Chronic disease and where you live: Built and natural environment relationships with physical activity, obesity, and diabetes.

BACKGROUND: Diabetes is among the most prevalent non-communicable diseases causing significant morbidity and mortality globally. The aetiology and disease development of diabetes are influenced by genetic, lifestyle, and environmental factors. Due to an increasing number of cases each year, it is imperative to improve the understanding of modifiable environmental risk and protective factors. In this study we aimed to analyse associations between built and natural environment features and diabetes prevalence; and two major risk factors: physical activity and obesity and their mediation effects. METHODS: We analysed relationships between walkability and park availability with physical activity, obesity, and diabetes, using self-reported data from a large cross-sectional survey in British Columbia, Canada (n = 22,418). We validated results with an independent cohort (n = 11,972) in a subset of the analyses. The outcome measures included walking, moderate to vigorous physical activity (MVPA), body mass index (BMI), and diabetes. Built and natural environment features within a 1 km road network buffer around residential postal code centroids were assessed using validated indicators of walkability and park availability. We used general linear multivariable models (GLM) to examine the direct relationship between environmental features, physical activity, obesity, and diabetes respectively. Path models were developed to analyse mediation effects of physical activity and obesity on the association between environmental indicators and diabetes. The relative contribution of direct versus indirect effects was assessed. All models were adjusted for age, gender, income. ethnicity, years lived in neighbourhood and regional accessibility. RESULTS: Walkable neighbourhoods and areas with greater park availability were associated with lower rates of diabetes. There was a direct association of walkability and park availability on physical activity (highest vs. lowest quintile OR = 1.15; 95% CI: 1.00, 1.33 and OR = 1.28, 95% CI: 1.13, 1.45 respectively), obesity (highest vs. lowest quintile OR = 0.58, 95% CI: 0.49, 0.70 and OR = 0.57, 95% CI: 0.48, 0.68 respectively) and diabetes (highest vs. lowest quintile OR = 0.62, 95% CI: 0.45, 0.85, and OR = 0.63, 95% CI: 0.47, 0.84 respectively). Results were similar in the independent cohort. The associations between diabetes and walkability and park availability respectively were partly mediated by obesity (41% of total association for walkability and 53% of total association for park availability). The mediating effect of physical activity was negligible. CONCLUSION: Results support investments in walkability through active transportation and transit infrastructure. Changes in zoning and subdivision regulations governing land use actions are required to enable compact mixed-use environments with access to parks and high quality transit service. Future studies including cost-benefit analyses of health-related economic impacts of such investments can contribute to evidence-based decisions for healthier cities.

Community design and hypertension: Walkability and park access relationships with cardiovascular health.

BACKGROUND: There is an increased literature focusing on the role of the built and natural environments in preventing hypertension. However, very few studies have quantitively analyzed specific pathways through which urban form affects blood pressure levels. OBJECTIVES: To examine how features of the built and natural environments relate to hypertension and the mediating role of transportation and leisure walking and body mass index in this relationship. METHODS: We examined the association between neighbourhood walkability and park availability with hypertension through generalized linear models in two independent population cohorts. One Cohort was 22,418 adults (My Health My Community) and the other cohort was 11,972 adults (BC Generations Project). We employed a path analysis modelling approach to explore the presence and significance of mediating factors that may contribute to any association between walkability or park availability and hypertension. This study intentionally employed walkability measures enforced through municipal zoning and subdivision regulations legally underpinned by health, safety, and welfare. All models were adjusted for socioeconomic and other characteristics where data were available. RESULTS: Our analysis of two population-based Canadian cohorts consistently found that higher levels of walkability and park accessibility were both associated with significantly lower odds of self-reported hypertension, especially for lower income individuals. Mediation analysis showed that obesity accounted for 50% and 52.9% of the total effect of walkability and park accessibility on hypertension, respectively. DISCUSSION: We suggest an integrated population health approach that considers multimorbidity as a result of exposure to car-dependent areas and the lack of green spaces. Longitudinal research is needed to document causal effects of built and natural environments on hypertension.

Evaluation of a multisectoral intervention to mitigate the risk of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission in long-term care facilities.

OBJECTIVE: A Canadian health authority implemented a multisectoral intervention designed to control severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission during long-term care facility (LTCF) outbreaks. The primary objective was to evaluate the effectiveness of the intervention 14 days after implementation. DESIGN: Quasi-experimental, segmented regression analysis. INTERVENTION: A series of outbreak measures classified into 4 categories: case and contact management, proactive case detection, rigorous infection control practices and resource prioritization and stewardship. METHODS: A mixed-effects segmented Poisson regression model was fitted to the incidence rate of coronavirus disease 2019 (COVID-19), calculated every 2 days, within each facility and case type (staff vs residents). For each facility, the outbreak time period was segmented into an early outbreak period (within 14 days of the intervention) and postintervention period (beyond 14 days following the intervention). Model outputs quantified COVID-19 incidence trend and rate changes between these 2 periods. A secondary model was constructed to identify effect modification by case type. RESULTS: The significant upward trend in COVID-19 incidence rate during the early outbreak period (rate ratio [RR], 1.07; 95% confidence interval [CI], 1.03-1.11; P < .001) reversed during the postintervention period (RR, 0.73; 95% CI, 0.67-0.80; P < .001). The average trend did not differ by case type during the early outbreak period (P > .05) or the postintervention period (P > .05). However, staff had a 70% larger decrease in the average rate of COVID-19 during the postintervention period than residents (RR, 0.30; 95% CI, 0.10-0.88; P < .05). CONCLUSIONS: Our study provides evidence for the effectiveness of this intervention to reduce the transmission of COVID-19 in LTCFs. This intervention can be adapted and utilized by other jurisdictions to protect the vulnerable individuals in LTCFs.