Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA.
Department of Anesthesiology, Wake Forest Baptist Health, Winston-Salem, North Carolina, USA.
J Am Med Inform Assoc. 2019 Aug 1;26(8-9):767-777. doi: 10.1093/jamia/ocz050.
Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic.
We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models.
Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states.
Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations.
Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.
研究个体、地理和经济特征是否可以预测特定药物在药物过量死亡中的数据缺失情况，因为这种数据缺失会导致药物过量死亡率数据出现偏差，而后者是衡量阿片类药物流行程度的关键数据来源。
我们采用多方法分析方法，结合了 2014-2016 年的全国性数据来源（死亡率、人口统计学、经济和地理），对过量死亡记录中药物分类缺失的情况进行了分析（根据死亡证明中 ICD-10 T50.9 的使用情况定义）。我们在多变量逻辑回归模型中检查了个体差异，在空间分析（热图）中检查了地理差异，并在时间趋势分析（描述性统计）以及个体和县级多变量逻辑回归模型的组合中检查了经济差异。
我们的分析一致发现，女性、白人、非西班牙裔、受过大学教育、年龄在 30-59 岁之间、来自贫困县的死者中，未分类的药物过量死亡率更高。尽管未分类的药物过量死亡率随着时间的推移而降低，但贫富县之间的差距仍然存在。州内和州际之间也存在显著的地理差异。
鉴于死亡率数据在衡量阿片类药物流行程度方面的重要作用，了解特定药物参与情况数据缺失背后的个体和社区不平等现象非常重要。了解这些不平等现象可以增强我们对阿片类药物危机的理解，并为更公平的资源分配提供数据驱动的干预和政策措施。
未分类的药物过量死亡背后存在多种个体、地理和经济差异，这对公共卫生信息学和解决阿片类药物危机具有重要意义。
