Background News media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective Using data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking). Methods COVID-19–related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19–related official updates). Results In addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.
Legalization of recreational cannabis in Ontario included the legalization of flower and herbs (Phase 1, October 2018), and was followed by the deregulation of cannabis retailers and sales of edibles (Phase 2, February 2020). Research on the impact of cannabis legalization on acute care utilization is nascet; no research has investigated potential age, gender, and geographically vulnerable subgroup effects. Residents living in Northern Ontario not only have higher levels of substance use problems, but also have inadequate access to primary healthcare. Our study investigated the impact of Ontario’s recreational cannabis policy (including Phase 1 and 2) on cannabis-attributable emergency department (ED) visits, and estimated the impact separately for different age and gender groups, with additional analyses focused on Northern Ontarians. We created a cohort of adults (18 and over) eligible for provincial universal health insurance with continuous coverage from 2015–2021 (n = 14,900,820). An interrupted time series was used to examine the immediate impact and month-to-month changes in cannabis-related ED visits associated with Phase 1 & 2 for each subgroup. While Northern Ontario has higher rates of cannabis-related ED visits, both Northern and Southern Ontario show similar patterns of changes. Phase 1 was associated with significant increases in adults 25–64, with the strongest increases seen in women 45–64. Month-to-month trends were flattened in most groups compared to pre-legalization. Phase 2 was associated with significant immediate increases for adults aged 18–44 in both genders, but the increases were larger in women than men. No significant month-to-month changes were detected in this period. While current preventive efforts are largely focused on reducing cannabis-related harms in youths and younger adults, our results show that adults 25–64, particularly women, have been significantly impacted by cannabis policies. Further research on gender-specific cannabis dosage and targeted interventions for adult women should be investigated. Legalization did not appear to have a differential impact on Northern versus Southern Ontario, but higher rates of ED visits in the North should be addressed.
Background There is growing evidence that lesbian, gay, and bisexual (LGB) adults experience more sleep problems than the general population. As LGB individuals experience a significantly greater risk of family rejection and low family support, our study investigates the role of family support as a potential determinant of LGB sleep problems over a prolonged period, and whether friend support (i.e. chosen family) can mitigate the effect of low family support. Given the importance of sleep on mental and physical health, study results may help shed light on persistent health disparities across sexual orientations. Methods Our sample included 1703 LGB individuals from the UK Household Longitudinal Study (UKHLS). Mixed-effect logistic regressions were used to estimate the effect of family and friend support on the development of sleep problems after 24 months while controlling for potential confounders. A modified Pittsburgh Sleep Quality Index was used to measure 1) presence of any sleep problems, 2) short sleep duration, and 3) poor sleep quality. Results Family support at baseline was independently associated with all sleep problems in our study after 24-months: 1 SD increase in family support was associated with a 0.94 times lower risk of sleep problems (95% C.I = 0.90-0.98), a 0.88 times lower risk of short sleep duration (95% C.I = 0.81-0.95), and a 0.92 times lower risk of sleep quality (95% C.I = 0.93-0.98). Support from one’s chosen family (proxied by friend support) did not mitigate the effects of low family support on sleep problems. Conclusions Our study found a consistent effect of family support across all sleep outcomes along with evidence of a persistent effect after 24 months. Our findings point to the importance of targeting family support in designing interventions aimed at reducing LGB sleep problems.
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