Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.
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Authors
Dwyer-Lindgren, Laura
Gutreuter, Steve
O'Driscoll, Megan
Bajaj, Sumali
Ashton, Rob
Hill, Alexandra
Russell, Emma
Esra, Rachel
Dolan, Nicolas
Anifowoshe, Yusuf O
Woodbridge, Mark
Fellows, Ian
Haeuser, Emily
Okonek, Taylor
Thomas, Matthew L
Wakefield, Jon
Berry, Jonathan
Sabala, Tomasz
Heard, Nathan
Delgado, Stephen
Jahn, Andreas
Kalua, Thokozani
Chimpandule, Tiwonge
Auld, Andrew
Kim, Evelyn
Payne, Danielle
FitzJohn, Richard G
Wanyeki, Ian
Shiraishi, Ray W
Publication Date
2021-09Journal Title
J Int AIDS Soc
ISSN
1758-2652
Publisher
Wiley
Volume
24 Suppl 5
Pages
e25788
Language
eng
Type
Article
This Version
VoR
Physical Medium
Print
Metadata
Show full item recordCitation
Eaton, J. W., Dwyer-Lindgren, L., Gutreuter, S., O'Driscoll, M., Stevens, O., Bajaj, S., Ashton, R., et al. (2021). Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.. J Int AIDS Soc, 24 Suppl 5 e25788. https://doi.org/10.1002/jia2.25788
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 2016-2018. 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 was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 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% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. CONCLUSIONS: The Naomi model synthesizes 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.
Identifiers
External DOI: https://doi.org/10.1002/jia2.25788
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329591
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