The authors regret an unfortunate coding error in the optimal portfolio analysis presented in Table 3 of the published paper. This was discovered while performing new analyses on the data used in this study for another study. In the optimal portfolio analysis, the estimates of the multiple discrete-continuous extreme value (MDCEV) choice model were used to calculate the expected utility of each feasible portfolio (i.e., every possible combination of policy alternatives given the budget constraint, with attribute levels averaged over the sample). The expected utility was calculated correctly, but when preparing Table 3 for the paper, the portfolios were ranked incorrectly (i.e., by portfolio costs instead of by their expected utility). As a result, the top ten portfolios presented in Table 3 in the paper were not in fact the top ten portfolios in terms of expected utility, but in terms of implied additional expenditure on long-term care. In this corrigendum, we present the correct Table 3 and discuss these findings. The authors wish to emphasize that all the other analyses and results, i.e., the descriptive results (Figures 2, 3, S1 – S3, S8 – S14), the MDCEV estimates (Tables 2, S3, and S5), and the Latent Class Cluster Analysis (Tables 4, 5, and S2 and Figures S4 – S7) stay the same. Moreover, the abstract and highlights, and thereby the main message of the paper, remain unchanged. After ranking the portfolios by their expected utility, as originally intended, Table 3 reads as follows: [Table presented] The correct top ten portfolios indicate a stronger preference for the institutional and home-based formal care policy alternatives (i.e., increasing capacity of nursing homes, nursing care and social care at home, and introducing care homes), relative to those previously presented. At the same time, they indicate a lower preference for providing respite care to informal caregivers and introducing compulsory social service for young adults. Also, increasing the use of supportive care technologies is less preferred, but remains included in most of the top-ranked portfolios. This implies that portfolios involving a moderate increase in formal care options were generally preferred. The results reinforce the paper's conclusion that respondents seemed to prefer distributing public resources towards multiple policy alternatives over investing substantially in one or two particular policy alternatives. Finally, while the ten highest-ranked portfolios do not exhaust the budget constraint entirely, like in Table 3 of the paper, most portfolios still imply an expenditure increase close to exhausting the budget constraint. Based on the above, the subsection ‘Optimal portfolio composition’ of the Results section in the paper should be as follows: “Table 3 shows the ten portfolios with the highest expected utility. For example, portfolio 10 includes an increase in the capacity of nursing homes and nursing care at home by 10,000 places each, the introduction of care homes with 20,000 places (i.e., two times 10,000 places), an increase in the capacity of social care at home by 10,000 places, and the provision of respite care to informal caregivers for a maximum of three months, while increase in use of supportive care technologies and compulsory social service for young adults are not selected. Several patterns can be observed from the top ten portfolios. For example, each of these portfolios included at least one of the policy alternatives regarding nursing care and at least one regarding social care. Besides, all portfolios contained five or six of the seven policy alternatives. Additionally, both institutional care alternatives and both home-based care alternatives were included in all of the ten highest ranked portfolios, with the increased use of supportive care technologies also included at least once in seven out of the ten highest-ranked portfolios. All ten highest-ranked portfolios resulted in substantial expenditure increases, with eight of the ten portfolios nearly exhausting the resource constraint (i.e., > €90 euros per adult per month).” The sentences related to this part of the analysis in the Conclusion and Discussion section of the paper should be as follows: “In the optimal portfolios, increased capacity of institutional and home-based care and use of supportive care technologies were often included.” “Most of the highest-ranked portfolios nearly exhausted the budget constraint.” “The policy alternatives regarding the increased capacity of institutional and home-based care and use of supportive care technologies are particularly encouraged, conditional on the policies’ effectiveness and efficiency in practice. While various forms of institutional and home-based care are more commonly adopted and arguably less challenging to implement, the use of supportive care technologies in long-term care remains relatively limited.” Finally, the optimal portfolio composition in the sensitivity analysis also changes. It is similar to what is presented in the corrected Table 3 above, but with portfolios 6 and 7 and portfolios 8 and 9, respectively, reversed. The authors would like to apologise for any inconvenience caused.

Background: The outbreak of COVID-19 was followed by an unprecedented package of measures to protect public health. Over 150 countries mandated school closures to reduce the risk of transmission. Decisions on whether to close schools involve trade-offs between important effects on public health, learning outcomes, well-being of children, productivity of parents. Objectives: Investigate Peruvian citizens’ preferences for schools opening during a public-health crisis such as the COVID-19 pandemic in two scenarios: (i) when the threat from COVID-19 is low and schools are open; and, (ii) when the threat from COVID-19 is high and schools are closed. Methods: We conducted a Participatory Value Evaluation (PVE) from 22 September to 17 October 2022, on which 2007 respondents assessed which policy measures to implement in the two scenarios. (i) In Scenario 1 “Schools are open”, children go to school, teachers and parents go to their jobs, but children still experience learning deficits from previous school closures. (ii) In Scenario 2 “Schools are closed”, children cannot go to school and do not receive any formal teaching, leading to learning losses; many teachers must change careers; and, many parents have to stay at home to take care of their children and lose income. Respondents were shown a range of policy measures in each of the scenarios and received information about the effects of each measure on public health, children's well-being and learning loss. Results: We found that most respondents in Scenario 1 preferred mandatory vaccination for teachers and quarantine measures. In Scenario 2 we found that most respondents were positive towards reopening school policies. In both Scenarios respondents prioritized mandatory vaccination and quarantine measures over other mitigation measures. In Scenario 2, most respondents from the Highland region selected opening schools with 100% on-location teaching while hybrid teaching was mostly selected in the Coast region. Most respondents (82%) evaluated PVE as a good method to involve citizens in policy decision-making. Conclusions: Policies that focus on prevention (e.g. mandatory vaccination for teachers and quarantine measures) can count on substantial support in a scenario when schools are open. The strong preference for opening schools with a noticeable difference in the way classes are provided (e.g. teaching on location most preferred by respondents from the Highlands and hybrid teaching by respondents from the Coast) show the importance of introducing differentiated strategies among regions.

Many countries face rapidly ageing populations, resulting in a rising demand for long-term care (LTC) for older people and an increased pressure on LTC systems. In responding to this development, governments face challenging trade-offs between different policy measures and their effects. To inform allocation decisions, this study elicited citizens’ policy preferences for LTC for older people in the Netherlands in 2040. We conducted a constrained portfolio choice experiment, in which 997 respondents composed a portfolio of their preferred policies, subject to a budget constraint, while being presented with the expected effects of their choices. Choices were analysed using a Multiple Discrete Continuous Extreme Value (MDCEV) choice model and a Latent Class Cluster Analysis (LCCA). The results suggest a preference for distributing resources towards multiple policies, including both nursing and social care, over investing heavily in one or two particularly. Also, most respondents chose portfolios constituting a substantial public expenditure increase, suggesting a widespread willingness to accept a tax increase to allow for this. Preferences were particularly heterogeneous with respect to expenditure levels and the adoption of supportive care technologies and compulsory social service for young adults. Policymakers may use these results to support the selection of a portfolio of LTC policies that aligns with public preferences.

Objective: In many countries, the incidence of skin cancer is growing rapidly, resulting in a substantive health and economic burden. While the wide range of available skin cancer prevention policies may have large individual and societal benefits, many countries still lack a policy strategy, and little is known about public preferences for collective prevention policy measures. We elicited these preferences using a discrete choice experiment (DCE) in Austria, the Netherlands, and Spain to inform policy action. Methods: Respondents were asked to choose twelve times between two packages of different prevention policies. Each package was described by its estimated effectiveness and costs. Before and after the DCE, respondents were asked for their support for any policy action. We quota-sampled adult citizens in each of the countries from an online panel (N = 2,442). The choice data were analyzed using multinomial logit (MNL) and mixed multinomial logit (MMNL) models. Results: Almost all attributes significantly influenced respondents’ choices, with the tax attribute being most influential in each country. Among the six policy measures, information campaigns and a price reduction of sunscreen were the most preferred policy measures, and the prohibition of solar bed sales and solaria the least preferred. Preference structures were largely consistent across the countries. Finally, most respondents supported policy action, particularly after the DCE. Conclusions: Citizens in the three countries recommended their governments to take policy action against the increasing incidence of skin cancer. The results provide policymakers with directions for publicly supported policy action, which should be complemented with additional information on preference heterogeneity, citizens' argumentation, and policies’ relative (cost-)effectiveness. The suggestion that preferences for policy action adapted over the course of completing the DCE survey should be further examined.

Objective: Increasing healthcare expenditures require governments to make difficult prioritization decisions. Considering public preferences can help raise citizens’ support. Previous research has predominantly elicited preferences for the allocation of public resources towards specific treatments or patient groups and principles for resource allocation. This study contributes by examining public preferences for budget allocation over various healthcare purposes in the Netherlands. Methods: We conducted a Participatory Value Evaluation (PVE) choice experiment in which 1408 respondents were asked to allocate a hypothetical budget over eight healthcare purposes: general practice and other easily accessible healthcare, hospital care, elderly care, disability care, mental healthcare, preventive care by encouragement, preventive care by discouragement, and new and better medicines. A default expenditure was set for each healthcare purpose, based on current expenditures. Respondents could adjust these default expenditures using sliders and were presented with the implications of their adjustments on health and well-being outcomes, the economy, and the healthcare premium. As a constraint, the maximum increase in the mandatory healthcare premium for adult citizens was €600 per year. The data were analysed using descriptive statistics and a Latent Class Cluster Analysis (LCCA). Results: On average, respondents preferred to increase total expenditures on all healthcare purposes, but especially on elderly care, new and better medicines, and mental healthcare. Three preference clusters were identified. The largest cluster preferred modest increases in expenditures, the second a much higher increase of expenditures, and the smallest favouring a substantial reduction of the healthcare premium by decreasing the expenditure on all healthcare purposes. The analyses also demonstrated substantial preference heterogeneity between clusters for budget allocation over different healthcare purposes. Conclusions: The results of this choice experiment show that most citizens in the Netherlands support increasing healthcare expenditures. However, substantial heterogeneity was identified in preferences for healthcare purposes to prioritize. Considering these preferences may increase public support for prioritization decisions.

Participatory value evaluation (PVE) has recently been introduced in the field of health as a new method to elicit stated preferences for public policies. PVE is a method in which respondents in a choice experiment are presented with various policy options and their attributes, and are asked to compose their portfolio of preference given a public-resource constraint. This paper aims to illustrate PVE’s potential for informing healthcare decision making and to position it relative to established preference-elicitation methods. We first describe PVE and its theoretical background. Next, by means of a narrative review of the eight existing PVE applications within and outside the health domain, we illustrate the different implementations of the main features of the method. We then compare PVE to several established preference-elicitation methods in terms of the structure and nature of the choice tasks presented to respondents. The portfolio-based choice task in a PVE requires respondents to consider a set of policy alternatives in relation to each other and to make trade-offs subject to one or more constraints, which more closely resembles decision making by policymakers. When using a flexible budget constraint, respondents can trade-off their private income with public expenditures. Relative to other methods, a PVE may be cognitively more demanding and is less efficient; however, it seems a promising complementary method for the preference-based assessment of health policies. Further research into the feasibility and validity of the method is required before researchers and policymakers can fully appreciate the advantages and disadvantages of the PVE as a preference-elicitation method.