{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:25:30Z","timestamp":1787235930031,"version":"build-2736575974"},"reference-count":35,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n To complete the Ending the HIV Epidemic initiative in areas with high HIV incidence, there needs to be a greater understanding of the demographic, behavioral, and geographic factors that influence the rate of new HIV diagnoses. This information will aid the creation of targeted prevention and intervention efforts.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to identify the geographic distribution of risk groups and their role within potential transmission networks in Florida.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Public data from the Florida Department of Health and behavioral data from the Surveillance Tools and Reporting System between 2012 and 2022 were used in these analyses. We analyzed records as a combination of variables of interest (gender, age, race or ethnicity, and HIV risk group) to create demographic-behavioral profiles (DBPs) that represent the profiles of people newly diagnosed with HIV. We then used the resulting DBPs to characterize Florida counties and HIV coordination areas and calculated the county-to-county and area-to-area rank (Spearman) correlation. We then drew a dendrogram based on the correlation matrix and identified clusters of similar counties and areas. Lastly, network analysis used HIV contact tracing data from the Surveillance Tools and Reporting System to identify HIV transmission and exposure contact networks and characterized large networks by DBPs and geolocation.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We identified 37 DBPs. The largest DBPs were Hispanic and non-Hispanic Black males aged 25\u201049 reporting male-to-male sexual contact (n=7539 and n=4329, respectively), non-Hispanic White males aged 25\u201049 reporting male-to-male sexual contact (n=4221), and non-Hispanic Black females aged 25\u201049 reporting heterosexual contact (n=3371). The state could be broken up generally into 2 transmission and exposure clusters by region: Northwestern or Northern and Central or Southern. We identified several counties with similar DBPs that were not in the same HIV coordination area. A total of 3097 contact networks were identified among 7944 people with HIV contact tracing data. Most (n=2508, 81%) networks involve only 2 people, 11% (n=349) involve 3 people, 7% (n=224) involve 4 to 19 people, and 6 networks involve 20 or more people. As network size increases, the proportion of people within the network who identify as female, non-Hispanic Black, aged older than 50 years, and exposed to HIV via heterosexual contact decreases.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We identified distinct risk groups and clusters of transmission and exposure throughout Florida. These results can help regions identify health disparities and allocate their HIV prevention and intervention resources accordingly. The goal of this work was to highlight areas of need in a high-incidence setting, not to contribute to existing stigma against vulnerable groups, and it is important to consider the ethics and possible harm of advanced methodologies such as contact network analysis when addressing public health problems.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/65573","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T17:17:20Z","timestamp":1750871840000},"page":"e65573-e65573","source":"Crossref","is-referenced-by-count":2,"title":["Behavioral and Demographic Profiles of HIV Transmission and Exposure Networks in Florida: Network Analysis of HIV Contact Tracing Data"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5519-3853","authenticated-orcid":false,"given":"Yiyang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2546-507X","authenticated-orcid":false,"given":"Christina","family":"Parisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0913-5668","authenticated-orcid":false,"given":"Chaoyue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-2601","authenticated-orcid":false,"given":"Rebecca","family":"Fisk-Hoffman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-2102","authenticated-orcid":false,"given":"Marco","family":"Salemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2027-058X","authenticated-orcid":false,"given":"Diego","family":"Viteri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-7012","authenticated-orcid":false,"given":"Brandi","family":"Danforth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9021-5595","authenticated-orcid":false,"given":"Mattia","family":"Prosperi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5704-3533","authenticated-orcid":false,"given":"Simone","family":"Marini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"R1","unstructured":"Diagnoses of HIV infection in the United States and dependent areas, 2019. 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