Estimating Incidence from Prevalence in Generalised HIV Epidemics: Methods and Validation

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.

Results from a Method for Estimating HIV Incidence Based on the First Cd4 Count among Treatment-Naïve Cases: Brazil, 2004-2013

“…HIV incidence can also be calculated based on the change in HIV prevalence estimated at two points in time [18]. The assumption underlying this methodology is that the number of new infections is the number of prevalent cases at the second point in time minus the number of cases who survived between the two time points.…”

Hepatitis C Virus Acquisition among Injecting Drug Users: A Cohort Analysis of a National Repeated Cross-sectional Survey of Needle and Syringe Program Attendees in Australia, 1995–2004

“…To date, there has been limited published data internationally on trends in HCV infection and related risk factors in IDU populations over significant time periods, with studies examining trends in prevalent HCV infection being limited to IDUs recruited via drug treatment facilities 11 or comparison of different data sources over time. 12 While studies published in HIV [13][14][15] and cancer epidemiology 16 have used various statistical methods to estimate trends in incident infection from prevalence data when cohort studies were not feasible, we are not aware of similar methods being applied to routinely collected HCV prevalence data.…”

An old enzyme for current needs: adenosine deaminase and a dendritic cell vaccine for HIV

“…Another mathematical model developed by Hallett et al uses successive rounds of national cross-sectional HIV prevalence data to estimate HIV incidence by age in the general population (Hallett et al, 2008). Still other dynamic models have been developed to generate HIV incidence estimates (Williams et al, 2001;Gregson et al, 1996).…”

“…This incidence estimate is then used by Spectrum to estimate the numbers of people living with HIV, new infections and deaths. Incidence by age group is informed by age patterns of incidence derived from populationbased surveys (Hallett et al, 2008). In addition, EPP now allows for the prevalence trend to be informed by multiple population-based surveys and a changing urban-rural ratio over time.…”