Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cost-effectiveness analysis (CEA) can help identify the trade-offs decision makers face when confronted with alternative courses of action for the implementation of public health strategies. Application of CEA alongside implementation scientific studies remains limited. We aimed to identify areas for future development in order to enhance the uptake and impact of model-based CEA in implementation scientific research. RECENT FINDINGS: Important questions remain about how to broadly implement evidence-based public health interventions in routine practice. Establishing population-level implementation strategy components and distinct implementation phases, including planning for implementation, the time required to scale-up programs, and sustainment efforts required to maintain them, can help determine the data needed to quantify each of these elements. Model-based CEA can use these data to determine the added value associated with each of these elements across systems, settings, population subgroups, and levels of implementation to provide tailored guidance for evidence-based public health action. There is a need to integrate implementation science explicitly into CEA to adequately capture diverse real-world delivery contexts and make detailed, informed recommendations on the aspects of the implementation process that provide good value. We describe examples of how model-based CEA can integrate implementation scientific concepts and evidence to help tailor evaluations to local context. We also propose six distinct domains for methodological advancement in order to enhance the uptake and impact of model-based cost-effectiveness analysis in implementation scientific research.
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 | 0.271 | 0.501 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.015 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".