Dataset Information

Methods and Assumptions for Estimating Key HIV Indicators in the UNAIDS Annual Estimates Process.

ABSTRACT:

Background

Each year UNAIDS supports national teams to estimate key HIV indicators using their latest data. These estimates are produced using a collection of models and software tools. This paper describes the demographic and HIV projection models used in this process.

Methods

The demographic model (DemProj) projects the population by sex and single age for each year of the estimate. This information is fed into the HIV model (AIDS Impact Model) to estimate key HIV indicators. The model uses program, survey and surveillance data along with incidence trends produced through 1 of several separate models, to estimate new HIV infections, HIV-related deaths, and the population living with HIV by sex, age, CD4 category, and treatment status.

Results

These models allow the annual production of estimates of key HIV indicators including uncertainty intervals. This information is used to track progress toward national and global goals and to develop national strategic plans, Global Fund applications and PEPFAR country operational plans.

Conclusions

Under the guidance of the UNAIDS Reference Group on Estimates, Modeling and Projections, these models are updated on a regular basis in response to evolving programmatic needs, new data, and analyses. This process of continuous review and improvement has led to mature models that make the best use of available data to provide estimates of indicators important to monitoring progress and developing future plans.

SUBMITTER: Stover J

PROVIDER: S-EPMC10769177 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Methods and Assumptions for Estimating Key HIV Indicators in the UNAIDS Annual Estimates Process.

Stover John J Glaubius Robert R

Journal of acquired immune deficiency syndromes (1999) 20240104 1S

<h4>Background</h4>Each year UNAIDS supports national teams to estimate key HIV indicators using their latest data. These estimates are produced using a collection of models and software tools. This paper describes the demographic and HIV projection models used in this process.<h4>Methods</h4>The demographic model (DemProj) projects the population by sex and single age for each year of the estimate. This information is fed into the HIV model (AIDS Impact Model) to estimate key HIV indicators. Th ...[more]

PMID: 38180735

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Project description:BackgroundMore than 300 cities including the city of Amsterdam in the Netherlands have joined the UNAIDS Fast-Track Cities initiative, committing to accelerate their HIV response and end the AIDS epidemic in cities by 2030. To support this commitment, we aimed to estimate the number and proportion of Amsterdam HIV infections that originated within the city, from Amsterdam residents. We also aimed to estimate the proportion of recent HIV infections during the 5-year period 2014-2018 in Amsterdam that remained undiagnosed.MethodsWe located diagnosed HIV infections in Amsterdam using postcode data (PC4) at time of registration in the ATHENA observational HIV cohort, and used HIV sequence data to reconstruct phylogeographically distinct, partially observed Amsterdam transmission chains. Individual-level infection times were estimated from biomarker data, and used to date the phylogenetically observed transmission chains as well as to estimate undiagnosed proportions among recent infections. A Bayesian Negative Binomial branching process model was used to estimate the number, size, and growth of the unobserved Amsterdam transmission chains from the partially observed phylogenetic data.ResultsBetween 1 January 2014 and 1 May 2019, there were 846 HIV diagnoses in Amsterdam residents, of whom 516 (61%) were estimated to have been infected in 2014-2018. The rate of new Amsterdam diagnoses since 2014 (104 per 100,000) remained higher than the national rates excluding Amsterdam (24 per 100,000), and in this sense Amsterdam remained a HIV hotspot in the Netherlands. An estimated 14% [12-16%] of infections in Amsterdan MSM in 2014-2018 remained undiagnosed by 1 May 2019, and 41% [35-48%] in Amsterdam heterosexuals, with variation by region of birth. An estimated 67% [60-74%] of Amsterdam MSM infections in 2014-2018 had an Amsterdam resident as source, and 56% [41-70%] in Amsterdam heterosexuals, with heterogeneity by region of birth. Of the locally acquired infections, an estimated 43% [37-49%] were in foreign-born MSM, 41% [35-47%] in Dutch-born MSM, 10% [6-18%] in foreign-born heterosexuals, and 5% [2-9%] in Dutch-born heterosexuals. We estimate the majority of Amsterdam MSM infections in 2014-2018 originated in transmission chains that pre-existed by 2014.ConclusionsThis combined phylogenetic, epidemiologic, and modelling analysis in the UNAIDS Fast-Track City Amsterdam indicates that there remains considerable potential to prevent HIV infections among Amsterdam residents through city-level interventions. The burden of locally acquired infection remains concentrated in MSM, and both Dutch-born and foreign-born MSM would likely benefit most from intensified city-level interventions.FundingThis study received funding as part of the H-TEAM initiative from Aidsfonds (project number P29701). The H-TEAM initiative is being supported by Aidsfonds (grant number: 2013169, P29701, P60803), Stichting Amsterdam Dinner Foundation, Bristol-Myers Squibb International Corp. (study number: AI424-541), Gilead Sciences Europe Ltd (grant number: PA-HIV-PREP-16-0024), Gilead Sciences (protocol numbers: CO-NL-276-4222, CO-US-276-1712, CO-NL-985-6195), and M.A.C AIDS Fund.

Project description:ABSTRACT Background: HIV/AIDS programmes and organisations around the world use routinely updated estimates of the UNAIDS 90-90-90 targets to track progress and prioritise further programme implementation. Any bias in these estimates has the potential to mislead organisations on where gaps exist in HIV testing and treatment programmes. Objective: To measure the extent of undisclosed HIV testing history and its impact on estimating the proportion of people living with HIV (PLHIV) who know their HIV status (the ‘first 90’ of the UNAIDS 90-90-90 targets). Methods: We conducted a retrospective cohort study using population-based HIV serological surveillance conducted between 2010 and 2016 and linked, directly observed HIV testing records in Kisesa, Tanzania. Generalised estimating equations logistic regression models were used to detect associations with non-disclosure of HIV testing history adjusting for demographic, behavioural, and clinical characteristics. We compared estimates of the ‘first 90’ using self-reported survey data only and augmented estimates using information from linked records to quantify the absolute and relative impact of undisclosed HIV testing history. Results: Numbers of participants in each of the survey rounds ranged from 7171 to 7981 with an average HIV prevalence of 6.9%. Up to 33% of those who tested HIV-positive and 34% of those who tested HIV-negative did not disclose their HIV testing history. The proportion of PLHIV who reported knowing their status increased from 34% in 2010 to 65% in 2016. Augmented estimates including information from directly observed testing history resulted in an absolute impact of 6.7 percentage points and relative impact of 12.4%. Conclusions: In this population, self-reported testing history in population-based HIV serological surveys under-estimated the percentage of HIV positives that are diagnosed by a relative factor of 12%. Research should be employed in other surveillance systems that benefit from linked data to investigate how bias may vary across settings.

Project description:BackgroundIn line with the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 target, Norway aims for at least 90% of people living with HIV (PLHIV) to know their HIV-status. We produced current estimates of the number of PLHIV and undiagnosed population in Norway, overall and for six key subpopulations: Norwegian-born men who have sex with men (MSM), migrant MSM, Norwegian-born heterosexuals, migrant Sub-Saharan Africa (SSA)-born heterosexuals, migrant non-SSA-born heterosexuals and people who inject drugs.MethodsWe used the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool on Norwegian HIV surveillance data through 2018 to estimate incidence, time from infection to diagnosis, PLHIV, and the number and proportion undiagnosed. As surveillance data on CD4 count at diagnosis were not collected in Norway, we ran two models; using default model CD4 assumptions, or a proxy for CD4 distribution based on Danish national surveillance data. We also generated alternative overall PLHIV estimates using the Spectrum AIDS Impact Model, to compare with those obtained from the ECDC tool.ResultsEstimates of the overall number of PLHIV in 2018 using different modelling approaches aligned at approximately 5000. In both ECDC models, the overall number undiagnosed decreased continuously from 2008. The proportion undiagnosed in 2018 was lower using default model CD4 assumptions (7.1% [95%CI: 5.3-8.9%]), than the Danish CD4 proxy (10.2% [8.3-12.1%]). This difference was driven by results for heterosexual migrants. Estimates for Norwegian-born MSM, migrant MSM and Norwegian-born heterosexuals were similar in both models. In these three subpopulations, incidence in 2018 was < 30 new infections, and the number undiagnosed had decreased in recent years. Norwegian-born MSM had the lowest estimated number of undiagnosed infections (45 [30-75], using default CD4 assumptions) and undiagnosed fraction (3.6% [2.4-5.7%], using default CD4 assumptions) in 2018.ConclusionsResults allow cautious confidence in concluding that Norway has achieved the first UNAIDS 90-90-90 target, and clearly highlight the success of prevention strategies among MSM. Estimates for subpopulations strongly influenced by migration remain less clear, and future modelling should appropriately account for all-cause mortality and out-migration, and adjust for time of in-migration.

Dataset Information

Methods and Assumptions for Estimating Key HIV Indicators in the UNAIDS Annual Estimates Process.

Background

Methods

Results

Conclusions

Publications

Methods and Assumptions for Estimating Key HIV Indicators in the UNAIDS Annual Estimates Process.

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