A Systematic Review of Methods Used by Pediatric Cost-Utility Analyses to Include Family Spillover Effects

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.