Development and Validation of a Sociodemographic and Behavioral Characteristics-Based Risk-Score Algorithm for Targeting HIV Testing Among Adults in Kenya

To inform targeted HIV testing, we developed and externally validated a risk-score algorithm that incorporated behavioral characteristics. Outpatient data from five health facilities in western Kenya, comprising 19,458 adults ≥ 15 years tested for HIV from September 2017 to May 2018, were included i...

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Bibliographic Details
Published in:AIDS and behavior 2021-02, Vol.25 (2), p.297-310
Main Authors: Muttai, Hellen, Guyah, Bernard, Musingila, Paul, Achia, Thomas, Miruka, Fredrick, Wanjohi, Stella, Dande, Caroline, Musee, Polycarp, Lugalia, Fillet, Onyango, Dickens, Kinywa, Eunice, Okomo, Gordon, Moth, Iscah, Omondi, Samuel, Ayieko, Caren, Nganga, Lucy, Joseph, Rachael H., Zielinski-Gutierrez, Emily
Format: Article
Language:English
Subjects:
Adult
Adults
Algorithms
Confidence intervals
Demography
Discrimination
Health care facilities
Health Psychology
HIV
HIV Infections - diagnosis
HIV Infections - epidemiology
HIV Infections - prevention & control
HIV Testing
Human immunodeficiency virus
Humans
Infectious Diseases
Kenya - epidemiology
Medical tests
Medicine
Medicine & Public Health
Original Paper
Public Health
Risk
Risk Factors
Risk taking
Sociodemographics
Socioeconomic Factors
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Summary:To inform targeted HIV testing, we developed and externally validated a risk-score algorithm that incorporated behavioral characteristics. Outpatient data from five health facilities in western Kenya, comprising 19,458 adults ≥ 15 years tested for HIV from September 2017 to May 2018, were included in univariable and multivariable analyses used for algorithm development. Data for 11,330 adults attending one high-volume facility were used for validation. Using the final algorithm, patients were grouped into four risk-score categories: ≤ 9, 10–15, 16–29 and ≥ 30, with increasing HIV prevalence of 0.6% [95% confidence interval (CI) 0.46–0.75], 1.35% (95% CI 0.85–1.84), 2.65% (95% CI 1.8–3.51), and 15.15% (95% CI 9.03–21.27), respectively. The algorithm’s discrimination performance was modest, with an area under the receiver-operating-curve of 0.69 (95% CI 0.53–0.84). In settings where universal testing is not feasible, a risk-score algorithm can identify sub-populations with higher HIV-risk to be prioritized for HIV testing.
ISSN:1090-7165
1573-3254
DOI:10.1007/s10461-020-02962-7