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The embedding effect represents one of the most challenging problems of the contingent valuation (CV) method. The purpose of this study is to address the issue of embedding effect in the context of museums, by estimating the size and extent of the problem. A CV study conducted in Denmark asked respondents to evaluate both the Danish museum-system (inclusive good) and the specific local museum in their municipality of residence (embedded good). Typically, an equal or very close willingness-to-pay (WTP) for the two goods would indicate the presence of an embedding effect. However, we show that this will only be the case for some respondents, while for others the similarity between the two WTP reflects an exclusive interest for the local museum. We demonstrate the existence of these two different types of respondents by using a latent class model where respondents are clustered based on answers to the CV survey. Consequently, the aggregate WTP and the bias associated with the embedding effect are estimated. Our results have implications in terms of the reliability of CV studies and its implementation in actual policy issues, demonstrating that the right questions and techniques will facilitate the separation of 'true' answers from biased ones. [ABSTRACT FROM AUTHOR]

Background: Latent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios. Methods: We generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G 2 and χ 2 statistics indicated lack-of-fit globally or for each test pair. Results: Across all settings, residual correlation plots, pairwise G 2 and χ 2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G 2 and χ 2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests. Conclusions: Residual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates. [ABSTRACT FROM AUTHOR]

In the absence of a gold standard, sensitivity and specificity could be evaluated using the Bayesian latent class model underlying true positives and true negatives. However, when the frequency of positivity is limited owing to low prevalence, the estimates may be strongly influenced by prior information or small-sample bias. In this study, we evaluated the performance of the Bayesian latent class model with two independent tests under a rare frequency of positivity, varying the strength and way of giving prior information under the conditional independence assumption is satisfied. Throughout the simulation experiments, a small-sample bias led to underestimation and overestimation when the frequency of positivity was low. Furthermore, we observed that placing an informative prior distribution for only one of the two tests resulted in a constant bias of sensitivity or specificity for the other test, regardless of the strength of the prior information. [ABSTRACT FROM AUTHOR]

In addition to the deadly effects of the COVID-19 pandemic, positive changes have been made in air quality. It is important for decision-makers to know whether people's preferences change as the environment changes. This study tests the stability of public's preferences and willingness to pay (WTP) for air quality improvement before and after the COVID-19 pandemic, using a choice experiment. To explore the determinants of the public's preference heterogeneity for air quality improvement, we introduced the risk perception psychological factor and further compared the public's preference heterogeneity before and after the COVID-19 pandemic based on a latent class model. The results suggest that the characteristics of respondents' preference categories are similar in the face of a public health crisis and an uncertain future, and the heterogeneous preference of respondents for air quality improvement before the COVID-19 pandemic (2019) and after the COVID-19 pandemic (2021) which can be described in terms of three classes: "sensation preferable," "health preferable," and "price sensitive." However, the sources of influencing preference heterogeneity before and after the COVID-19 pandemic are different, which are reflected in risk perception and socio-economic characteristic variables. After the COVID-19 pandemic, females with stronger perceived risk effects and lower acceptability of air pollution will pay more attention to the health attributes of air quality improvements. The results also reveal that respondents show different WTP for air quality improvement attributes, with respondents before the COVID-19 pandemic showing higher WTP than those after. The contribution of this study is to provide theoretical insights to explore the effects of stability of public preferences in the context of public health emergencies, and to provide valuable guidance for policy makers to formulate governance measures in line with public demands. [ABSTRACT FROM AUTHOR]

Single-item assessments have recently become popular in various fields, and researchers have developed methods for estimating the reliability of single-item assessments, some based on factor analysis and correction for attenuation, and others using the double monotonicity model, Guttman's λ6, or the latent class model. However, no empirical study has investigated which method best estimates the reliability of single-item assessments. This study investigated this question using a simulation study. To represent assessments as they are found in practice, the simulation study varied several aspects: the item discrimination parameter, the test length of the multi-item assessment of the same construct, the sample size, and the correlation between the single-item assessment and the multi-item assessment of the same construct. The results suggest that by using the method based on the double monotonicity model and the method based on correction for attenuation simultaneously, researchers can obtain the most precise estimate of the range of reliability of a single-item assessment in 94.44% of cases. The test length of a multi-item assessment of the same construct, the item discrimination parameter, the sample size, and the correlation between the single-item assessment and the multi-item assessment of the same construct did not influence the choice of method choice. [ABSTRACT FROM AUTHOR]

On the path to carbon neutrality by 2050, the expansion and improvement of high-voltage power transmission lines (HV-PTL) is a critical and unavoidable energy issue that may become a potential source of public conflict. However, the discussion on this issue in South Korea has been insufficient. In this study, we analyzed public attitudes toward HV-PTLs and classified people into subgroups based on their common characteristics using latent class models. In addition, we used the contingent valuation method to estimate the public's willingness to pay (WTP) for the conversion of overhead lines to underground cables, focusing on the differences among the identified subgroups. According to the empirical analysis, people in South Korea can be divided into four classes based on their perceived need for resident participation in the decision-making process, perceived health risk, and perceived property loss risk. According to the WTP analysis using the spike model, the estimated mean and median WTP values were KRW 4184 (USD 3.8) and KRW 2632 (USD 2.4) per household per month, respectively. In addition, we found a significant difference in WTP by class. Those who perceived HV-PTL as risky were less willing to pay for undergrounding projects compared with those whose perceived risk was relatively low. This study contributes to the understanding of academics and policymakers on the relationship between public attitudes toward HV-PTL and overhead to underground conversion. [ABSTRACT FROM AUTHOR]

Achieving sustainability goals in the food system should be informed by consumer demand that signals the market trend and drives systemic changes. This study examines the efficiency of sustainability cues in influencing consumer choices of seafood among consumers in Southern China. The preference and willingness to pay for each seafood attribute are estimated using the Latent Class Logit model. The results show that respondents strongly believed in governmental certifications and were more willing to pay for domestic seafood certified to public standards over imported seafood with third-party sustainability certifications. By integrating individual characteristics into the membership function, this study finds that the preference for sustainability cues and other authenticity cues is related to seafood consumption habits and education. The study highlights the efficiency heterogeneity of sustainability cues, providing valuable insights for formulating public policy and developing marketing strategies that promote sustainable consumption. [ABSTRACT FROM AUTHOR]

In a recent 12-week smoking cessation trial, varenicline tartrate failed to show significant improvements in enhancing end-of-treatment abstinence when compared with placebo among adolescents and young adults. The original analysis aimed to assess the average effect across the entire population using timeline followback methods, which typically involve overdispersed binomial counts. We instead propose to investigate treatment effect heterogeneity among latent classes of participants using a Bayesian beta-binomial piecewise linear growth mixture model specifically designed to address longitudinal overdispersed binomial responses. Within each class, we fit a piecewise linear beta-binomial mixed model with random changepoints for each study group to detect critical windows of treatment efficacy. Using this model, we can cluster subjects who share similar characteristics, estimate the class-specific mean abstinence trends for each study group, and quantify the treatment effect over time within each class. Our analysis identified two classes of subjects: one comprising high-abstinent individuals, typically young adults and light smokers, in which varenicline led to improved abstinence; and another comprising low-abstinent individuals for whom varenicline showed no discernible effect. These findings highlight the importance of tailoring varenicline to specific participant subgroups, thereby advancing precision medicine in smoking cessation studies. [ABSTRACT FROM AUTHOR]

This research addressed two questions: (1) what are the associations of built environments with public bike use, and (2) how different are the associations among sociodemographic groups? The study sampled commuters entering or leaving the metro stations in Xinyi District, Taipei, Taiwan. Their mode choices of connecting trips between trip origins/destinations and metro stations were analyzed using logit and latent class models. Empirical evidence revealed that attributes of density, diversity, distance to transit, and distribution of rental stations result in more significant effects on public bike use than attributes of design and destination accessibility in general. The three segments of respondents that indicated partially dissimilar associations of built environments with public bike use were identified. The empirical results contributed new evidence to the study issues and benefited the development of customized strategies based on travelers' sociodemographic attributes and built environments for promoting public bike use. [ABSTRACT FROM AUTHOR]

Policymakers need to develop measures to counter overcrowding and relocate excess tourism in congested areas to others with fewer visitors. This article proposes a methodological framework consisting of a survey and statistical tools to a gain deeper understanding of tourist profiles and characteristics and determine how covariates affect the ratio of each group in the market. Spatial analysis is performed to determine regional cluster patterns and propose measures aimed at alleviating congested areas by redirecting tourism to other destinations with less pressure without losing the economic impact of tourism. The proposed methodology is applied to international tourists in Spain and reveals some relevant aspects of four international tourism profiles. The analysis confirms the existence of spatial dependence between Spanish regions, suggesting that the application of public policies in one region could have implications for neighbouring ones. [ABSTRACT FROM AUTHOR]

Abstract Background Latent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios. Methods We generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, $${G}^{2}$$ G 2 and $${\chi }^{2}$$ χ 2 statistics indicated lack-of-fit globally or for each test pair. Results Across all settings, residual correlation plots, pairwise $${G}^{2}$$ G 2 and $${\chi }^{2}$$ χ 2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, $${G}^{2}$$ G 2 and $${\chi }^{2}$$ χ 2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests. Conclusions Residual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

This paper addresses the problem of full-model estimation for non-parametric finite mixture models. It presents an approach for selecting the number of components and the subset of discriminative variables (i.e. the subset of variables having different distributions among the mixture components) by considering an upper bound on the number of components (this number being allowed to increase with the sample size). The proposed approach considers a discretization of each variable into B bins and a penalization of the resulting log-likelihood. Considering that the number of bins tends to infinity as the sample size tends to infinity, we prove that our estimator of the model (number of components and subset of relevant variables for clustering) is consistent under a suitable choice of the penalty term. The relevance of our proposal is illustrated on simulated and benchmark data. [ABSTRACT FROM AUTHOR]

The popularity of must-see tourism destinations raises concerns about negative impacts including pollution, vandalism, and overcrowding in cultural heritage sites and the surrounding landscape. To address these challenges, understanding visitors' choice behavior and motivations is essential. This study aims to identify visitor segments based on their location choice and formulate policy recommendations to address overtourism challenges by examining visitors' choice behavior in a stated choice experiment. It provides evidence-based policy suggestions for mitigating mass tourism effects. The choice experiment was implemented in an online survey which was completed by a sample of 437 individuals who had paid a visit to Amsterdam in recent years. The results shed light on how individuals trade-off location attributes in making a choice between heritage destinations. A latent class analysis revealed three segments which can be labeled as cultural-attraction seekers, selective sightseers, and city-life lovers. The results show that crowdedness and entrance fees for additional experiences significantly influence visitors' location choices across all segments. [ABSTRACT FROM AUTHOR]

Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction. [ABSTRACT FROM AUTHOR]

Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case–control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed significant challenges to global public health organisations and governments, leading to countermeasures like hand sanitizer availability, social distancing, and mandatory face mask wearing, which have disrupted the public transportation sector and impacted the virus spread. Anticipating the effects of circumstances like a pandemic on mobility is essential for operators and managers of public transportation systems to effectively and safely manage the system. In this study, the measures taken during the pandemic, such as those mentioned above, were considered as indicators in the latent class model (LCM) for modal shifting. The model incorporates sociodemographic variables as covariates to understand their impact on modal shifting from public transport to private cars. An online survey with 53,973 valid responses was conducted in Istanbul, Turkiye. As a result of the LCM with covariates, two-latent-class model, the best fit among models ranging from two to six latent classes, emerged. Class-1 participants show increased sensitivity to the pandemic, shifting to private mode, while Class-2 participants are less concerned and tend to maintain their existing mode. The model suggests using LCM with covariates to estimate the modal shift from public transportation to private cars in any given situation.

AbstractInvestigating the determinant of consumer preference for game meat safety is crucial in promoting game meat consumption. In this study, we examined the impact on consumer preferences for a safety attribute, taking the individual risk attitude and the risk perception into account. We used data from a survey and a labeled discrete choice experiment involving 476 Italian consumers and analyzed it with a latent class model. The results show that all consumers gain utility from game meat with this safety claim. Furthermore, consistent groups of consumers who dislike game meat are likely to be risk-averse and perceive game meat as hazardous. Therefore, a marketing or public policy strategy aimed at promoting game meat consumption should focus on reinforcing consumers’ trust in more stringent safety standards. For instance, highlighting the safety measures and inspections involved in the production of game meat can help increase consumer confidence in its safety. [ABSTRACT FROM AUTHOR]

Objective: To explore the latent class characteristics of college students' mental Huagong. Methods: A questionnaire survey was carried out on 510 college students by using Collage Mental Huagong Scale(CMHGS) and the Self-assessment Scale of College Students' Innovation and Entrepreneurship Ability(CSIEASAS). Results: The college students' mental Huagong can be divided into high Huagong group (40%), high-level low will group (6%), medium-level low emotion group (48%) and low Huagong group (5%). There were significant differences in the scores of entrepreneurial personality, basic entrepreneurial ability, core entrepreneurial ability and social adaptability among the four Latent class model of collage students' mental Huagong. Conclusion: There are four Latent class model of college students' mental Huagong, and there are significant differences in each dimensions of college students' abilities of innovation and entrepreneurship among each latent class models of college students' mental Huagong. [ABSTRACT FROM AUTHOR]

This study explores tourists' preferences for bike-sharing services in the context of tourism. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, a latent class behavior model was developed to investigate preferences for bike-sharing services from tourists' point of view. The results show that tourists prefer a bike-sharing service that ensures round-the-clock availability, is accessible within a 5-min walk from both the origin and destination, features bikes stationed at specific designated locations, and provides a variety of payment options at an affordable rate of USD 1.00 per h. Under these attributes, about 41.63% of tourists are likely to choose a bike-sharing option for their travel tours. These findings offer valuable insights for traffic management authorities and policymakers, demonstrating that bike-sharing services can be hailed as an effective complement to existing transportation modes and can help bridge the gap between supply and demand in tourist cities. [ABSTRACT FROM AUTHOR]

Recent U.S. National Restaurant Surveys show four out of ten hot trending concepts from the consumers' point of view are concepts relating to local sourcing, including hyper-local, locally sourced meat and seafood, locally sourced produce, and farm/estate branded items. Consumers are signaling that they value locally sourced food; therefore, restaurants need to establish a legitimate and efficient way to assure consumers that they are offering a verifiable local sourcing value proposition. In this study, we apply a "tractor" symbol index system designed for a local restaurant tourism directory to understand consumer willingness-to-pay (WTP) for local sourcing intensity signaled by restaurants and measure choice variables across alternative restaurant formats. Stated preference data and a discrete choice experiment (DCE) survey were conducted in Kentucky on over 1600 consumers. Results of the latent class analysis show there is a significant interest in local sourcing within each restaurant format. [ABSTRACT FROM AUTHOR]

We analyze why the impact of wine tourism in Spain lags behind France and Italy. We argue that a key factor is a lack of attraction of foreign enotourists, especially among tourists who visit a wine PDO region anyway. The international tourist profiles visiting such Spanish wine PDOs are identified and segmented with the ultimate objective of fine-tuning the gastro-touristic offer in these regions. In particular, an innovative two-step Latent Class Model (LCM) with covariates is developed. The first step concerns the sociodemographic profiles of potential international enotourists, which is therefore complementary to existing segmentations of actual enotourists. The second step uses touristic activities in order to profile the possible ways of reaching different types of tourists. The daily expenditure and the tourists' valuation are included as covariates, allowing to predict the probability of class membership, and thus to define priority target profiles for market positioning. [ABSTRACT FROM AUTHOR]

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers' attribute preferences. Design/methodology/approach: A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles. Findings: Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures. Research limitations/implications: Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives. Originality/value: This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model. [ABSTRACT FROM AUTHOR]

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one's behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service. [ABSTRACT FROM AUTHOR]

Many countries are actively promoting a community solar program, which is a community energy program centered on solar photovoltaic power generation, as a public participation-type energy policy. In this study, we analyze the preferences of potential participants in community solar programs from the perspective of policymakers to identify public acceptance and draw policy implications. As a methodology, the choice experiment with a stated preference method is used to analyze the preferences on the individual attributes of community solar energy in terms of business model targeting Korean who are the potential participants. Furthermore, we observed the homogeneity of preferences within each group and the heterogeneity of preferences between groups by dividing the groups based on the latent class model estimation. The results of this study show that the attributes of the community solar program, such as the operating agent, contract period, expected return, and participatory management, have a significant impact on the acceptance of potential consumers, and that there are clear differences between potential classes. The results can be used as basic data for the community energy projects and can contribute to designing succecesful energy policies not only for Korea but also for governments of other countries pursuing carbon neutrality.

Abstract Background Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. Methods We included 659 individuals aged $$\ge 16$$ ≥ 16 years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl–Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. Results Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden’s Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. Conclusion Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Summary Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

This study aimed to estimate the prevalence of poultry aspergillosis and evaluate the accuracy of histopathology (test under evaluation) and mycological culture (an imperfect reference test). Farms raising layer and breeder or broiler birds, with suspected aspergillosis cases, clinical or subclinical, were eligible and visited for sampling. After necropsy, histopathology and mycological culture examinations were conducted by two evaluators. A Bayesian latent class model was used to estimate the accuracy of histopathology when compared to the imperfect reference test, mycological culture. A total of 142 chicken farms, 96 laying and breeding hen farms, and 46 broiler farms were used for the study. True aspergillosis median prevalence was estimated at 63.7% (95% credibility intervals, CrI: 53.8%, 73.0%) in layers and breeders and at 65.2% (95% CrI: 50.2%, 78.3%) in the broiler farms’ population. The median diagnostic sensitivity of histopathology and culture were estimated at, respectively, 98.8% (95% CrI: 94.6%, 100.0%) and 90.4% (95% CrI: 83.6%, 95.3%). Tests’ diagnostic specificity was estimated at, respectively, 97.3% (95% CrI: 87.7%, 99.9%) and 95.7% (95% CrI: 91.8%, 98.2%). Both tests had very high and comparable positive predictive values, but, in a population where disease prevalence was 25%, histopathology had a higher negative predictive value than culture.

ImportanceWhen an emerging infectious disease outbreak occurs, such as COVID-19, institutions of higher education (IHEs) must weigh decisions about how to operate their campuses. These decisions entail whether campuses should remain open, how courses should be delivered (in-person, online, or a mixture of the two), and what safety plans should be enacted for those on campus. These issues have weighed heavily on campus administrators during the on-going COVID-19 pandemic. However, there is still limited knowledge about how such decisions affect students' enrollment decisions and campus safety in practice when considering compliance.ObjectivesTo assess 1) students' willingness to comply with health protocols and contrast their perception of their classmates' compliance, 2) whether students prefer in-person or online learning during a pandemic, and 3) the importance weights of different aspects of campus operations (i.e., modes of course delivery and safety plans) for students when they decide to enroll or defer.Design setting and participantsAn internet-based survey of college students took place from June 25, 2020 to July 10, 2020. Participants included 398 industrial engineering students at the Georgia Institute of Technology, a medium-size public university in Atlanta, Georgia. The survey included a discrete choice experiment with questions that asked students to choose whether to enroll or defer when presented with hypothetical scenarios related to Fall 2020 modes of course delivery and aspects of campus safety. The survey also asked students about expected compliance with health protocols, whether they preferred in-person or online courses, and sociodemographic information.Main outcomes and measuresWe examine students' willingness to comply with potential health protocols. We estimated logistic regression models to infer significant factors that lead to a student's choice between in-person and online learning. Additionally, we estimated discrete choice models to infer the importance of different modes of course delivery and safety measures to students when deciding to enroll or defer.ResultsThe survey response rate was 20.8%. A latent class model showed three classes of students: those who were "low-concern" (comprising a 29% expected share of the sample), those who were "moderate-concern" (54%) and those who were "high-concern" (17%). We found that scenarios that offered an on-campus experience with large classes delivered online and small classes delivered in-person, strict safety protocols in terms of mask-wearing, testing, and residence halls, and lenient safety protocols in terms of social gatherings were broadly the scenarios with the highest expected enrollment probabilities. The decision to enroll or defer for all students was largely determined by the mode of delivery for courses and the safety measures on campus around COVID-19 testing and mask-wearing. A logistic regression model showed that a higher perceived risk of infection of COVID-19, a more suitable home environment, being older, and being less risk-seeking were significant factors for a person to choose online learning. Students stated for themselves and their classmates that they would comply with some but not all health protocols against COVID-19, especially those limiting social gatherings.Conclusions and relevanceThe majority of students indicated a preference to enroll during the COVID-19 pandemic so long as sufficient safety measures were put in place and all classes were not entirely in-person. As IHEs consider different options for campus operations during pandemics, they should consider the heterogeneous preferences among their students. Offering flexibility in course modes may be a way to appeal to many students who vary in terms of their concern about the pandemic. At the same time, since students overall preferred some safety measures placed around mask-wearing and COVID-19 testing on campus, IHEs may want to recommend or require wearing masks and doing some surveillance tests for all students, faculty, and staff. Students were expecting themselves and their fellow classmates to comply with some but not all health protocols, which may help IHEs identify protocols that need more education and awareness, like limits on social gatherings and the practice of social distancing at social gatherings.

Artificial intelligence, such as ChatGPT, is advancing rapidly, driving significant transformations in various social areas such as education. This study aimed to divide the student group and test the acceptance of ChatGPT in different student groups through the established model. This study used college students as the research object and the latent class model to distinguish the AI digital ability of students. In this study, the sample of students was divided into groups of weak and strong AI digital based on the latent class model. Taking the whole sample, students with weak and strong digital ability as the research objects, the structural equation model of technology acceptance model and planned behavior theory were established to study the acceptance of ChatGPT among different student groups. The results showed significant heterogeneity in the acceptance of ChatGPT among the student groups. Across the whole sample, perceived behavioral control was the most important factor affecting students' acceptance of the ChatGPT. For students with weak AI digital ability, both attitude and perceived behavioral control play significant roles in their acceptance of ChatGPT. Students with strong AI digital ability are influenced by attitude, subjective norms, and perceived behavioral control when considering their acceptance of ChatGPT. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the prevalence of poultry aspergillosis and evaluate the accuracy of histopathology (test under evaluation) and mycological culture (an imperfect reference test). Farms raising layer and breeder or broiler birds, with suspected aspergillosis cases, clinical or subclinical, were eligible and visited for sampling. After necropsy, histopathology and mycological culture examinations were conducted by two evaluators. A Bayesian latent class model was used to estimate the accuracy of histopathology when compared to the imperfect reference test, mycological culture. A total of 142 chicken farms, 96 laying and breeding hen farms, and 46 broiler farms were used for the study. True aspergillosis median prevalence was estimated at 63.7% (95% credibility intervals, CrI: 53.8%, 73.0%) in layers and breeders and at 65.2% (95% CrI: 50.2%, 78.3%) in the broiler farms' population. The median diagnostic sensitivity of histopathology and culture were estimated at, respectively, 98.8% (95% CrI: 94.6%, 100.0%) and 90.4% (95% CrI: 83.6%, 95.3%). Tests' diagnostic specificity was estimated at, respectively, 97.3% (95% CrI: 87.7%, 99.9%) and 95.7% (95% CrI: 91.8%, 98.2%). Both tests had very high and comparable positive predictive values, but, in a population where disease prevalence was 25%, histopathology had a higher negative predictive value than culture. [ABSTRACT FROM AUTHOR]

An electric scooter sharing (ESS) service offers improved convenience by providing accessibility to public transportation stations and mobility for short-distance trips. However, with the growing global popularity of ESS services, concerns related to safety and accidents have also increased. Previous studies explored the factors impacting preferences for ESS services; however, few have considered ways to enhance safety and convenience for users. Therefore, this study investigated the factors that influence people's choice of ESS service for enhancing safety and convenience. Data was collected in Seoul, Korea, and a latent class model was developed, allowing us to estimate the willingness-to-pay (WTP) of various attributes. The estimation results revealed that the "Exclusive e-scooter lane" attribute was the most preferred, followed by "provision of real-time driving information." The WTP for "Exclusive e-scooter lane" was 47.1% above the base fare. In the estimation results of the class membership model, the preferred attributes were different depending on the class characteristics. The study further revealed variations in WTP for safety and convenience among users and non-users, as well as among non-users depending on their intent to use the ESS service. Furthermore, the proposed attributes constituted factors that affect the safety and convenience of ESS users, and that users were willing to pay an additional cost for them. • Investigating the preferences of ESS service users is essential for establishing operation strategy. • We identified factors with potential positive impact on ESS safety and convenience. • 1000 potential users were surveyed and a latent class model was constructed. • Users were most willing to pay for exclusive e-scooter lane. • Heterogeneity analysis: age and intention to use ESS varied willingness to pay. [ABSTRACT FROM AUTHOR]

Designing incentives for agri-environmental public good provision with threshold effects calls for payment mechanisms favouring critical mass participation and continuity of commitments at the landscape scale. We conducted a choice experiment to test the acceptability of a bonus in a scheme for improving river water quality in France. We introduce a sponsorship bonus each time the farmer convinces a peer into entering the scheme, which can be combined with a collective result bonus per hectare if the river reaches a higher step on the water quality scale. We consider the involvement of local financers could increase the willingness to pay beyond opportunity costs and income foregone and propose higher levels of payment than agri-environmental schemes. Results suggest a sponsorship bonus on its own is cost-effective. We characterize respondents’ heterogeneity and identify three groups based on choice patterns: (i) “pro-environment individualists”, (ii) “management change averse” farmers, and (iii) “pro-incentive” farmers. [ABSTRACT FROM AUTHOR]

This paper investigates the latent classes of parking preference for drivers and the economic effects after implementing Parking Space Comprehensive Utilization (PSC) in traditional business districts (TBD), with a particular focus on the parking preferences of electric vehicle users (EVU). Firstly, Exploratory Factor Analysis (EFA) is used to reduce dimensionality and determine the latent structure. Then, based on the Latent Class Model (LCM), the customers are classified, and the proportion of each class under various latent variables is analyzed. Finally, the paper conducts a quantitative analysis of economic effects by considering different psychological factors across different customer classes. With the data obtained from revealed preference (RP) and stated preference (SP) surveys, this paper identifies the customers' preferences for the three scenarios presented. The results show that (1) customers can be classified into four classes: core customers (CCS, 34%), potential customers (PCS, 29%), regular customers (RCS, 22%), and marginal customers (MCS, 15%), among which EVU do not show a significant preference for parking charging facilities in TBD; (2) the potential economic improvements for these four classes are: 9%, 12%, 8%, and 10%; (3) CCS has the greatest potential to increase store revenue by ¥7041, while PCS has the greatest potential to increase store customer flow by 31%. These findings provide a valuable reference for decision-making by TBD store managers. [ABSTRACT FROM AUTHOR]

ObjectivesThis study aimed to examine the association between different dimensions of bi-directional intergenerational support and the social participation patterns of rural older adults, while also exploring the heterogeneity of these older adults by gender and age.MethodsBased on longitudinal survey data from the ‘Well-Being of Older People in Anhui Province (WESAP)’ in 2018 and 2021, this study used latent class analysis to identify social participation patterns and used multinomial logistic regressions to explore the relationship between intergenerational support and social participation patterns among rural older adults.ResultsFirst, the social participation patterns of rural older persons can be divided into four categories: leisure type (9%), work type (11%), housekeeping type (57.9%) and family labor type (22.1%). Second, there is heterogeneity in the relationship between intergenerational support and social participation patterns in older people, among them, receiving financial support decreased the likelihood of older adults being categorized as work type and family labor type by 14 and 7.7%, respectively, while providing financial support increased the likelihood of older adults belonging to the family labor type by 7.5%; receiving caring support increased the likelihood of older parents being categorized as leisure type by 6%, while providing caring support decreased the likelihood of older parents being categorized as leisure type by 10%; emotional support is related to all patterns of social participation among older adults. Third, the effects of intergenerational support vary across subgroups based on gender and age.ConclusionTwo-way intergenerational support was significantly associated with social participation among rural older adults, and there was significant heterogeneity in this association. Children and older persons should be encouraged to strengthen two-way intergenerational mobility to give full play to the positive impact of different dimensions of intergenerational support on the social participation of older persons in rural areas, ultimately enhancing the quality of life for rural older adults.

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co‐existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model‐based (including frequentist and Bayesian) approaches and algorithm‐based approaches. To evaluate their performance, we compare these approaches using real‐life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed. [ABSTRACT FROM AUTHOR]

This paper investigates heterogeneous mobile app usage patterns through customer segmentation and further examines how customer characteristics are correlated with these heterogeneous usage patterns. The research utilizes a unique individual-level mobile app usage dataset and employs a latent class model incorporating concomitant variables. The results uncover four mobile customer segments and show that: (1) customer mobility is most positively associated with the usage pattern of Social-Type Users, with the highest social app usage but the lowest entertainment app usage; (2) customer phone price is most positively correlated with the usage pattern of Entertainment-Type Users, with the highest entertainment and e-commerce app usage; and (3) customer's number of app downloads is most positive for the usage pattern of Information-Type Users, with the highest information and travel app usage. This study contributes to the literatures on customer segmentation and mobile app usage. The findings offer important implications for app managers. [ABSTRACT FROM AUTHOR]

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers. [ABSTRACT FROM AUTHOR]

Cancer is a major contributor to global disease burden. Many countries experienced or are experiencing the transition that non‐infection‐related cancers replace infection‐related cancers. We aimed to characterise burden changes for major types of cancers and identify global transition patterns. We focused on 10 most common cancers worldwide and extracted age‐standardised incidence and mortality in 204 countries and territories from 1990 to 2019 through the Global Burden of Disease Study. Two‐stage modelling design was used. First, we applied growth mixture models (GMMs) to identify distinct trajectories for incidence and mortality of each cancer type. Next, we performed latent class analysis to detect cancer transition patterns based on the categorisation results from GMMs. Kruskal‐Wallis H tests were conducted to evaluate associations between transition patterns and socioeconomic indicators. Three distinct patterns were identified as unfavourable, intermediate and favourable stages. Trajectories of lung and breast cancers had the strongest association with transition patterns among men and women. The unfavourable stage was characterised by rapid increases in lung, breast and colorectal cancers alongside stable or decreasing burden of gastric, cervical, oesophageal and liver cancers. In contrast, the favourable stage exhibited rapid declines in most cancers. The unfavourable stage was associated with lower sociodemographic index, health expenditure, gross domestic product per capita and higher maternal mortality ratio (P <.001 for all associations). Our findings suggest that unfavourable, intermediate and favourable transition patterns exist. Countries and territories in the unfavourable stage tend to be socioeconomically disadvantaged, and tailored intervention strategies are needed in these resource‐limited settings. [ABSTRACT FROM AUTHOR]

As Korea provides a fare-free policy for subways only, there are objections to geographical equity, and the need to expand it to the entire public transportation system is being discussed. However, expanding policy scope in line with an aging society will soon pose sustainability problems. Hence, policy changes, similar to that of countries that provide fare-discount policies for the elderly or apply different discount rates for each travel mode, are needed. However, providing the same policies for all cities may differ from the benefits the target group wants. Thus, this study investigated the preference of the elderly living in major cities in South Korea for discount policies by travel mode. The study aims to provide a strategy for choosing the travel mode that should provide discount policies by combining regional and individual attributes. The latent class model is employed to classify stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. In addition, providing additional bus discount policies only for trunk lines will help improve sustainability. [ABSTRACT FROM AUTHOR]

This study clarifies the consumption trends and patterns of olive oil in Japan before and after the COVID-19 outbreak. Three years of scanner panel data from 2019 to 2021 was used in this analysis. The latent class model was adopted to discover heterogenous groups of olive oil consumption. The results identified four distinctive classes. Although about 40 percent of consumers did not change their purchase behavior throughout the period, other consumers moved their consumption toward high-, medium- or low-priced oil. The membership function of consumer attributes was also estimated. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation of discrete latent variable (DLV) models is usually performed by the expectation-maximization (EM) algorithm. A well-known drawback is related to the multimodality of the log-likelihood function so that the estimation algorithm can converge to a local maximum, not corresponding to the global one. We propose a tempered EM algorithm to explore the parameter space adequately for two main classes of DLV models, namely latent class and hidden Markov. We compare the proposal with the standard EM algorithm by an extensive Monte Carlo simulation study, evaluating both the ability to reach the global maximum and the computational time. We show the results of the analysis of discrete and continuous cross-sectional and longitudinal data referring to some applications of interest. All the results provide supporting evidence that the proposal outperforms the standard EM algorithm, and it significantly improves the chance to reach the global maximum. The advantage is relevant even considering the overall computing time. [ABSTRACT FROM AUTHOR]

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24‐hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24‐hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape‐invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24‐hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24‐hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism. [ABSTRACT FROM AUTHOR]

The soaring economic development of export activities of handicrafts centralized in emerging urban regions in Vietnam has accelerated the increase in the occurrence of diseases and threats to ecosystems induced by water pollution. We design a discrete choice experiment to elicit the willingness-to-pay of handicraft enterprises to restore the environment and diminish health risks from polluted wastewater through water quality improvement under different scenarios. Estimates from five latent classes reveal that one-half of entrepreneurs strongly value the provision of wastewater treatment services, and their decisions are mostly driven by preferences to reduce the risk of sickness caused by water pollution. This finding lends support to the argument that self-interested preferences predominate pro-environmental behavior in the readiness to pay for water quality services. While entrepreneurs' preferences attributed to ecological remediation seem to vary according to their educational background, the status-quo group shows low environmental awareness. This divergent behavioral pattern suggests that the design of wastewater management policies requires a mixture of measures that aim at different groups of individuals pursuing economic incentives and the creation of awareness. [ABSTRACT FROM AUTHOR]