Navigating Public Policy Responses to a Pandemic: The Balancing Act Between Physical Health, Mental Health, and Household Income.

OBJECTIVE: During COVID-19, governments imposed restrictions that reduced pandemic-related health risks but likely increased personal and societal mental health risk, partly through reductions in household income. This study aimed to quantify the public's willingness to accept trade-offs between pandemic health risks, household income reduction, and increased risk of mental illness that may result from future pandemic-related policies. METHODS: A total of 547 adults from an online panel participated in a discrete choice experiment where they were asked to choose between hypothetical future pandemic scenarios. Each scenario was characterized by personal and societal risks of dying from the pandemic, experiencing long-term complications, developing anxiety/depression, and reductions in household income. A latent class regression was used to estimate trade-offs. RESULTS: Respondents state a willingness to make trade-offs across these attributes if the benefits are large enough. They are willing to accept 0.8% lower household income (0.7-1.0), 2.7% higher personal risk of anxiety/depression (1.8-3.6), or 3.2% higher societal rate of anxiety/depression (1.7-4.7) in exchange for 300 fewer deaths from the pandemic. CONCLUSION: Results reveal that individuals are willing to accept lower household income and higher rates of mental illness, both personal and societal, if the physical health benefits are large enough. Respondents placed greater emphasis on maintaining personal, as opposed to societal, mental health risk and were most interested in preventing pandemic-related deaths. Governments should consider less restrictive policies when pandemics have high morbidity but low mortality to avoid the prospect of improving physical health while simultaneously reducing net social welfare.

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework.

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.

Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis.

OBJECTIVE: To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure. METHODS: A best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred. RESULTS: Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization. CONCLUSION: Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

Patient Preferences in Pulmonary Arterial Hypertension, a Latent Class Analysis to Identify Preference Heterogeneity.

OBJECTIVES: Pulmonary arterial hypertension (PAH) is a chronic, progressive disease of the pulmonary circulation characterized by vascular remodeling that, if untreated, can lead to right heart dysfunction and death. This analysis measured heterogeneity in patient preferences for PAH-specific treatment regimens. METHOD: Adult patients with PAH with slight to marked limitations during physical activity were recruited through a patient organization in Germany. Participants completed an online best-worst scaling case 3 survey. Patients chose among 3 hypothetical treatment profiles defined by 6 benefits and risks at varying levels. Participants completed 12 choice tasks. Preference heterogeneity was assessed using latent class analysis. RESULTS: A total of 83 participants (76% female) completed the survey. Best-fit model revealed 4 classes. Class 1 (19% of participants) assigned importance to multiple attributes particularly side effects, class 2 (34%) to physical activity limitations, class 3 (30%) to survival and physical activity limitations, and class 4 (17%) to survival. No differences in sociodemographic characteristics were observed across classes. Compared with other classes, class 4 was most likely to report having marked physical activity limitations (79%) and needing daily help (100%), while considering higher daily activity levels to be ordinary (walking >1 km [71%] or climbing several flights of stairs [50%]). CONCLUSION: This first patient preference study in a PAH population suggests that physical activity limitations in addition to survival matter most to patients; however, preference heterogeneity between groups of patients was observed. Patient preferences should be considered in treatment decision making to better balance patient's expectations regarding the known risk-benefit ratio of treatment.

Which body functions and activities matter for stroke patients? Study protocol for best-Worst scalings to value core elements of the International Classification of Functioning, Disability and Health.

BACKGROUND: Stroke is a common, serious, and disabling healthcare problem with increasing incidence and prevalence. Following a stroke, identifying the factors associated with decisions about rehabilitation interventions is important to assess rehabilitation after stroke. The aim is to guide clinical staff to make patient-centered decisions. Fundamentally, decision makers cannot draw on evidence to consider the relevance of distinct functions and activities from the patient's perspective. Until now, outcomes of rehabilitation are generally categorized using the International Classification of Functioning, Disability and Health (ICF). This can be seen as a conceptual basis for the assessment of health and disability. Since the ICF does not distinguish importance between these aspects there is a need to value the most important clinical factors as well as related activities from a patients and public perspective to help guide therapists in effectively designing post-acute rehabilitation care for individuals following stroke. The research question is which ICF body functions and activities are of value to stroke patients? Which trade-offs are patients willing to make within the core elements? Health preference research (HPR) answers the need to develop additional preference weights for certain ICF dimensions. Patient preference information (PPI) values health conditions based on the ICF from a patient perspective. METHODS: In this study we conduct three best-worst scaling (BWS) experiments to value body function and activities from patients' and public perspective. Out of all ICF dimensions this research covers health conditions relevant to stroke patients in terms of body function, perception, and activities of daily living. Stroke patients as well as members of the general population will be recruited to participate in the online BWS surveys. Fractional, efficient designs are applied regarding the survey design. Conditional and multinominal logit analyses will be used as the main analysis method, with the best-worst count analysis as a secondary analysis. The survey is being piloted prior to commencing the process of data collection. Results are expected by the autumn of 2023. DISCUSSION: The research will add to the current literature on clinical decision-making in stroke rehabilitation and the value of certain body functions as well as related activities in neurorehabilitation. Moreover, the study will show whether body functions and activities that are currently equally weighted in international guidelines are also equally important from the point of view of those affected, or whether there are disconcordances in terms of differences between public judgements and patients' preferences.

Trade-Offs between Vaccine Effectiveness and Vaccine Safety: Personal versus Policy Decisions.

OBJECTIVE: We aimed to investigate whether individuals' trade-offs between vaccine effectiveness and vaccine safety vary if they are asked to consider the perspective of a policymaker making decisions for others compared with the decisions they would make for themselves. METHOD: A web-enabled discrete choice experiment survey was administered between 1 April and 1 May 2022 to participants recruited from the general population of two Southeast Asian countries (Indonesia and Vietnam). In each country, 500 participants were randomly assigned to make decisions regarding coronavirus disease 2019 (COVID-19) vaccines for others as a policymaker or in a personal capacity for their own use. Vaccines were characterized by three attributes: (1) effectiveness of the vaccine in reducing infection rate; (2) effectiveness of the vaccine in reducing hospitalization among those infected; and (3) risk of death from vaccine-related serious adverse events. A mixed logit model was utilized for analyses. RESULTS: Based on the attributes and levels used in this study, the most important vaccine attribute was the risk of death from vaccine-related adverse events, followed by effectiveness in reducing infection rate and hospitalizations. Compared with personal decisions, the mean probability of choosing a vaccine was (1) lower, and (2) more sensitive to the changes in risk of death from adverse events in policy decisions (p ≤ 0.01). CONCLUSIONS AND RELEVANCE: Our results suggest that, in the face of an infectious disease pandemic, individuals are likely to be more risk-averse to vaccine-related deaths when making decisions for others as a policymaker than they would for themselves.

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment.

BACKGROUND: Strokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients' unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. OBJECTIVE: This study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. METHODS: To obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. RESULTS: The literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. CONCLUSIONS: Our results will inform decision makers about patients' and publics' acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46056.

Comparison of Preferences and Data Quality between Discrete Choice Experiments Conducted in Online and Face-to-Face Respondents.

INTRODUCTION: Discrete choice experiments (DCE) are increasingly being conducted using online panels. However, the comparability of such DCE-based preferences to traditional modes of data collection (e.g., in-person) is not well established. In this study, supervised, face-to-face DCE was compared with its unsupervised, online facsimile on face validity, respondent behavior, and modeled preferences. METHODS: Data from face-to-face and online EQ-5D-5L health state valuation studies were compared, in which each used the same experimental design and quota sampling procedure. Respondents completed 7 binary DCE tasks comparing 2 EQ-5D-5L health states presented side by side (health states A and B). Data face validity was assessed by comparing preference patterns as a function of the severity difference between 2 health states within a task. The prevalence of potentially suspicious choice patterns (i.e., all As, all Bs, and alternating As/Bs) was compared between studies. Preference data were modeled using multinomial logit regression and compared based on dimensional contribution to overall scale and importance ranking of dimension-levels. RESULTS: One thousand five Online respondents and 1,099 face-to-face screened (F2FS) respondents were included in the main comparison of DCE tasks. Online respondents reported more problems on all EQ-5D dimensions except for Mobility. The face validity of the data was similar between comparators. Online respondents had a greater prevalence of potentially suspicious DCE choice patterns ([Online]: 5.3% [F2FS] 2.9%, P = 0.005). When modeled, the relative contribution of each EQ-5D dimension differed between modes of administration. Online respondents weighed Mobility more importantly and Anxiety/Depression less importantly. DISCUSSION: Although assessments of face validity were similar between Online and F2FS, modeled preferences differed. Future analyses are needed to clarify whether differences are attributable to preference or data quality variation between modes of data collection.

Divergent preferences for enhanced HIV testing options among high-risk populations in northern Tanzania: a short report.

To achieve the UNAIDS target of diagnosing 95% of all persons living with HIV, enhanced HIV testing services with greater attractional value need to be developed and implemented. We conducted a discrete choice experiment (DCE) to quantify preferences for enhanced HIV testing features across two high-risk populations in the Kilimanjaro Region in northern Tanzania. We designed and fielded a survey with 12 choice tasks to systematically recruited female barworkers and male mountain porters. Key enhanced features included: testing availability on every day of the week, an oral test, integration of a general health check or an examination for sexually transmitted infections (STI) with HIV testing, and provider-assisted confidential partner notification in the event of a positive HIV test result. Across 300 barworkers and 440 porters surveyed, mixed logit analyses of 17,760 choices indicated strong preferences for everyday testing availability, health checks, and STI examinations. Most participants were averse to oral testing and confidential partner notification by providers. Substantial preference heterogeneity was observed within each risk group. Enhancing HIV testing services to include options for everyday testing, general health checks, and STI examinations may increase the appeal of HIV testing offers to high-risk populations.Trial registration: ClinicalTrials.gov identifier: NCT02714140.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report.

OBJECTIVES: Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences. METHODS: An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends. RESULTS: Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model. CONCLUSIONS: Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics.

PROBLEM: Policymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions. METHODS: An online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best-worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people's acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection. RESULTS: Data from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income. DISCUSSION: The results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population's preferences will help inform decisions that consider people's perspectives and expectations for the future. Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

The Impact of Gastrointestinal Symptoms on Patients' Well-Being: Best-Worst Scaling (BWS) to Prioritize Symptoms of the Gastrointestinal Symptom Score (GIS).

BACKGROUND: The gastrointestinal symptom score (GIS) is used in a standardized form to ascertain dyspeptic symptoms in patients with functional dyspepsia in clinical practice. As a criterion for evaluating the effectiveness of a treatment, the change in the summed total point value is used. The total score ranges from 0 to 40 points, in which a higher score represents a more serious manifestation of the disease. Each symptom is included with equal importance in the overall evaluation. The objective of this study was to test this assumption from a patients' perspective. Our aim was to measure the priorities of patients for the ten gastrointestinal symptoms by using best-worst scaling. METHOD: A best-worst scaling (BWS) object scaling (Case 1) was applied. Therefore, the symptoms of the GIS were included in a questionnaire using a fractional factorial design (BIBD-balanced incomplete block design). In each choice set, the patients selected the component that had the most and the least impact on their well-being. The BIB design generated a total of 15 choice sets, which each included four attributes. RESULTS: In this study, 1096 affected patients were asked for their priorities regarding a treatment of functional dyspepsia and motility disorder. Based on the data analysis, the symptoms abdominal cramps (SQRT (B/W): -1.27), vomiting (SQRT (B/W): -1.07) and epigastric pain (SQRT (B/W): -0.76) were most important and thus have the greatest influence on the well-being of patients with functional dyspepsia and motility disorders. In the middle range are the symptoms nausea (SQRT (B/W): -0.69), acid reflux/indigestion (SQRT (B/W): -0.29), sickness (SQRT (B/W): -0.26) and retrosternal discomfort (SQRT (B/W): 0.26), whereas the symptoms causing the least impact are the feeling of fullness (SQRT (B/W): 0.80), early satiety (SQRT (B/W): 1.54) and loss of appetite (SQRT(B/W): 1.95). DISCUSSION: Unlike the underlying assumption of the GIS, the BWS indicated that patients did not weight the 10 symptoms equally. The results of the survey show that the three symptoms of vomiting, abdominal cramps and epigastric pain are weighted considerably higher than symptoms such as early satiety, loss of appetite and the feeling of fullness. The evaluation of the BWS data has illustrated, however, that the restrictive assumption of GIS does not reflect the reality of dyspeptic patients. CONCLUSIONS: In conclusion, a preference-based GIS is necessary to make valid information about the real burden of illness and to improve the burden of symptoms in the indication of gastrointestinal conditions. The findings of the BWS demonstrate that the common GIS is not applicable to represent the real burden of disease. The results suggest the potential modification of the established GIS by future research using a stated preference study.

Public preferences for the allocation of donor organs for transplantation: A discrete choice experiment.

This study aimed to assess public preferences for the allocation of donor organs in Germany with the focus on ethical principles of distributive justice. We performed a discrete choice experiment (DCE) using a self-completed online questionnaire. Based on a systematic review and focus group discussions, six attributes, each with two-four levels, were selected (corresponding principle of distributive justice in brackets), including (1) life years gained after transplantation (principle of distributive justice: effectiveness/benefit - utilitarianism), (2) quality of life after transplantation (effectiveness/benefit - utilitarianism), (3) chance for a further donor organ offer (principle of distributive justice: medical urgency - favouring the worst-off), (4) age (medical and social risk factors: sociodemographic status), (5) registered donor (principle of distributive justice: value for society), and (6) individual role in causing organ failure (principle of distributive justice: own fault). Each respondent was presented with eight choice sets and asked to choose between two hypothetical patients without an opt-out. Data were analysed using conditional logit, mixed logit and latent class models. The final sample comprised 1028 respondents. Choice decisions were significantly influenced by all attributes except chance for a further donor organ offer. The attributes of good quality of life after transplantation, younger age, and no individual role in causing organ failure had the greatest impact on choice decisions. Life years gained after transplantation and being a registered donor were less important for the public. The latent class model identified four classes with preference heterogeneities. Respondents preferred to allocate deceased donor organs by criteria related to effectiveness/benefit, whereas medical urgency was of minor importance. Therefore, a public propensity for a rational, utilitarian, ethical model of allocation could be identified. Public preferences can help to inform policy to warrant socially responsible allocation systems and thus improve organ donation rates.

Patient preferences in the treatment of hemophilia A: A latent class analysis.

OBJECTIVE: To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A. METHODS: Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences. RESULTS: The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership. CONCLUSIONS: The LCM analysis addresses heterogeneity in respondents' choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.

Personalized diabetes management: what do patients with diabetes mellitus prefer? A discrete choice experiment.

BACKGROUND: There are unresolved procedural and medical problems in the care of diabetes, which cause high costs for health systems. These include the inadequate glycemic adjustment, care gaps, therapeutic inertia, and a lack of motivation. Personalized diabetes management can be seen as a kind of "standard process" that provides both physicians and patients with a framework. The aim of this empirical survey is the evaluation of patient preferences regarding personalized diabetes management. The purpose of this experiment is to demonstrate the properties of the programs that are relevant for the choice of insulin-based therapy regimens for patients with type II diabetes mellitus. METHODS: A discrete choice experiment (DCE) was applied to identify preferences for a personalized diabetes management in patients with type II diabetes mellitus. Six attributes were included. The DCE was conducted in June 2017 using a fractional factorial design, and the statistical data analysis used random effect logit models. RESULTS: N = 227 patients (66.1% male) were included. The preference analysis showed dominance for the attribute "occurrence of severe hypoglycemias per year" [level difference (LD) 2765]. Preference analysis also showed that participants weight the "risk of myocardial infarction (over 10 years)" (LD 1.854) highest among the side effects. Within the effectiveness criterion of "change in the long-term blood glucose level (HbA1c)" a change at an initial value of 9.5% (LD 1.146) is weighted slightly higher than changes at 7.5% (LD 1.141). Within the random parameter logit estimation, all coefficients proved to be significantly different from zero at the level p ≤ 0.01. The latent class analysis shows three heterogeneous classes, each showing clearly different weights of the therapeutic properties. This results in a clear three-folding: for 1/3 of the respondents the change of the long-term blood sugar (HbA1c value) is the top objective. Another third is solely interested in the short-term effectiveness of the therapy in the sense of the occurrence of severe hypoglycemias per year. The last third of the interviewees finally focuses on the follow-up regarding cardiovascular events. Overall, there were five structural and personality traits which have an influence on the respective probability of the class membership. DISCUSSION/CONCLUSION: This study identifies and weights the key decision-making criteria for optimal management of diabetes from the perspective of patients. It was shown that the effectiveness of a care program is the most important from the perspective of the patient and avoiding severe a hypoglycemia has the greatest influence on the choice. The risk of myocardial infarction as a follow-up disease and the long-term adjustment of the blood glucose follow the importance. In the analysis of possible subgroup differences by means of latent class analysis, it was found that three preference patterns exist within the sample. The generated preference data can be used for the design of personalized management approaches. It remains open to the extent to which expert opinions and patient preferences diverge.

Comparison of online and face-to-face valuation of the EQ-5D-5L using composite time trade-off.

OBJECTIVE: The aim of this study was to compare online, unsupervised and face-to-face (F2F), supervised valuation of EQ-5D-5L health states using composite time trade-off (cTTO) tasks. METHODS: The official EuroQol experimental design and valuation protocol for the EQ-5D-5L of 86 health states were implemented in interviewer-assisted, F2F and unsupervised, online studies. Validity of preferences was assessed using prevalence of inconsistent valuations and expected patterns of TTO values. Respondent task engagement was measured using number of trade-offs and time per task. Trading patterns such as better-than-dead only was compared between modes. Value sets were generated using linear regression with a random intercept (RILR). Value set characteristics such as range of scale and dimension ranking were evaluated between modes. RESULTS: Five hundred one online and 1,134 F2F respondents completed the surveys. Mean elicited TTO values were higher online than F2F when compared by health state severity. Compared to F2F, a larger proportion of online respondents did not assign the poorest EQ-5D-5L health state (i.e., 55555) the lowest TTO value ([Online] 41.3% [F2F] 12.2%) (p < 0.001). A higher percentage of online cTTO tasks were completed in 3 trade-offs or fewer ([Online] 15.8% [F2F] 3.7%), (p < 0.001). When modeled using the RILR, the F2F range of scale was larger than online ([Online] 0.600 [F2F] 1.307) and the respective dimension rankings differed. CONCLUSIONS: Compared to F2F data, TTO tasks conducted online had more inconsistencies and decreased engagement, which contributed to compromised data quality. This study illustrates the challenges of conducting online valuation studies using the TTO approach.

Increasing respondent engagement in composite time trade-off tasks by imposing three minimum trade-offs to improve data quality.

BACKGROUND: Web-based surveys are increasingly utilized for health valuation studies but may be more prone to lack of engagement and, therefore, poor data validity. The objective of this study was to evaluate the effect of imposed engagement (i.e., at least three trade-offs) in the composite time trade-off (cTTO) task. METHODS: The EQ-5D-5L valuation study protocol and study design were adapted for online, unsupervised completion in two arms: base case and engagement. Validity of preferences was assessed using the prevalence of inconsistent valuations and expected patterns of TTO values. Respondent task engagement was measured using time per task. Value sets were generated using linear regression with a random intercept (RILR). RESULTS: The base case (n = 501) and engagement arms (n = 504) clustered at different TTO values: [base case] 0, 1; [engagement] -0.5, 0.45, 0.6. Mean TTO values were lower for the engagement arm. Engagement respondents did not spend more time per TTO task: [base case] 63.3 s (SD 77.9 s); [engagement] 64.7 s (SD 73.3 s); p = 0.36. No significant difference was found between arms for prevalence of respondents with at least one inconsistent TTO value: [base case] 61.1%; [engagement] 63.5%; p = 0.43. Both value sets had significant intercepts far from 1: [base case] 0.846; [engagement] 0.783. The relative importance of the EQ-5D dimensions also differed between arms. CONCLUSIONS: Both online arms had poor quality data. A minimum trade-off threshold did not improve engagement nor face validity of the data, indicating that modifications to the number of iterations are insufficient alone to improve data quality/validity of online TTO studies.

What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects.

Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods.