Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Jan 2;24(1):147-52.
doi: 10.1097/QAD.0b013e32833062dc.

Estimates of HIV incidence from household-based prevalence surveys

Affiliations

Estimates of HIV incidence from household-based prevalence surveys

Timothy B Hallett et al. AIDS. .

Abstract

Objective: To estimate HIV incidence in the general population in countries where there have been two recent household-based HIV prevalence surveys (the Dominican Republic, Mali, Niger, Tanzania, and Zambia).

Methods: We applied a validated method to estimate HIV incidence using HIV prevalence measurement in two surveys.

Results: We estimate incidence among men and women aged 15-44 years to be: 0.5/1000 person-years at risk in the Dominican Republic 2002-2007, 1.1/1000 in Mali 2001-2006, 0.6/1000 in Niger 2002-2006, 3.4/1000 in Tanzania 2004-2008, and 11.2/1000 in Zambia 2002-2007. The groups most at risk in these epidemics are typically 15-24-year-old women and 25-39-year-old men. Incidence appears to have declined in recent years in all countries, but only significantly among men in the Dominican Republic and Tanzania and women in Zambia.

Conclusion: Using prevalence measurements to estimate incidence reveals the current level and age distribution of new infections and the trajectory of the HIV epidemic. This information is more useful than prevalence data alone and should be used to help determine priorities for interventions.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1. Estimates of HIV incidence
Estimates are for men (triangles) and women (circles) per 1000 person-years at risk. Error bars shows 95% bootstrap intervals; bars approaching the zero line indicate that the observed change in prevalence can be completely ascribed due mortality. Note the differences in the scales of the vertical axes.

References

    1. Zaba B, Whitworth J, Marston M, Nakiyingi J, Ruberantwari A, Urassa M, et al. HIV and mortality of mothers and children: evidence from cohort studies in Uganda, Tanzania, and Malawi. Epidemiology. 2005;16:275–280. - PubMed
    1. Gregson S, Todd J, Zaba B. Sexual behaviour change in countries with generalised HIV epidemics? Evidence from population-based cohort studies in sub-Saharan Africa. Sex Transm Infect. 2009;85(Suppl 1):i1–i2. - PMC - PubMed
    1. Parekh BS, Kennedy MS, Dobbs T, Pau CP, Byers R, Green T, et al. Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence. AIDS Res Hum Retroviruses. 2002;18:295–307. - PubMed
    1. UNAIDS Reference Group on Estimates Modelling and Projections Statement on the use of the BED-assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring. Wkly Epidemiol Rec. 2006;81:40–41. - PubMed
    1. Macro International Inc. HIVPrevalence Estimates from the Demographic and Health Surveys. Macro International, Inc.; Calverton, Maryland, USA: 2008. http://www.measuredhs.com/topics/hiv/start.cfm.

Publication types