Quantifying preferences for asthma control in parents and adolescents using best-worst scaling

Client preferences for the design and delivery of injectable opioid agonist treatment services: Results from a best-worst scaling task

BACKGROUND AND AIMS

Clinical trials support injectable opioid agonist treatment (iOAT) for individuals with opioid use disorder (OUD) for whom other pharmacological management approaches are not well-suited. However, despite substantial research indicating that person-centered care improves engagement, retention and health outcomes for individuals with OUD, structural requirements (e.g. drug policies) often dictate how iOAT must be delivered, regardless of client preferences. This study aimed to quantify clients' iOAT delivery preferences to improve client engagement and retention.

DESIGN

Cross-sectional preference elicitation survey.

SETTING

Metro Vancouver, British Columbia, Canada.

PARTICIPANTS

124 current and former iOAT clients.

MEASUREMENTS

Participants completed a demographic questionnaire package and an interviewer-led preference elicitation survey (case 2 best-worst scaling task). Latent class analysis was used to identify distinct preference groups and explore demographic differences between preference groups.

FINDINGS

Most participants (n = 100; 81%) were current iOAT clients. Latent class analysis identified two distinct groups of client preferences: (1) autonomous decision-makers (n = 73; 59%) and (2) shared decision-makers (n = 51; 41%). These groups had different preferences for how medication type and dosage were selected. Both groups prioritized access to take-home medication (i.e. carries), the ability to set their own schedule, receiving iOAT in a space they like and having other services available at iOAT clinics. Compared with shared decision-makers, fewer autonomous decision-makers identified as a cis-male/man and reported flexible preferences.

CONCLUSIONS

Injectable opioid agonist treatment (iOAT) clients surveyed in Vancouver, Canada, appear to prefer greater autonomy than they currently have in choosing OAT medication type, dosage and treatment schedule.

Measuring the preferences of injectable opioid agonist treatment (iOAT) clients: Development of a person-centered scale (best-worst scaling)

BACKGROUND

Injectable opioid agonist treatment (iOAT) is effective for opioid use disorder (OUD), yet little is known about client preferences for accessing iOAT (e.g., with diacetylmorphine, hydromorphone, buprenorphine, fentanyl, etc.). Best-worst scaling (BWS) is a preference elicitation method from health economics that has never been applied to addiction care broadly, or iOAT specifically. We describe the stages of developing a BWS scale that assesses iOAT clients' treatment delivery preferences to inform program planning and maximize healthcare efficiency.

METHODS

We underwent several steps to reveal the relevant attributes/levels and design the scale structure. An initial list of potential attributes and levels was established from a literature review and prior qualitative data. Then, we conducted semi-structured interviews and focus groups with clients (n=21) on their iOAT preferences to confirm the attributes and prioritize/include new ones. Next, we conducted semi-structured interviews and focus groups with iOAT experts and stakeholders to receive their input on the draft list of attributes and levels. A BWS profile case design was piloted with iOAT clients (n=18) from different sites during a think aloud interview. After several rounds of revisions, the final version was tested by iOAT clients (n=2) before the scale was launched.

RESULTS

We developed a person-centered scale that assesses current and former iOAT clients' most and least wanted aspects of iOAT delivery. The final version yielded 7 unique attributes: choice of medication, choice of dose, convenience, location & space, scheduling & routines, staff & training, and types of services offered.

CONCLUSION

This scale can help expand iOAT programs in a way that is person-centered, rapid, and affordable. The methodology is a guide for other regions with similar populations who aim to develop strong quantitative methodologies that prioritize client collaboration.

Feasibility of a best-worst scaling exercise to set priorities for autism research

Background

The preferences of autism stakeholders regarding the top priorities for future autism research are largely unknown.

Objective

This study had two objectives: First, to examine what autism stakeholders think new research investments should be and the attributes of investment that they consider important, and second, to explore the feasibility, acceptability and outcomes of two prioritization exercises among autism stakeholders regarding their priorities for future research in autism.

Design

This was  a prospective stakeholder‐engaged iterative study consisting of best–worst scaling (BWS) and direct prioritization exercise.

Setting and Participants

A national snowball sample of 219 stakeholders was included: adults with autism, caregivers, service providers and researchers.

Main Outcome Measures

The main outcomes measures were attributes that participants value in future research investments, and priority research investments for future research.

Results

Two hundred and nineteen participants completed the exercises, of whom 11% were adults with autism, 58% were parents/family members, 37% were service providers and 21% were researchers. Among stakeholders, the BWS exercises were easier to understand than the direct prioritization, less frequently skipped and yielded more consistent results. The proportion of children with autism affected by the research was the most important attribute for all types of stakeholders. The top three priorities among future research investments were (1) evidence on which child, family and intervention characteristics lead to the best/worst outcomes; (2) evidence on how changes in one area of a child's life are related to changes in other areas; and (3) evidence on dietary interventions. Priorities were similar for all stakeholder types.

Conclusions

The values and priorities examined here provide a road map for investigators and funders to pursue autism research that matters to stakeholders.

Patient or Public Contribution

Stakeholders completed a BWS and direct prioritization exercise to inform us about their priorities for future autism research.

Experiences and Treatment Preferences in Patients With Psoriatic Arthritis: A Cross-Sectional Study in the ArthritisPower Registry

Introduction

Despite recent advances in treatment for psoriatic arthritis (PsA), many patients experience inadequate response or intolerance to therapy, indicating that unmet treatment-related needs remain. To further characterize these unmet needs, we evaluated patients’ experiences regarding the burden of PsA symptoms and disease impacts, and patients’ preferences for treatment.

Methods

Patients from ArthritisPower, a rheumatology research registry, completed a web-based survey. Object case best–worst scaling (BWS) was used to evaluate the relative burden of 11 PsA-related symptoms and the relative importance of improvement in nine PsA-related disease impacts. BWS data were analyzed using a random-parameters logit model. Patient demographics, preferences for mode and frequency of therapy, and preferences for methotrexate were analyzed descriptively.

Results

Among the 332 participants, most were White (94%), female (80%), with mean age of 54 years (SD 11.4). In the BWS, joint pain was the most bothersome symptom, followed by other musculoskeletal pain and fatigue. The BWS for disease impacts found that improvements in the ability to perform physical activities were most important, followed by improvements in the ability to function independently, sleep quality, and the ability to perform daily activities. The most burdensome symptoms and desired disease impact improvements were similar in patients regardless of their experience with biologic disease-modifying antirheumatic drugs. The most preferred mode and frequency of treatment administration was oral, once-daily medication (preferred by 38% of respondents), and 74% prioritized therapies that significantly improved joint-related symptoms versus psoriasis-related symptoms. The majority of respondents (65%) preferred PsA treatment regimens that did not include methotrexate.

Conclusions

Patients with PsA from a rheumatology registry found musculoskeletal pain symptoms to be the most bothersome and prioritized improvements to functional impacts of their disease. These findings can better inform development of new therapies and guide shared patient-provider treatment decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-022-00436-x.

Clarifying Values: An Updated and Expanded Systematic Review and Meta-Analysis

Background

Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment.

Purpose

To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods.

Data Sources

MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, and CINAHL.

Study Selection

We included articles that described randomized trials of 1 or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods.

Data Extraction

Two independent reviewers extracted details about each values clarification method and its evaluation.

Data Synthesis

Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-incongruent choices (risk difference, –0.04; 95% confidence interval [CI], –0.06 to –0.02; P < 0.001) and decisional conflict (standardized mean difference, –0.20; 95% CI, –0.29 to –0.11; P < 0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (χ² = 9.25, P = 0.01). There were no differences between different values clarification methods regarding decisional conflict (χ² = 6.08, P = 0.05).

Limitations

Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories.

Conclusions

Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

Patient preferences for treatment of metastatic melanoma

AIM

To investigate patient preferences for clinical attributes of first-line metastatic melanoma treatments.

MATERIALS & METHODS

A discrete-choice experiment and best-worst scaling exercise were used to assess relative preferences for treatment attributes.

RESULTS

The 200 survey respondents had distinct preferences. Avoiding a 30% risk of colitis or hormone gland problems and avoiding severe fever were more important to respondents than avoiding a 20% risk of extreme sun sensitivity (p < 0.05). Patients preferred taking pills to receiving intravenous infusions in a clinic. When attributes were combined, approximately 85% of respondents preferred a risk profile similar to targeted therapy over a profile similar to immunotherapy, holding efficacy constant.

CONCLUSION

Taking patient preferences into account can help patients get the full benefit from metastatic melanoma therapies.

Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review

BACKGROUND

Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood.

OBJECTIVE

We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications.

METHODS

We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist.

RESULTS

We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences between classes. Less than half of the studies reported that individual characteristics were included in the segmentation models, and 30% reported that post-estimation analyses were conducted to examine class characteristics. While a higher percentage of studies discussed clinical implications of the segmentation results, an increasing number of studies proposed policy recommendations based on segmentation results since 2010.

CONCLUSIONS

LCA is increasingly used to study preference heterogeneity in health and support decision-making. However, there is little consensus on best practices as its application in health is relatively new. With an increasing demand to study preference heterogeneity, guidance is needed to improve the quality of applications of segmentation methods in health to support policy development and clinical practice.

Eliciting Preferences for Clinical Follow-Up in Patients with Head and Neck Cancer Using Best-Worst Scaling

OBJECTIVES

There are no commonly accepted standards for monitoring patients treated for head and neck cancer. The aim of this study was to assess patients' preferences for different aspects of follow-up.

METHODS

A best-worst survey was conducted in a sample of head and neck cancer patients in clinical follow-up at the National Cancer Institute (Milan, Italy). Conditional logit regression with choice as the dependent variable was run to analyse the data. A covariate-adjusted analysis was performed in order to identify socio-demographic and clinical factors related to the selection of best-worst items. The participants were asked to report any difficulties encountered during the survey.

RESULTS

A total of 143 patients, predominantly male (74%) and with a mean age of 58 years were enrolled in the survey. The strongest positive preference was expressed for a hospital-based program of physical examinations with frequency decreasing over time. Conversely, the lowest valued item was not performing any positron emission tomography (PET) scan during follow-up. Patients with high educational levels were more likely to value attending a primary care-based program and undergoing intensive radiological investigations. Other patient-specific variables significantly associated with the choice of items were employment and living status, time already spent in follow-up and number of treatments received.

CONCLUSIONS

Overall, patients were more likely to choose an intensive follow-up scheme broadly consistent with the program currently administered by the hospital. There is little evidence of preference heterogeneity that might justify customized programs based on demographics. The best-worst scaling task appeared feasible for most participants.

Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview

Best-worst scaling (BWS), also known as maximum-difference scaling, is a multiattribute approach to measuring preferences. BWS aims at the analysis of preferences regarding a set of attributes, their levels or alternatives. It is a stated-preference method based on the assumption that respondents are capable of making judgments regarding the best and the worst (or the most and least important, respectively) out of three or more elements of a choice-set. As is true of discrete choice experiments (DCE) generally, BWS avoids the known weaknesses of rating and ranking scales while holding the promise of generating additional information by making respondents choose twice, namely the best as well as the worst criteria. A systematic literature review found 53 BWS applications in health and healthcare. This article expounds possibilities of application, the underlying theoretical concepts and the implementation of BWS in its three variants: 'object case', 'profile case', 'multiprofile case'. This paper contains a survey of BWS methods and revolves around study design, experimental design, and data analysis. Moreover the article discusses the strengths and weaknesses of the three types of BWS distinguished and offered an outlook. A companion paper focuses on special issues of theory and statistical inference confronting BWS in preference measurement.

Using Best-Worst Scaling to Investigate Preferences in Health Care

INTRODUCTION

Best-worst scaling (BWS) is becoming increasingly popular to elicit preferences in health care. However, little is known about current practice and trends in the use of BWS in health care. This study aimed to identify, review and critically appraise BWS in health care, and to identify trends over time in key aspects of BWS.

METHODS

A systematic review was conducted, using Medline (via Pubmed) and EMBASE to identify all English-language BWS studies published up until April 2016. Using a predefined extraction form, two reviewers independently selected articles and critically appraised the study quality, using the Purpose, Respondents, Explanation, Findings, Significance (PREFS) checklist. Trends over time periods (≤2010, 2011, 2012, 2013, 2014 and 2015) were assessed further.

RESULTS

A total of 62 BWS studies were identified, of which 26 were BWS object case studies, 29 were BWS profile case studies and seven were BWS multi-profile case studies. About two thirds of the studies were performed in the last 2 years. Decreasing sample sizes and decreasing numbers of factors in BWS object case studies, as well as use of less complicated analytical methods, were observed in recent studies. The quality of the BWS studies was generally acceptable according to the PREFS checklist, except that most studies did not indicate whether the responders were similar to the non-responders.

CONCLUSION

Use of BWS object case and BWS profile case has drastically increased in health care, especially in the last 2 years. In contrast with previous discrete-choice experiment reviews, there is increasing use of less sophisticated analytical methods.

Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women

PURPOSE

Best-worst scaling (BWS) is a survey method for assessing individuals' priorities. It identifies the extremes-best and worst items, most and least important factors, biggest and smallest influences-among sets. In this article, we demonstrate an application of BWS in a primary care setting to illustrate its use in identifying patient priorities for services.

METHODS

We conducted a BWS survey in 2014 in Boston, Massachusetts, to assess the relative importance of 10 previously identified attributes of Papanicolaou (Pap) testing services among women experiencing homelessness. Women were asked to evaluate 11 sets of 5 attributes of Pap services, and identify which attribute among each set would have the biggest and smallest influence on promoting uptake. We show how frequency analysis can be used to analyze results.

RESULTS

In all, 165 women participated, a response rate of 72%. We identified the most and least salient influences on encouraging Pap screening based on their frequency of report among our sample, with possible standardized scores ranging from+1.0 (biggest influence) to -1.0 (smallest influence). Most important was the availability of support for issues beyond health (+0.39), while least important was the availability of accommodations for personal hygiene (-0.27).

CONCLUSIONS

BWS quantifies patient priorities in a manner that is transparent and accessible. It is easily comprehendible by patients and relatively easy to administer. Our application illustrates its use in a vulnerable population, showing that factors beyond those typically provided in health care settings are highly important to women in seeking Pap screening. This approach can be applied to other health care services where prioritization is helpful to guide decisions.

Parents and adolescents preferences for asthma control: a best-worst scaling choice experiment using an orthogonal main effects design

Background

The preferences of parents and children with asthma influence their ability to manage a child’s asthma and achieve good control. Potential differences between parents and adolescents with respect to specific parameters of asthma control are not considered in clinical asthma guidelines. The objective was to measure and compare the preferences of parents and adolescents with asthma with regard to asthma control parameters using best worst scaling (BWS).

Methods

Fifty-two parents of children with asthma and 44 adolescents with asthma participated in a BWS study to quantify preferences regarding night-time symptoms, wheezing/chest tightening, changes in asthma medications, emergency visits and physical activity limitations. Conditional logit regression was used to determine each group’s utility for each level of each asthma control parameter.

Results

Parents displayed the strongest positive preference for the absence of night-time symptoms (β = 2.09, p < 0.00001) and the strongest negative preference for 10 emergency room visits per year (β = −2.15, p < 0.00001). Adolescents displayed the strongest positive preference for the absence of physical activity limitations (β = 2.17, p < 0.00001) and the strongest negative preference for ten physical activity limitations per month (β = −1.97). Both groups were least concerned with changes to medications.

Conclusion

Parents and adolescents placed different weights on the importance of asthma control parameters and each group displayed unique preferences. Understanding the relative importance placed on each parameter by parents and adolescents is essential for designing effective patient-focused disease management plans.

Electronic supplementary material

The online version of this article (doi:10.1186/s12890-015-0141-9) contains supplementary material, which is available to authorized users.