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Observational studies suggest that 25-hydroxy vitamin D (25(OH)D) concentration is inversely associated with pain. However, findings from intervention trials are inconsistent. We assessed the effect of vitamin D supplementation on pain using data from a large, double-blind, population-based, placebo-controlled trial (the D-Health Trial). 21,315 participants (aged 60-84 years) were randomly assigned to a monthly dose of 60,000 IU vitamin D3 or a matching placebo. Pain was measured using the 6-item Pain Impact Questionnaire (PIQ-6), administered 1, 2 and 5 years after enrollment. We used regression models (linear for continuous PIQ-6 score and log-binomial for binary categorizations of the score, namely ‘some or more pain impact’ and ‘presence of any bodily pain’) to estimate the effect of vitamin D on pain. We included 20,423 participants who completed ≥1 PIQ-6. In blood samples collected from 3943 randomly selected participants (∼800 per year) the mean (SD) 25(OH)D concentrations were 77 (SD 25) and 115 (SD 30) nmol/L in the placebo and vitamin D groups, respectively. Most (76%) participants were predicted to have 25(OH)D concentration >50 nmol/L at baseline. The mean PIQ-6 was similar in all surveys (∼50.4). The adjusted mean difference in PIQ-6 score (vitamin D cf placebo) was 0.02 (95% CI, -0.20 to 0.25). The proportion of participants with some or more pain impact and with presence of bodily pain was also similar between groups (both prevalence ratios 1.01, 95% CI 0.99 to 1.03). In conclusion, supplementation with 60,000 IU of vitamin D3 per month had negligible effect on bodily pain.
The current study used data from an ethnically diverse population from South London to examine ethnic differences in physical and mental multimorbidity among working age (18–64 years) adults in the context of depression and anxiety.
The study included 44 506 patients who had previously attended Improving Access to Psychological Therapies services in the London Borough of Lambeth. Multinomial logistic regression examined cross-sectional associations between ethnicity with physical and mental multimorbidity. Patterns of multimorbidity were identified using hierarchical cluster analysis.
Within 44 056 working age adults with a history of depression or anxiety from South London there were notable ethnic differences in physical multimorbidity. Adults of Black Caribbean ethnicity were more likely to have physical multimorbidity [adjusted relative risk ratio (aRRR) = 1.25, 95% confidence interval (CI) 1.15–1.36] compared to adults of White ethnicity. Relative to adults of White ethnicity, adults of Asian ethnicity were more likely to have physical multimorbidity at higher thresholds only (e.g. 4 + conditions; aRRR = 1.53, 95% CI 1.17–2.00). Three physical (atopic, cardiometabolic, mixed) and three mental (alcohol/substance use, common/severe mental illnesses, personality disorder) multimorbidity clusters emerged. Ethnic minority groups with multimorbidity had a higher probability of belonging to the cardiometabolic cluster.
In an ethnically diverse population with a history of common mental health disorders, we found substantial between- and within-ethnicity variation in rates of physical, but not mental, multimorbidity. The findings emphasised the value of more granular definitions of ethnicity when examining the burden of physical and mental multimorbidity.
Because decision making is complicated, political elites seek advice when making decisions, and the ways in which they use that advice has systematic features. But, analyses of decision making among elites usually fail to account for advice. We take advantage of unique information about the advice provided to one set of elites to empirically uncover the effect of advice. Specifically, we examine law clerk recommendations on cert to Justice Blackmun. We find that, even after controlling for known determinants of cert and considering sequential decision making, the advice of a trusted advisor matters greatly.
Using a new measure of urbanity for every federal electoral district in Canada from 1896 to the present, this article describes the long-term development of the urban-rural divide in Canadian federal elections. We focus on three questions: (1) when the urban-rural divide has existed in Canada, identifying three main periods—the 1920s, the 1960s and 1993–present—in which the urban-rural cleavage has been especially important in federal elections; (2) where the urban-rural divide has existed, finding that in the postwar period the urban-rural cleavage is a pan-Canadian phenomenon; and (3) how well urbanity predicts district-level election outcomes. We argue that the urban-rural divide is important for understanding election outcomes during several periods of Canadian political development, and never more so than in recent decades. We conclude by discussing the implications of our findings for research on urban-rural cleavages, Canadian electoral politics and Canadian political development.
To enable new research on local ideology and representation in Canada, we construct a latent measure of the policy ideology of 37,500 Canadian Election Study respondents using 56 policy-relevant questions and then use multilevel regression and poststratification to estimate the average ideological position of each of Canada's 338 federal electoral districts and 250 largest municipalities. We use these local ideology estimates to examine ideological representation in Canadian municipal politics. Combining our municipal ideology estimates with elite survey data from more than 900 Canadian municipal politicians, we find evidence of a strong relationship between mass and elite ideology. This relationship is consistent across differing municipal population sizes and institutional structures. We conclude with additional detail on our publicly available individual and aggregate measures and describe their potential uses for future research on ideology and representation in Canadian politics at all levels.
ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.
Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.
Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.
Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.
All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.
The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
Municipal governments are experts in social non-distancing. From swimming pools to libraries, streetcars to public parks, municipalities bring residents together and move them around—services vital to a vibrant community in ordinary times, but potentially disastrous in a pandemic. Municipal decisions to shutter these services and enforce social distancing are thus crucial for a successful COVID-19 response.
Despite aspirations to be a world-class national curriculum, the Australian Curriculum (AC) has been criticised as ‘manifestly deficient’ (Australian Government Department of Education and Training, 2014 p. 5) as an inclusive curriculum, failing to meet the needs of all students with disabilities (SWD) and their teachers. There is a need for research into the daily attempts of educators to navigate the tension between a ‘top-down’ system-wide curriculum and a ‘bottom-up’ regard for individual student needs, with a view to informing both policy and practice. This article is the first of two research papers in which we report the findings from a national online Research in Special Education (RISE) Australian Curriculum Survey of special educators in special schools, classes, and units regarding their experience using the AC to plan for and teach SWD. Survey results indicated (a) inconsistent use of the AC as the primary basis for developing learning objectives and designing learning experiences, (b) infrequent use of the achievement standards to support assessment and reporting, and (c) considerable supplementation of the AC from other resources when educating SWD. Overall, participants expressed a lack of confidence in translating the AC framework into a meaningful curriculum for SWD. Implications for policy, practice, and future research are discussed.
Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.
Ceramic fiber–matrix composites (CFMCs) are exciting materials for engineering applications in extreme environments. By integrating ceramic fibers within a ceramic matrix, CFMCs allow an intrinsically brittle material to exhibit sufficient structural toughness for use in gas turbines and nuclear reactors. Chemical stability under high temperature and irradiation coupled with high specific strength make these materials unique and increasingly popular in extreme settings. This paper first offers a review of the importance and growing body of research on fiber–matrix interfaces as they relate to composite toughening mechanisms. Second, micropillar compression is explored experimentally as a high-fidelity method for extracting interface properties compared with traditional fiber push-out testing. Three significant interface properties that govern composite toughening were extracted. For a 50-nm-pyrolytic carbon interface, the following were observed: a fracture energy release rate of ∼2.5 J/m2, an internal friction coefficient of 0.25 ± 0.04, and a debond shear strength of 266 ± 24 MPa. This research supports micromechanical evaluations as a unique bridge between theoretical physics models for microcrack propagation and empirically driven finite element models for bulk CFMCs.
Much of the global agricultural by products go waste, especially in developing nations where much of their revenues depend on the exports of raw agricultural products. Such waste streams, if converted to “value added” products could serve as additional source of revenue while simultaneously having a positive impact on the socio-economic well being of the people. We present a preliminary investigation on utilizing chemical activation technique and ball milling to convert agricultural waste streams such as cocoa pod, coconut husk, palm midrib and calabash commonly found in Ghana into ultra-high surface area activated carbon. Such activated carbons are suitable for myriads of applications in environmental remediation, climate management, energy storage and conversion systems (batteries and supercapacitors), and improving crop productivity. We achieved BET surface area as high as ∼ 3000 m2/g.
Epigenetic modifications to the genome are a key mechanism involved in the biological encoding of experience. Animal studies and a growing body of literature in humans have shown that early adversity is linked to methylation of the gene for the glucocorticoid receptor (GR), which is a key regulator of the hypothalamic–pituitary–adrenal axis as well as a broad range of physiological systems including metabolic and immune function. One hundred eighty-four families participated, including n = 74 with child welfare documentation of moderate-severe maltreatment in the past 6 months. Children ranged in age from 3 to 5 years, and were racially and ethnically diverse. Structured record review and interviews in the home were used to assess a history of maltreatment, other traumas, and contextual life stressors, and a composite variable assessed the number exposures to these adversities. Methylation of regions 1D, 1F, and 1H of the GR gene was measured via sodium bisulfite pyrosequencing. The composite measure of adversity was positively correlated with methylation at exons 1D and 1F in the promoter of the GR gene. Individual stress measures were significantly associated with a several CpG sites in these regions. GR gene methylation may be a mechanism of the biobehavioral effects of adverse exposures in young children.
Despite widespread recognition that the physiological systems underlying stress reactivity are well coordinated at a neurobiological level, surprisingly little empirical attention has been given to delineating precisely how the systems actually interact with one another when confronted with stress. We examined cross-system response proclivities in anticipation of and following standardized laboratory challenges in 664 4- to 14-year-olds from four independent studies. In each study, measures of stress reactivity within both the locus coeruleus-norepinephrine system (i.e., the sympathetic and parasympathetic branches of the autonomic nervous system) and the corticotrophin releasing hormone system (i.e., the hypothalamic–pituitary–adrenal axis) were collected. Latent profile analyses revealed six distinctive patterns that recurred across the samples: moderate reactivity (average cross-system activation; 52%–80% of children across samples), parasympathetic-specific reactivity (2%–36%), anticipatory arousal (4%–9%), multisystem reactivity (7%–14%), hypothalamic–pituitary–adrenal axis specific reactivity (6%–7%), and underarousal (0%–2%). Groups meaningfully differed in socioeconomic status, family adversity, and age. Results highlight the sample-level reliability of children's neuroendocrine responses to stress and suggest important cross-system regularities that are linked to development and prior experiences and may have implications for subsequent physical and mental morbidity.
We present preliminary results on a processing protocol by chemical activation that transforms organic waste product such as coconut husk into high surface area activated carbon. Dried raw materials of the coconut husk were carbonized anaerobically into char. The char was impregnated with KOH of different ratios and were activated at 800°C and 900°C. The transmission electron microscope was used to acquire structural and morphological information of the activated carbon, and the surface area and porosity analysis were performed using Micromeritics ASAP 2020 analyzer. The activated carbons show both micropores and mesopores with specific surface area as high as 2900m2/g.
We have derived and tested a simple analytical model for placing limits on the transit timing variations of circumbinary exoplanets. These are generally of days in magnitude, dwarfing those found in multi-planet systems. The derived method is fast, efficient and is accurate to approximately 1% in predicting limits on the possible times of transits over a 3-year campaign.