Study finds advances in estimating hiv incidence in epidemics

Study finds advances in estimating hiv incidence in epidemics

An article published in the open-access journal PLoS Medicine reports on the development and testing of two user-friendly methods that use changes in cross-sectional HIV prevalence (the fraction of the population infected with HIV) to estimate HIV incidence (the number of new infections occurring during a specific time period). Timothy Hallett (Imperial College London) and colleagues suggest that the incidence of HIV can be estimated from repeat surveys of prevalence with enough accuracy to monitor the epidemic.

Currently, 33 million people are infected with HIV (the virus that causes AIDS), and AIDS has already killed more than 25 million people. Working to thwart this epidemic, governments and international agencies have been assessing the impact of interventions by keeping track of how the virus spreads. Usually, agencies monitor generalized epidemics (ones that have spread to the whole population) by determining the prevalence of HIV infection among women who attend antenatal clinics. More accurate measures of HIV prevalence are being acquired by testing blood for antibodies related to the AIDS virus (serological testing).

Researchers, though still interested in prevalence, are also concerned with the incidence of the virus in order to gage how the epidemic changes over time and how the virus is transmitted. However, measures of incidence are generally more costly - researchers would have to identify individuals, test their blood and then repeatedly follow up the same individuals. Hallett and colleagues tackle this problem by developing mathematical methods that allow one to use prevalence data to estimate the incidence of HIV in generalized epidemics.

Realizing that changes in HIV incidence and mortality rates contribute to changes in HIV prevalence, the researchers developed models that include methods to disentangle these features. The first method combines information on mortality rates collected in cohort studies of HIV infection, while the second method uses data collected in long-running cohort studies that focuses on survival after HIV infection. Computer-simulated data and real data on HIV prevalence and incidence from cohort studies in Zimbabwe, Uganda, and Tanzania were used to assess the accuracy of the two methods. Both estimation methods resulted in accurate predictions of HIV incidence from the simulated data. When the data from Africa were used, the average difference between actual incidence measurements and estimates was 19% from the first method 14% from the second method.

"Neither method tends to systematically over- or underestimate incidence," write the authors.

These findings indicate that repeat surveys of prevalence can help estimate HIV incidence rates with sufficient accuracy to monitor the epidemic. One potential weakness of the study is that it may be difficult to generalize to other parts of Africa where HIV epidemics are restricted to subsets of the populations. As the availability of prevalence measures from blood tests increases, the new methods presented for estimating HIV incidence from HIV prevalence could be very useful for tracking the progress of national epidemics and for informing HIV control programs.

Estimating incidence from prevalence in generalised HIV epidemics: Methods and validation

Hallett TB, Zaba B, Todd J, Lopman B, Wambura M, et al.

PLoS Medicine . 5(4): e80.


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Jacob Konikoff - Cross-Sectional HIV Incidence Estimation: Approaches and Challenges (Video Medical And Professional 2020).

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