Date Published: April 8, 2008
Publisher: Public Library of Science
Author(s): Timothy B Hallett, Basia Zaba, Jim Todd, Ben Lopman, Wambura Mwita, Sam Biraro, Simon Gregson, J. Ties Boerma, Peter Ghys
Abstract: BackgroundHIV surveillance of generalised epidemics in Africa primarily relies on prevalence at antenatal clinics, but estimates of incidence in the general population would be more useful. Repeated cross-sectional measures of HIV prevalence are now becoming available for general populations in many countries, and we aim to develop and validate methods that use these data to estimate HIV incidence.Methods and FindingsTwo methods were developed that decompose observed changes in prevalence between two serosurveys into the contributions of new infections and mortality. Method 1 uses cohort mortality rates, and method 2 uses information on survival after infection. The performance of these two methods was assessed using simulated data from a mathematical model and actual data from three community-based cohort studies in Africa. Comparison with simulated data indicated that these methods can accurately estimates incidence rates and changes in incidence in a variety of epidemic conditions. Method 1 is simple to implement but relies on locally appropriate mortality data, whilst method 2 can make use of the same survival distribution in a wide range of scenarios. The estimates from both methods are within the 95% confidence intervals of almost all actual measurements of HIV incidence in adults and young people, and the patterns of incidence over age are correctly captured.ConclusionsIt is possible to estimate incidence from cross-sectional prevalence data with sufficient accuracy to monitor the HIV epidemic. Although these methods will theoretically work in any context, we have able to test them only in southern and eastern Africa, where HIV epidemics are mature and generalised. The choice of method will depend on the local availability of HIV mortality data.
Partial Text: Monitoring the continuing spread of the HIV epidemic is essential for determining public health priorities, assessing the impact of interventions, and making estimates of current and future health care needs . Currently, surveillance systems in generalized epidemics primarily rely on HIV prevalence (fraction of population infected) data collected from women attending selected antenatal clinics [2,3]. Interpretation of these data is complicated by natural epidemiological changes that arise from the long and variable incubation of HIV and AIDS-related mortality [4,5], by biases in the sample due to subfertility associated with bacterial sexually transmitted infections and HIV , and by the disproportionate selection of surveillance sites in urban areas . Recently, serological testing has been included in household health surveys, such as the Demographic and Health Surveys (DHS), giving estimates of HIV prevalence in the general adult population based on a standard methodology [8–10]. Measures that relate to the general population are more useful, but there remain several important limitations in using prevalence data to monitor the epidemic. These limitations include the following. (i) Decreases in prevalence do not necessarily indicate a reduction in risk of infection ; (ii) changes in prevalence lag behind real changes in risk, particularly at older ages; (iii) comparisons of prevalence between countries can be confounded by different survival times following infection (e.g., if survival following infection is shorter in Asia than in Europe or Africa [11–13], then similar prevalence levels could mask higher incidence rates in Asia); and (iv) the weak association between prevalent infection and risk makes it difficult to identify “high-risk” groups (e.g., higher average prevalence among women does not necessarily mean they are at overall greater risk than men).
We developed two methods for estimating HIV incidence in the general population using successive rounds of cross-sectional prevalence data, and tested how well these methods perform using model-simulated data and real data from three African cohort studies. Spreadsheets for implementation of both methods to estimate incidence are provided in Text S2. Both methods provided good estimates of incidence in adults and young people and captured the pattern of incidence with respect to age. Since incidence is not routinely measured directly at large scales, these methods could be of substantial use in monitoring and comparing the progress of national epidemics, contributing to the interpretation of observed behavioural or epidemiological trends, and refining estimates of disease burden, treatment needs, and the future course of the epidemic. The serial measurements of cross-sectional prevalence that are required for these methods will soon be available from household surveys with HIV testing, such as DHS.