\/jats:p>\n                  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