COVID-19 clinical trial participation and awareness in Texas.

The COVID-19 pandemic required the rapid development of COVID-19 vaccines and treatments, necessitating quick yet representative clinical trial enrollment to evaluate these preventive measures. However, misinformation around the COVID-19 pandemic and general concerns about clinical trial participation in the U.S. hindered clinical trial enrollment. This study assessed awareness of, willingness to participate in, and enrollment in COVID-19 vaccine and treatment clinical trials in Texas. A quota sample of 1,089 Texas residents was collected online from June - July 2022. Respondents were asked if they were aware of, willing to participate in, and had enrolled in clinical trials for COVID-19 vaccines or treatments. Overall, 45.8% of respondents reported being aware of clinical trials for COVID-19 treatments or vaccines, but only 21.7% knew how to enroll and only 13.2% had enrolled in a COVID-19 clinical trial. Respondents with bachelor's or graduate degrees were more likely to be aware of clinical trials, more likely to have enrolled in trials, and more willing to participate in treatment trials. Women were less willing to participate and less likely to have enrolled in COVID-19 clinical trials than men. Respondents aged 55 years and older were more willing to participate, but less likely to have enrolled in COVID-19 clinical trials than 18-to-24-year-olds. Common reasons given for not participating in clinical trials included concerns that COVID-19 treatments may not be safe, government distrust, and uncertainty about what clinical trial participation would entail. Substantial progress is needed to build community awareness and increase enrollment in clinical trials.

The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations.

Understanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero-inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero-inflated or hurdle, and marginalized zero-inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step-by-step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution-based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results.

Do brief motivational interventions increase motivation for change in drinking among college students? A two-step meta-analysis of individual participant data.

BACKGROUND: Brief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two-step meta-analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI). METHODS: We included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually-delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random-effects meta-analysis (step 2). RESULTS: The overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [-0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation-targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [-0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [-0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta-regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = -0.050, z = -2.80, p = 0.005 for k = 14). CONCLUSIONS: Although BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation-targeted content components are provided.

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

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.

Demographic and Psychosocial Correlates of COVID-19 Vaccination Status among a Statewide Sample in Texas.

The COVID-19 pandemic has been a global public health concern since early 2020 and has required local and state-level responses in the United States. There were several Food and Drug Administration (FDA) approved vaccines available for the prevention of COVID-19 as of August 2022, yet not all states have achieved high vaccination coverage. Texas is a particularly unique state with a history of opposing vaccination mandates, as well as a large and ethnically/racially diverse population. This study explored the demographic and psychosocial correlates of COVID-19 vaccinations among a statewide sample in Texas. A quota sample of 1089 individuals was surveyed online from June-July 2022. The primary outcome in this study was COVID-19 vaccination status (fully vaccinated, partially vaccinated, or unvaccinated) and included independent variables related to demographics, COVID-19 infection/vaccine attitudes and beliefs, and challenges related to the COVID-19 pandemic. Hispanic/Latinx individuals were more likely than non-Hispanic White individuals to be partially vaccinated as opposed to unvaccinated. Higher education levels and confidence that the FDA would ensure a safe COVID-19 vaccine were strongly associated with a higher likelihood of being fully vaccinated. In addition, some challenges brought on by the pandemic and concerns about becoming infected or infecting others were associated with a higher likelihood of being partially or fully vaccinated. These findings emphasize the need to further investigate the interaction between individual and contextual factors in improving COVID-19 vaccination rates, especially among vulnerable and disadvantaged populations.

Delta-8 THC Retail Availability, Price, and Minimum Purchase Age.

Background: Retail sales of Delta-8 tetrahydrocannabinol (THC) products have increased in the U.S. market since the passing of the 2018 Farm Bill, and there is currently little regulation of marketing/sales and limited related safety standards in many states. After thousands of calls to poison control centers (40% for individuals under 18 years old and 70% requiring health care facility evaluation), the Food and Drug Administration issued warnings on Delta-8 THC products, stating their psychoactive effects and that some manufacturers may synthesize Delta-8 using unsafe household chemicals. The current study describes the Delta-8 THC retail sales environment in Fort Worth, Texas. Given its relatively inexpensive manufacturing and that low prices are a major determinant of cannabis use, the price of Delta-8 THC products was examined. This study also examined whether retail outlets in areas with greater socioeconomic deprivation had higher odds of selling Delta-8 THC products. This is important because if Delta-8 THC retailers are disproportionately located in more socioeconomically deprived communities, residents of these communities can more easily access these products and may have higher risk of adverse consequences. Methods: Potential Delta-8 THC retailers were selected by identifying lists of current retail locations with alcohol, cannabidiol, and/or tobacco licenses in Fort Worth. Trained research assistants called outlets in September and October 2021 to query about sales of products containing Delta-8 THC. The response rate was 69% (n=1,223). Outlets' 9-digit zip codes were merged with Area Deprivation Index scores. Products and purported minimum age were described. Chi-squared and Student's t-tests were used. Results: Eleven percent of outlets (n=133) reported selling Delta-8 THC. Ninety-six percent sold vapes and/or "flower" (i.e., hemp leaves coated with Delta-8 THC distillate) and 76% sold edibles. Among the least expensive products available, edibles cost, on average, $8.58 less than flower/vapes (p<0.001). Outlets that sold Delta-8 THC were located in areas with greater deprivation (p=0.02). Most reported a minimum purchase age of 21; however, 4% reported 18 years or no minimum age. Conclusions: Delta-8 THC retail outlets were disproportionately located in areas with more socioeconomic deprivation. Legal intervention such as zoning, minimum age, and tax laws may help reduce Delta-8 THC-related disparities.

Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia.

Importance: Increasing evidence suggests that low socioeconomic status and geographic residence in disadvantaged neighborhoods contribute to disparities in breast cancer outcomes. However, little epidemiological research has sought to better understand these disparities within the context of location. Objective: To examine the association between neighborhood deprivation and racial disparities in mortality among Black and White patients with breast cancer in the state of Georgia. Design, Setting, and Participants: This population-based cohort study collected demographic and geographic data from patients diagnosed with breast cancer between January 1, 2004, and February 11, 2020, in 3 large health care systems in Georgia. A total of 19 580 patients with breast cancer were included: 12 976 from Piedmont Healthcare, 2285 from Grady Health System, and 4319 from Emory Healthcare. Data were analyzed from October 2, 2020, to August 11, 2022. Exposures: Area deprivation index (ADI) scores were assigned to each patient based on their residential census block group. The ADI was categorized into quartile groups, and associations between ADI and race and ADI × race interaction were examined. Main Outcomes and Measures: Cox proportional hazards regression models were used to compute hazard ratios (HRs) and 95% CIs associating ADI with overall mortality by race. Kaplan-Meier curves were used to visualize mortality stratified across racial and ADI groups. Results: Of the 19 580 patients included in the analysis (mean [SD] age at diagnosis, 58.8 [13.2] years), 3777 (19.3%) died during the course of the study. Area deprivation index contributed differently to breast cancer outcomes for Black and White women. In multivariable-adjusted models, living in a neighborhood with a greater ADI (more deprivation) was associated with increased mortality for White patients with breast cancer; compared with the ADI quartile of less than 25 (least deprived), increased mortality HRs were found in quartiles of 25 to 49 (1.22 [95% CI, 1.07-1.39]), 50 to 74 (1.32 [95% CI, 1.13-1.53]), and 75 or greater (1.33 [95% CI, 1.07-1.65]). However, an increase in the ADI quartile group was not associated with changes in mortality for Black patients with breast cancer (quartile 25 to 49: HR, 0.81 [95% CI, 0.61-1.07]; quartile 50 to 74: HR, 0.91 [95% CI, 0.70-1.18]; and quartile ≥75: HR, 1.05 [95% CI, 0.70-1.36]). In neighborhoods with an ADI of 75 or greater, no racial disparity was observed in mortality (HR, 1.11 [95% CI, 0.92-1.36]). Conclusions and Relevance: Black women with breast cancer had higher mortality than White women in Georgia, but this disparity was not explained by ADI: among Black patients, low ADI was not associated with lower mortality. This lack of association warrants further investigation to inform community-level approaches that may mitigate the existing disparities in breast cancer outcomes in Georgia.

Development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention for adults with alcohol misuse who are experiencing homelessness.

BACKGROUND: Adults experiencing homelessness have much higher rates of alcohol misuse than housed individuals. This study describes the development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention (JITAI) to reduce alcohol use among adults experiencing homelessness. METHODS: We conducted a pilot trial (N = 41; mean age [SD] = 45.2 [11.5]; 19.5% women) of the Smart-T Alcohol JITAI where participants completed brief ecological momentary assessments (EMAs) each day, received personalized treatment messages following each EMA, and accessed on-demand intervention content for 4 weeks. The prediction algorithm and treatment messages were developed based on an independent but similar sample as part of the trial. We examined three drinking outcomes: daily drinking (yes/no), drinks per day, and heavy episodic drinking, controlling for scores on the Alcohol Use Disorders Identification Test (AUDIT) at baseline, age, and sex using quadratic growth curve models. RESULTS: Over the 4-week period, participants showed a decline in all alcohol use outcomes. Participants also reported high levels of satisfaction with the JITAI. CONCLUSIONS: Use of the Smart-T Alcohol JITAI was well received and provided encouraging evidence that it may reduce any drinking, drinks per day, and heavy episodic drinking among adults experiencing homelessness.

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

Background: 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, the impact of child adjustment problems on maternal mental health may be exacerbated in contexts of sectarian violence. Methods: 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, 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. Results: 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. Conclusion: This study advances understanding of the bidirectional impact between maternal mental health and adolescent internalizing over time in contexts of political violence.

Relations Among Father's Presence, Family Conflict, and Adolescent Adjustment in Northern Ireland.

Burgeoning evidence identifies the influence of fathers and, relatedly, fathers in the family context (e.g., family conflict), on adolescent adjustment. However, little is known about the significance of fathers' presence in contexts of environmental risk. In a unique social-political context of economic and sociopolitical adversity, this study examined relations between adolescent adjustment, fathers' presence, and family conflict in families in Belfast, Northern Ireland. Based on responses from 999 adolescents (M = 12.18 years; SD = 1.82) and their mothers, participating from 2006 to 2012, fathers' presence was linked with reduced internalizing symptoms, and family conflict was related to both internalizing and externalizing problems. The discussion considers the implications for understanding family dynamics related to adolescent adjustment in contexts of environmental adversity.

Bayesian Genome-wide TWAS Method to Leverage both cis- and trans-eQTL Information through Summary Statistics.

Transcriptome-wide association studies (TWASs) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans-expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian genome-wide TWAS (BGW-TWAS) method that leverages both cis- and trans-eQTL information for a TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis- and trans-eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWASs achieved higher power compared to existing TWAS methods that do not assess trans-eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N = ∼3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of ZC3H12B and Alzheimer dementia (AD) (p value = 5.42 × 10-13), neurofibrillary tangle density (p value = 1.89 × 10-6), and global measure of AD pathology (p value = 9.59 × 10-7). These associations for ZC3H12B were completely driven by trans-eQTL. Additionally, the GReX of KCTD12 was found to be significantly associated with ß-amyloid (p value = 3.44 × 10-8) which was driven by both cis- and trans-eQTL. Four of the top driven trans-eQTL of ZC3H12B are located within APOC1, a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N = ∼54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1, as well as ZC3H12B.

Testing structural models of psychopathology at the genomic level.

Genome-wide association studies (GWAS) have revealed hundreds of genetic loci associated with the vulnerability to major psychiatric disorders, and post-GWAS analyses have shown substantial genetic correlations among these disorders. This evidence supports the existence of a higher-order structure of psychopathology at both the genetic and phenotypic levels. Despite recent efforts by collaborative consortia such as the Hierarchical Taxonomy of Psychopathology (HiTOP), this structure remains unclear. In this study, we tested multiple alternative structural models of psychopathology at the genomic level, using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best-fitting model included four correlated higher-order factors - externalizing, internalizing, thought problems, and neurodevelopmental disorders - which showed distinct patterns of genetic correlations with external validity variables and accounted for substantial genetic variance in their constituent disorders. A bifactor model including a general factor of psychopathology as well as the four specific factors fit worse than the above model. Several model modifications were tested to explore the placement of some disorders - such as bipolar disorder, obsessive-compulsive disorder, and eating disorders - within the broader psychopathology structure. The best-fitting model indicated that eating disorders and obsessive-compulsive disorder, on the one hand, and bipolar disorder and schizophrenia, on the other, load together on the same thought problems factor. These findings provide support for several of the HiTOP higher-order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels.

Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium.

BACKGROUND: Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts. METHODS: Individual level aggression data on 86,559 children (42,468 twin pairs) were available in five European twin cohorts measured by different instruments. A phenotypic reference panel was collected which enabled a model-based phenotype harmonization approach. A bi-factor integration model in the integrative data analysis framework was developed to model aggression across studies while adjusting for rater, age, and sex. Finally, harmonized aggression scores were analyzed to estimate contributions of genes, environment, and social interaction to aggression. The large sample size allowed adequate power to test for sibling interaction effects, with unique dynamics permitted for opposite-sex twins. RESULTS: The best-fitting model found a high level of overall heritability of aggression (~60%). Different heritability rates of aggression across sex were marginally significant, with heritability estimates in boys of ~64% and ~58% in girls. Sibling interaction effects were only significant in the opposite-sex twin pairs: the interaction effect of males on their female co-twin differed from the effect of females on their male co-twin. An aggressive female had a positive effect on male co-twin aggression, whereas more aggression in males had a negative influence on a female co-twin. CONCLUSIONS: Opposite-sex twins displayed unique social dynamics of aggressive behaviors in a joint analysis of a large, multinational dataset. The integrative data analysis framework, applied in combination with a reference panel, has the potential to elucidate broad, generalizable results in the investigation of common psychological traits in children.

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.

Metataxonomic Analysis of Individuals at BMI Extremes and Monozygotic Twins Discordant for BMI.

OBJECTIVE: The human gut microbiota has been demonstrated to be associated with a number of host phenotypes, including obesity and a number of obesity-associated phenotypes. This study is aimed at further understanding and describing the relationship between the gut microbiota and obesity-associated measurements obtained from human participants. SUBJECTS/METHODS: Here, we utilize genetically informative study designs, including a four-corners design (extremes of genetic risk for BMI and of observed BMI; N = 50) and the BMI monozygotic (MZ) discordant twin pair design (N = 30), in order to help delineate the role of host genetics and the gut microbiota in the development of obesity. RESULTS: Our results highlight a negative association between BMI and alpha diversity of the gut microbiota. The low genetic risk/high BMI group of individuals had a lower gut microbiota alpha diversity when compared to the other three groups. Although the difference in alpha diversity between the lean and heavy groups of the BMI-discordant MZ twin design did not achieve significance, this difference was observed to be in the expected direction, with the heavier participants having a lower average alpha diversity. We have also identified nine OTUs observed to be associated with either a leaner or heavier phenotype, with enrichment for OTUs classified to the Ruminococcaceae and Oxalobacteraceae taxonomic families. CONCLUSION: Our study presents evidence of a relationship between BMI and alpha diversity of the gut microbiota. In addition to these findings, a number of OTUs were found to be significantly associated with host BMI. These findings may highlight separate subtypes of obesity, one driven by genetic factors, the other more heavily influenced by environmental factors.

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.