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Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in Sub-Saharan Africa

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Title: Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in Sub-Saharan Africa
Authors: Eaton, J
Dwyer-Lindgren, L
Gutreuter, S
O'Driscoll, M
Stevens, O
Bajaj, S
Ashton, R
Hill, A
Russell, E
Esra, R
Dolan, N
Anifowoshe, Y
Woodbridge, M
Fellows, I
Glaubius, R
Haeuser, E
Okonek, T
Stover, J
Thomas, M
Wakefield, J
Wolock, T
Berry, J
Sabala, T
Heard, N
Delgado, S
Jahn, A
Kalua, T
Chimpandule, T
Auld, A
Kim, E
Payne, D
Johnson, LF
Fitzjohn, R
Wanyeki, I
Mahy, M
Shiraishi, RW
Item Type: Journal Article
Abstract: Introduction: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. Methods: Small-area regressions for HIV prevalence, ART coverage, and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016 to 2018. Results: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi’s districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV were among ages 35-39 for both women and men, while the most untreated PLHIV were among ages 25-29 for women and 30-34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe City, an estimated 71% (95% CI 61–79%) resided in Lilongwe City, 20% (14–27%) in Lilongwe district outside the metropolis, and 9% (6–12%) in neighbouring Dowa district. Thirty-eight percent (26–50%) of Lilongwe Rural residents and 39% (27–50%) of Dowa residents received treatment at facilities in Lilongwe City. Conclusions: The Naomi model synthesises multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.
Issue Date: Sep-2021
Date of Acceptance: 19-Jul-2021
URI: http://hdl.handle.net/10044/1/90881
DOI: 10.1002/jia2.25788
ISSN: 1758-2652
Publisher: International AIDS Society
Start Page: 1
End Page: 13
Journal / Book Title: Journal of the International AIDS Society
Volume: 24
Issue: S5
Copyright Statement: © 2021 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: National Institutes of Health
National Institutes of Health
Bill & Melinda Gates Foundation
Medical Research Council (MRC)
Funder's Grant Number: 1R03AI125001-01A1
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
Bayesian statistics
HIV estimates
joint modelling
routine data
small-area estimation
Bayesian statistics
HIV estimates
joint modelling
routine data
small-area estimation
1103 Clinical Sciences
1117 Public Health and Health Services
1199 Other Medical and Health Sciences
Publication Status: Published
Online Publication Date: 2021-09-21
Appears in Collections:Faculty of Medicine
School of Public Health
Faculty of Natural Sciences

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