Evidence map›Paper›PMID 40361019›Full record

ArticleBMC public health2025

Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality.

Andrew Deas, Adam Spannaus, Hashan Fernando, Heidi A Hanson, Anuj J Kapadia, Jodie Trafton, Vasileios Maroulas

Abstract read
In one paragraph

Article in BMC public health, 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. A spatiotemporal analysis of opioid prescriptions in Indiana from 2015 to 2019.Substance abuse treatment, prevention, and policy · 2025
    Article
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

7 authors.

Andrew DeasDepartment of Mathematics, University of Tennessee, Circle Dr, Knoxville, 37916, TN, USA. deasaj@ornl.gov.
Adam SpannausComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Bethel Valley Road, Oak Ridge, 37830, TN, USA.
Hashan FernandoThe Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Middle Dr, Knoxville, 37996, TN, USA.
Heidi A HansonComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Bethel Valley Road, Oak Ridge, 37830, TN, USA.
Anuj J KapadiaComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Bethel Valley Road, Oak Ridge, 37830, TN, USA.
Jodie TraftonOffice of Mental Health and Suicide Prevention, Veterans Health Administration, Willow Road, Palo Alto, 94025, CA, USA.
Vasileios MaroulasDepartment of Mathematics, University of Tennessee, Circle Dr, Knoxville, 37916, TN, USA.

Funding

U.S. Department of Energy DE-AC05-00OR22725
6 · The paper itself

Abstract

backgroundThe opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates.

methodsThis study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter's predicted rates across the nation's counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally.

resultsOur spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation's most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift.

conclusionsThe initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use.

Indexed as

Analgesics, OpioidOpioid-Related DisordersPersons with DisabilitiesVulnerable PopulationsHumansSpatio-Temporal AnalysisUnited StatesAnalgesics, OpioidHeat mapsHotspot identificationKalman filterOpioid epidemicOpioid vulnerability

Identifiers

PMID40361019
PMCPMC12070698

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.