The interplay of psychological resilience and adolescent mobile phone addiction in Henan province, China: insights from latent class analysis.

Background: The aim of this study was to classify distinct subgroups of adolescents based on the severity levels of their mobile phone addiction and to investigate how these groups differed in terms of their psychosocial characteristics. We surveyed a total of 2,230 adolescents using three different questionnaires to assess the severity of their mobile phone addiction, stress, anxiety, depression, psychological resilience, and personality. Latent class analysis was employed to identify the subgroups, and we utilized Receiver Operating Characteristic (ROC) curves and multinomial logistic regression for statistical analysis. All data analyses were conducted using SPSS 26.0 and Mplus 8.5. Methods: We classified the subjects into subgroups based on their mobile phone addiction severity, and the results revealed a clear pattern with a three-class model based on the likelihood level of mobile phone addiction (p < 0.05). We examined common trends in psychosocial traits such as age, grade at school, parental education level, anxiety levels, and resilience. ROC analysis of sensitivity versus 1-specificity for various mobile phone addiction index (MPAI) scores yielded an area under the curve (AUC) of 0.893 (95% CI, 0.879 to 0.905, p < 0.001). We also determined diagnostic value indices for potential cutoff points ranging from 8 to 40. The optimal cutoff value for MPAI was found to be >14, which corresponded to the maximum Youden index (Youden index = 0.751). Results: The latent classification process in this research confirmed the existence of three distinct mobile phone user groups. We also examined the psychosocial characteristics that varied in relation to the severity levels of addiction. Conclusion: This study provides valuable insights into the categorization of adolescents based on the severity of mobile phone addiction and sheds light on the psychosocial characteristics associated with different addiction levels. These findings are expected to enhance our understanding of mobile phone addiction traits and stimulate further research in this area.

Family structure and children's risk of child protective services re-reports.

BACKGROUND: Single parent families are at higher risk of re-report to Child Protective Services (CPS) than two-parent families. Yet, how single-family homes differ in risk from two-parent families remains under researched. OBJECTIVE: To identify heterogenous patterns of child and caregiver factors among CPS-involved families and the subsequent risk for CPS re-report based on child and family characteristics (i.e., sociodemographic information, family structure, and risk indicators). PARTICIPANTS AND SETTING: Data were from the 2017 National Child Abuse and Neglect Data System Child File (N = 249,026). METHODS: We conducted latent class analysis (LCA) to identify discrete patterns (i.e., classes) based on child and caregiver risk indicators (e.g., substance use, behavioral health). We then used logistic regression to examine family structure and other family characteristics and CPS indicators predicted CPS re-report for each class. RESULTS: Results yielded five distinct classes: 1) Financial Stressors (25 % of the sample); 2) Caregiver Substance Use (16 %); 3) Complex Household Stressors (3 %); 4) Child Disabilities (4 %); and 5) Minimal Household Stressors (53 %). Family structure was significantly associated with CPS re-reports for Classes 1, 2, and 5. For Class 1, single father families had increased odds of CPS re-report compared to other family structures. For Classes 2 and 5, single father families' odds of CPS re-reports were greater than those of married families, but lower than single mother families. CONCLUSIONS: Children growing up in single father families have different likelihoods of repeat CPS involvement compared to those in single mother and married families. Financial stressors and parental substance use within single father families should be addressed.

Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis.

BACKGROUND: Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes. METHODS: Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership. RESULTS: A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77). CONCLUSIONS: Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Profiles of healthcare use of persons living with dementia: A population-based cohort study.

AIM: Persons living with dementia are a heterogeneous population with complex needs whose healthcare use varies widely. This study aimed to identify the healthcare use profiles in a cohort of persons with incident dementia, and to describe their characteristics. METHODS: This is a retrospective cohort study of health administrative data in Quebec (Canada). The study population included persons who: (i) had an incident dementia diagnosis between 1 April 2015 and 31 March 2016; (ii) were aged ≥65 years and living in the community at the time of diagnosis. We carried out a latent class analysis to identify subgroups of healthcare users. The final number of groups was chosen based on clinical interpretation and statistical indicators. RESULTS: The study cohort consisted of 15 584 individuals with incident dementia. Four profiles of healthcare users were identified: (i) Low Users (36.4%), composed of individuals with minimal healthcare use and fewer comorbidities; (ii) Ambulatory Care-Centric Users (27.5%), mainly composed of men with the highest probability of visiting cognition specialists; (iii) High Acute Hospital Users (23.6%), comprised of individuals mainly diagnosed during hospitalization, with higher comorbidities and mortality rate; and (iv) Long-Term Care Destined Users (12.5%), who showed the highest proportion of antipsychotics prescriptions and delayed hospitalization discharge. CONCLUSIONS: We identified four distinct subgroups of healthcare users within a population of persons living with dementia, providing a valuable context for the development of interventions tailored to specific needs within this diverse population. Geriatr Gerontol Int 2024; ••: ••-••.

Symptom cluster study undergoing chemotherapy in breast cancer patients: Latent class analysis and contemporaneous network analysis.

Objective: This study aims to explore the subgroups and networks of symptom clusters in breast cancer patients undergoing chemotherapy, and to provide effective interventions for the core symptoms. Methods: A cross-sectional survey was conducted at four comprehensive hospitals in Foshan City, China, from August to November 2023. A total of 292 participants completed the social determinants of health questionnaire, the numerical rating scale (NRS), the Pittsburgh sleep quality index (PSQI), the Chinese version of the cancer fatigue scale (CFS), and the hospital anxiety and depression Scale (HADS). Latent class analysis (LCA) was utilized to distinguish subgroups, and network analysis was utilized to identify core symptoms among different subgroups. Results: Breast cancer patients undergoing chemotherapy exhibit symptoms were divided into two subgroups: the high burden group of symptoms (72.3%, Class 1) and the low burden group of symptoms (27.7%, Class 2). Education attainment, work status, family monthly income per capita, and daily sleep duration (hours) were associated with subgroup membership. "Panic feelings" (# HADS-A11) were the core symptom in both the full sample and Class 2, while "tension or pain" (# HADS-A1) was the core symptom in Class 1. Conclusions: The core symptoms of fear, enjoyment, nervousness, and pain varied across subgroups of patients and could inform the current strategies for symptom management in breast cancer chemotherapy patients.

Development and Bayesian validation of a competitive inhibition ELISA for detection of antibodies against Brucella abortus in cattle.

Bovine brucellosis is a zoonotic disease caused by Brucella abortus, responsible for abortions in cows. It is endemic in low- and middle-income countries, where the brucellosis control and eradication programs are based on compulsory vaccination, detection of infected cattle through serologic assays, and culling of infected animals at slaughterhouses. The development of high sensitivity and specificity, and low-cost serologic assays guarantee their implementation in countries where the disease is endemic. The aim of the present study was to develop and validate a competitive inhibition enzyme-linked immune assay (ciELISA) to detect anti-B. abortus antibodies in sera from cattle. The developed ciELISA was validated using 2833 serum samples from dairy and beef cattle. From these, 1515 sera were from uninfected cows that belonged to free of brucellosis herds and 1318 were from infected cows that belonged positive to brucellosis herds. Sera were analyzed with the developed ciELISA, the buffer plate antigen (BPA) test, and the complement fixation test (CFT). The brucellosis status of the herds was officially established according to the country legislation and consistent for at least 5 years and was defined for each cow using the CFT as gold standard. The cutoff for the ciELISA was calculated using a ROC curve and its sensitivity and specificity were analyzed using the Bayesian Latent Class Model (BLCM) approach. The agreement among tests was calculated using the kappa (κ) value. In addition, 15 calves were vaccinated with 3×1010 viable cells of B. abortus Strain 19 vaccine, and the dynamics of antibodies were measured by CFT, buffered plate antigen (BPA) test, and the developed ciELISA. The obtained cutoff for ciELISA was ≥ 47 percentage of inhibition (% I), at the BLCM approach the sensitivity was 99.01% (95% CI: 97.55-100) and the specificity 98.74% (95% CI: 97.68-99.8). The κ between the ciELISA and BPA was κ = 0.88 and between the ciELISA and CFT κ = 0.95. Antibodies against B. abortus were detected in all the vaccinated calves 7 days after vaccination (AV) by the three assays, at day 135 AV all the calves were negative to CFT (15/15), 93.3% (14/15) to ciELISA and 73.3% (11/15) to BPA, and at day 190 AV all the calves were negative to the three assays. The developed ciELISA showed a very good performance, could detect the majority of vaccinated animals as negative after 135 days and could be used for the detection of anti-B. abortus antibodies in serum samples for the brucellosis control and eradication program.

Latent classification model for censored longitudinal binary outcome.

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.

Gastrointestinal and Neuropsychological Symptoms Are Associated With Distinct Vomiting Profiles in Patients Receiving Chemotherapy.

OBJECTIVES: To identify subgroups of patients with distinct chemotherapy-induced vomiting (CIV) profiles; determine how these subgroups differ on several demographic, clinical, and symptom characteristics; and evaluate factors associated with chemotherapy-induced nausea and CIV profiles. SAMPLE & SETTING: Adult patients (N = 1,338) receiving cancer chemotherapy. METHODS & VARIABLES: Data were collected on demographic, clinical, and symptom characteristics. Differences among subgroups of patients with distinct CIV profiles were evaluated using parametric and nonparametric tests. RESULTS: Three CIV profiles (None, Decreasing, and Increasing) were identified. Compared with the None class, Decreasing and Increasing classes were more likely to have lower household income and a higher comorbidity burden, as well as to report higher rates of dry mouth, nausea, diarrhea, depression, anxiety, sleep disturbance, morning fatigue, and pain interference. IMPLICATIONS FOR NURSING: Clinicians need to assess common and distinct risk factors for CIV and chemotherapy-induced nausea.

Co-occurring conditions during pregnancy and hospitalizations in the first year postpartum among persons with opioid use disorder.

INTRODUCTION: Pregnant persons with opioid use disorder (OUD) face a multitude of comorbid conditions that may increase the risk of adverse drug and health outcomes. This study characterizes typologies of comorbidities among pregnant persons with OUD and assesses the associations of these typologies with hospitalizations in the first year postpartum. METHODS: A cohort of pregnant persons with OUD at delivery in 2018 were identified in a Pennsylvania statewide hospital dataset (n = 2055). Latent class analysis assessed 12 comorbid conditions including substance use disorders (SUDs), mental health conditions, and infections. Multivariable logistic regressions examined the association between comorbidity classes and hospitalizations (all-cause, OUD-specific, SUD-related, mental health-related) during early (0-42 days) and late (43-365 days) postpartum. RESULTS: A three-class model best fit the data. Classes included low comorbidities (56.9% of sample; low prevalence of co-occurring conditions), moderate polysubstance/depression (18.4%; some SUDs, all with depression), and high polysubstance/bipolar disorder (24.7%; highest probabilities of SUDs and bipolar disorder). Overall, 14% had at least one postpartum hospitalization. From 0 to 42 days postpartum, the moderate polysubstance/depression and high polysubstance/bipolar disorder classes had higher odds of all-cause and mental health-related hospitalization, compared to the low comorbidities class. From 43 to 365 days postpartum, the high polysubstance/bipolar disorder class had higher odds of all-cause hospitalizations, while both the high polysubstance/depression and moderate polysubstance/bipolar disorder classes had higher odds of SUD-related and mental health-related hospitalizations compared to the low comorbidities class. CONCLUSIONS: Findings highlight the need for long-term, multidisciplinary healthcare delivery interventions to address comorbidities and prevent adverse postpartum outcomes.

Healthcare Needs Patterns and Pattern-Predicting Factors in Dementia: Results of the Comprehensive, Computerized Unmet Needs Assessment from the Randomized, Controlled Interventional Trial InDePendent.

Background: Determining unmet need patterns and associated factors in primary care can potentially specify assessment batteries and tailor interventions in dementia more efficiently. Objective: To identify latent unmet healthcare need patterns and associated sociodemographic and clinical factors. Methods: This Latent Class Analysis (LCA) includes n = 417 community-dwelling people living with dementia. Subjects completed a comprehensive, computer-assisted face-to-face interview to identify unmet needs. One-hundred-fifteen predefined unmet medical, medication, nursing, psychosocial, and social care needs were available. LCA and multivariate logistic regressions were performed to identify unmet needs patterns and patient characteristics belonging to a specific pattern, respectively. Results: Four profiles were identified: [1] "few needs without any psychosocial need" (n = 44 (11%); mean: 7.4 needs), [2] "some medical and nursing care needs only" (n = 135 (32%); 9.7 needs), [3] "some needs in all areas" (n = 139 (33%); 14.3 needs), and [4] "many medical and nursing needs" (n = 99 (24%); 19.1 needs). Whereas the first class with the lowest number of needs comprised younger, less cognitively impaired patients without depressive symptoms, the fourth class had the highest number of unmet needs, containing patients with lower health status, less social support and higher comorbidity and depressive symptoms. Better access to social care services and higher social support reduced unmet needs, distinguishing the second from the third class (9.7 versus 14.3 needs). Conclusions: Access to the social care system, social support and depressive symptoms should be assessed, and the patient's health status and comorbidities monitored to more comprehensively identify unmet needs patterns and more efficiently guide tailored interventions.

Dietary patterns and multiple chronic diseases in older adults.

BACKGROUND: The prevalence rate of multiple chronic diseases among the elderly is relatively high, posing a risk to their health and also imposing a financial burden on them. Optimal dietary patterns have positive effects on multiple chronic diseases. This study aimed to identify dietary patterns associated with multiple chronic diseases in older adults. METHODS: Dietary intake was assessed through two non-consecutive 24-hour dietary recalls. The presence of multiple chronic diseases was assessed based on the existence of dyslipidemia, hypertension, chronic kidney disease, sleep disorders, diabetes, moderate or severe depressive symptoms, and cognitive impairment, with two or more of these conditions being considered. Latent class analysis was used to identify types of multiple chronic diseases, and two-step cluster analysis was used to determine individual dietary patterns. Logistic regression analysis with robust standard errors was conducted to determine the associations between dietary patterns and types of multiple chronic diseases. RESULTS: Three dietary patterns and three types of multiple chronic diseases were identified. Individuals following a diet rich in legumes, meat, vegetables and fruits (HLMVF dietary pattern) were 59% less likely to have the cardiometabolic cognitive impairment comorbidity (CCC) than those following a diet rich in milk and eggs but with low grain intake (HME-LG) (OR = 0.41, 95% CI: 0.27-0.64, P < 0.001) and 66% less likely to have the especially sleep disorders comorbidity (ESC) than those following a diet rich in grains but lacking milk and eggs (HG-LME) (OR = 0.34, 95% CI: 0.14-0.87, P < 0.05). DISCUSSION: The HLMVF dietary pattern may serve as a healthy dietary pattern to reduce the incidence of multiple chronic diseases and should be promoted among the older adult population.

Molecular Endotypes of Idiopathic Pulmonary Fibrosis: A Latent Class Analysis of Two Multicenter Observational Cohorts.

Rationale: Idiopathic pulmonary fibrosis (IPF) causes irreversible fibrosis of the lung parenchyma. While antifibrotic therapy can slow IPF progression, treatment response is variable. There exists a critical need to develop a precision medicine approach to IPF. Objective: To identify and validate biologically driven molecular endotypes of IPF. Methods: Latent class analysis (LCA) was independently performed in prospectively recruited discovery (n=875) and validation (n=347) cohorts. Twenty-five plasma biomarkers associated with fibrogenesis served as class-defining variables. The association between molecular endotype and 4-year transplant-free survival was tested using multivariable Cox regression adjusted for baseline confounders. Endotype-dependent differential treatment response to future antifibrotic exposure was then assessed in a pooled cohort of patients naïve to antifibrotic therapy at time of biomarker measurement (n=555). Results: LCA independently identified two latent classes in both cohorts (p<0.0001). WAP four-disulfide core domain protein 2 (WFDC2) was the most important determinant of class membership across cohorts. Membership in Class 2 was characterized by higher biomarker concentrations and higher risk of death or transplantation (discovery: HR 2.02 [95% CI 1.64-2.48]; p<0.001; validation: HR 1.95 [1.34-2.82]; p<0.001). In pooled analysis, significant heterogeneity in treatment effect was observed between endotypes (pinteraction=0.030), with a favorable antifibrotic response in Class 2 (HR 0.64 [0.45-0.93]; p=0.018) but not in Class 1 (HR 1.19 [0.77-1.84]; p=0.422). Conclusions: In this multicohort study, we identified two novel molecular endotypes of IPF with divergent clinical outcomes and response to antifibrotics. Pending further validation, these endotypes could enable a precision medicine approach for future IPF clinical trials.

Replication and reliability of Parkinson's disease clinical subtypes.

BACKGROUND: We recently identified three distinct Parkinson's disease subtypes: "motor only" (predominant motor deficits with intact cognition and psychiatric function); "psychiatric & motor" (prominent psychiatric symptoms and moderate motor deficits); "cognitive & motor" (cognitive and motor deficits). OBJECTIVE: We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes. METHODS: We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification. RESULTS: First, LCA supported the three-class solution - Motor Only, Psychiatric & Motor, and Cognitive & Motor- in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively. CONCLUSIONS: These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.

Novel Irritable Bowel Syndrome Subgroups are Reproducible in the Global Adult Population.

BACKGROUND & AIMS: Current classification systems for irritable bowel syndrome (IBS) based on bowel habit do not consider psychological impact. We validated a classification model in a UK population with confirmed IBS, using latent class analysis, incorporating psychological factors. We applied this model in the Rome Foundation Global Epidemiological Survey (RFGES), assessing impact of IBS on the individual and the health care system, and examining reproducibility. METHODS: We applied our model to 2195 individuals in the RFGES with Rome IV-defined IBS. As described previously, we identified 7 clusters, based on gastrointestinal symptom severity and psychological burden. We assessed demographics, health care-seeking, symptom severity, and quality of life in each. We also used the RFGES to derive a new model, examining whether the broader concepts of our original model were replicated, in terms of breakdown and characteristics of identified clusters. RESULTS: All 7 clusters were identified. Those in clusters with highest psychological burden, and particularly cluster 6 with high overall gastrointestinal symptom severity, were more often female, exhibited higher levels of health care-seeking, were more likely to have undergone previous abdominal surgeries, and had higher symptom severity and lower quality of life (P < .001 for trend for all). When deriving a new model, the best solution consisted of 10 clusters, although at least 2 seemed to be duplicates, and almost all mapped on to the previous clusters. CONCLUSIONS: Even in the community, our original clusters derived from patients with physician-confirmed IBS identified groups of individuals with significantly higher rates of health care-seeking and abdominal surgery, more severe symptoms, and impairments in quality of life.

Patterns and determinants of tobacco purchase behaviors among male cigarette smokers in Vietnam: A latent class analysis.

INTRODUCTION: Understanding smokers' purchasing patterns can aid in customizing tobacco control initiatives aimed at reducing the tobacco smoking prevalence. Therefore, this study identified cigarette purchase behavior among Vietnamese male smokers and associated demographic and consumption factors. METHODS: We analyzed a secondary dataset of male current tobacco smokers (n=3983) who participated in the Vietnam Global Adult Tobacco Survey in 2015. We applied the latent class analysis (LCA) to identify the classes of purchase behavior among cigarette smokers (n=1241). Multinomial logistic regression was performed to identify demographics (education level, ethnicity, partnership status, and household socioeconomic status) and cigarette consumption variables (smoking years and heavy smoking status) related to purchase behavior classes. The results are reported as an adjusted relative risk ratio (ARRR). RESULTS: The LCA identified four cigarette purchase behaviors classes: Class 1 (price-insensitive and purchased international brand: 44.4%), Class 2 (price-sensitive and purchased domestic brand: 27.6%), Class 3 (price-sensitive and purchased cigarettes in a street vendor: 18.6%), and Class 4: price-sensitive and purchased loose/carton cigarette: 9.4%). The poorer economic groups were more likely to belong to the three price-sensitive classes. Heavy smokers and those who had smoked for a longer period were more likely to belong to Class 3 (ARRR=2.33; 95% CI: 1.51-3.58 and ARRR=1.02; 95% CI: 1.001-1.05, respectively) and Class 4 (ARRR=2.94; 95% CI: 1.71-5.06 and ARRR=1.05; 95% CI: 1.02-1.08, respectively). CONCLUSIONS: Varied purchasing behaviors among male cigarette smokers, influenced by divergent price sensitivities and economic backgrounds, underscore the need for comprehensive tobacco control. Future efforts should include targeted policy interventions, behavior modification, and reshaping social norms.

Adverse Childhood Experiences and Their Relationships with Perceived Stress and Self-Care Behaviors in African American Social Work Students: Insights from a Latent Class Analysis.

Based on stress sensitization theory and stress proliferation theory, this study was designed to identify adverse childhood experience (ACE) classes and their relationships with perceived stress and self-care behaviors. Hypotheses were that (a) there would be diverse ACE classes among African American social work students; (b) the identified classes embedded in high/multiple ACEs would have greater levels of perceived stress than those in low ACEs; and (c) the identified classes embedded in high/multiple ACEs would have lower levels of self-care behaviors than those in low ACEs. Recruited from one of the South's historically Black colleges and universities, 186 African American social work students completed an online survey. Latent class analysis found three classes fit the data best: low ACEs, high divorce/abuse/neglect, and high/multiple ACEs. Students in the high divorce/abuse/neglect class had the greatest levels of perceived stress and significantly greater perceived stress levels than the low ACEs class. The low ACEs class had greater self-care behaviors than students in the other two ACEs classes. The study revealed diverse ACE classes and the effect of more ACEs on greater perceived stress and lower self-care behaviors, supporting the importance of using a range of approaches to support African American social work students with different ACEs.

Latent class analysis of migraine associated vestibular-auditory symptoms.

OBJECTIVE: This study aimed to identify the potential subgroups of migraines based on the patterns of migraine associated symptoms, vestibular and auditory symptoms using latent class analysis and to explore their characteristics. METHOD: A total of 555 patients with migraine participated in the study. Symptoms such as nausea, vomiting, photophobia, phonophobia, osmophobia, visual symptoms, vestibular symptoms (dizziness, vertigo), and auditory symptoms (tinnitus, hearing loss, aural fullness) were assessed. Latent class analysis was performed to identify subgroups of migraines. Covariates such as gender, age of migraine onset, frequency of migraine attacks per month, and family history were also considered. RESULTS: The analysis revealed four latent classes: the Prominent Vestibular; Prominent Nausea; Presenting Symptoms but not prominent or dominant; and Sensory Hypersensitivity groups. Various covariates, such as gender, age of migraine onset, and frequency of migraine attacks, demonstrated significant differences among the four groups. The Sensory Hypersensitivity group showed the presence of multiple sensory symptoms, earlier age of migraine onset, and higher proportion of females. The Prominent Vestibular group had the highest probability of dizziness or vertigo but lacked the presence of auditory symptoms. The Prominent Nausea group exhibited prominent nausea. The Presenting Symptoms but not prominent or dominant group comprised individuals with the highest migraine attacks per month and proportion of chronic migraine. CONCLUSION: This study identifies four subgroups of migraines based on the patterns of symptoms. The findings suggest potential different but overlapped mechanisms behind the vestibular and auditory symptoms of migraine. Considering the different patterns of migraine-related symptoms may provide deeper insights for patients' prognosis and clinical decision-making.

Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan.

BACKGROUND: In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions. METHODS: This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates. RESULTS: A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3. CONCLUSIONS: This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.

Ocular Signs and Testing Most Compatible with Sarcoidosis-Associated Uveitis: A Latent Class Analysis.

Purpose: This study aims to explore the potential subgroups of sarcoidosis-associated uveitis (SAU) within a multicenter cohort of uveitis participants. Design: Cross-sectional study. Participants: A cohort of 826 uveitis patients from a uveitis registry from 19 clinical centers in 12 countries between January 2011 and April 2015. Methods: We employed a latent class analysis (LCA) incorporating recommended tests and clinical signs from the revised International Workshop on Ocular Sarcoidosis (IWOS) to identify potential SAU subgroups within the multicenter uveitis cohort. Additionally, we assessed the performance of the individual tests and clinical signs in classifying the potential subclasses. Main Outcome Measures: Latent subtypes of SAU. Results: Among 826 participants included in this analysis, the 2-class LCA model provided a best fit, with the lowest Bayesian information criteria of 7218.7 and an entropy of 0.715. One class, consisting of 548 participants, represented the non-SAU, whereas the second class, comprised of 278 participants, was most representative of SAU. Snowballs/string of pearls vitreous opacities had the best test performance for classification, followed by bilaterality and bilateral hilar lymphadenopathy (BHL). The combination of 4 tests with the highest classification importance, including snowballs/string of pearls vitreous opacities, periphlebitis and/or macroaneurysm, bilaterality, and BHL, demonstrated a sensitivity of 84.8% and a specificity of 95.4% in classifying the SAU subtypes. In the exploratory analysis of the 3-class LCA model, which had comparable fit indices as the 2-class model, we identified a candidate non-SAU subtype, candidate SAU subtype with pulmonary involvement, and a candidate SAU with less pulmonary involvement. Conclusions: Latent class modeling, incorporating tests and clinical signs from the revised IWOS criteria, effectively identified a subset of participants with clinical features indicative of SAU. Though the sensitivity of individual ocular signs or tests was not perfect, using a combination of tests provided a satisfactory performance in classifying the SAU subclasses identified by the 2-class LCA model. Notably, the classes identified by the 3-class LCA model, including a non-SAU subtype, an SAU subtype with pulmonary involvement, and an SAU subtype with less pulmonary involvement, may have potential implication for clinical practice, and hence should be validated in further research. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Latent Classes of Simultaneous Alcohol and Cannabis Use and Associations with Consequences using Daily Data.

Objective: Simultaneous alcohol and cannabis use (i.e., marijuana, [SAM], using alcohol and cannabis so effects overlap) is associated with increased consumption and consequences compared to single-substance use. SAM use prevalence is increasing, yet there is heterogeneity in use patterns among those engaging in SAM use, which may lead to differential consequences. Method: This study drew on daily data to characterize latent profiles of cannabis, alcohol, and SAM use patterns and to test class differences on related consequences after 3 months among college students engaging in SAM use (77.08% White, 51.67% female). Class indicators were 10 person-level substance use variables derived from repeated daily surveys. Results: Results yielded a three-class solution: Heavy Alcohol, Cannabis, and SAM (Heavy Use, n = 105); Heavy Alcohol-Light Cannabis (n = 75); and Light Alcohol-Heavy Cannabis (n = 60). There were significant person-level differences between classes on all substance use indicators (e.g., quantity and frequency of alcohol, cannabis, and SAM) but not sex or race/ethnicity. At 3-month follow-up, the Heavy Use class endorsed more SAM consequences than the other classes. The Heavy Use class did not differ on alcohol or cannabis consequences compared to the Heavy Alcohol-Light Cannabis or Light Alcohol-Heavy Cannabis classes, respectively. The Light Alcohol-Heavy Cannabis class endorsed the fewest alcohol consequences. The Heavy Alcohol-Light Cannabis class endorsed the fewest cannabis consequences. Conclusions: Findings highlight distinct patterns of co-use and their association with consequences at follow-up. Heavy alcohol or cannabis use was associated with consequences for that substance, but heavy use of only one substance was not indicative of SAM-specific consequences.