Various fiscal policies have been proposed to incentivise healthy and sustainable diets, with differing attributes and levels. Public support can influence the ability for such policies to be implemented but is currently poorly understood, so two discrete choice experiments (DCEs) were used to measure such preferences among 2020 adults in the UK. The attributes and levels for 192 policy options were identified using a scoping review that comprehensively documented existing or proposed food-based fiscal policies. For health-related taxes and subsidies, options that prioritised protecting the NHS budget were most preferred for all respondents, which held across variation in 9 respondent characteristics. For sustainability taxes, avoiding donating to other countries was a clear priority for all respondents, and again held across 9 respondent characteristics. These results could help to shape UK policy while these methods could be used to understand policy preferences elsewhere.

Abstract Background Wearing facemasks and practising social distancing slow the spread of respiratory pathogens. However, in the event of a new pandemic emerging, the willingness of populations to voluntarily adopt these behaviours is unclear. Methods A discrete choice experiment was conducted among 2,006 UK-based adults. Participants were presented with hypothetical scenarios describing the emergence of a respiratory virus pandemic and were asked to choose when they would wear facemasks and practise social distancing. A mixed multinomial logit model was used to jointly estimate how disease severity and prevalence, uncertainty in these quantities, and individual-level characteristics influence behavioural choices. Findings Participants were averse to facemasks and social distancing in the absence of pandemic risk. For each ten-unit increase in severity (10 additional hospitalisations/1,000 infections), the odds of always wearing a facemask outside the home increased by 15.9% (95%CI: 14.3%, 17.5%), relative to rarely/never, and the odds of avoiding all people as much as possible increased by 16.4% (14.6%, 18.2%), relative to not avoiding anyone. Greater disease prevalence, uncertainty in disease severity or disease prevalence, a university education, prior COVID-19 vaccination and non-white ethnicity were also associated with choosing to always wear facemasks and avoid all people as much as possible. The probability of participants choosing to rarely/never wear facemasks varied from 13.4% (11.9%, 14.9%) in the lowest-risk scenario to 1.4% (1.2%, 1.7%) in the highest-risk scenario. Interpretation Perceived risks of disease and associated uncertainty drive intention of UK adults to adapt their behaviour in a future pandemic. Funding Medical Research Foundation. Research in Context Evidence before this study In the aftermath of the COVID-19 pandemic, evidence on intended behaviour change during a future pandemic is scarce in the UK and globally. We searched PubMed with no language restrictions from 01/01/2020 to 18/05/2026 using the terms (“behavior” OR “behaviour” OR “mask” OR “distancing” OR “voluntary” OR “intention“) AND (“future pandemic” OR “next pandemic” OR “hypothetical pandemic” OR “Disease-X” OR “Pathogen-X”). An overview of UK biosecurity priorities highlights behaviour change as a critical yet understudied component of pandemic preparedness. A systematic review identified strong preferences for voluntary compliance with facemasks and social distancing, rather than mandatory enforcement. Choice experiments assessed the intention of different populations to get vaccinated or travel during the next pandemic, but no studies evaluated intention to wear facemasks and practise social distancing, nor associations between behaviour change and epidemic characteristics. Added value of this study This choice experiment among 2,006 UK-based adults is the first of its kind to assess population preferences for wearing facemasks and practising social distancing during the next respiratory virus pandemic. Although participants were averse to both behaviours in the absence of risk, they were increasingly likely to prefer them given higher estimates of disease prevalence or severity. Participants preferred to exercise greater caution when faced with greater epidemic uncertainty, and to exercise caution to a similar degree across both behaviours, instead of favouring one over the other. Associations with individual-level characteristics, including education, ethnicity and prior COVID-19 vaccination, highlight groups with less intention to modify their behaviour and potentially at greater risk of infection and transmission. These estimated relationships between behaviour change and epidemic characteristics may be harnessed in transmission dynamic modelling, outbreak forecasting and risk assessment. Implications of all the available evidence In the event of a future pandemic, adults in the UK intend to protect themselves and others to a greater degree when faced with increasing or more uncertain epidemic risk. Clear and concise communication of risk and its associated uncertainty may better enable populations to adapt their behaviour as appropriate to the evolving epidemiological context. Reducing uncertainty via investment in epidemiological surveillance could inadvertently reduce voluntary risk mitigation by excluding probabilities of very high risk.

Unsustainably high numbers of patients attending emergency departments (ED) is a serious issue worldwide, with consequences for the quality and timeliness of emergency care. Avoidable visits, i.e. unnecessary or that should be dealt with elsewhere, exacerbate this issue. Most studies focussed on avoidable attendances use clinical data collected by hospital staff, while this study relies on survey data collected from patients asked to recall their last ED attendance and reflect on its necessity. We apply a Recursive Bivariate Probit model to quantify the factors affecting patients' perception of an ED visit being avoidable (or not), unveiling how it relates to socio-demographic and contextual factors. We find that patients who do not trust their General Practitioner (GP) are less likely to think their ED visit was avoidable. The perception of whether an ED visit was avoidable is also associated with symptoms experienced, patients' ethnicity and waiting time for a GP appointment.

Understanding food purchasing behaviours is complex because people make both choices among goods and volumes of those goods that they choose. We use the novel Multiple Discrete-Continuous Extreme Value (MDCEV) model, capable of handling both aspects of behaviour, on real-world food shopping behaviour data from a clinical trial. We compared the impact of providing general dietary advice, general dietary advice plus personalised shopping advice, or taxation, and combinations thereof, on the amount of saturated fat in consumers’ shopping baskets, using simulation. We used supermarket loyalty card data from a randomized controlled trial of 111 adults with raised cholesterol in Oxfordshire (UK). A Danish fat tax simulation alone is less effective than the tax in combination with dietary and shopping advice. These data illustrate the potential of MDCEV models for these behaviours and, by extension, informing food policies.

Multi-Cancer tests (MCTs) hold potential to detect cancer across multiple sites and some predict the origin of the cancer signal. Understanding stakeholder preferences for MCTs could help to develop appealing MCTs, encouraging their adoption. Discrete Choice Experiments (DCEs) conducted online in England. GPs (n = 251) and the general public (n = 1005) preferred MCTs that maximised negative predictive value, positive predictive value, and could test for a larger number of cancer sites. A reduction of the NPV of 4.0% was balanced by a 12.5% increase in the PPV for people and a 32.5% increase in PPV for GPs. People from ethnic minority backgrounds placed less importance on whether MCTs can detect multiple cancers. People with more knowledge and experience of cancer placed substantial importance on the MCT being able to detect cancer at an early stage. Both GPs and members of the public preferred the MCT reported in the SYMPLIFY study to FIT, PSA, and CA125, and preferred the SYMPLIFY MCT to 91% (GPs) and 95% (people) of 2048 simulated MCTs. These findings provide a basis for designing clinical implementation strategies for MCTs, according to their performance characteristics.

Background and ObjectiveOral corticosteroids (OCS) are the guideline recommended treatment for all asthma attacks, but benefits must be considered alongside the potential for cumulative side-effects. There is interest in trialling biomarker-directed management of attacks to rationalise OCS treatment in those with least benefit. Understanding stakeholder perspectives on the risks and benefits associated with OCS treatment can inform trial design and shared decision-making discussions in clinical practice. The aim was to examine patients' and healthcare professionals' preferences for the risks and benefits associated with OCS treatment for asthma attacks.MethodsDiscrete choice experiment (DCE) by patients with asthma and HCPs in the UK and New Zealand. Preferences were analysed using logit models.ResultsEight hundred and twenty-four patients and 171 HCPs completed the DCE. Avoiding the risks of permanent side effects had the greatest impact on treatment preference by patients and HCPs. Avoidance of side effects was weighted higher by patients than HCPs. Patients with uncontrolled asthma were more prepared to trade risk for benefit. Symptom recovery was the most valued clinical benefit to patients and HCPs. Patients preferred 'improving lung function' over 'avoiding additional GP treatment or hospitalisation', whereas HCPs preferred avoidance of further healthcare utilisation. Based on their responses we estimated the minimum clinically important difference for the treatment failure outcome at 20%.ConclusionPatients and HCPs will trade-off treatment benefits to avoid the side-effects associated with OCS. The risk-benefit balance of OCS should feature in shared decision-making discussions with patients experiencing outpatient asthma attacks. The findings support developing trials to personalise acute asthma treatment.

Governments can utilise fiscal measures, through subsidies and taxes, to promote healthy and environmentally sustainable food choices. Despite their potential, implementing subsidies and taxes is often contested because of the cost, anticipated efficacy, ideological basis of these policies, and the wide range of ways they might be implemented. Deliberative methods are useful for converging debate to understand whether and how policy decisions on contentious issues are supported by the public. In October 2023, we held two deliberative forums with members of the public in UK locations experiencing high rates of deprivation: one in Govanhill, Glasgow (n = 13) and one in Bridlington (n = 11). We developed 16 food subsidy or tax scenarios from a systematic scoping review of the literature. We presented scientific evidence on related issues and facilitated deliberations, culminating in each forum ranking their preferred subsidy or tax scenarios. Though each forum's preferences differed, overall participants favoured the implementation of a population-wide tax on high carbon foods, preferred more subsidy than tax scenarios, and preferred population-wide policies to policies that targeted people experiencing low income. Our findings demonstrate the public's interest in government fiscal action to create a fairer, healthier, and more sustainable food system.

BACKGROUND AND AIMS:Some states have banned flavors in various tobacco products. This can reduce use of banned products and induce substitution towards non-banned products. The net impact must be determined empirically. The aim of this study is to evaluate the impacts of these bans on both use and substitution. SETTING:US tobacco market. PARTICIPANTS:3220 individuals aged 18-41 in the United States who smoked and/or vaped (past 30-day use) completed an online survey. MEASUREMENTS:Multinomial logistic models regressed changes in tobacco product use between two time periods on: states with and without bans on flavored e-cigarettes and menthol cigarettes, individuals' characteristics, and other state-level tobacco policies. Estimated models were used to simulate impacts of flavor bans for people who dual use. RESULTS:Policies' impacts were only observed for those who dual use cigarettes and e-cigarettes. Most who dual use did not change their tobacco product use regardless of state policy. Some significant differences were found by states for those who quit both products. Massachusetts, with bans on both flavored e-cigarettes and menthol cigarettes, had the greatest predicted rate of quitting both products (9 %) compared to states without (3 %). States with e-cigarette flavors bans had higher cessation of e-cigarette use among those who dual use. CONCLUSIONS:Flavor bans on cigarettes and e-cigarettes were associated with reduced vaping among those who dual use. Massachusetts saw a higher proportion of quitting all tobacco products, likely because people who smoked in Massachusetts could not substitute with flavored e-cigarettes which had been banned.

Arguably the key issue in modelling discrete choice data is capturing preference heterogeneity. This can be through observed characteristics, and/or using techniques for capturing random heterogeneity across respondents. On the latter, in health economics, the two main approaches are the mixed multinomial logit (MMNL) and the latent class (LC) model. In this paper, we revisit the discrete mixture (DM) model as a third alternative to these. The DM model is similar to LC but allows for any combination of preferences across attributes, rather than grouping preferences as is the case in LC. We next develop a generalised discrete mixture (GDM) model. Additional boosting parameters in the class allocation component allow the model to collapse to a standard DM or LC structure as best fits the data at hand. This means that the model, by definition, performs at least as well as the best of a standard DM and a LC model; or better than both. Additional benefits include that it (a) allows the data to tell us the underlying correlations of preferences, (b) does not rely on distributions as is the case for mixed logit models, meaning estimation times are reduced and it does not require assumptions on the distribution of preferences. Exercises on simulated data show the unlikely conditions under which a LC model would be preferred to a DM. The convention of labelling latent classes, we believe, is questionable in many cases. The GDM is suitable in all cases. We show in empirical data that the GDM substantially outperforms LC models, granting a more detailed depiction of respondents' preferences.

BackgroundAny sample of individuals has its own unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. Many parametric specifications for these models are available. We test a range of alternative assumptions and model averaging to test if or how model outputs are affected.DesignScoping review of current modeling practices. Seven alternative distributions and model averaging over all distributional assumptions were compared on 4 datasets: 2 were stated preference, 1 was revealed preference, and 1 was simulated. Analyses examined model fit, preference distributions, willingness to pay, and forecasting.ResultsAlmost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness to pay varied significantly across specifications and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowing for greater flexibility and further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions affected predictions from models.LimitationsOur focus was on mixed logit models since these models are the most common in health, although latent class models are also used.ConclusionsThe standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Implications. Researchers should test alternative assumptions to normal distributions in their models.HighlightsHealth modelers use normal mixing distributions for preference heterogeneity.Alternative distributions offer more flexibility and improved model fit.Model averaging offers yet more flexibility and improved model fit.Distributions and willingness to pay differ substantially across alternatives.

Background:Hospitals in China are classified into tiers (1, 2 or 3), with the largest (tier 3) having more equipment and specialist staff. Differential health insurance cost-sharing by hospital tier (lower deductibles and higher reimbursement rates in lower tiers) was introduced to reduce overcrowding in higher tier hospitals, promote use of lower tier hospitals, and limit escalating healthcare costs. However, little is known about the effects of differential cost-sharing in health insurance schemes on choice of hospital tiers. Methods:In a 9-year follow-up of a prospective study of 0.5 M adults from 10 areas in China, we examined the associations between differential health insurance cost-sharing and choice of hospital tiers for patients with a first hospitalisation for stroke or ischaemic heart disease (IHD) in 2009-2017. Analyses were performed separately in urban areas (stroke: n = 20,302; IHD: n = 19,283) and rural areas (stroke: n = 21,130; IHD: n = 17,890), using conditional logit models and adjusting for individual socioeconomic and health characteristics. Findings:About 64-68% of stroke and IHD cases in urban areas and 27-29% in rural areas chose tier 3 hospitals. In urban areas, higher reimbursement rates in each tier and lower tier 3 deductibles were associated with a greater likelihood of choosing their respective hospital tiers. In rural areas, the effects of cost-sharing were modest, suggesting a greater contribution of other factors. Higher socioeconomic status and greater disease severity were associated with a greater likelihood of seeking care in higher tier hospitals in urban and rural areas. Interpretation:Patient choice of hospital tiers for treatment of stroke and IHD in China was influenced by differential cost-sharing in urban areas, but not in rural areas. Further strategies are required to incentivise appropriate health seeking behaviour and promote more efficient hospital use. Funding:Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, China Ministry of Science and Technology, and National Natural Science Foundation of China.

Importance Etiologic diagnoses for rare diseases can involve a diagnostic odyssey, with repeated health care interactions and inconclusive diagnostics. Prior studies reported cost savings associated with genome-wide sequencing (GWS) compared with cytogenetic or molecular testing through rapid genetic diagnosis, but there is limited evidence on whether diagnosis from GWS is associated with reduced health care costs. Objective To measure changes in health care costs after diagnosis from GWS for Canadian and English children with suspected rare diseases. Design, Setting, and Participants This cohort study was a quasiexperimental retrospective analysis across 3 distinct English and Canadian cohorts, completed in 2023. Mixed-effects generalized linear regression was used to estimate associations between GWS and costs in the 2 years before and after GWS. Difference-in-differences regression was used to estimate associations of genetic diagnosis and costs. Costs are in 2019 US dollars. GWS was conducted in a research setting (Genomics England 100 000 Genomes Project [100KGP] and Clinical Assessment of the Utility of Sequencing and Evaluation as a Service [CAUSES] Research Clinic) or clinical outpatient setting (publicly reimbursed GWS in British Columbia [BC], Canada). Participants were children with developmental disorders, seizure disorders, or both undergoing GWS between 2014 and 2019. Data were analyzed from April 2021 to September 2023. Exposures GWS and genetic diagnosis. Main Outcomes and Measures Annual health care costs and diagnostic costs per child. Results Study cohorts included 7775 patients in 100KGP, among whom 788 children had epilepsy (mean [SD] age at GWS, 11.6 [11.1] years; 400 female [50.8%]) and 6987 children had an intellectual disability (mean [SD] age at GWS, 8.2 [8.4] years; 2750 female [39.4%]); 77 patients in CAUSES (mean [SD] age at GWS, 8.5 [4.4] years; 33 female [42.9%]); and 118 publicly reimbursed GWS recipients from BC (mean [SD] age at GWS, 5.5 [5.2] years; 58 female [49.2%]). GWS diagnostic yield was 143 children (18.1%) for those with epilepsy and 1323 children (18.9%) for those with an intellectual disability in 100KGP, 47 children (39.8%) in the BC publicly reimbursed setting, and 42 children (54.5%) in CAUSES. Mean annual per-patient spending over the study period was $5283 (95% CI, $5121-$5427) for epilepsy and $3373 (95% CI, $3322-$3424) for intellectual disability in the 100KGP, $724 (95% CI, $563-$886) in CAUSES, and $1573 (95% CI, $1372-$1773) in the BC reimbursed setting. Receiving a genetic diagnosis from GWS was not associated with changed costs in any cohort. Conclusions and Relevance In this study, receiving a genetic diagnosis was not associated with cost savings. This finding suggests that patient benefit and cost-effectiveness should instead drive GWS implementation.

AbstractIntroductionCardiovascular disease (CVD) is a leading cause of death in the UK and globally. People identified as being at high risk may receive further investigations or preventive treatment. Polygenic risk scores (PRSs) give a summary of overall underlying genetic risk, and may be used to give additional information that GPs can use alongside other information about the patient to determine which interventions, if any, would be beneficial.Methods and AnalysisTwo discrete choice experiments (DCEs) with 2000 participants recruited from the UK general adult population. The first DCE aims to determine people’s attitudes about getting their PRS in the context of cardiovascular disease, and what factors may influence this. The second DCE aims to determine how people are likely to react to this risk information, and their stated probability of undergoing further investigation or interventions for disease management. This aims to provide new, quantitative information of whether individuals’ health-related behaviour is likely to be modified by knowledge of one’s PRS. Results from the pilot study will be used to inform the design of the main study, and the analysis will use multinomial logit models. Marginal rates of substitution between attributes, and heterogeneity analysis comparing people with different demographic characteristics, will also be carried out.Ethics and DisseminationEthics approval (reference: R89898/RE001) was obtained through the Medical Sciences Interdivisional Research Ethical Committee (MS IDREC) at the University of Oxford. The results of this research will be submitted to academic journals and will be presented at conferences.

In the article cited above, funding information was inadvertently omitted for authors Paul Aveyard and Susan A. Jebb. The following text has been added:

ObjectivesDevelop a score summarising how successfully a child with any surgical condition has been treated, and test the clinical validity of the score.DesignDiscrete choice experiment (DCE), and secondary analysis of data from six UK-wide prospective cohort studies.Participants253 people with lived experience of childhood surgical conditions, 114 health professionals caring for children with surgical conditions and 753 members of the general population completed the DCE. Data from 1383 children with surgical conditions were used in the secondary analysis.Main outcome measuresNormalised importance value of attribute (NIVA) for number/type of operations, hospital-treated infections, quality of life and duration of survival (reference attribute).ResultsQuality of life and duration of survival were the most important attributes in deciding whether a child had been successfully treated. Parents, carers and previously treated adults placed equal weight on both attributes (NIVA=0.996; 0.798 to 1.194). Healthcare professionals placed more weight on quality of life (NIVA=1.469; 0.950 to 1.987). The general population placed more weight on survival (NIVA=0.823; 95% CI 0.708 to 0.938). The resulting score (the Children's Surgery Outcome Reporting (CSOR) Treatment Success Score (TSS)) has the best possible value of 1, a value of 0 describes palliation and values less than 0 describe outcomes worse than palliation. CSOR TSSs varied clinically appropriately for infants whose data were included in the UK-wide cohort studies.ConclusionsThe CSOR TSS summarises how successfully children with surgical conditions have been treated, and can therefore be used to compare hospitals' observed and expected outcomes.

Introduction E-cigarette flavor bans could reduce or exacerbate population health harms. To determine how US e-cigarette flavor restrictions might influence tobacco use behavior, this study assesses responses to real-world and hypothetical flavor bans among young adults who use flavored e-cigarettes.Aims and Methods An online, national survey of young adults ages 18-34 who use flavored e-cigarettes was conducted in 2021 (n = 1253), oversampling states affected by e-cigarette flavor restrictions. Participants were asked about their responses to real-world changes in the availability of flavored e-cigarettes. Unaffected participants were asked to predict their responses under a hypothetical federal e-cigarette flavor ban.Results The most common response to real-world changes in flavored e-cigarettes availability was to continue vaping (similar to 80%). Among those who exclusively vaped, 12.5% switched to combustible tobacco. Quitting all forms of tobacco was selected by 5.3% of those exclusively vape versus 4.2% who dual use. Under a hypothetical federal ban, more than half of respondents stated they would continue vaping; 20.9% and 42.5% of those who exclusively vape versus dual use would use combustible tobacco. Quitting all tobacco products was endorsed by 34.5% and 17.2% of those who exclusively vape versus dual use.Conclusions Young adults who vape flavored e-cigarettes have mixed responses to e-cigarette flavor bans. Under both real-world and hypothetical e-cigarette flavor bans, most who use flavored e-cigarettes continue vaping. Under a real-world ban, the second most common response among those who exclusively vape is to switch to smoking; under a hypothetical federal ban, it is to quit all tobacco.Implications This is the first national survey to directly ask young adults who use flavored e-cigarettes about their responses to real-world changes in flavored e-cigarette availability due to state and local flavor restrictions. The survey also asked individuals to predict their responses under a hypothetical federal e-cigarette flavor ban. Most who use flavored e-cigarettes would continue vaping following e-cigarette flavor restrictions, but many would switch to or continue using combustible tobacco, highlighting potential negative public health consequences of these policies. Policymakers must consider the impact of e-cigarette flavor bans on both e-cigarette and cigarette use.

Objective: To understand preferences for features of weight loss programmes among adults with, or at risk of, type 2 diabetes in the UK. Research Design and Methods: A discrete choice experiment with 3,960 UK adults living with overweight, (675 with type 2 diabetes). Preferences for seven characteristics of weight loss programmes were analysed. Simulations from choice models using the experimental data predicted uptake of available weight loss programmes. Patient groups comprising those who have experience with weight loss programmes, including from minority communities, informed the experimental design. Results: Preferences did not differ between people with or without type 2 diabetes. Preferences were strongest for the type of diet. Healthy eating was most preferred relative to total diet replacement (TDR) (OR=2.24, 95%CI: 2.04-2.44). Individual interventions were more popular than groups (OR=1.40, 95%CI: 1.34-1.47). People preferred programmes offering weight loss of 10-15 kg (OR=1.37, 95%CI: 1.28-1.47) compared with 2-5kg. Online content was preferred over in-person contacts (OR=1.24, 95%CI: 1.18-1.30). There were few differences in preferences by gender and ethnicity though weight loss was more important for women than for men, and individuals from ethnic minority populations identified more with programmes where others shared their characteristics. Modelling suggested that tailoring programmes to individual preferences could increase participation by around 17 percentage points (68% in relative terms). Conclusions: Offering a range of weight loss programmes targeting the preferred attributes of different patient groups could potentially encourage more people to participate in weight loss programmes and support people living with overweight to reduce their weight.