Imperial College London

Jeff Eaton

Faculty of MedicineSchool of Public Health

Senior Lecturer in HIV Epidemiology
 
 
 
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Contact

 

jeffrey.eaton

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Maheu-Giroux:2019:10.1097/QAD.0000000000002386,
author = {Maheu-Giroux, M and Marsh, K and Doyle, C and Godin, A and Delaunay, CL and Johnson, LF and Jahn, A and Abo, K and Mbofana, F and Boily, M-C and Buckeridge, DL and Hankins, C and Eaton, JW},
doi = {10.1097/QAD.0000000000002386},
journal = {AIDS},
pages = {S255--S269},
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},
url = {http://dx.doi.org/10.1097/QAD.0000000000002386},
volume = {33},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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, howe
AU - Maheu-Giroux,M
AU - Marsh,K
AU - Doyle,C
AU - Godin,A
AU - Delaunay,CL
AU - Johnson,LF
AU - Jahn,A
AU - Abo,K
AU - Mbofana,F
AU - Boily,M-C
AU - Buckeridge,DL
AU - Hankins,C
AU - Eaton,JW
DO - 10.1097/QAD.0000000000002386
EP - 269
PY - 2019///
SN - 0269-9370
SP - 255
TI - National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the "first 90" from program and survey data
T2 - AIDS
UR - http://dx.doi.org/10.1097/QAD.0000000000002386
UR - https://www.ncbi.nlm.nih.gov/pubmed/31764066
UR - https://insights.ovid.com/crossref?an=00002030-900000000-96771
UR - http://hdl.handle.net/10044/1/75346
VL - 33
ER -