Recommendations for Emerging Good Practice and Future Research in Relation to Family and Caregiver Health Spillovers in Health Economic Evaluations: A Report of the SHEER Task Force
Bibliographic record
Abstract
BACKGROUND: Omission of family and caregiver health spillovers from the economic evaluation of healthcare interventions remains common practice. When reported, a high degree of methodological inconsistency in incorporating spillovers has been observed. AIM: To promote emerging good practice, this paper from the Spillovers in Health Economic Evaluation and Research (SHEER) task force aims to provide guidance on the incorporation of family and caregiver health spillovers in cost-effectiveness and cost-utility analysis. SHEER also seeks to inform the basis for a spillover research agenda and future practice. METHODS: A modified nominal group technique was used to reach consensus on a set of recommendations, representative of the views of participating subject-matter experts. Through the structured discussions of the group, as well as on the basis of evidence identified during a review process, recommendations were proposed and voted upon, with voting being held over two rounds. RESULTS: This report describes 11 consensus recommendations for emerging good practice. SHEER advocates for the incorporation of health spillovers into analyses conducted from a healthcare/health payer perspective, and more generally inclusive perspectives such as a societal perspective. Where possible, spillovers related to displaced/foregone activities should be considered, as should the distributional consequences of inclusion. Time horizons ought to be sufficient to capture all relevant impacts. Currently, the collection of primary spillover data is preferred and clear justification should be provided when using secondary data. Transparency and consistency when reporting on the incorporation of health spillovers are crucial. In addition, given that the evidence base relating to health spillovers remains limited and requires much development, 12 avenues for future research are proposed. CONCLUSIONS: Consideration of health spillovers in economic evaluations has been called for by researchers and policymakers alike. Accordingly, it is hoped that the consensus recommendations of SHEER will motivate more widespread incorporation of health spillovers into analyses. The developing nature of spillover research necessitates that this guidance be viewed as an initial roadmap, rather than a strict checklist. Moreover, there is a need for balance between consistency in approach, where valuable in a decision making context, and variation in application, to reflect differing decision maker perspectives and to support innovation.
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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 | 0.786 | 0.842 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.035 | 0.034 |
| Open science | 0.025 | 0.029 |
| Research integrity | 0.053 | 0.050 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".