Estimation and prediction of the HIV-AIDS-epidemic under conditions of HAART using mixtures of incubation time distributions

Stat Med. 2008 Mar 15;27(6):781-94. doi: 10.1002/sim.2974.


The estimation of the HIV-AIDS epidemic by means of back-calculation (BC) has been difficult since the introduction of highly active anti-retroviral therapy (HAART) because the incubation time distributions needed for BC were poorly known. Moreover, it has been assumed that if the general public is aware that effective treatments are available then the majority of infected people would be known, and therefore a hidden epidemic was assumed not to exist. Nevertheless, it was suspected that not every infected person would come to the attention of health-care providers, and therefore estimates independent of the patients' registration were necessary. In this paper, the incubation time distributions for HIV treated with the HAART regimen are derived from a cohort study. By using estimates of the proportion treated according to the HAART regimen and the incubation time distributions estimated in the era before the implementation of HAART (pre-HAART), new marginal population incubation time distributions for each of the three risk groups (homosexuals, drug users and others) were constructed. The BC was performed using an empirical Bayesian approach based on the latter incubation time distribution.

MeSH terms

  • Acquired Immunodeficiency Syndrome / drug therapy
  • Acquired Immunodeficiency Syndrome / epidemiology
  • Acquired Immunodeficiency Syndrome / virology
  • Antiretroviral Therapy, Highly Active*
  • Bayes Theorem
  • CD4 Lymphocyte Count
  • Disease Progression
  • Forecasting
  • HIV Infections / drug therapy
  • HIV Infections / epidemiology*
  • HIV Infections / virology
  • Homosexuality, Male / statistics & numerical data
  • Humans
  • Incidence
  • Male
  • Models, Statistical
  • Netherlands / epidemiology
  • Population Surveillance / methods*
  • Risk
  • Statistical Distributions
  • Substance Abuse, Intravenous / epidemiology
  • Time Factors
  • Virus Latency* / drug effects