Justin M. Luningham

This study assesses COVID-19 booster intentions and hesitancy in Texas, a state known for its diversity and libertarian values. A survey was conducted with 274 participants residing in Texas between June and July 2022. The analysis examined sociodemographic and health-related factors, trusted information sources, and preventive behaviors. The survey focused on vaccinated participants and their intention to receive the booster dose, which was categorized into three outcomes: yes, no, and not sure. Machine learning techniques were employed to analyze the survey responses of vaccinated participants to identify the most critical factors. Among the participants, 113 expressed their intention to get the booster (41.2%), 107 did not plan to receive the booster (39.1%), and 54 remained undecided (19.7%). Our findings indicate that the perception of vaccine safety significantly influenced the decision to receive the booster dose. Those who reported trust in social media contacts as reliable information sources were more likely to intend to boost. Additionally, among those hospitalized when diagnosed with COVID-19, the largest proportion were unwilling to receive the booster (47.0%) compared to those who intended to receive the booster (33.3%). In contrast, most of those who believed they would be hospitalized if infected with COVID-19 intended to get the booster. Other factors did not demonstrate a significant association. Our findings are highly transferable and can offer valuable insights, particularly for countries where COVID-19 remains prevalent and are pivotal both presently and in the future for developing strategies to improve booster uptake and shape public health initiatives in epidemic and pandemic outbreaks.

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.

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.

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.

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.

Among women in the US, breast cancer is the most commonly diagnosed cancer and is the second-leading cause of cancer death.¹ Despite an emphasis on early detection and the availability of effective treatments, racial disparities in breast cancer mortality persist.^2-4 The cause of these inequities is complex. Some factors associated with disparities in breast cancer mortality include socioeconomic status (SES), geographic residency,² insurance coverage, and access to screening and care.

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.

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.

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.