Discrete choice experiments in health economics: distinguishing between the method and its application

Public preferences for online medical consultations in China: a discrete choice experiment

Background

Online medical consultation (OMC) is significant to promote the utilization and accessibility of healthcare resources and save time on consultation. However, the usage and public acceptance rates of it are still low in China. Meanwhile, few studies have focused on consumers' demand of OMC services. This study aims to identify attributes that influence users' preference for OMC services, quantify the value of these characteristics, and compare their relative importance.

Methods

A nationwide discrete choice experiment was conducted to survey Chinese residents' preference choices for six attributes of OMC services. Conditional logit model and mixed logit model were used to analyze respondents' preference. Willingness to pay and heterogeneity were estimated by the mixed logit model.

Results

A total of 856 respondents completed the study, and 668 questionnaires passed the consistency test. All of 6 attributes in the study were statistically significant except for "Doctor's professional title - Associate Senior." When choosing OMC services, respondents preferred to spend as little time and money as possible on a large online medical platform to consult a high-rated physician with a senior title from a well-known Grade-A tertiary hospital. Besides, respondents valued doctor's evaluation score most and were willing to pay ¥107 to obtain the services of higher-scored doctors.

Conclusion

The study measured Chinese residents' preferences for six attributes of OMC and showed the heterogeneity of attributes among subgroups. Our findings suggested that OMC services providers should reduce the customers' waiting time, improve the quality of services and enhance professional skills to meet the customers' requirements. More research on preferences for OMC needs to be conducted in China, especially for key populations such as patients with chronic diseases.

Continuing medical education in China: evidence from primary health workers' preferences for continuing traditional Chinese medicine education

BACKGROUND

Continuing Medical Education (CME) is an important part of the training process for health workers worldwide. In China, training in Traditional Chinese Medicine (TCM) not only improves the expertise of medical workers, but also supports the Chinese Government's policy of promoting TCM as an equal treatment to western medicine. CME, including learning Traditional Chinese Medicine Technologies (TCMTs), perform poorly and research into the motivation of health workers to engage in CME is urgently required. Using a discrete choice experiment, this study assessed the CME learning preferences of primary health workers, using TCMT as a case study of CME programs.

METHODS

We conducted a discrete choice experiment among health workers in Shandong Province, Guizhou Province, and Henan provinces from July 1, 2021 to October 1, 2022 on the TCMT learning preferences of primary health workers. The mixed logit model and latent class analysis model were used to analyze primary health workers' TCMT learning preferences.

RESULTS

A total of 1,063 respondents participated in this study, of which 1,001 (94.2%) passed the consistency test and formed the final sample. Our key finding was that there were three distinct classes of TCMT learners. Overall, the relative importance of the seven attributes impacting the learning of TCMTs were: learning expenses, expected TCMT efficacy, TCMT learning difficulty, TCMT mode of learning, TCMT type, time required to learn, and expected frequency of TCMT use. However, these attributes differed significantly across the three distinct classes of TCMT learners. Infrequent users (class 1) were concerned with learning expenses and learning difficulty; workaholics (class 2) focused on the mode of learning; and pragmatists (class 3) paid more attention to the expected TCMT efficacy and the expected frequency of TCMT use. We recommend targeted strategies to motivate TCMT learning suited to the requirements of each class of TCMT learners.

CONCLUSION

Rather than a single TCMT medical education program for primary health workers, CME programs should be targeted at different classes of TCMT learners.

Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment

Introduction

There have been promising developments in technologies and associated algorithm-based prescribing (‘stratified approach’) to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors.

Objective

This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed ‘biologic calculator’).

Methods

An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a ‘biologic calculator’ compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models.

Results

Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies.

Conclusion

Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00533-z.

Preferences heterogeneity of health care utilization of community residents in China: a stated preference discrete choice experiment

Background

To tackle the issue with the low usage of primary healthcare service in China, it is essential to align resource distribution with the preferences of the community residents. There are few academic researches for describing residents’ perceived characteristics of healthcare services in China. This study aims to investigate the preferences of healthcare services utilization in community residents and explore the heterogeneity. The findings will be useful for the policy makers to take targeted measures to tailor the provision of healthcare services.

Methods

The face-to-face interviews and surveys were conducted to elicit four key attributes (care provider; mode of services; cost; travel time) of the preference from community residents for healthcare utilization. A rational test was presented first to confirm the consistency, and then 16 pairs of choice tasks with 12 sociodemographic items were given to the respondents. Two hypothetical options for each set, without an opt-out option, were presented in each choice task. The latent class analysis (LCA) was used to analyse the data.

Results

Two thousand one hundred sixty respondents from 36 communities in 6 cities were recruited for our study. 2019 (93.47%) respondents completed valid discrete choice experiment (DCE) questionnaires. The LCA results suggested that four groups of similar preferences were identified. The first group (27.29%) labelled as “Comprehensive consideration” had an even preference of all four attributes. The second group (37.79%) labelled as “Price-driven” preferred low-price healthcare services. The third group labelled as “Near distance” showed a clear preference for seeking healthcare services nearby. The fourth group (34.18%) labelled as “Quality seeker” preferred the healthcare service provided by experts. Willingness to pay (WTP) results showed that people were willing to accept CNY202.12($29.37) for Traditional Chinese Medicine (TCM) services and willing to pay CNY604.31($87.81) for the service provided by experts.

Conclusions

Our study qualitatively measures the distinct preferences for healthcare utilization in community residents in China. The results suggest that the care provider, mode of services, travel time and cost should be considered in priority setting decisions. The study, however, reveals substantial disagreement in opinion of TCM between different population subgroups.

Investigating the Heterogeneity in Women's Preferences for Breast Screening: Does the Communication of Risk Matter?

BACKGROUND

The relative benefits and risks of screening programs for breast cancer have been extensively debated.

OBJECTIVES

To quantify and investigate heterogeneity in women's preferences for the benefits and risks of a national breast screening program (NBSP) and to understand the effect of risk communication format on these preferences.

METHODS

An online discrete choice experiment survey was designed to elicit preferences from female members of the public for an NBSP described by three attributes (probability of detecting a cancer, risk of unnecessary follow-up, and out-of-pocket screening costs). Survey respondents were randomized to one of two surveys, presenting risk either as percentages only or as icon arrays and percentages. Respondents were required to choose between two hypothetical NBSPs or no screening in 11 choice sets generated using a Bayesian D-efficient design. The trade-offs women made were analyzed using heteroskedastic conditional logit and scale-adjusted latent class models.

RESULTS

A total of 1018 women completed the discrete choice experiment (percentages-only version = 507; icon arrays and percentages version = 511). The results of the heteroskedastic conditional logit model suggested that, on average, women were willing-to-accept 1.72 (confidence interval 1.47-1.97) additional unnecessary follow-ups and willing-to-pay £79.17 (confidence interval £66.98-£91.35) for an additional cancer detected per 100 women screened. Latent class analysis indicated substantial heterogeneity in preferences with six latent classes and three scale classes providing the best fit. The risk communication format received was not a predictor of scale class or preference class membership.

CONCLUSIONS

Most women were willing to trade-off the benefits and risks of screening, but decision makers seeking to improve uptake should consider the disparate needs of women when configuring services.

The Value of Clinical Needs Assessments for Point-of-Care Diagnostics

Most entrepreneurial ventures fail long before the core technology can be brought to the marketplace because of disconnects in performance and usability measures such as accuracy, cost, complexity, assay stability, and time requirements between technology developers' specifications and needs of the end-users. By going through a clinical needs assessment (CNA) process, developers will gain vital information and a clear focus that will help minimize the risks associated with the development of new technologies available for use within the health care system. This article summarizes best practices of the principal investigators of the National Institute of Biomedical Imaging and Bioengineering point-of-care (POC) centers within the National Institute of Biomedical Imaging and Bioengineering POC Technologies Research Network. Clinical needs assessments are particularly important for product development areas that do not sufficiently benefit from traditional market research, such as grant-funded research and development, new product lines using cutting-edge technologies developed in start-up companies, and products developed through product development partnerships for low-resource settings. The objectives of this article were to (1) highlight the importance of CNAs for development of POC devices, (2) discuss methods applied by POC Technologies Research Network for assessing clinical needs, and (3) provide a road map for future CNAs.

Preferences for cancer treatments: an overview of methods and applications in oncology

This review provides cancer clinicians and researchers with an overview of methods for assessing preferences, with examples and recommendations for their application in oncology. Decisions about cancer treatments involve trade-offs between their relative benefits and harms. An individual's preference for a cancer treatment reflects their evaluation of the relative benefits and harms in comparison with a given alternative or alternatives. Methods of preference assessment include the ranking or rating scale, standard gamble (SG), time trade-off (TTO), visual analogue scale, discrete choice experiment (DCE), and multi-attribute utility instrument (MAUI). The choice of method depends on the purpose of preference assessment; the ranking or rating scale, SG, TTO, and DCEs are best suited to clinical decisions, whereas MAUIs are best suited to health policy decisions. Knowledge of patients' preferences for cancer treatments can better inform clinical decisions about patient management by enabling the tailoring of decisions to individual patients' values, attitudes, and priorities and health policy decisions through economic evaluations of cancer treatments and their suitability for coverage by health payers.

Discrete choice experiments for complex health-care decisions: does hierarchical information integration offer a solution?

This paper describes an application of hierarchical information integration (HII) discrete choice experiments. We assessed theoretical and construct validity, as well as internal consistency, to investigate whether HII can be used to investigate complex multi-faceted health-care decisions (objective 1). In addition, we incorporated recent advances in mixed logit modelling (objective 2). Finally, we determined the response rate and predictive ability to study the feasibility of HII to support health-care management (objective 3). The clinical subject was the implementation of the guideline for breast cancer surgery in day care, which is a complex process that involves changes at the organizational and management levels, as well as the level of health-care professionals and that of patients.We found good theoretical and construct validity and satisfactory internal consistency. The proposed mixed logit model, which included repeated measures corrections and subexperiment error scale variations, also performed well. We found a poor response, but the model had satisfactory predictive ability. Therefore, we conclude that HII can be used successfully to study complex multi-faceted health-care decisions (objectives 1 and 2), but that the feasibility of HII to support health-care management, in particular in challenging implementation projects, seems less favourable (objective 3).

Conducting discrete choice experiments to inform healthcare decision making: a user's guide

Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.