Predictors Of Class Membership Research Articles

Phenotypic subtypes of Xia-Gibbs syndrome: a latent class analysis.

Xia-Gibbs syndrome (XGS) is a rare neurodevelopmental disorder with considerable clinical heterogeneity. To further characterize the syndrome's heterogeneity, we applied latent class analysis (LCA) on reported cases to identify phenotypic subtypes. By searching PubMed, Embase, China National Knowledge Infrastructure and Wanfang databases from inception to February 2024, we enrolled 97 cases with nonsense, frameshift or missense variants in the AHDC1 gene. LCA was based on the following 6 phenotypes with moderate occurrence and low missingness: ataxia, seizure, autism, sleep apnea, short stature and scoliosis. After excluding cases with missing data on all LCA variables or with unmatched phenotype-genotype information, a total of 85 cases were selected for LCA. Models with 1-5 classes were compared based on Akaike Information Criterion, Bayesian Information Criterion, Sample-Size Adjusted BIC and entropy. We used multinomial logistic regression (MLR) analyses to investigate the phenotype-genotype association and potential predictors for class membership. LCA revealed 3 distinct classes labeled as Ataxia subtype (n = 11 [12.9%]), Sleep apnea & short stature subtype (n = 23 [27.1%]) and Neuropsychological subtype (n = 51 [60.0%]). The commonest Neuropsychological subtype was characterized by high estimated probabilities of seizure, ataxia and autism. By adjusting for sex, age and variant type, MLR showed no significant association between phenotypic subtype and variant position. Age and variant type were identified as predictors of class membership. The findings of this review offer novel insights for different presentations of XGS. It is possible to deliver targeted monitoring and treatment for each subtype in the early stage.

Healthcare avoidance and delay among trans adults: Associations with mental and behavioral health outcomes.

Healthcare avoidance and delay (HAD) is prevalent among transgender (trans) populations. This study sought to identify patterns of HAD and examine associations between HAD and 5 behavioral health outcomes among trans adults: depression, anxiety, tobacco and alcohol use, and intimate partner violence (IPV). This study used survey data collected in 2023 from 789 trans adults in Washington state. In 2024, a latent class analysis using four indicators of HAD was conducted. Adjusted multinomial models predicted class membership by 10 demographic and socioeconomic characteristics and adjusted logistic regression models predicted each outcome by class. The sample was predominantly White (60.3%), trans women (82.7%), and financially stable (59.6%). Most participants were assigned to the Low HAD class (58.9%), followed by the Stigma HAD class (33.6%), and Combined HAD class (i.e., both cost and stigma, 7.5%). Combined HAD class members were younger, more likely to be trans men or nonbinary, and occupied lower socioeconomic positions than other classes. Compared to the Low HAD class, Stigma HAD class members were more likely to live in rural or suburban areas. Combined HAD and Stigma HAD class membership was associated with increased odds of depressive symptoms, anxious symptoms, and IPV. While the demographic profiles of the Combined HAD and Stigma HAD classes were distinct, the behavioral health of these groups was comparable. Preventing HAD among trans adults and mitigating its potential health consequences requires greater attention to healthcare affordability and health services acceptability.

Trajectories of Cancer Survivors' Spiritual Well-Being Through the Transition From Treatment to Early Survivorship.

Spirituality is an important domain of well-being for cancer survivors, yet we know little about the different trajectories of survivors' spiritual well-being across the transition from active treatment to survivorship. Further, the specific psychosocial resources and coping efforts that might predict distinct trajectories of spiritual well-being have yet to be identified. In this study, we characterized trajectories of survivors' spiritual well-being (peace, meaning, faith) across the first year of survivorship and examined whether social support and coping strategies predicted these trajectories. Participants (N=482) completed five surveys over the course of a year following a diagnosis of breast (63.5%), prostate (25.7%), or colorectal cancer (10.8%). We used latent class linear mixed modeling to identify spiritual well-being trajectory classes (FACIT-Sp) and employed multinomial logistic regression models to examine whether social support and specific coping styles predicted class membership. While the majority of our sample had moderate levels of spiritual well-being, over one-third reported very low levels of peace. Distinct latent classes for peace (four classes), meaning (five classes), and faith (five classes) were identified among adult cancer survivors transitioning from treatment to survivorship. Higher social support and adaptive coping predicted greater likelihood of belonging to classes that maintained higher levels of peace, meaning, and faith following cancer treatment. Cancer survivors show unique trajectories of spiritual well-being as they transition from active treatment to survivorship. Social support and coping may be important resources for maintaining spiritual well-being during this critical transition period.

Latent class analysis of actigraphy within the depression early warning (DEW) longitudinal clinical youth cohort

BackgroundWearable-generated data yield objective information on physical activity and sleep variables, which, are in turn, related to the phenomenology of depression. There is a dearth of wearable-generated data regarding physical activity and sleep variables among youth with clinical depression.MethodsLongitudinal (up to 24 months) quarterly collections of wearable-generated variables among adolescents diagnosed with current/past major depression. Latent class analysis was employed to classify participants on the basis of wearable-generated: Activity, Sleep Duration, and Sleep efficiency. The Patient Health Questionnaire adapted for adolescents (PHQ-9-A), and the Ruminative Response Scale (RRS) at study intake were employed to predict class membership.ResultsSeventy-two adolescents (72.5% girls) were recruited over 31 months. Activity, Sleep Duration, and Sleep efficiency were reciprocally correlated, and wearable-generated data were reducible into a finite number (3 to 4) of classes of individuals. A PHQ-A score in the clinical range (14 and above) at study intake predicted a class of low physical activity (Acceleration) and a class of shorter Sleep Duration.LimitationsLimited power related to the sample size and the interim nature of this study.ConclusionsThis study of wearable-generated variables among adolescents diagnosed with clinical depression shows that a large amount of longitudinal data is amenable to reduction into a finite number of classes of individuals. Interfacing wearable-generated data with clinical measures can yield insights on the relationships between objective psychobiological measures and symptoms of adolescent depression, and may improve clinical management of depression.

Obsessive-compulsive symptom trajectories from pregnancy through the postpartum: examining longitudinal course and risk factors during the COVID-19 pandemic.

The peripartum is a time of increased risk for the development or worsening of obsessive-compulsive (OC) symptoms. Existing evidence suggests that OC symptoms commonly surge immediately postpartum followed by a gradual recovery. Yet how societal conditions, such as a widespread public health crisis, may affect this pattern remains unknown. Increased OC symptom prevalence in the general population during the COVID-19 pandemic coupled with the pre-existing vulnerability of peripartum women suggests they may have been at particularly high risk for sustained symptoms. Thus, the aim of the current study was to examine trajectories in OC symptoms, particularly contamination concerns, in women who gave birth during the COVID-19 pandemic, and to look at risk and resiliency factors that may influence symptom course METHODS: A sample of 164 US pregnant women were recruited during the COVID-19 pandemic. They reported OC symptoms at 3 timepoints: April/May, 2020, July, 2020, and February, 2022. We used a growth mixture modeling approach to examine OC symptom trajectories from pregnancy through the postpartum across these timepoints, comparing the goodness of fit of models with 1-5 trajectory classes for overall OC symptoms and for contamination-related OC symptoms. For total OC symptoms, two classes of trajectories were identified: recovering (29.27%) versus stable low (70.73%) symptoms; for contamination symptoms alone, a subset of total symptoms, three trajectories were identified: recovering (8.54%), stable low symptoms (68.29%), and stable high symptoms (23.17%). Peripartum stress, but not sociodemographic or obstetric factors, predicted class membership. These findings suggest that peripartum OC symptoms, particularly related to contamination, may persist beyond the postpartum period in some women, particularly for women exposed to elevated stress in pregnancy and the postpartum.

Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups. Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data. Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%. Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure.

Using latent class analysis to identify Finnish gambler types and potential risk

ABSTRACT The trend of increasing liberalization in gambling markets has been matched by a need for both effective approaches to promote responsible gambling practices and for improved prevention strategies. Given that the majority of players do not experience problematic gambling, it is in the public interest that knowledge is generated which helps identify activities or clusters of activities which are associated with at-risk behaviors. This study uses a representative sample of the Finnish population aged 15–74, to identify distinct types of gamblers based on their behavioral patterns and predictors of class membership via Latent Class Analysis. Cross-sectional random sample data were collected in 2019 (n = 3148). In addition to confirming existing knowledge for gamblers characterized by high engagement and high risk, it offered insights into three further classes: the largest (ME-HR, 45%), was characterized by moderate engagement, but participated in activities associated with higher levels of risk. Additionally, low-risk classes were differentiated by both gambling preferences and demographic characteristics. Given that the largest class was associated with significant potential for the development of problematic behaviors, this work makes several recommendations for preventative actions, including targeted awareness campaigns and psychoeducation addressing erroneous beliefs about gambling.

The effects of free prescriptions on community pharmacy selection: A discrete choice experiment

BackgroundLittle is known about preferences for community pharmacies, particularly the influence of prescription co-payments, and for New Zealand's priority populations, Māori and Pacific Peoples. Improving understanding of community pharmacy preferences will enable tailoring services to meet community needs. ObjectivesThis study aims to investigate New Zealanders’ preferences for community pharmacies when collecting prescriptions. Additionally, variations in preferences for community pharmacy attributes between different latent and demographic groups were examined. MethodsFocus group discussions with various community groups were thematically analyzed to develop six attributes: location, wait time, customer service, prescription co-payments, parking availability, and nearby businesses. Participants were asked to complete an online survey involving 12 choice tasks, where they had to choose their preferred option of 3 unlabeled pharmacies along with demographic questions. A mixed multinomial logit model and latent classes analysis were used to assess and compare the participant preferences. ResultsThe sample of 553 participants, representative of the New Zealand population, resulted in 19,908 observations for analysis. The most attractive pharmacy attribute was its proximity, being within a 10-min travel distance from home or work. The importance of prescription co-payments is evident, with free prescriptions being the second most attractive attribute level and $15 NZD prescription co-payments being the least appealing. Different classes placed importance on different attributes, the largest of which prioritized prescription co-payments. Including demographic characteristics did not improve model accuracy nor predict class membership. ConclusionsUnder current policy, the most effective way for pharmacies to attract business is by offering free prescriptions. However, the trend of adopting lower-cost models may reduce the quality of care they deliver. Policy decision-makers must decide if they are comfortable with this potential impact.

Substance use patterns and mental health comorbidities in youth with a history of depression or suicidality: Findings from TX-YDSRN

BackgroundThere is a robust relationship between depression and substance use in youth, with higher levels of substance use associated with greater depressive symptomatology. However, previous research has examined individual substances, without consideration of psychiatric comorbidities. Here, we investigate patterns of substance use among depressed and/or suicidal youth within the context of psychiatric comorbidities. Methods945 youth with depression and/or suicidality from the Texas Youth Depression and Suicide Research Network (TX-YDSRN) were assessed for current use of alcohol, nicotine, cannabis, and other drugs and comorbid psychiatric diagnoses. We used latent class analysis to identify patterns of past-year substance use, then examined if demographics or psychiatric disorders predicted class membership. ResultsWe identified three patterns of substance use: non-use (63.4 %), moderate likelihood of using alcohol, nicotine and cannabis (23.8 %), and high likelihood of using all substances (12.7 %). Compared to non-users, individuals in the moderate and high likelihood classes were more likely to be older. Individuals in the high likelihood class were more likely to have a substance use disorder, ADHD, and higher suicidality scores. LimitationsWe cannot ascertain the causal or temporal ordering of substance use and psychiatric diagnoses due to the cross-sectional nature of the study. ConclusionsUsing a brief, self-report measure of substance use, we identified three classes of substance users differing in probability of past-year use, which were predicted by older age and some psychiatric comorbidities. While research on universal screening of substance use in youth remains limited, we discuss who may benefit from such screening among depressed youth.

Identifying electric vehicle charging styles among consumers: a latent class cluster analysis

The market share of electric vehicles (EVs) is growing rapidly, making it crucial to understand the charging behaviour of current and prospective users. Such understanding is essential for designing policies that positively influence consumers’ charging behaviour and facilitate EV adoption. In this study, we examined the heterogeneity in charging preferences of 994 respondents across Australia using a latent class cluster model that considers indicators of charging behaviour as outcomes of interest. We used sociodemographic characteristics, travel needs, and EV adoption status as covariates to predict class membership. Our findings indicate five segments of consumers with distinct charging preferences: routine-focused frugals, cost-oriented deliberators, range seekers, flexibility seekers, and indifferent late adopters. These segments differ in the importance they attach to charging attributes, their coping strategies with limited battery resources, and their risk attitude. Our results suggest that a uniform approach to EV-related policies is not appropriate, as each consumer segment has unique charging preferences and requirements. Furthermore, the study emphasizes the significance of accounting for charging behaviour heterogeneity in demand modelling, as assumptions in current models may not accurately represent the decision-making of most segments.

Study on ring-road incident duration based on latent class accelerated hazard model.

Accurately estimating the duration of freeway incidents can enhance emergency management practices and reduce the likelihood of secondary incidents. To investigate the mechanisms through which key factors influence incident duration, this study sorted out the characteristics and variables of the incident duration on a special freeway in Zhejiang Province, that is, the ring road, and developed a latent class accelerated hazard model. Heterogeneity was incorporated into the model. Three distributions (Weibull, Log-normal, and Log-logistic) were compared, and the Log-logistic distribution exhibited superior performance. The analysis revealed two distinct latent classes: Latent Class 1 and Class 2, had class membership probability of 0.53 and 0.47, respectively, with a total of 11 variables being statistically significant at the 0.05 significance level. It is worth noting that, some neglected explanatory variables are discussed in depth in this study. For example, the mechanism of which specific lane is closed has an impact on the incident duration, rather than a general discussion of the number of lane closures. Furthermore, the way in which the driver involved in the incident reports to the police has a significant impact on the duration of incidents. Notably, potential heterogeneity and its influencing mechanism are captured in the model. Additionally, by predicting class membership using posterior probabilities, it was determined that most data points were more likely to belong to Class 1, and the incident duration primarily ranged between 0 and 60 minutes. These findings are helpful to reduce the duration of incidents on ring-roads and freeways in China, and provide theoretical support for the formulation of freeway incident management and treatment policies.

The Association of Stigma, School, and Family Factors with Patterns of Substance Use Among LGBTQ Youth.

Polysubstance use is associated with myriad short- and long-term health outcomes. Although prior research has documented differences in polysubstance use between lesbian, gay, bisexual, transgender, queer/questioning, and other sexual and gender minoritized (LGBTQ +) youth and their heterosexual/cisgender counterparts, as well as between subgroups of LGBTQ + youth, it is unknown how personal, family, and school factors are associated with substance use patterns among LGBTQ + youth. Using a large, national sample of 9646 LGBTQ + youth ages 13-17, we used latent class analysis to examine patterns of alcohol, tobacco, and marijuana use and to determine whether personal, family, and school factors predict class membership. We identified five classes of substance use: polysubstance use, polysubstance experimentation, dual alcohol and cannabis, alcohol, and no use. Greater depression and LGBTQ + victimization, and an ability to be oneself at school, were associated with greater odds of membership in the polysubstance use class, while higher levels of family connection and having a Gender Sexuality Alliance (GSA) at school were associated with lower odds of membership in the polysubstance use class. Our analysis also revealed sociodemographic differences in class membership. These findings highlight potential mechanisms for intervention to reduce polysubstance use among LGBTQ + youth.

Involuntary psychiatric hospitalisation – differences and similarities between patients detained under the mental health act and according to the legal guardianship legislation

BackgroundInvoluntary psychiatric hospitalisation occurs under different legal premises. According to German law, detention under the Mental Health Act (MHA) is possible in cases of imminent danger of self-harm or harm to others, while detention according to the legal guardianship legislation (LGL) serves to prevent self-harm if there is considerable but not necessarily imminent danger. This study aims to compare clinical, sociodemographic and environmental socioeconomic differences and similarities between patients hospitalised under either the MHA or LGL.MethodsWe conducted a retrospective health records analysis of all involuntarily hospitalised cases in the four psychiatric hospitals of the city of Cologne, Germany, in 2011. Of the 1,773 cases, 87.3% were detained under the MHA of the federal state of North Rhine-Westphalia and 6.4% were hospitalised according to the federal LGL. Another 6.3% of the cases were originally admitted under the MHA, but the legal basis of detention was converted to LGL during the inpatient psychiatric stay (MHA→LGL cases). We compared sociodemographic, clinical, systemic and environmental socioeconomic (ESED) variables of the three groups by means of descriptive statistics. We also trained and tested a machine learning-based algorithm to predict class membership of the involuntary modes of psychiatric inpatient care.ResultsCases with an admission under the premises of LGL lived less often on their own, and they were more often retired compared to MHA cases. They more often had received previous outpatient or inpatient treatment than MHA cases, they were more often diagnosed with a psychotic disorder and they lived in neighbourhoods that were on average more socially advantaged. MHA→LGL cases were on average older and more often retired than MHA cases. More often, they had a main diagnosis of an organic mental disorder compared to both MHA and LGL cases. Also, they less often received previous psychiatric inpatient treatment compared to LGL cases. The reason for detention (self-harm or harm to others) did not differ between the three groups. The proportion of LGL and MHA cases differed between the four hospitals. Effect sizes were mostly small and the balanced accuracy of the Random Forest was low.ConclusionWe found some plausible differences in patient characteristics depending on the legal foundation of the involuntary psychiatric hospitalisation. The differences relate to clinical, sociodemographic and socioeconomical issues. However, the low effect sizes and the limited accuracy of the machine learning models indicate that the investigated variables do not sufficiently explain the respective choice of the legal framework. In addition, we found some indication for possibly different interpretation and handling of the premises of the law in practice. Our findings pose the need for further research in this field.

Childhood maltreatment exposure is differentially associated with transdiagnostic perinatal depression symptoms

History of childhood maltreatment (CM) is common and robustly associated with prenatal and postpartum (perinatal) depression. Given perinatal depression symptom heterogeneity, a transdiagnostic approach to measurement could enhance understanding of patterns between CM and perinatal depression. MethodsIn two independently collected samples of women receiving care at perinatal psychiatry clinics (n = 523 and n = 134), we categorized longitudinal symptoms of perinatal depression, anxiety, stress, and sleep into transdiagnostic factors derived from the Research Domain Criteria and depression literatures. We split the perinatal period into four time points. We conducted a latent profile analysis of transdiagnostic factors in each period. We then used self-reported history of CM (total exposure and subtypes of abuse and neglect) to predict class membership. ResultsA three-class solution best fit our data. In relation to positive adaptive functioning, one class had relatively more positive symptoms (high adaptive), one class had average values (middle adaptive), and one class had fewer adaptive symptoms (low adaptive). More total CM and specific subtypes associated with threat/abuse increased an individual's likelihood of being in the Low Adaptive class in both samples (ORs: 0.90–0.97, p < .05). LimitationsGeneralizability of our results was curtailed by 1) limited racial/ethnic diversity and 2) missing data. ConclusionsOur results support taking a person-centered approach to characterize the relationship between perinatal depression and childhood maltreatment. Given evidence that increased exposure to childhood maltreatment is associated with worse overall symptoms, providers should consider incorporating preventative, transdiagnostic interventions for perinatal distress in individuals with a history of childhood maltreatment.

Latent classes of early childhood development and their predictors in Low- and middle-income countries: Results from multiple indicator cluster surveys 2010 - 2020

Investing in early childhood development (ECD) is critical for individual and societal development. Variable-centered research on ECD has shown that family wealth, maternal education, and parenting practices predict childhood outcomes overall. However, little is known about differences in the ECD patterns and their predictors. This study examined the latent classes of ECD using data from three waves of the Multiple Indicators Cluster Surveys (MICS) conducted in 29 low- and middle-income countries (LMICs) between 2010 and 2020 (MICS 4, 5, and 6) and identified their predictors at different ecological levels. The total sample size for analyses was 226,374 (nMICS4 = 70,082, nMICS5 = 91,652, nMICS6 = 64,640; Mage = 47.23(months), SDage = 6.87). Three classes, Learning Challenged but On Track for Physical and Social-emotional Development, Academically Challenged but Approaches-to-Learning Competent, On Track for Physical and Social-emotional Development, and Competent across All Domains, were consistently identified across MICS 4–6 using latent class analysis. Three variables, all at the microsystem level, predicted class membership with acceptable effect sizes in one or more waves of the MICS data: preschool attendance, number of books at home, and maternal education. The study has implications for future research and the development of policies aimed at monitoring and supporting ECD in LMICs.

Patterns of comorbid PTSD, depression, alcohol use disorder, and insomnia symptoms in firefighters: A latent profile analysis

BackgroundFirefighters are an at-risk population for multiple psychiatric conditions, including posttraumatic stress disorder (PTSD), depression, alcohol use disorders (AUDs), and insomnia. These disorders are likely to co-occur; however, patterns of comorbidity have scarcely been investigated in firefighters. We aimed to identify subgroups of comorbidity of PTSD, depression, AUDs, and insomnia in a nationwide population of firefighters in South Korea. MethodsA total of 54,054 firefighters responded to an online survey. Latent classes of comorbidity were categorized using latent profile analysis (LPA) based on the symptom scores of PTSD, depression, AUDs, and insomnia. Analysis of variance was performed to compare the characteristics of the identified classes, and multinomial logistic regression was conducted to examine whether anger reactions, resilience, and number of traumatic events predicted class membership. ResultsThe LPA identified four subgroups: minimal symptoms (n = 42,948, 79.5 %), predominant PTSD (n = 2858, 5.3 %), subthreshold symptoms and comorbidity (n = 7003, 13.0 %), and high symptoms and comorbidity (n = 1245, 2.3 %). Three comorbidity classes were defined based on severity and one class showed predominant PTSD symptoms. Number of traumatic exposures predicted predominant PTSD, while resilience and anger reactions predicted severity of comorbidities. LimitationsThe cross-sectional design and usage of self-reported questionnaires are limitations of this study. ConclusionsThe severity of PTSD, depression, AUDs and insomnia tend to correlate and co-occur in firefighters. Our findings highlight the need to assess comorbid symptoms in firefighters and need to reduce anger reactions and enhance resilience in those with multiple comorbidities.

Unraveling Heterogeneity in Online Shopping and Travel Behavior Through Latent Class Modeling

While existing literature has extensively explored the impact of online shopping on travel behavior, few studies have undertaken segmentation analysis to uncover hidden behavioral heterogeneity. This study fills this gap by addressing heterogeneity and identifying distinct shopper segments based on online shopping and shopping travel behaviors, with a focus on product types. Data collected in November and December 2021 from 1,747 shoppers in Florida were analyzed using Latent Class Analysis (LCA) with covariates. Sociodemographic and residential characteristics, COVID-19 influences, attitudes, and perceptions of channel-specific factors served as active and inactive covariates to predict class membership. Our model identified six classes of shoppers, with short-distance dual-channel shoppers representing the largest class (28.4%) and exclusive online shoppers the smallest (6.2%). Dual-channel shopaholics, overrepresented by Gen Zers, Millennials, Blacks, and workers, exhibited high average monthly vehicle miles traveled (VMT) across all product types and a strong potential for complementary shopping behavior. Conversely, exclusive online shoppers overrepresented by members of the silent generation, those who live alone, have no vehicle, and do not enjoy shopping, demonstrated potential substitutive shopping behavior. In general, single-channel shoppers showed lower monthly VMT than their dual-channel counterparts across all product types. These findings contribute to a deeper understanding of shopping behavior, offering insights for a more accurate quantification of the net traffic and environmental impacts of e-commerce. Additionally, they provide valuable considerations for designing segment-specific policies aimed at minimizing complementary shopping and maximizing substitutive shopping.

Trusted Sources of Information and COVID-19 Vaccination Among Black Adults in Chicago.

Examine trust in sources of COVID-19 information and vaccination status. Cross-sectional. Chicago, Illinois. Convenience sample of 538 Black adults surveyed between September 2021 and March 2022. Trust in sources of COVID-19 information, COVID-19 vaccination. Using latent class analysis, we identified classes of trust in sources of COVID-19 information. We considered predictors of class membership using multinomial logistic regression and examined unadjusted and adjusted associations between trust class membership and COVID-19 vaccination while accounting for uncertainty in class assignment. Our analytic sample (n = 522) was predominantly aged 18-34 (52%) and female (71%). Results suggested a four-class solution: (1) low trust, (2) high trust in all sources, (3) high trust in doctor and government, and (4) high trust in doctor, faith leader, and family. Unadjusted odds of vaccination were greater in the high trust in all sources (OR 2.0, 95% CI 1.2-3.2), high trust in doctor and government (OR 2.7, 95% CI 1.4-5.3), and high trust in doctor, faith leader, and family classes (OR 2.1, 95% CI 1.2, 3.9) than the low trust class. However, these associations were not significant after adjustment for sociodemographic and health status factors. Although COVID-19 vaccination varied across trust classes, our adjusted findings do not suggest a direct association between trust and vaccination, reflecting complexities in the vaccine decision-making process.

Trajectories of fatigue and related outcomes following mild acquired brain injury: a multivariate latent class growth analysis.

Fatigue is a common symptom following acquired brain injury although the severity and course differs for many individuals. This longitudinal study aimed to identify latent trajectory classes of fatigue and associated outcomes following mild brain injury. 204 adults with mild traumatic brain injury (159; 78%) or minor stroke (45; 22%) were assessed 4 times over 1 year. Subjective measures of fatigue, anxiety, depression, cognitive complaints and societal participation were collected. Multivariate Latent Class Growth Analysis identified classes of participants with similar longitudinal patterns. Demographic and injury characteristics were used to predict class membership. Analysis revealed four classes. Class 1 (53%) had mild, decreasing fatigue with no other problems. Class 2 (29%) experienced high persistent fatigue, moderate cognitive complaints and societal participation problems. Class 3 (11%) had high persistent fatigue with anxiety, depression, cognitive complaints and participation problems. Class 4 (7%) experienced decreasing fatigue with anxiety and depression but no cognitive or participation problems. Women and older individuals were more likely to be in class 2. Half the participants had a favourable outcome while the remaining classes were characterised by persistent fatigue with cognitive complaints (class 2), decreasing fatigue with mood problems (class 4) or fatigue with both cognitive and mood problems (class 3). Fatigue treatment should target combinations of problems in such individual trajectories after mild brain injury.

Psychological and social determinants of adaptation: the impact of finances, loneliness, information access and chronic stress on resilience activation.

Many people who face adversity, such as disasters, demonstrate resilience. However, less is known about reactions to large scale disasters with longer recovery periods. The concern is that protracted disasters may result in more chronic or accumulated stressors with an uncertain or unknown end point and can exhaust the natural coping methods and ability to rebound. Thus, understanding patterns of longer-term disaster recovery, inclusive of resilience, is needed. Further resilience is not individual specific rather social determinants, such as support networks and available resources, are contributing factors. The purpose of this study is to improve understanding of mental health and resilience during increased stress, we aim to identify profiles of adaptation and psychological and social determinants that predict membership within predominant symptom groupings. We conducted an exploratory cross-section study (N = 334) with two phases of multivariate analysis. Latent profile models were estimated to identify groups based on depression, anxiety, and resilience scores. The second phase included a step-wise multinomial logistic regression to predict class membership. We identified four distinct groups: 33% of participants were categorized as anxious, 18% depressed, 9% comorbid, and 40% with above average levels of resilience. Psychosocial factors such as demographics, trauma history, information access, loneliness, and lack of financial resources predicted poorer mental health outcomes and lower resilience. This study identified factors that contribute to overall wellbeing despite chronic stressors. Social determinants of adaptation, found in this study population, include loneliness, finances, and information access. The findings from this study support the need for both psychological and social adaption supports, inclusive of mental health treatment, to strengthen resilience activation.