Does alcohol use have a causal effect on HIV incidence and disease progression? A review of the literature and a modeling strategy for quantifying the effect
Bibliographic record
Abstract
In the first part of this review, the nature of the associations between alcohol use and HIV/AIDS is discussed. Alcohol use has been found to be strongly associated with incidence and progression of HIV/AIDS, but the extent to which this association is causal has traditionally remained in question. Experiments where alcohol use has been manipulated as the independent variable have since helped establish a causal effect of alcohol use on the intention to engage in condomless sex. As the intention to engage in condomless sex is a surrogate measure of actual condom use behavior, which itself is linked to HIV incidence and re-infection, the causal chain has been corroborated. Moreover, there are biological pathways between alcohol use and the course of HIV/AIDS, only in part being mediated by adherence to antiretroviral medication. In the second part of the contribution, we provide suggestions on the quantification of the link between alcohol use and HIV incidence, using risk relations derived from experimental data. The biological links between alcohol use and course of HIV/AIDS are difficult to quantify given the current state of knowledge, except for an operationalization for the link via adherence to medication based on meta-analyses. The suggested quantifications are exemplified for South Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".