Suppr超能文献

利用疑似潜在药物过量追踪器(SPOT)实时预测药物过量导致的意外死亡:公共卫生意义。

Predicting accidental drug overdose as the cause of fatality in near real-time using the Suspected Potential Overdose Tracker (SPOT): public health implications.

机构信息

Columbia University School of Social Work, New York, NY, USA.

Friends Research Institute, Inc, Baltimore, MD, USA.

出版信息

BMC Public Health. 2022 Jul 8;22(1):1311. doi: 10.1186/s12889-022-13700-0.

Abstract

BACKGROUND

Effective responses to the worsening drug overdose epidemic require accurate and timely drug overdose surveillance data. The objectives of this paper are to describe the development, functionality, and accuracy of the Suspected Potential Overdose Tracker (SPOT) for predicting accidental drug overdose as the cause and manner of death in near real-time, and public health implications of adopting the tool.

METHODS

SPOT was developed to rapidly identify overdose deaths through a simple and duplicable process using data collected by death investigators. The tool assigns each death a ranking of 1 through 3 based on the likelihood of it being an unintentional drug overdose, with 1 representing the highest likelihood that the death will be confirmed as an unintentional drug overdose and 3 representing the lowest. We measured the accuracy of the tool for predicting overdose deaths by comparing potential overdose deaths in New York City from 2018-2020 that were identified using SPOT to finalized death certificates. We also calculated the proportion of death certificate-confirmed overdoses that were missed by the SPOT tool and the proportion of type 1 errors.

RESULTS

SPOT captured up to 77% of unintentional drug overdose deaths using data collected within 72 h of fatality. The tool predicted unintentional drug overdose from 2018 to 2020 with 93-97% accuracy for cases assigned a ranking of 1, 87-91% accuracy for cases assigned a ranking of 2, and 62-73% accuracy for cases assigned a ranking of 3. Among all unintentional overdose deaths in 2018, 2019, and 2020, 21%, 28%, and 33% were missed by the SPOT tool, respectively. During this timeframe, the proportion of type 1 errors ranged from 15%-23%.

CONCLUSIONS

SPOT may be used by health departments, epidemiologists, public health programs, and others to monitor overdose fatalities before death certificate data becomes available. Improved monitoring of overdose fatalities allows for rapid data-driven decision making, identification of gaps in public health and public safety overdose response, and evaluation and response to overdose prevention interventions, programs, and policies.

摘要

背景

有效应对日益恶化的药物过量流行需要准确和及时的药物过量监测数据。本文的目的是描述可疑潜在过量追踪器 (SPOT) 的开发、功能和准确性,以实时预测意外药物过量作为死因和死亡方式,并说明采用该工具的公共卫生意义。

方法

SPOT 的开发是为了通过使用死因调查员收集的数据,快速识别过量死亡,采用简单且可重复的流程。该工具根据药物过量的可能性,将每个死亡分配 1 到 3 的排名,其中 1 表示死亡将被确认为意外药物过量的可能性最高,3 表示可能性最低。我们通过比较使用 SPOT 识别的 2018 年至 2020 年纽约市的潜在过量死亡与最终死亡证明,来衡量该工具预测过量死亡的准确性。我们还计算了 SPOT 工具错过的经死亡证明确认的过量死亡比例和 1 类错误的比例。

结果

SPOT 使用在死亡后 72 小时内收集的数据,可捕获高达 77%的意外药物过量死亡。该工具在 2018 年至 2020 年期间预测意外药物过量,对排名 1 的病例的准确率为 93-97%,对排名 2 的病例的准确率为 87-91%,对排名 3 的病例的准确率为 62-73%。在 2018 年、2019 年和 2020 年所有意外药物过量死亡中,SPOT 工具分别遗漏了 21%、28%和 33%。在这段时间内,1 类错误的比例范围为 15%-23%。

结论

卫生部门、流行病学家、公共卫生计划和其他机构可以在死亡证明数据可用之前使用 SPOT 监测过量死亡。对过量死亡的监测得到改善,使数据驱动的决策更加迅速,识别公共卫生和公共安全应对药物过量的差距,以及评估和应对药物过量预防干预、方案和政策成为可能。

相似文献

7
Buprenorphine infrequently found in fatal overdose in New York City.在纽约市,丁丙诺啡很少在致命过量用药情况中被发现。
Drug Alcohol Depend. 2015 Oct 1;155:298-301. doi: 10.1016/j.drugalcdep.2015.08.007. Epub 2015 Aug 15.

本文引用的文献

9
10
Federal Response to the Opioid Crisis.应对阿片类药物危机的联邦对策。
Curr HIV/AIDS Rep. 2018 Aug;15(4):293-301. doi: 10.1007/s11904-018-0398-8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验