Evidence map›Paper›PMID 40065757›Full record

ArticlePreventive medicine reports2025

Leveraging spatiotemporal Bayesian analysis to unravel polysubstance use and overdose risk: Opportunities and challenges.

Kechna Cadet, Michael R Desjardins, Christopher Morrison, Silvia S Martins

Abstract read
In one paragraph

Article in Preventive medicine reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

4 authors.

Kechna CadetDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States of America.
Michael R DesjardinsDepartment of Epidemiology and Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Christopher MorrisonDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States of America.
Silvia S MartinsDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States of America.

Funding

Substance Abuse Epidemiology Training Program (SAETP) at Columbia UniversityT32DA031099 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI DEBORAH S HASIN, Silvia Saboia Martins · 2012 to 2026
$6.1M
NIDA NIH HHS L60 DA061545NIDA NIH HHS T32 DA031099
6 · The paper itself

Abstract

In the current wave of the opioid epidemic, the prevalence of polysubstance use continues to complicate drug-related deaths. Most studies to date use non-spatial statistical approaches to examine the association between polysubstance use and overdose risk, without considering the spatial distribution of these latent sub-patterns of use. This paper describes the utility and potential impact of using disease mapping and Bayesian spatiotemporal approaches for analyzing and monitoring polysubstance use and overdose risk to better respond to the ongoing opioid epidemic. We discuss the application of Bayesian spatiotemporal approaches in analyzing polysubstance use among people who use drugs. Bayesian spatiotemporal analyses offer a salient approach to detecting localized distributions of overdose events and tailor local interventions to community needs in order to reduce polysubstance use and related adverse health among people who use drugs. This can help improve precision and efficacy response in reducing polysubstance use adverse outcomes and optimize resource allocation.

Indexed as

Disease mappingGeospatial healthOverdosePolysubstance usePublic healthSpatiotemporal analysis

Identifiers

PMID40065757
PMCPMC11891691

What OpenQuestion holds

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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.