title: Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.
abstract: BACKGROUND
  Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies.
  
  
  METHODS
  We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands.
  
  
  RESULTS
  The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years.
  
  
  CONCLUSIONS
  This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.
authors.affiliation: aStichting HIV Monitoring, Amsterdam, The Netherlands; bUniversity College London, London, United Kingdom; cMRC Biostatistics Unit, Cambridge, United Kingdom; dEuropean Centre for Disease Prevention and Control, Stockholm, Sweden; eNational Institute for Public Health and the Environment, Bilthoven, The Netherlands; fInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; and gDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Ard
authors.initials: A
authors.lastname: van Sighem
authors.forename: Fumiyo
authors.initials: F
authors.lastname: Nakagawa
authors.forename: Daniela
authors.initials: D
authors.lastname: De Angelis
authors.forename: Chantal
authors.initials: C
authors.lastname: Quinten
authors.forename: Daniela
authors.initials: D
authors.lastname: Bezemer
authors.forename: Eline Op
authors.initials: EO
authors.lastname: de Coul
authors.forename: Matthias
authors.initials: M
authors.lastname: Egger
authors.forename: Frank
authors.initials: F
authors.lastname: de Wolf
authors.forename: Christophe
authors.initials: C
authors.lastname: Fraser
authors.forename: Andrew
authors.initials: A
authors.lastname: Phillips
mesh_terms: D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology*; D018451:Homosexuality, Male; D006801:Humans; D015994:Incidence; D008297:Male; D008962:Models, Theoretical; D009426:Netherlands / Q000453:epidemiology; D062486:Public Health Surveillance / Q000379:methods*; D013997:Time Factors