Preference Elicitation Techniques Used in Valuing Children's Health-Related Quality-of-Life: A Systematic Review

The randomized controlled trial (NAVKIDS2) of a patient navigator program created for children with chronic kidney disease

Patient navigators enable adult patients to circumnavigate complex health systems, improving access to health care and outcomes. Here, we aimed to evaluate the effects of a patient navigation program in children with chronic kidney disease (CKD). In this multi-center, randomized controlled trial, we randomly assigned children (aged 0-16 years) with CKD stages 1-5 (including children on dialysis or with kidney transplants), from low socioeconomic status backgrounds, and/or residing in remote areas, to receive patient navigation at randomization (immediate) or at six months (waitlist). The primary outcome was self-rated health (SRH) of participating children at six months, using intention to treat analysis. Secondary outcomes included caregivers' SRH and satisfaction with health care, children's quality of life, hospitalizations, and missed school days. Repeated measures of the primary outcome from baseline to six months were analyzed using cumulative logit mixed effects models. Semi-structured interviews were thematically evaluated. Of 398 screened children, 162 were randomized (80 immediate and 82 waitlist); mean age (standard deviation) of 8.8 (4.8) years with 64.8% male. SRH was not significantly different between the immediate and wait-listed groups at six months. There were also no differences across all secondary outcomes between the two groups. Caregivers' perspectives were reflected in seven themes: easing mental strain, facilitating care coordination, strengthening capacity to provide care, reinforcing care collaborations, alleviating family tensions, inability to build rapport and unnecessary support. Thus, in children with CKD, self-rated health may not improve in response to a navigator program, but caregivers gained skills related to providing and accessing care.

The development of a capability wellbeing measure in economic evaluation for children and young people aged 11-15

The capability approach provides a broad evaluative space for making funding decisions for health and care interventions, with capability wellbeing as the outcome of value. A range of capability measures have been developed for the economic evaluation of health and care interventions for adults. However, such measures have not been previously developed for children and young people for this purpose and may be valuable. This study aimed to identify important capabilities for children and young people aged 11-15, and to develop these into attributes for an economic measure that can inform funding decisions. Thirty-three qualitative in-depth interviews were undertaken with children and young people aged 11-15 (n = 19) and parents (n = 14) in urban and rural areas of England between September 2019 and November 2021. Purposeful maximum variation sampling ensured representation from different backgrounds. Children and young people were asked to think of things important to them and place these on sticky notes around a drawing/photograph of themselves; the interview asked them about these important things. Parents were asked to identify factors that enhanced and negatively impacted their child's quality of life. Analysis using constant comparison facilitated exploration of similarities and differences in important capabilities. A second phase of semi-structured interviews with children and young people (n = 15) explored how these attributes should be expressed in a meaningful way. Eight overarching capability wellbeing themes were identified, with some variation across children and young people, and parent groups: Fun and enjoyment; Learning and experiencing; Attachment; Emotional security andsupport; Achievement; Identity and choice; Physical safety; Aspiration. Potentially, this information will help to provide an alternative approach to the measurement of benefits to children and young people for economic evaluation of health and care interventions, one that will be better able to capture benefits associated with interventions to improve the social determinants of health.

Valuing quality of life for economic evaluations in cancer: navigating multiple methods

INTRODUCTION

Utility values offer a quantitative means to evaluate the impact of novel cancer treatments on patients' quality of life (QoL). However, the multiple methods available for valuing QoL present challenges in selecting the most appropriate method across different contexts.

AREAS COVERED

This review provides cancer clinicians and researchers with an overview of methods to value QoL for economic evaluations, including standalone and derived preference-based measures (PBMs) and direct preference elicitation methods. Recent developments are described, including the comparative performance of cancer-specific PBMs versus generic PBMs, measurement of outcomes beyond health-related QoL, and increased use of discrete choice experiments to elicit preferences. Recommendations and considerations are provided to guide the choice of method for cancer research.

EXPERT OPINION

We foresee continued adoption of the QLU-C10D and FACT-8D in cancer clinical trials given the extensive use of the EORTC QLQ-C30 and FACT-G in cancer research. While these cancer-specific PBMs offer the convenience of eliciting utility values without needing a standalone PBM, researchers should consider potential limitations if they intend to substitute them for generic PBMs. As the field advances, there is a greater need for consensus on the approach to selection and integration of various methods in cancer clinical trials.

Who should value children's health and how? An international Delphi study

Valuing child health necessitates normative methodological decisions on whose preferences should be elicited and who should be imagined as experiencing impaired health. Formal guidance is limited and expert consensus unclear. This study sought to establish the degree of consensus among expert stakeholders on normative issues of who to ask and who should be imagined when valuing child health (7-17 years) to inform UK health technology assessment. Sixty-two experts (n = 47 in Round 2) from 18 countries participated in a modified, two-round online Delphi survey (Round 1: May-June 2023; Round 2: September-October 2023). Participants were expert stakeholders in child health valuation, including academics (n = 38); industry/consultancy representatives (including the charity/not-for-profit sector; n = 13); and UK policy/government representatives (n = 11). The Delphi survey was modified between rounds and consisted of 9-point Likert, categorical, multiple-choice, and free-text questions on normative issues in valuing child health. Responses were analysed descriptively and thematically. An a priori criterion of ≥75% agreement was established for formal consensus, while areas approaching consensus (≥70% agreement) and without consensus were identified as a future research primer. Consensus was observed that older adolescents (aged 16-17 years) and adults (18+ years) should be asked to value child health states. There was consensus that the former should think about themselves when valuing the health states and the latter should imagine a child of some form (e.g., imagining themselves as a child or another hypothetical child). However, no consensus was evident on what form this should take. Several other methodological issues also reached consensus. These findings are largely consistent with recent views elicited qualitatively from members of the public and other stakeholders on normative issues in valuing child health. The results mean that, contrary to what has been done in previous child health valuation studies, efforts should be made to involve both older adolescents (16+ years) and adults in child health valuation.

A happiness approach to valuing health states for children

Preference weights are widely used to score generic health states into utility indexes for estimation of quality adjusted life years (QALYs) and to aid health care funding decisions. To date, health state utilities are predominantly derived using stated preference methods based on decision utility. This paper tests an alternative and generates preference weights using experienced utility for children based on the Child Health Utility 9D (CHU9D) descriptive system. We estimate the relative values of the CHU9D health states with regard to experienced utility, where experienced utility is approximated by self-reported happiness. A nationally-representative longitudinal survey was used including 6090 Australian children aged 12-17 years surveyed over 2014-2018. The derived weights were then applied to calculate the utility decrements for a few common child health conditions. We found that the estimated utility decrements are largely similar to those estimated using the published CHU9D Australian adolescent weights based on decision utility, except for pain and depression. A smaller utility decrement for pain and a larger utility decrement for depression were indicated by experienced utility. We contribute to the literature by showing that using experienced utility methods to generate preference weights for health states is possible, and we discuss some important methodological challenges for future studies such as the impracticability of anchoring to 'dead' when utilizing experienced utility.

The RETRIEVE Checklist for Studies Reporting the Elicitation of Stated Preferences for Child Health-Related Quality of Life

BACKGROUND

Recent systematic reviews show varying methods for eliciting, modelling, and reporting preference-based values for child health-related quality-of-life (HRQoL) outcomes, thus producing value sets with different characteristics. Reporting in many of the reviewed studies was found to be incomplete and inconsistent, making them difficult to assess. Checklists can help to improve standards of reporting; however, existing checklists do not address methodological issues for valuing child HRQoL. Existing checklists also focus on reporting methods and processes used in developing HRQoL values, with less focus on reporting of the values' key characteristics and properties. We aimed to develop a checklist for studies generating values for child HRQoL, including for disease-specific states and value sets for generic child HRQoL instruments.

DEVELOPMENT

A conceptual model provided a structure for grouping items into five modules. Potential items were sourced from an adult HRQoL checklist review, with additional items specific to children developed using recent reviews. Checklist items were reduced by eliminating duplication and overlap, then refined for relevance and clarity via an iterative process. Long and short checklist versions were produced for different user needs. The resulting long RETRIEVE contains 83 items, with modules for reporting methods (A-D) and characteristics of values (E), for researchers planning and reporting child health valuation studies. The short RETRIEVE contains 14 items for decision makers or researchers choosing value sets.

CONCLUSION

Applying the RETRIEVE checklists to relevant studies suggests feasibility. RETRIEVE has the potential to improve completeness in the reporting of preference-based values for child HRQOL outcomes and to improve assessment of preference-based value sets.

Child-Parent Agreement in the Assessment of Health-Related Quality of Life Using the CHU9D and the PedsQLTM

OBJECTIVE

This study examined the inter-rater agreement between child-self and parental proxy health-related quality of life (HRQoL) ratings (overall and domain level) using two different generic child-specific measures, the Child Health Utility 9D (CHU9D) and the Pediatric Quality of Life Inventory (PedsQLTM), in a community-based sample of Australian children. A secondary objective was to investigate the impact of age on child-parent agreement across the dimensions of the two measures.

METHODS

A total of 85 child-parent dyads (children aged 6-12 years) recruited from the community completed the self and proxy versions of the CHU9D and the PedsQLTM, respectively. The inter-rater agreement was estimated using Concordance Correlation Coefficients (CCC) and Gwet's Agreement Coefficient (AC1) for the overall sample and across age-groups.

RESULTS

Agreement was low for overall HRQoL for both the CHU9D (CCC = 0.28) and the PedsQLTM (CCC = 0.39). Across the CHU9D dimensions, agreement was the highest for 'sad' (AC1 = 0.83) and lowest for 'tired' (AC1 = 0.31). The PedsQLTM demonstrated stronger agreement (AC1 = 0.41-0.6) for the physical health dimension but weaker for the psychosocial dimensions (AC1 < 0.4). Except for the 'tired' dimension, agreement was consistent across age-groups with the CHU9D, whilst the PedsQLTM showed poor agreement for most of the psychosocial health items among the older age-groups only (8-10 and 11-12 years).

CONCLUSION

This study highlights that the agreement between child and parent proxy reported HRQoL may be influenced by both the measure used and the age of the child. These findings may have implications for the economic evaluation of healthcare interventions and services in child populations when both child and proxy perspectives are considered in the assessment of child HRQoL.

Patient Preferences in Diagnostic Imaging: A Scoping Review

BACKGROUND

As new diagnostic imaging technologies are adopted, decisions surrounding diagnostic imaging become increasingly complex. As such, understanding patient preferences in imaging decision making is imperative.

OBJECTIVES

We aimed to review quantitative patient preference studies in imaging-related decision making, including characteristics of the literature and the quality of the evidence.

METHODS

The Pubmed, Embase, EconLit, and CINAHL databases were searched to identify studies involving diagnostic imaging and quantitative patient preference measures from January 2000 to June 2022. Study characteristics that were extracted included the preference elicitation method, disease focus, and sample size. We employed the PREFS (Purpose, Respondents, Explanation, Findings, Significance) checklist as our quality assessment tool.

RESULTS

A total of 54 articles were included. The following methods were used to elicit preferences: conjoint analysis/discrete choice experiment methods (n = 27), contingent valuation (n = 16), time trade-off (n = 4), best-worst scaling (n = 3), multicriteria decision analysis (n = 3), and a standard gamble approach (n = 1). Half of the studies were published after 2016 (52%, 28/54). The most common scenario (n = 39) for eliciting patient preferences was cancer screening. Computed tomography, the most frequently studied imaging modality, was included in 20 studies, and sample sizes ranged from 30 to 3469 participants (mean 552). The mean PREFS score was 3.5 (standard deviation 0.8) for the included studies.

CONCLUSIONS

This review highlights that a variety of quantitative preference methods are being used, as diagnostic imaging technologies continue to evolve. While the number of preference studies in diagnostic imaging has increased with time, most examine preventative care/screening, leaving a gap in knowledge regarding imaging for disease characterization and management.

Understanding the valuation of paediatric health-related quality of life: a qualitative study protocol

INTRODUCTION

There is evidence from previous studies that adults value paediatric health-related quality of life (HRQoL) and adult HRQoL differently. Less is known about how adolescents value paediatric HRQoL and whether their valuation and decision-making processes differ from those of adults. Discrete choice experiments (DCEs) are widely used to develop value sets for measures of HRQoL, but there is still much to understand about whether and how the methods choices in the implementation of DCE valuation tasks, such as format, presentation and perspective, affect the decision-making process of participants. This paper describes the protocol for a qualitative study that aims to explore the decision-making process of adults and adolescents when completing DCE valuation tasks. The study will also explore the impact of methodological choices in the design of DCE studies (including decisions about format and presentation) on participants' thinking process.

METHODS AND ANALYSIS

An interview protocol has been developed using DCE valuation tasks. Interviews will be conducted online via Zoom with both an adolescent and adult sample. In the interview, the participant will be asked to go through some DCE valuation tasks while 'thinking aloud'. After completion of the survey, participants will then be asked some predetermined questions in relation to various aspects of the DCE tasks. Interviews will be recorded and transcribed and analysed using a thematic analysis approach.

ETHICS AND DISSEMINATION

Ethics approval for this study has been received for the adult sample (UTS ETH20-9632) as well as the youth sample (UTS ETH22-6970) from the University of Technology Sydney Human Research Ethics Committee. Results from this study will inform the methods to be used in development of value sets for use in the health technology assessment of paediatric interventions and treatments. Findings from this study will also be disseminated through national/international conferences and peer-reviewed journals.

Analytical Considerations When Anchoring Discrete Choice Experiment Values Using Composite Time Trade-Off Data: The Case of EQ-5D-Y-3L

Discrete choice experiments (DCEs) are becoming increasingly used to elicit preferences for children's health states. However, DCE data need to be anchored to produce value sets, and composite time trade-off (cTTO) data are typically used in the context of EQ-5D-Y-3L valuation. The objective of this paper is to compare different anchoring methods, summarise the characteristics of the value sets they produce, and outline key considerations for analysts. Three anchoring methods were compared using data from published studies: (1) rescaling using the mean value for the worst health state; (2) linear mapping; and (3) hybrid modelling. The worst state rescaling value set had the largest range. The worst state rescaling and linear mapping value sets preserved the relative importance of the dimensions from the DCE, whereas the hybrid model value set did not. Overall, the predicted values from the hybrid model value set were more closely aligned with the cTTO values. These findings are relatively generalisable. Deciding upon which anchoring approach to use is challenging, as there are numerous considerations. Where cTTO data are collected for more than one health state, anchoring on the worst health state will arguably be suboptimal. However, the final choice of approach may require value judgements to be made. Researchers should seek input from relevant stakeholders when commencing valuation studies to help guide decisions and should clearly set out their rationale for their preferred anchoring approach in study outputs.