Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati EMS data, and other datasets. From the publicly available National Survey on Drug Use and Health (NSDUH), we developed a model of opioid misuse and assigned probability of misuse to each synthetic individual. We then analyzed EMS overdose data in the context of local level misuse and demographic characteristics. The main results show locations where there is a dramatic variation in ratio values between overdose events and the number of misusers. We concluded that, for optimal efficacy, intervention strategies should consider the existence of exceptional geographic locations with extremely high or low values of this ratio.

中文翻译：

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利用合成种群了解阿片类药物相关过量和预测的阿片类药物滥用的地理空间格局

俄亥俄州在全美过量死亡的流行中处于领先地位，其中多数是由阿片类药物引起的。通过这项研究，我们估计了通过EMS呼叫测得的阿片类药物过量与模型预测的药物滥用之间的关联。RTI开发的综合人口统计上代表了辛辛那提的每个家庭，并允许人们开发一个地理上明确的模型，该模型将辛辛那提EMS数据和其他数据集联系起来。根据可公开获得的全国药物使用和健康调查（NSDUH），我们开发了阿片类药物滥用模型，并为每个合成个体分配了滥用可能性。然后，我们在局部滥用和人口统计学特征的背景下分析了EMS过量数据。主要结果显示了在用药过量事件和误用次数之间比率值有显着变化的位置。