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Record W4211013501 · doi:10.1101/2022.02.08.22270670

Using routine programmatic data to estimate the population-level impacts of HIV self-testing: The example of the ATLAS program in Cote d’Ivoire

2022· preprint· en· W4211013501 on OpenAlexaff
Arlette Simo Fotso, Cheryl Johnson, Anthony Vautier, Konan Blaise Kouamé, Papa Moussa Diop, Romain Silhol, Mathieu Maheu‐Giroux, Marie‐Claude Boily, Nicolas Rouveau, Clémence Doumenc-Aïdara, Rachel Baggaley, E. Ehui, Joseph Larmarange

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsPopulationHuman immunodeficiency virus (HIV)MedicineCote d ivoireAtlas (anatomy)Linear regressionDemographyFamily medicineStatisticsEnvironmental healthMathematicsHumanities

Abstract

fetched live from OpenAlex

Abstract Background HIV self-testing (HIVST) is recommended by the World Health Organization as an additional HIV testing approach. Since 2019, it has been implemented in Côte d’Ivoire through the ATLAS project, including primary and secondary distribution channels. While the discreet and flexible nature of HIVST makes it appealing for users, it also makes the monitoring and estimation of the population-level programmatic impact of HIVST programs challenging. We used routinely collected data to estimate the effects of ATLAS’ HIVST distribution on access to testing, conventional testing (self-testing excluded), diagnoses, and antiretroviral treatment (ART) initiations in Côte d’Ivoire. Methods We used the ATLAS project’s programmatic data between the third quarter (Q) of 2019 (Q3 2019) and Q1 2021, in addition to routine HIV testing services program data obtained from the President’s Emergency Plan for AIDS Relief dashboard. We performed ecological time series regression using linear mixed models. Findings The results are presented for 1000 HIVST kits distributed through ATLAS. They show a negative but nonsignificant effect of the number of ATLAS HIVST on conventional testing uptake (−190 conventional tests [95% CI: −427 to 37, p=0·10]). We estimated that for 1000 additional HIVST distributed through ATLAS, +590 [95% CI: 357 to 821, p<0·001] additional individuals have accessed HIV testing, assuming an 80% HIVST utilization rate (UR) and +390 [95% CI: 161 to 625, p<0·001] assuming a 60% UR. The statistical relationship between the number of HIVST and HIV diagnoses was significant and positive (+8 diagnosis [95% CI: 0 to 15, p=0·044]). No effect was observed on ART initiation (−2 ART initiations [95% CI: −8 to 5, p=0·66]). Interpretations Social network-based HIVST distribution had a positive impact on access to HIV testing and diagnoses in Cote d’Ivoire. This approach offers a promising way for countries to assess the impact of HIVST programs. Funding Unitaid 2018-23-ATLAS Research in context Evidence before this study We searched PubMed between November 9 and 12, 2021, for studies assessing the impact of HIVST on HIV testing, ‘conventional’ testing, HIV diagnoses and ART initiation. We searched published data using the terms “HIV self-testing” and “HIV testing”; “HIV self-testing” and “traditional HIV testing” or “conventional testing”; “HIV self-testing” and “diagnosis” or “positive results”; and “HIV self-testing” and “ART initiation” or “Antiretroviral treatment”. Articles with abstracts were reviewed. No time or language restriction was applied. Most studies were individual-based randomized controlled trials involving data collection and some form of HIVST tracking; no studies were conducted at the population level, none were conducted in western Africa and most focused on subgroups of the population or key populations. While most studies found a positive effect of HIVST on HIV testing, evidence was mixed regarding the effect on conventional testing, diagnoses, and ART initiation. Added value of this study HIVST can empower individuals by allowing them to choose when, where and whether to test and with whom to share their results and can reach hidden populations who are not accessing existing services. Inherent to HIVST is that there is no automatic tracking of test results and linkages at the individual level. Without systematic and direct feedback to program implementers regarding the use and results of HIVST, it is difficult to estimate the impact of HIVST distribution at the population level. Such estimates are crucial for national AIDS programs. This paper proposed a way to overcome this challenge and used routinely collected programmatic data to indirectly estimate and assess the impacts of HIVST distribution in Côte d’Ivoire. Implications of all the available evidence Our results showed that HIVST increased the overall HIV testing uptake and diagnoses in Côte d’Ivoire without significantly reducing conventional HIV testing uptake. We demonstrated that routinely collected programmatic data could be used to estimate the effects of HIVST kit distribution outside a trial environment. The methodology used in this paper could be replicated and implemented in different settings and enable more countries to routinely evaluate HIVST programming at the population level.

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)0.011
metaresearch head score (Gemma)0.026
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.585

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.235
GPT teacher head0.446
Teacher spread0.211 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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