OBJECTIVES: The authors compared 3 practical methods to estimate human HIV incidence rates using existing data from persons seeking anonymous testing in San Francisco between 1996-2002. Each method was assessed for strengths and limitations. METHODS: Three different approaches were used to determine HIV incidence: one based on self-reported dates of prior tests, one based on linking records of prior tests using an anonymous unique testing code, and one based on the Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS). RESULTS: The 3 methods found comparable rates of seroconversion overall (1.0, 1.2, and 1.3 per 100 person-years) and among men who have sex with men (1.4, 1.6, and 2.0 per 100 person-years). Incidence for all 3 methods saw a peak during 1999 followed by a decline. Greatest variability of incidence was observed among lower-risk populations, in whom few infections were expected. CONCLUSIONS: The 3 methods had complementary strengths and limitations, which may prevent proper interpretation of HIV incidence if any one method is analyzed alone. HIV incidence rates among persons seeking HIV testing should be interpreted cautiously using corroborative data on risk behavior and sexually transmitted diseases and other contextual information.
OBJECTIVES: The authors compared 3 practical methods to estimate human HIV incidence rates using existing data from persons seeking anonymous testing in San Francisco between 1996-2002. Each method was assessed for strengths and limitations. METHODS: Three different approaches were used to determine HIV incidence: one based on self-reported dates of prior tests, one based on linking records of prior tests using an anonymous unique testing code, and one based on the Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS). RESULTS: The 3 methods found comparable rates of seroconversion overall (1.0, 1.2, and 1.3 per 100 person-years) and among men who have sex with men (1.4, 1.6, and 2.0 per 100 person-years). Incidence for all 3 methods saw a peak during 1999 followed by a decline. Greatest variability of incidence was observed among lower-risk populations, in whom few infections were expected. CONCLUSIONS: The 3 methods had complementary strengths and limitations, which may prevent proper interpretation of HIV incidence if any one method is analyzed alone. HIV incidence rates among persons seeking HIV testing should be interpreted cautiously using corroborative data on risk behavior and sexually transmitted diseases and other contextual information.
Authors: Janet M McNicholl; J Steven McDougal; Punneeporn Wasinrapee; Bernard M Branson; Michael Martin; Jordan W Tappero; Philip A Mock; Timothy A Green; Dale J Hu; Bharat Parekh Journal: PLoS One Date: 2011-03-04 Impact factor: 3.240
Authors: Hasina Samji; Jia Hu; Michael Otterstatter; Mark Hull; Troy Grennan; David Moore; Mark Gilbert; Rob Higgins; Jason Wong Journal: PLoS One Date: 2022-01-27 Impact factor: 3.240
Authors: D Callander; M Stoové; A Carr; J F Hoy; K Petoumenos; M Hellard; J Elliot; D J Templeton; S Liaw; D P Wilson; A Grulich; D A Cooper; A Pedrana; B Donovan; J McMahon; G Prestage; M Holt; C K Fairley; N McKellar-Stewart; S Ruth; J Asselin; P Keen; C Cooper; B Allan; J M Kaldor; R Guy Journal: BMC Infect Dis Date: 2016-12-12 Impact factor: 3.090