用尿液药物测试数据为美国产生过量死亡的及时估计
John V Myers1, Charles Marks2, Joanne Kim1
1Department of Biomedical Informatics, The Ohio State University, Columbus.
JAMA network open
|June 9, 2025
概括
汇总的尿液药物测试 (UDT) 数据可以有效地估计数据滞后期间的国家药物过量死亡. 这种方法准确地捕捉了过量使用的趋势,包括COVID-19大流行期间的增加.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 毒理学 毒理学 毒理学
背景情况:
- 美国的临时致命药物过量估计有6个月的滞后.
- 准确,及时的数据对于了解过量使用趋势至关重要,尤其是在COVID-19大流行等公共卫生危机期间.
- 需要实时数据源来识别过量轨迹的突然变化.
研究的目的:
- 评估聚合尿液药物测试 (UDT) 数据在6个月数据滞后窗口内估计国家药物过量死亡的有用性.
- 评估UDT数据是否能及时提供对过量死亡趋势的见解.
主要方法:
- 一项横截面研究利用了来自美国医疗机构的超过310万个UDT标本 (2015-2025).
- 每月的UDT阳性率和药物水平 (芬太尼,甲胺) 被聚合.
- 基于4年的数据进行训练的通用线性模型使用了UDT数据来估计6个月滞后期的过量死亡,并与CDC死亡人数进行比较.
主要成果:
- 基于UDT的模型在估计过量死亡时,与基线ARIMA模型 (MAPE9.0%) 相比,显示出更高的准确性 (MAPE7.1%).
- 该模型成功地确定了COVID-19大流行开始时过量死亡人数的显著增加.
- 从2019年到2024年8月,在美国记录了超过537,000例过量死亡.
结论:
- 汇总的尿液药物测试数据显示,对估计当前药物过量死亡趋势有前途.
- 这种方法可以提供至关重要的最新信息,为公共卫生干预提供信息.
- 未来的模型改进可以纳入额外的数据来源,如药物扣押和综合征监测.
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