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

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.

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023

“…To facilitate cross-country comparison, key population HIV prevalence and ART coverage data were compared to estimates for HIV prevalence or ART coverage among the total population estimates for a given age, sex, year, and first administrative level (henceforth ‘province’). Province-level estimates of age/sex-specific HIV prevalence and ART coverage were extracted from UNAIDS Naomi subnational estimates for 2020 23,24 and projected back in time 2000-2021 parallel to the Spectrum 27 15-49 national HIV prevalence and ART coverage trajectories.…”

“…Geographic boundaries from the Global Rural/Urban Mapping Project (GRUMP) and UNAIDS Naomi subnational district populations were used to derive total population denominators for subnational locations in which key population surveys were conducted. [23][24][25] Each KPSE was matched to the total population denominator by age, sex, year, and area. String matching was used to assign a GRUMP or Naomi area name to each survey area, thereby matching to a total population denominator.…”

“…The copyright holder for this preprint this version posted July 29, 2022. ; https://doi.org/10.1101/2022.07.27.22278071 doi: medRxiv preprint the total population estimates for a given age, sex, year, and first administrative level (henceforth 'province'). Province-level estimates of age/sex-specific HIV prevalence and ART coverage were extracted from UNAIDS Naomi subnational estimates for 2020 23,24 and projected back in time 2000-2021 parallel to the Spectrum 27…”

HIV prevention for the next decade: Appropriate, person-centred, prioritised, effective, combination prevention

“…Developments in phylogenetics, mathematical modelling, and enhanced epidemiological surveillance are providing insights into the dynamics of transmission and acquisition of HIV infection related to age and gender [ 15 ]; increasing the geographic resolution [ 16 ]; and predicting current and future contributions to new infections of people with acute and early infection, those PLHIV who have not been previously diagnosed, those PLHIV who are not on treatment, those PLHIV whose virus is resistant to treatment, and those PLHIV who are no longer taking effective treatment [ 17 ].…”

Characterizing and mapping the spatial variability of HIV risk among adolescent girls and young women: A cross-county analysis of population-based surveys in Eswatini, Haiti, and Mozambique

“…As a result, we had to incorporate HIV-positive girls in our final size estimations by making the percentage of AGYW at risk out of all AGYW. Combining the percentage of AGYW at risk with subnational estimates of HIV prevalence (e.g., Naomi estimates [ 43 ]) could improve the accuracy of HIV risk estimates among HIV-negative AGYW. We also assumed that the proportion of AGYW at risk and HIV-negative remained constant between the time of survey data collection (2017 for Haiti, 2015 for Mozambique, and 2016–2017 for Eswatini) and when we paired these data with 2020 WorldPop population projections.…”