基于药物相关网络搜索的机器学习实现药物过量死亡的精确时空映射。

Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches.

机构信息

Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America.

Georgia State University, Atlanta, Georgia, United States of America.

出版信息

PLoS One. 2020 Dec 7;15(12):e0243622. doi: 10.1371/journal.pone.0243622. eCollection 2020.

DOI:10.1371/journal.pone.0243622

PMID:33284864

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7721465/

Abstract

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004-2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE's error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005-2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.

摘要

注射毒品者（PWID）因过量死亡（ODD）、感染艾滋病毒、乙型肝炎（HBV）和丙型肝炎病毒（HCV）以及非传染性健康状况而面临更高的风险。确定 PWID 社区的时空位置对于制定高效、具有成本效益的公共卫生干预措施，以减少与注射毒品使用（IDU）相关的发病率和死亡率至关重要。报告的 ODD 是不同地理区域 IDU 程度的有力指标。然而，ODD 的量化可能需要时间，由于包括死亡调查和药物测试在内的一系列因素，ODD 的报告可能会延迟。这种延迟的 ODD 报告可能会影响对传染病的有效早期干预。我们提出了一种新模型，即动态过量脆弱性估计器（DOVE），用于评估和时空映射美国不同司法管辖区的 ODD。我们使用谷歌®网络搜索量（即包含某些词的所有搜索量的一部分）来识别 2004 年至 2017 年期间报告的 ODD 率与毒品相关搜索词之间的强关联。开发了一个机器学习模型（极端随机森林）来生成州和县级别的年度 ODD 估计值，以及州级别的每月估计值。关于每年的 ODD 总数，2005 年至 2017 年期间，DOVE 在全美范围内的误差仅为 3.52%（中位数绝对误差，MAE）。DOVE 估计 2017 年报告的 70237 例 ODD 中的 66463 例（94.48%）。对于该年，各州年度估计的个体 ODD 率的 MAE 分别为 4.43%、7.34%和 12.75%，县年度估计和州月度估计。这些结果表明，DOVE 的 ODD 估计值适用于大多数州的动态 IDU 评估，这可能会提醒人们注意与 IDU 相关的发病率和死亡率可能增加的情况。DOVE 生成的 ODD 估计值为 ODD 的时空映射提供了机会。及时识别 PWID 中潜在的死亡趋势，可能有助于通过将公共卫生干预措施针对最脆弱的 PWID 社区，制定有效的 ODD 预防和乙型肝炎、丙型肝炎和艾滋病毒感染消除计划。