Reimbursement decisions for orphan drugs are complex, as conventional cost-effectiveness frameworks may not fully capture broader societal values. To investigate public preferences regarding the inclusion of rare disease drugs in China's Basic Medical Insurance (BMI) using a multi-center discrete choice experiment. From July to September 2024, a face-to-face, filed-based discrete choice experiment (DCE) was conducted across four regions in China. Using non probability sampling technique which is known as Quota sampling,participants evaluated drug profiles varying in six attributes. Mixed logit model and latent class model were used for analysis. Relative importance, willingness to pay (WTP), scenario analysis and preference heterogeneity were estimated. Of the 761 respondents retained after initial data cleaning, 622 passed the internal consistency check and were included in the final analysis. Most participants were urban residents (62.7%), employed (66.6%), and covered by urban and rural resident basic medical insurance (BMI) (57.7%). The general public preferred including orphan drugs in BMI coverage when treatments provided greater health gains, targeted diseases with moderate untreated life expectancy, and lacked existing reimbursed alternatives. In contrast, poorer untreated quality of life and larger increases in BMI financing reduced utility. Health gains were the most important, followed by increases in BMI financing, untreated quality of life, existing BMI coverage, and untreated life expectancy. Annual WTP was ¥1.225 per capita for quality-adjusted life years (QALY) gains (0.01-4), with lower WTP observed among respondents facing poorer untreated quality of life. Scenario analysis showed that 78.5% would support reimbursement for drugs providing maximal health gains (0.01-4). Preference heterogeneity was observed across age, sex, education, household income, insurance type, and urban-rural residence. Latent class analysis identified two subgroups. The "life-saving group" (23%) showed strong preferences for greater disease severity and larger health gains, whereas the "pragmatist group" (77%) demonstrated relatively flatter preferences and negative utility associated with declining untreated life expectancy. While health gains drive preferences, the public also values equity, disease severity and affordability. Policymakers must integrate these multidimensional social values to ensure legitimacy and fairness in allocating finite resources for rare diseases. Members of the public were involved in the development of the DCE. In the attribute development phase, members of the public participated in a pilot study to help ensure the clarity and comprehensibility of attribute descriptions and levels. And their feedback informed revisions to the questionnaire. In the main study, members of the public participated as respondents, providing data on societal preferences for orphan drug reimbursement.

Informal caregivers' preferences for self-care support services in caring for community-dwelling older adults with disabilities: A discrete choice experiment.

Health status in the valuation of benefits from improved air quality: Evidence from a systematic review and meta-regression of stated preference studies.

Preferences for palliative care services for patients with advanced cancer from the demand-supply perspective: A multicenter discrete choice experiment.

Patient and caregiver priorities of treatment characteristics influencing preferences for tyrosine kinase inhibitors in anaplastic lymphoma kinase-positive metastatic non-small cell lung cancer.

Nursing students' educational preferences for integrated teaching methods: A best-worst scaling study.

To overcome barriers to the implementation of effective interventions, it is important to understand the factors which may influence stakeholders' implementation decisions. We aimed to identify these factors and quantify their relative importance in the decision-making process. Stakeholders included those wishing to implement an intervention (e.g., health service researchers and providers), and those funding implementation efforts (e.g., administrators and policymakers). We utilized discrete choice experiment methodology to explore several factors relevant to implementation decisions. The survey instrument was developed through a systematic review and expert consultation process. Conditional and mixed logit models were used to capture observed and unobserved preference heterogeneity. We tested the robustness of our results with subgroup and sensitivity analyses. We received 131 survey responses. There was a strong preference for intervention factors that 1) met stakeholders' needs, 2) had excellent levels of evidence, and 3) demonstrated strong organizational readiness. Respondents showed a preference for the mid-level cost ($150,000) over the lowest cost level ($100,000) within the experimental cost attribute (relative to an annual budget of $5 million). This suggests that respondents may prioritize the other three factors over the cost of implementation. These results remained relatively consistent across professional subgroups and sensitivity analyses. Meeting stakeholders' needs, levels of evidence, and organizational readiness had a large influence on implementation decisions. These factors were ranked of higher importance than implementation costs, administrative processes, and the presence of local champions within an organization. Designing research programs that support these influential factors will encourage successful implementation and ensure optimal healthcare delivery.

Understanding the population's preferences for health insurance plays an important role in optimizing insurance scheme design and improving enrollment rate. This study aims to quantitatively investigate preference for supplementary voluntary health insurance (SVHI) from a multi-site survey and examine its heterogeneity in China. A discrete choice experiment was conducted in Shandong, Henan and Sichuan provinces using multi-stage stratified sampling method. Five SVHI attributes were identified: premium, benefit package, deductible, reimbursement rate, and reimbursement for pre-existing conditions. Choice sets were generated using a D-efficient design, grouped into two blocks randomly assigned to respondents, with each set comprising two SVHI options and an opt-out. Data were collected via face-to-face computer-assisted interviews. Mixed logit models was used to estimate preference weights, willingness-to-pay (WTP) and attribute importance scores. Preference heterogeneity was analyzed by disease-related financial risk awareness, numeracy, and health insurance knowledge, demographic, socioeconomic, and health characteristics. Of the 1326 respondents who completed the questionnaire, 1254 were included in the analysis. Reimbursement rate was the most important attribute (34.26%), followed by premium (25.06%), benefit package (17.60%), deductible (17.50%) and reimbursement for pre-existing conditions (5.58%). Overall, respondents expressed the highest WTP (USD 48.50) for improving the reimbursement rate from 50% to 90%, while they showed lowest WTP (USD 8.60) for decreasing deductible from USD 2777.78 to 1388.89. Heterogeneity analysis revealed stronger enrollment preferences among respondents with risk awareness, higher health insurance knowledge, higher numeracy, higher educational attainment, higher income, and those living in urban areas. In addition, higher levels of risk awareness, insurance knowledge, numeracy, income, and education were associated with increased WTP for SVHI attributes. Preference heterogeneity by risk awareness and insurance knowledge suggests need for targeted risk information communication and education campaign to promote SVHI uptake, and diverse insurance design tailored to socioeconomic differences in preferences for attributes.

ABSTRACT International student mobility offers considerable economic benefits to host countries, yet it is increasingly challenged by shifting policies on funding, migration, and tuition fees. To inform strategies aimed at maximizing these benefits, this paper investigates the determinants of international students' institutional choices and their decisions to remain in the host country post-graduation. We conduct two Discrete Choice Experiments, enriched with detailed demographic information. We find that tuition fees are the most important determinant of university choice. Moreover, our results suggest that international students are willing to forego up to €8597 in annual tuition fees to attend a higher ranked university. For post-graduation decisions, easing immigration hurdles increases the probability of staying by 11.62 to 23.99 percentage points. Next, we show substantial preference heterogeneity across different student groups. The stay intention is 9 percentage points higher among visa-requiring students and between 4.6 and 6.2 percentage points higher among students with their own income source, suggesting that integration mechanisms may strengthen willingness to stay. These findings provide actionable insights for policymakers seeking to design targeted strategies to attract and retain international talent.

Current olfactory tests for untrained dogs have limitations due to use of a single odorant, requirement for ambulation, and potential for vision interference. We hypothesized that our new dogSIT odorant test battery measures would be reliable, repeatable, and age-associated in untrained companion dogs. Gently restrained dogs were presented with 18 odorants (in 9 pairs, one pair was subsequently excluded from analysis); odorants were contained in identical stainless-steel tea-balls. Odorants were presented for 30s in a habituation-dishabituation paradigm (4 consecutive presentations of the same odorant followed by a different odorant). Interaction times were measured post-hoc using masked video analysis; outcome measures represented novel interactions, habituation, and dishabituation. We assessed inter-rater reliability and test-retest repeatability (n = 25); and analyzed age, sex, and bodyweight covariates (n = 65). Outcome measures had significant inter-rater and test-retest repeatability. There was heterogeneity in odorant preference between dogs. Age, and to a lesser extent bodyweight and sex, were associated with outcome measures relating to novel odorant interaction and dishabituation. Our dogSIT battery is reliable, repeatable, and identified reduced odorant interaction behaviors in older dogs. Our findings have implications for research on food motivation and scent discrimination in aging dogs.

Patient Preference Information (PPI) is increasingly applied across the medical device lifecycle to inform product design, clinical trial planning, regulatory submissions, labeling, and shared decision-making. However, cross-company learnings remain fragmented. This review synthesizes five recent industry case studies illustrating diverse applications of PPI: Edwards Lifesciences (transcatheter tricuspid valve replacement), CVRx (Barostim neuromodulation therapy for heart failure), Medtronic (renal denervation for hypertension), Johnson and Johnson (lung cancer interception therapy), and Abbott (leadless pacemaker feature optimization). Across cases, quantitative stated-preference methods, particularly discrete-choice experiments, predominated. Evidence informed pivotal trial design and regulatory submissions (Edwards Lifesciences, Medtronic), FDA-approved labeling (CVRx), preventive therapy development (Johnson and Johnson), and device feature prioritization post-approval (Abbott). Common enablers included early engagement with regulators, rigorous instrument development with qualitative pretesting, and proactive dissemination. Recurring challenges involved recruiting representative samples, addressing preference heterogeneity, and demonstrating return on investment. These findings underscore the feasibility and value of PPI in advancing patient-centered innovation and regulatory decision-making across the medical device lifecycle.

This paper develops a hybrid credit risk model within a multi-regime utility indifference pricing framework to value two distinct types of Contingent Convertible (CoCo) bonds: principal write-down and equity-conversion structures. The trigger mechanism is modelled using a structural approach, under which a write-down or conversion is activated once the issuing firm’s equity value falls below a prespecified threshold, moving the firm into a distressed regime. Within this regime, we adopt a reduced-form model to capture the risk of sudden default. We derive closed-form solutions for the indifference prices of both bond types. Numerical results reveal that the indifference prices of write-down and equity-conversion CoCo bonds exhibit distinct sensitivities to investors’ risk aversion. We further show that heterogeneity in risk preferences and beliefs about default risk generates substantial dispersion in valuations, helping to explain the diverse demand patterns observed in the CoCo market.

AI-enabled platforms increasingly rely on user information to improve personalisation and service quality, but information disclosure simultaneously exposes users to privacy risks. This paper develops a Stackelberg game framework to investigate users’ equilibrium information disclosure under heterogeneous risk preferences. The model endogenizes the interaction among AI learning capability, user information disclosure, and platform security investment decisions. We further characterise optimal platform security strategies and examine the welfare implications of government security regulation. Our analysis generates several insights. First, stronger AI learning capability increases users’ equilibrium willingness to disclose information. Second, higher disclosure costs reduce disclosure incentives and weaken AI system performance. Third, platforms are more likely to adopt stronger security investment when security implementation costs are low or when the proportion of information-sensitive users is high. Finally, the optimal degree of security regulation may vary non-monotonically with security implementation costs. At both low and high levels of security implementation cost, relatively lenient regulation may improve overall social welfare.

ObjectivesPopulation aging and the weakening of traditional care systems pose growing challenges to China’s eldercare system, creating increasing demand for technology-assisted healthcare solutions. This study aimed to examine the preferences and willingness to pay (WTP) of old aged Chinese adults for assistive healthcare technologies.MethodsA discrete choice experiment with six attributes (service location, service function, technology difficulty, technology security, out-of-pocket payments and quality of life) was conducted with 235 older adults. The conditional logit and mixed logit models were used to assess the utility, relative importance score, and WTP for attributes and preference heterogeneity.ResultsAll six attributes had a significant impact on older people’s preferences. Participants preferred assistive technologies that could be used at home, focused on daily living assistance, featured low technology difficulty, ensured high security, improved quality of life, and incurred lower out-of-pocket payments, with the maximum WTP of $129.747 per month. Notable preference heterogeneity was observed, with females placing greater value on security, rural residents showing stronger preferences for home-based care, and individuals with chronic diseases prioritizing quality-of-life improvements.ConclusionsChinese older adults prioritized assistive technology with high technology security, better quality of life, home-based, low technology difficulty, lower out-of-pocket costs and offering daily living assistance services. We recommend policymakers build secure assistive technology systems; develop and promote low-difficulty and user-friendly technology solutions; implement differentiated pricing strategies to enhance the affordability and accessibility of assistive technology.

PurposeAcute leukemia (AL) is an aggressive cancer of white blood cells that progresses rapidly and requires intensive treatment. While first-line therapies can induce remission, this is not the case for all patients (refractory disease), and many patients experience a relapse. In these situations, treatment decisions become complex due to trade-offs between efficacy, toxicity and quality of life. Despite recognition of the importance of patient preferences in treatment decision-making, limited evidence exists on how individuals with AL value different treatment features.Patients and MethodsA web-based discrete choice experiment (DCE) was conducted across the UK, USA, France, Germany and Italy to quantify treatment preferences among adults with AL. Attributes were identified through literature review, formative qualitative research and patient input. Each participant completed 12 choice tasks comparing hypothetical treatment profiles varying by chance of response, duration of response, quality of life during treatment and response and mode of administration. Data were analyzed using mixed logit and latent class models to estimate conditional relative attribute importance (RAI) scores, explore preference heterogeneity and assess trade-offs.ResultsA total of 267 respondents completed the study. Across the full sample, the chance of response was the most influential treatment attribute (RAI=62%), followed by quality of life during response (14%), duration of response (9%), quality of life during treatment (8%) and mode of administration (7%). Three latent preference classes were identified: efficacy-focused (47%), convenience- and efficacy-focused (21%) and balanced decision-makers (32%). Respondents in the USA were more likely to belong to the convenience- and efficacy-focused group, suggesting that contextual factors influence preferences.ConclusionPatients with AL largely prioritize treatment efficacy but also value quality of life and convenience, indicating that treatment decisions are multifaceted and context dependent. Incorporating patient preferences into clinical development and health technology assessment can promote more person-centered approaches to care.

This study aimed to derive a Korean value set for the SF-6Dv2. Following an adapted international protocol, an online discrete choice experiment with duration (DCETTO) was conducted among 3800 Korean adults representative of the general population. The experimental design included three components: choice sets from a Core Module, choice sets pairing commonly occurring health states (Common Module), and triplet choice sets with death options (Triplet Module). Preferences were analyzed using conditional and mixed logit models. Model 9 (based on the Core Module) was selected as the preferred model for its superior logical consistency, including monotonicity of coefficients and avoidance of counterintuitive results. The pain dimension had the largest utility decrement at its worst level (- 0.496), followed by physical functioning (- 0.480), mental health (- 0.391), social functioning (- 0.329), vitality (- 0.178), and role limitations (- 0.138). The utility for the worst possible health state (556555) was estimated at - 1.012, with 31.7% of all states valued as worse than dead. Significant preference heterogeneity was observed across dimensions, indicating substantial individual-level variation in health state valuations. While including death comparisons (Triplet Module) produced less-negative worst-state utilities, it was not recommended as the primary value set due to its non-efficient experimental design and logical monotonicity issues necessitating greater level collapsing. We derived the first Korean SF-6Dv2 value set. This enables quality-adjusted life-year calculations from both SF-36v2 and SF-6Dv2 data, supporting economic evaluations in Korea.

Son preference remains a key driver of gender inequality in India, yet most studies treat its determinants as uniform across space, obscuring critical subnational variation. Addressing this gap, this study investigates the geographic heterogeneity of son preference and examines how its predictors vary spatially. Data and methods Using district-level data on 102,045 ever married women aged 15-49 from the National Family Health Survey-5 (2019-2021), we applied a spatially explicit analytical framework, including choropleth mapping, Global Moran's I, hotspot analysis, Local Indicators of Spatial Association (LISA) cluster mapping, kriging interpolation, and Geographically Weighted Regression (GWR). The use of GWR was justified by significant spatial non-stationarity (Koenker BP = 32.78, p < 0.001) detected in OLS diagnostics. Son preference prevalence ranged 8.2%-42.3%. Global Moran's I (0.397, z = 57.86, p < 0.001) confirmed significant clustering. OLS identified five significant predictors: parity 2, no mass media exposure, household size 5-8, illiterate mothers, and younger maternal age, explaining 64% variance. GWR demonstrated superior fit (AICc: 4459.98 vs 4478.46; adjusted R2: 0.65 vs 0.64). Local R2 ranged 0.45-0.71, highest in northern/central districts. Women's illiteracy (β: 0.40-0.62), large household size (β: 0.35-0.83), and younger mothers showed strongest associations in northern/central India, but negligible effects in southern regions, confirming spatial heterogeneity. Son preference is a spatially embedded social process, shaped by localized patriarchy, economy, and institutions. Geographically targeted, gender-transformative policies such as conditional incentives for girls' schooling and localized media campaigns effectively address the structural devaluation of daughters.

Globally and in South Africa, cisgender men underutilize available effective HIV-prevention strategies. Early-stage research on emerging HIV prevention methods provide an opportunity to assess factors influencing the initiation and persistent use of novel drug delivery platforms for long-acting preexposure prophylaxis (LA-PrEP) among male users. We conducted the SAMURAI randomized crossover study, where MSM and men who have sex only with women (MSW) in Cape Town and Johannesburg, South Africa, were administered placebo versions of bimonthly gluteal injections for six months and a six-month implant inserted in the inner upper arm in a randomized sequential order. We measured acceptability and defined adherence as the initiation and continued use of each product. We further monitored adverse events, serious adverse events (SAEs), injection/implant site reactions (ISRs), and social harms throughout the study and assessed LA-PrEP preference at study exit. From July 2022 to June 2024, 184 cisgender men (n = 84 MSM, n = 100 MSW) were enrolled and retained with an 86% retention rate. More participants initiated injections than implants (97.8 vs. 91.3%, P = 0.007). Of those, 80.6% persisted with injections. For implants, 91.1% persisted (median 6.0 months, range 2.3-14.6), with 9% requesting early removal. Persistent use was significantly higher for implants (aRR 1.17, 95% CI: 1.07-1.27, P < 0.001). User satisfaction was high (injectables: 8.6, implants: 8.4; P = 0.44), with similar likelihood for future use (8.9 vs. 8.7; P = 0.28). Among those who tried both methods, 48.4% preferred injections and 47.7% preferred implants. All reported adverse events were unrelated to product use, 83 ISRs (most implant) documented, and 2% cited study-related SH. Men in South Africa expressed strong interest in LA-PrEP, with these two novel drug delivery platforms rated highly acceptable. Following user experience with both placebo options, preferences for injections or implants were nearly equivalent, reinforcing heterogeneity of preferences and the importance of PrEP choice. Significant differences between product initiation and persistence suggested that delivery form familiarity and visit frequency tied to clinic burden influenced men's behavior, with implications for persistence in future LA-PrEP method use.

Digital Health Technologies (DHTs) are expanding rapidly, offering new opportunities to support care delivery. Their adoption, however, depends on how well they match patients' needs and expectations. Accurately assessing patient preferences is challenging due to diverse user profiles and varying methods used to measure preferences. A synthesis of current evidence is needed to clarify what patients value in DHTs. This study synthesizes evidence on patient preferences for DHTs-including eHealth, telehealth, telemedicine, and mHealth-and examines the methods used to elicit these preferences, highlighting opportunities to improve adoption and design. We conducted a scoping review of literature published from 2000 to 2026 following PRISMA-ScR guidelines. Searches were performed in PubMed, EMBASE, CINAHL, Scopus, and Web of Science. Two reviewers independently screened titles, abstracts, and full texts for eligibility. Data were charted and narratively synthesized, with study characteristics categorized by methodology (qualitative, quantitative, mixed methods) and preference elicitation techniques. Of 2,419 records identified, 115 underwent full-text screening and 85 met all inclusion criteria: 27 qualitative studies (31.76%), 45 quantitative studies (52.95%), and 13 mixed-methods studies (15.29%). Quantitative studies primarily applied attribute-based methods (e.g., Discrete Choice Experiments, Conjoint Analysis, Best-Worst Scaling), and six studies used the Contingent Valuation method to estimate total willingness-to-pay for DHTs. Qualitative studies employed thematic analysis, deductive, inductive, and immersion-crystallization approaches. Across studies, patients consistently emphasized cost, privacy, convenience, and personalization of DHTs as key concerns. Only a few studies estimated the relative importance of the attributes or the marginal willingness to pay for the device's features. Considerable heterogeneity in preferences was observed by DHT type, patients' health condition and age. Quantitative, qualitative, and mixed-method approaches provide complementary insights into how patients perceive and value DHTs. Patients consistently seek personalized, easy-to-use, secure, and affordable solutions, which is particularly important for older adults managing chronic conditions. Given the diversity of patient preferences, it is essential to consider these differences when developing digital health technologies to ensure they effectively support patients and address their needs.

The ability to quickly capture and adapt to customer preferences is central for firms seeking to offer personalized products and improve retention. This objective becomes challenging when individual-level data on customer interactions are limited, as is often the case for new customers or short consumption sessions. To this end, we propose meta-temporal processes (MetaTP), a meta-learning framework that enables scalable personalization from a small number of individual observations. MetaTP is trained across a large collection of session-based tasks, allowing it to improve data efficiency and transfer shared structure across customers. To model customer interactions over time, MetaTP integrates a Transformer-based architecture that captures sequential consumption patterns within sessions. This design uncovers dynamic preference heterogeneity and enables accurate predictions. We illustrate MetaTP through an application on customer sequential consumption of digital products, focusing on the lukewarm stage of the customer journey, a transition period characterized by limited individual observations. Empirically, MetaTP outperforms a comprehensive set of benchmark methods in few-shot prediction and reveals meaningful patterns of preference evolution through its interpretable parameters. Managerially, we demonstrate how firms can leverage MetaTP to optimize personalized recommendations with limited individual data, including product sequencing decisions and both open-loop and closed-loop session completion strategies.