Using age‐specific mortality of HIV infected persons to predict Anti‐Retroviral Treatment need: a comparative analysis of data from five African population‐based cohort studies
Objectives To present a simple method for estimating population‐level anti‐retroviral therapy (ART) need that does not rely on knowledge of past HIV incidence.
Methods A new approach to estimating ART need is developed based on calculating age‐specific proportions of HIV‐infected adults expected to die within a fixed number of years in the absence of treatment. Mortality data for HIV‐infected adults in the pre‐treatment era from five African HIV cohort studies were combined to construct a life table, starting at age 15, smoothed with a Weibull model. Assuming that ART should be made available to anyone expected to die within 3 years, conditional 3‐year survival probabilities were computed to represent proportions needing ART. The build‐up of ART need in a successful programme continuously recruiting infected adults into treatment as they age to within 3 years of expected death was represented by annually extending the conditional survival range.
Results The Weibull model: survival probability in the infected state from age 15 = exp(−0.0073 × (age − 15)1.69) fitted the pooled age‐specific mortality data very closely. Initial treatment need for infected persons increased rapidly with age, from 15% at age 20–24 to 32% at age 40–44 and 42% at age 60–64. Overall need in the treatment of naïve population was 24%, doubling within 5 years in a programme continually recruiting patients entering the high‐risk period for dying.
Conclusion A reasonable projection of treatment need in an ART naive population can be made based on the age and gender profile of HIV‐infected people.
Document Type: Research Article
Affiliations: 1: London School of Hygiene and Tropical Medicine, London, UK 2: Medical Research Council/Uganda Virus Research Institute, Entebbe, Uganda 3: National Institute for Medical Research, Mwanza, Tanzania 4: Africa Centre for Health and Population Studies, Somkhele, University of KwaZulu Natal, KwaZulu Natal, South Africa 5: Imperial College, London, UK 6: Karonga Prevention Study, Chilumba, Malawi
Publication date: 2012-08-01