Measuring health system resource use for economic evaluation: a comparison of data sources
Bibliographic record
Abstract
A key challenge for evaluators and health system planners is the identification, measurement and valuation of resource use for economic evaluation. Accurately capturing all significant resource use is particularly difficult in the Australian context where there is no comprehensive database from which researchers can draw. Evaluators and health system planners need to consider different approaches to data collection for estimating resource use for economic evaluation, and the relative merits of the different data sources available. This paper illustrates the issues that arise in using different data sources using a sub-sample of the data being collected for an economic evaluation. Specifically, it compares the use of Australia's largest administrative database on resource use, the Health Insurance Commission database, with the use of patient-supplied data. The extent of agreement and discrepancies between the two data sources is investigated. Findings from this study and recommendations as to how to deal with different data sources are presented.
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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 | 0.343 | 0.622 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.022 | 0.051 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".