Childhood aggression and the co-occurrence of behavioural and emotional problems

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 psychop

Testing models for the underlying structure of psychopathology using genetic correlations

In this project, we will test alternative structural models of psychopathology using genetic correlations among relevant disorders and traits derived from GWAS summary statistics

Bidirectional Maternal Mental Health and Adolescent Internalizing

Emerging evidence indicates the existence of bidirectional relations between mothers’ mental health and adolescent adjustment, but few studies have examined these relations in contexts of high environmental adversity, including economic deprivation and political violence. Given other empirical connections between political violence and adolescent adjustment problems (Cummings et al., 2017), the impact of child adjustment problems on maternal mental health may be exacerbated in contexts of sectarian violence. Addressing this gap, latent change score modeling was used to examine interrelations between trajectories of maternal mental health and adolescent internalizing symptoms over time in communities afflicted by political conflict. Over six years, a total of 999 adolescent-mother dyads participated in a longitudinal study in Belfast, Northern Ireland. Six-hundred ninety-five families were originally recruited in year 1, with 304 recruited to supplement the sample in year 3; the largest available sample for a given year was 760 families. Models including maternal mental health, adolescent internalizing symptomatology, and political violence (i.e., sectarian antisocial behavior) as a time-varying covariate were tested. Results demonstrated that for both mothers and adolescents in a dyadic pairing, higher rates of symptomology in one member of the dyad were related to symptoms observed in the other member. Results also suggest that political violence and factors related to social deprivation increased symptoms across the dyad. This study advances understanding of the bidirectional impact between maternal mental health and adolescent internalizing over time in contexts of political violence.National Institute of Child Health and Human DevelopmentOffice of First Minister & Deputy First Minister, Government of Northern Irelan

Sum Scores in Twin Growth Curve Models:Practicality Versus Bias

To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g. a sum score) is a simple and practical approach for modeling such data, but this simplicity can come at a cost in longitudinal studies, where the relevance of individual items often changes as a function of age. Such changes violate the assumptions of longitudinal measurement invariance (MI), and this violation has the potential to obfuscate the interpretation of the results of latent growth models fit to sum scores. The objectives of this study are (1) to investigate the extent to which violations of longitudinal MI lead to bias in parameter estimates of the average growth curve trajectory, and (2) whether absence of MI affects estimates of the heritability of these growth curve parameters. To this end, we analytically derive the bias in the estimated means and variances of the latent growth factors fit to sum scores when the assumption of longitudinal MI is violated. This bias is further quantified via Monte Carlo simulation, and is illustrated in an empirical analysis of aggression in children aged 3-12 years. These analyses show that measurement non-invariance across age can indeed bias growth curve mean and variance estimates, and our quantification of this bias permits researchers to weigh the costs of using a simple sum score in longitudinal studies. Simulation results indicate that the genetic variance decomposition of growth factors is, however, not biased due to measurement non-invariance across age, provided the phenotype is measurement invariant across birth-order and zygosity in twins

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update

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 indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (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 2 empirical examples. This study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multigroup 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

Demographic disparities in the limited awareness of alcohol use as a breast cancer risk factor: empirical findings from a cross-sectional study of U.S. women

Abstract 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

Associations between health insurance status, neighborhood deprivation, and treatment delays in women with breast cancer living in Georgia

Abstract Background Little is known regarding the association between insurance status and treatment delays in women with breast cancer and whether this association varies by neighborhood socioeconomic deprivation status. Methods In this cohort study, we used medical record data of women diagnosed with breast cancer between 2004 and 2022 at two Georgia‐based healthcare systems. Treatment delay was defined as >90 days to surgery or >120 days to systemic treatment. Insurance coverage was categorized as private, Medicaid, Medicare, other public, or uninsured. Area deprivation index (ADI) was used as a proxy for neighborhood‐level socioeconomic status. Associations between delayed treatment and insurance status were analyzed using logistic regression, with an interaction term assessing effect modification by ADI. Results Of the 14,195 women with breast cancer, 54% were non‐Hispanic Black and 52% were privately insured. Compared with privately insured patients, those who were uninsured, Medicaid enrollees, and Medicare enrollees had 79%, 75%, and 27% higher odds of delayed treatment, respectively (odds ratio [OR]: 1.79, 95% confidence interval [CI]: 1.32–2.43; OR: 1.75, 95% CI: 1.43–2.13; OR: 1.27, 95% CI: 1.06–1.51). Among patients living in low–deprivation areas, those who were uninsured, Medicaid enrollees, and Medicare enrollees had 100%, 84%, and 26% higher odds of delayed treatment than privately insured patients (OR: 2.00, 95% CI: 1.44–2.78; OR: 1.84, 95% CI: 1.48–2.30; OR: 1.26, 95% CI: 1.05–1.53). No differences in the odds of delayed treatment by insurance status were observed in patients living in high‐deprivation areas. Discussion/Conclusion Insurance status was associated with treatment delays for women living in low‐deprivation neighborhoods. However, for women living in neighborhoods with high deprivation, treatment delays were observed regardless of insurance status

Bidirectional effects between maternal mental health and adolescent internalizing problems across six years in Northern Ireland.

BackgroundEmerging evidence indicates the existence of bidirectional relations between mothers' mental health and adolescent adjustment, but few studies have examined these relations in contexts of high environmental adversity, including economic deprivation and political violence. Given other empirical connections between political violence and adolescent adjustment problems, the impact of child adjustment problems on maternal mental health may be exacerbated in contexts of sectarian violence.MethodsAddressing this gap, latent change score modeling was used to examine interrelations between trajectories of maternal mental health and adolescent internalizing symptoms over time in communities afflicted by political conflict. Over six years, 999 adolescent-mother dyads participated in a longitudinal study in Belfast, Northern Ireland. Six-hundred ninety-five families were originally recruited in year 1, with 304 recruited to supplement the sample in year 3; the largest available sample for a given year was 760 dyads. Models including maternal mental health, adolescent internalizing symptomatology, and political violence (i.e., sectarian antisocial behavior) as a time-varying covariate were tested.ResultsResults demonstrated that for both mothers and adolescents in a dyadic pairing, higher rates of symptomology in one member of the dyad were related to symptoms observed in the other member. Results also suggest that political violence and factors related to social deprivation increased symptoms across the dyad.ConclusionThis study advances understanding of the bidirectional impact between maternal mental health and adolescent internalizing over time in contexts of political violence

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia