Shannon M. Monnat is with the Maxwell School of Citizenship and Public Affairs, Lerner Center for Public Health Promotion, Center for Policy Research, and the Department of Sociology, Syracuse University, Syracuse, NY. David J. Peters and Andrew Hochstetler are with the Department of Sociology, Iowa State University, Ames. Mark T. Berg is with the Department of Sociology and Public Policy Center, University of Iowa, Iowa City.
Am J Public Health. 2019 Aug;109(8):1084-1091. doi: 10.2105/AJPH.2019.305136. Epub 2019 Jun 20.
To examine associations of county-level demographic, socioeconomic, and labor market characteristics on overall drug mortality rates and specific classes of opioid mortality. We used National Vital Statistics System mortality data (2002-2004 and 2014-2016) and county-level US Census data. We examined associations between several census variables and drug deaths for 2014 to 2016. We then identified specific classes of counties characterized by different levels and rates of growth in mortality from specific opioid types between 2002 to 2004 and 2014 to 2016. We ran multivariate and multivariable regression models to predict probabilities of membership in each "opioid mortality class" on the basis of county-level census measures. Drug mortality rates overall are higher in counties characterized by more economic disadvantage, more blue-collar and service employment, and higher opioid-prescribing rates. High rates of prescription opioid overdoses and overdoses involving both prescription and synthetic opioids cluster in more economically disadvantaged counties with larger concentrations of service industry workers. High heroin and "syndemic" opioid mortality counties (high rates across all major opioid types) are more urban, have larger concentrations of professional workers, and are less economically disadvantaged. Syndemic opioid counties also have greater concentrations of blue-collar workers. Census data are essential tools for understanding the importance of place-level characteristics on opioid mortality. National opioid policy strategies cannot be assumed universally applicable. In addition to national policies to combat the opioid and larger drug crises, emphasis should be on developing locally and regionally tailored interventions, with attention to place-based structural economic and social characteristics.
为了考察县级人口、社会经济和劳动力市场特征与总体药物死亡率以及特定类别的阿片类药物死亡率之间的关联。我们使用了国家生命统计系统的死亡率数据(2002-2004 年和 2014-2016 年)和县级美国人口普查数据。我们研究了 2014 年至 2016 年期间几个普查变量与药物死亡之间的关联。然后,我们确定了具有不同水平和特定类型阿片类药物死亡率增长率的特定类别的县,这些增长率来自 2002 年至 2004 年和 2014 年至 2016 年。我们运行了多变量和多变量回归模型,根据县级人口普查数据预测每个“阿片类药物死亡率类别”的成员资格概率。总体而言,经济劣势较大、蓝领和服务业就业较多以及阿片类药物处方率较高的县的药物死亡率较高。高处方类阿片类药物过量率和涉及处方类和合成类阿片类药物的过量率集中在经济劣势较大的县,这些县的服务业工人浓度较高。高海洛因和“综合征”类阿片类药物死亡率县(所有主要阿片类药物类型的高死亡率)更城市化,专业工人的浓度更高,经济劣势较小。综合征类阿片类药物县的蓝领工人浓度也更高。人口普查数据是了解地方特征对阿片类药物死亡率的重要性的重要工具。国家阿片类药物政策战略不能被认为是普遍适用的。除了打击阿片类药物和更大的毒品危机的国家政策外,还应重点制定针对当地和地区的定制干预措施,注意基于地点的结构性经济和社会特征。