Keeping an eye on cost: What can eye tracking tell us about attention to cost information in discrete choice experiments?

Are we ready for the next pandemic? Public preferences and trade-offs between vaccine characteristics and societal restrictions across 21 countries

In vaccination decisions, individuals must weigh the benefits against the risks of remaining unvaccinated and potentially facing social restrictions. Previous studies have focused on individual preferences for vaccine characteristics and societal restrictions separately. This study aims to quantify public preferences and the potential trade-offs between vaccine characteristics and societal restrictions, including lockdowns and vaccine mandates, in the context of a future pandemic. We conducted a discrete choice experiment (DCE) involving 47,114 respondents from 21 countries between July 2022 and June 2023 through an online panel. Participants were presented with choices between two hypothetical vaccination programs and an option to opt-out. A latent class logit model was used to estimate trade-offs among attributes. Despite some level of preference heterogeneity across countries and respondents' profiles, we consistently identified three classes of respondents: vaccine refusers, vaccine-hesitant, and pro-vaccine individuals. Vaccine attributes were generally deemed more important than societal restriction attributes. We detected strong preferences for the highest levels of vaccine effectiveness and for domestically produced vaccines across most countries. Being fully vaccinated against COVID-19 was the strongest predictor of pro-vaccine class preferences. Women and younger people were more likely to be vaccine refusers compared to men and older individuals. In some countries, vaccine hesitancy and refusal were linked to lower socioeconomic status, whereas in others, individuals with higher education and higher income were more likely to exhibit hesitancy. Our findings emphasize the need for tailored vaccination programs that consider local contexts and demographics. Building trust in national regulatory authorities and international organizations through targeted communication, along with investing in domestic production facilities, can improve vaccine uptake and enhance public health responses in the future.

Choice Consistency in Discrete Choice Experiments: Does Numeracy Skill Matter?

OBJECTIVES

This study investigated the relationship between numeracy skills (NS) and choice consistency in discrete choice experiments (DCEs).

METHODS

A DCE was conducted to explore patients' preferences for kidney transplantation in Italy. Patients completed the DCE and answered 3-item numeracy questions. A heteroskedastic multinomial logit model was used to investigate the effect of numeracy on choice consistency.

RESULTS

Higher NS were associated with greater choice consistency, increasing the scale to 1.63 (P < .001), 1.39 (P < .001), and 1.18 (P < .001) for patients answering 3 of 3, 2 of 3, and 1 of 3 questions correctly, respectively, compared with those with no correct answers. This corresponded to 63%, 39%, and 18% more consistent choices, respectively. Accounting for choice consistency resulted in varying willingness-to-wait (WTW) estimates for kidney transplant attributes. Patients with the lowest numeracy (0/3) were willing to wait approximately 42 months [95% CI: 29.37, 54.68] for standard infectious risk, compared with 33 months [95% CI: 28.48, 38.09] for 1 of 3, 28 months [95% CI: 25.13, 30.32] for 2 of 3, and 24 months [95% CI: 20.51, 27.25] for 3 of 3 correct answers. However, WTW differences for an additional year of graft survival and neoplastic risk were not statistically significant across numeracy levels. Supplementary analyses of 2 additional DCEs on COVID-19 vaccinations and rheumatoid arthritis, conducted online, supported these findings: higher NS were associated with more consistent choices across different disease contexts and survey formats.

CONCLUSIONS

The findings suggested that combining patients with varying NS could bias WTW estimates, highlighting the need to consider numeracy in DCE data analysis and interpretation.

The use of machine learning to understand the role of visual attention in multi-attribute choice

Whether eye movements (as a measure of visual attention) contribute to the understanding of how multi-attribute decisions are made, is still a matter of debate. In this study, we show how machine learning methods can be used to separate the effects of the information presented, eye movement patterns, and attention to specific information. We also show how to deal with data from a relatively small sample of participants, often found in eye tracking studies that require in-lab testing. We make use of a dataset of 30 females who decided whether or not to accept screening for Chlamydia in 21 different scenarios. For this dataset, we find that eye movements did not add to the prediction of choice beyond the information presented to participants. Future studies should determine whether the same conclusion holds for other eye tracking datasets.

Comparing heuristic valuation processes between health state valuation from child and adult perspectives

OBJECTIVES

Health state valuation assumes that respondents trade off between all aspects of choice tasks and maximize their utility. Yet, respondents may use heuristic valuation processes, i.e., strategies to simplify or avoid the trade-offs that are core to health state valuation. The objective of this study is to explore if heuristic valuation processes are more prevalent for valuation from a 10-year-old child's perspective compared to the use of an adult perspective.

METHODS

We reused existing data in which EQ-5D health states were valued from adult and child perspectives with composite time trade-off (cTTO) and discrete choice experiment (DCE) tasks. Our analyses focused on comparing completion time and responding patterns across both perspectives. We also explored how reflective of a set of heuristic strategies respondents' choices were in both perspectives.

RESULTS

We found no evidence for systematic differences in completion time across perspectives. Generally, we find different responding patterns in child perspectives, e.g., more speeding, dominance violations, and clustering of utilities at 1.0, 0.8, and 0. Very few heuristic strategies provide a coherent explanation for the observed DCE responses.

CONCLUSION

Our results provide some, albeit indirect, evidence for differences in heuristic valuation processes between perspectives, although not across all data sources. Potential effects of heuristic valuation processes, such as transfer of responsibility, may be identified through studying responding patterns in cTTO and DCE responses.

Making Use of Technology to Improve Stated Preference Studies

The interest in quantifying stated preferences for health and healthcare continues to grow, as does the technology available to support and improve health preference studies. Technological advancements in the last two decades have implications and opportunities for preference researchers designing, administering, analysing, interpreting and applying the results of stated preference surveys. In this paper, we summarise selected technologies and how these can benefit a preference study. We discuss empirical evaluations of the technology in preference research, with examples from health where possible. The technologies reviewed include serious games, virtual reality, eye tracking, innovative formats and decision aids with values clarification components. We conclude with a critical reflection on the benefits and limitations of implementing (often costly) technology alongside stated preference studies.