Evidence map›Paper›PMID 40164400›Full record

ArticlePreventive medicine2025

Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.

John C Halifax, Bennett Allen, Claire Pratty, Victoria Jent, Alexandra Skinner, Magdalena Cerdá, Brandon D L Marshall, Daniel B Neill, Jennifer Ahern

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Preventive medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

John C HalifaxDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA. Electronic address: john_halifax@berkeley.edu.
Bennett AllenCenter for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA.
Claire PrattyDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, USA.
Victoria JentCenter for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA.
Alexandra SkinnerDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, USA.
Magdalena CerdáCenter for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA.
Brandon D L MarshallDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, USA.
Daniel B NeillCenter for Urban Science and Progress, New York University, New York, NY, USA; Department of Computer Science, Courant Institute for Mathematical Sciences, New York University, New York, NY, USA; Robert F. Wagner Graduate School of Public Service, New York University, New York, NY, USA.
Jennifer AhernDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.

Funding

Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention TrialR01DA046620 · NIDA · BROWN UNIVERSITY · PI CERDA, MAGDALENA, MARSHALL, BRANDON DAVID LEWIS · 2019 to 2023
$3.8M
ACL HHS K01CE003586NCIPC CDC HHS K01 CE003586NIDA NIH HHS R01 DA046620
6 · The paper itself

Abstract

objectivesTo evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose prevention resources.

methodsWe evaluated seven combinations of data from six administrative data sources (American Community Survey (ACS) five-year estimates, built environment, emergency medical services non-fatal overdose response, prescription drug monitoring program, carceral release, and historical fatal overdose data). Fatal overdoses in Rhode Island census block groups (CBGs) were predicted using two machine learning approaches: linear regressions and random forests embedded in a nested cross-validation design. We evaluated performance using mean squared error and the percentage of statewide overdoses captured by CBGs forecast to be in top percentiles from 2019 to 2021.

resultsLinear models trained on ACS data combined with one other data source performed well, and comparably to models trained on all available data. Those including emergency medical service, prescription drug monitoring program, or carceral release data with ACS data achieved a priori goals for percentage of statewide overdoses captured by CBGs prioritized by models on average.

conclusionsPrioritizing neighborhoods for overdose prevention with forecasting is feasible using a simple-to-implement model trained on publicly available ACS data combined with only one other administrative data source in Rhode Island, offering a starting point for other jurisdictions.

Indexed as

Drug OverdoseMachine LearningResidence CharacteristicsEmergency Medical ServicesForecastingHumansInformation SourcesRhode IslandCommunity healthForecastMachine learningOverdoseSubstance use

Identifiers

PMID40164400
PMCPMC12648265

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.