Although restrictive eating behaviors are very common during early childhood, their precise nature and clinical correlates remain unclear. Especially, there is little evidence on restrictive eating behaviors in older children and their associations with children's shape concern. The present population-based study sought to delineate subgroups of restrictive eating patterns in N = 799 7-14 year old children. Using Latent Class Analysis, children were classified based on six restrictive eating behaviors (for example, picky eating, food neophobia, and eating-related anxiety) and shape concern, separately in three age groups. For cluster validation, sociodemographic and objective anthropometric data, parental feeding practices, and general and eating disorder psychopathology were used. The results showed a 3-cluster solution across all age groups: an asymptomatic class (Cluster 1), a class with restrictive eating behaviors without shape concern (Cluster 2), and a class showing restrictive eating behaviors with prominent shape concern (Cluster 3). The clusters differed in all variables used for validation. Particularly, the proportion of children with symptoms of avoidant/restrictive food intake disorder was greater in Cluster 2 than Clusters 1 and 3. The study underlined the importance of considering shape concern to distinguish between different phenotypes of children's restrictive eating patterns. Longitudinal data are needed to evaluate the clusters' predictive effects on children's growth and development of clinical eating disorders.

Introduction: Long–standing disparities in colorectal cancer (CRC) outcomes and survival between Whites and Blacks have been observed. A person–centered approach using latent class analysis (LCA) is a novel methodology to assess and address CRC health disparities. LCA can overcome statistical challenges from subgroup analyses that would normally impede variable–centered analyses like regression. Aim was to identify risk profiles and differences in malignant CRC survivorship outcomes. Methods: We conducted an LCA on the Surveillance, Epidemiology, and End Results data from 1975 to 2016 for adults ≥18 (N = 525,245). Sociodemographics used were age, sex/gender, marital status, race, and ethnicity (Hispanic/Latinos) and stage at diagnosis. To select the best fitting model, we employed a comparative approach comparing sample-size adjusted BIC and entropy; which indicates a good separation of classes. Results: A four–class solution with an entropy of 0.72 was identified as: lowest survivorship, medium-low, medium-high, and highest survivorship. The lowest survivorship class (26% of sample) with a mean survival rate of 53 months had the highest conditional probabilities of being 76–85 years–old at diagnosis, female, widowed, and non-Hispanic White, with a high likelihood with localized staging. The highest survivorship class (53% of sample) with a mean survival rate of 92 months had the highest likelihood of being married, male with localized staging, and a high likelihood of being non-Hispanic White. Conclusion: The use of a person–centered measure with population-based cancer registries data can help better detect cancer risk subgroups that may otherwise be overlooked.

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

Context: Colorectal cancer (CRC) surgical treatment delay (TD) has been associated with mortality and morbidity; however, disparities by TD profiles are unknown. Objectives: This study aimed to identify CRC patient profiles of surgical TD while accounting for differences in sociodemographic, health insurance, and geographic characteristics. Design: We used latent class analysis (LCA) on 2005-2015 Tennessee Cancer Registry data of CRC patients and observed indicators that included sex/gender, age at diagnosis, marital status (single/married/divorced/widowed), race (White/Black/other), health insurance type, and geographic residence (non-Appalachian/Appalachian). Setting: The state of Tennessee in the United States that included both Appalachian and non-Appalachian counties. Participants: Adult (18 years or older) CRC patients (N = 35 412) who were diagnosed and surgically treated for in situ (n = 1286) and malignant CRC (n = 34 126). Main Outcome Measure: The distal outcome of TD was categorized as 30 days or less and more than 30 days from diagnosis to surgical treatment. Results: Our LCA identified a 4-class solution and a 3-class solution for in situ and malignant profiles, respectively. The highest in situ CRC patient risk profile was female, White, aged 75 to 84 years, widowed, and used public health insurance when compared with respective profiles. The highest malignant CRC patient risk profile was male, Black, both single/never married and divorced/separated, resided in non-Appalachian county, and used public health insurance when compared with respective profiles. The highest risk profiles of in situ and malignant patients had a TD likelihood of 19.3% and 29.4%, respectively. Conclusions: While our findings are not meant for diagnostic purposes, we found that Blacks had lower TD with in situ CRC. The opposite was found in the malignant profiles where Blacks had the highest TD. Although TD is not a definitive marker of survival, we observed that non-Appalachian underserved/underrepresented groups were overrepresented in the highest TD profiles. The observed disparities could be indicative of intervenable risk.

There are common pitfalls and neglected areas when using clustering approaches to solve educational problems. A clustering algorithm is often used without the choice being justified. Few comparisons between a selected algorithm and a competing algorithm are presented, and results are presented without validation. Lastly, few studies fully utilize data provided in an educational environment to evaluate their findings. In response to these problems, this thesis describes a rigorous study comparing two clustering algorithms in the context of an educational digital library service, called the Instructional Architect. First, a detailed description of the chosen clustering algorithm, namely, latent class analysis (LCA), is presented. Second, three kinds of preprocessed data are separately applied to both the selected algorithm and a competing algorithm, namely, K-means algorithm. Third, a series of comprehensive evaluations on four aspects of each clustering result, i.e., intra-cluster and inter-cluster distances, Davies-Bouldin index, users' demographic profile, and cluster evolution, are conducted to compare the clustering results of LCA and K-means algorithms. Evaluation results show that LCA outperforms K-means in producing consistent clustering results at different settings, finding compact clusters, and finding connections between users' teaching experience and their effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.

<p><b>Objectives</b> The study will assess patterns of known individual, interpersonal, and community-level circumstances leading to suicide to understand how these factors can co-occur. These patterns will help focus on prevention strategies.</p><p><b>Methods</b> Data was collected from the Iowa Violent Death Reporting System, Census data from the American Community Survey, and 2010 rural-urban commuting area codes from the Economic Research Service. The study consisted of three steps. The first step latent class analysis was conducted on data from suicide deaths from Iowa in 2016-2018 to create classes of patterns of circumstances leading to suicide. The second step maximum probability assignment was used to assign the sample of 1,276 to the created latent classes. Finally, in the third step bivariate regressions were ran to understand the relationship between the created latent classes and the rurality variable (nonmetropolitan vs metropolitan).</p><p><b>Results </b>Five latent classes of distinct patterns of suicide factors emerged. Class 1 is physical health problems living in areas that are average on all community-level variables. This class 1 is seen to happen with higher odds in nonmetropolitan areas. Class 2 is interpersonal problems in areas where living alone is high. This class 2 happened with higher odds in nonmetropolitan areas. Class 3 is mental health problems or depressed mood with no legal problems in areas that had lower educational attainment. This class 3 did not indicate greater odds based on rurality. Class 4 is history of mental health treatment in well-off areas. This class 4 was seen to happen with higher odds in metropolitan areas. Class 5 is substance abuse problems in poorer areas. This class 5 did not indicate greater odds based on rurality. All the classes shared a common theme of experiencing mental health issues or being in a depressed mood.</p><p><b>Conclusions </b>Suicide is a complex concern that could be classified into several classes that have distinct patterns of suicide factors. These classes and patterns help with identifying what services and interventions are needed in certain communities. Overall, providing support in regards to mental health as well as intervening in childhood to support positive development may provide substantial mitigation to the odds of committing suicide. In investigating these patterns, future prevention and intervention effort can take into consideration these patterns to tailor to the individual and the environments where they live.</p>

Helicopter parenting has emerged as a prevalent phenomenon in families with adult children. Due to its developmentally inappropriate nature, helicopter parenting sometimes serves as a risk factor for children. In addition, culture and parents’ gender shape parenting and adult children’s outcomes. The purpose of the present study was to identify multidimensional constructs of helicopter parenting among college students and describe how latent classes of helicopter parenting of mothers and fathers are related to college students’ game and social media addictive behaviors in the United States and China. Using a three-step latent class approach, data from 1402 mother and young-adult child (MC) and 1225 father and young-adult child (FC) pairs in the United States and 527 MC and 426 FC pairs in China were analyzed. Four helicopter parenting latent classes (strong, strong but weak direct intervention, weak but strong academic management, and weak) were identified among MC and FC pairs in the United States, but three latent classes (strong, strong but weak direct intervention, and weak) were identified in China. In addition, college students whose parents were in the strong helicopter parenting class reported a higher level of game and social media addictive behaviors than those in weak and weak but strong academic management classes in the United States, but not in China. These findings indicate that helicopter parenting is multidimensional in nature in both American and Chinese families, but the impact of helicopter parenting on college students’ game and social media addictive behaviors differs between the two countries.

Previous research has revealed heterogeneity in outcome trajectories among individuals seeking psychotherapy. However, questions remain as to the number, nature, and predictors of these trajectories. Therefore, the present study had three aims: 1) to identify heterogeneous latent groups among treatment trajectories of 212 clients with major depressive disorder (MDD) seeking psychotherapy at a community mental health training clinic; 2) to identify significant associations between clinical and demographic variables and group membership; and 3) to identify correlations between trajectory shape and positive treatment outcome. Prior to treatment, participants provided demographic information and completed symptom severity ratings. Once in treatment, participants completed a self-report of distress via the Outcome Questionnaire (OQ-45) at every session. Growth mixture modeling was utilized to identify distinct patient subgroups based on outcome trajectories among the sample. Three distinct latent classes of treatment trajectory were identified, providing evidence of heterogeneity in treatment trajectories among individuals with MDD. Baseline distress, pre-treatment work problems, and sleep difficulties were found to be predictive of an individual's membership in a specific trajectory group. Finally, specific shapes of change, namely early response and sudden gains, were associated with positive treatment outcome. Findings from this study can be used to identify patients at risk for treatment failure, allowing clinicians to intervene earlier to enhance mid-treatment feedback and prognosis.

(1) Background: The aim of the present study was to examine symptom classes of major depressive disorder (MDD), prolonged grief disorder (PGD), and posttraumatic stress disorder (PTSD) in a sample of suicide-bereaved individuals, while accounting for loss-related characteristics. (2) Methods: A latent class analysis was conducted to identify classes of the suicide bereaved, sharing symptom profiles, in a German suicide-bereaved sample (N = 159). (3) Results: Our analyses revealed three main classes: a resilient class (16%), a class with high endorsement probability for PGD symptoms (50%), and a class with high endorsement probability for combined PGD/PTSD symptoms (34%). Prolonged grief and intrusive symptoms emerged across all classes, while MDD showed low endorsement probability. Our results indicate an association between class membership and time passed since the loss; however, this applies only to the comparison between the PGD and the resilient class, and not for the PGD/PTSD class. (4) Conclusions: Our results may provide information about the predictability of symptom clusters following suicide bereavement. The findings also represent a significant step towards tailoring treatments based on the needs of relevant suicide-bereaved subgroups through a symptom-level approach. Time passed since loss might explain differences between symptom clusters.

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>