American Journal of Epidemiology Vol. 144, No. 7: 682-695
Copyright © 1996 by The Johns Hopkins University School of Hygiene and Public Health
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Human Immunodeficiency Virus Infection Dynamics in East Africa Deduced from Surveillance Data
1The unit of Surveillance Evaluation and Forecasting, Global Program on AIDS, World Health Organization Geneva, Switzerland
2The unit of Coordination and Monitoring of National Program Support, Global Program on AIDS, World Health Organization Entebbe, Uganda
3The National Sexuaily Transmitted Disease and Acquired Immunodeficiency Syndrome Control Program, Ministry of Heatth Entebbe, Uganda
Reprint requests to Dr. Rand L. Stonebumer, International Center for Migration and Health, 24 Avenue de Beau-SéJour, CH-1206 Geneva, Switzerland.
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. Am J Epidemiol 1996;144:682–95.
cohort effect; cohort studies; epidemiologic factors; forecasting; HIV infections; models, theoretical; population surveillance
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