Human immunodeficiency virus infection dynamics in east Africa deduced from surveillance data.

ABSTRACT

Knowledge of human immunodeficiency virus type 1 (HIV) incidence patterns in East African HIV epidemics like that in Uganda is fundamental for guiding interventions and forecasting the future course of the pandemic, yet they are difficult to determine from surveillance data. The authors deduce hypotheses of HIV incidence dynamics from birth cohort analyses of Ugandan acquired immunodeficiency syndrome (AIDS) incidence from 1987 to 1992 and from the age and sex distribution of sexually transmitted disease an age dependency for HIV risk; a period effect of varying HIV incidence growth; and a replenishment of HIV-susceptible populations through demographic renewal. The hypotheses are tested by incorporating them into a model that generates patterns of HIV incidence, prevalence, and AIDS cases that are consistent with empiric data. When applied to Uganda, the modeled HIV incidence is characterized by a short temporal concentration of high incidence, followed by a decline, stabilization, and concentration in younger ages. The ensuing HIV dynamics result in a rapid build-up and subsequent stabilization of prevalence and mortality in years 10 and 13, respectively, after epidemic onset. When this model is used to forecast scenarios from 1980 to 2000, HIV prevalence declines in some populations, which is different from earlier scenarios. The techniques presented provide an empiric basis to better direct interventions, forecast epidemic impacts, and evaluate determinants of changing incidence and prevalence patterns.

ABSTRACT

PIP Knowledge of HIV incidence patterns helps to guide interventions and forecast the future course of the HIV/AIDS pandemic. The authors deduce hypotheses of HIV incidence dynamics from birth cohort analyses of Ugandan AIDS incidence during 1987-92 and from the age and sex distribution of sexually transmitted disease. The hypotheses are then tested by incorporating them into a model which generates patterns of HIV incidence, prevalence, and AIDS cases consistent with empirical data. Applied to Uganda, the modeled HIV incidence is characterized by a short temporal concentration of high incidence, followed by a decline, stabilization, and concentration in younger ages. A rapid build-up is then envisaged followed by a stabilization of prevalence and mortality in years 10 and 13, respectively, after epidemic onset. When the model is used to forecast scenarios over the period 1980-2000, HIV prevalence declines in some populations, different from earlier scenarios. The techniques presented in this paper provide an empirical basis upon which to better direct interventions, forecast epidemic impacts, and evaluate the determinants of changing incidence and prevalence patterns.