To implement and evaluate a point-of-care (POC) molecular testing platform for respiratory viruses in congregate living settings (CLS).

Prospective quality improvement study.

Seven CLS, including three nursing homes and four independent-living facilities.

Residents of CLS.

A POC platform for COVID-19, influenza A and B, and respiratory syncytial virus was implemented at participating CLS from December 1, 2022 to April 15, 2023. Residents with respiratory symptoms underwent paired testing, with respiratory specimens tested first with the POC platform and then delivered to an off-site laboratory for multiplex respiratory virus panel (MRVP) polymerase chain reaction (PCR) as per standard protocol. Turn-around time and diagnostic accuracy of the POC platform were compared against MRVP PCR. In an exploratory analysis, time to outbreak declaration among participating CLS was compared against a convenience sample of 19 CLS that did not use the POC platform.

A total of 290 specimens that underwent paired testing were included. Turn-around time to result was significantly shorter with the POC platform compared to MRVP PCR, with median difference of 36.2 hours (interquartile range 21.8–46.4 hours). The POC platform had excellent diagnostic accuracy compared to MRVP PCR, with area under the curve statistic of .96. Time to outbreak declaration was shorter in CLS that used the POC platform compared to CLS that did not.

Rapid POC testing platforms for respiratory viruses can be implemented in CLS, with high diagnostic accuracy, expedited turn-around times, and shorter time to outbreak declaration.

Parkinsonism and Parkinson's disease (PD) have been described as consequences of repetitive head impacts (RHI) from boxing, since 1928. Autopsy studies have shown that RHI from other contact sports can also increase risk for neurodegenerative diseases, including chronic traumatic encephalopathy (CTE) and Lewy bodies. In vivo research on the relationship between American football play and PD is scarce, with small samples, and equivocal findings. This study leveraged the Fox Insight study to evaluate the association between American football and parkinsonism and/or PD Diagnosis and related clinical outcomes.

Fox Insight is an online study of people with and without PD who are 18+ years (>50,000 enrolled). Participants complete online questionnaires on motor function, cognitive function, and general health behaviors. Participants self-reported whether they "currently have a diagnosis of Parkinson's disease, or parkinsonism, by a physician or other health care professional." In November 2020, the Boston University Head Impact Exposure Assessment was launched in Fox Insight for large-scale data collection on exposure to RHI from contact sports and other sources. Data used in this abstract were obtained from the Fox Insight database https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp on 01/06/2022. The sample includes 2018 men who endorsed playing an organized sport. Because only 1.6% of football players were women, analyses are limited to men. Responses to questions regarding history of participation in organized football were examined. Other contact and/or non-contact sports served as the referent group. Outcomes included PD status (absence/presence of parkinsonism or PD) and Penn Parkinson's Daily Activities Questionnaire-15 (PDAQ-15) for assessment of cognitive symptoms. Binary logistic regression tested associations between history and years of football play with PD status, controlling for age, education, current heart disease or diabetes, and family history of PD. Linear regressions, controlling for these variables, were used for the PDAQ-15.

Of the 2018 men (mean age=67.67, SD=9.84; 10, 0.5% Black), 788 (39%) played football (mean years of play=4.29, SD=2.88), including 122 (16.3%) who played youth football, 494 (66.0%) played high school, 128 (17.1%) played college football, and 5 (0.7%) played at the semi-professional or professional level. 1738 (86.1%) reported being diagnosed with parkinsonism/PD, and 707 of these were football players (40.7%). History of playing any level of football was associated with increased odds of having a reported parkinsonism or PD diagnosis (OR=1.52, 95% CI=1.14-2.03, p=0.004). The OR remained similar among those age <69 (sample median age) (OR=1.45, 95% CI=0.97-2.17, p=0.07) and 69+ (OR=1.45, 95% CI=0.95-2.22, p=0.09). Among the football players, there was not a significant association between years of play and PD status (OR=1.09, 95% CI=1.00-1.20, p=0.063). History of football play was not associated with PDAQ-15 scores (n=1980) (beta=-0.78, 95% CI=-1.59-0.03, p=0.059) among the entire sample.

Among 2018 men from a data set enriched for PD, playing organized football was associated with increased odds of having a reported parkinsonism/PD diagnosis. Next steps include examination of the contribution of traumatic brain injury and other sources of RHI (e.g., soccer, military service).

Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Problems in learning that sights, sounds, or situations that were once associated with danger have become safe (extinction learning) may explain why some individuals suffer prolonged psychological distress following traumatic experiences. Although simple learning models have been unable to provide a convincing account of why this learning fails, it has recently been proposed that this may be explained by individual differences in beliefs about the causal structure of the environment.

Here, we tested two competing hypotheses as to how differences in causal inference might be related to trauma-related psychopathology, using extinction learning data collected from clinically well-characterised individuals with varying degrees of post-traumatic stress (N = 56). Model parameters describing individual differences in causal inference were related to multiple post-traumatic stress disorder (PTSD) and depression symptom dimensions via network analysis.

Individuals with more severe PTSD were more likely to assign observations from conditioning and extinction stages to a single underlying cause. Specifically, greater re-experiencing symptom severity was associated with a lower likelihood of inferring that multiple causes were active in the environment.

We interpret these results as providing evidence of a primary deficit in discriminative learning in participants with more severe PTSD. Specifically, a tendency to attribute a greater diversity of stimulus configurations to the same underlying cause resulted in greater uncertainty about stimulus-outcome associations, impeding learning both that certain stimuli were safe, and that certain stimuli were no longer dangerous. In the future, better understanding of the role of causal inference in trauma-related psychopathology may help refine cognitive therapies for these disorders.

Intellectual and developmental disabilities (IDDs) and psychiatric disorders frequently co-occur. Although each has been associated with negative outcomes, their combined effect has rarely been studied.

To examine the likelihood of five negative health and healthcare outcomes for adults with IDD and mental health/addiction disorders (MHAs), both separately and together. For each outcome, demographic, clinical and system-level factors were also examined.

Linked administrative data-sets were used to identify adults in Ontario, Canada, with IDD and MHA (n = 29 476), IDD-only (n = 35 223) and MHA-only (n = 727 591). Five outcomes (30-day readmission, 30-day repeat ED visit, delayed discharge, long-term care admission and premature mortality) were examined by logistic regression models with generalised estimating equation or survival analyses. For each outcome, crude (disorder groups only) and complete (adding biosocial covariates) models were run using a general population reference group.

The IDD and MHA group had the highest proportions across outcomes for both crude and complete models. They had the highest adjusted ratios for readmissions (aOR 1.93, 95%CI 1.88–1.99), repeat ED visit (aOR 2.00, 95%CI 1.98–2.02) and long-term care admission (aHR 12.19, 95%CI 10.84–13.71). For delayed discharge, the IDD and MHA and IDD-only groups had similar results (aOR 2.00 (95%CI 1.90–2.11) and 2.21 (95%CI 2.07–2.36). For premature mortality, the adjusted ratios were similar for all groups.

Poorer outcomes for adults with IDD, particularly those with MHA, suggest a need for a comprehensive, system-wide approach spanning health, disability and social support.

Perinatal mental illnesses are a major public health issue, which untreated can have devastating impacts on women and their families. Problems with emotion regulation are a common feature across perinatal mental illnesses.

This study sought to evaluate the impacts of dialectical behaviour therapy (DBT) skills groups for mothers and babies in a community perinatal service. We hypothesised that community perinatal DBT skills groups that included babies would reduce distress and improve emotional regulation.

A mixed-methods within-subjects design was utilised with outcome measures collected pre- and post-intervention. Qualitative interviews exploring mothers’ experiences of bringing their baby to group were also conducted.

Results indicated that DBT skills groups significantly improved levels of psychological distress and emotional regulation.

Community perinatal DBT skills groups are effective when babies are present. Moreover, benefits of including babies were identified, under the themes of Self as Mother, Shared Experience, and Impact of Babies.

Depression is associated with lower educational attainment, but there has been little investigation of long-term educational trajectories in large cohorts with diagnosed depression.

To describe the educational attainment trajectories of children with a depression diagnosis in secondary care, and to investigate whether these trajectories vary by sociodemographic characteristics.

We identified new referrals to South London and Maudsley's NHS Foundation Trust between 2007 and 2013 who received a depression diagnosis at under 18 years old. Linking their health records to the National Pupil Database, we standardised their performance on three assessments (typically undertaken at ages 6–7 years (school Year 2), 10–11 (Year 6) and 15–16 (Year 11)) relative to the local reference population in each academic year. We used mixed models for repeated measures to estimate attainment trajectories.

In our sample of 1492 children, the median age at depression diagnosis was 15 years (interquartile range = 14–16). Their attainment showed a decline between school Years 6 and 11. Attainment was consistently lower among males and those eligible for free school meals. Black ethnic groups also showed lower attainment than White ethnic groups between Years 2 and 6, but showed a less pronounced drop in attainment at Year 11.

Those who receive a depression diagnosis during their school career show a drop in attainment in Year 11. Although this pattern was seen among multiple sociodemographic groups, gender, ethnicity and socioeconomic status predict more vulnerable subgroups within this clinical population who might benefit from additional educational support or more intensive treatment.

In Ontario, there are approximately 66,000 adults living with a diagnosis of intellectual and developmental disabilities (IDD). These individuals are nearly twice as likely to experience an injury compared to the general population. Falls are an important contributor to injuries in persons with IDD and in the general population, and are consistently found to be the leading cause of traumatic brain injury (TBI). There is currently no literature that quantitatively examines TBI among persons with IDD. The purpose of this study was to compare the risk of TBI for adults with and without IDD in Ontario over time and by demographic information.

Using administrative health databases, two main cohorts were identified: (1) adults with IDD, and (2) a random 10% sample of adults without IDD. Within each cohort, annual crude and adjusted incidence of TBI were calculated among unique individuals for each fiscal year from April 1, 2002 to March 31, 2017.

Over the 15-year study period, the average annual adjusted incidence of TBI was approximately 2.8 new cases per 1000 among Ontario adults with IDD, compared to approximately 1.53 per 1000 among those without IDD. In both cohorts, a higher proportion of TBI cases were younger (19–29 years) and male.

During the study period, persons with IDD experienced a significantly higher risk of TBI compared to the general population indicating the possibility, and need, for targeted TBI prevention.