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