National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Division of Violence Prevention, US Centers for Disease Control and Prevention, Atlanta, Georgia.
JAMA Netw Open. 2022 Jul 1;5(7):e2223033. doi: 10.1001/jamanetworkopen.2022.23033.
Opioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more.
To build and validate a statistical model for estimating national opioid overdose deaths in near real time.
DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, signals from 5 overdose-related, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality.
Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths.
Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System.
Statistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and -1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model).
Results of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.
阿片类药物过量在美国是一个主要的公共卫生问题; 然而，全国范围内的过量死亡数据要延迟几个月甚至更长时间。
建立并验证一种用于实时估算全国阿片类药物过量死亡人数的统计模型。
设计、设置和参与者：在这项横断面研究中，我们结合了来自美国 2014 年至 2019 年的五个与过量相关的代理数据来源的信号，这些信号涵盖了健康、执法和在线数据，使用 LASSO（最小绝对收缩和选择算子）回归模型进行组合，并对 2018 年和 2019 年的阿片类药物过量死亡进行每周预测，以验证模型性能。结果还与基线 SARIMA（季节性自回归综合移动平均）模型进行了比较，SARIMA 是预测伤害死亡率最常用的方法之一。
来自国家综合征监测计划的阿片类药物过量急诊就诊时间序列数据、国家法医实验室信息系统中非法市场流通的海洛因和合成阿片类药物数量数据、谷歌上海洛因和合成阿片类药物搜索量数据以及 Twitter 和 Reddit 上海洛因和合成阿片类药物发布量数据用于训练和验证阿片类药物过量死亡预测模型。
对 2018 年和 2019 年美国每周阿片类药物过量死亡人数进行了基于模型的预测，并与国家生命统计系统实际观察到的阿片类药物过量死亡人数进行了比较。
使用 5 个实时代理数据来源的统计模型分别估计了 2018 年和 2019 年的全国阿片类药物过量死亡率，误差分别为 1.01%和-1.05%。考虑到每周预测的准确性，基于机器学习的方法每周估计值（均方根误差）的平均误差为 2018 年 60.3 例过量死亡（SARIMA 模型为 310.2 例过量死亡），2019 年 67.2 例过量死亡（SARIMA 模型为 83.3 例过量死亡）。
这项连续横断面研究的结果表明，代理行政数据源可用于估计全国阿片类药物过量死亡率趋势，以便更及时地了解这一公共卫生问题。
Zotero 插件
