title: Assessing evidence for behaviour change affecting the course of HIV epidemics: a new mathematical modelling approach and application to data from Zimbabwe.
abstract: BACKGROUND
  Determining whether interventions to reduce HIV transmission have worked is essential, but complicated by the potential for generalised epidemics to evolve over time without individuals changing risk behaviour. We aimed to develop a method to evaluate evidence for changes in risk behaviour altering the course of an HIV epidemic.
  
  
  METHODS
  We developed a mathematical model of HIV transmission, incorporating the potential for natural changes in the epidemic as it matures and the introduction of antiretroviral treatment, and applied a Bayesian Melding framework, in which the model and observed trends in prevalence can be compared. We applied the model to Zimbabwe, using HIV prevalence estimates from antenatal clinic surveillance and house-hold based surveys, and basing model parameters on data from sexual behaviour surveys.
  
  
  RESULTS
  There was strong evidence for reductions in risk behaviour stemming HIV transmission. We estimate these changes occurred between 1999 and 2004 and averted 660,000 (95% credible interval: 460,000-860,000) infections by 2008.
  
  
  CONCLUSIONS
  The model and associated analysis framework provide a robust way to evaluate the evidence for changes in risk behaviour affecting the course of HIV epidemics, avoiding confounding by the natural evolution of HIV epidemics.
authors.affiliation: Imperial College London, London, UK. kkallio@liv.ac.uk
authors.forename: Timothy B
authors.initials: TB
authors.lastname: Hallett
authors.forename: Simon
authors.initials: S
authors.lastname: Gregson
authors.forename: Owen
authors.initials: O
authors.lastname: Mugurungi
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Gonese
authors.forename: Geoff P
authors.initials: GP
authors.lastname: Garnett
mesh_terms: D000293:Adolescent; D000328:Adult; D044966:Anti-Retroviral Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D015658:HIV Infections / Q000188:drug therapy / Q000453:epidemiology* / Q000517:prevention & control* / Q000635:transmission; D015438:Health Behavior / Q000208:ethnology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011247:Pregnancy; D011295:Prenatal Care; D015995:Prevalence; D012309:Risk-Taking; D012725:Sexual Behavior / Q000706:statistics & numerical data*; D055815:Young Adult; D015030:Zimbabwe / Q000453:epidemiology
chemical_list: D044966:Anti-Retroviral Agents