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Background: Metabolic risk factors, low cognitive function and history of depression are known risk factors for future depressive episodes. This paper aims to evaluate the potential interactions between these factors on the risk of a major depressive episodes in middle-age.Methods: Baseline and follow-up data from a population-based study of Quebec, Canada were used. The sample consisted of 1788 adults between 40 and 69 years of age without diabetes. Cognitive function and metabolic risk factors were assessed at baseline. Three cognitive domains were assessed: processing speed, episodic memory and executive function. History of depression was assessed five years later by a clinical interview. Logistic regression analysis was conducted to evaluate interactions between individual metabolic factors, low cognitive function, and depression history.Results: Participants with a comorbidity of at least one metabolic factor, history of depression and low cognitive function had the highest risk of experiencing a depressive episode in middle age. The highest risk was observed in individuals with abdominal obesity, low cognitive function, and a history of depression (OR= 8.66, 95% CI 3.83-19.59). The risks for those with abdominal obesity only, depression history only, and low cognitive function were 1.20 (95%CI 0.71-2.02), 3.10 (95%CI 1.81-5.24), and 1.39 (95%CI 0.72-2.67), respectively.Limitations: Depression was only assessed at follow-up.Conclusion: Metabolic risk factors comorbid with low cognitive function in middle-aged individuals with a history of depression were associated with an increased risk of a future depressive episode. This study highlights the importance of screening for metabolic and cognitive comorbidities in patients with a history of depression. [ABSTRACT FROM AUTHOR]

Introduction: Validity of research linking built environments to health relies on the availability and reliability of data used to measure exposures. As cities transform, it is important to track when and where urban changes occur, to provide detailed information for urban health intervention research. This paper presents an online observation method of the implementation of traffic-calmingfeatures using Google Street View Time Machine. The method is used to validate an existingadministrative database detailing the implementation of curb extensions and speed bumps.Methods: Online observation of curb extensions and speed bumps was conducted for four boroughsin Montreal, Canada, in autumn 2016, and compared with administrative data documenting traffic-calming measures implemented between 2008 and 2014. All images available through the Time Machine function between 2007 and 2016 for 708 intervention sites were visualized online. Records in the administrative database were compared to real-world Google Street View observations and tested in terms of sensitivity, specificity, and positive predicted value.Results: Google Street View Time Machine allowed the visualization of a median of seven different dates per street intersection and six dates per street segment. This made it possible to analyze built environment changes within 3,973 distinct time periods at 708 locations. Validation of the administrative data regarding presence of an intervention showed 99% (95% CI=97%, 99%) sensitivity, 58% (95% CI=51%, 64%) specificity, and 77% (95% CI=73%, 81%) positive predictive value.Conclusions: Google Street View Time Machine allowed past (2007-2016) online documentation of microscale urban interventions-curb extensions and speed bumps. The proposed method offers a new way to document historic changes to the built environment, which will be useful for urban health intervention research. [ABSTRACT FROM AUTHOR]

Background: Ten U.S. states, Canada, and Uruguay have passed laws to legalize the production and sale of cannabis for non-medical purposes. Available research has documented rapidly falling prices and changing product mixes, but many details are not well understood: particularly, the popularity, prices, and product characteristics of different cannabis edibles and extract-based products - each offering different ways to consume cannabis, with unclear health consequences.Methods: This paper analyzes data from Washington's recreational cannabis market, which has recorded over 110 million retail item-transactions from July 2014 to October 2017. Previous research on price and product trends has focused mostly on herbal cannabis, which accounts for the majority, but a decreasing share, of sales. This paper applies advanced text-analytic methods to provide new insights, including (A) estimating potency data for edibles and (B) identifying extract sub-types. Patterns and trends are described, across product types, regarding THC and CBD profiles and price per THC.Results: Extracts accounted for 28.5% of sales in October 2017. Of extracts categorized to subtype, nearly half were identified as "dabs", and another half "cartridges". In October 2017, price per 10 mg THC was roughly $3 among edibles, 70 cents among extract cartridges, and 30-40 cents for other flower and other extracts; solid concentrates offered the lowest priced THC among extract products. Price declines continue but have slowed. High-CBD chemovars are becoming more common, but still are almost non-existent in flower marijuana and rare (1% of sales) among extract products.Conclusion: As Washington's recreational cannabis market has developed over three and a half years, trends identified in that market may serve as an early indication of potential issues in other states. Legislators and regulators in other jurisdictions with commercial non-medical cannabis markets may wish to establish policies responsive to these trends in product popularity, price, and potency. [ABSTRACT FROM AUTHOR]

This paper is based on a qualitative study conducted in a rural community in British Columbia, Canada. Ethnographic methods were used to: (1) to bring youth voice to the literature on emotional distress; and (2) to capture the ways in which context shapes young peoples' experiences of emotional distress within their everyday lives. Our findings demonstrate how socio-structural contextual factors such as the local economy, geographical segregation, racism, ageism, and cutbacks in health and social service programming operate to create various forms of disconnection, and intersect in young peoples' lives to shape their experiences of emotional distress. [ABSTRACT FROM AUTHOR]

Abstract: Background: We have proposed that declines in adaptive capacity, defined as the ability to adapt to multiple stressors, may serve as an indicator of risk for fatigue. A comprehensive measure of adaptive capacity does not exist. Objectives: In this paper we describe construction of an instrument to measure adaptive capacity, the Adaptive Capacity Index (ACI). Design: Descriptive and psychometric. Setting: Six sites providing palliative care in Western Canada. Participants: Inclusion criteria: ≥18 years old, diagnosed with advanced cancer, able to read and write English, Mini-Mental Status Exam score ≥22. Pilot study n =48; Main study n =225 stratified using the Edmonton Symptom Assessment Scale (ESAS) tiredness score (≥0 to ≤2 n =60; ≥3 to ≤6 n =108; ≥7 and ≤10 n =57). Methods: Following ethics approval, 17 experts in symptom management assisted with content validation and consenting individuals completed the Functional Assessment of Cancer Therapy-Fatigue (FACT-F), the Profile of Mood States-Vigor short form (POMS-Vsf), and the ACI. A research assistant collected demographic information and assigned an Eastern Cooperative Oncology Group (ECOG) score. Data were analyzed using descriptive and inferential statistics (i.e., exploratory factor analyses, correlation, multivariate analyses of variance, and multiple regression). Results: Five 6-item ACI factors/subscales (Cognitive Function, Stamina/Muscle Endurance, Sleep Quality, Emotional Reactivity, and Social Interaction) were identified. The ACI-total scale and its subscales were internally consistent (Cronbach''s alpha 0.76–0.89), and were significantly correlated with each other, and with each fatigue measure (Pearson''s r ranging from −0.724 to 0.634). The ACI total score was sensitive to changes in the ESAS tiredness score. Stamina/Muscle Endurance, Cognitive Function, and Sleep Quality predicted 60.8% of the variance in FACT-F. Stamina/Muscle Endurance and Social Interaction predicted 36.8% of the variance in POMS-Vsf. Stamina/Muscle Endurance and Sleep Quality predicted 8% of the variance in ECOG. Conclusions: The ACI is reliable and has beginning evidence of validity. In future studies we will examine relationships between ACI subscale scores and subsequent increases in fatigue and explore linkages to physiological processes. We will also establish ACI norms for early and late stage cancers and explore variations in ACI subscale scores base on age or gender. [Copyright &y& Elsevier]