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Holistic and ecological approaches to mental health support have been identified by many as best-practice approaches for creating and sustaining well-being. Those who experience mental health challenges including, for example, Borderline Personality Disorder (BPD), often struggle to find adequate, sustainable and ongoing care from clinical settings, and recent research shows that additional strategies such as peer support programs, arts approaches, and holistic school-based collaboration work effectively. More generally, the mainstream population continues to suffer from outdated, overly medicalised and frequently inaccurate notions of the experience of poor mental health and the effective support of those who suffer, and peer- and school-integrated approaches go some way toward better education about mental ill-health. This article uses a creative ecologies model (Harris, 2016) to help schools implement student- and family-led support programs for students experiencing mental health challenges in school settings.

This article considers the related concepts of 'rehearsal' [Harney, Stefano, and Fred Moten. 2013. "The Undercommons: Fugitive Planning & Black Study." Brooklyn: Minor Compositions] and 'catastrophe' [Stengers, Isabelle. 2015. "In Catastrophic Times: Resisting the Coming Barbarism." Translated by Andrew Goffey. London: Open Humanities Press] as activism, focussing upon eco-somatic drama processes engaged through "In Your Arms" (IYA). This participatory arts project for babies and their primary caregivers was co-created in Naarm (Melbourne), Australia, by a four-person artist ensemble, ten pre-crawling babies, and their mothers. Multi-modalities were used, including improvisatory movement scores, natural props, sound-making and listening practices. The article suggests new ways to consider drama activity at a micro-sensorial scale, offering practices that cultivate environmental attunement and care from the very first months of life.

Abstract When particles are deposited at a fluid interface they tend to aggregate by capillary attraction to minimize the overall potential energy of the system. In this work, we embed floating millimetric disks with permanent magnets to introduce a competing repulsion effect and study their pattern formation in equilibrium. The pairwise energy landscape of two disks is described by a short-range attraction and long-range repulsion (SALR) interaction potential, previously documented in a number of microscopic condensed matter systems. Such competing interactions enable a variety of pairwise equilibrium states, including the possibility of a local minimum energy corresponding to a finite disk spacing. Two-dimensional (2D) experiments and simulations in confined geometries demonstrate that as the areal packing fraction is increased, the dilute repulsion-dominated lattice state becomes unstable to the spontaneous formation of localized clusters, which eventually merge into a system-spanning striped pattern. Finally, we demonstrate that the equilibrium pattern can be externally manipulated by the application of a supplemental vertical magnetic force that remotely enhances the effective capillary attraction.

Objectives Individual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia. Approach Using linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death. Results We identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]). Conclusions We found significant associations between machine learning-derived clusters of chronic conditions and dementia. Implications Unsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.

BackgroundSeveral social determinants of health and other structural factors drive racial and ethnic disparities in COVID-19 risk, morbidity, and mortality. Public-private collaborations with community pharmacies have been successful in expanding access to COVID-19 testing and reaching historically underserved communities. The objectives of this study were to describe individuals who sought testing for COVID-19 at a national community pharmacy chain and to understand potential racial and ethnic inequities in testing access, positivity, and infection with emerging variants of concern.MethodsWe conducted a cross-sectional study of individuals aged ≥18 who were tested for COVID-19 (SARS-CoV-2) at a Walgreens pharmacy or Walgreen-affiliated mass testing site between May 1, 2021 and February 28, 2022. Positivity was defined as the proportion of positive tests among all administered tests. A geographically balanced random subset of positive tests underwent whole genome sequencing to identify specific viral variants (alpha, delta, and omicron). Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) to compare the likelihood of testing positive and testing positive with an emerging variant of concern across race and ethnicity groups.ResultsA total of 18,576,360 tests were analyzed (16.0% tests were positive for COVID-19; 59.5% of tests were from White individuals and 13.1% were from Black individuals). American Indian or Alaska Native (OR = 1.12; 95%CI = 1.10–1.13), Hispanic or Latino (1.20; 95%CI = 1.120, 1.21), and Black (1.12; 95%CI = 1.12, 1.13) individuals were more likely to test positive for COVID-19 compared to White individuals. Non-White individuals were also more likely to test positive for emerging variants of concern (e.g., Black individuals were 3.34 (95%CI = 3.14–3.56) times more likely to test positive for omicron compared to White individuals during the transition period from delta to omicron).DiscussionUsing a national database of testing data, we found racial and ethnic differences in the likelihood of testing positive for COVID-19 and testing positive for emerging viral strains. These results demonstrate the feasibility of public-private collaborations with local pharmacies and pharmacy chains to support pandemic response and reach harder to reach populations with important health services.

BackgroundA wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). ObjectiveThis study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. MethodsWe used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. ResultsAn NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. ConclusionsThe evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), and our HEC population (recall = N/A, precision = 0.9160).

Abstract When a solid body floats at the interface of a vibrating liquid bath, the motion of the object generates outwardly propagating surface waves. We here demonstrate that chiral objects on a vibrating fluid interface are set into steady rotation, with the angular speed and direction of rotation controlled by the interplay between object geometry and driving parameters. Scaling laws and a simplified model of the wavefield reveal the underlying physical mechanism of rotation, while collapsing measurements of the angular velocity across parameters. Leveraging the control over the chiral object’s direction of rotation, we demonstrate that a body with an asymmetric mass distribution and chirality can be remotely steered along two-dimensional trajectories via modulation of the driving frequency. This accessible and tunable macroscopic system serves as a potential platform for explorations of chiral active and driven matter, and demonstrates a mechanism by which wave-mediated forces can be manipulated for directed propulsion.

IntroductionCOVID-19 booster vaccines are highly effective at reducing severe illness and death from COVID-19. Research is needed to identify whether racial and ethnic disparities observed for the primary series of the COVID-19 vaccines persist for booster vaccinations and how those disparities may vary by other characteristics. We aimed to measure racial and ethnic differences in booster vaccine receipt among U.S. Medicare beneficiaries and characterize potential variation by demographic characteristics.MethodsWe conducted a cohort study using CVS Health and Walgreens pharmacy data linked to Medicare claims. We included community-dwelling Medicare beneficiaries aged ≥66 years who received two mRNA vaccine doses (BNT162b2 and mRNA-1273) as of 8/1/2021. We followed beneficiaries from 8/1/2021 until booster vaccine receipt, death, Medicare disenrollment, or end of follow-up (12/31/2021). Adjusted Poisson regression was used to estimate rate ratios (RRs) and 95% confidence intervals (CIs) comparing vaccine uptake between groups.ResultsWe identified 11,339,103 eligible beneficiaries (mean age 76 years, 60% female, 78% White). Overall, 67% received a booster vaccine (White = 68.5%; Asian = 67.0%; Black = 57.0%; Hispanic = 53.3%). Compared to White individuals, Black (RR = 0.78 [95%CI = 0.78–0.78]) and Hispanic individuals (RR = 0.72 [95% = CI 0.72–0.72]) had lower rates of booster vaccination. Disparities varied by geographic region, urbanicity, and Medicare plan/Medicaid eligibility. The relative magnitude of disparities was lesser in areas where vaccine uptake was lower in White individuals.DiscussionRacial and ethnic disparities in COVID-19 vaccination have persisted for booster vaccines. These findings highlight that interventions to improve vaccine uptake should be designed at the intersection of race and ethnicity and geographic location.

Abstract Introduction: Housing instability is a social determinant of health associated with multiple negative health outcomes including substance use disorders (SUDs). Real-world evidence of housing instability is needed to improve translational research on populations with SUDs. Methods: We identified evidence of housing instability by leveraging structured diagnosis codes and unstructured clinical data from electronic health records of 20,556 patients from 2017 to 2021. We applied natural language processing with named-entity recognition and pattern matching to unstructured clinical notes with free-text documentation. Additionally, we analyzed semi-structured addresses containing explicit or implicit housing-related labels. We assessed agreement on identification methods by having three experts review of 300 records. Results: Diagnostic codes only identified 58.5% of the population identifiable as having housing instability, whereas 41.5% are identifiable from addresses only (7.1%), clinical notes only (30.4%), or both (4.0%). Reviewers unanimously agreed on 79.7% of cases reviewed; a Fleiss’ Kappa score of 0.35 suggested fair agreement yet emphasized the difficulty of analyzing patients having ambiguous housing situations. Among those with poisoning episodes related to stimulants or opioids, diagnosis codes were only able to identify 63.9% of those with housing instability. Conclusions: All three data sources yield valid evidence of housing instability; each has their own inherent practical use and limitations. Translational researchers requiring comprehensive real-world evidence of housing instability should optimize and implement use of structured and unstructured data. Understanding the role of housing instability and temporary housing facilities is salient in populations with SUDs.

Introduction: Stay-at-home orders during the COVID-19 pandemic decreased population mobility to reduce SARS-CoV-2 infection rates. We empirically tested the hypothesis that this public health measure was associated with a higher likelihood of opioid- and stimulant-involved deaths occurring in homes located in Cook County, Illinois. Methods: The stay-at-home period was from March 21, 2020 to May 30, 2020. We analyzed overdose data from the Cook County Medical Examiner's Office using a death location description from case investigations categorized as home, medical, motel, scene, and other. Two groups of decedents were defined as either having an opioid or stimulant listed in the primary cause of death field. We modeled a weekly time series to detect changes in deaths (number) and trends during segmented time periods. Chi-square or Fisher's exact and adjusted logistic regression was used for testing the differences between the stay-at-home and a 13-week preceding period. Results: There were 4,169 and 2,012 opioid- and stimulant-involved deaths, respectively, from 2018 to 2020. Both groups were demographically similar: 75% male, 52% White, and aged 45 years (mean). In the 13 weeks before stay-at-home orders, 51% of opioid-involved deaths occurred in homes, which increased to 59% (p