Availability of timely methadone treatment in the United States and Canada during COVID-19: a census tract-level analysis☆

Objectives We sought to compare timely access to methadone treatment in the United States (US) and Canada during the COVID-19 pandemic. Methods We conducted a cross-sectional study of census tracts and aggregated dissemination areas (used for rural Canada) within 14 US and 3 Canadian jurisdictions in 2020. We excluded census tracts or areas with a population density of less than one person per square km. Data from a 2020 audit of timely medication access was used to determine clinics accepting new patients within 48 hours. Unadjusted and adjusted linear regressions were performed to examine the relationship between area population density and sociodemographic covariates and three outcome variables: 1) driving distance to the nearest methadone clinic accepting new patients, 2) driving distance to the nearest methadone clinic accepting new patients for medication initiation within 48 hours, and 3) the difference in the driving distance between the first and second outcome. Results We included 17,611 census tracts and areas with a population density greater than one person per square kilometer. After adjusting for area covariates, US jurisdictions were a median of 11.6 miles (p value <0.001) further from a methadone clinic accepting new patients and 25.1 miles (p value <0.001) further from a clinic accepting new patients within 48 hours than Canadian jurisdictions. Conclusions These results suggest that the more flexible Canadian regulatory approach to methadone treatment is associated with a greater availability of timely methadone treatment and reduced urban-rural disparity in availability, compared to the US.

Availability of timely methadone treatment in the United States and Canada during COVID-19: A census tract-level analysis.

OBJECTIVES: We sought to compare timely access to methadone treatment in the United States (US) and Canada during the COVID-19 pandemic. METHODS: We conducted a cross-sectional study of census tracts and aggregated dissemination areas (used for rural Canada) within 14 US and 3 Canadian jurisdictions in 2020. We excluded census tracts or areas with a population density of less than one person per square km. Data from a 2020 audit of timely medication access was used to determine clinics accepting new patients within 48 h. Unadjusted and adjusted linear regressions were performed to examine the relationship between area population density and sociodemographic covariates and three outcome variables: 1) driving distance to the nearest methadone clinic accepting new patients, 2) driving distance to the nearest methadone clinic accepting new patients for medication initiation within 48 h, and 3) the difference in the driving distance between the first and second outcome. RESULTS: We included 17,611 census tracts and areas with a population density greater than one person per square kilometer. After adjusting for area covariates, US jurisdictions were a median of 11.6 miles (p value <0.001) further from a methadone clinic accepting new patients and 25.1 miles (p value <0.001) further from a clinic accepting new patients within 48 h than Canadian jurisdictions. CONCLUSIONS: These results suggest that the more flexible Canadian regulatory approach to methadone treatment is associated with a greater availability of timely methadone treatment and reduced urban-rural disparity in availability, compared to the US.

Spatial and temporal patterns in Canadian COVID-19 crowdfunding campaigns.

Online charitable crowdfunding has become an increasingly prevalent way for Canadians to deal with costs that they would otherwise not be able to shoulder on their own. With the onset of COVID-19 and related lockdown measures, there is evidence of a surge in crowdfunding use relating to the pandemic. This study gathered, classified, and analysed Canadian crowdfunding campaigns created in response to COVID-19 from GoFundMe.com, a popular crowdfunding platform. Spatio-temporal analysis of classified campaigns allowed for observation of emergent trends in the distribution of pandemic-related need incidence and financial support throughout the pandemic. Campaigns raising money on behalf of established charities were the most common in the sample, and accounted for the greatest portion of funding raised, while campaigns for businesses made up a small proportion. Dense metropolitan areas accounted for the vast majority of campaign locations, and total sample funding was disproportionately raised by campaigners in Ontario and British Columbia.

Environmental Preferences and Concerns of Recreational Road Runners.

Recreational road running is growing in popularity and has been linked to numerous mental and physical health benefits. However, we know little about what environmental preferences or concerns runners have regarding participation in the sport, and whether differences exist across age and gender. We conducted a cross-sectional survey on recreational road runners to investigate the type of built and natural environments road runners prefer, as well as the safety and health concerns that may affect runners' choice of environment. Responses were analyzed by age and gender. A total of 1228 road runners responded to the survey; 59.6% of respondents were women and 32.1% of respondents were men. Most respondents preferred to run on asphalt or sidewalk surfaces, and preferred well-lit, tree-lined routes. Major concerns for both men and women include animals and dangerous road conditions. Men and women differed significantly in their responses to the importance of running around others and their primary concerns while running. Results of this study serve to deepen our understanding of recreational road runners' environmental preferences and concerns, providing valuable information for public health officials and city planners alike. This information must be considered if we are to continue to encourage uptake of running as a sport and reap its health effects.

The need for GIScience in mapping COVID-19.

Since first being tracked in China in late 2019, the effects of the COVID-19 coronavirus have shaped global patterns of morbidity and mortality, as well as exposed the strengths and limitations of health care systems and social safety nets. Without question, reporting of its impact has been bolstered in large part through near real-time daily mapping of cases and fatalities. Though these maps serve as an effective political and social tool in communicating disease impact, most visualizations largely over-emphasize their usefulness for tracking disease progression and appropriate responses. Messy and inconsistent health data are a big part of this problem, as is a paucity of high-resolution spatial data to monitor health outcomes. Another issue is that the ease of producing out-of-the box products largely out paces the response to the core challenges inherent in the poor quality of most geo-referenced data. Adopting a GIScience approach, and in particular, making use of location-based intelligence tools, can improve the shortcomings in data reporting and more accurately reveal how COVID-19 will have a long-term impact on global health.

Street masking: a network-based geographic mask for easily protecting geoprivacy.

BACKGROUND: Geographic masks are techniques used to protect individual privacy in published maps but are highly under-utilized in research. This leads to continual violations of individual privacy, as sensitive health records are put at risk in unmasked maps. New approaches to geographic masking are required that foster accessibility and ease of use, such that they become more widely adopted. This article describes a new geographic masking method, called street masking, that reduces the burden on users of finding supplemental population data by instead automatically retrieving OpenStreetMap data and using the road network as a basis for masking. We compare it to donut geomasking, both with and without population density taken into account, to evaluate its efficacy against geographic masks that require slightly less and slightly more supplemental data. Our analysis is performed on synthetic data in three different Canadian cities. RESULTS: Street masking performs similarly to population-based donut geomasking with regard to privacy protection, achieving comparable k-anonymity values at similar median displacement distances. As expected, distance-based donut geomasking performs worst at privacy protection. Street masking also performs very well regarding information loss, achieving far better cluster preservation and landcover agreement than population-based donut geomasking. Distance-based donut geomasking performs similarly to street masking, though at the cost of reduced privacy protection. CONCLUSION: Street masking competes with, if not out-performs population-based donut geomasking and does so without requiring any supplemental data from users. Moreover, unlike most other geographic masks, it significantly minimizes the risk of false attribution and inherently takes many geographic barriers into account. It is easily accessible for Python users and provides the foundation for interfaces to be built for non-coding users, such that privacy can be better protected in sensitive geospatial research.