Search

Introduction: There were repeated reports of increased cannabis sales, use and health impacts in Canada during the COVID‐19 pandemic. However, it was unclear whether the increases were due to pandemic effects or industry expansion. Methods: We performed interrupted time series regressions of monthly per capita legal cannabis sales from March 2019 to February 2021, first with national averages, then with provincial/territorial data after adjusting for store density. We considered two interruption alternatives: January 2020, when product variety increased; and March 2020, when pandemic restrictions began. Results: The provincial/territorial regression with the January interruption explained R2 = 69.6% of within‐jurisdiction variation: baseline monthly per capita sales growth averaged $0.21 (95% confidence interval [CI] 0.15, 0.26), sales immediately dropped in January by $1.02 (95% CI −1.67, −0.37), and monthly growth thereafter increased by $0.16 (95% CI 0.06, 0.25). With the March interruption, the regression instead explained 68.7% of variation: baseline sales growth averaged $0.14 (95% CI 0.06, 0.22), there was no immediate drop and growth thereafter increased by $0.22 per month (95% CI 0.08, 0.35). Discussion and Conclusions: Increasing cannabis sales during the pandemic was consistent with pre‐existing trends and increasing store numbers. The extra increased growth was more aligned with January's new product arrivals than with March's pandemic measures, though the latter cannot be ruled out. We found little evidence of pandemic impacts on Canada's aggregate legal cannabis sales. We therefore caution against attributing increased population‐level cannabis use or health impacts primarily to the pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Understanding the inequitable impacts of the ongoing COVID-19 pandemic on youth mental health are leading priorities. Existing research has linked income inequality in schools to adolescent depression, however, it is unclear if the onset of the pandemic exacerbated the effects of income inequality on adolescent mental health. The current study aimed to quantify the association between income inequality and adolescent mental health during COVID-19. Material and methods: Longitudinal data were taken from three waves (2018/19 to 2020/21) of the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) school-based study. Latent Growth Curve modelling was used to assess the association between Census District (CD)-level income inequality and depressive symptoms before and after the onset of COVID-19. Results: The study sample included 29,722 students across 43 Census divisions in British Columbia, Alberta, Ontario, and Quebec. The average age of the sample at baseline was 14.9 years [standard deviation (SD) = 1.5] and ranged between 12 and 19 years of age. Most of the sample self-reported as white (76.3%) and female (54.4%). Students who completed the COMPASS survey after the onset of COVID reported 0.20-unit higher depressive scores (95% CI = 0.16, 0.24) compared to pre-COVID. The adjusted analyses indicated that the association between income inequality on anxiety scores was strengthened following the onset of COVID-19 (β = 0.02, 95% CI = 0.0004, 0.03), indicating that income inequality was associated with a greater increase in anxiety scores during COVID-19. Discussion: The adjusted results indicate that the association between income inequality and adolescent anxiety persisted and was heightened at the onset of COVID-19. Future studies should use quasi-experimental methods to strengthen this finding. The current study can inform policy and program discussions regarding the effects of the COVID-19 pandemic and pandemic recovery for young Canadians and relevant social policies for improving adolescent mental health. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to estimate associations between COVID-19 incidence and mortality with neighbourhood-level immigration, race, housing, and socio-economic characteristics. Methods: We conducted a population-based study of 28,808 COVID-19 cases in the provincial reportable infectious disease surveillance systems (Public Health Case and Contact Management System) which includes all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020. Residents of congregate settings, Indigenous communities living on reserves or small neighbourhoods with populations <1,000 were excluded. Comparing neighbourhoods in the 90th to the 10th percentiles of socio-demographic characteristics, we estimated the associations between 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics and COVID-19 incidence and mortality using Poisson generalized linear mixed models. Results: Neighbourhoods with the highest proportion of immigrants (relative risk (RR): 4.0, 95%CI:3.5–4.5) and visible minority residents (RR: 3.3, 95%CI:2.9–3.7) showed the strongest association with COVID-19 incidence in adjusted models. Among individual race groups, COVID-19 incidence was highest among neighbourhoods with the high proportions of Black (RR: 2.4, 95%CI:2.2–2.6), South Asian (RR: 1.9, 95%CI:1.8–2.1), Latin American (RR: 1.8, 95%CI:1.6–2.0) and Middle Eastern (RR: 1.2, 95%CI:1.1–1.3) residents. Neighbourhoods with the highest average household size (RR: 1.9, 95%CI:1.7–2.1), proportion of multigenerational families (RR: 1.8, 95%CI:1.7–2.0) and unsuitably crowded housing (RR: 2.1, 95%CI:2.0–2.3) were associated with COVID-19 incidence. Neighbourhoods with the highest proportion of residents with less than high school education (RR: 1.6, 95%CI:1.4–1.8), low income (RR: 1.4, 95%CI:1.2–1.5) and unaffordable housing (RR: 1.6, 95%CI:1.4–1.8) were associated with COVID-19 incidence. Similar inequities were observed across neighbourhood-level sociodemographic characteristics and COVID-19 mortality. Conclusions: Neighbourhood-level inequities in COVID-19 incidence and mortality were observed in Ontario, with excess burden experienced in neighbourhoods with a higher proportion of immigrants, racialized populations, large households and low socio-economic status. [ABSTRACT FROM AUTHOR]