Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada.
Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
PLoS One. 2021 Mar 11;16(3):e0248285. doi: 10.1371/journal.pone.0248285. eCollection 2021.
Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada's National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto's neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.
伤害已成为破坏性且常被低估的公共卫生问题。在加拿大，伤害是导致 65 岁以下人群潜在寿命损失的主要原因。然而，伤害的地理模式在空间和时间上都很明显，这表明有可能在邻里尺度上对政策进行空间优化，以降低伤害风险、促进大都市地区的预防和控制。在本文中，使用加拿大国家门诊护理报告系统评估了 2004 年至 2010 年多伦多的非故意和故意伤害，探索了整个城市伤害的空间关系，同时还使用了多伦多幸福感数据。这些发现得到了证实，对报告的超过 170 万起伤害进行了全局和局部水平的空间自相关分析。对多伦多邻里的细分进一步确定了全市最脆弱的社区，整个城市的空间分布特征明显。各个社区都需要制定针对伤害预防的不同政策。这是本文的主要创新点之一。对三个回归模型进行了比较。结果表明，空间回归模型的性能显著更强，表明应该将空间回归用于伤害研究。多伦多幸福感数据在评估非故意伤害、发病率和跌倒方面表现相当不错。但在理解故意伤害的动态方面表现欠佳。这些结果为在邻里层面制定定制化的伤害预防措施提供了一个框架，为像多伦多这样的发达城市的医疗领域的规划和参与式决策提供了重要依据。
