Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA.
Int J Drug Policy. 2021 Oct;96:103395. doi: 10.1016/j.drugpo.2021.103395. Epub 2021 Jul 31.
BACKGROUND: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.
背景：由于非无菌注射吸毒，美国（USA）多个地区正经历着高比率的药物过量和血液传染病（包括 HIV 和丙型肝炎病毒（HCV））爆发。我们旨在确定美国罗得岛易受药物过量和传染病爆发影响的社区。主要目的是试用机器学习方法，以确定哪些邻里层面的因素对于在全州范围内创建“脆弱性评估得分”很重要。次要目的是让利益相关者试用交互式地图工具并可视化结果。
方法：从 2018 年 9 月至 2019 年 11 月，我们开展了一项名为“村庄项目”（Vulnerability Investigation of underlying Local risk And Geographic Events，VILLAGE）的邻里层面脆弱性评估和利益相关者参与流程。我们使用机器学习方法（LASSO、Elastic Net 和 RIDGE）开发了一个预测分析模型，以确定具有较高药物过量、HIV 和 HCV 爆发脆弱性的地区，使用药物过量死亡的人口普查区计数作为注射吸毒模式和相关健康结果的替代指标。利益相关者审查了地图工具的表面有效性和社区分布。
结果：机器学习预测模型适合估计邻里层面相对易受爆发影响的程度。模型中重要的变量包括住房费用负担、先前的药物过量死亡、住房密度和教育水平。确定了 89 个人口普查区（37%）没有先前的药物过量死亡，被认为容易受到这种爆发的影响，其中 9 个被确定为脆弱性评估得分在前 25%。结果以脆弱性分层图和在线交互式地图工具进行了传播。
结论：机器学习方法非常适合预测易受爆发影响的社区。这些方法有望成为评估结构脆弱性和在地方一级预防爆发的工具。
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