ArticleThe International journal on drug policy2021
Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.
Article in The International journal on drug policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Substance use and co-occurring mental health conditions among adolescents and young adults in North America: a systematic review.BMC psychiatry · 2026Pooled it
- "Sometimes I'm interested in seeing a fuller story to tell with numbers" Implementing a forecasting dashboard for harm reduction and overdose prevention: a qualitative assessment.BMC public health · 2025Trial
- Identifying communities at elevated risk of hepatitis C infection in Los Angeles County: an ecological study using machine learning.BMC public health · 2026Article
- Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.Lancet regional health. Americas · 2026Review
- An Overdose Forecasting Dashboard for Local Harm-Reduction Response.Health promotion practice · 2025Article
- Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.Preventive medicine · 2025Article
- Public Health Dashboards in Overdose Prevention: The Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action.Journal of medical Internet research · 2024Article
- Understanding Regional Patterns of Overdose Deaths Related to Opioids and Psychostimulants.Substance use & misuse · 2024Observational
- Creation and Validation of a New Socio-built Environment Index Measure of Opioid Overdose Risk for Use in Both Non-urban and Urban Settings.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Preparing for the next outbreak: A review of indices measuring outbreak preparedness, vulnerability, and resilience.Preventive medicine reports · 2023Review
- Application of machine learning algorithms for localized syringe services program policy implementation - Florida, 2017.Annals of medicine · 2022Article
- Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's "triple wave".Pharmacology, biochemistry, and behavior · 2022Article
- Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial.Addiction (Abingdon, England) · 2022Article
- Cardiovascular Autonomic Function Changes and Predictors During a 2-Year Physical Activity Program in Rheumatoid Arthritis: A PARA 2010 Substudy.Frontiers in medicine · 2021Article
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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13 authors.
Funding
Abstract
backgroundMultiple 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.
methodsFrom 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.
resultsMachine 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.
conclusionMachine 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.
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