Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study.

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.

Derivation of a UK preference-based value set for the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS) to allow estimation of Mental Well-being Adjusted Life Years (MWALYs).

BACKGROUND: The Mental Well-being Adjusted Life Year (MWALY) is an alternative outcome measure to the quality-adjusted life year (QALY) in economic evaluations of interventions aimed at improving mental well-being. However, there is a lack of preference-based mental well-being instruments for capturing population mental well-being preferences. OBJECTIVES: To derive a UK preference-based value set for the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS). METHODS: 225 participants that were interviewed between December 2020 and August 2021 completed 10 composite time trade-off (C-TTO) and 10 discrete choice experiment (DCE) interviewer-administered exercises. Heteroskedastic Tobit and conditional logit models were used to model C-TTO and DCE responses respectively. The DCE utility values were rescaled to a C-TTO comparable scale through anchoring and mapping. An inverse variance weighting hybrid model (IVWHM) was used to derive weighted-average coefficients from the modelled C-TTO and DCE coefficients. Model performance was assessed using statistical diagnostics. RESULTS: The valuation responses confirmed the feasibility and face validity of the C-TTO and DCE techniques. Apart from the main effects models, statistically significant associations were estimated between the predicted C-TTO value and participants' SWEMWBS scores, gender, ethnicities, education levels, and the interaction terms between age and useful feeling. The IVWHM was the most optimal model with the fewest logically inconsistent coefficients and the lowest pooled standard errors. The utility values generated by the rescaled DCE models and the IVWHM were generally higher than those of the C-TTO model. The predictive ability of the two DCE rescaling methods was similar according to the mean absolute deviation and root mean square deviation statistics. CONCLUSIONS: This study has produced the first preference-based value set for a measure of mental well-being. The IVWHM provided a desirable blend of both C-TTO and DCE models. The value set derived by this hybrid approach can be used for cost-utility analyses of mental well-being interventions.

Can decision field theory enhance our understanding of health-based choices? Evidence from risky health behaviors.

Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision-making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices.

Toward the diagnosis of rare childhood genetic diseases: what do parents value most?

Genomic testing is becoming routine for diagnosing rare childhood genetic disease. Evidence underlying sustainable implementation is limited, focusing on short-term endpoints such as diagnostic yield, unable to fully characterize patient and family valued outcomes. Although genomic testing is becoming widely available, evidentiary and outcomes uncertainty persist as key challenges for implementation. We examine whether the current evidence base reflects public tolerance for uncertainty for genomics to diagnose rare childhood genetic disease. We conducted focus groups with general population parents in Vancouver, Canada, and Oxford, United Kingdom, to discuss expectations and concerns related to genomic testing to diagnose rare childhood genetic disease. Applying a purposive sampling technique, recruitment continued until thematic saturation was reached. Transcripts were analysed using thematic analysis. Thirty-three parents participated across four focus groups. Participants valued causal diagnoses alongside management strategies to improve patient health and wellbeing. Further, participants valued expanding the evidence base to reduce evidentiary uncertainty while ensuring security of information. Willingness to pay out of pocket for testing reflected perceived familial health benefit. Diagnostic yield fails to fully capture valued outcomes, and efforts to resolve uncertainty better reflect public priorities. Evaluations of genomic testing that fully integrate valued endpoints are necessary to ensure consistency with best practices and public willingness to accept the uncertain familial benefit.

Patterns of Youth Cigarette Experimentation and Onset of Habitual Smoking.

INTRODUCTION: Although research suggests that youth e-cigarette experimentation is associated with later combustible cigarette experimentation, it is unclear how this relates to habitual smoking. This study assesses how minors' patterns of combustible cigarette and e-cigarette experimentation relate to habitual smoking at ages 18-21 years. METHODS: Between November 2016 and May 2017, a cross-sectional, online survey of current and retrospective cigarette use was fielded among individuals aged 18-21 years who had tried combustible cigarettes or e-cigarettes (n=1,424). Logistic regressions tested how experimentation patterns prior to age 18 years related to two indicators of current habitual smoking: daily smoking and current established smoking (past 30-day use among those who had smoked ≥100 cigarettes). RESULTS: Respondents who first tried combustible cigarettes or e-cigarettes as minors (n=1,096) were more likely to be current established users (64%) than those who first experimented at ages 18-21 years (41%). Experimentation patterns in individuals aged <18 years beginning with combustible cigarettes were the most predictive of later smoking. Relative to those who first experimented at ages >17 years (n=328), trying only combustible cigarettes as a minor yielded a 175% increase in one's odds of being an established smoker (AOR=2.75, 95% CI=1.99, 3.79) and a 161% increase for daily smoking (AOR=2.61, 95% CI=1.75, 3.90). Trying combustibles and then e-cigarettes yielded sizable increases in both habitual smoking measures, whereas trying e-cigarettes before combustibles yielded smaller effects. Trying only e-cigarettes as a minor yielded a 78% decrease in both outcomes, relative to those who did not try either product as minors. CONCLUSIONS: Minors who tried combustible cigarettes were more likely to be habitual smokers at ages 18-21 years than those who tried e-cigarettes alone.

The impact of flavors, health risks, secondhand smoke and prices on young adults' cigarette and e-cigarette choices: a discrete choice experiment.

AIMS: To estimate young adults' preferences for cigarettes and e-cigarettes, and how preferences vary by policy-relevant factors. A related aim was to provide information on potential substitution/complementarity across cigarettes and e-cigarettes ahead of policy selection. DESIGN: An online discrete choice experiment (DCE) in which respondents chose their preferred option among cigarettes, two types of e-cigarettes (disposable/reusable) and 'none'. Each cigarette-type was characterized by policy-relevant attributes: flavors, short-term health risks to self, secondhand smoke risks and price. A latent class model identified smoking types that respond differently to these. SETTING: US tobacco market. PARTICIPANTS: A total of 2003 young adults (aged 18-22 years) who ever tried either cigarettes or e-cigarettes, recruited via the survey platform Qualtrics, matched to the 2015 National Health Interview Survey by age, gender, education and census region. MEASUREMENTS: Respondents' DCE choices. FINDINGS: Young adults fell into two broad categories. One latent group, termed 'prefer smoking group', preferred cigarettes and another, 'prefer vaping group', preferred e-cigarettes. The 'prefer smoking group' preferred lower prices and lower health harms more than other attributes. The 'prefer vaping group' valued these, although price less intensely, and valued health and fruit/candy flavors more. CONCLUSION: Banning all flavors in cigarettes and e-cigarettes might improve the health of young adults who ever tried either cigarettes or e-cigarettes. Young adult ever-triers might be deterred from smoking by increasing cigarette prices and encouraged to switch to e-cigarettes by reducing the health harms of e-cigarettes. Reducing health harms of e-cigarettes could also make the 'prefer vaping group' less likely to quit, resulting in increased health harm.