Membership Probabilities Research Articles

The Gaia-ESO Survey: Homogenisation of stellar parameters and elemental abundances

The Gaia-ESO Survey is a public spectroscopic survey that targeted ≳105 stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public release in May 2022. Unlike other spectroscopic surveys, Gaia-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O (Teff ~ 30 000–52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. In the final data release (internal data release 6) of the Gaia-ESO Survey, we provide the final database containing a large number of products, such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, for example, activity and accretion indicators in young stars and membership probability in star clusters for more than 114 000 stars. The spectral analysis is coordinated by a number of working groups (WGs) within the survey, each specialised in one or more of the various stellar samples. Common targets are analysed across WGs to allow for comparisons (and calibrations) amongst instrumental setups and spectral types. Here we describe the procedures employed to ensure all survey results are placed on a common scale in order to arrive at a single set of recommended results for use by all survey collaborators. We also present some general quality and consistency checks performed on the entirety of the survey results.

Who feels marginalized? A latent class analysis of perceived societal marginalization

Previous research has followed a binary perspective in analyzing the effects of modernization and globalization, often assumed to produce the societal camps of both “winners” and “losers” in economic, cultural, and political terms. However, our understanding of these individual differences and the social groups that perceive themselves as disadvantaged by society is still limited. Hence, in contrast to previous findings we argue that the phenomenon may be better grasped by following a “pattern approach.” Hence, in three different samples (total N = 2,566), we applied Latent Class Analysis to investigate subgroups representing individual differences in Perceived Societal Marginalization (PSM). Furthermore, we explored sociodemographic, personal-resource-related, attitudinal, and personality determinants of people’s class membership probabilities to better understand the subgroups—or: patterns—we identified in substantive terms. In the discussion, we focus on the implications of our findings regarding societal cleavages, prejudice toward minority groups, and the support of far-right political parties.

Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort.

To gain better understanding of osteoarthritis (OA) heterogeneity and its predictors for distinguishing OA phenotypes. This could provide the opportunity to tailor prevention and treatment strategies and thus improve care. Ten year follow-up data from CHECK (1002 early-OA subjects with first general practitioner visit for complaints ≤6 months before inclusion) was used. Data were collected on WOMAC (pain, function, stiffness), quantitative radiographic tibiofemoral (TF) OA characteristics, and semi-quantitative radiographic patellofemoral (PF) OA characteristics. Using functional data analysis, distinctive sets of trajectories were identified for WOMAC, TF and PF characteristics, based on model fit and clinical interpretation. The probabilities of knee membership to each trajectory were used in hierarchical cluster analyses to derive knee OA phenotypes. The number and composition of potential phenotypes was selected again based on model fit (silhouette score) and clinical interpretation. Five trajectories representing different constant levels or changing WOMAC scores were identified. For TF and PF OA, eight and six trajectories respectively were identified based on (changes in) joint space narrowing, osteophytes and sclerosis. Combining the probabilities of knees belonging to these different trajectories resulted in six clusters ('phenotypes') of knees with different degrees of functional (WOMAC) and radiographic (PF) parameters; TF parameters were found not to significantly contribute to clustering. Including baseline characteristics as well resulted in eight clusters of knees, dominated by sex, menopausal status and WOMAC scores, with only limited contribution of PF features. Several stable and progressive trajectories of OA symptoms and radiographic features were identified, resulting in phenotypes with relatively independent symptomatic and radiographic features. Sex and menopausal status may be especially important when phenotyping knee OA patients, while radiographic features contributed less. Possible phenotypes were identified that, after validation, could aid personalized treatments and patients selection.

Astrophysical Parameters of the Open Cluster NGC 2509

This study presents structural and fundamental astrophysical parameters of poorly studied open cluster NGC 2509. We used the third photometric and astrometric data release of the Gaia (Gaia DR3) to perform analyses. By taking into account the Gaia DR3 astrometric data, we calculated the membership probabilities of the stars in the region of NGC 2509. As a result of the membership analysis, 244 stars with membership probabilities P  50% were determined as the physical members of the cluster. The colour excess, distance and age were obtained simultaneously by fitting solar metallicity PARSEC isochrones to G×GBP-GRP colour-magnitude diagram. We considered the most likely cluster member stars during the fitting procedure and calculated the colour excess, distance and age of the NGC 2509 as E(GBP-GRP) = 0.100±0.184 mag, d = 2518±667 pc and t = 1.5±0.1 Gyr, respectively.

Differentiation of vaginal cells from epidermal cells using morphological and autofluorescence properties: Implications for sexual assault casework involving digital penetration

Analysis of DNA mixtures from sexual assault evidence is an ongoing challenge for DNA casework laboratories. To assist the forensic scientist address source and activity level propositions there is a significant need for new techniques that can provide information as to the source of DNA, particularly for sexual assault samples that do not involve semen. The goal of this study was to develop a new biological signature system that provides additional probative value to samples comprised of mixtures of epidermal and vaginal cells, as may be observed in cases involving digital penetration. Signatures were based on morphological and autofluorescence properties of individual cells collected through Imaging Flow Cytometry (IFC). Comparisons to reference cell populations from vaginal tissue and epidermal cells collected from hands showed strong multivariate differences across > 80 cellular measurements. These differences were used to build a predictive framework for classifying unknown cell populations as originating from epithelial cells associated with digital penetration or epidermal tissue. As part of the classification scheme, posterior probabilities of specific tissue group membership were calculated for each cell, along with multivariate similarity to that tissue type. We tested this approach on cell populations from reference tissue as well as mock casework samples involving hand swabbings following digital vaginal penetration. Many more cells classifying as non-epidermal tissue were detected in digital penetration hand swab samples than control hand swabbings. Minimum interpretation thresholds were developed to minimize false positives; these thresholds were also effective when screening licked hands, indicating the potential utility of this method for a variety of biological mixture types and depositional events relevant to forensic casework. Results showed that samples collected subsequent to digital penetration possessed markedly higher numbers of cells classifying as vaginal tissue as well as higher posterior probabilities for vaginal tissue (≥ 0.90) compared to cell populations collected from hands without prior contact with vaginal tissue. Additionally, digital penetration cell populations may be resolved from saliva cell populations and other non-target tissue types.

Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients

Background and objectiveAdherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplished using group-based trajectory models (GBTM) of monthly PDC. The objective is to assess nintedanib adherence trajectories using GBTM and identify characteristics of patients within each trajectory group.MethodsIndividuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 10/1/2014–12/31/2018 were identified in 100% Medicare claims and enrollment data. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) initiating nintedanib. A series of GBTMs of adherence was estimated to identify the best-fitting specification. Patients were then grouped based on their estimated adherence trajectories. Associations between baseline patient characteristics, including demographics, comorbidities, and health care use, and group membership probabilities were quantified as odds ratios using fractional multinomial logit modeling.ResultsAmong the 1,798 patients initiating nintedanib, mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectory groups: high adherence (43.1%), moderate adherence (11.9%), high-then-poor adherence (10.4%), delayed-poor adherence (13.2%), and early-poor adherence (21.5%). The principal factors associated with higher odds of being in at least one of the poor-adherence groups were older age, female sex, race and ethnicity other than non-Hispanic white, and number of medications during baseline.ConclusionsGBTM identified distinct patterns of nintedanib adherence for the IPF patient cohort. Identifying adherence trajectory groups and understanding the characteristics of their members provide more actionable information to personalize interventions than conventional metrics of medication adherence.

Astrophysical Parameters of the Open Cluster Berkeley 6

In this study, the structural and basic astrophysical parameters of poorly studied open cluster Berkeley 6 is calculated. Analyses of the cluster are carried out using the third photometric, spectroscopic and astrometric data release of Gaia (Gaia DR3). The membership probabilities of stars located through the cluster region are calculated by considering their astrometric data. Thus, we identified 119 physical members for Berkeley 6. The colour excess, distance and age of the cluster are determined simultaneously on the colour-magnitude diagram. We fitted solar metallicity PARSEC isochrones to the colour-magnitude diagram by considering the most probable member stars and obtained E(GBP-GRP) colour excess as 0.918±0.145 mag. Distance and age of the cluster are determined as d=2625±337 pc and t=350±50 Myr, respectively.

Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision

The current literature includes limited information on the classification precision of Bayes estimation for latent class analysis (BLCA). (1) Objectives: The present study compared BLCA with the robust maximum likelihood (MLR) procedure, which is the default procedure with the Mplus 8.0 software. (2) Method: Markov chain Monte Carlo simulations were used to estimate two-, three-, and four-class models measured by four binary observed indicators with samples of 1000, 750, 500, 250, 100, and 75 observations, respectively. With each sample, the number of replications was 500, and entropy and average latent class probabilities for most likely latent class membership were recorded. (3) Results: Bayes entropy values were more stable and ranged between 0.644 and 1. Bayes’ average latent class probabilities ranged between 0.528 and 1. MLR entropy values ranged between 0.552 and 0.958. and MLR average latent class probabilities ranged between 0.539 and 0.993. With the two-class model, BLCA outperformed MLR with all sample sizes. With the three-class model, BLCA had higher classification precision with the 75-sample size, whereas MLR performed slightly better with the 750- and 1000-sample sizes. With the 4-class model, BLCA underperformed MLR and had an increased number of unsuccessful computations, particularly with smaller samples.

Longitudinal latent polysubstance use patterns among a cohort of people who use opioids in Vancouver, Canada.

Polysubstance use (PSU) practices are increasing among people who use opioids (PWUO). However, several aspects of longitudinal PSU patterns among PWUO remain understudied. This study aims to identify person-centred longitudinal patterns of PSU among a cohort of PWUO. Using longitudinal data (2005-2018) from three prospective cohort studies including people who use drugs in Vancouver, Canada, we used repeated measures latent class analysis to identify different PSU classes among PWUO. Multivariable generalised estimating equations models weighted by the respective posterior membership probabilities were applied to identify covariates of membership in different PSU classes over time. Overall, 2627 PWUO (median age at baseline: 36 [quartile 1-3: 25-45]) were included between 2005 and 2018. We found five distinct PSU patterns, including low/infrequent probability of regular substance use (Class 1; 30%), primarily opioid and methamphetamine use (Class 2; 22%), primarily cannabis use (Class 3; 15%), primarily opioid and crack use (Class 4; 29%) and frequent PSU (Class 5; 4%). Membership in Class 2, 4 and 5 was positively associated with several behavioural and socio-structural adversities. Findings of this longitudinal study suggest PSU is the norm among PWUO and highlights the heterogeneous characteristics of PWUO. The diversities within the population of PWUO need to be recognised in addiction care and treatment as well as optimising resource allocation in the response to the overdose crisis.

Mapping GPs' motivation — it's not all about the money: a nationwide cross-sectional survey study from Denmark.

Understanding physicians' motivation may be essential for policymakers if they are to design policies that cater to physicians' wellbeing, job retention, and quality of care. However, physicians' motivation remains an understudied area. To map GPs' work motivation. A cross-sectional analysis using registry and survey data from Denmark. Survey data were used to measure four types of motivation: extrinsic motivation, intrinsic motivation, user orientation, and public service motivation. These were combined with register data on the characteristics of the GP, practice, and area. Using latent profile analysis, the heterogeneity in GPs' motivation was explored; the associations between GPs' motivation and the GP, practice, and area characteristics were estimated using linear regression analyses. There was substantial heterogeneity in GPs' motivations. Five classes of GPs were identified with different work motivations: class 1 'it is less about the money' - probability of class membership 53.2%; class 2 'it is about everything' - 26.5%; class 3 'it is about helping others' - 8.6%; class 4 'it is about the work' - 8.2%; and class 5 'it is about the money and the patient' - 3.5%. Linear regression analyses showed that motivation was associated with GP, practice, and area characteristics to a limited extent only. GPs differ in their work motivations. The finding that, for many GPs, 'it is not all about the money' indicated that their different motivations should be considered when designing new policies and organisational structures to retain the workforce and ensure a high quality of care.

Differential Unmet Needs and Experience of Restorative Dental Care in Trajectories of Dental Caries Experience: A Birth Cohort Study

Dental caries is a chronic and cumulative disease but little has been reported on the continuity of the disease and its treatment through life. Group-based multi-trajectory modeling was used to identify developmental trajectories of untreated carious tooth surfaces (DS), restored tooth surfaces (FS), and teeth extracted due to caries (MT) from ages 9 to 45 years in a New Zealand longitudinal birth cohort, the Dunedin Multidisciplinary Health and Development Study (n = 975). Associations between early-life risk factors and trajectory group membership were examined by specifying the probability of group membership according to a multinomial logit model. Six trajectory groups were identified and labeled: “low caries rate”; “moderate caries rate, maintained”; “moderate caries rate, unmaintained”; “high caries rate, restored”; “high caries rate, tooth loss”; and “high caries rate, untreated caries”. The two moderate-caries-rate groups differed in count of FS. The three high-caries-rate groups differed in the relative proportion of accumulated DS, FS, and MT. Early childhood risk factors associated with less favorable trajectories included higher dmfs scores at age 5, lack of exposure to community water fluoridation during the first 5 years of life, lower childhood IQ, and low childhood socioeconomic status. Parent self-ratings of their own or their child’s oral health as “poor” were associated with less favorable caries experience trajectories. Children who had clinical signs of dental caries together with a parent rating of child’s oral health as poor were more likely to follow a less favorable caries trajectory. Higher deciduous dentition caries experience at age 5 years was associated with less favorable caries trajectories, as were children whose parents gave “poor” ratings of their own or their child’s oral health. These findings highlight the considerable intergenerational continuity in dental caries experience from early childhood to midlife. Subjective measures of child oral health are informative and might aid as predictors of adult caries experience in cases where childhood dental clinical data were not available.

Identifying and describing trajectories of alcohol use frequency and binge drinking frequency among those age 15-30 in a national cohort of U.S. adolescents: a group-based trajectory modeling approach.

Analyzing long-term trajectories of alcohol use has the potential to strengthen policy and intervention priorities and timing. We identified and described trajectories of alcohol use and binge drinking frequency from mid-adolescence to early adulthood and measured the association of the role of early drinking initiation with trajectory membership. Longitudinal cohort study SETTING: United States PARTICIPANTS: The National Longitudinal Survey of Youth 1997 is a nationally representative cohort of youth age 12-16 at baseline. The analytic sample included individuals who participated in >2 annual interviews between age 15 and 30 (n=8,809). Participants self-reported the number of days in the past 30 days they: (1) drank alcohol and (2) binge drank [>5 drinks in one occasion]. We used group-based trajectory modeling to identify distinct trajectories from age 15-30 of past 30-day drinking and past 30-day binge drinking. Using multinomial logistic regression, we evaluated associations between early drinking initiation (≤14 years) and key demographics with trajectory membership. We identified 5 past 30-day drinking groups: late-escalating (16.0%), moderate-frequency (19.0%), high-frequency (11.1%), low-frequency (35.4%) and no/infrequent (18.4%). Early drinking initiation (versus later) was associated with higher odds of membership in the moderate (adjusted multinomial odds ratio [aMOR] = 4.32; 95% confidence interval [CI]: 3.65, 5.12) and high-frequency groups (aMOR = 4.17; 95%CI: 3.50, 4.97) than in the no/infrequent comparator trajectory. We identified 5 groups with distinct binge drinking frequency patterns: later escalating (9.9%), high-frequency (3.9%), low frequency (28.7%), earlier onset (9.5%), and no/infrequent (48.0%). Early initiation was associated with increased odds of membership in earlier onset and high-frequency groups compared with the no/infrequent group. For both outcomes additional differences in probability of group membership were identified by gender, racial identity, parental factors (religiosity, high school completion), and household characteristics (household size, income, and region of residence). Youth in the United States appear to follow heterogeneous drinking and binge drinking trajectories from adolescence into adulthood. These may include higher-use trajectories as well as trajectories with different escalation timing (e.g., earlier vs. later). Early initiation of drinking may increase risk of membership in higher- and earlier-use trajectory groups.

Food values and personality traits in the United States and Norway

Values and personality affect food choices, and values are associated with personality. Food values are values that have been developed specifically within a food context. Our primary objective was to investigate the associations between these food values and the Big Five personality traits. An online survey with a choice experiment on food values and questions about socioeconomic characteristics and personality traits were conducted in Norway and the US. A latent class logit model was used to segment respondents based on the relative importance they assigned to the different food values. Personality traits and socioeconomic characteristics were used to determine the probability of membership in each segment. A health, an altruistic, a rational, and a hedonistic segment were identified in both countries. In addition, Norwegian natural and welfare segments and US safety and indeterminate segments were identified. Several personality traits and socioeconomic characteristics were significant in defining the segments, and these traits and characteristics can potentially be used for marketing purposes of foods with sustainability-related attributes. Marketing activities can be targeted at segments who emphasize sustainability-related aspects of food consumption such as environmental impact, fairness, and animal welfare.

Identifying cluster members of NGC 869 and NGC 884 using a combined machine learning method

This work describes a method for calculating cluster membership probability from big, high dimensional newly released GAIA-EDR3 observations that combine the Gaussian Mixture Model (GMM) with the Random Forest (RF). This technique has the considerable advantage of rapidly identifying possible cluster members in big data sets using modest training samples. The result indicates that the double clusters NGC 869 has 20270 points ultra-high (p≥ 0.8) probable members, 9294 are high (p≥ 0.6) probable members, and 6181 stars are moderate (0.2 <= p < 0.6) members. NGC 884 has ultra-high (p≥ 0.8) probable members of 21317, high (p≥ 0.6) probable members with 10871 and moderate (0.2 <= p < 0.6) of 5998. Cluster centers of NGC 884 and NGC 869 were also determined to be at α = 2h 22m 17s, δ = 57h 8m 60s and α = 2h 19m 2s, δ = 57h 7m 48s respectively. The double cluster also has a mean proper motion of -0.690 ± 12 mas/yr, or -1.150 ± 16 mas/yr, and a mean parallax of 0.410 ± 04 mas. The old cluster member in NGC 884 was quantified to be ± 621, and the new cluster member to be ± 1129. The new members of NGC 869 are ± 1511, up from the previous 920 members. We also found that the combined GMM and RF are effective in identifying cluster membership using the GAIA-EDR3 observations.

CCD UBV and Gaia DR3 based analysis of NGC 189, NGC 1758 and NGC 7762 open clusters

This paper presents photometric, astrometric, and kinematic analyses of the open clusters NGC 189, NGC 1758 and NGC 7762 based on CCD UBV photometric and Gaia Data Release 3 (DR3) data. According to membership analyses, we identified 32, 57 and 106 most probable member stars with membership probabilities P⩾0.5 in NGC 189, NGC 1758 and NGC 7762, respectively. The color excesses and photometric metallicities of each cluster were determined separately using UBV two-color diagrams. The color excess E(B-V) is 0.590±0.023 mag for NGC 189, 0.310±0.022 mag for NGC 1758 and 0.640±0.017 mag for NGC 7762. The photometric metallicity [Fe/H] is -0.08±0.03 dex for both NGC 189 and NGC 1758, and -0.12±0.02 dex for NGC 7762. Distance moduli and ages of the clusters were obtained by comparing PARSEC isochrones with the color-magnitude diagrams constructed from UBV and Gaia photometric data. During this process, we kept as constant color excess and metallicity for each cluster. The estimated isochrone distance is 1201±53 pc for NGC 189, 902±33 pc for NGC 1758 and 911±31 pc for NGC 7762. These are compatible with the values obtained from trigonometric parallax. Ages of the clusters are 500±50 Myr, 650±50 Myr and 2000±200 Myr for NGC 189, NGC 1758 and NGC 7762, respectively. Galactic orbit integration of the clusters showed that NGC 1758 completely orbits outside the solar circle, while NGC 189 and NGC 7762 enter the solar circle during their orbits.

중학생 비행 유형에 대한 초등학교 6학년 시점 예측요인 탐색

Objectives The purpose of this study is to classify middle school students accoring to the change of their delinquency measures and to find predictors of delinquency among variables in their 6th grade.&#x0D; Methods Using Gyeonggi Education Panel Study(GEPS) data for the elementary students panel from 3th year(6th grade in elementary school) to 6th year(3rd grade in middle school), latent growth mixture modeling was applied to classfy middle school students' pattern of delinquency changes, and multinomial logistic regression was conducted to find predictor variables.&#x0D; Results With latent growth mixture analyses, three latent classes were identified: ‘decreasing from high level’, ‘increasing from low level’, and ‘low-level’. The probability of class membership was significantly predicted by gender, physical self-concept, academic achievement, parental attachment, parental monitoring, and peer relationship.&#x0D; Conclusions It was suggested to develop a policy-based support system to affect variables in elementary school that predicts students' delinquency in middle school and that teachers in elementary schools can detect easily. Specifically, an early intervention for students with low academic achievement was suggested as an effective approach to prevent delinquency.

The new detection of blue straggler stars in 50 open clusters using Gaia DR3

Context. The particularly abundant presence of blue straggler stars (BSS) in Galactic open clusters offers favorable conditions for detailed studies on the statistical properties and the origin of the blue straggler population. With the help of Gaia DR3, the number of identified open clusters continuously increases, and the determination of star cluster members is more reliable. Aims. We performed a more thorough search for BSS in newly found open clusters based on Gaia data. Methods. We implemented a uniform membership determination for over one thousand newly identified open clusters with larger sky coverage based on the astrometric and photometric data from Gaia DR3. The membership probabilities of stars were assigned by the pyUPMASK algorithm. Then we estimated the physical parameters of these clusters by isochrone fitting on their CMDs and picked out BSS in the specific region of these CMDs. Results. We identified 138 BSS that had not been reported before in 50 open clusters. Conclusions. Compared with recent catalogs that present more than 1500 BSS in 339 open clusters, our new catalog increased the number of BSS in Galactic open clusters by about 10%, and the number of open clusters with BSS by nearly 17%. In the future, more accurate abundance measurements are anticipated to better probe the origin of BSS in open clusters.

The association of gestational age at birth with trajectories of early childhood developmental delay among late preterm and early term born children: A longitudinal analysis of All Our Families pregnancy cohort.

Like infants born very preterm (<32 weeks), late preterm (≥34 and <37 weeks) and early term (≥37 and <39 weeks) births have been associated with increased risk of developmental delay (DD); yet, the evidence remains heterogeneous across the continuum of gestational ages, hindering early identification and intervention. To estimate the association of gestational age at birth with early childhood trajectories of DD in early childhood for infants born ≥34 and <41 weeks, and determine how various maternal, pregnancy and infant characteristics relate to these trajectory groups. Analysis of mother-child dyad data with infants born ≥34 and <41 weeks gestational age within an observational pregnancy cohort in Alberta, Canada, from 2008 to 2011 (n=2644). The association between gestational age and trajectories of the total number of Ages and Stages Questionnaire domains indicating risk of DD from 1 through 5 years of age were estimated using group-based trajectory modelling along with other perinatal risk factors. Three distinct trajectory groups were identified: low-risk, moderate-risk (transiently at risk of DD in one domain over time) and high-risk (consistently at risk of delay in ≥2 domains over time). Per week of decreasing gestational age, the risk ratio of membership in the high-risk group increases by 1.77 (95% confidence interval [CI] 1.43, 2.20) or 1.84 (95% CI 1.49, 2.27) relative to the moderate-risk and low-risk respectively. Increasing maternal age, identifying as Black, indigenous or a person of colour, elevated maternal depressive symptoms in pregnancy, and male infant sex were associated with high- and moderate-risk trajectories compared to the low-risk trajectory. In combination with decreasing gestational age, poor maternal mental health and social determinants of health increase the probability of membership in trajectories with increased risk of DD, suggesting that additional monitoring of children born late preterm and early term is warranted.

Sexual Identity Disclosure and Alcohol Experiences Among LGBTQ+ Adolescents

Sexual and gender minority youth (SGMY) report greater alcohol use in comparison to their heterosexual counterparts. Prior research has found that elevated alcohol use among SGMY can be explained by minority stress experiences. Sexual identity outness may be another factor that drives alcohol use among SGMY, given that outness is associated with alcohol use among older sexual and gender minority samples. We examined how patterns of sexual identity outness were associated with lifetime alcohol use, past-30-day alcohol use, and past-30-day heavy episodic drinking. Data were drawn from the LGBTQ National Teen Survey (N = 8884). Participants were SGMY aged 13 to 17 (mean age = 15.59) years living in the US. Latent class analysis was used to identify sexual identity outness patterns. Multinomial regressions were used to examine the probability of class membership by alcohol use. Six outness classes were identified: out to all but teachers (n = 1033), out to siblings and peers (n = 1808), out to siblings and LGBTQ+ peers (n = 1707), out to LGBTQ+ peers (n = 1376), mostly not out (n = 1653), and very much not out (n = 1307). SGMY in classes characterized by greater outness to peers, friends, and family had greater odds of lifetime alcohol use compared with SGMY in classes characterized by lower outness. These findings suggest that SGMY with greater sexual identity outness may be a target for alcohol use prevention programming. Differences in sexual identity outness may be explained by minority stress factors.

Patterns of parent screen use, child screen time, and child socio-emotional problems at 5 years.

Digital media screens have become an essential part of our family life. However, we have insufficient knowledge about parental screen use patterns and how these affect children's socio-emotional development. In total, 867 Canadian parents of 5-year-old children from the TARGet Kids! Cohort (73.1% mothers, mean ± SD age=38.88 ± 4.45 years) participated in this study from 2014 to the end of 2019. Parents reported parental and child time on television (TV) and handheld devices and completed the Strengths and Difficulties Questionnaire (SDQ). Latent profile analysis identified six latent profiles of parent screen use: low handheld users (P1, reference; n=323), more TV than handheld (P2; n=261), equal TV and handheld (P3; n=177), more handheld than TV (P4; n=57), high TV and handheld (P5; n=38), and extremely high TV and handheld (P6; n=11). Parents that were more likely to belong to P6 were also more likely to be living in single-parent households compared to P1 (estimate=-1.49 ± 0.70), p=.03). High membership probability for P2 (estimate=-0.67 ± 0.32, p=.04) and P4 (estimate=-1.42 ± 0.40, p < 0.001) was associated with lower household income compared to P1. Children of parents with higher P4 (χ2 =12.32, p < 0.001) or P5 (χ2 =9.54, p=.002) membership probability had higher total screen time compared to P1. Finally, a higher likelihood to belong to P6 (χ2 =6.82, p=.009) was associated with a higher SDQ Total Difficulties Score compared to P1. Thus, patterns of parent screen use were associated with child screen use and child socio-emotional problems. The emerging link between parental screen use profiles and child behaviors suggests the need for more research on parent screen time.