{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T12:42:37Z","timestamp":1782996157798,"version":"3.54.5"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"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>Background<\/jats:title>\n                    <jats:p>Viral hepatitis remains a major public health challenge in China, contributing significantly to global disease burden. Understanding spatiotemporal patterns is crucial for developing effective control strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted spatiotemporal analysis of viral hepatitis surveillance data from 31 provinces in mainland China spanning 2007\u20132023, encompassing 21,346,298 reported cases of hepatitis A, B, C, and E. Methods included descriptive epidemiological analysis, spatial autocorrelation using Moran\u2019s\n                      <jats:italic>I<\/jats:italic>\n                      statistics, and spatial correlation analysis with transportation networks and water systems using Geographic Information Systems (GIS).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Hepatitis B dominated with 78.63% of cases, followed by hepatitis C at 12.51%. The annual incidence of hepatitis A decreased from 7.20 to 1.06 (85.3% reduction, APC = \u221215.2%); hepatitis C increased from 3.03 to 13.82 (356% growth, APC = +11.8%); hepatitis B showed slight decline (APC = \u22120.6%); and hepatitis E remained relatively stable (APC = +1.4%). Spatial autocorrelation analysis revealed significant clustering patterns (Global Moran\u2019s\n                      <jats:italic>I<\/jats:italic>\n                      : 0.412 for hepatitis A, 0.387 for hepatitis B, 0.523 for hepatitis C, and 0.298 for hepatitis E;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01), with persistent high-incidence clusters in western provinces (Xinjiang, Tibet, and Qinghai) for hepatitis A and C, while hepatitis E showed significant clustering in eastern coastal regions (Zhejiang, Jiangsu, and Shandong). Infrastructure analysis demonstrated significant negative correlations between transportation proximity and incidence (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings validate hepatitis A vaccination as a cost-effective elimination strategy that can be replicated in resource-limited settings; identify the need for immediate policy action on hepatitis C; and demonstrate that investment in transportation infrastructure can reduce hepatitis burden by improving healthcare access in remote areas. These studies provide theoretical references for China to achieve the 2030 elimination target and offer transferable strategies for countries facing similar geographical and socio-economic challenges.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1660415","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T06:33:33Z","timestamp":1764830013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatial and temporal analysis of viral hepatitis in mainland China from 2007 to 2023: implications for targeted public health interventions"],"prefix":"10.3389","volume":"13","author":[{"given":"Qiang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi 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