{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:30:39Z","timestamp":1786516239298,"version":"build-2736575974"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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>The disease burden of liver fibrosis-cirrhosis complicated with pneumonia-influenza has become a major public health concern in the world, yet the long-term trends, and core influencing factors remain unclear. This study aimed to systematically characterize the ecological associations between U.S. county-level health-related risk phenotypes and comorbidity mortality, and to identify area-level factors associated with mortality variation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were integrated from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) and 2020 County Health Rankings (CHR) databases. Joinpoint regression was used to examine long-term mortality trends from 1999 to 2023. Unsupervised clustering (PCA combined with HCPC) was then applied to classify county-level phenotypic profiles. Spatial autocorrelation analysis (global and local Moran\u2019s I) was performed to evaluate geographic clustering, and generalized linear models were constructed to assess phenotypic associations with mortality and to identify key correlated health behavior factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 435\u202fU.S. counties with complete mortality data from 2018 to 2023 were enrolled, with a total of 32,093 deaths recorded from this comorbidity during the study period. County-level comorbidity mortality showed a phased increase from 2011 to 2023 (Annual Percentage Change [APC]\u202f=\u202f6.28%). Three heterogeneous county-level ecological phenotypes were identified via unsupervised clustering. No significant spatial autocorrelation was detected for county-level comorbidity mortality (global Moran\u2019s I\u202f=\u202f\u22120.0003,\n                      <jats:italic>p<\/jats:italic>\n                      =\u202f0.317), indicating a random spatial distribution without geographic clustering. Phenotype 2, geographically concentrated in the Appalachian and southeastern states, exhibited the highest county-level comorbidity mortality (27.8 per 1,000,000 population) and showed an independent statistical association with elevated mortality relative to other phenotypes (adjusted rate ratio [RR]\u202f=\u202f1.304). Furthermore, the county-level average number of physically unhealthy days, disconnected youth rate, and adults with obesity rate were not only strongly associated with high mortality in Phenotype 2 but also positively correlated with county-level single-disease mortality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>County-level comorbidity mortality from this condition showed a phased upward trend with acceleration after 2011 and significant phenotypic specificity. The observed ecological associations between high-risk phenotypes and correlated area-level indicators provide observational reference for geographically tailored public health strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1884089","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:55:20Z","timestamp":1786514120000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Trends and factors associated with liver fibrosis-cirrhosis complicated with pneumonia-influenza mortality in U.S. Counties: a CDC WONDER and CHR database analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Yanqiu","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Digestive Diseases, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China","place":["Changsha City, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueming","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Digestive Diseases, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China","place":["Changsha City, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/j.jhep.2018.11.015","article-title":"Influenza virus infection as precipitating event of acute-on-chronic liver failure","volume":"70","author":"Schutte","year":"2019","journal-title":"J Hepatol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.jceh.2018.04.006","article-title":"A\/H1N1\/09 influenza is associated with high mortality in liver cirrhosis","volume":"9","author":"Premkumar","year":"2019","journal-title":"J Clin Exp 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