Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in Sub-Saharan Africa
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Author(s)
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.
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.
Date Issued
2021-09
Date Acceptance
2021-07-19
Citation
Journal of the International AIDS Society, 2021, 24 (S5), pp.1-13
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.
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.
License URL
Sponsor
National Institutes of Health
National Institutes of Health
UNAIDS
Bill & Melinda Gates Foundation
Medical Research Council (MRC)
UNAIDS
Identifier
https://onlinelibrary.wiley.com/doi/10.1002/jia2.25788
Grant Number
1R03AI125001-01A1
5776-ICS-DHHS-6664
2017/778519
INV-006733
MR/R015600/1
2019/974072
Subjects
Science & Technology
Life Sciences & Biomedicine
Immunology
Infectious Diseases
Bayesian statistics
HIV estimates
joint modelling
routine data
small-area estimation
ANTIRETROVIRAL THERAPY
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
Date Publish Online
2021-09-21