Random Parameter Logit Model Research Articles

The Impacts of Health and Environmental Information Nudges on Meat Choices: Where Does Goat Meat Fit?

ABSTRACTAmidst a recent surge in US goat meat imports to meet growing demand, this study contributes to the meat demand literature by examining consumer preferences for goat meat, a relatively healthy and environmentally friendly alternative to other popular meats. We employed a discrete choice experiment on representative US meat consumers and randomly assigned respondents to health and environmental information treatments. Further, information treatments were categorized into goat‐focused comparative and generic information treatments. Estimates from the random parameter logit model reveal that health information treatments outperform environmental information treatments in influencing consumer preferences for goat meat. Additionally, the results show that goat‐focused comparative information is more effective than generic information treatment. The market share simulations under different price scenarios suggest decreasing goat meat price to $4/lb. and informing respondents of the health benefits of goat meat over beef can yield up to 21% market share of the total US meat market for goat meat. These findings underscore the potential of goat meat in the US meat market, particularly when coupled with health information nudges.

Enhancing community-based earthquake disaster management: resident preferences and adaptive capacity for resilience

Earthquake disasters remain a persistent global challenge, requiring innovative approaches to disaster management. Community-based earthquake disaster management (CBEDM) integrates social, environmental, and political factors, emphasizing residents’ adaptive capacity and disaster prevention preferences. This study employs the Choice Experiment (CE) method within an adaptive capacity framework to “quantify residents’ preferences, adaptive behaviours, and willingness to participate (WTP)” in CBEDM scenarios. Using the Random Parameter Logit (RPL) model, the research reveals strong community support for CBEDM programs and highlights three key findings: first, “collaboration between local governments, non-profits, and communities enhances disaster preparedness and post-disaster recovery”; second, “active community participation reduces disaster impact and raises awareness”; third, “empowering social organizations ensures efficient recovery services and social inclusion.” These insights provide evidence-based recommendations for aligning disaster management strategies with residents’ preferences and fostering adaptive behaviours to mitigate earthquake risks.

Educational disparities shifts in motorcyclist injury severity: Accounting for unobserved heterogeneity

Traffic crashes involving motorcyclists are the dominant public health concern in developing countries. While existing literature has mainly focused on the severity of various risk factors contributing to motorcyclist injury severity, such as helmet use and road and environmental conditions, the role of the driver’s educational background remains a relatively under-researched subject. Educational disparities can significantly impact the risk perception decision-making behavior of motorcyclists, and their complex interplay with other factors can potentially influence injury severity outcomes. Aiming to fill the highlighted research gaps, the current study employs a random parameter logit (RPL) approach by capturing the heterogeneity in individual responses to provide nuanced insights and relationships between motorcyclist educational disparities and their severity outcomes. Four RPL models that account for heterogeneity in means and variances of motorcyclist riders with different levels of education (illiterate, high school, college, and graduation) are developed. The empirical analysis is based on a database of three years (2020 to 2022) of motorcycle accidents maintained by RESCUE 1122 in Rawalpindi, Pakistan. Model assessment for heterogeneity in the means and variances highlighted unique indicators for riders with different educational backgrounds. Model results revealed that severe and fatal injury outcomes are affected by attributes including temporal indicators (weekend, day, year, winter, and autumn), rider profiles (male, young-, middle-, and older-ages), road characteristics (60–70 km/h, lane 3), weather conditions (raining, sunny), and vehicle and crash characteristics (crash with a truck or car, over-speeding, distractions, and slipping). Models’ estimation results indicate notable differences among the injury-severity contributing factors for various RPL models, highlighting the significance of motorcyclists’ educational backgrounds and unobserved heterogeneity. The recent findings can provide valuable insights and guide researchers and practitioners to develop effective countermeasures and targeted interventions to tackle the safety concerns associated with various motorcyclists’ varying education levels.

Partially temporally constrained modeling of speeding crash-injury severities on freeways and non-freeways before, during, and after the stay-at-home order.

Speeding crashes remain high injury severities after the stay-at-home order in California, highlighting a need for further investigation into the fundamental cause of this increment. To systematically explore the temporal impacts of the stay-at-home order on speeding behaviors and the corresponding crash-injury outcomes, this study utilizes California-reported single-vehicle speeding crashes on freeways (access-controlled) and non-freeways (non-access-controlled) before, during, and after the order. Significant injury factors and in-depth heterogeneity across observations are identified by random parameter logit models with heterogeneity in means and variances. Without segmenting the data by periods, the partially temporally constrained approach is employed to statistically determine varying and stabilized parameters over time through the whole dataset. Different likelihood ratio tests reveal significant temporal instabilities and stabilities of factors between two roadways and three periods. The potential impacts of observation selection issues on the marginal effect calculations of the partially constrained models are also systematically investigated. Significant variations in the probability of severe injury rate per week after the order are also found based on the Mann-Whitney U tests. The hysteretic effects of the order on the crash frequency and severity are observed on both freeways and non-freeways. Overall, seven variables are found to have stable effects, while fifteen variables exhibit unstable effects over time. Significant temporal variations in driver behaviors, including driving under the influence, cell phone usage, hit-and-run, failure to use seat belt, entering or leaving the ramp, and reaction to previous collisions, are observed before, during, or after the order. Specific countermeasures and effects of heterogeneity in means and variances are also discussed. These findings provide insights into understanding the temporal impacts of the stay-at-home order on injury severities, which are valuable to decision-makers and researchers for future order practice, restriction improvement, and complementary policy development.

Exploring Adolescents' Contraceptive Preferences and Trade-Offs: Findings From a Discrete Choice Experiment in Kenya.

A focus on contraceptive preferences is essential to the provision of person-centered care. Adolescent girls and young women (AGYW) in the WHO African Region experience disparities in contraceptive access and use and reproductive health outcomes. Despite increasing recognition of AGYW needs as unique, their preferences are understudied, limiting strategies to improve contraceptive care access and quality among marginalized youth populations. We designed and conducted a discrete choice experiment among five hundred 15-20-year-old AGYW in Kisumu, Kenya, to examine the relative importance of trade-offs between contraceptive methods and service delivery attributes. Participants answered eight choice sets including three alternatives: two hypothetical contraceptive options characterized by seven attributes (effectiveness, bleeding pattern, duration of use, privacy, access location, counseling source, and cost) and a "no method" opt-out. We used random-parameters logit models to estimate preference weights and trade-offs among alternatives. The bleeding pattern was the most important determinant of stated choice, with a strong preference for unchanged or irregular bleeding over heavier bleeding or amenorrhea. Participants preferred the lowest chance of method failure as well as the longest duration of use (one year) over daily use or use during/after sex. Parous AGYW were more likely to prefer the longest duration of use. The ability to keep method use completely private was also an influential choice, particularly among 15-17-year-olds. AGYW traded effectiveness and increased cost for preferred bleeding patterns and privacy. The opt-out "no method" alternative was chosen only 2.7 percent of the time, indicating a strong preference for pregnancy prevention. Our findings highlight key insights for enhancing the person-centeredness of contraceptive care for AGYW: Kenyan AGYW place a high value on preferred menstrual bleeding patterns, high method effectiveness, longer duration of use, and the ability to keep method use private. Preference data can inform programs, including contraceptive decision-support interventions, to improve AGYW access to quality preference-sensitive contraceptive services.

Directional Heterogeneity in WTP For Multiple Attributes of Beach Recreation: An Application of Future Travel Cost Increase Method to A Labelled Discrete Choice Experiment

This study examines directional heterogeneity in distance-decay effects on willingness-to-pay (WTP) and their significance in welfare estimates, benefits transfer, and cost-benefit analysis. We used the future travel cost (FTC) increase method in a labelled discrete choice experiment (DCE) and applied random parameter logit (RPL) models to estimate distance-decay effects in WTP for multiple attributes of beach recreation, such as coastal water quality and beach cleanliness. The results show that accounting for directional heterogeneity for multiple attributes, rather than a single attribute, yields notably different willingness-to-pay (WTP) estimates. For coastal water quality, directional effects are seen across two directions, while beach cleanliness shows directional effects in all directions. These findings highlight significant directional heterogeneity in WTP held by individual respondents for multiple attributes across different directions exist, which would help policymakers lacking financial resources to generate revenues for sustainable coastal resource management.

How does habit strength affect reactions to warning labels? evidence from Chinese consumers' choices of carbonated beverages.

Effectively regulating the excessive consumption of sugar-sweetened beverages (SSBs) has been an important task for public health authorities around the world. The rapid increase in SSB consumption in China necessitates robust regulations. This study employed a choice experiment to simulate the market scenario in which a text warning label was presented on SSBs. We draw a sample of 1200 participants from five major cities across China to analyze the warning label's effectiveness in reducing urban consumers' purchases of SSBs, with a particular emphasis on how an individual's habit strength affects their reaction to the warning label. Our analysis unveiled varying levels of habit strength among respondents: 35.33% showed weak habits, 56.25% displayed moderate habits, and 8.42% demonstrated strong habits. The random parameters logit model results showed a significant overall decrease in SSB purchases when a warning label was present, indicated by a significant negative coefficient associated with the label. However, when examining the impact of warning labels across different habit strengths, a significant effect was observed only in the moderate habit group. Furthermore, within this group, the warning label was more effective for those with correct health knowledge and who were not overweight or obese.

Heterogeneities in Farmers’ Preference for Advisory Services: A Choice Experiment of Vegetable Growers in North-Western Ethiopia

This study investigated vegetable farmers’ willingness-to-pay (WTP) for private agricultural advisory services in northwestern Ethiopia. Understanding farmers’ preferences is a crucial step for modernizing agricultural advisory services and transforming smallholder agri-food systems into a sustainable path. Discrete choice experiment data from 393 farm households were analyzed using a random parameter logit model (RPL). The results revealed that vegetable farmers are willing to pay for practice-oriented private advisory services. The result also showed the existence of heterogeneity in farmers’ preference for features of vegetable advisory services. Household heads’ educational status and age influenced preferences for advisory service features. The result is substantiated by the fact that merely 25.5% of the sample households acquired formal education. Farmers in general preferred extension services with frequent expert visits and practical engagement on-farm as opposed to digitized options that rely on short message service (SMS) and voice-based guidance. Additionally, farmers are willing to pay an ETB 120.89 and ETB 203.94 monthly fee for an extension service that emphasizes fruity and root and tuber vegetables, respectively, as opposed to leafy vegetables. The findings imply initiatives that push for commercializing agricultural advisory services should strive to achieve a balance between the practical application and digitization of extension services accounting for the heterogeneous preferences of smallholder farmers.

Investigating Influence Factors on Traffic Safety Based on a Hybrid Approach: Taking Pedestrians as an Example.

Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian-vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors "Workday or not" (Not), "Signal control mode" (No signal and Other security facilities), and "Road safety attribute" (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels.

Study on the travelers’ P&R usage behavior during snowfall in the corridor valley pattern city

According to the characteristics of corridor valley pattern cities, this paper presents four aspects of terrain space perception. Using a structural equation model (SEM), the four-dimensional terrain space perception is analyzed through second-order confirmatory factor analysis. A random parameter Logit model (RPLM) was developed to examine the impact of individual socioeconomic attributes, objective built environment, travel characteristics, and terrain space perception on travelers’ Park and Ride (P&R) use behavior during snowfall conditions. Additionally, a Logit model was utilized to assess how individual socioeconomic attributes of travelers in corridor valley pattern cities influence their perception of terrain space during snowfall weather. The findings indicate that the four latent variables related to terrain space perception proposed in this paper effectively explain travelers’ perception of terrain space in corridor valley pattern cities, and the observed variables also support these latent variables. The subjective and objective built environment of corridor valley pattern cities significantly affect travelers’ P&R usage behavior during snowfall weather, and there is heterogeneity in the terrain space perception during snowfall weather among different travelers in corridor valley pattern cities. Furthermore, the gender, age, income, and experience of using P&R of travelers in corridor valley pattern cities play a significant role in moderating terrain space perception.

Patient Preferences for Features Associated With Leadless Versus Conventional Transvenous Cardiac Pacemakers.

Regulatory approval of the first dual-chamber leadless pacemaker system provides patients an alternative to conventional transvenous pacemakers. The study objective was to quantify the preferences of patients for pacemaker features. Patients with a de novo (ie, initial) pacemaker indication were recruited from 7 US sites to complete a Web-based discrete-choice experiment survey between May 11, 2022, and May 24, 2023. Patients chose between pairs of experimentally designed, hypothetical pacemakers that varied according to type (removable leadless, nonremovable leadless, or conventional transvenous); battery life (5, 8, 12, or 15 years); time since regulatory approval (2 or 10 years); discomfort for 6 months (none or discomfort); and complication risk and infection risk (1%, 5%, or 10%/20% for each). Patients' choice data were analyzed using random-parameter logit models and latent-class analysis. Among 117 participants, the mean (SD) age was 67.3 (14.6) years, 94% were white, and 42% were female. On average, patients' survey responses revealed a preference for removable leadless pacemakers (β, 0.340; SE, 0.096) over both nonremovable leadless pacemakers (β, -0.310; SE, 0.131; P=0.001) and conventional transvenous pacemakers (β, -0.030; SE, 0.119; P=0.031). However, latent-class analysis revealed 2 distinct preference classes. One class preferred leadless pacemakers (50.5%), and the other class preferred conventional transvenous pacemakers (49.5%). The conventional pacemaker class prioritized pacemakers with 10 rather than 2 years since regulatory approval (P<0.001), whereas the leadless pacemaker class was insensitive to years since regulatory approval (P=0.83). Complication risks and infection risks were found to be the most influential. All else equal, patients would accept maximum risks of complications or infections ranging about 5% to 18% to receive their preferred pacemaker type. Latent-class analysis revealed strong patient preferences for the type of pacemaker, with a nearly equal split between recent leadless pacemaker technology and conventional transvenous pacemakers. These findings can inform shared decision-making between health care providers and patients. URL: https://www.clinicaltrials.gov; Unique identifier: NCT05327101.

What rural ecological governance goals do local residents prefer? Evidence from Hainan, China

Rural ecosystems restoration requires the joint participation of rural residents and government in governance. This study aims to analyze whether rural ecological governance goals are consistent with rural residents' preferences and how they affect rural residents' utility levels, which are key to rural residents' ecological governance participation behavior. Using survey data from 653 rural residents in Hainan Island, China, this paper applies a choice experiment method and a random parameter logit model to identify differences in rural residents' preferences for different rural ecological governance policy goals and their governance participation behaviors. The results show that increasing the coverage rate of sewage treatment facilities, reducing fertilizer and pesticide application, and increasing rural biodiversity can significantly enhance rural residents' ecological governance participation utility, thereby promoting their choice of ecological governance options. Rural residents' participation behavior in rural ecological governance is also influenced by individual and household characteristics such as ethnicity, labor size, net farm income, ICT usage, and ecological knowledge level. Based on these findings, policymakers should strengthen communication of governance policies and objectives, respect rural residents' policy goal preferences, prioritize the implementation of rural ecological governance models that increase the coverage of wastewater treatment facilities, reduce fertilizer and pesticide application, and increase rural biodiversity, and rely on ICT devices to popularize ecological knowledge among rural residents.

Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach

Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.

Econometric analysis of consumers’ preference heterogeneity for yoghurt and ice cream products in Tanzania: A latent class model and mixed logit model

In Tanzania, a growing upper and middle classes, particularly among urbanites, exhibit distinct preferences for higher-quality processed foods, including dairy products. This study examines variations in consumer preferences and their willingness to pay for yogurt and ice cream, which serve as stand-ins for processed milk products. The analysis is based on a discrete choice experiment involving 400 participants in Dar es Salaam. A random parameter logit model was utilized to account for preference heterogeneity, while latent class models (LCMs) were applied to uncover the underlying factors driving these differences in preferences. Our findings reveal three distinct consumer classes: processed milk sceptics (who prefer unprocessed dairy products), processed milk advocates (who prefer processed products), and neutral consumers (indifferent between processed and unprocessed milk). Preferences are influenced by product attributes, socioeconomic characteristics, and attitudes towards processed foods. The results indicate that Tanzanian consumers place the greatest value on sensory attributes, packaging, and the product's origin (local versus imported). This research offers fresh perspectives on the intricate preferences of dairy consumers in Tanzania, a topic that has been relatively underexplored. The findings suggest that producers and marketers must adapt to the dynamic market by balancing intrinsic and extrinsic factors against price. Understanding consumers' socioeconomic and product attributes is essential for increasing market share and effectively segmenting markets. These findings would be useful incorporated into strategic planning to enhance the competitiveness and sustainability of Tanzania's dairy industry.

Why they take the risk to perform a direct left turn at intersections: A data-driven framework for cyclist violation modeling

Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered. To bridge these gaps, this study proposes a DLT detection framework based on bike sharing trajectories. Moreover, this study seeks to understand the contributing factors to DLT behavior using the random parameters logit model with heterogeneity in means and variances (RPLHMV) to account for unobserved heterogeneity in the DLT cases dataset. Statistical analysis shows that DLT is most likely to occur on weekdays during peak periods under large commuting demand. As to what caused the DLT violations, law-obeying cyclists are more susceptible to external events, while risk-taking cyclists are subtly undermined by their habits. In addition, the model of RPLHMV reveals several significant contributing factors to the propensity of DLT violations, such as event time, available passing time for left-turning bicycles, and average cycling speed, whereas the indicator variables of actual waiting time, available passing space for left-turning bicycles, and preference for DLT violation become the emerging influential variables. This study is expected to help better understand DLT occurrence and propose countermeasures more efficiently for reducing cyclists’ DLT rate.

An Exploratory Assessment of Driver Injury Severities in Large-Truck Crashes Involving Fatigued and Non-Fatigued Driving

Fatigue poses a persistent safety challenge for long-haul truck drivers, significantly elevating the risk of highway crashes and compromising daily work performance. This study investigates the factors influencing driver injury severity in single-large truck crashes due to fatigued driving, compared with non-fatigued driving (normal driving). Using Florida crash data from 2011 to 2019 (inclusive), covering both fatigue and non-fatigue-related crashes, this analysis employs random parameters logit models to account for potential unobserved heterogeneity in means and variances. A comprehensive array of factors affecting driver injury severity, including spatial and temporal characteristics, vehicle and traffic attributes, roadway conditions, and driver-specific characteristics, are examined. Strikingly, different parameter estimates emerge for fatigue and non-fatigue-related crashes, indicating fundamental dissimilarities in unobserved heterogeneity in large truck crash data. The estimated model results unveil distinct marginal effects between fatigued and non-fatigued driving, particularly concerning severe injury crashes, suggesting significant variation in driver behavior based on fatigue levels. These findings contribute substantially to the growing literature on the intrinsic disparities between fatigued and non-fatigued driving behaviors. Moreover, this research underscores the potential ramifications of these distinctions on the safety performance of commercial trucks, highway safety technologies, and policy-related safety countermeasures. Understanding the unique characteristics of fatigued driving can inform targeted interventions to bolster safety within the commercial trucking industry and mitigate the impact of severe crashes resulting from fatigued driving.

What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.

One of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted screening application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community's preference for using the AiSTi app. We used a discrete choice experiment (DCE) to understand community preferences regarding the attributes of the AiSTi app for checking anogenital skin lesions. The DCE design included the attributes: data type; AI accuracy; verification of result by clinician; details of result; speed; professional support; and cost. The anonymous DCE survey was distributed to clients attending MSHC and through social media channels in Australia between January and March 2024. Participant preferences on various app attributes were examined using random parameters logit (RPL) and latent class analysis (LCA) models. The median age of 411 participants was 32 years (interquartile range 26-40 years), with 64% assigned male at birth. Of the participants, 177 (43.1%) identified as same-sex attracted and 137 (33.3%) as heterosexual. In the RPL model, the most influential attribute was the cost of using the app (24.1%), followed by the clinician's verification of results (20.4%), the AI accuracy (19.5%) and the speed of receiving the result (19.1%). The LCA identified two distinct groups: 'all-rounders' (88%), who considered every attribute as important, and a 'cost-focussed' group (12%), who mainly focussed on the price. On the basis of the currently available app attributes, the predicted uptake was 72%. In the short term, a more feasible scenario of improving AI accuracy to 80-89% with clinician verification at a $5 cost could increase uptake to 90%. A long-term optimistic scenario with AI accuracy over 95%, no clinician verification and no cost could increase it to 95%. Preferences for an AI-assisted screening app targeting STI-related anogenital skin lesions are one that is low-cost, clinician-verified, highly accurate and provides results rapidly. An app with these key qualities would substantially improve user uptake.

Branding locally produced chicken in Ghana: An application of discrete choice experiment

Branding is gradually becoming an essential component of successful agribusinesses and a significant motivation to boost modern agriculture. The study aimed to investigate broiler farmers' choices and preferences for branding locally produced chicken in Ghana. Primary data was collected from 562 broiler producers using a multistage sampling technique. The Discrete Choice Experiment model, which comprises random parameter logit and conditional logit models, was used to analyse farmers' preferences for branding attributes and willingness to brand locally produced chicken. Branding attributes of brand source, production claim, form of branded chicken, percentage of farm use, and price were analysed. Averagely, farmers were willing to pay US$0.59 to brand 1.9 kg of processed chicken. Further, household size, brand source, production claim, form of branded chicken, percentage of farm use, and membership of a farmer-based organisation positively influenced farmers' willingness to brand locally produced chicken. In contrast, price negatively influenced farmers’ preference for branding. Farmers should produce brands with these unique attributes to boost sales, improve income and enhance the competitiveness in the poultry industry.

Biologics and oral systemic treatment preferences in patients and physicians for moderate-to-severe atopic dermatitis: a discrete choice experiment in the United Kingdom and Germany

Background: As the available treatments for moderate-to-severe atopic dermatitis (AD) expand, understanding patient and physician preferences becomes crucial for informed decision-making. Objective: To quantify patient and physician preferences for biologics and oral systemic AD treatment attributes. Materials and methods: We conducted a cross-sectional, online discrete choice experiment (DCE) involving 306 AD patients and 206 physicians throughout the United Kingdom and Germany. Qualitative interviews identified the key attributes for inclusion in the DCE. Each choice task comprised two hypothetical patient profiles. Data were analyzed using a random-parameters logit model. Results: Results indicated a significant emphasis on efficacy, with reducing sleep disturbance and itch ranking first and second among patients, and the reverse for physicians. Time to itch relief was the third most important efficacy attribute for both groups, but relatively more important for patients than for physicians. For both groups, the risk of eye problems was the most important safety concern of those included. Mode of administration was not of great importance compared to efficacy and safety attributes. Conclusions: Our findings suggest patients prioritize sleep disturbance, an attribute not captured in prior preference studies in AD, time to itch relief and itch. These findings emphasize the importance of addressing sleep-related issues, whilst also targeting fast itch control, to enhance patients’ well-being.

Farmers’ preferences for rice bean production traits in western Kenya

Rice bean (Vigna umbellata) is an important legume for food and nutritional security. However, the level of its cultivation in western Kenya, where there is a serious malnutrition challenge, is still low due to a lack of insights into its attributes. Hence, to bridge this knowledge gap, this study analyzed smallholder farmers' preferences for production traits. We collected data from 204 farmers through a choice experiment (CE) approach in upper and lower midland agro-ecological zones. Subsequently, we used the random parameter logit (RPL) model to analyze the data. Results showed that farmers preferred all attribute levels presented except 61 % or more pest resistance. The lower midland farmers preferred compensation of Kenya shillings (Ksh) 34.93 for yields of 9 or more bags per acre; Ksh 52.22 for 61 to 79 maturity days and; Ksh 20.24 for 40 %–60 % pest resistance level. In comparison, upper midland farmers preferred compensation of Ksh 66.44 for 6 to 8 bags per acre; Ksh 53.82 for 9 or more bags per acre; no compensation for 50 to 60 maturity days; and Ksh 132.98 for 40 %–60 % pest resistance. However, farmers preferred no compensation for intact pods. The observed differences in farmers' preferences for rice bean attributes are explained by heterogeneity in the agroecological conditions, farmers' socio-economic characteristics, and risk perceptions. The findings should inform targeted extension and breeding programs that fit farmers’ diverse environments and resource endowments.