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National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the "first 90" from program and survey data

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Title: National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the "first 90" from program and survey data
Authors: Maheu-Giroux, M
Marsh, K
Doyle, C
Godin, A
Delaunay, CL
Johnson, LF
Jahn, A
Abo, K
Mbofana, F
Boily, M-C
Buckeridge, DL
Hankins, C
Eaton, JW
Item Type: Journal Article
Abstract: OBJECTIVE: HIV testing services (HTS) are a crucial component of national HIV responses. Learning one's HIV diagnosis is the entry point to accessing life-saving antiretroviral treatment and care. Recognizing the critical role of HTS, the Joint United Nations Programme on HIV/AIDS (UNAIDS) launched the 90-90-90 targets stipulating that by 2020, 90% of people living with HIV know their status, 90% of those who know their status receive antiretroviral therapy, and 90% of those on treatment have a suppressed viral load. Countries will need to regularly monitor progress on these three indicators. Estimating the proportion of people living with HIV who know their status (i.e., the "first 90"), however, is difficult. METHODS: We developed a mathematical model (henceforth referred to as "F90") that formally synthesizes population-based survey and HTS program data to estimate HIV status awareness over time. The proposed model uses country-specific HIV epidemic parameters from the standard UNAIDS Spectrum model to produce outputs that are consistent with other national HIV estimates. The F90 model provides estimates of HIV testing history, diagnosis rates, and knowledge of HIV status by age and sex. We validate the F90 model using both in-sample comparisons and out-of-sample predictions using data from three countries: Côte d'Ivoire, Malawi, and Mozambique. RESULTS: In-sample comparisons suggest that the F90 model can accurately reproduce longitudinal sex-specific trends in HIV testing. Out-of-sample predictions of the fraction of PLHIV ever tested over a 4-to-6-year time horizon are also in good agreement with empirical survey estimates. Importantly, out-of-sample predictions of HIV knowledge are consistent (i.e., within 4% points) with those of the fully calibrated model in the three countries when HTS program data are included. The F90 model's predictions of knowledge of status are higher than available self-reported HIV awareness estimates, however, suggesting -in line with previous studies- that these self-reports could be affected by non-disclosure of HIV status awareness. CONCLUSION: Knowledge of HIV status is a key indicator to monitor progress, identify bottlenecks, and target HIV responses. The F90 model can help countries track progress towards their "first 90" by leveraging surveys of HIV testing behaviors and annual HTS program data.
Issue Date: 15-Dec-2019
Date of Acceptance: 1-Nov-2019
URI: http://hdl.handle.net/10044/1/75346
DOI: 10.1097/QAD.0000000000002386
ISSN: 0269-9370
Publisher: Lippincott, Williams & Wilkins
Start Page: S255
End Page: S269
Journal / Book Title: AIDS
Volume: 33
Copyright Statement: This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Sponsor/Funder: Medical Research Council (MRC)
Bill & Melinda Gates Foundation
Medical Research Council (MRC)
Funder's Grant Number: MR/K010174/1B
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
Bayesian statistics
knowledge of HIV status
mathematical modeling
population health
treatment and care cascade
06 Biological Sciences
11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
Publication Status: Published
Conference Place: England
Open Access location: https://pdfs.journals.lww.com/aidsonline/9000/00000/National_HIV_testing_and_diagnosis_coverage_in.96771.pdf
Online Publication Date: 2019-11-19
Appears in Collections:School of Public Health