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S7.3 Impact of the Avahan intervention on HIV/STI transmission amongst high and low-risk groups: an interim modelling assessment

2011· article· en· W2054388644 on OpenAlexaff
Peter Vickerman, Michael Pickles, Catherine M Lowndes, B M Ramesh, Reynold Washington, Stephen Moses, Kathleen Deering, Sushena Reza‐Paul, Anna Vassall, Janet Bradley, James Blanchard, Michel Alary, Marie‐Claude Boily

Bibliographic record

VenueSexually Transmitted Infections · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsCentre hospitalier universitaire de QuébecUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsMedicinePopulationCondomDemographyTransmission (telecommunications)Men who have sex with menSyphilisEnvironmental healthHuman immunodeficiency virus (HIV)ImmunologyTelecommunications

Abstract

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Objective To estimate the potential HIV-impact of Avahan, the India AIDS Initiative, among targeted high-risk groups (including female sex workers (FSWs), their clients and men who have sex with men (MSM)) and the general population in different districts of Karnataka, Andhra Pradesh, Tamil Nadu and Maharashtra. Design Impact evaluation involving mathematical modelling using detailed serial cross-sectional surveys on sexual behaviour and STI/HIV prevalence (IBBA) among targeted high-risk groups and the general population. Methods A bespoke detailed deterministic model, parameterised with district specific IBBA data, was used to simulate HIV/HSV-2/syphilis transmission in high-risk groups and the general population in different districts. Latin hypercube sampling within a Bayesian framework was used to identify multiple parameter sets that reproduced multiple rounds of HIV prevalence data among FSWs, MSM and clients in all districts and the general population for some districts. The framework was used to test which of two hypotheses (H1 and H2) for time trends in consistent condom use (CCU) among FSWs derived from independent data sources, was more consistent with observed HIV trends, and if these trends could have occurred without post-Avahan increases in CCU (two null hypotheses were assumed—one being more (H0b) and less conservative(H0a)). The most likely CCU hypothesis was used to predict the intervention impact on HIV prevalence/incidence and HIV infections prevented. Results Using the most likely CCU hypothesis for each district (H1), results so far suggest that the increase in condom use post-Avahan may have resulted in between 21 and 45% of new HIV infections being averted among FSWs in Mysore, Belgaum and Bellary respectively from 2004 to 2007. Similar results were obtained for clients but the absolute number averted was 2–8 fold more. Model projections (Abstract S7.3 figure 1) suggest that this has resulted the large decrease in HIV prevalence observed in these districts, and that this would not have occurred in the absence of Avahan. The syphilis treatment component alone prevented <9 and 13% of new HIV infections over 1 and 10 years. Impact projections for the general population and additional districts will be presented. Abstract S7.3 Figure 1 Predicted FSW HIV prevalence over time for the most likely hypothesis (H1) and the two null hypotheses H0a and H0b used to simulate control groups (constant or slowly increasing condom use since start of Avahan) in A) Mysore, B) Belgaum, and C) Bellary districts. Shown on the graphs are the mean (dark lines black, blue and red) and the 75% credibility intervals (shaded area) for each hypothesis. The paler grey area represents the 95% credibility intervals. Also shown is the available IBBA survey prevalence data (mean and 95% CI). Conclusions These Bayesian modelling results, combined with observed HIV prevalence trends and evidence of successful implementation and scale-up of Avahan, provides plausible evidence that Avahan has reduced HIV transmission to a large extent among high-risk groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.186
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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 teacher head, 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
Published2011
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