Targeting an Alcohol Intervention Cost‐Effectively to Persons Living with <scp>HIV</scp>/<scp>AIDS</scp> in East Africa
Bibliographic record
Abstract
BACKGROUND: In the current report, we ask if targeting a cognitive behavioral therapy (CBT)-based intervention aimed at reducing hazardous alcohol consumption to HIV-infected persons in East Africa would have a favorable value at costs that are feasible for scale-up. METHODS: Using a computer simulation to inform HIV prevention decisions in East Africa, we compared 4 different strategies for targeting a CBT intervention-(i) all HIV-infected persons attending clinic; (ii) only those patients in the pre-antiretroviral therapy (ART) stages of care; (iii) only those patients receiving ART; and (iv) only those patients with detectable viral loads (VLs) regardless of disease stage. We define targeting as screening for hazardous alcohol consumption (e.g., using the Alcohol Use Disorders Identification Test and offering the CBT intervention to those who screen positive). We compared these targeting strategies to a null strategy (no intervention) or a hypothetical scenario where an alcohol intervention was delivered to all adults regardless of HIV status. RESULTS: An intervention targeted to HIV-infected patients could prevent 18,000 new infections, add 46,000 quality-adjusted life years (QALYs), and yield an incremental cost-effectiveness ratio of $600/QALY compared to the null scenario. Narrowing the prioritized population to only HIV-infected patients in pre-ART phases of care results in 15,000 infections averted, the addition of 21,000 QALYs and would be cost-saving, while prioritizing based on an unsuppressed HIV-1 VL test results in 8,300 new infections averted, adds 6,000 additional QALYs, and would be cost-saving as well. CONCLUSIONS: Our results suggest that targeting a cognitive-based treatment aimed at reducing hazardous alcohol consumption to subgroups of HIV-infected patients provides favorable value in comparison with other beneficial strategies for HIV prevention and control in this region. It may even be cost-saving under certain circumstances.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".