Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
ACE Research Lab, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
PLoS One. 2021 Mar 11;16(3):e0247050. doi: 10.1371/journal.pone.0247050. eCollection 2021.
Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring.
In this study, we relied on emergency medical services data to investigate the geographical and temporal patterns in opioid-suspected overdose incidents in one of the largest and most ethnically diverse metropolitan areas (Houston Texas). Using a cross sectional design and Bayesian spatiotemporal models, we identified zip code areas with excessive opioid-suspected incidents, and assessed how the incidence risks were associated with zip code level socioeconomic characteristics. Our analysis suggested that opioid-suspected overdose incidents were particularly high in multiple zip codes, primarily south and central within the city. Zip codes with high percentage of renters had higher overdose relative risk (RR = 1.03; 95% CI: [1.01, 1.04]), while crowded housing and larger proportion of white citizens had lower relative risks (RR = 0.9; 95% CI: [0.84, 0.96], RR = 0.97, 95% CI: [0.95, 0.99], respectively).
Our analysis illustrated the utility of Bayesian spatiotemporal models in assisting the development of targeted community strategies for local prevention and harm reduction efforts.
阿片类药物相关的过量死亡是美国头号意外死因，要制定解决这一流行问题的区域性策略，首先需要更好地了解过量用药事件的发生地点和时间。
在这项研究中，我们依赖于紧急医疗服务数据，调查了美国最大和种族最多样化的大都市区之一（德克萨斯州休斯顿）中阿片类药物疑似过量用药事件的地理和时间模式。采用横截面设计和贝叶斯时空模型，我们确定了阿片类药物疑似事件过多的邮政编码区域，并评估了邮政编码级别的社会经济特征与发病风险的关联。我们的分析表明，多个邮政编码区域的阿片类药物疑似过量用药事件特别高，主要集中在城市的南部和中部。租房者比例高的邮政编码地区的过量用药相对风险更高（RR=1.03；95%CI：[1.01,1.04]），而住房拥挤和白人比例较高的邮政编码地区的相对风险较低（RR=0.9；95%CI：[0.84,0.96]，RR=0.97，95%CI：[0.95,0.99]）。
我们的分析说明了贝叶斯时空模型在协助制定针对特定社区的预防和减少伤害策略方面的效用。
