Evaluating Alternative Designs of a Multilevel HIV Intervention in Maharashtra, India: The Impact of Stakeholder Constraints
Bibliographic record
Abstract
Background. Multilevel interventions combine individual component interventions, and their design can be informed by decision analysis. Our objective was to identify the optimal combination of interventions for alcohol-using HIV+ individuals on antiretroviral drug therapy in Maharashtra, India, explicitly considering stakeholder constraints. Methods. Using an HIV simulation, we evaluated the expected net monetary benefit (ENMB), the probability of lying on the efficiency frontier (PEF), and annual program costs of 5,836 unique combinations of 15 single-focused HIV risk-reduction interventions. We evaluated scenarios of 1) no constraints (i.e., maximize expected value), 2) short-term budget constraints (limits on annual programmatic costs of US$200,000 and $400,000), and 3) a constraint stemming from risk aversion (requiring that the strategy has >50% PEF). Results. With no constraints, the combination including long individual alcohol counseling, text-message adherence support, long group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $428,886; PEF ∼27%) maximized ENMB and would be the optimal design. With a cost constraint of $400,000, the combination including long individual alcohol counseling, text-message adherence support, brief group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $374,745; PEF ∼4%) maximized ENMB. With a cost constraint of $200,000, the combination including long individual alcohol counseling, text-message adherence support, and brief group counseling for sex-risk (annual cost = $187,335; PEF ∼54%) maximized ENMB. With the risk aversion constraint, the same configuration ( long individual alcohol counseling, text-message support, and brief group counseling for sex-risk) maximized health benefit. Conclusion. Evaluating the costs, risks, and projected benefits of alternatives supports informed decision making prior to initiating study; however, stakeholder constraints should be explicitly included and discussed when using decision analyses to guide study design.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".