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Record W4400896333 · doi:10.1002/jia2.26334

Cost‐effectiveness analysis of community‐led HIV self‐testing among key populations in Côte d'Ivoire, Mali, and Senegal

2024· article· en· W4400896333 on OpenAlexafffund
Ingrid Jiayin Lu, Romain Silhol, Marc d’Elbée, Marie‐Claude Boily, Nirali Soni, Odette Ky‐Zerbo, Anthony Vautier, Arlette Simo Fotso, Kéba Badiane, Métogara Mohamed Traoré, Fern Terris‐Prestholt, Joseph Larmarange, Mathieu Maheu‐Giroux

Bibliographic record

VenueJournal of the International AIDS Society · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité LavalCentre hospitalier universitaire de QuébecMcGill University
FundersEuropean and Developing Countries Clinical Trials PartnershipMedical Research CouncilImperial College LondonCanadian Institutes of Health ResearchUniversité de MontpellierFaculty of Medicine and Health, University of SydneyInstitut National de la Santé et de la Recherche MédicaleCanada Research ChairsMcGill UniversityUnitaidEuropean CommissionForeign, Commonwealth and Development OfficeUniversité de BordeauxUniversité Laval
KeywordsMedicineHuman immunodeficiency virus (HIV)Men who have sex with menCost effectivenessCost-effectiveness analysisMarginal costEnvironmental healthDemographyFamily medicineEconomics

Abstract

fetched live from OpenAlex

INTRODUCTION: HIV self-testing (HIVST) is a promising strategy to improve diagnosis coverage among key populations (KP). The ATLAS (Auto Test VIH, Libre d'Accéder à la connaissance de son Statut) programme implemented HIVST in three West African countries, distributing over 380,000 kits up between 2019 and 2021, focussing on community-led distribution by KP to their peers and subsequent secondary distribution to their partners and clients. We aim to evaluate the cost-effectiveness of community-led HIVST in Côte d'Ivoire, Mali and Senegal. METHODS: An HIV transmission dynamics model was adapted and calibrated to country-specific epidemiological data and used to predict the impact of HIVST. We considered the distribution of HIVST among two KP-female sex workers (FSW), and men who have sex with men (MSM)-and their sexual partners and clients. We compared the cost-effectiveness of two scenarios against a counterfactual without HIVST over a 20-year horizon (2019-2039). The ATLAS-only scenario mimicked the 2-year implemented ATLAS programme, whereas the ATLAS-scale-up scenario achieved 95% coverage of HIVST distribution among FSW and MSM by 2025 onwards. The primary outcome is the number of disability-adjusted life-years (DALY) averted. Scenarios were compared using incremental cost-effectiveness ratios (ICERs). Costing was performed using a healthcare provider's perspective. Costs were discounted at 4%, converted to $USD 2022 and estimated using a cost-function to accommodate economies of scale. RESULTS: The ATLAS-only scenario was highly cost-effective over 20 years, even at low willingness-to-pay thresholds. The median ICERs were $126 ($88-$210) per DALY averted in Côte d'Ivoire, $92 ($88-$210) in Mali and 27$ ($88-$210) in Senegal. Scaling-up the ATLAS programme would also be cost-effective, and substantial epidemiological impacts would be achieved. The ICERs for the scale-up scenario were $199 ($122-$338) per DALY averted in Côte d'Ivoire, $224 ($118-$415) in Mali and $61 ($18-$128) in Senegal. CONCLUSIONS: Both the implemented and the potential scale-up of community-led HIVST programmes in West Africa, where KP are important to overall transmission dynamics, have the potential to be highly cost-effective, as compared to a scenario without HIVST. These findings support the scale-up of community-led HIVST to reach populations that otherwise may not access conventional testing services.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.398
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

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