In Michigan, the COVID-19 pandemic severely impacted Black and Latinx communities. These communities experienced higher rates of exposure, hospitalizations, and deaths compared to Whites. We examine the impact of the pandemic and reasons for the higher burden on communities of color from the perspectives of Black and Latinx community members across four Michigan counties and discuss recommendations to better prepare for future public health emergencies.

Using a community-based participatory research approach, we conducted semi-structured interviews (n = 40) with Black and Latinx individuals across the four counties. Interviews focused on knowledge related to the pandemic, the impact of the pandemic on their lives, sources of information, attitudes toward vaccination and participation in vaccine trials, and perspectives on the pandemic’s higher impact on communities of color.

Participants reported overwhelming effects of the pandemic in terms of worsened physical and mental health, financial difficulties, and lifestyle changes. They also reported some unexpected positive effects. They expressed awareness of the disproportionate burden among Black and Latinx populations and attributed this to a wide range of disparities in Social Determinants of Health. These included racism and systemic inequities, lack of access to information and language support, cultural practices, medical mistrust, and varied individual responses to the pandemic.

Examining perspectives and experiences of those most impacted by the pandemic is essential for preparing for and effectively responding to public health emergencies in the future. Public health messaging and crisis response strategies must acknowledge the concerns and cultural needs of underrepresented populations.

Childhood adversity is associated with increased later mental health problems and suicidal behaviour. Opportunities for earlier healthcare identification and intervention are needed.

To determine associations between hospital admissions for childhood adversity and mental health in children who later die by suicide.

Population-based longitudinal case-control study. Scottish in-patient general and psychiatric records were summarised for individuals born 1981 or later who died by suicide between 1991 and 2017 (cases), and matched controls (1:10), for childhood adversity and mental health (broadly defined as psychiatric diagnoses and general hospital admissions for self-harm and substance use).

Records were extracted for 2477 ‘cases’ and 24 777 ‘controls’; 2106 cases (85%) and 13 589 controls (55%) had lifespan hospitalisations. Mean age at death was 23.7; 75.9% were male. Maltreatment or violence-related childhood adversity codes were recorded for 7.6% cases aged 10–17 (160/2106) versus 2.7% controls (371/13 589), odds ratio = 2.9 (95% CI, 2.4–3.6); mental health-related admissions were recorded for 21.7% cases (458/2106), versus 4.1% controls (560/13 589), odds ratio = 6.5 (95% CI, 5.7–7.4); 80% of mental health admissions were in general hospitals. Using conditional logistic models, we found a dose-response effect of mental health admissions <18y, with highest adjusted odds ratio (aOR) for three or more mental health admissions: aORmale = 8.17 (95% CI, 5.02–13.29), aORfemale = 15.08 (95% CI, 8.07–28.17). We estimated that each type of childhood adversity multiplied odds of suicide by aORmale = 1.90 (95% CI, 1.64–2.21), aORfemale = 2.65 (95% CI, 1.94–3.62), and each mental health admission by aORmale = 2.06 (95% CI, 1.81–2.34), aORfemale = 1.78 (95% CI, 1.50–2.10).

Our lifespan study found that experiencing childhood adversity (primarily maltreatment or violence-related admissions) or mental health admissions increased odds of young person suicide, with highest odds for those experiencing both. Healthcare practitioners should identify and flag potential ‘at-risk’ adolescents to prevent future suicidal acts, especially those in general hospitals.

Definitive diagnosis of Alzheimer’s disease (AD) is often unavailable, so clinical diagnoses with some degree of inaccuracy are often used in research instead. When researchers test methods that may improve clinical accuracy, the error in initial diagnosis can penalize predictions that are more accurate to true diagnoses but differ from clinical diagnoses. To address this challenge, the current study investigated the use of a simple bias adjustment for use in logistic regression that accounts for known inaccuracy in initial diagnoses.

A Bayesian logistic regression model was developed to predict unobserved/true diagnostic status given the sensitivity and specificity of an imperfect reference. This model considers cases as a mixture of true (with rate = sensitivity) and false positives (rate = 1 - specificity) while controls are mixtures of true (rate = specificity) and false negatives (rate = 1 - sensitivity). This bias adjustment was tested using Monte Carlo simulations over four conditions that varied the accuracy of clinical diagnoses. Conditions utilized 1000 iterations each generating a random dataset of n = 1000 based on a true logistic model with an intercept and three arbitrary predictors. Coefficients for parameters were randomly selected in each iteration and used to produce a set of two diagnoses: true diagnoses and observed diagnoses with imperfect accuracy. Sensitivity and specificity of the simulated clinical diagnosis varied with each of the four conditions (C): C1 = (0.77, 0.60), C2 = (0.87, 0.44), C3 = (0.71, 0.71), and C4 = (0.83, 0.55), which are derived from published values for clinical AD diagnoses against autopsy-confirmed pathology. Unadjusted and bias-adjusted logistic regressions were then fit to the simulated data to determine the models’ accuracy in estimating regression parameters and prediction of true diagnosis.

Under all conditions, the bias-adjusted logistic regression model outperformed its unadjusted counterpart. Root mean square error (the variability of estimated coefficients around their true parameter values) ranged from 0.23 to 0.79 for the unadjusted model versus 0.24 to 0.29 for the bias-adjusted model. The empirical coverage rate (the proportion of 95% credible intervals that include their true parameter) ranged from 0.00 to 0.47 for the unadjusted model versus 0.95 to 0.96 for the bias-adjusted model. Finally, the bias-adjusted model produced the best overall diagnostic accuracy with correct classification of true diagnostic values about 78% of the time versus 62-72% without adjustment.

Results of this simulation study, which used published AD sensitivity and specificity statistics, provide evidence that bias-adjustments to logistic regression models are needed when research involves diagnoses from an imperfect standard. Results showed that unadjusted methods rarely identified true effects with credible intervals for coefficients including the true value anywhere from never to less than half of the time. Additional simulations are needed to examine the bias-adjusted model’s performance under additional conditions. Future research is needed to extend the bias adjustment to multinomial logistic regressions and to scenarios where the rate of misdiagnosis is unknown. Such methods may be valuable for improving detection of other neurological disorders with greater diagnostic error as well.

Learning curve patterns on list-learning tasks can help clinicians determine the nature of memory difficulties, as an “impaired” score may actually reflect attention and/or executive difficulties rather than a true memory impairment. Though such pattern analysis is often qualitative, there are quantitative methods to assess these concepts that have been generally underutilized. This study aimed to develop a model that decomposes learning over repeated trials into separate cognitive processes and then include other testing data to predict performance at each trial as a function of general cognitive functioning.

Data for CVLT-II learning trials were obtained from an outpatient neuropsychology service within an academic medical center referred for clinical reasons. Participants with a cognitive diagnosis of non-demented (ND) or probable Alzheimer’s disease (AD) were included. The final sample consisted of 323 ND [Mage = 58.6 (14.8); Medu = 15.4 (2.7); 55.7% female] and 915 AD [Mage = 72.6 (9.0); Medu = 14.2 (3.1); 60.1% female cases. A Bayesian non-linear beta-binomial multilevel model was used, which uses three parameters to predict CVLT-II recall-by-trial: verbal attention span (VAS), maximal learning potential (MLP), and learning rate (LR). Briefly, VAS predicts expected first trial performance while MLP, conversely, predicts the expected best performance as trials are repeated, and LR weights the influence of VAS versus MLR over repeated trials. Predictors of these parameters included age, education, sex, race, and clinical diagnosis, in addition to raw scores on Trail Making Test Parts A and B, phonemic (FAS) fluency, animal fluency, Boston Naming Test, Wisconsin Card Sorting Test (WCST) Categories Completed, and then age-adjusted scaled scores from WAIS-IV Digit Span, Block Design, Vocabulary, and Coding. Random intercepts were included for each parameter and extracted for comparison of residual differences by diagnosis.

The model explained 84% of the variance in CVLT-II raw scores. VAS reduced with age and time-to-complete Trails B but improved with both verbal fluencies and confrontation naming. MLP increased as a function of WAIS Digit Span, animal fluency, confrontation naming, and WCST categories completed. Finally, LR was greater for females and WAIS-IV Coding and Vocabulary performances but reduced with age. Participants with AD had lower estimates of all three parameters: Cohen’s d = 2.49 (VAS) - 3.48 (LR), though including demographic and neuropsychological tests attenuated differences, Cohen’s d = 0.34 (LR) - 0.95 (MLP).

The resulting model highlights how non-memory neuropsychological deficits affect list-learning test performance. At the same time, the model demonstrated that memory patterns on the CVLT-II can still be identified beyond other confounding deficits since having AD affected all parameters independent of other cognitive impairments. The modeling approach can generate conditional learning curves for individual patient data, and when multiple diagnoses are included in the model, a person-fit statistic can be computed to return the mostly likely diagnosis for an individual. The model can also be used in research to quantify or adjust for the effect of other patient data (e.g., neuroimaging, biomarkers, medications).

Sleep problems associated with poor mental health and academic outcomes may have been exacerbated by the COVID-19 pandemic.

To describe sleep in undergraduate students during the COVID-19 pandemic.

This longitudinal analysis included data from 9523 students over 4 years (2018–2022), associated with different pandemic phases. Students completed a biannual survey assessing risk factors, mental health symptoms and lifestyle, using validated measures. Sleep was assessed with the Sleep Condition Indicator (SCI-8). Propensity weights and multivariable log-binomial regressions were used to compare sleep in four successive first-year cohorts. Linear mixed-effects models were used to examine changes in sleep over academic semesters and years.

There was an overall decrease in average SCI-8 scores, indicating worsening sleep across academic years (average change −0.42 per year; P-trend < 0.001), and an increase in probable insomnia at university entry (range 18.1–29.7%; P-trend < 0.001) before and up to the peak of the pandemic. Sleep improved somewhat in autumn 2021, when restrictions loosened. Students commonly reported daytime sleep problems, including mood, energy, relationships (36–48%) and concentration, productivity, and daytime sleepiness (54–66%). There was a consistent pattern of worsening sleep over the academic year. Probable insomnia was associated with increased cannabis use and passive screen time, and reduced recreation and exercise.

Sleep difficulties are common and persistent in students, were amplified by the pandemic and worsen over the academic year. Given the importance of sleep for well-being and academic success, a preventive focus on sleep hygiene, healthy lifestyle and low-intensity sleep interventions seems justified.

Trauma is prevalent amongst early psychosis patients and associated with adverse outcomes. Past trials of trauma-focused therapy have focused on chronic patients with psychosis/schizophrenia and comorbid Post-Traumatic Stress Disorder (PTSD). We aimed to determine the feasibility of a large-scale randomized controlled trial (RCT) of an Eye Movement Desensitization and Reprocessing for psychosis (EMDRp) intervention for early psychosis service users.

A single-blind RCT comparing 16 sessions of EMDRp + TAU v. TAU only was conducted. Participants completed baseline, 6-month and 12-month post-randomization assessments. EMDRp and trial assessments were delivered both in-person and remotely due to COVID-19 restrictions. Feasibility outcomes were recruitment and retention, therapy attendance/engagement, adherence to EMDRp treatment protocol, and the ‘promise of efficacy’ of EMDRp on relevant clinical outcomes.

Sixty participants (100% of the recruitment target) received TAU or EMDR + TAU. 83% completed at least one follow-up assessment, with 74% at 6-month and 70% at 12-month. 74% of EMDRp + TAU participants received at least eight therapy sessions and 97% rated therapy sessions demonstrated good treatment fidelity. At 6-month, there were signals of promise of efficacy of EMDRp + TAU v. TAU for total psychotic symptoms (PANSS), subjective recovery from psychosis, PTSD symptoms, depression, anxiety, and general health status. Signals of efficacy at 12-month were less pronounced but remained robust for PTSD symptoms and general health status.

The trial feasibility criteria were fully met, and EMDRp was associated with promising signals of efficacy on a range of valuable clinical outcomes. A larger-scale, multi-center trial of EMDRp is feasible and warranted.