Translating and disseminating a localised economic model to support implementation of the ‘Ending the HIV Epidemic’ initiative to public health policymakers
Bibliographic record
Abstract
Background: Despite significant progress in HIV treatment and prevention, the US remains far from its goal of 'Ending the HIV Epidemic' by 2030. Economic models using local data can synthesise the evidence to help policymakers allocate HIV resources efficiently, but persistent research-to-practice gaps remain. Little is known about how to facilitate the use of economic modelling data among local public health policymakers in real-world settings. Aims and objectives: To explore the dissemination of results from a locally-calibrated economic model for HIV prevention and treatment and identify the factors influencing potential uptake of the model for public health decision making at the local level. Methods: Four virtual focus groups with 26 local health department policymakers in Baltimore, Miami, Seattle, and New York City were held between July 2020 and May 2021. Qualitative content analysis of transcripts identified key themes around using the localised economic model in policy decisions. Results: Participants were interested in using local data in their decisions to allocate resources for HIV prevention/treatment. Six themes emerged: 1) importance of understanding local policy context; 2) health equity considerations; 3) using evidence to support current priorities; 4) difficulty of changing strategies, even incrementally; 5) bang for the incremental buck (efficiency) vs. previous impact; and 6) community values. Conclusion and relevance: To optimise acceptance and use of results from economic models, researchers should engage with local community members and public health decision makers early to understand budgetary and community priorities. Participants prioritised evidence that supports their existing strategies, considers budgets and funding streams, and improves health equity; however, real-world budget constraints and conflicting interests serve as barriers to implementing model recommendations and reaching national goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".