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Background: Alcohol use is an established yet modifiable risk factor for breast cancer. However, recent research indicates that the vast majority of U.S. women are unaware that alcohol use is a risk factor for breast cancer. There is limited information about the sociodemographic characteristics and alcohol use correlates of awareness of the alcohol use and breast cancer link, and this is critically important for health promotion and intervention efforts. In this study, we assessed prevalence of the awareness of alcohol use as a risk factor for breast cancer among U.S. women and examined sociodemographic and alcohol use correlates of awareness of this link., Methods: We conducted a 20-minute online cross-sectional survey, called the ABLE (Alcohol and Breast Cancer Link Awareness) survey, among U.S. women aged 18 years and older (N = 5,027) in the fall of 2021. Survey questions assessed awareness that alcohol use increases breast cancer risk (yes, no, don't know/unsure); past-year alcohol use and harmful drinking via the Alcohol Use Disorders Identification Test (AUDIT); and family, health, and sociodemographic characteristics. We conducted multivariate multinomial regression analysis to identify correlates of awareness that alcohol use increases breast cancer risk., Results: Overall, 24.4% reported that alcohol use increased breast cancer risk, 40.2% reported they were unsure, and 35.4% reported that there was no link between alcohol use and breast cancer. In adjusted analysis, awareness of alcohol use as a breast cancer risk factor, compared to not being aware or unsure, was associated with being younger (18-25 years old), having a college degree, and having alcohol use disorder symptoms. Black women were less likely than white women to report awareness of the alcohol use and breast cancer link., Conclusions: Overall, only a quarter of U.S. women were aware that alcohol use increases breast cancer risk, although 40% expressed uncertainty. Differences in awareness by age, level of education, race and ethnicity and level of alcohol use offer opportunities for tailored prevention interventions, while the overall low level of awareness calls for widespread efforts to increase awareness of the breast cancer risk from alcohol use among U.S. women., (© 2024. The Author(s).)

Childhood aggression and its resulting consequences inflict a huge burden on affected children, their relatives, teachers, peers and society as a whole. Aggression during childhood rarely occurs in isolation and is correlated with other symptoms of childhood psychopathology. In this paper, we aim to describe and improve the understanding of the co-occurrence of aggression with other forms of childhood psychopathology. We focus on the co-occurrence of aggression and other childhood behavioural and emotional problems, including other externalising problems, attention problems and anxiety-depression. The data were brought together within the EU-ACTION (Aggression in Children: unravelling gene-environment interplay to inform Treatment and InterventiON strategies) project. We analysed the co-occurrence of aggression and other childhood behavioural and emotional problems as a function of the child's age (ages 3 through 16 years), gender, the person rating the behaviour (father, mother or self) and assessment instrument. The data came from six large population-based European cohort studies from the Netherlands (2x), the UK, Finland and Sweden (2x). Multiple assessment instruments, including the Child Behaviour Checklist (CBCL), the Strengths and Difficulties Questionnaire (SDQ) and Multidimensional Peer Nomination Inventory (MPNI), were used. There was a good representation of boys and girls in each age category, with data for 30,523 3- to 4-year-olds (49.5% boys), 20,958 5- to 6-year-olds (49.6% boys), 18,291 7- to 8-year-olds (49.0% boys), 27,218 9- to 10-year-olds (49.4% boys), 18,543 12- to 13-year-olds (48.9% boys) and 10,088 15- to 16-year-olds (46.6% boys). We replicated the well-established gender differences in average aggression scores at most ages for parental ratings. The gender differences decreased with age and were not present for self-reports. Aggression co-occurred with the majority of other behavioural and social problems, from both externalising and internalising domains. At each age, the co-occurrence was particularly prevalent for aggression and oppositional and ADHD-related problems, with correlations of around 0.5 in general. Aggression also showed substantial associations with anxiety-depression and other internalizing symptoms (correlations around 0.4). Co-occurrence for self-reported problems was somewhat higher than for parental reports, but we found neither rater differences, nor differences across assessment instruments in co-occurrence patterns. There were large similarities in co-occurrence patterns across the different European countries. Finally, co-occurrence was generally stable across age and sex, and if any change was observed, it indicated stronger correlations when children grew older. We present an online tool to visualise these associations as a function of rater, gender, instrument and cohort. In addition, we present a description of the full EU-ACTION projects, its first results and the future perspectives.

Latent variable mixture models (LVMMs) are models for multivariate observed data from a potentially heterogeneous population. The responses on the observed variables are thought to be driven by one or more latent continuous factors (e.g. severity of a disorder) and/or latent categorical variables (e.g., subtypes of a disorder). Decomposing the observed covariances in the data into the effects of categorical group membership and the effects of continuous trait differences is not trivial, and requires the consideration of a number of different aspects of LVMMs. The first part of this paper provides the theoretical background of LVMMs and emphasizes their exploratory character, outlines the general framework together with assumptions and necessary constraints, highlights the difference between models with and without covariates, and discusses the interrelation between the number of classes and the complexity of the within-class model as well as the relevance of measurement invariance. The second part provides a growth mixture modeling example with simulated data and covers several practical issues when fitting LVMMs., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population. A previous study illustrated a bootstrap approach to gauge this model selection uncertainty using two empirical examples. The current study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multi-group and mixture model comparisons. These simulations show that bootstrap selection rates can provide additional information over and above simply relying on the size of AIC and BIC differences in a given sample.