Three-cluster Solution Research Articles

Socio-Demographic and Loyalty Assessment of Brand Proud Consumers: A Segmentation Study

Emotion-based consumer-brand relationships determine current and future consumption and significantly impact brand loyalty, enhancing the brand’s profit potential. This study is a pioneering attempt to establish distinct consumer segments based on brand pride. Five hundred twenty-two respondents were surveyed, using existing scales on brand loyalty, opposition brand loyalty, and a newly developed 12-item two-dimensional brand pride scale. A cluster solution was arrived at by conducting Hierarchical and k-means cluster analysis. Chi-square analysis distinguished the clusters on demographics, product, and brand choices. One-way ANOVA analysis gave the cluster-wise difference regarding brand loyalty and opposition brand loyalty. The analysis indicated a three-cluster solution: Distinctives, Interdependents, and Aficionados. The Aficionados demonstrated high collective and individualistic brand pride. Distinctives were medium on individualistic and low on collectivistic brand pride. Interdependents were low on individualistic and were more attuned to collectivistic brand pride. The Interdependents were younger than the other two cohorts. The Aficionados demonstrated higher brand and opposition brand loyalty than the other groups. Identifying diverse consumer segments and their demographic and brand profiling has significant implications for marketers to design segment-centric branding strategies. The validation of the segment solution across geographies would further establish the generalizability of the findings.

Neuropsychiatric feature-based subgrouping reveals neural sensory processing spectrum in female FMR1 premutation carriers: A pilot study.

Fragile X Syndrome (FXS) is rare genetic condition characterized by a repeat expansion (CGG) in the Fragile X messenger ribonucleoprotein 1 (FMR1) gene where individuals with greater than 200 repeats are defined as full mutation. FXS clinical presentation often includes intellectual disability, and autism-like symptoms, including anxiety and sensory hypersensitivities. Individuals with 55 to <200 CGG repeats are said to have the FMR1 premutation, which is not associated with primary characteristics of the full mutation, but with an increased risk for anxiety, depression, and other affective conditions, as well as and impaired cognitive processing differences that vary in severity. Defining subgroups of premutation carriers based on distinct biological features may identify subgroups with varying levels of psychiatric, cognitive, and behavioral alterations. The current pilot study utilized 3 cluster subgroupings defined by previous k means cluster analysis on neuropsychiatric, cognitive, and resting EEG variables in order to examine basic sensory auditory chirp task-based EEG parameters from 33 females with the FMR1 premutation (ages 17-78). Based on the predefined, neuropsychiatric three-cluster solution, premutation carriers with increased neuropsychiatric features and higher CGG repeat counts (cluster 1) showed decreased stimulus onset response, similar to previous ERP findings across a number of psychiatric disorders but opposite to findings in individuals with full mutation FXS. Premutation carriers with increased executive dysfunction and resting gamma power (cluster 2) exhibited decreased gamma phase locking to a chirp stimulus, similar to individuals with full mutation FXS. Cluster 3 members, who were relatively unaffected by psychiatric or cognitive symptoms, showed the most normative task-based EEG metrics. Our findings suggest a spectrum of sensory processing characteristics present in subgroups of premutation carriers that have been previously understudied due to lack of overall group differences. Our findings also further validate the pre-defined clinical subgroups by supporting links between disturbances in well-defined neural pathways and behavioral alterations that may be informative for identifying the mechanisms supporting specific risk factors and divergent therapeutic needs in individuals with the FMR1 premutation.

At-risk mental states and personality traits: A cluster analysis approach on a group of help-seeking young adults.

Research on the relationship between personality and psychosis onset is growing, with the goal of preventing or intervening early in patients' vulnerability. The identification of individuals with at-risk mental states has enabled the development of early intervention strategies, such as Programma 2000, a youth mental health service that was implemented in Milan (Italy). Focusing on the 18-25 age range-the time window with the highest incidence of psychotic onset-this study aims to identify the personality traits that may characterize the at-risk mental states and the social functioning of a group of help-seeking young adults. The sample includes 169 people (48.5% males and 51.5% females). Data were collected during an initial assessment that comprised the Social and Occupational Functioning Assessment Scale, the Personality Inventory for DSM-5, the Checklist ERIraos and a clinical session. Results identified a three-cluster solution based on the Checklist scores: Cluster 1 'Not at psychotic risk'; Cluster 2 'At intermediate risk'; Cluster 3 'With psychotic onset'. The multivariate analysis of the variance of personality traits shows significant differences among the clusters in negative affect, detachment and disinhibition. Higher scores in these traits may distinguish individuals, not at psychotic risk from those at intermediate risk or with psychotic onset. Moreover, social functioning was found to be negatively associated with clusters of psychotic risk. Findings from this study highlighted the need to evaluate personalized interventions targeting such personality traits that could prevent psychotic transition and promote psychological well-being.

Evaluating 87Sr/86Sr and Sr/Ca ratios in otoliths of different European freshwater fish species as fishery management tool in an Alpine foreland with limited geological variability

The focus of this study was to assess the potential of otolith microchemistry as a fishery management tool for different European freshwater fish species in an Alpine foreland with a diverse range of different water bodies but low geological variation. 87Sr/86Sr isotope ratios and Sr/Ca ratios in water samples from 26 habitat sites in a pre-alpine catchment region around lake Chiemsee, Germany, an important region for recreational and economic fisheries, were analysed. 87Sr/86Sr isotope ratios and the Sr/Ca ratios in otoliths of 246 fish out of 16 species were determined using (laser ablation) inductively coupled plasma mass spectrometry ((LA)-ICP-MS). Habitats could be differentiated into seven clusters when using the 87Sr/86Sr isotope and Sr/Ca ratios in water in combination. When using the 87Sr/86Sr isotope ratio as individual parameter, the strontium isotope groups (SIGs) based on the 87Sr/86Sr isotope ratios including the measurement uncertainties justified a three-cluster solution. The direct comparison of 87Sr/86Sr isotope ratios in water and otolith samples allowed to identify fish that a) might have migrated, b) were transferred from other water bodies or c) stocked from fish farms. Sr/Ca ratios in water and otoliths were highly correlated, although significant differences between species from the same environment were documented. Sr/Ca ratios in sagittae of Perca fluviatilis were about 64–60% of those in sagittae of Coregonus spp and of lapilli of roach Rutilus rutilus from the same habitats, respectively. Partition coefficients for water to otolith Sr/Ca ratios were calculated and differed between species. Discrimination of fish otoliths according to habitat clusters by combining 87Sr/86Sr and Sr/Ca ratios was possible with success rates of up to 100% for whitefish Coregonus spp., 90% for European perch Perca fluviatilis, 87% for cyprinids, and 89% European grayling Thymallus thymallus, and 68% for salmonids. When using the 87Sr/86Sr isotope ratios as single parameter, assignment results were generally worse, except for salmonids, where an improved accuracy of 79% could be achieved, or for cyprinids when the number of clusters was reduced from five to three. The study indicates that otolith microchemistry has great potential to serve as a fishery management tool at smaller spatial scales such as in the studied Alpine foreland when combining the 87Sr/86Sr and Sr/Ca ratios, and considering the type and spatial distribution of habitats, the species and question under investigation.

Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.

Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep-wake disorders using unsupervised machine learning approaches. We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. Hierarchical clustering analysis was applied on test items and participants to investigate common patterns of symptoms co-occurrence, and further quantitatively investigated with clustering methods to find groups that may represent similar psychiatric phenotypes. First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of "mixed" symptoms, "depressed affect and nervousness", and "troubled sleep and interpersonal problems". A highly similar clustering solution was independently established in the replication sample. Second, four groups of participants could be separated, and these groups scored differently on the item clusters. We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiety, and poor sleep. We identified four groups of participants that share (sub)clinical symptoms and might benefit from similar prevention or treatment strategies, despite potentially diverging, or lack of, diagnoses.

A Pilot Study of Polysubstance Use Sequences across the Lifespan among Assiniboine and Sioux People Who Use Injection Drugs.

Compared with other racial/ethnic groups in the United States, American Indians/Alaska Natives have the highest rates of acute Hepatitis C Virus (HCV) infection, the highest HCV-related mortality, and one of the fastest climbing rates of drug overdose deaths involving stimulants. In this pilot study, a life history calendar was administered to Indigenous people who use injection drugs (IPWIDs) to understand sequences of polysubstance use across the lifespan. 40 IPWIDs completed a questionnaire and life history calendar. Social sequence analysis was used to examine patterns in sequential phenomena among substances reported over years of the lifespan. Most participants (55%) began injecting substances before the age of 21, 62.5% shared syringes with others, and 45% had ever been diagnosed with HCV. An appreciably large increase in the use of stimulants occurred between the year prior to and following injection initiation (33% to 82%). A three-cluster solution distinguished younger IPWIDs transitioning into polysubstance use involving stimulants and/or narcotic analgesics from adults using narcotic analgesics with stimulants over longer periods of time, and adults most focused on stimulant use over time. Findings from this pilot study contribute to an understanding of how methamphetamine injection plays a role in the HCV epidemic among IPWIDs.

Characterizing emotion profiles in non-clinical population: A cluster analytical study.

Emotion processing and beliefs about pleasure can influence the development and severity of depressive symptoms. This cluster analysis study aimed to profile a large sample of college students using pleasure experience, emotion expression and regulation as well as beliefs about pleasure. We also aimed to validate the resultant clusters in terms of depressive symptoms. A set of checklists capturing beliefs about pleasure and the three facets of emotion processing was administered to 1028 college students. A two-stage cluster analysis was used to analyze the profile of these emotional aspects in these college students. Our results showed that a three-cluster solution best fit the data. Cluster 1 (n=536) was characterized by moderate levels of beliefs about pleasure, pleasure experience, emotion expression, and regulation; Cluster 2 (n=402) was characterized by generally high levels of beliefs about pleasure, pleasure experience, emotion expression, and regulation; Cluster 3 (n=90) was characterized by relatively low levels of beliefs about pleasure, pleasure experience, emotion expression, and regulation. The three clusters differed significantly in the severity of depressive symptoms. Our findings suggest the existence of three emotional subtypes, which may be useful in early detection of youth at risk of developing depression.

The Influence of Personality Traits on Specific Coping Styles and the Development of Posttraumatic Stress Symptoms following Acute Coronary Syndrome: A Cluster Analytic Approach

Objective: A growing body of literature suggests a relationship between personality traits and posttraumatic stress disorder (PTSD) symptoms after acute coronary events (ACS). However, specific personality profiles have not been examined in patients after ACS. Thus, the aim of the present study was to examine personality profiles created from response patterns on the resilience, alexithymia and type D personality (TDP) scales and to examine associations with PTSD symptoms, symptom clusters and coping styles among a sample of ACS patients. Methods: A cluster analytic approach was utilized to identify risk profiles based on personality variables and a series of ANOVAs in 154 patients. Post hoc analyses were conducted to examine the relationship between each profile, and interviewer-rated PTSD symptoms and different coping styles. Results: The analyses indicated a three-cluster solution, including low- (high resilience, low alexithymia and non-TDP), medium- (average resilience, average alexithymia and non-TDP) and high-risk (low resilience, high alexithymia and TDP) profiles. Clusters differed significantly in all three coping subscales. At 3-month follow up, clusters differed significantly in all three PTSD subscales (re-experiencing, avoidance and hyperarousal). At 12-month follow up, the differences remained significant for the hyperarousal subscale only. Conclusions: The personality profiles identified and the respective associations to PTSD symptoms and coping strategies highlight the potential impact for the psychological adjustment following ACS.

Finding the vulnerable postoperative population: A two-step cluster analysis of the PAIN-OUT registry.

Identifying predictors of poor postoperative outcomes is crucial for planning personalized pain treatments. The aim of this study was to examine pain outcomes using cluster analysis in N=2678 patients from the PAIN-OUT registry at first postoperative day. Indicator variables of the clustering analysis assessed multiple domains, such as clinical and surgical conditions, analgesic-anaesthetic variables, desire for more pain treatment and outcome variables of the International Pain Outcome Questionnaire (IPO) summarized as factor scores. Two-step cluster identified the three-cluster solution as the optimal. Two empirical groups (C1 and C2) included patients with good postoperative outcomes discriminated by peripheral nerve block use, while the other cluster (C3) grouped patients with the worst outcomes, where all patients desired more pain treatment. C3 comprised about 20% of the participants, mostly lower limb, abdominal and spine procedures. The best predictors of belonging to C3 included younger age, being male, preoperative opioid use, bone and fracture reduction procedures, institution, number of comorbidities and morphine equivalents in the recovery room. IPO factor scores can be used to select pain outcomes phenotypes in large clinical databases. Most of the predictors were present before the recovery period so perioperative planning should focus in the preoperative and intraoperative periods. Improvement of postoperative pain requires assessment methods that go beyond pain intensity scores. We perform a cluster analysis among PAIN-OUT patients that revealed a cluster of vulnerable postoperative patients, using a novel composite measure of postoperative outcomes: the factor scores of the International Pain Outcomes Questionnaire. By changing the focus from pain intensity to multidimensional pain outcomes, male gender and number of comorbidities appeared as new risk factors for worse postoperative outcomes. The study also identified procedures that require urgent quality improvements.

Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline.

Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. Individual MoCA item data from the Alzheimer's Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Role of stressful life events in homelessness in Nicaragua: an intragroup analysis in a low-income country

ABSTRACT The purpose of the study is to analyse the stressful life events (SLEs) suffered by 62 people living homeless (32 men; 30 women) in Nicaragua, and how the different SLEs are related to patterns and trajectories in homelessness. The sample was classified based on SLEs, and the relationship between the resulting subgroups and sociodemographic characteristics, homelessness trajectories, health, substances consumption, social support, and outlook for the future was examined. Results suggest that a three-cluster solution was theoretically and structurally meaningful: (a) Cluster A was mainly characterized by a higher prevalence of SLEs and problems in the family from an early age, consisting of young people consuming substances, primarily drinking alcohol and sniffing glue; (b) Cluster B was characterized by low levels of SLEs, but with alcohol problems; and (c) Cluster C was characterized by a high number of violent SLEs from an early age, made up of people an older age group, who have been living homeless for longer and in suffering from poor health. Increased knowledge about the different subgroups and trajectories of people living homeless in Nicaragua can help with the design of public policy, intervention programs and social care tools that are sensitive to these differences.

Measuring the pathways model through the gambling pathways questionnaire: the psychometric properties of the Italian version

ABSTRACT Over the last decade, gambling opportunities worldwide have increased significantly, providing continual access to more people. Thus, standardized measures to assess various facets of Gambling Disorder and holistic approaches to identifying sub-groups of disordered gamblers are needed. The Gambling Pathways Questionnaire (GPQ), based on the Pathways Model of problem gambling, is designed to assign gamblers to subtypes at intake and identify a variety of salient etiological factors. This study sought to validate the Italian version of the GPQ. After the translation and adaptation of the scale to the Italian language and culture, the GPQ was administered to 490 treatment-seeking disordered gamblers (84% males, M age = 43.77, SD = 13.25) who attended 54 treatment centers distributed across all of Italy. Confirmatory factor analysis (CFA) supported the six-factor structure and internal coherence coefficients showed good to very good reliability. Moreover, cluster analysis revealed that the three-cluster solution produced the best model fit, which generally corresponded with the Pathways Model. The Italian version of the GPQ evidenced good psychometric properties, and the Italian-adapted scoring corresponds to the cluster analysis.

MP06-02 NON-MUSCLE-INVASIVE MICROPAPILLARY BLADDER CANCER HAS A DISTINCT LNCRNA PROFILE ASSOCIATED WITH UNFAVORABLE PROGNOSIS

You have accessJournal of UrologyCME1 May 2022MP06-02 NON-MUSCLE-INVASIVE MICROPAPILLARY BLADDER CANCER HAS A DISTINCT LNCRNA PROFILE ASSOCIATED WITH UNFAVORABLE PROGNOSIS Joep de Jong, Begoña Valderrama, Julia Perera, Nuria Juanpere, Paloma Cejas, Henry Long, Mar Alba, Ewan Gibb, and Joaquim Bellmunt Joep de JongJoep de Jong More articles by this author , Begoña ValderramaBegoña Valderrama More articles by this author , Julia PereraJulia Perera More articles by this author , Nuria JuanpereNuria Juanpere More articles by this author , Paloma CejasPaloma Cejas More articles by this author , Henry LongHenry Long More articles by this author , Mar AlbaMar Alba More articles by this author , Ewan GibbEwan Gibb More articles by this author , and Joaquim BellmuntJoaquim Bellmunt More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002523.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Molecular subtyping of bladder cancer has revealed luminal tumors generally have a more favorable prognosis. However, some aggressive forms of variant histology, including micropapillary, are often classified luminal. In previous work, we found long non-coding RNA (lncRNA) expression profiles could identify a subgroup of luminal bladder tumors with less aggressive biology and better outcomes. In the present study, we aimed to investigate whether lncRNA expression profiles could identify high-grade T1 micropapillary bladder cancer with differential outcome. METHODS: LncRNAs were quantified from RNA-seq data from a HG T1 bladder cancer cohort that was enriched for primary micropapillary cases (15/84). Unsupervised consensus clustering of variant lncRNAs identified a three-cluster solution, which was characterized using a panel of micropapillary-associated biomarkers, molecular subtypes, gene signatures and survival analysis. A single-sample genomic signature was trained using lasso-penalized logistic regression to classify micropapillary-like gene-expression, as characterized by lncRNA clustering. The genomic classifier (GC) was tested on luminal tumors derived from the TCGA cohort (N=202). Primary endpoints were overall, progression-free and high-grade recurrence-free survival. RESULTS: Primary micropapillary HGT1 showed decreased FGFR3, SHH and p53 pathway activity relative to tumors with conventional urothelial carcinoma. Many bladder cancer-associated lncRNAs were downregulated in micropapillary tumors, including UCA1, LINC00152, and MALAT1. Unsupervised consensus clustering resulted in a lncRNA cluster (LC1) with worse prognosis that was enriched for primary micropapillary histology and the Luminal Unstable (LumU) molecular subtype. Interestingly, LC1 appeared to better identify aggressive HG T1 disease, compared to stratifying outcomes using primary histologic characteristics. A signature trained to identify LC1 cases showed good performance in the testing cohort, identifying seven cases with significantly worse survival (p<0.001). CONCLUSIONS: Using the lncRNA transcriptome we identified a subgroup of aggressive HGT1 bladder cancer that was enriched with micropapillary histology. These data suggest that lncRNAs can facilitate the identification of aggressive micropapillary-like tumors, potentially improving patient management. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e77 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Joep de Jong More articles by this author Begoña Valderrama More articles by this author Julia Perera More articles by this author Nuria Juanpere More articles by this author Paloma Cejas More articles by this author Henry Long More articles by this author Mar Alba More articles by this author Ewan Gibb More articles by this author Joaquim Bellmunt More articles by this author Expand All Advertisement PDF DownloadLoading ...

Non-muscle-invasive micropapillary bladder cancer has a distinct lncRNA profile associated with unfavorable prognosis.

Molecular subtyping of bladder cancer has revealed luminal tumors generally have a more favourable prognosis. However, some aggressive forms of variant histology, including micropapillary, are often classified luminal. In previous work, we found long non-coding RNA (lncRNA) expression profiles could identify a subgroup of luminal bladder tumors with less aggressive biology and better outcomes. In the present study, we aimed to investigate whether lncRNA expression profiles could identify high-grade T1 micropapillary bladder cancer with differential outcome. LncRNAs were quantified from RNA-seq data from a HGT1 bladder cancer cohort that was enriched for primary micropapillary cases (15/84). Unsupervised consensus clustering of variant lncRNAs identified a three-cluster solution, which was further characterised using a panel of micropapillary-associated biomarkers, molecular subtypes, gene signatures, and survival analysis. A single-sample genomic signature was trained using lasso-penalized logistic regression to classify micropapillary-like gene-expression, as characterised by lncRNA clustering. The genomic classifier (GC) was tested on luminal tumors derived from the TCGA cohort (N = 202). Patient and tumor characteristics were compared between subgroups by using X2 tests and two-sided Wilcoxon rank-sum tests. Primary endpoints were overall, progression-free and high-grade recurrence-free survival, calculated as the date of high-grade T1 disease at TURBT till date of death from any cause, progression, or recurrence, respectively. Survival rates were estimated using weighted Kaplan-Meier (KM) curves. Primary micropapillary HGT1 showed decreased FGFR3, SHH, and p53 pathway activity relative to tumors with conventional urothelial carcinoma. Many bladder cancer-associated lncRNAs were downregulated in micropapillary tumors, including UCA1, LINC00152, and MALAT1. Unsupervised consensus clustering resulted in a lncRNA cluster 1 (LC1) with worse prognosis that was enriched for primary micropapillary histology and the Luminal Unstable (LumU) molecular subtype. Interestingly, LC1 appeared to better identify aggressive HGT1 disease, compared to stratifying outcomes using primary histologic characteristics. A signature trained to identify LC1 cases showed good performance in the testing cohort, identifying seven cases with significantly worse survival (p < 0.001). Limitations include the retrospective nature of the study and the lack of a validation cohort. Using the lncRNA transcriptome we identified a subgroup of aggressive HGT1 bladder cancer that was enriched with micropapillary histology. These data suggest that lncRNAs can facilitate the identification of aggressive micropapillary-like tumors, potentially improving patient management.

Prevalence of comorbidities, and affective disorders in epilepsy: A latent class analysis approach

ObjectiveEpilepsies are severe chronic neurological diseases that impair several domains in life and are often accompanied by various somatic and psychiatric comorbidities. Associations between epilepsy and its comorbidities remain poorly understood. As epidemiological research mainly relies on cross-sectional designs and descriptive results, homogeneities regarding comorbidities in individuals suffering from epilepsy remain uncovered. Therefore, we aimed to identify clusters of individuals based on selected seizure-related variables and somatic comorbidities, and their respective risk of experiencing affective disorders, using a Latent Class Analysis (LCA). MethodsLatent class analysis, is a model-driven statistical approach, which aims at latent, unobservable clusters on selected disease features. LCA has therefore the potential for uncovering previously unobservable groups or classes with similar comorbidity patterns. It allows for comparisons between those classes regarding risk or promotive factors – such as affective disorders. Our data derives from the Austrian cohort of the European Study on Burden and Care of Epilepsy (ESBACE; http://www.esbace.eu/). In ESBACE, multiple factors were collected to get a detailed picture on prevalence, epilepsy-related variables and comorbidities in a population-based cohort from the region of Salzburg, Austria. We used LCA to identify epilepsy-somatic-comorbidity-clusters and further, compared them to the observed the risk of suffering from affective disorders. ResultsThe prevalence of epilepsy in the study region was 9.14/1000 inhabitants. LCA unveiled a three-cluster solution, of which one cluster, mainly consisting of individuals with mixed seizure types, higher age, and discrete somatic comorbidities (stroke, cardiovascular – and respiratory/pulmonary diseases) had a higher risk of experiencing affective disorders. SignificanceTo our knowledge, this is the first large scale study that uses LCA to identify epilepsy-related comorbidity phenotypes, and therefore it might open a new way for epidemiological research.

Engaging with Social Media Influencers on Youtube: A Cluster Analysis

Social media platforms have created a new industry where both influencers and consumers are empowered. Consumers do not only consume but also contribute to the content they face on social media. Through their narratives and content, they may even become social media influencers who have the power to shape the attitudes and behavior of fellow consumers. An important social medium, YouTube, allows people to engage with social media influencers by liking, commenting, sharing, etc. However, engagement practices are not similarly shared among every YouTube user, and people have different reaction styles. Thus, measuring success in creating engaging content becomes a controversial issue for brand endorsement. This research aims to uncover consumer typologies in terms of engagement behavior with social media influencers on YouTube. The influential motives of engagement for each typology are also analyzed in order to describe the groups. For this purpose, 341 participants from Amazon’s Mechanical Turk (MTurk) platform participated in an online survey, and a two-step cluster analysis was conducted with eleven common social media engagement practices with influencers. The results implied a three-cluster solution, and the clusters were profiled according to several social media engagement motivations. The groups were named as ‘positively active followers,’ ‘passive followers,’ and ‘analytical followers.’ Implications for brand endorsement and content marketing strategies are discussed.

Toward a Multidimensional Understanding of Misophonia Using Cluster-Based Phenotyping.

Misophonia is a condition characterized by hypersensitivity and strong emotional reactivity to specific auditory stimuli. Misophonia clinical presentations are relatively complex and reflect individualized experiences across clinical populations. Like some overlapping neurodevelopmental and neuropsychiatric disorders, misophonia is potentially syndromic where symptom patterns rather than any one symptom contribute to diagnosis. The current study conducted an exploratory k-means cluster analysis to evaluate symptom presentation in a non-clinical sample of young adult undergraduate students (N = 343). Individuals participated in a self-report spectrum characteristics survey indexing misophonia, tinnitus severity, sensory hypersensitivity, and social and psychiatric symptoms. Results supported a three-cluster solution that split participants on symptom presentation: cluster 1 presented with more severe misophonia symptoms but few overlapping formally diagnosed psychiatric co-occurring conditions; cluster 3 was characterized by a more nuanced clinical presentation of misophonia with broad-band sensory hypersensitivities, tinnitus, and increased incidence of social processing and psychiatric symptoms, and cluster 2 was relatively unaffected by misophonia or other sensitivities. Clustering results illustrate the spectrum characteristics of misophonia where symptom patterns range from more “pure” form misophonia to presentations that involve more broad-range sensory-related and psychiatric symptoms. Subgroups of individuals with misophonia may characterize differential neuropsychiatric risk patterns and stem from potentially different causative factors, highlighting the importance of exploring misophonia as a multidimensional condition of complex etiology.

Work in Hotels Under Conditions Related to the Covid-19 Disease

Hotel employees are at increased risk of infection with SARS-Cov-2. Therefore, governments have taken many systematic measures to protect employees and employers (hoteliers). Since previous studies have almost entirely overlooked hotel employees' perspectives on the pandemic, this study determines how heterogeneously Slovenian hotel employees perceived the disease and their own work during and after the health crisis. The Two-Step and K-Means clustering methods were used to identify clusters of respondents. The One-way ANOVA with post hoc Tukey's HSD test was employed to verify the significant differences among groups/items. The sample included 301 completed questionnaires. A three-cluster solution with statistically significant differences was identified. Cluster 3, the largest, is particularly interesting, as its members are the most pessimistic of all. In contrast, Cluster 1, which is much smaller, included the most optimistic members (plain optimists). Cluster 2 is also interesting, as its members tend to be more extroverted – expressing the highest psychological burden and pessimistic views about the future provision of hotel services. Managers should provide clear instructions, education/training, tighten control, and do everything possible to prevent oversensitivity and excessively defensive responses that could hinder operational work.

A Pilot Exploratory Study to Form Subgroups Using Cluster Analysis of Family Needs Survey Scores for Providing Tailored Support to Parents Caring for a Population-Based Sample of 5-Year-Old Children with Developmental Concerns.

In a population-based developmental screening program, healthcare providers face a practical problem with respect to the formation of groups to efficiently address the needs of the parents whose children are screened positive. This small-scale pilot study explored the usefulness of cluster analysis to form type-specific support groups based on the Family Needs Survey (FNS) scores. All parents (N = 68), who accompanied their 5-year-old children to appointments for formal assessment and diagnostic interviews in the second phase of screening, completed the FNS as part of a developmental questionnaire package. The FNS scores of a full dataset (N = 55) without missing values were subjected to hierarchical and K-means cluster analyses. As the final solution, hierarchical clustering with a three-cluster solution was selected over K-means clustering because the hierarchical clustering solution produced three clusters that were similar in size and meaningful in each profile pattern: Cluster 1—high need for information and professional support (N = 20); Cluster 2—moderate need for information support (N = 16); Cluster 3—high need for information and moderate need for other support (N = 19). The range of cluster sizes was appropriate for managing and providing tailored services and support for each group. Thus, this pilot study demonstrated the utility of cluster analysis to classify parents into support groups, according to their needs.

Mapping Actuarial Criteria for Parkinson's Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes.

Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD.