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Preference Variation: Where Does Health Risk Attitude Come Into the Equation?

  • Samare P.I. Huls
    Correspondence
    Correspondence: Samare P.I. Huls, MSc, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands.
    Affiliations
    Department of Health Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, The Netherlands
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  • Jorien Veldwijk
    Affiliations
    Department of Health Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
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  • Joffre D. Swait
    Affiliations
    Department of Health Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, The Netherlands
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  • Jennifer Viberg Johansson
    Affiliations
    Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden

    Department of New Technologies and the Human Future, The Institute for Future Studies, Stockholm, Sweden
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  • Mirko Ancillotti
    Affiliations
    Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
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  • Esther W. de Bekker-Grob
    Affiliations
    Department of Health Technology Assessment, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, The Netherlands
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Open AccessPublished:June 22, 2022DOI:https://doi.org/10.1016/j.jval.2022.05.005

      Highlights

      • Decisions about health often involve risk, and risk preferences may vary among people.
      • Interest in health preference heterogeneity and the role of risk is increasing.
      • Modeling health risk attitude as an individual characteristic underlying preference heterogeneity has the potential to improve model fit and model interpretations.
      • Further research into the relationship between health risk attitude and preference heterogeneity is warranted.

      Abstract

      Objectives

      Decisions about health often involve risk, and different decision makers interpret and value risk information differently. Furthermore, an individual’s attitude toward health-specific risks can contribute to variation in health preferences and behavior. This study aimed to determine whether and how health-risk attitude and heterogeneity of health preferences are related.

      Methods

      To study the association between health-risk attitude and preference heterogeneity, we selected 3 discrete choice experiment case studies in the health domain that included risk attributes and accounted for preference heterogeneity. Health-risk attitude was measured using the 13-item Health-Risk Attitude Scale (HRAS-13). We analyzed 2 types of heterogeneity via panel latent class analyses, namely, how health-risk attitude relates to (1) stochastic class allocation and (2) systematic preference heterogeneity.

      Results

      Our study did not find evidence that health-risk attitude as measured by the HRAS-13 distinguishes people between classes. Nevertheless, we did find evidence that the HRAS-13 can distinguish people’s preferences for risk attributes within classes. This phenomenon was more pronounced in the patient samples than in the general population sample. Moreover, we found that numeracy and health literacy did distinguish people between classes.

      Conclusions

      Modeling health-risk attitude as an individual characteristic underlying preference heterogeneity has the potential to improve model fit and model interpretations. Nevertheless, the results of this study highlight the need for further research into the association between health-risk attitude and preference heterogeneity beyond class membership, a different measure of health-risk attitude, and the communication of risks.

      Keywords

      Introduction

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      systematically reviewed individual characteristics underlying the decision-making process and their relation to preference heterogeneity. They identified risk attitude as 1 important, yet marginally studied, factor relating to preference heterogeneity; experts agreed with this assessment.
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      ) are increasingly found to be related to preference heterogeneity,
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      the complexities associated with the operationalization of risk attitude hamper studying the relationship between risk attitude and preference heterogeneity.
      • Russo S.
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      • et al.
      Understanding patients’ preferences: a systematic review of psychological instruments used in patients’ preference and decision studies.
      Therefore, the purpose of this study is to determine whether and how health-risk attitude and heterogeneity of health preferences are related by means of 3 case studies, using the relatively new 13-item Health-Risk Attitude Scale (HRAS-13), which aims to overcome some of the operational complexities.
      • Huls S.P.I.
      • van Osch S.M.C.
      • Brouwer W.B.F.
      • van Exel J.
      • Stiggelbout A.M.
      Psychometric evaluation of the Health-Risk Attitude Scale (HRAS-13): assessing the reliability, dimensionality and validity in the general population and a patient population.
      To assess the relationship between the HRAS-13 and heterogeneity, we studied 2 types of heterogeneity, namely, (1) stochastic class assignment and (2) systematic preference heterogeneity.

      Methods

      Case Studies

      To study the association between health-risk attitude and heterogeneity of preferences, we selected 3 DCE case studies in the health domain that had at least 1 attribute that implicitly or explicitly concerned risks and for which we could gain the authors’ consent to share the data for this purpose. The studies differed in terms of their topic, country, study population, number of respondents, and their DCE design leading to an increased generalizability of the results. An overview of the case studies and their DCE designs can be found in Table 1.
      • Visser L.A.
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      • de Bekker-Grob E.W.
      • Redekop W.K.
      An implantable device to treat multiple sclerosis: a discrete choice experiment on patient preferences in three European countries.
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      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      • Arslan I.G.
      • Huls S.P.I.
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      • et al.
      Patients’, healthcare providers’, and insurance company employees’ preferences for knee and hip osteoarthritis care: a discrete choice experiment.
      Attributes and levels were selected based on literature reviews, focus groups, and interviews; these are presented in Table 2. The first case study concerned the treatment preferences of patients with multiple sclerosis (MS) in The Netherlands, France, and the UK.
      • Visser L.A.
      • Huls S.P.I.
      • Uyl-de Groot C.A.
      • de Bekker-Grob E.W.
      • Redekop W.K.
      An implantable device to treat multiple sclerosis: a discrete choice experiment on patient preferences in three European countries.
      Inclusion criteria were the following: aged 18 years or older, diagnosis of MS, and living in one of these 3 European countries. Respondents were recruited online via the commercial survey sampling company Survey Engine (N = 753). Three of 4 attributes were explicitly described as risks to survey respondents. The second study analyzed preferences regarding antibiotics usage in Sweden.
      • Ancillotti M.
      • Eriksson S.
      • Andersson D.I.
      • Godskesen T.
      • Nihlén Fahlquist J.
      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      An online sample of respondents between 18 and 65 years old was recruited from the Swedish general public (N = 378). Respondents were recruited online via Dynata, a commercial survey sample provider. Three of 5 attributes concerned risk, 2 of them in percent and the third in words. The third study concerned care for hip and knee osteoarthritis (HKOA) in The Netherlands.
      • Arslan I.G.
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      Patients’, healthcare providers’, and insurance company employees’ preferences for knee and hip osteoarthritis care: a discrete choice experiment.
      Respondents aged 45 years and older with knee or hip osteoarthritis were recruited online, also via Dynata (N = 648). In contrast to the other 2 studies, none of the attributes were explicitly described to respondents as being related to risks. Nevertheless, based on the relationship between time preference and risk aversion, “waiting time in weeks” was classified as a risk attribute.
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      The number and type of professionals involved were also classified as a risk attribute because health anxiety increases the belief that specialist referral is needed, and health anxiety was found to be driven by risk aversion.
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      More details about the 3 studies are published elsewhere.
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      • Huls S.P.I.
      • Uyl-de Groot C.A.
      • de Bekker-Grob E.W.
      • Redekop W.K.
      An implantable device to treat multiple sclerosis: a discrete choice experiment on patient preferences in three European countries.
      • Ancillotti M.
      • Eriksson S.
      • Andersson D.I.
      • Godskesen T.
      • Nihlén Fahlquist J.
      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      • Arslan I.G.
      • Huls S.P.I.
      • Bekker-Grob E.W. de
      • et al.
      Patients’, healthcare providers’, and insurance company employees’ preferences for knee and hip osteoarthritis care: a discrete choice experiment.
      Table 1Case study and DCE design characteristics.


      Characteristics
      Study 1

      Visser et al
      • Visser L.A.
      • Huls S.P.I.
      • Uyl-de Groot C.A.
      • de Bekker-Grob E.W.
      • Redekop W.K.
      An implantable device to treat multiple sclerosis: a discrete choice experiment on patient preferences in three European countries.
      Study 2

      Ancillotti et al
      • Ancillotti M.
      • Eriksson S.
      • Andersson D.I.
      • Godskesen T.
      • Nihlén Fahlquist J.
      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      Study 3

      Arslan et al
      • Arslan I.G.
      • Huls S.P.I.
      • Bekker-Grob E.W. de
      • et al.
      Patients’, healthcare providers’, and insurance company employees’ preferences for knee and hip osteoarthritis care: a discrete choice experiment.
      Case studies
      TopicMultiple sclerosisAntibioticsHKOA
      CountryThe Netherlands, France, United KingdomSwedenThe Netherlands
      Study populationPatients with MS, ≥18 years oldGeneral public, 18-65 years oldHKOA patients, ≥45 years old
      Number of respondents753378648
      DCE design
      Number of attributes456
      Number of choice sets per block151612
      Number of blocks232
      Number of alternatives3 including opt-out22
      Risk attributesRisk of relapse (%), reducing disease progression (%), risk of side effects (words and %)Contribution to resistance (words), risk of side effects (%), treatment failure (%)Waiting time in weeks (words), professionals involved (words)
      Number of latent classes in original study234
      Note. Attributes and levels were selected based on literature review, focus groups, and interviews; they are presented in Table 2.
      DCE indicates discrete choice experiment; HKOA, hip and knee osteoarthritis; MS, multiple sclerosis.
      Table 2Attributes and levels.
      Study 1—MSStudy 2—antibioticsStudy 3—HKOA
      AttributesLevelsAttributesLevelsAttributesLevels
      1.Risk of relapse
      Attributes with an asterisk implicitly or explicitly concerned risks.
      30% less riskContribution to resistance
      Attributes with an asterisk implicitly or explicitly concerned risks.
      LowWaiting time in weeks
      Attributes with an asterisk implicitly or explicitly concerned risks.
      0
      50% less riskMedium2
      70% less riskHigh4
      2.Reducing disease progression
      Attributes with an asterisk implicitly or explicitly concerned risks.
      20% less progressionNumber of days treatment3 daysProfessionals involved
      Attributes with an asterisk implicitly or explicitly concerned risks.
      GP
      40% less progression7 daysOrthopedist
      60% less progression14 daysGP and orthopedist
      3.Risk of side effects
      Attributes with an asterisk implicitly or explicitly concerned risks.
      Very common mild (> 10%)Risk of side effects
      Attributes with an asterisk implicitly or explicitly concerned risks.
      1%Price in Euros0
      Common moderate (1%-10%)5%45
      Rare severe (0.1%-1%)10%90
      20%
      4.Mode of administrationInjections (1× per week)Treatment failure
      Attributes with an asterisk implicitly or explicitly concerned risks.
      80%Time per consult in minutes10
      Injections (3× per week)85%15
      Pills (1× per day)90%30
      Pills (2× per day)95%
      Implant (1× per year)
      Implant (1× per 3 years)
      5.Costs100 kr.Travel time in kilometers1
      250 kr.7
      400 kr.20
      1000 kr.
      6.Equipment availableDirect
      Indirect
      GP indicates general practitioner; HKOA, hip and knee osteoarthritis; kr., Swedish Krona; MS, multiple sclerosis.
      Attributes with an asterisk implicitly or explicitly concerned risks.

      DCE Design and Questionnaire

      In all studies, a Bayesian heterogeneous DCE design was created using Ngene ChoiceMetrics software
      Ngene user manual. ChoiceMetrics.
      to maximize D-efficiency. Initial priors were based on literature, focus groups, and interviews with experts or members of the study population in the prepiloting phase. Based on the results of a standard multinomial logit model, the priors and the design were optimized once 10% of the required sample completed the questionnaire. In study 1, the final experimental design contained 30 choice tasks that were divided into 2 blocks of 15 choice tasks. Each choice task had 2 generic alternatives (“treatment 1” and “treatment 2”) that were characterized by a selection of attribute levels, and the third alternative (“no treatment”) allowed respondents to not choose any of the alternatives presented (opt-out). The design of the second study consisted of 48 unique choice tasks divided over 3 blocks of 16 choice tasks. Each choice task had 2 generic alternatives. In study 3, the design consisted of 24 choice tasks and was divided into 2 blocks of 12 choice tasks. Again, each choice task had 2 generic alternatives. Examples of the choice tasks are given in Appendix Figures 1 to 3 in Appendix A in Supplemental Materials found at https://doi.org/10.1016/j.jval.2022.05.005.
      To assess health-risk attitude, we used the 13-item HRAS-13.
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      Psychometric evaluation of the Health-Risk Attitude Scale (HRAS-13): assessing the reliability, dimensionality and validity in the general population and a patient population.
      The HRAS-13 is context specific, and its items relate to medical treatment, the importance of health, general attitude toward risk taking in health and care, and consideration of the future consequences of health behaviors. Advantages of using this scale are that (1) context-specific scales are found to better predict risk behavior
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      ; and (3) it avoids the need to differentiate between risk-taking behavior and risk perception.
      • Weber E.U.
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      ,
      • Huls S.P.I.
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      The items of the HRAS-13 were rated on a 7-point Likert scale from “completely disagree” to “completely agree.” Total scores for the HRAS-13 were obtained by reverse-scoring 7 of the items that are phrased negatively and then summing the scores to each item. Scores range between 13 and 91. Respondents with a lower HRAS-13 score are more health risk averse, whereas a higher HRAS-13 score indicates a more risk-prone attitude toward health risks. In addition, the questionnaires contained questions about health, age, sex, and education level. Health was measured using a visual analog scale ranging from 0 to 100 in study 1 and study 3, while using a 5-point Likert scale from “very poor” to “very good” in study 2. Age was measured on a continuous scale, with sex as “female,” “male,” or “other.” Education level was measured according to the European Qualification Framework and categorized as “low,” “medium,” or “high” in accordance with the Dutch Qualification Framework and Statistics Netherlands.
      NLQF-niveaus
      Netherlands qualification framework (NLQF).
      ,
      Opleidingsniveau
      Central Bureau of Statistics, Centraal Bureau Voor de Statistiek.
      In addition, study 1 and study 2 also contained questions about health literacy and numeracy. Health literacy was measured using the Communicative Health Literacy and Critical Health Literacy scales.
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      respondents with an average score of 2 or lower were categorized as “inadequate,” those between 2 and 3 were categorized as “problematic,” and those with an average score larger than 3 were deemed “sufficient.” The Dutch version of the scale
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      Developing a measure of communicative and critical health literacy: a pilot study of Japanese office workers.
      and the Swedish version.
      • Wångdahl J.M.
      • Mårtensson L.I.
      The communicative and critical health literacy scale-- Swedish version.
      In addition, the Dutch version uses a 4-point Likert scale rather than a 5-point Likert scale. Study 1 was based on the Dutch version (and translated from Dutch to English and French); study 2 was based on the Swedish version. For comparability between studies, health literacy was calculated using only the 5 items that were in the Swedish version, and responses in study 2 were recoded so that they were also rated on a 4-point Likert scale (divide each item score by 5 and multiply by 4/5). Numeracy was measured using the 3-item version of the subjective numeracy scale.
      • McNaughton C.D.
      • Cavanaugh K.L.
      • Kripalani S.
      • Rothman R.L.
      • Wallston K.A.
      Validation of a short, 3-item version of the subjective numeracy scale.
      Based again on de Bekker-Grob et al
      • de Bekker-Grob E.W.
      • Donkers B.
      • Bliemer M.C.J.
      • Veldwijk J.
      • Swait J.D.
      Can healthcare choice be predicted using stated preference data?.
      ,
      • de Bekker-Grob E.W.
      • Donkers B.
      • Veldwijk J.
      • et al.
      What factors influence non-participation most in colorectal cancer screening? A discrete choice experiment.
      and Ancillotti et al,
      • Ancillotti M.
      • Eriksson S.
      • Andersson D.I.
      • Godskesen T.
      • Nihlén Fahlquist J.
      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      items were scored on a 6-point Likert scale ranging from “not good at all/never” to “extremely good/very often.” Respondents with an average score below 2 were categorized as “inadequate,” those with a score between 3 and 4 were categorized as “problematic,” and those with an average score higher than 5 were deemed “sufficient.”

      Analysis of Health-Risk Attitude and Preference Heterogeneity

      Panel latent class models were used to analyze heterogeneity of preferences. These models account for the multiple choice sets each respondent completed (ie, panel structure), and they capture unobserved heterogeneity of preferences using a discrete number of classes (ie, latent classes).
      • Hess S.
      Latent class structures: taste heterogeneity and beyond.
      • Greene W.H.
      • Hensher D.A.
      A latent class model for discrete choice analysis: contrasts with mixed logit.
      • Swait J.
      A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data.
      Following random utility theory, class allocation of respondent n in class c is based on choices for choice set s of each alternative j and is given by Unsj|c. The utility consists of an observable component V and a random component εnsj|c that is formally written as follows:
      Unsj|c=VXnsj,βc+εnsj|c.
      (1)


      Here βc is a class-specific vector describing the preference weights of the attributes and levels Xnsj for respondent n for choice set s in alternative j. The exact model specification differed per study; the specification of the alternative specific constant(s), linearity of the attributes, and the number of classes were based on model fit and with consideration for class size and interpretability of the main-effects model.
      To understand whether and how health-risk attitude and preference heterogeneity are related, we analyzed 2 types of heterogeneity, namely, (1) stochastic class assignment and (2) systematic preference heterogeneity. Both types of heterogeneity were included jointly to disentangle the different potential sources of preference heterogeneity. The impact of health-risk attitude on stochastic class assignment was included to analyze whether health-risk attitude could distinguish preferences between classes, that is, whether it distinguished preferences for risk-related attributes and nonrisk-related attributes. For matters of completeness, the class assignment model also included other variables based on their relationship with health-risk attitude or preference heterogeneity. The propensity of class membership φnc is specified as a linear-in-parameters function consisting of a constant term δ0|c plus the variables health
      • Dieteren C.M.
      • Brouwer W.B.F.
      • van Exel J.
      How do combinations of unhealthy behaviors relate to attitudinal factors and subjective health among the adult population in The Netherlands? [published correction appears in BMC Public Health. 2020;20(1):1808].
      ,
      • Huls S.P.I.
      • van Osch S.M.C.
      • Brouwer W.B.F.
      • van Exel J.
      • Stiggelbout A.M.
      Psychometric evaluation of the Health-Risk Attitude Scale (HRAS-13): assessing the reliability, dimensionality and validity in the general population and a patient population.
      ,
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      (dichotomized based on median split, good vs rest), age
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      ,
      • Bansback N.
      • Harrison M.
      • Sadatsafavi M.
      • Stiggelbout A.
      • Whitehurst D.G.T.
      Attitude to health risk in the Canadian population: a cross-sectional survey.
      (continuous), sex
      • Rosen A.B.
      • Tsai J.S.
      • Downs S.M.
      Variations in risk attitude across race, gender, and education.
      ,
      • Huls S.P.I.
      • van Osch S.M.C.
      • Brouwer W.B.F.
      • van Exel J.
      • Stiggelbout A.M.
      Psychometric evaluation of the Health-Risk Attitude Scale (HRAS-13): assessing the reliability, dimensionality and validity in the general population and a patient population.
      ,
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      ,
      • Bansback N.
      • Harrison M.
      • Sadatsafavi M.
      • Stiggelbout A.
      • Whitehurst D.G.T.
      Attitude to health risk in the Canadian population: a cross-sectional survey.
      (male vs female), education level
      • Rosen A.B.
      • Tsai J.S.
      • Downs S.M.
      Variations in risk attitude across race, gender, and education.
      ,
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      (high vs rest), and if applicable numeracy
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      ,
      • Bansback N.
      • Harrison M.
      • Sadatsafavi M.
      • Stiggelbout A.
      • Whitehurst D.G.T.
      Attitude to health risk in the Canadian population: a cross-sectional survey.
      and health literacy
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      ,
      • Veldwijk J.
      • van der Heide I.
      • Rademakers J.
      • et al.
      Preferences for vaccination: does health literacy make a difference?.
      (sufficient vs rest); thus:
      φnc=δ0|c+γ1|cHRAS scoren+γ2|cgood healthn+γ3|cage n+γ4|cmalen+γ5|chigh educationn+γ6|csufficient literacyn+γ7|csufficient numeracyn+ωnc.
      (2)


      The stochastic term wnc is assumed to be extreme value type 1 (Gumbel) independent and identically distributed across classes, yielding a polytomous multinomial logit model for the probability of class membership:
      πnc=expφnc¯c'=1Cexpφnc'¯.
      (3)


      Note that the coefficient vector for 1 class must be set to 0.
      Statistically significant γ coefficients (as indicated by P <.05) indicate that a certain variable contributed to the class assignment model. For example, a positive and statistically significant γ coefficient of HRAS score in class 1 would mean that respondents with higher HRAS scores are more likely to be allocated to class 1 than the reference class. Nevertheless, a nonsignificant coefficient means that differences in HRAS scores do not explain differences in overall preference structures between the classes.
      In parallel, we assessed the relationship between health-risk attitude and systematic preference heterogeneity by interacting the risk-related attributes with respondents’ health-risk attitude. A statistically significant HRAS interaction term (again as indicated by P <.05) with a risk-related attribute, for example, in class 1, is interpreted as health-risk attitude explaining preference heterogeneity of that attribute within that class.
      To assess the impact of including health-risk attitude, in each study, we compared log-likelihood of the model that included health-risk attitude in the class allocation model and used interactions with a model that did not do either but was equal in all other aspects. Log-likelihood statistics were compared using likelihood ratio tests, given that the number of classes is equal between models. All analyses were performed in Nlogit 6.

      Results

      Respondents

      Given the varying study contexts, inclusion criteria, and study designs, the 3 studies had different types of respondents (see Table 1
      • Visser L.A.
      • Huls S.P.I.
      • Uyl-de Groot C.A.
      • de Bekker-Grob E.W.
      • Redekop W.K.
      An implantable device to treat multiple sclerosis: a discrete choice experiment on patient preferences in three European countries.
      • Ancillotti M.
      • Eriksson S.
      • Andersson D.I.
      • Godskesen T.
      • Nihlén Fahlquist J.
      • Veldwijk J.
      Preferences regarding antibiotic treatment and the role of antibiotic resistance: a discrete choice experiment.
      • Arslan I.G.
      • Huls S.P.I.
      • Bekker-Grob E.W. de
      • et al.
      Patients’, healthcare providers’, and insurance company employees’ preferences for knee and hip osteoarthritis care: a discrete choice experiment.
      for an overview of the case studies). The studies consisted of 753, 378, and 648 respondents, respectively. In study 2, the general public sample, HRAS-13 scores were generally higher (more positive attitude toward health risks) and more dispersed than in the MS sample (study 1) and the HKOA sample (study 3). In study 1, respondents were less healthy (mean = 60.6) than in study 3 (mean = 68.8); they were younger (mean = 42.0), mostly female (67.9%), and highly educated (47.3%). Furthermore, the sample of study 1 was less literate and slightly less numerate than in study 2. In the second study, most people had a good (43.1%) or very good (15.3%) health. The sample of study 2 was slightly older than in the first study, but younger than in the third. As in study 1, most respondents were highly educated (51.3%). In study 3, respondents were oldest (mean = 61.7), 55.4% were female, and fewer (25.3%) were highly educated than in the other studies. No data were collected on health literacy and numeracy. An overview of these respondent characteristics can be found in Table 3, whereas more information about the relationship between HRAS-13 scores and other background variables is presented in Appendix Table 1 in Appendix B in Supplemental Materials found at https://doi.org/10.1016/j.jval.2022.05.005.
      Table 3Respondent characteristics per study.
      CharacteristicCategoryStudy 1—MSStudy 2—antibioticsStudy 3—HKOA
      n (%)n (%)n (%)
      n753 (100)378 (100)648 (100)
      HRAS-13 score, mean (SD)44.5 (9.2)60.2 (9.8)49.0 (5.4)
      HRAS-13 score, median466050
      HRAS-13 score, range18-7019-8629-65
      Health, mean (SD)60.6 (20.3)-68.8 (19.6)
      Health, median65Good73
      Health, categoriesVery poor-6 (1.6)-
      Poor-38 (10.1)-
      Neutral-113 (29.9)-
      Good-163 (43.1)-
      Very good-58 (15.3)-
      Health, categories median splitHigh: > median386 (51.3)58 (15.3)323 (49.8)
      Low: ≤ median367 (48.7)320 (84.7)325 (50.2)
      Age, mean (SD)42.0 (12.1)43.3 (13.6)61.7 (8.9)
      SexFemale512 (67.9)208 (55.0)359 (55.4)
      Male241 (32.1)169 (44.7)289 (44.6)
      Other0 (0.0)1 (0.3)0 (0.0)
      Education levelLow188 (25.0)70 (18.5)207 (31.9)
      Medium201 (26.7)108 (28.6)275 (42.4)
      High356 (47.3)194 (51.3)164 (25.3)
      Other8 (1.1)6 (1.6)2 (0.3)
      Health literacyInadequate96 (12.7)8 (2.1)-
      Problematic497 (66.0)117 (31.0)-
      Sufficient160 (21.2)253 (66.9)-
      NumeracyInadequate51 (6.8)23 (6.1)-
      Problematic331 (44.0)154 (40.7)-
      Sufficient371 (49.3)201 (53.2)-
      HKOA indicates hip and knee osteoarthritis; HRAS-13, 13-item Health-Risk Attitude Scale; MS, multiple sclerosis.

      Health-Risk Attitude and Preference Heterogeneity

      An overview of the results per study is presented in Tables 4 and 5 described below; for further information, the full results are presented in Appendix Tables 2 to 4 in Appendix C in Supplemental Materials found at https://doi.org/10.1016/j.jval.2022.05.005. In none of the studies were HRAS-13 scores statistically significantly related to stochastic classification of preferences. This indicates that parameters in the utility function were not jointly dependent on health-risk attitude for any of the classes in any of the studies. Nevertheless, numeracy was related to class allocation (P = .02) in study 1. In study 2, age (P = .004) and health literacy contributed to class allocation (P = .012) in class 1 and 2, respectively. In study 3, age explained class allocation in 2 classes (P = .004 and P = .040).
      Table 4Overview results per study.
      Type of heterogeneityClassStudy 1—MSCoeff.P valueStudy 2—antibioticsCoeff.P valueStudy 3—HKOACoeff.P value
      Stochastic class allocation1HRAS−0.002.839HRAS0.018.316HRAS0.007.794
      2HRAS0.000-HRAS−0.002.924HRAS−0.001.980
      3---HRAS0.000-HRAS0.036.301
      4------HRAS0.000-
      Systematic heterogeneity1Risk relapse (%)−0.009<.001Resistance (med)0.012.262Waiting time−0.023.001
      Progression (%)−0.017<.001Resistance (high)0.004.794Orthopedist0.026.327
      Side effects (mod.)−0.002.391Side effects (5%)0.001.949GP and orthopedist−0.002.953
      Side effects (sev.)−0.002.002Side effects (10%)0.000.989
      Side effects (20%)−0.001.934
      Treatment failure (%)−0.004.577
      2Risk relapse (%)0.012.003Resistance (med)−0.010.320Waiting time−0.004.441
      Progression (%)−0.007.026Resistance (high)−0.005.578Orthopedist0.031.167
      Side effects (mod.)0.002.772Side effects (5%)0.002.834GP and orthopedist0.026.198
      Side effects (sev.)0.003.318Side effects (10%)0.012.285
      Side effects (20%)0.014.197
      Treatment fail (%)−0.016.019
      3---Resistance (med)0.007.466Waiting time0.036.001
      Resistance (high)−0.004.733Orthopedist−0.041.317
      Side effects (5%)−0.001.949GP and orthopedist0.023.616
      Side effects (10%)−0.005.655
      Side effects (20%)−0.023.057
      Treatment fail (%)−0.001.847
      4-----Waiting time0.008.053
      Orthopedist0.025.913
      GP and orthopedist0.027.056
      Coeff. indicates coefficient; GP, general practitioner; HKOA, hip and knee osteoarthritis; HRAS, Health-Risk Attitude Scale; med, medium; mod., moderate; MS, multiple sclerosis; sev., severe.
      Table 5Model fit per study.
      StatisticStudy 1 - MSStudy 2 - antibioticsStudy 3 - HKOA
      Log-likelihood (-)Excluding HRAS9389.983009.384215.38
      Including HRAS9383.082999.734196.44
      Number of parametersExcluding HRAS294147
      Including HRAS386162
      Likelihood ratio testχ213.819.337.9
      df92015
      P value.130.502<.001
      HKOA indicates hip and knee osteoarthritis; HRAS, Health-Risk Attitude Scale; MS, multiple sclerosis.
      In contrast, systematic heterogeneity as measured by interactions between health-risk attitude and risk attributes was present in some risk attributes of the studies. In study 1, the MS patient sample with 3 risk attributes phrased using percent, we found systematic preference heterogeneity for all risk attributes in the first and largest class. In this class, health-risk attitude significantly moderated the effect of reducing the risk of relapse and reducing disease progression (P < .001 for both) and the risk of rare severe side effects (P = .020). In the second class, only the interaction between health-risk attitude and reducing risk of relapse (P = .003) was significant. In addition, the second study, concerning the antibiotics context with a general public sample, had 3 classes and 3 risk attributes. Health-risk attitude explained part of the heterogeneity for treatment failure rate (P = .019) in one of the classes but not in the other 2. Nevertheless, the interaction effects with the other risk attributes were not significant in any of the classes. In the third study about patients’ preferences for HKOA treatment, 2 attributes implicitly concerned risk. In 2 of the 4 classes, health-risk attitude explained heterogeneity of preferences for waiting time (P = .001 for both) but not for professionals involved.
      As shown in Table 5, inclusion of HRAS-13 scores significantly improved the model fit only in study 3 (χ2 = 37.9, df = 15, P < .001). In the other studies, the improvement was not statistically significant.

      Discussion and Conclusions

      Hence, where does health-risk attitude come into the equation when researching preference variation? Our study did not find evidence that health-risk attitude as measured by the HRAS-13 distinguishes people between classes. Nevertheless, we did find evidence that the HRAS-13 can distinguish people’s preferences for some risk attributes within classes. This association between health-risk attitude and preference heterogeneity was stronger in the case studies where respondents were sampled from a patient population than in the case study that used a general public sample. Respondents in the patient samples were also more health risk averse than members of the general public. In the first case study, which used a patient sample, health-risk attitude explained the heterogeneity of preferences for most attributes in both classes, but it did not significantly improve the model fit. In the third study, which also used a patient sample, health-risk attitude was related to heterogeneity of preferences for one attribute in 2 of 4 health preference classes. Although the 2 risk attributes of this study only implicitly concerned risk, it was the only study in which the model fit statistically significantly improved by incorporating health-risk attitude.
      Furthermore, we found that numeracy, health literacy, and age affected stochastic class allocation, meaning that these characteristics could distinguish preferences between classes for risk-related attributes and nonrisk-related attributes. In the study where numeracy affected class allocation, all risk attributes were phrased using percent. In the study where health literacy affected class allocation, one of the risk attributes was described in words. Moreover, numeracy and literacy were among the characteristics that improved external validity when accounted for in preference heterogeneity in de Bekker-Grob et al
      • de Bekker-Grob E.W.
      • Swait J.D.
      • Kassahun H.T.
      • et al.
      Are healthcare choices predictable? The impact of discrete choice experiment designs and models.
      and among the psychological constructs with the strongest consensus to be included in preference studies in the review of Russo et al.
      • Russo S.
      • Monzani D.
      • Pinto C.A.
      • et al.
      Taking into account patient preferences: a consensus study on the assessment of psychological dimensions within patient preference studies.
      Our results suggest that risks are in some way related to preference heterogeneity, either directly when health-risk attitude distinguishes people’s preferences within classes or indirectly when people have varying levels of numeracy and literacy.
      The relevance of these results is threefold. First, the impact of health-risk attitude on preferences should be explored beyond class membership by interacting the health-risk attitude with the risk-related attributes. This is expected to be mostly relevant in contexts where alternatives largely vary in terms of benefit-risk, when treatment outcomes are highly uncertain, or when patients are risk averse. In those contexts, accounting for health-risk attitude has the potential to improve model fit and model interpretations. Second, the impact of health-risk attitude on preferences should be explored using a different measure than the HRAS-13. Given that we did not find strong evidence for this using the HRAS-13, which is a health-specific instrument of which the items cover a broad range of health domains,
      • Huls S.P.I.
      • van Osch S.M.C.
      • Brouwer W.B.F.
      • van Exel J.
      • Stiggelbout A.M.
      Psychometric evaluation of the Health-Risk Attitude Scale (HRAS-13): assessing the reliability, dimensionality and validity in the general population and a patient population.
      an option would be to use a more targeted measure of health-risk attitude in DCEs. In addition, one could research the relationship between health preference heterogeneity and measures that use a narrower definition of risk attitude (eg, the standard gamble method
      • Wakker P.
      • Deneffe D.
      Eliciting von Neumann-Morgenstern utilities when probabilities are distorted or unknown.
      • Bleichrodt H.
      • Pinto J.L.
      • Wakker P.P.
      Making descriptive use of prospect theory to improve the prescriptive use of expected utility.
      • Bleichrodt H.
      • Abellan-Perpiñan J.M.
      • Pinto-Prades J.L.
      • Mendez-Martinez I.
      Resolving inconsistencies in utility measurement under risk: tests of generalizations of expected utility.
      or the Balloon Analog Risk Task
      • Lejuez C.W.
      • Read J.P.
      • Kahler C.W.
      • et al.
      Evaluation of a behavioral measure of risk taking: the balloon analogue risk task (BART).
      ). Such studies can confirm whether indeed health-risk attitude is not linked to preferences as strongly as anticipated
      • Russo S.
      • Monzani D.
      • Pinto C.A.
      • et al.
      Taking into account patient preferences: a consensus study on the assessment of psychological dimensions within patient preference studies.
      ,
      • Russo S.
      • Jongerius C.
      • Faccio F.
      • et al.
      Understanding patients’ preferences: a systematic review of psychological instruments used in patients’ preference and decision studies.
      or whether it could be explained by the relatively low levels of variance in the HARS-13 scores in the case studies. As outlined in the Methods section, we do recommend sticking to a health-specific measure of risk attitude. Third, given that numeracy and health literacy were found to affect stochastic class allocation, our results add to existing literature that stresses the importance of the communication of risks (ie, presentation, framing, training materials, and analysis) in DCEs (eg, Harrison et al,
      • Harrison M.
      • Rigby D.
      • Vass C.
      • Flynn T.
      • Louviere J.
      • Payne K.
      Risk as an attribute in discrete choice experiments: a systematic review of the literature.
      Veldwijk et al,
      • Veldwijk J.
      • van der Heide I.
      • Rademakers J.
      • et al.
      Preferences for vaccination: does health literacy make a difference?.
      and Peters et al
      • Peters E.
      • Hart P.S.
      • Fraenkel L.
      Informing patients: the influence of numeracy, framing, and format of side effect information on risk perceptions.
      ). In this study, we analyzed a wide range of risk attributes. Although we did not observe clear differences in the relationship between health-risk attitude and preference heterogeneity based on the type or phrasing of the risk attribute, we find that numeracy explained heterogeneity in the study in which risks were presented using percent, whereas literacy explained heterogeneity in the study where some risk attributes were phrased using percent and some using words.
      A strength of this study is that it is among the first to research health-risk attitude as an individual characteristic underlying heterogeneity in health preferences and thereby responds to the call for this type of research.
      • Russo S.
      • Monzani D.
      • Pinto C.A.
      • et al.
      Taking into account patient preferences: a consensus study on the assessment of psychological dimensions within patient preference studies.
      ,
      • Russo S.
      • Jongerius C.
      • Faccio F.
      • et al.
      Understanding patients’ preferences: a systematic review of psychological instruments used in patients’ preference and decision studies.
      ,
      • Harrison M.
      • Rigby D.
      • Vass C.
      • Flynn T.
      • Louviere J.
      • Payne K.
      Risk as an attribute in discrete choice experiments: a systematic review of the literature.
      The case studies provide a cross-European comparison in 3 different health contexts with varying degrees of risk and study population leading to an increased generalizability of the results. Nevertheless, the differences in samples also make it harder to identify the source of similarities and differences in results between the studies. Given that secondary data were used for the current study, comparability across the studies is limited. In future research, it would be interesting to set up studies with the specific aim to compare the impact of health-risk attitude across different populations and risk attributes. It should also be noted that it is unclear whether respondents’ level of perceived riskiness of the attributes is in line with what was determined by the researchers. Given that risk perception and risk behavior are not always aligned,
      • Weber E.U.
      • Blais A.-R.
      • Betz N.E.
      A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors.
      we recommend future research in this area to also elicit respondents’ risk perception at an early stage of DCE development. Furthermore, this research focused on improving model fit and model interpretations from the perspective of internal validity. Given the mixed evidence regarding the predictive ability of survey-based measures of risk attitude,
      • Zhang D.C.
      • Highhouse S.
      • Nye C.D.
      Development and validation of the general risk propensity scale (GRiPS).
      ,
      • Charness G.
      • Garcia T.
      • Offerman T.
      • Villeval M.C.
      Do measures of risk attitude in the laboratory predict behavior under risk in and outside of the laboratory?.
      ,
      • Islam A.
      • Smyth R.
      • Tan H.A.
      • Wang L.C.
      Survey measures versus incentivized measures of risk preferences: evidence from sex workers’ risky sexual transactions.
      it would be interesting to also study whether and how health-risk attitude and heterogeneity of health preferences are related from the perspective of external validity and individual-level prediction accuracy, for example, as in de Bekker-Grob et al.
      • de Bekker-Grob E.W.
      • Donkers B.
      • Bliemer M.C.J.
      • Veldwijk J.
      • Swait J.D.
      Can healthcare choice be predicted using stated preference data?.
      In conclusion, our study did not find evidence that health-risk attitude as measured by the HRAS-13 distinguishes people between classes. Nevertheless, we did find evidence that the HRAS-13 can distinguish people’s preferences for risk attributes within classes. This phenomenon was more pronounced in the patient samples than in the general population sample. Furthermore, we found that preference heterogeneity is affected by numeracy and health literacy. These results warrant the relevance of further research into preference heterogeneity beyond class membership, a different measure of health-risk attitude, and the communication of risks.

      Article and Author Information

      Author Contributions: Concept and design: Huls, Veldwijk, Viberg Johansson, Ancillotti, De Bekker-Grob
      Acquisition of data: Huls, Veldwijk, Ancillotti
      Analysis and interpretation of data: Huls, Veldwijk, Swait, Viberg Johansson, De Bekker-Grob
      Drafting of the manuscript: Huls
      Critical revision of the paper for important intellectual content: Huls, Veldwijk, Swait, Viberg Johansson, Ancillotti, De Bekker-Grob
      Statistical analysis: Huls, Veldwijk, Swait
      Provision of study materials or patients: Ancillotti
      Supervision: Veldwijk, De Bekker-Grob
      Conflict of Interest Disclosures: Ms Huls and Dr Swait and Dr de Bekker-Grob reported receiving support from the Erasmus Initiative “Smarter Choices for Better Health” during the conduct of the study. Dr Swait reported being a partner at Advanis Inc until 2020. No other disclosures were reported.
      Funding/Support: Dr Swait reported receiving funding from University of Padova, Italy for consulting work outside of the submitted work.
      Role of the Funder/Sponsor: The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the manuscript for publication.

      Acknowledgment

      The authors thank L.A. Visser, C.A. Uyl-de Groot, W.K. Redekop, S. Eriksson, D.I. Andersson, T. Godskesen, J. Nihlén Fahlquist, I.G. Arslan, R. Rozendaal, M.C.T. Persoons, M.E. Spruijt-van Hell, P.J.E. Bindels, S.M.A. Bierma-Zeinstra, and D. Schiphof for agreeing to use the data for the purpose of this article. The authors also thank associate editor Dr. Lysbeth Floden and the anonymous reviewers of Value in Health for their valuable suggestions.
      Declarations: Data and code are available on request.
      Ethics Approval: Data reused in this study were ethically tested and approved respectively by the Medical Ethical Testing Committee of the Erasmus Medical Centre (MEC-2019-0248), Uppsala Regional Ethical Review Board (Dnr 2018/293), and the Medical Ethical Testing Committee of the Erasmus Medical Centre (MEC-2018-1076).

      Supplemental Materials

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