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BACKGROUND: The COVID-19 pandemic prompted the launch of the US Department of Health and Human Services' COVID-19 Public Education Campaign to boost vaccine confidence and uptake among adults, as vaccines are key to preventing severe illness and death. OBJECTIVE: Past segmentation research relevant to COVID-19 behavior has found important differences in attitudes, sociodemographics, and subsequent COVID-19 prevention behaviors across population segments. This study extends prior work by incorporating a more comprehensive set of attitudes, behaviors, and sociodemographic variables to identify population segments by differing levels of COVID-19 vaccine confidence and evaluate differences in their subsequent uptake of COVID-19 prevention behaviors. METHODS: Data were obtained from 5 waves (January 2021 to June 2022) of a web-based longitudinal, probability-based panel survey of US adults (N=4398) administered in English and in Spanish. Participants were recruited from NORC at the University of Chicago's national AmeriSpeak panel and were invited to participate across multiple waves. Latent class cluster analysis estimated segments of respondents based on over 40 COVID-19 attitudes, beliefs, behaviors, and sociodemographics as reported in wave 1. Survey-weighted cross-tabulations and bivariate regression analyses assessed differences in COVID-19 vaccine uptake, booster uptake, mask use, and social distancing in all segments across all 5 survey waves. RESULTS: A total of 6 segments (hardline nonintenders, prevention-compliant nonintenders, burned-out waiters, anxious waiters, skeptical confidents, and ready confidents) were identified, which differed by their COVID-19 vaccine confidence, prevention-related attitudes and behaviors, and sociodemographics. Cross-tabulations and regression results indicated significant segment membership differences in COVID-19 vaccine and booster timing, mask use, and social distancing. Results from survey-weighted cross-tabulations comparing COVID-19 vaccine and booster uptake across segments indicate statistically significant differences in these outcomes across the 6 segments (P<.001). Results were statistically significant for each segment (P<.01 for booster uptake among burned-out waiters; P<.001 for all other coefficients), indicating that, on average, respondents in segments with lower intentions to vaccinate reported later receipt of COVID-19 vaccines and boosters relative to the timing of vaccine and booster uptake among ready confidents. CONCLUSIONS: Results extend previous research by showing that initial beliefs and behaviors relevant to COVID-19 vaccination, mask use, and social distancing are important for understanding differences in subsequent compliance with recommended COVID-19 prevention measures. Specifically, we found that across respondent segments, the probability of vaccine and booster uptake corresponded with both COVID-19 vaccine confidence and mask use and social distancing compliance; more compliant segments were more likely to get vaccinated or boosted than less compliant segments given similar levels of vaccine confidence. These findings help identify appropriate audiences for campaigns. Results highlight the use of a comprehensive list of attitudes, behaviors, and other individual-level characteristics that can serve as a basis for future segmentation efforts relevant to COVID-19 and other infectious diseases.

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The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective -- either within clusters (internal) or between clusters (external) -- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

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The potential of ecotourism is expanding year on year. Given the broad nature of the tourism market, it is necessary to identify smaller groups of individuals that have common traits. This study aims to explore if there are any segments of ecotourist based on attitude and behavior towards ecotourism, profiles the segments according to their sociodemographic characteristics, and analyzes the extent to which they differ by country of origin. The study used a sample of 809 ecotourists from Spain and Chile, while latent class cluster analysis was employed as the segmentation method. The ecotourists were divided into three segments (Basic ecotourists-ecotourists of thought-, Average ecotourists, and Conscientious and active ecotourists) and the bias-adjusted three-step methodology was used to examine the differences in the country of origin. In sum, the largest portion of tourists falls into the category of the average ecotourist segment, which lies between the two aforementioned segments and exhibits moderate attitudes and intentions to engage in ecotourism and pay a premium for it. This segment is more prevalent in Spain compared to Chile. This research adds to the body of knowledge on the marketing of ecotourism behavior by modeling the heterogeneity in the ecotourists' attitudes and behaviors. This study suggests that ecotourism organizations, whether they are accommodation providers, tourism operators, or companies that organize activities in nature, should be aware of the unique qualities of their customers, as well as their preferences, in order to create successful marketing strategies.

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This study aims a more thorough understanding of individuals' motivations and determinants of working from home (WFH) at various phases of the pandemic. To achieve this research goal, we analyze attitudes towards WFH, the profiles of various types of workers engaged in WFH, and the determinants of the current and future expected frequency of WFH among 816 workers in Hong Kong. We identify four types of teleworkers: (1) those with little employer support, (2) those distracted with tech problems, (3) those with good home office, and (4) those with substantial employer support. Separate latent-class choice models present that WFH frequencies in early phases of the pandemic (and at the moment), attitudes towards WFH, and certain constraining/facilitating factors affect the (expected) frequency of WFH. This study provides valuable insights into the types of teleworkers and the determinants of WFH, which will help policymakers create ways to encourage (or discourage) the future frequency of WFH.

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Cases of dengue and chikungunya fever are escalating all over India. Both viruses share a common vector, the "Aedes" mosquito. Due to similar clinical symptoms, both the dengue (DENV) and chikungunya (CHIKV) virus can circulate as co-infection. There is very limited data available on dengue-chikungunya co-infection in Uttarakhand, India. The purpose of this study was to determine the seroprevalence of dengue and chikungunya virus infections, as well as their co-infection, in patients presenting with clinical symptoms. Serum samples of clinically suspected patients from the tertiary care hospital of Uttarakhand were collected, and Latent Class Cluster Analysis was performed for clinical profiling. ELISA was performed for DENV and CHIKV. 279 cases were enrolled, out of which 222 (79.5%) came positive for dengue NS1 Ag, 143 (51.2%) for dengue IgM, 98 (35.1%) for IgG followed by 16 (5.7%) of CHIKV IgM, and 4 (1.4%) were NS1 Ag with CHIKV IgM. Among the clinical features, fever (n = 270, 96.8%) was the most common symptom in all suspected dengue and chikungunya cases. Other symptoms like chills (n = 254, 91.0%), arthralgia (n = 241, 86.4%), and headache (n = 240, 86.0%) were present in a significant number. Results showed fewer odds of getting both DENV and CHIKV infection simultaneously, but the risk is still not negligible. This study explores the clinical presentation of the suspected dengue-chikungunya case. The increasing incidence of dengue and chikungunya and their co-infection necessitate the authorities' active surveillance of endemic regions and effective patient care management.

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BACKGROUND: A significant proportion of women with preeclampsia (PE) exhibit persistent postpartum hypertension (PHTN) at 3 months postpartum associated with cardiovascular morbidity. This study aimed to screen patients with PE to identify the high-risk population with persistent PHTN. METHODS: This retrospective cohort study enrolled 1,000 PE patients with complete parturient and postpartum blood pressure (BP) profiles at 3 months postpartum. The enrolled patients exhibited new-onset hypertension after 20 weeks of pregnancy, while those with PE superimposed upon chronic hypertension were excluded. Latent class cluster analysis (LCCA), a method of unsupervised learning in machine learning, was performed to ascertain maternal exposure clusters from eight variables and 35 subordinate risk factors. Logistic regression was applied to calculate odds ratios (OR) indicating the association between clusters and PHTN. RESULTS: The 1,000 participants were classified into three exposure clusters (subpopulations with similar characteristics) according to persistent PHTN development: high-risk cluster (31.2%), medium-risk cluster (36.8%), and low-risk cluster (32.0%). Among the 1,000 PE patients, a total of 134 (13.4%) were diagnosed with persistent PHTN, while the percentages of persistent PHTN were24.68%, 10.05%, and 6.25% in the high-, medium-, and low-risk clusters, respectively. Persistent PHTN in the high-risk cluster was nearly five times higher (OR, 4.915; 95% confidence interval (CI), 2.92-8.27) and three times (OR, 2.931; 95% CI, 1.91-4.49) than in the low- and medium-risk clusters, respectively. Persistent PHTN did not differ between the medium- and low-risk clusters. Subjects in the high-risk cluster were older and showed higher BP, poorer prenatal organ function, more adverse pregnancy events, and greater medication requirement than the other two groups. CONCLUSION: Patients with PE can be classified into high-, medium-, and low-risk clusters according to persistent PHTN severity; each cluster has cognizable clinical features. This study's findings stress the importance of controlling persistent PHTN to prevent future cardiovascular disease.

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OBJECTIVE: Public knowledge of early onset symptoms and risk factors (RF) of acute myocardial infarction (AMI) is very important for prevention, recurrence and guide medical seeking behaviours. This study aimed to identify clusters of knowledge on symptoms and RFs of AMI, compare characteristics and the awareness of the need for prompt treatment. DESIGN: Multistage stratified sampling was used in this cross-sectional study. Latent GOLD Statistical Package was used to identify and classify the respondent subtypes of the knowledge on AMI symptoms or modifiable RFs. Multivariable logistic regression was performed to identify factors that predicted high knowledge membership. PARTICIPANTS: A structured questionnaire was used to interview 4200 community residents aged over 35 in China. 4122 valid questionnaires were recovered. RESULTS: For AMI symptoms and RFs, the knowledge levels were classified into two or three distinct clusters, respectively. 62.7% (Symptom High Knowledge Cluster) and 39.5% (RF High Knowledge Cluster) of the respondents were able to identify most of the symptoms and modifiable RFs. Respondents who were highly educated, had higher monthly household income, were insured, had regular physical examinations, had a disease history of AMI RFs, had AMI history in immediate family member or acquaintance or had received public education on AMI were observed to have higher probability of knowledge on symptoms and RFs. There was significant difference in awareness of the prompt treatment in case of AMI occurs among different clusters. 'Calling an ambulance' was the most popular option in response of seeing others presenting symptoms of AMI. CONCLUSIONS: A moderate or relatively low knowledge on AMI symptoms and modifiable RFs was observed in our study. Identification of Knowledge Clusters could be a way to detect specific targeted groups with low knowledge of AMI, which may facilitate health education, further reduce the prehospital delay in China and improve patient outcomes.

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Purpose: Interventions that are tailored to the specific psychosocial needs of people with diabetes may be more effective than a "one size fits all" approach. The purpose of this study is to identify patient profiles with distinct characteristics to inform the development of tailored interventions. Methods: A latent class cluster analysis was conducted with data from the ENCOURAGE trial based on participant responses to 6 baseline psychosocial measures, including trust in physicians, perceived discrimination, perceived efficacy in patient-physician interactions, social support, patient activation, and diabetes distress. The trial's primary outcomes were hemoglobin A1c, body mass index, systolic blood pressure, low-density lipoprotein cholesterol, and quality of life; secondary outcomes were diabetes distress and patient engagement. Results: Three classes of participants were identified: Class 1 (n = 72) had high trust, activation, perceived efficacy and social support; low diabetes distress; and good glycemic control (7.1 ± 1.3%). Class 2 (n = 178) had moderate values in all measures with higher baseline A1c (8.1 ± 2.1%). Class 3 (n = 155) had high diabetes distress; low trust, patient engagement, and perceived efficacy; with similar baseline A1c (8.2 ± 2.1%) as Class 2. Intervention effects differed for these 3 classes. Conclusion: Three distinct subpopulations, which exhibited different responses to the ENCOURAGE intervention, were identified based on baseline characteristics. These groups could be used as intervention targets. Future studies can determine whether these approaches can be used to target scarce resources efficiently and effectively in real-world settings to maximize the impact of interventions on population health, especially in impoverished communities.

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With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the "intelligent lockdown", daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted. For this group, policy is required to ensure when (which days) and where (geographical) telework takes place, such that public transport operators can better plan and operate their services. For those with low willingness, it is essential that the government provides tools to companies (especially in education and vital sector) such that they can be better prepared for teleworking (mostly during but also after the pandemic). Employers on the other hand need to better support their employees, such that they stay in contact with colleagues and their concentration and productivity can increase.

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The ongoing COVID-19 pandemic has drastically altered daily life in cities across the world. To slow the spread of COVID-19, many countries have introduced mobility restrictions, ordered the temporary closure of businesses, and encouraged social distancing. These policies have directly and indirectly influenced travel behaviour, particularly modal preferences. The purpose of this paper to explore modality profiles for non-mandatory trips and analyze how they have changed in response to the pandemic and pandemic-related public health policies. The data used for this study were collected from web-based surveys conducted in the Greater Toronto Area. Modality profiles were identified through the application of latent class cluster analysis, with six modality profiles being identified for both the pre-pandemic and pandemic periods. The results indicate that the importance of public transit has declined during the pandemic, while the roles of private vehicles and active modes have become more prominent. However, individuals' changes in modal preferences vary based on their pre-pandemic modality profile. In particular, it appears that pre-pandemic transit users with access to a private vehicle have substituted public transit for travel by private vehicle, while those without private vehicle access are continuing to use public transit for non-mandatory trips. Consequently, pandemic-related transportation policies should consider those who do not have access to a private vehicle and aim to help those making non-mandatory trips using transit or active modes comply with local public health guidelines while travelling. The results highlight how the changes in modal preferences that occurred due to the pandemic differ among different segments of the population.

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Using an experimental methodology based on investment games, we examine whether smallholder rice farmers from Nueva Ecija, Philippines have heterogeneous preferences for improvements in 10 rice varietal traits. We use a latent class cluster approach to identify different segments of rice producing households and their distinct preferences for trait improvements. These clusters were characterised post hoc using household, farm, and marketing characteristics. On average, farmers invested the most in rice varietal trait improvements that offered opportunities to reduce losses caused by lodging, insects and diseases. We found four classes of farmers with distinct preferences for improvements in variety traits. The clusters were significantly different in terms of household and farm characteristics. These findings can guide breeding research in the development of varieties that have the traits farmers identified for improvement, and that will address the unique needs of distinct farmer segments.

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The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.

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Our objective was to identify meaningful subgroups of buyers based on psychological risk factors for compulsive buying. A community sample of 242 adult women fulfilled an online survey exploring buying habits and motives, impulsivity, self-esteem, and severity of compulsive buying. A latent class cluster analysis was performed. A nonproblematic cluster (28%) was characterized by low levels of impulsivity and buying motives. An intermediary cluster (51%) was characterized by higher levels of positive and negative reinforcement-related buying motives. Both clusters were characterized by a low frequency of compulsive buying (2 and 8%, respectively), but the severity of compulsive buying was higher for the intermediary cluster. A third cluster (21%) was characterized by a higher frequency of compulsive buying (43%), a higher severity of compulsive buying, a stronger feeling of losing control, and higher levels of negative urgency and coping motive. These results present similarities with the Interaction of Person-Affect-Cognition-Execution (I-PACE) model of addiction and the negative reinforcement model of drug addiction, which both postulate that negative feelings play a central role in motivating and maintaining addiction. These results also echo other typologies performed in problem gamblers and problematic videogame users. These similarities of psychological profiles with other addictive behaviors, and with common symptoms and clinical expressions, are supplementary arguments to consider conceptualizing compulsive buying as an addictive disorder.

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OBJECTIVES: Internet gambling is often considered as a risk factor for gambling problems compared with land-based gambling. In parallel, this online activity generates data that can be useful to characterize Internet gambling behaviours. The objectives were to define a typology of online lotteries and scratch games gamblers' behaviours in order to identify early risky gambling behaviours and to classify gamblers in order to identify individuals with global risky gambling behaviours. METHODS: We performed a multilevel latent class cluster based on player account-based data of 10,000 gamblers from a French online operator. RESULTS: We identified seven clusters of online lotteries and scratch games gamblers' behaviours. A small cluster (3%) was characterized by a very high gambling activity, a high probability of chasing behaviour, a large proportion of bets concerning instant lotteries and scratch games, and a high proportion of women. We also found a group of gamblers having an 81% probability of being each month in this cluster. CONCLUSIONS: The identification of distinct clusters of gambling behaviours and of groups of gamblers having different probabilities of being in these clusters through time could allow the implementation of personalized prevention measures according to the gamblers' profile.

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The changing nature of employment in recent decades, due to an increased emphasis on flexibility and competitiveness in European labour markets, compels the need to assess the consequences of contemporary employment situations for workers. This article aims to study the relation between the quality of employment and the health and well-being of European workers, using data from the 2010 European Working Conditions Survey. A typology of employment arrangements, mapping out employment quality in the European labour force, is constructed by means of a Latent Class Cluster Analysis. This innovative approach shows that it is possible to condense multiple factors characterising the employment situation into five job types: Standard Employment Relationship-like (SER-like), instrumental, precarious unsustainable, precarious intensive and portfolio jobs. Binary logistic regression analyses show that, controlling for other work quality characteristics, this employment quality typology is related to self-perceived job satisfaction, general health and mental health. Precarious intensive jobs are associated with the worst and SER-like jobs with the best health and well-being situation. The findings presented in this study indicate that, among European wage workers, flexible and de-standardised employment tends to be related to lower job satisfaction, general health and mental health. The quality of employment is thus identified as an important social determinant of health (inequalities) in Europe.

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Latent class (LC) cluster analysis of a set of subscale lz person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students (N = 91,648) on the nine-subscale School Attitude Questionnaire Internet (SAQI). Inspection of the class-specific lz mean and variance structure combined with explanatory analysis of class membership showed that the data included a poor-fit class, a class showing good fit combined with social desirability bias, a good-fit class, and two classes that were more difficult to interpret. A comparison of multinomial logistic regression predicting class membership and multiple regression predicting continuous person fit showed that LC cluster analysis provided information about aberrant responding unattainable by means of linear multiple regression. It was concluded that LC person-fit analysis has added value to common approaches to explaining aberrant responding to multiscale measures.

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BACKGROUND: Cancer is prevalent in the rapidly growing Chinese American community, yet little is known about the symptom experience to guide comprehensive treatment planning. This study evaluated symptom prevalence and patient subgroups with symptom distress in a large sample of Chinese American cancer patients. METHODS: Patients were consecutively recruited from 4 oncology practices, and they completed a translated cancer symptom scale. Latent class cluster analysis was used to identify subgroups of patients with distinct symptom distress profiles. RESULTS: There were 1436 patients screened; 94.4% were non-English-speaking, and 45.1% were undergoing cancer therapy. The cancers included breast (32.6%), lung (14.8%), head and neck (12.5%), and hematologic cancer (10.1%). Overall, 1289 patients (89.8%) had 1 or more symptoms, and 1129 (78.6%) had 2 or more. The most prevalent symptoms were a lack of energy (57.0%), dry mouth (55.6%), feeling sad (49.3%), worrying (47.5%), and difficulty sleeping (46.8%). Symptoms causing "quite a bit" or "very much" distress included difficulty sleeping (37.9%), a lack of appetite (37.2%), feeling nervous (35.8%), pain (35.2%), and worrying (34.0%). Four patient subgroups were identified according to the probability of reporting moderate to high symptom distress: very low physical and psychological symptom distress (49.5%), low physical symptom distress and moderate psychological symptom distress (25.2%), moderate physical and psychological symptom distress (17.4%), and high physical and psychological symptom distress (7.8%). CONCLUSIONS: Symptom prevalence is high in community-dwelling Chinese American cancer patients, and nearly half experience severe distress (rated as "quite a bit" or "very much" distressing) from physical symptoms, psychological symptoms, or both. These data have important implications for the development of effective symptom control interventions.

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One of the most fundamental, but also controversial, food trends of the past years is convenience food. This article investigates the underexplored relationship between the heterogeneity in (convenience) food consumption (a feature of a food culture's cuisine) and meal patterns (characteristics of a food culture's structure). This study hopes to illustrate that convenience food can be interpreted both as a means to maintain a food culture's structure and as a means to overturn it. Latent Class Cluster Analysis is performed using data from the HBS 2005 survey on families' food expenditures to conceptualize convenience-orientation and to examine the relationships with families' meal behaviors. Whereas outsourcing cooking is most prevalent among single-person households; two-or more-person households are most likely to buy unprocessed and natural foods and to spend most time cooking and eating in. A higher consumption of convenience food is also more likely to affect individuals' kitchen than table habits.

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The fate of captured and released fish in recreational fisheries depends in large part on fisher handling and behavior. As such, there is a need for promoting adoption of responsible fishing practices. We interviewed recreational sockeye salmon anglers in the lower Fraser River, British Columbia, to assess their awareness of responsible fishing practices and identify gaps where improved education could promote conservation-oriented behaviors. Based on our interview data, we developed three latent class models of salmon angler typologies based on: 1) anglers' fishing behaviors and preferences, 2) anglers' perceived risks to salmon survival due to post-capture live release, and 3) anglers' level of support for education programs. In the first model, we identified salmon-only anglers (33% of sample), lake-species specialists (46%), and all-around anglers (21%). These classes were differentiated primarily by non-salmon fishing activities (e.g., other target species). In the second model, we found four classes of anglers who differed with regards to key factors they thought affected post-release survival: air exposure (39% of sample); water temperature (24%); hook location (22%); and revival effort (15%). In the third model, we found anglers were either supporters (73%) or non-supporters (27%) of angler education programs. Heterogeneity existed among anglers but we found no correlations in angler classes across models, nor any significant demographic or experiential predictors of class membership. Respondents generally had high awareness and application of catch-and-release best practices, with lake-species specialists rating a higher awareness and usage of recommended catch-and-release technique, and were significantly more likely to cut the line on deeply hooked fish than other groups. Our findings provide resource managers with important insight into the attitudes and behaviors of sockeye salmon anglers in the important lower Fraser River recreational fishery. Our findings also highlight, however, the need for further research on the determinants of angler beliefs and behavior in order to customize programs to build anglers' awareness and adoption of responsible fishing practices.

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