Patterns of pain medication usage and self-reported pain in older Irish adults with osteoarthritis: A latent class analysis of data from the Irish Longitudinal Study on Ageing.

BACKGROUND: This study aimed to identify and describe links between pain medication use and self-reported pain among people aged ≥ 50 years with osteoarthritis (OA) in an Irish population, and to examine the relationships between pain, medication usage and socioeconomic and clinical characteristics. METHODS: Secondary data analysis of wave 1 cross-sectional data from The Irish Longitudinal Study on Ageing (TILDA) was undertaken of 1042 people with self-reported doctor-diagnosed OA. We examined use of medications typically included in OA clinical guidelines, including non-opioid analgesics (e.g. paracetamol), topical and oral non-steroidal anti-inflammatory drugs (NSAIDs), opioids and nutraceuticals. Latent Class Analysis (LCA) was used to identify underlying clinical subgroups based on medication usage patterns, and self-reported pain severity. Multinomial logistic regression was used to explore sociodemographic and clinical characteristic links to latent class membership. RESULTS: A total of 358 (34.4%) of the 1042 people in this analysis were taking pain medications including oral NSAIDs (17.5%), analgesics (11.4%) and opioids (8.7%). Nutraceutical (glucosamine/chondroitin) use was reported by 8.6% and topical NSAID use reported by 1.4%. Three latent classes were identified: (1) Low medication use/no pain (n = 382, 37%), (2) low medication use/moderate pain (n = 523, 50%) and (3) moderate medication use/high pain (n = 137, 13%). Poorer self-rated health and greater sleep disturbance were associated with classes 2 and 3; depressive symptoms and female gender were associated with class 2, and retirement associated with class 3. CONCLUSIONS: Whilst pain medication use varied with pain severity, different medication types reported broadly aligned with OA guidelines. The two subgroups exhibiting higher pain levels demonstrated poorer self-rated health and greater sleep disturbance.

Latent class analysis-derived classification improves the cancer-specific death stratification of lymphomas: A large retrospective cohort study.

Lymphomas have diverse etiologies, treatment approaches, and prognoses. Accurate survival estimation is challenging for lymphoma patients due to their heightened susceptibility to non-lymphoma-related mortality. To overcome this challenge, we propose a novel lymphoma classification system that utilizes latent class analysis (LCA) and incorporates demographic and clinicopathological factors as indicators. We conducted LCA using data from 221,812 primary lymphoma patients in the Surveillance, Epidemiology, and End Results (SEER) database and identified four distinct LCA-derived classes. The LCA-derived classification efficiently stratified patients, thereby adjusting the bias induced by competing risk events such as non-lymphoma-related death. This remains effective even in cases of limited availability of cause-of-death information, leading to an enhancement in the accuracy of lymphoma prognosis assessment. Additionally, we validated the LCA-derived classification model in an external cohort and observed its improved prognostic stratification of molecular subtypes. We further explored the molecular characteristics of the LCA subgroups and identified potential driver genes specific to each subgroup. In conclusion, our study introduces a novel LCA-based lymphoma classification system that provides improved prognostic prediction by accounting for competing risk events. The proposed classification system enhances the clinical relevance of molecular subtypes and offers insights into potential therapeutic targets.

Meritocratic beliefs in the United States, Finland, and China: A multidimensional approach using latent class analysis.

This study employs latent class analysis (LCA) as a novel methodology to investigate the multidimensional nature of meritocratic beliefs, addressing the limitations of traditional unidimensional approaches. Using data from the International Social Survey Program 2009 for the United States, Finland, and China, this study demonstrates several advantages of this multidimensional approach. First, LCA effectively identifies dual consciousness, where individuals simultaneously endorse meritocratic and structuralist explanations of social stratification. The analysis reveals three distinct narratives explaining social stratification: purely meritocratic beliefs, predominantly meritocratic beliefs, and dual consciousness. While all three subtypes consider merits highly important, they differ in their perceived importance of structural factors. Second, LCA facilitates cross-national comparisons, unveiling qualitative typological variations in meritocratic beliefs across countries. Unique country-specific subtypes or patterns emerge: Finland exhibits purely meritocratic beliefs, the United States shows predominantly meritocratic beliefs, and China demonstrates a dominance of dual consciousness. Although dual consciousness exists in all three countries, its prevalence varies significantly-dominant in China, moderate in the United States, and least in Finland. Third, this study reveals that the effect of education on meritocratic beliefs varies across the three countries. Education strengthens individual meritocratic beliefs in the United States, weakens them in Finland, and shows no significant effect in China. These findings highlight both within-country and across-country heterogeneity of meritocratic beliefs, underscoring the importance of a multidimensional approach.

Perception towards electronic health records & uptake of digital health IDs among the urban residents in northern India: A mixed methods study.

Background & objectives Ayushman Bharat Digital Mission (ABDM) envisages a unique digital health ID for all citizens of India, to create electronic health records (EHR) of individuals. The present study assessed the uptake of Digital Health IDs by the patient and general population, their attitude toward EHR, and explored the barriers to digital ID and utilizing electronic health records services. Methods A concurrent explanatory mixed methods study was undertaken in Chandigarh, India, with an analytical cross-sectional design as a quantitative part and a qualitative descriptive study. The study participants were 419 individuals aged ≥18 yr who attended the urban primary healthcare centre (n=399) and the community-based screening camps (n=20) between July 2021 and January 2022. Latent Class Analysis (LCA) was undertaken to identify hidden sub-population characteristics. In-depth interviews were done to identify the barriers to health ID uptake. Results The digital health ID uptake rate was 78 per cent (n=327). Among the study participants, those who were aware of EHR, those who wanted a national EHR system, those who were confident with the government on EHR security, and those who were willing to make national EHR accessible for research showed significantly higher digital health ID uptake than their counterparts. The themes identified under barriers of uptake from the qualitative interviews were lack of awareness, technology-related (including digital literacy) and utility-related. Interpretation & conclusions Increasing EHR awareness, digital health literacy, and enacting data protection laws may improve the acceptance of the digital health ecosystem in India.

Distinct Phenotypic Groups and Related Clinical Outcomes in Patients With Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a heterogeneous disorder with varying risks of clinical outcomes, including sudden cardiac death (SCD). We aimed to identify distinct phenotypes among patients with HCM in relation to SCD risk factors, interpret their clinical characteristics, and examine their outcomes. METHODS AND RESULTS: This retrospective study analyzed 1231 consecutive patients with HCM from 2 tertiary hospitals. We performed latent class analysis to categorize patients into phenotypic groups. Three distinct phenotypic groups were identified using latent class analysis. Group 1 (n=554) consisted of young patients with HCM with minimal SCD risk factors and favorable cardiac remodeling. Group 2 (n=114) comprised young patients with HCM and a high prevalence of SCD risk factors, whereas Group 3 (n=563) included older patients (median age, 68 years). Over a median 6.5-year follow-up, 34 SCD-related events, 131 cardiovascular events, 133 all-cause mortality events, and 70 noncardiovascular mortality events were observed. Group 2 exhibited the highest rate of SCD-related events (5-year SCD rate: Group 1 versus 2 versus 3: 0.8% versus 8.2% versus 4.0%, respectively, P<0.001), and cardiovascular events were more frequent in Groups 2 and 3 compared with Group 1. All-cause and noncardiovascular mortality were the most frequent in Group 3. A simplified decision tree was developed for the straightforward assignment of phenotypic group membership, demonstrating fair concordance. CONCLUSIONS: This study identified 3 distinct clinical phenotypes in patients with HCM, each associated with different SCD risks and outcomes. Data-driven phenotyping of patients with HCM offers effective risk stratification and may optimize patient management.

Depression symptoms are associated with demographic characteristics, nutritional status, and social support among young adults in Chile: a latent class analysis.

BACKGROUND: Depressive disorders are a critical public health concern in Chile. Nonetheless, there is a lack of evidence regarding the identification of depressive symptom clusters. The objective was to identify depressive symptom clusters among Chilean young adults and examine how demographic, and lifestyle factors as well as social support can influence and predict them. METHODS: Cross-sectional study conducted among 1,000 participants from the Limache cohort 2. A latent class analysis (LCA) was performed to identify depressive symptom clusters, using the Patient Health Questionnaire (PHQ-9). Multinomial logistic regression was then applied to explore the associations between identified classes and potential predictors. The models were adjusted by age and sex. RESULTS: Three latent classes of depressive symptoms were identified: minimal (25.7%); somatic (50.7%) and severe (23.6%). In the severe class for eight out nine depressive symptoms the probabilities were above 50%, and the probability of suicidal ideation was almost a third in this class. Being female (Adjusted Odds ratio [AOR], 2.49; 95% confidence interval [CI] [1.63-3.81]), current smoker (AOR, 1.74; 95% CI [1.15-2.65]), having basic education (AOR, 3.12; 95% CI [1.30-7.53]) and obesity (AOR, 2.72; 95% CI [1.61-4.59]) significantly increased the likelihood of belonging to severe class. Higher social support decreased the odds of being in the somatic (OR, 0.96; 95% CI [0.93-0.98]) and severe (OR, 0.92; 95% CI [0.90-0.94]) classes. CONCLUSIONS: These findings highlight the importance of individualized intervention strategies for depression management. Also, the study suggests that nutritional status and social support should be considered when addressing depression in this population.

Patterns of Poly-Victimization Among Early Adolescents: A Latent Class and Two-Wave Latent Transition Analysis.

Background: Poly-victimization involves more than just counting incidents; it varies in severity and type among adolescents and can change over time. Objective: The aim is to identify latent classes of poly-victimization among children in early adolescence, investigate transition probabilities between these latent categories, and examine the influencing factors. Methods: We used stratified cluster random sampling to select 2275 junior high students from five rural middle schools in Shantou and Jieyang, China, and surveyed them in two waves. Latent Class Analysis (LCA) and Latent Transition Analysis (LTA) identified latent classes of poly-victimization, and multi-factor logistic regression examined factors influencing the probability of students transitioning between these latent classes. Results: LCA identified three categories of poly-victimization: low poly-victimization, group, and high child maltreatment and peer and sibling victimization. The probabilities of remaining in the high child maltreatment and peer and sibling victimization group, transitioning to the transition group, or shifting to the low poly-victimization group were 37.00%, 29.20%, and 33.80%, respectively. Most transition group members remained in the same group, with a conversion probability of 77.10%, followed by transitioning to the low poly-victimization group with a probability of 15.80%. Physically healthy children, compared to those with disabilities or illnesses, were less likely to switch from the low poly-victimization group to the transition group (OR=0.034) or the high child maltreatment and peer and sibling victimization group (OR=0.14). Non-left-behind children, compared to left-behind children, have a higher probability of switching from the high child maltreatment and peer and sibling victimization group to the low poly-victimization group (OR=6.905). Conclusion: The high child maltreatment and peer and sibling victimization group had similar probabilities of transitioning into other categories. Physical illness or disability, as well as being left behind, are significant risk factors for children transitioning from the low-harm group to the high-harm group.

Prenatal Substance Exposure and Infant Discharge Placement: Results From the ACT NOW Study.

It is critical that researchers gather evidence of factors that identify infants at risk of out-of-home placement based on types of substance exposures and demographic characteristics. This study applied a validated medical record data extraction tool on data derived from a multi-site (N = 30) pediatric clinical trials network (ISPCTN) study of Neonatal Opioid Withdrawal (ACT NOW study). Participants included 1808 birthing parent-infant dyads with documented NOWS scoring or prenatal opioid exposure. Non-Hispanic White pregnant persons comprised the largest proportion of the sample (69.8%), followed by Non-Hispanic Black (11.6%), Non-Hispanic Multiracial and Other race (8.5%), and Hispanic (6.2%). Most notably, infant prenatal substance exposure across alcohol, cocaine, meth/amphetamine, and opioids, had the lowest possibility of discharging to parent(s). Additionally, latent class analysis identified distinct classes of substance use during pregnancy that were associated with different probabilities of discharging to parent(s). Specifically, less than half of infants (47%-49%) in the Poly-use and Meth/amphetamine classes were discharged to their parent(s). Severity of infant withdrawal symptoms influenced placement decisions within the Poly-use and Prescription Opioid classes. Findings can inform standard practices for increasing support for pregnant persons and substance-exposed infants including identification, subsequent referrals, communication with Child Protective Services, and plans of safe care.

Unraveling Perceived Ostracism: The Role of Antagonistic Traits and Attachment Orientation.

Perceived ostracism (e.g., feeling ignored and excluded) can lead to psychological distress. There has been little empirical research into the types (profiles) of people more likely to perceive ostracism. The present study (N = 604) used latent class analysis (LCA) to (a) explore classes based on antagonistic traits (narcissism, machiavellianism, psychopathy, and sadism)while controlling for attachment orientation (attachment anxiety and attachment avoidance) and (b) examine whether such classes could reliably differentiate levels of self-reported perceived ostracism. We extracted five classes: (a) Average Low, (b) the Non-Antagonisers, (c) Average High, (d) Spiteful Manipulators, and (e) the High Antagonisers. Those in the High Antagonisers class reported significantly higher levels of perceived ostracism compared to all other classes. No other differences between classes were observed. There were also significant positive relationships for avoidant and anxious attachment on perceived ostracism, respectively. This study provides new insight into the profiles of individuals who may be more likely to perceive ostracism. However, further research is needed to explore the association between personality and perceived ostracism. Researchers may consider measuring the potential outcomes following perceived ostracism for such groups and/or design potential interventions for those at risk of such experiences.

Classes of child-mother attachment disorganization from infancy to the preschool years.

Mary Main's operationalization of infant attachment disorganization contributed to our understanding of attachment and psychopathology. Her exploration of attachment patterns at age 6 with Jude Cassidy laid the foundations for studying attachment post-infancy. They found remarkable correspondence from age 1 to age 6 in the disorganization spectrum and documented the emergence of role-reversal. This study proposes a person-centered approach to explore classes of children with respect to attachment disorganization at four time points between infancy and late preschool. Participants (n = 205) were recruited in the UK and formed a socioeconomically diverse community sample of mother-child dyads. We identified three classes of children: 1) a stable organized group; 2) an unstable group becoming organized; and 3) an unstable group becoming disorganized. Results show that major loss predicts membership of the third class of children. These findings contribute to our understanding of disorganization across multiple periods, and thus to Mary Main's legacy.

Self-Efficacy in Nursing Competencies during Students' Clinical Practicum: The Development of a Self-Assessment Scale.

The evaluation of the competencies corresponding to the different professional profiles of future nursing graduates is fundamental to their training. In this regard, students' self-evaluation must be part of their training. This study aimed to develop and psychometrically test the Perceived Self-Efficacy in Nursing Competencies (PSENC) Scale. This study was conducted in two phases: selecting and adjusting items and assessing the instrument's psychometric properties. A sample of 1416 students completed the scale online. Exploratory factor and confirmatory factor analyses were conducted. Inferential analysis was carried out. The exploratory factor analysis of the PSENC scale with 20 items resulted in five factors (76.3% of variance). All factors showed Cronbach's alpha coefficients > 0.70. The confirmatory factor analysis measurement model showed satisfactory and adequate goodness-of-fit indices. The developed scale showed the psychometric adequacy and usefulness to the self-assessment of nursing students regarding their self-efficacy expectations in competencies during their clinical practicum. This study was not registered.

Subgroup Behaviors and Factors Influencing Compliance With COVID-19 Preventive Measures Among Undergraduate Students in Southern Thailand.

Objective: To investigate clusters of students' COVID-19 preventive behaviors and their associated factors. Methods: We surveyed undergraduate students using an online questionnaire at a regional university in southern Thailand, between April and June 2022. Statistical analyses included latent class analysis and multinomial regression analysis. Results: Three latent classes were identified: moderately consistent practitioner (7.5%), high compliance overall (48.9%), and good compliance with routine safeguards (43.6%). Females tended to have high compliance overall (RRR 2.46 95% CI 1.23-4.94), and higher academic performance was associated with high compliance overall and good routine safeguards. Perceived threats from COVID-19 were associated with good compliance with routine safeguards (RRR 4.21 95% CI 1.70-10.45). Benefits of actions and clear cues to action were associated with high overall compliance (RRR 5.24 95% CI 2.13-12.90). Students who perceived feasibility were more likely to be moderately consistent practitioners. Conclusion: The common clusters of the students' preventive behaviors were high compliance overall and good compliance with routine preventions. Female, academic performance, perceived threats, and perceived benefits and cues to action were associated with compliance.

Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Background: Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. Methods: We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. Results: LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52 - 0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. Conclusions: In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

The longitudinal study of the relationship between social participation pattern and depression symptoms in frail older adults.

Background: Mental health challenges are encountered by frail older adults as the population ages. The extant literature is scant regarding the correlation between depressive symptoms and social participation among frail older adults. Methods: This study is based on an analysis of data from China Health and Retirement Longitudinal Study (CHARLS) participants aged 60 and older who are frail. A frailty index (FI) was developed for the purpose of assessing the frailty level of the participants. Additionally, latent class analysis (LCA) was employed to classify the participants' social engagement patterns in 2015 and 2018. The study used ordered logistic regression to examine the relationship between social participation type and depressive symptoms. We also used Latent Transition Analysis (LTA) methods to explore the impact of changes in social activity types on depressive symptoms after three years of follow-up in 2018. In addition, the response surface analysis (RSM) investigation explored the relationship among FI, depression, and social participation. Results: A total of 4,384 participants completed the baseline survey; three years later, 3,483 were included in the follow-up cohort. The baseline survey indicates that female older adults in rural areas who are single, have lower incomes, shorter sleep durations, and lighter weights exhibited more severe depressive symptoms. Social participation patterns were categorized into five subgroups by LCA. The findings indicate that individuals classified as "board game enthusiasts" (OR, 0.62; 95% CI, 0.47-0.82) and those as "extensive social interaction" (OR,0.67; 95% CI, 0.49-0.90) have a significantly lower likelihood of developing depressive symptoms compared to the "socially isolated" group. We also discovered that "socially isolated" baseline participants who transitioned to the "helpful individual" group after three years had significantly greater depressed symptoms (OR, 1.56; 95% CI, 1.00-2.44). More social activity types and less FI are linked to lower depression in our study. Conclusion: The results of the study emphasize the importance of social participation patterns and the number of social participation types in relation to the severity of depression among frail older adults individuals. This study's findings may provide important insights for addressing depressive symptoms in frail older adults person.

Housing instability patterns among low-income, urban Black young adults in California and associations with mental health outcomes: baseline data from a randomized waitlist-controlled trial.

BACKGROUND: Deep-rooted racial residential segregation and housing discrimination have given rise to housing disparities among low-income Black young adults in the US. Most studies have focused on single dimensions of housing instability, and thus provide a partial view of how Black young adults experience multiple, and perhaps overlapping, experiences of housing instability including homelessness, frequent moves, unaffordability, or evictions. We aimed to illuminate the multiple forms of housing instability that Black young adults contend with and examine relationships between housing instability and mental health outcomes. METHODS: Using baseline data from the Black Economic Equity Movement (BEEM) guaranteed income trial with 300 urban low-income Black young adults (aged 18-24), we conducted a three-stage latent class analysis using nine housing instability indicators. We identified distinct patterns by using fit indices and theory to determine the optimal number of latent classes. We then used multinomial logistic regression to identify subpopulations disproportionately represented within unstable housing patterns. Finally, we estimated associations between housing experience patterns and mental health outcomes: depression, anxiety, and hope. RESULTS: We found high prevalence of housing instability with 27.3% of participants reporting experiences of homelessness in the prior year and 39.0% of participants reporting multiple measures of housing instability. We found the 4-class solution to be the best fitting model for the data based on fit indices and theory. Latent classes were characterized as four housing experience patterns: 1) more stably housed, 2) unaffordable and overcrowded housing, 3) mainly unhoused, and 4) multiple dimensions of housing instability. Those experiencing unaffordable and overcrowded housing and being mainly unhoused were more than four times as likely to have symptoms of depression (Unaffordable: aOR = 4.57, 95% CI: 1.64, 12.72; Unhoused: aOR = 4.67, 95% CI:1.18, 18.48) and more than twice as likely to report anxiety (Unaffordable: aOR = 2.28, 95% CI: 1.03, 5.04; Unhoused: aOR = 3.36, 95% CI: 1.12, 10.05) compared to the more stably housed pattern. We found that hope scores were similarly high across patterns. CONCLUSIONS: High prevalence of housing instability and mental health challenges among low-income Black young adults demands tailored interventions to reduce instability, given widening racial disparities and implications for future well-being into adulthood.

User profiles in digitalized healthcare: active, potential, and rejecting - a cross-sectional study using latent class analysis.

BACKGROUND: There is evidence of different use by different groups of people for general health-related applications. Yet, these findings are lacking for digitalized healthcare services. It is also unclear whether typical use patterns can be found and how user types can be characterized. METHODS: The analyses are based on data from 1 821 respondents to the Health Related Beliefs and Health Care Experiences in Germany panel (HeReCa). Digitalized healthcare services, that were used to determine the user types, include for example sick notes before/after examination and disease related training. User types were determined by latent class analysis. Individual groups were characterized using multinomial logistic regressions, taking into account socioeconomic and demographic factors as well as individual attitudes towards digitalization in the healthcare system. RESULTS: Three types were identified: rejecting (27.9%), potential (53.8%) and active (18.3%). Active participants were less likely to be employed, less likely to be highly educated and less skeptical of digital technologies. Potential users were the youngest, most highly-educated and most frequently employed group, with less skepticism than those who rejected. Rejecters were the oldest group, more likely to be female and of higher socio-economic status. CONCLUSIONS: Socio-demographic and socio-economic differences were identified among three user types. It can therefore be assumed that not all population groups will benefit from the trend towards digitalization in healthcare. Steps should be taken to enhance access to innovations and ensure that everyone benefits from them.

Novel application of latent class analysis to outcome assessment in traumatic brain injury with multiple injury subtypes or poly-TBI.

OBJECTIVE: The aim of this study was to stratify poly-traumatic brain injury (poly-TBI) patterns into discrete classes and to determine the association of these classes with mortality and withdrawal of life-sustaining treatment (WLST). METHODS: The authors performed a single-center retrospective review of their institutional trauma registry from 2018 to 2020 to identify patients with traumatic brain injury (TBI). Patients were included if they had moderate to severe TBI, defined as Glasgow Coma Scale score ≤ 12 and Abbreviated Injury Scale (AIS) head score ≥ 3, and the presence of more than one TBI subtype. TBI subtypes were defined as subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), and epidural hemorrhage (EDH). Latent class analysis was used to identify patient classes based on TBI subtypes and Rotterdam CT (RCT) scores. The authors then evaluated class membership in relation to categorical outcomes of in-hospital mortality and WLST by using Lanza et al.'s method. RESULTS: A total of 125 patients met inclusion criteria for poly-TBI. Latent class analysis yielded 3 poly-TBI classes: class 1-mixed; class 2-SDH/SAH; and class 3-EDH/SAH. Class 1-mixed had a higher likelihood of SDH, SAH, and ICH, and a lower likelihood of EDH. Class 2-SDH/SAH had a higher likelihood of only SDH and SAH. Class 3-EDH/SAH had a higher likelihood of EDH and SAH, and a lower likelihood of SDH and ICH. Class 1-mixed was relatively more likely to have an RCT score of 2. Class 2-SDH/SAH was relatively more likely to have an RCT score of 2, 3, and 4. Class 3-EDH/SAH had a higher likelihood of an RCT score of 3, 4, and 5. Class 1-mixed had significantly lower mortality (χ2 = 7.968; p = 0.005) and less WLST (χ2 = 4.618; p = 0.032) than Class 2-SDH/SAH. Class 2-SDH/SAH had the highest probability of death (0.612), followed by class 3-EDH/SAH (0.385) and class 1-mixed (0.277). Similarly, class 2-SDH/SAH had the highest WLST probability (0.498), followed by class 3-EDH/SAH (0.615) and class 1-mixed (0.238). CONCLUSIONS: Distinct poly-TBI classes were associated with increased in-hospital mortality and WLST. Further research with larger datasets will allow for more comprehensive poly-TBI class definitions and outcomes analysis.

Types of depression in patients with coronary heart disease: Results from the THORESCI study.

BACKGROUND: Both coronary heart diseases (CHD) and depression are highly prevalent and bidirectionally related. The precise nature of this relationship remains unclear. Defining depressive subtypes could help unravel this relationship. Therefore, the aim of this study was to explore depressive subtypes in patients with CHD. METHODS: 1530 patients (21.3 % women, mean age: 64.7 years (SD = 10.1)) were included in latent class analysis with nine indicators derived from the PHQ-9 and BDI-II representing symptoms of depression as described in the DSM-5 criteria. The best-fitting latent class model was confirmed with double cross-validation. Classes were characterized using demographic, medical, psychiatric, and cardiovascular (risk) factors. RESULTS: A 3-class model demonstrated the best fit to the data, resulting in a depressed (5.4 %), fatigued (13.5 %), and non-depressed class (81.1 %). Having medical comorbidities, a history of psychiatric problems, negative affectivity, and anxiety symptoms increased the odds of belonging to the depressed group (OR 3.02, 95%CI 1.19-7.68, OR 3.61, 95%CI 1.44-9.02, OR 1.16, 95%CI 1.04-1.30, and OR 1.89, 95%CI 1.66-2.15, respectively). Belonging to the fatigued group was associated with increased odds of having an elective PCI (OR 2.12, 95%CI 1.27-3.55), insufficient physical activity (OR 2.19, 95%CI 1.20-3.99), comorbid medical conditions (OR 2.15, 95%CI 1.21-3.81), a history of psychiatric problems (OR 2.25, 95%CI 1.25-4.05), and anxiety symptoms (OR 1.48, 95%CI 1.34-1.63) compared with the non-depressed group. LIMITATIONS: Future studies should include more people with depressive symptoms. CONCLUSIONS: Patients with CHD and medical or psychiatric risk factors should be offered support to decrease or prevent depressive or fatigue symptoms.

The association between poverty and longitudinal patterns of adverse childhood experiences across childhood and adolescence: Findings from a prospective population-based cohort study in the UK.

BACKGROUND: Adverse childhood experiences (ACEs) affect up to half the general population, they are known to co-occur, and are particularly common among those experiencing poverty. Yet, there are limited studies examining specific patterns of ACE co-occurrence considering their developmental timing. OBJECTIVE: To examine the longitudinal co-occurrence patterns of ACEs across childhood and adolescence, and to examine the role of poverty in predicting these. PARTICIPANTS AND SETTING: The sample was 8859 children from the Avon Longitudinal Study of Parents and Children, a longitudinal prospective population-based UK birth cohort. METHODS: Repeated measures of ten ACEs were available, occurring in early childhood (birth-5 years), mid-childhood (6-10 years), and adolescence (11-16 years). Latent class analysis was used to identify groups of children with similar developmental patterns of ACEs. Multinomial regression was used to examine the association between poverty during pregnancy and ACE classes. RESULTS: Sixteen percent of parents experienced poverty. A five-class latent model was selected: "Low ACEs" (72·0 %), "Early and mid-childhood household disharmony" (10·6 %), "Persistent parental mental health problems" (9·7 %), "Early childhood abuse and parental mental health problems" (5·0 %), and "Mid-childhood and adolescence ACEs" (2·6 %). Poverty was associated with a higher likelihood of being in each of the ACE classes compared to the low ACEs reference class. The largest effect size was seen for the "Early and mid-childhood household disharmony" class (OR 4·70, 95 % CI 3·68-6·00). CONCLUSIONS: A multifactorial approach to preventing ACEs is needed - including support for parents facing financial and material hardship, at-risk families, and timely interventions for those experiencing ACEs.

The genetic architecture of substance use and its diverse correlations with mental health traits.

Although harmful substance use is common and represented by shared symptom features and high genetic correlations, the underlying genetic relationships between substance use traits have not been fully explored. We have investigated the genetic architecture of substance use traits through exploratory and confirmatory factor analyses using genomic structural equation modeling (Genomic SEM), and explored genetic correlations between different aspects of substance use and mental health-related traits. Genomic SEM was used to identify latent factors representing the relationships between 14 substance use traits (alcohol, nicotine, cannabis and opioid use), and to confirm or modify existing latent factors for 38 mental health-related traits. A bi-factor model best explained the genetic overlap between substance use traits, including a general substance use factor and two sub-factors representing genetic liability specific to alcohol use or smoking. The SNP-based heritability of these factors ranged from 2 to 7 % and each factor had 10 or more independent significant SNPs identified. Bivariate correlations revealed patterns of genetic overlap with other mental health-related factors unique to each substance use factor. Variations in the genetic overlap between psychiatric traits and different aspects of substance use can be used to further investigate the pleiotropy present between these traits, and explore commonalities in etiology.