A Systematic Review of Methods Used by Pediatric Cost-Utility Analyses to Include Family Spillover Effects
Bibliographic record
Abstract
BACKGROUND: A child's health condition affects family members' health and well-being. However, pediatric cost-utility analysis (CUA) commonly ignores these family spillover effects leading to an incomplete understanding of the cost and benefits of a child's health intervention. Methodological challenges exist in assessing, valuing, and incorporating family spillover effects. OBJECTIVE: This study systematically reviews and compare methods used to include family spillover effects in pediatric CUAs. METHODS: A literature search was conducted in MEDLINE, Embase, EconLit, Cochrane collection, CINAHL, INAHTA, and the Pediatric Economic Database Evaluation (PEDE) database from inception to 2020 to identify pediatric CUAs that included family spillover effects. The search was updated to 2021 using PEDE. The data describing in which family members spillover effects were measured, and how family spillover effects were measured, incorporated, and reported, were extracted. Common approaches were grouped conceptually. Further, this review identified theories or theoretical frameworks used to justify approaches for integrating family spillover effects into CUA. RESULTS: Of 878 pediatric CUAs identified, 35 included family spillover effects. Most pediatric CUAs considered family spillover effects on one family member. Pediatric CUAs reported eight different approaches to measure the family spillover effects. The most common method was measuring the quality-adjusted life years (QALY) loss of the caregiver(s) or parent(s) due to a child's illness or disability using an isolated approach whereby family spillover effects were quantified in individual family members separately from other health effects. Studies used four approaches to integrate family spillover effects into CUA. The most common method was to sum children's and parents/caregivers' QALYs. Only two studies used a theoretical framework for incorporation of family spillover effects. CONCLUSIONS: Few pediatric CUAs included family spillover effects and the observed variation indicated no consensus among researchers on how family spillover effects should be measured and incorporated. This heterogeneity is mirrored by a lack of practical guidelines by Health Technology Assessment (HTA) agencies or a theoretical foundation for including family spillover effects in pediatric CUA. The results from this review may encourage researchers to develop a theoretical framework and HTA agencies to develop guidelines for including family spillover effects. Such guidance may lead to more rigorous and standardized methods for including family spillover effects and better-quality evidence to inform decision-makers on the cost-effectiveness of pediatric health interventions.
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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 | 0.071 | 0.288 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".