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Progression of a large syphilis outbreak in rural North Carolina through space and time: Application of a Bayesian Maximum Entropy graphical user interface.
Fox, Lani C; Miller, William C; Gesink, Dionne; Doherty, Irene; Hampton, Kristen H; Leone, Peter A; Williams, Delbert E; Akita, Yasuyuki; Dunn, Molly; Serre, Marc L.
Afiliação
  • Fox LC; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
  • Miller WC; Lani Fox Geostatistical Consulting, Claremont, California, United States of America.
  • Gesink D; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
  • Doherty I; School of Medicine, Division of Infectious Diseases University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
  • Hampton KH; Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, Ohio, Unites States of America.
  • Leone PA; Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Williams DE; School of Medicine, Division of Infectious Diseases University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
  • Akita Y; Julius L. Chambers Biomedical/Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, United States of America.
  • Dunn M; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
  • Serre ML; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PLOS Glob Public Health ; 3(5): e0001714, 2023.
Article em En | MEDLINE | ID: mdl-37141185
ABSTRACT
In 2001, the primary and secondary syphilis incidence rate in rural Columbus County, North Carolina was the highest in the nation. To understand the development of syphilis outbreaks in rural areas, we developed and used the Bayesian Maximum Entropy Graphical User Interface (BMEGUI) to map syphilis incidence rates from 1999-2004 in seven adjacent counties in North Carolina. Using BMEGUI, incidence rate maps were constructed for two aggregation scales (ZIP code and census tract) with two approaches (Poisson and simple kriging). The BME maps revealed the outbreak was initially localized in Robeson County and possibly connected to more urban endemic cases in adjacent Cumberland County. The outbreak spread to rural Columbus County in a leapfrog pattern with the subsequent development of a visible low incidence spatial corridor linking Roberson County with the rural areas of Columbus County. Though the data are from the early 2000s, they remain pertinent, as the combination of spatial data with the extensive sexual network analyses, particularly in rural areas gives thorough insights which have not been replicated in the past two decades. These observations support an important role for the connection of micropolitan areas with neighboring rural areas in the spread of syphilis. Public health interventions focusing on urban and micropolitan areas may effectively limit syphilis indirectly in nearby rural areas.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article