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Epidemic trajectories can be substantially impacted by people modifying their behaviours in response to changes in their perceived risk of spreading or contracting the disease. However, most infectious disease models assume a stable population behaviour. We present a flexible new class of models, called behavioural change individual‐level models (BC‐ILMs), that incorporate both individual‐level covariate information and a data‐driven behavioural change effect. Focusing on spatial BC‐ILMs, we consider four “alarm” functions to model the effect of behavioural change as a function of infection prevalence over time. Through simulation studies, we find that if behavioural change is present, using an alarm function, even if specified incorrectly, will result in an improvement in posterior predictive performance over a model that assumes stable population behaviour. The methods are applied to data from the 2001 U.K. foot and mouth disease epidemic. The results show some evidence of a behavioural change effect, although it may not meaningfully impact model fit compared to a simpler spatial ILM in this dataset. [ABSTRACT FROM AUTHOR]

This commentary argues that financial incentives for employees who meet body mass index requirements reinforce healthism, a false and oppressive ideology. Healthism is the view that personal health is the vehicle of well-being and that health is achieved by taking personal responsibility for habit modification. Healthist views about body shape and body weight enforce oppressive norms and can lead to pernicious harms, especially to members of vulnerable groups. Overall, this article argues that persons and organizations ought not to label behaviors that influence body shape and weight in normative terms, such as "ideal" or "healthy." [ABSTRACT FROM AUTHOR]

There are no studies of the safety and effectiveness of telehealth psychiatric treatment of partial hospital level of care, in general, and for borderline personality disorder (BPD) in particular. In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project, the authors compared the effectiveness of their partial hospital treatment program in treating patients with BPD. For both the in-person and telehealth partial hospital level of care, patients with BPD were highly satisfied with treatment and reported a significant reduction in symptoms from admission to discharge. Both groups reported a significant improvement in functioning, coping ability, positive mental health, and general well-being. A large effect size of treatment was found in both treatment groups. No patients attempted suicide. Telehealth partial hospital treatment was as effective as in-person treatment in terms of patient satisfaction, symptom reduction, and improved functioning and well-being for patients with BPD. [ABSTRACT FROM AUTHOR]

Crisis standards of care (CSC) are a significant change in the standard level of medical care that can be given compared to normal healthcare operations. So, if resource allocation strategies rely on predictive measures of healthcare outcomes to decide who gets scarce resources in a crisis, those resource allocation strategies risk perpetuating inequities among historically disadvantaged groups. [Extracted from the article]

Background: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.Methods: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.Results: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.Conclusions: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region. [ABSTRACT FROM AUTHOR]

With current trends in cannabis legalization, large efforts are being made to understand the effects of less restricted legislation on human consumption, health, and abuse of these products. Little is known about the effects of cannabis legalization and increased cannabis use on vulnerable populations, such as dogs. The objective of this study was to examine the effects of different state-level cannabis legislation, county-level socioeconomic factors, and dog-level characteristics on dog cannabis poisoning reports to an animal poison control center (APCC). Data were obtained concerning reports of dog poisoning events, county characteristics, and state cannabis legislation from the American Society for the Prevention of Cruelty to Animals' (ASPCA) APCC, the US Census Bureau, and various public policy-oriented and government websites, respectively. A multilevel logistic regression model with random intercepts for county and state was fitted to investigate the associations between the odds of a call to the APCC being related to a dog being poisoned by a cannabis product and the following types of variables: dog characteristics, county-level socioeconomic characteristics, and the type of state-level cannabis legislation. There were significantly higher odds of a call being related to cannabis in states with lower penalties for cannabis use and possession. The odds of these calls were higher in counties with higher income variability, higher percentage of urban population, and among smaller, male, and intact dogs. These calls increased throughout the study period (2009–2014). Reporting of cannabis poisonings were more likely to come from veterinarians than dog owners. Reported dog poisonings due to cannabis appear to be influenced by dog-level and community-level factors. This study may increase awareness to the public, public health, and veterinary communities of the effects of recreational drug use on dog populations. This study highlights the need to educate dog owners about safeguarding cannabis products from vulnerable populations. [ABSTRACT FROM AUTHOR]

Multiple challenges exist integrating research into clinical practice, particularly in acute care settings where randomized controlled trials may be impractical or unethical. Partial or day hospitals are one such setting. As compared to outpatients and inpatients, relatively little research is conducted or reported in partial hospital program (PHP) patients, leaving providers in this setting without a solid empirical basis from which to draw. We report treatment outcomes and patient satisfaction from the first 750 patients enrolled in a clinical research PHP utilizing the acceptance and commitment therapy (ACT) treatment model. ACT is a well-established, transdiagnostic behavior therapy. However, to date no study has examined the use of ACT in acute day hospitals. We hypothesized that applying ACT with this patient group would result in significantly improved depression, anxiety, functioning, and quality of life from intake to discharge. We additionally expected that patients would report high satisfaction with ACT treatment groups. Patients (n = 750 full sample, n = 518 completed treatment) completed daily measures of symptoms, functioning, and ACT processes at intake and discharge, and postgroup satisfaction surveys. Results showed significant improvements in symptoms and impairment. ACT process variables also increased over the course of treatment. Importantly, patient satisfaction was high for all groups. Thus, ACT provides an appropriate, flexible, effective, and satisfactory model for this patient group. A comparison of our findings using ACT to the limited research using other models in PHP settings is discussed, as well as challenges in the overall process of integrating research into routine clinical care. [ABSTRACT FROM AUTHOR]

Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipoprotein(a) (Lp(a), or remnant lipoproteins (RLP) using electron microscopy combined with machine learning tools from microliter samples of human plasma. In the reported method, lipoproteins were absorbed onto electron microscopy (EM) support films from diluted plasma and embedded in thin films of methyl cellulose (MC) containing mixed metal stains, providing intense edge contrast. The results show that LPs have a continuous frequency distribution of sizes, extending from LDL (> 15 nm) to intermediate density lipoprotein (IDL) and very low-density lipoproteins (VLDL). Furthermore, mixed metal staining produces striking "positive" contrast of specific antibodies attached to lipoproteins providing quantitative data on apolipoprotein(a)-positive Lp(a) or apolipoprotein B (ApoB)-positive particles. To enable automatic particle characterization, we also demonstrated efficient segmentation of lipoprotein particles using deep learning software characterized by a Mask Region-based Convolutional Neural Networks (R-CNN) architecture with transfer learning. In future, EM and machine learning could be combined with microarray deposition and automated imaging for higher throughput quantitation of lipoproteins associated with CVD risk. [ABSTRACT FROM AUTHOR]