Evidence map›Paper›PMID 38710239›Full record

ArticleAnnals of epidemiology2024

Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

Kristina Yamkovoy, Prasad Patil, Devon Dunn, Elizabeth Erdman, Dana Bernson, Pallavi Aytha Swathi, Samantha K Nall, Yanjia Zhang, Jianing Wang, Lauren Brinkley-Rubinstein and 3 more

Abstract read
In one paragraph

Article in Annals of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. A chain of survival for drug overdose.Resuscitation plus · 2025
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Kristina YamkovoyUniversity of Colorado School of Medicine, Division of General Internal Medicine, Aurora, CO, USA.
Prasad PatilBoston University School of Public Health, Boston, MA, USA.
Devon DunnMassachusetts Department of Public Health, Boston, MA, USA.
Elizabeth ErdmanMassachusetts Department of Public Health, Boston, MA, USA.
Dana BernsonMassachusetts Department of Public Health, Boston, MA, USA.
Pallavi Aytha SwathiUniversity of Colorado School of Medicine, Division of General Internal Medicine, Aurora, CO, USA.
Samantha K NallUniversity of Colorado School of Medicine, Division of General Internal Medicine, Aurora, CO, USA.
Yanjia ZhangBoston University School of Public Health, Boston, MA, USA.
Jianing WangMassachusetts General Hospital, Boston MA, USA.
Lauren Brinkley-RubinsteinDuke University, Department of Population Health Sciences, Durham, NC, USA.
Katherine H LeMastersUniversity of Colorado School of Medicine, Division of General Internal Medicine, Aurora, CO, USA.
Laura F WhiteBoston University School of Public Health, Boston, MA, USA.
Joshua A BarocasUniversity of Colorado School of Medicine, Division of General Internal Medicine, Aurora, CO, USA; University of Colorado School of Medicine, Division of Infectious Diseases, Aurora, CO, USA. Electronic address: Joshua.Barocas@CUAnschutz.edu.

Funding

Development of a novel community-based high-performance surveillance network for drug useDP2DA051864 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2020
$2.3M
Maximizing Investigators' Research Award (R35)R35GM141821 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WHITE, LAURA FORSBERG · 2021 to 2025
$1.8M
Strategies to reduce serious bacterial infections and overdose among people who inject drugsK01DA051684 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2024
$809k
NIDA NIH HHS DP2 DA051864NIDA NIH HHS K01 DA051684NIGMS NIH HHS R35 GM141821
6 · The paper itself

Abstract

purposeIdentifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention.

methodsWe leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models.

resultsWe found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals.

conclusionsStratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.

Indexed as

Decision TreesOpiate OverdoseAdultAnalgesics, OpioidEthnicityFemaleHumansMaleMassachusettsMiddle AgedOpioid-Related DisordersPrisonersPrisonsRacial GroupsWhiteYoung AdultAnalgesics, OpioidAlgorithmic biasDecision treesIncarcerationMachine learningOpioid overdoseSubstance use

Identifiers

PMID38710239
PMCPMC11117432

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Registered trials

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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.