A Checklist for the Conduct, Reporting, and Appraisal of Microcosting Studies in Health Care: Protocol Development
Bibliographic record
Abstract
BACKGROUND: Microcosting is a cost estimation method that requires the collection of detailed data on resources utilized, and the unit costs of those resources in order to identify actual resource use and economic costs. Microcosting findings reflect the true costs to health care systems and to society, and are able to provide transparent and consistent estimates. Many economic evaluations in health and medicine use charges, prices, or payments as a proxy for cost. However, using charges, prices, or payments rather than the true costs of resources can result in inaccurate estimates. There is currently no existing checklist or guideline for the conduct, reporting, or appraisal of microcosting studies in health care interventions. OBJECTIVE: The aim of this study is to create a checklist and guideline for the conduct, reporting, and appraisal of microcosting studies in health care interventions. METHODS: Appropriate potential domains and items will be identified through (1) a systematic review of all published microcosting studies of health and medical interventions, strategies, and programs; (2) review of published checklists and guidelines for economic evaluations of health interventions, and selection of items relevant for microcosting studies; and (3) theoretical analysis of economic concepts relevant for microcosting. Item selection, formulation, and reduction will be conducted by the research team in order to develop an initial pool of items for evaluation by an expert panel comprising individuals with expertise in microcosting and economic evaluation of health interventions. A modified Delphi process will be conducted to achieve consensus on the checklist. A pilot test will be conducted on a selection of the articles selected for the previous systematic review of published microcosting studies. RESULTS: The project is currently in progress. CONCLUSIONS: Standardization of the methods used to conduct, report or appraise microcosting studies will enhance the consistency, transparency, and comparability of future microcosting studies. This will be the first checklist for microcosting studies to accomplish these goals and will be a timely and important contribution to the health economic and health policy literature. In addition to its usefulness to health economists and researchers, it will also benefit journal editors and decision-makers who require accurate cost estimates to deliver health care.
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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 | 0.063 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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; both teacher heads 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".