Background: Urban green space may be important to mental health, but the association between long-term green space exposures and depression, anxiety, and cognitive function in adults remains unknown. Methods: We examined 8,144 adults enrolled in the CARTaGENE cohort in Quebec Canada. Average green space and change in green space with residential mobility were assessed using satellite-derived normalized difference vegetation index from 5-year residential address histories. Outcomes included depression and anxiety determined through medical record linkages, self-reported doctor diagnosis of depression, and the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7scales. Cognitive function was available for 6,658 individuals from computerized tests of reaction time, working memory, and executive function. We used linear and logistic multivariate models to assess associations between green space and each mental health and cognitive function measure. Results: In fully adjusted analyses, a 0.1 increase in residential normalized difference vegetation index within 500 m was associated with an odds ratio of 0.85 (95% CI: 0.76, 0.95) for a self-reported doctor diagnosis of depression and 0.81 (95% CI: 0.70, 0.93) for moderate anxiety assessed using the Generalized Anxiety Disorder 7 scale. Other models showed protective effects of urban green space on depression and anxiety but were not statistically significant, and the magnitude of association varied by green space exposure and mental health outcome assessment method. We did not observe any evidence of associations between green space and cognitive function. Conclusions: We observed some evidence to support the hypothesis that urban green space is associated with decreased depression and anxiety but not cognitive function.

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Objectives: Psychosocial factors have been hypothesized to increase the risk of cancer.This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer. Methods:The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2). Conclusion:PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses.

BackgroundPopulation health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information. ObjectivesThe main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations. MethodsData was extracted from CARTaGENE, a population-based cohort study in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l’assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index). ResultsAgreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores. DiscussionOverall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE’s participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec’s AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.