Creating an SF-6Dv2 social value set for New Zealand.

The SF-6D health descriptive system and its second version published in 2020, the SF-6Dv2, is used worldwide for valuing health-related quality of life (HRQoL) for economic evaluation and measuring patient-reported health outcomes. In this study, a valuation tool was developed and applied to create a social value set, comprising 18,750 health state values, for the SF-6Dv2 for New Zealand (NZ). This tool was adapted and extended from the one used to create a social value set for the EQ-5D-5L, a simpler health descriptive system with fewer dimensions and health states. The tool implements the PAPRIKA method, a type of adaptive discrete choice experiment, and a binary search algorithm to identify health states worse than dead and has extensive data quality controls to ensure the validity and reliability of the social value set derived from participants' personal value sets. The tool, accompanied by a short introductory video designed specifically for the SF-6Dv2, was distributed via an online survey to a large representative sample of adult New Zealanders in June-July 2022. The tool's data quality controls enabled participants who failed to understand or sincerely engage with the valuation tasks to be identified and excluded, resulting in the participants being pared down to a sub-sample of 2985 'high-quality' participants whose personal value sets were averaged for the social value set. These results, including participants' positive feedback, demonstrate the feasibility and acceptability of using the tool to value larger health descriptive systems such as the SF-6Dv2. Having successfully created an SF-6Dv2 social value set for NZ, the valuation tool can be readily applied to other countries, used to generate personal value sets for personalised medicine and adapted to create value sets for other health descriptive systems.

Assessing options for cannabis law reform: A Multi-Criteria Decision Analysis (MCDA) with stakeholders in New Zealand.

BACKGROUND: A number of jurisdictions are considering or implementing different options for cannabis law reform, including New Zealand. Multi-Criteria Decision Analysis (MCDA) helps facilitate the resolution of complex policy decisions by breaking them down into key criteria and drawing on the combined knowledge of experts from various backgrounds. AIMS: To rank cannabis law reform options by facilitating expert stakeholders to express preferences for projected reform outcomes using MCDA. METHODS: A group of cannabis policy experts projected the outcomes of eight cannabis policy options (i.e., prohibition, decriminalization, social clubs, government monopoly, not-for-profit trusts, strict regulation, light regulation, and unrestricted market) based on five criteria (i.e., health and social harm, illegal market size, arrests, tax income, treatment services). A facilitated workshop of 42 key national stakeholders expressed preferences for different reform outcomes and doing so generated relative weights for each criterion and level. The resulting weights were then used to rank the eight policy options. RESULTS: The relative weighting of the criteria were: "reducing health and social harm" (46%), "reducing arrests" (31%), "reducing the illegal market" (13%), "expanding treatment" (8%) and "earning tax" (2%). The top ranked reform options were: "government monopoly" (81%), "not-for-profit" (73%) and "strict market regulation" (65%). These three received higher scores due to their projected lower impact on health and social harm, medium reduction in arrests, and medium reduction in the illegal market. The "lightly regulated market" option scored lower largely due its projected greater increase in health and social harm. "Prohibition" ranked lowest due to its lack of impact on reducing the number of arrests or size of the illegal market. CONCLUSION: Strictly regulated legal market options were ranked higher than both the current prohibition, and alternatively, more lightly regulated legal market options, as they were projected to minimize health and social harms while substantially reducing arrests and the illegal market.

Rapid Development of a Tool for Prioritizing Patients with Coronavirus Disease 2019 for Intensive Care.

OBJECTIVES: To explain and demonstrate a new approach for rapidly developing a decision-support tool for prioritizing patients with coronovirus 2019 disease for admission to ICUs. DESIGN: An expert group used multi-criteria decision analysis methods to specify criteria and weights, representing their relative importance, for prioritizing patients with coronovirus 2019 disease with respect to likely clinical benefit. Specialized multi-criteria decision analysis software, implementing the "Potentially All Pairwise RanKings of all possible Alternatives" method to determine the weights, was used. Social equity considerations for prioritizing patients were also identified as important. SETTING: The prioritization tool was developed in New Zealand. SUBJECTS: An expert group comprising specialists from intensive care medicine and nursing, Maori (New Zealand's indigenous population) health, infectious diseases, and neonatology was formed. The group's work was supported by health economists and decision analysts and overseen by an ethicist and a senior representative from the New Zealand Ministry of Health. INTERVENTIONS: Multi-criteria decision analysis to create a prioritization tool. MEASUREMENTS AND MAIN RESULTS: The prioritization tool comprised eight criteria with respect to likely clinical benefit. In decreasing order of importance (weights in parentheses): Sequential Organ Failure Assessment score (15.7%), preexisting cardiovascular conditions (15.7%), functional capacity (15.7%), age (12.4%), preexisting respiratory conditions (11.1%), immunocompromised (11.1%), body mass index (9.2%), and other relevant medical conditions (9.2%). Two social equity considerations were also included in the overarching decision framework to be used alongside the clinical criteria: prioritizing Maori and Pacific people (and, potentially, other at-risk groups), and healthcare and other frontline workers. CONCLUSIONS: The criteria and weights in the prioritization tool can be easily revised as new evidence emerges. The approach for developing the tool could be used in other countries whose ICUs are at risk of being overwhelmed by the coronavirus disease 2019 pandemic to rapidly develop their own prioritization tools. In the event that future crises threaten to overload ICUs, other prioritization tools could also be rapidly developed.

A new tool for creating personal and social EQ-5D-5L value sets, including valuing 'dead'.

The EuroQol Group's health descriptive systems, the EQ-5D-3L and its successor introduced in 2009, the EQ-5D-5L, are widely used worldwide for valuing health-related quality of life for cost-utility analysis and patient-reported health outcome measures. A new online tool for creating personal and social EQ-5D-5L value sets was recently developed and trialled in New Zealand (NZ). The tool, which includes extensive checks of the quality of participants' data, implements the PAPRIKA method - a novel type of adaptive discrete choice experiment in the present context - and a binary search algorithm to identify any health states worse than dead. After development and testing, the tool was distributed in an online survey in February and March 2018 to a representative sample of NZ adults (N = 5112), whose personal value sets were created. The tool's extensive data quality checks resulted in a 'high-quality' sub-sample of 2468 participants whose personal value sets were, in effect, averaged to create a social value set for NZ. These results overall as well as feedback from participants indicates that the new valuation tool is feasible and acceptable to participants, enabling valuation data to be relatively easily and cheaply collected. The tool could also be used in other countries, tested against other methods for creating EQ-5D-5L value sets, applied in personalised medicine and adapted to create value sets for other health descriptive systems.

How Significant Are "High" Correlations Between EQ-5D Value Sets?

The calculation of quality-adjusted life years, as used for cost-utility analysis, depends on the availability of value sets representing people's preferences with respect to health-related quality of life (HRQoL). A value set consists of HRQoL index values for all health states representable by the particular descriptive system used, of which the EQ-5D (EuroQoL, 5 Dimensions) is by far the most widely used. High correlation coefficients for EQ-5D value sets derived from different samples-across countries and/or using different valuation techniques-are conventionally interpreted as evidence that the people in the respective samples have similar HRQoL preferences. However, EQ-5D value sets-for both versions of the system (EQ-5D-3L and EQ-5D-5L)-contain many inherent rankings of health state values by design. By calculating correlation coefficients for value sets created from random data, we demonstrate that "high" coefficients are artifacts of these inherent rankings, such as median Pearson's r = 0.783 for the EQ-5D-3L and 0.850 for the EQ-5D-5L instead of zero. Therefore, high correlation coefficients do not necessarily constitute evidence of meaningful associations in terms of similar HRQoL preferences. After calculating significance levels based on our simulations-available as an online resource for other researchers-we find that many high coefficients are not as significant as conventionally interpreted, whereas other coefficients are not significant. These "high" but insignificant correlations are in fact spurious.