Using contributing causes of death improves prediction of opioid involvement in unclassified drug overdoses in US death records.
Using contributing causes of death improves prediction of opioid involvement in unclassified drug overdoses in US death records.
复制标题
DOI：
10.1111/add.14943
复制
发表时间：
2020-07
期刊：
影响因子：
6
通讯作者：
Hill, Elaine L.
中科院分区：
文献类型：
--
作者：
Boslett, Andrew J.;Denham, Alina;Hill, Elaine L.
关键词：
在致命性药物过量中，有很大一部分缺少关于具体药物参与的信息，导致对阿片类药物相关死亡率的漏报和对阿片类药物流行程度的歪曲。我们的目标是比较预测美国死亡记录中非分类药物过量涉及阿片类药物的方法学方法，并使用表现最佳的方法估计1999年至2016年致命的阿片类药物过量数量。这是从国家卫生统计中心获得的对1999-2016年间吸毒过量情况的二次数据分析，详细说明了多种死因记录。美国。共有632,331人吸毒过量死亡。已知的药物过量占78.2%(N=494,316)，未分类的药物过量(ICD-10 T50.9)占21.8%(N=138,015)。已知的阿片类药物参与是使用ICD-10代码T40.0-40.4和T40.6定义的，这些代码记录在一组促成原因中。使用多种方法预测未分类药物过量涉及阿片类药物：Logistic回归和机器学习技术，纳入/排除致死原因，以及纳入/排除县级特征。在选择了预测能力最高的模型后，我们计算了阿片类药物相关死亡率的修正估计。Logistic回归和随机森林模型的表现类似。包括促成因素大大提高了预测的准确性，而包括县的特征并不能。使用优越的预测模型，我们发现1999-2016年未分类的药物过量中有71.8%涉及阿片类药物，这意味着与阿片类药物相关的死亡增加了99,160人，比报告的死亡人数高出约28%。重要的是，在低估阿片类药物过量方面存在显著的地理差异。在旨在纠正美国的阿片类药物死亡人数时，未来的报告和研究应将促成死亡的原因作为预测因素；关于统计建模，Logistic回归和随机森林在预测方面同样有效。
A substantial share of fatal drug overdoses is missing information on specific drug involvement, leading to underreporting of opioid related death rates and a misrepresentation of the extent of the opioid epidemic. We aimed to compare methodological approaches to predicting opioid involvement in unclassified drug overdoses in United States death records and to estimate the number of fatal opioid overdoses from 1999 to 2016 using the best performing method. This was a secondary data analysis of the universe of drug overdoses in 1999–2016 obtained from the National Center for Health Statistics Detailed Multiple Cause of Death records. United States. A total of 632,331 drug overdose decedents. Drug overdoses with known drug classification comprised 78.2% of the cases (N=494,316) and unclassified drug overdoses (ICD-10 T50.9) comprised 21.8% (N=138,015). Known opioid involvement was defined using ICD-10 codes T40.0–40.4 and T40.6, recorded in the set of contributing causes. Opioid involvement in unclassified drug overdoses was predicted using multiple methodological approaches: logistic regression and machine learning techniques, inclusion/exclusion of contributing causes of death, and inclusion/exclusion of county-level characteristics. Having selected the model with the highest predictive ability, we calculated corrected estimates of opioid related mortality. Logistic regression and random forest models perform similarly. Including contributing causes substantially improves predictive accuracy, while including county characteristics does not. Using superior prediction model, we found that 71.8% of unclassified drug overdoses in 1999–2016 involved opioids, translating into 99,160 additional opioid related deaths, or approximately 28% more than reported. Importantly, there is a striking geographic variation in undercounting of opioid overdoses. When aiming to correct opioid death counts in the United States, future reports and studies should include contributing causes of death as predictors; with respect to statistical modeling, logistic regression and random forests are equally effective in prediction.
登录
查看更多内容
影响因子：
12.7
作者：
Lowder, Evan M.;Ray, Bradley R.;Watson, Dennis P.
通讯作者：
Watson, Dennis P.
影响因子：
3.3
作者：
Ruhm, Christopher J.
通讯作者：
Ruhm, Christopher J.
影响因子：
4.7
作者：
Ruhm, Christopher J.
通讯作者：
Ruhm, Christopher J.
影响因子：
5.5
作者：
Ruhm, Christopher J.
通讯作者：
Ruhm, Christopher J.
影响因子：
6
作者：
Ruhm, Christopher J.
通讯作者：
Ruhm, Christopher J.