RESEARCH ARTICLE


Risk Factor Detection as a Metric of STARHS Performance for HIV Incidence Surveillance Among Female Sex Workers in Kigali, Rwanda



Sarah L Braunstein*, 1, #, Janneke H van de Wijgert 2, 3, Joseph Vyankandondera 2, 4, Evelyne Kestelyn2, Justin Ntirushwa 2, Denis Nash 1, 5
1 Mailman School of Public Health, Columbia University, New York, USA
2 Projet Ubuzima, Kigali, Rwanda
3 Academic Medical Center of the University of Amsterdam, Department of Internal Medicine, and Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
4 Belgian Development Agency, Kigali, Rwanda
5 CUNY School of Public Health, New York, USA


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© Braunstein et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the New York City Department of Health and Mental Hygiene, New York, USA; Tel: 347-396-7760; Fax: 347-396-7793; E-mail: braunstein.sarah@gmail.com
# Current Affiliation: New York City Department of Health and Mental Hygiene, New York, USA.


Abstract

Background:

The epidemiologic utility of STARHS hinges not only on producing accurate estimates of HIV incidence, but also on identifying risk factors for recent HIV infection.

Methods:

As part of an HIV seroincidence study, 800 Rwandan female sex workers (FSW) were HIV tested, with those testing positive further tested by BED-CEIA (BED) and AxSYM Avidity Index (Ax-AI) assays. A sample of HIV-negative (N=397) FSW were followed prospectively for HIV seroconversion. We compared estimates of risk factors for: 1) prevalent HIV infection; 2) recently acquired HIV infection (RI) based on three different STARHS classifications (BED alone, Ax-AI alone, BED/Ax-AI combined); and 3) prospectively observed seroconversion.

Results:

There was mixed agreement in risk factors between methods. HSV-2 coinfection and recent STI treatment were associated with both prevalent HIV infection and all three measures of recent infection. A number of risk factors were associated only with prevalent infection, including widowhood, history of forced sex, regular alcohol consumption, prior imprisonment, and current breastfeeding. Number of sex partners in the last 3 months was associated with recent infection based on BED/Ax-AI combined, but not other STARHS-based recent infection outcomes or prevalent infection. Risk factor estimates for prospectively observed seroconversion differed in magnitude and direction from those for recent infection via STARHS.

Conclusions:

Differences in risk factor estimates by each method could reflect true differences in risk factors between the prevalent, recently, or newly infected populations, the effect of study interventions (among those followed prospectively), or assay misclassification. Similar investigations in other populations/settings are needed to further establish the epidemiologic utility of STARHS for identifying risk factors, in addition to incidence rate estimation.

Keywords: HIV/AIDS, incidence, cross-sectional surveys, prospective studies, risk factors, Rwanda..