Abstract：
The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017-2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago.
摘要：
SARS-CoV-2(COVID-19)的全球大流行与美国阿片类药物使用障碍患者的负面影响有关。这项研究的重点是从地理空间角度分析美国COVID-19背景下阿片类药物参与的死亡率。我们调查了2020年阿片类药物相关死亡的时空模式，并将这些死亡的时空模式与前三年(2017-2019年)的模式进行了比较，以了解COVID-19大流行背景下的变化。使用反事实分析框架和时空随机森林（STRF）模型来估计与大流行有关的阿片类药物相关死亡人数的增加。为了进一步了解阿片类药物死亡与COVID-19相关因素之间的关系，我们为芝加哥市建立了一个时空随机森林模型，在2020年期间，与阿片类药物相关的死亡人数急剧增加。该模型确定的高级指标，如按人群调整的阳性COVID-19病例数和大流行期间在家居住时间的变化，用于生成芝加哥COVID-19大流行期间阿片类药物过量的脆弱性指数。
