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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

View ORCID ProfileM Maheu-Giroux, K Marsh, C Doyle, A Godin, C Lanièce Delaunay, LF Johnson, A Jahn, K Abo, F Mbofana, MC Boily, DL Buckeridge, C Hankins, JW Eaton
doi: https://doi.org/10.1101/532010
M Maheu-Giroux
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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  • ORCID record for M Maheu-Giroux
  • For correspondence: mathieu.maheu-giroux@mcgill.ca
K Marsh
2The Joint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland.
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C Doyle
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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A Godin
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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C Lanièce Delaunay
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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LF Johnson
3Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
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A Jahn
4Ministry of Health, Lilongwe, Malawi and I-TECH, Department of Global Health, University of Washington, Seattle, USA.
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K Abo
5Programme national de lutte contre le Sida, Abidjan, Côte d’Ivoire.
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F Mbofana
6Ministry of Health, Maputo, Mozambique.
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MC Boily
7Department of Infectious Disease Epidemiology, Imperial College London, St Mary’s Hospital, London, United Kingdom.
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DL Buckeridge
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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C Hankins
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.
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JW Eaton
7Department of Infectious Disease Epidemiology, Imperial College London, St Mary’s Hospital, London, United Kingdom.
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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 are 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 01, 2019.
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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
M Maheu-Giroux, K Marsh, C Doyle, A Godin, C Lanièce Delaunay, LF Johnson, A Jahn, K Abo, F Mbofana, MC Boily, DL Buckeridge, C Hankins, JW Eaton
bioRxiv 532010; doi: https://doi.org/10.1101/532010
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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
M Maheu-Giroux, K Marsh, C Doyle, A Godin, C Lanièce Delaunay, LF Johnson, A Jahn, K Abo, F Mbofana, MC Boily, DL Buckeridge, C Hankins, JW Eaton
bioRxiv 532010; doi: https://doi.org/10.1101/532010

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