Objectives: Taboo trade-offs can explain some of the (moral) difficulties in healthcare decision-making. The moral psychology literature suggests that individuals are averse to making trade-offs between attributes belonging to different values, such as (sacred) human lives versus (secular) money. We demonstrate and empirically test a discrete choice model designed to capture Taboo Trade-off Aversion (TTOA) behaviors in the healthcare domain. Methods: The linear-additive Random Utility Maximization (RUM) model is extended to capture TTOA behaviors by including penalties for taboo trade-offs. Using two Discrete Choice Experiments (DCEs) focusing on taboo trade-offs in public health policies, we empirically compare conventional linear-additive RUM models with TTOA models to explore differences in model and behavioral results. Results: We observe TTOA in both DCEs. In one DCE, the TTOA model separates TTOA effects from attribute-related parameters, showing inflated parameters in conventional RUM models when TTOA behavior is present. This discrepancy affected Willingness-To-Pay (WTP) estimates, with WTP to save an incremental patient life approximately 3.5 times higher in conventional RUM models compared to the TTOA models. The presence and magnitude of TTOA varied considerably across respondents. Latent Class (LC) models reveal that some respondent groups perceive trade-offs as taboo significantly, while others do not. Conclusions: Accounting for TTOA in RUM models may lead to more accurate behavioral information when choice behaviors are affected by taboo trade-offs. Researchers and policymakers can use TTOA models to obtain a more nuanced understanding of public acceptability in morally salient policy decisions – ultimately helping to navigate, rather than avoid, taboo trade-offs.

Background: Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare. Methods: An interdisciplinary literature search across four databases – PubMed, Scopus, Web of Science, and Arxiv – was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies. Results: DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the ‘moral’ cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making. Conclusions: By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making.

Objective: Here we investigate public preferences for coronavirus disease 2019 (COVID-19) certificates in the Netherlands, and whether these preferences differ between subgroups in the population. Methods: A survey including a discrete choice experiment was administered to 1500 members of the adult population of the Netherlands. Each participant was asked to choose between hypothetical COVID-19 certificates that differed in seven attributes: the starting date, and whether the certificate allowed gathering with multiple people, shopping without appointment, visiting bars and restaurants, visiting cinemas and theatres, attending events, and practising indoor sports. Latent class models (LCMs) were used to determine the attribute relative importance and predicted acceptance rate of hypothetical certificates. Results: Three classes of preference patterns were identified in the LCM. One class a priori opposed a certificate (only two attributes influencing preferences), another class was relatively neutral and included all attributes in their decision making, and the final class was positive towards a certificate. Respondents aged > 65 years and those who plan to get vaccinated were more likely to belong to the latter two classes. Being allowed to shop without appointment and to visit bars and restaurants was most important to all respondents, increasing predicted acceptance rate by 12 percentage points. Conclusions: Preferences for introduction of a COVID-19 certificate are mixed. A certificate that allows for shopping without appointment and visiting bars and restaurants is likely to increase acceptance. The support of younger citizens and those who plan to get vaccinated seems most sensitive to the specific freedoms granted by a COVID-19 certificate.