{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:47Z","timestamp":1782846227491,"version":"3.54.5"},"reference-count":51,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Condyloma acuminata (CA), the clinical manifestation of infection with low-risk human papillomaviruses 6 and 11, is a common sexually transmitted infection (STI) with recurrent lesions and notable psychosocial and health system burden. Recent evidence indicates a substantial global burden, with prevalence ranging from 0.5% to 33.1% and incidence ranging from 24 to 2940 per 100,000 person-years, varying by age, sex, time, and geography. In South Korea, national insurance data show sustained increases in patients receiving care for CA during 2010 to 2019. Beyond individual behaviors, spatial proximity and contextual factors can produce clustered STI risk. However, the municipal-level spatial distribution of CA in Korea and its contextual correlates remain understudied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify high-risk geographic clusters of CA in South Korea and determine the regional factors associated with its incidence rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted an ecological analysis using 2019 municipal-level data from the National Health Insurance Service of Korea. Spatial autocorrelation of CA incidence rates was evaluated using Moran\u2019s I, and clustering was assessed with Getis-Ord Gi* to detect high-risk clusters. We then analyzed potential regional determinants using two Bayesian spatial regression models: the intrinsic conditional autoregressive model and the Besag\u2013York\u2013Molli\u00e9 model. Key municipal-level variables included health behaviors, socioeconomic indicators, health care access, adult entertainment venue density, and risk of sexual violence. Results are reported as adjusted relative risks (aRRs) with 95% credible intervals (CrIs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 52,009 CA cases were identified in 2019, 70.03% (36,421\/52,009) of which were in men. We found significant positive spatial autocorrelation in CA incidence rates (Moran\u2019s I&gt;0,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001), indicating nonrandom spatial clustering. The Getis-Ord Gi* analysis revealed several high-incidence clusters (\n                      <jats:italic>hotspots<\/jats:italic>\n                      ) in metropolitan and southeastern regions of South Korea. In the Bayesian spatial models, higher CA incidence rates were associated with a greater share of the municipal budget spent on social welfare (aRR 1.005, 95% CrI 1.001\u20101.009), a higher percentage of single-person households (aRR 1.034, 95% CrI 1.025\u20101.043), and more adult entertainment establishments per 10,000 people (aRR 1.006, 95% CrI 1.001\u20101.012).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We identified significant geographic hotspots of CA and several community-level risk factors driving these patterns in South Korea. These findings highlight the importance of spatial surveillance and targeted public health interventions in high-risk areas. Adapting STI prevention programs to address local social determinants may help reduce the spread of CA in the identified hotspots.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/76751","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T20:52:13Z","timestamp":1762203133000},"page":"e76751-e76751","source":"Crossref","is-referenced-by-count":0,"title":["Spatial Clusters of Condyloma Acuminata and the Regional Risk Factors in South Korea: Bayesian Spatial Regression Analysis"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5763-4703","authenticated-orcid":false,"given":"Joonsu","family":"Jang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4559-4968","authenticated-orcid":false,"given":"Seyul","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6576-8916","authenticated-orcid":false,"given":"Byung Chul","family":"Chun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"issue":"6","key":"R1","first-page":"25","volume":"5","author":"Yanofsky","journal-title":"J Clin Aesthet Dermatol"},{"issue":"5","key":"R2","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1001\/jama.2018.20181","article-title":"Genital warts","volume":"321","author":"Grennan","journal-title":"JAMA"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2025.108029","article-title":"Epidemiologic, clinical, and economic burden of anogenital warts among adults: a global systematic review","author":"You","journal-title":"Int J Infect Dis"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","first-page":"56","DOI":"10.4111\/icu.20220318","article-title":"Trends of genital wart in Korea according to treatment method classification: big data analysis of health care in 2010-2019","volume":"64","author":"Kim","journal-title":"Investig Clin Urol"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1017\/S0950268817002813","article-title":"Annual prevalence and economic burden of genital warts in Korea: Health Insurance Review and Assessment (HIRA) service data from 2007 to 2015","volume":"146","author":"Park","journal-title":"Epidemiol Infect"},{"issue":"2","key":"R6","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.29420","article-title":"Risk factors for human papillomavirus infection and disease: a targeted literature summary","volume":"96","author":"Del Pino","journal-title":"J Med Virol"},{"issue":"10","key":"R7","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1086\/522863","article-title":"The burden of genital warts: a study of nearly 70,000 women from the general female population in the 4 Nordic countries","volume":"196","author":"Kjaer","journal-title":"J Infect Dis"},{"issue":"8","key":"R8","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s00384-014-1922-y","article-title":"The impact of smoking on HPV infection and the development of anogenital warts","volume":"29","author":"Kaderli","journal-title":"Int J Colorectal Dis"},{"issue":"4","key":"R9","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1215\/00703370-10054898","article-title":"Socially connected neighborhoods and the spread of sexually transmitted infections","volume":"59","author":"Newmyer","journal-title":"Demography"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/56229","article-title":"Spatiotemporal analysis of HIV\/AIDS incidence in China from 2009 to 2019 and its association with socioeconomic factors: geospatial study","volume":"10","author":"Xie","journal-title":"JMIR Public Health Surveill"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-025-24171-4","article-title":"How place shapes genital herpes simplex distribution in South Korea: a Bayesian spatial analysis using National Health Insurance Service data","volume":"25","author":"Jang","journal-title":"BMC Public Health"},{"issue":"8","key":"R12","doi-asserted-by":"publisher","DOI":"10.3346\/jkms.2025.40.e111","article-title":"Leveraging National Health Insurance Service Data for public health research in Korea: structure, applications, and future directions","volume":"40","author":"Lim","journal-title":"J Korean Med Sci"},{"issue":"3","key":"R13","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1093\/ije\/dyw253","article-title":"Data resource profile: the National Health Information database of the National Health Insurance Service in South Korea","volume":"46","author":"Cheol Seong","journal-title":"Int J Epidemiol"},{"key":"R14","unstructured":"Statistics Korea [Webpage in Korean]. 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