What Aspects of Illness Influence Public Preferences for Healthcare Priority Setting? A Discrete Choice Experiment in the UK.
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ABSTRACT: Background
Decisions on funding new healthcare technologies assume that all health improvements are valued equally. However, public reaction to health technology assessment (HTA) decisions suggests there are health attributes that matter deeply to them but are not currently accounted for in the assessment process. We aimed to determine the relative importance of attributes of illness that influence the value placed on alleviating that illness.Method
We conducted a discrete choice experiment survey that presented general public respondents with 15 funding decisions between hypothetical health conditions. The conditions were defined by five attributes that characterise serious illnesses, plus the health gain from treatment. Respondent preferences were modelled using conditional logistic regression and latent class analysis.Results
905 members of the UK public completed the survey in November 2017. Respondents generally preferred to provide treatments for conditions with 'better' characteristics. The exception was treatment availability, where respondents preferred to provide treatments for conditions where there is no current treatment, and were prepared to accept lower overall health gain to do so. A subgroup of respondents preferred to prioritise 'worse' health states.Conclusion
This study suggests a preference among the UK public for treating an unmet need; however, it does not suggest a preference for prioritising other distressing aspects of health conditions, such as limited life expectancy, or where patients are reliant on care. Our results are not consistent with the features currently prioritised in UK HTA processes, and the preference heterogeneity we identify presents a major challenge for developing broadly acceptable policy.
SUBMITTER: Morrell L
PROVIDER: S-EPMC8599241 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:When faced with an emergent epidemic with high mortality and morbidity potential, policy makers must decide what public health interventions to deploy at different stages of the outbreak. However, almost nothing is known about how the public view these interventions or how they trade off risks (of disease) with inconvenience (of interventions). In this paper, we aim to understand public perceptions on pandemic interventions, as well as to identify if there are any distinct respondent preference classes. DESIGN:A discrete choice experiment. SETTING:This study was fielded in Singapore between November 2012 and February 2013. PARTICIPANTS:A random sample of 500 Singapore residents aged 21 and over, including 271 women and 229 men, was analysed. OUTCOME MEASURES:Demographic information was collected from each participant. Participants were also shown a series of pairs of alternatives, each combining interventions and morbidity, mortality and cost outcomes and declared a preference for one combination. A random utility model was developed to determine the individual's preference for interventions and a hierarchical cluster analysis was performed to identify distinct respondent preference classes. RESULTS:On average, participants preferred more intense interventions, and preferred scenarios with fewer deaths and lower tax. The number of infections did not significantly influence respondents' responses. We identified two broad classes of respondents: those who were mortality averse and those who were expenditure averse. Education was found to be a predictor of group membership. CONCLUSION:Overall, there was considerable support for government interventions to prevent or mitigate outbreaks of emerging infectious diseases, including those that greatly restricted individual liberties, as long as the restrictions showed a reasonable chance of reducing the adverse health effects of the outbreak.
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Project description:BackgroundDeciding on pharmaceutical subsidy is regarded as a challenging issue for healthcare policymakers in Iran in most times. Public preferences, rarely attended in Iran, could be invaluable for including a particular drug in the list of subsidized medications.ObjectivesThe current study aims to elicit the public preferences to develop an evidence-based decision-making framework for entering a drug into the list of subsidies in Iran.MethodsDiscrete Choice Experiment (DCE) was employed to elicit the public preferences. Around 34 attributes were identified based on the systematic review and interview with 51 experts. By holding an expert panel, 7 attributes were finalized, namely: the survival after treatment, quality of life after treatment (QoL), alternative treatment, age group of the target population, cost burden for the government, disease severity, and drug manufacturer country. Next, 1224 households were selected for the survey in the city of Tehran, using random cluster sampling. Data were analyzed using conditional logit model.ResultsThe survival after treatment (β = 1.245; SE = 0.053) and disease severity (β =- 0.143; SE = 0.043) had the highest and lowest priority, respectively, in the preferences for allocating subsidy to a drug. In developed region, unlike the other two regions, the level of domestic drug production (β =- 0.302; SE = 0.073) was inversely associated with preferences toward allocating subsidy to a drug. In contrast to other districts, those living in district number one (β = 2.053; SE = 0.138) gave the highest value to promoting the QoL after treatment.ConclusionsIt is suggested that policymakers pay more attention to attributes such as effectiveness and alternative treatment when developing an evidence-based framework for entering a drug into the list of subsidies. This study highlighted the public belief in the government's subsidy for medicines, provided that, this results in an increased survival and QoL.
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Project description:ObjectivesWe aimed to elicit preferences of the French-speaking Quebec population regarding a COVID-19 vaccination program and to characterize individuals with respect to their vaccination behaviors.MethodsA discrete choice experiment was conducted in Autumn 2020 via a web-based survey. Its design included seven attributes: vaccine origin, vaccine effectiveness, side effects, protection duration, priority population, waiting time to get vaccinated, and recommender of the vaccine. Utilities were estimated using a mixed-logit model and a latent class logit model.ResultsOur sample included 1599 individuals. From this total, 119 always chose the opt-out option (7.4%). According to the mixed-logit model, the relative weights of attributes were as follows: effectiveness (28.48%), side effects (23.68%), protection duration (17.41%), vaccine origin (12.75%), recommender (11.96%), waiting time to get vaccinated (3.62%), and priority population (2.11%). Five classes were derived from the latent class logit model. Class 1 (9.13%) wanted to get vaccinated as fast as possible and was composed of uncertain and more vulnerable individuals. Class 5 (25.14%) was similar to the full sample, mostly favoring vaccination. Classes 2 (7.69%) and 4 (15.82%) included "vaccine hesitant and demanding" individuals but were different in their sociodemographic profiles. Finally, "anti-vaccine" and other "vaccine hesitant" individuals were in class 3 (42.21%).ConclusionsThis study showed the vaccine characteristics that are likely to improve vaccine uptake, which may more easily lead to herd immunity. Different profiles of respondents also showed various levels of acceptance toward a COVID-19 vaccination program, which may help to better understand vaccine hesitancy behaviors.
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Project description:ObjectivesIdentify Australian public preferences for antibiotic treatments in the context of antibiotic stewardship.MethodsA discrete choice experiment (DCE) was conducted in Australia to investigate the importance of seven attributes associated with antibiotic treatments and related stewardship practices: contribution to antimicrobial resistance (AMR), treatment duration, side effects, days needed to recover, days before taking antibiotics, treatment failure and out-of-pocket costs. The DCE data were analysed using conditional logit, mixed logit and latent class conditional logit models. The relative importance of each attribute was calculated.ResultsA total of 1882 respondents completed the survey; the main study sample consist of 1658 respondents (mean age 48 years) who passed quality checks. All seven attributes significantly influenced respondents' preferences for antibiotic treatments. Based on the designed attribute levels in the DCE, on average, out-of-pocket costs (32.8%) and contribution to antibiotic resistance (30.3%) were the most important attributes, followed by side effects (12.9%). Days before starting medication was least important (3.9%). Three latent classes were identified. Class 1 (including respondents who were more likely to be older and more health literate; 24.5%) gave contribution to antibiotic resistance greater importance in treatment preferences. Class 2 (including respondents more likely to report poorer health; 25.2%) gave out-of-pocket costs greater importance. The remaining (50.4%), who were generally healthier, perceived side effects as the most important attribute.ConclusionsDespite concerted public awareness raising campaigns, our results suggest that several factors may influence the preferences of Australians when considering antibiotic use. However, for those more likely to be aware of the need to preserve antibiotics, out-of-pocket costs and limiting the contribution to antibiotic resistance are the dominant influence. Delays in starting treatment were not important for any latent class, suggesting public tolerance for this measure. These results could help inform strategies to promote prudent antibiotic stewardship.
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