Evidence map›Paper›PMID 36931131›Full record

ArticleDrug and alcohol dependence2023

Opioid-related mortality: Dynamic temporal and spatial trends by drug type and demographic subpopulations, Massachusetts, 2005-2021.

Thomas J Stopka, Marc R Larochelle, Xiaona Li, Dana Bernson, Wenjun Li, Leland K Ackerson, Ric Bayly, Olaf Dammann, Cici Bauer

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  5. Widening Racial Disparities in the U.S. Overdose Epidemic.American journal of preventive medicine · 2025
    Article
  6. Article
  7. Article
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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

9 authors.

Thomas J StopkaDepartment of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States. Electronic address: thomas.stopka@tufts.edu.
Marc R LarochelleClinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, United States.
Xiaona LiDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States.
Dana BernsonOffice of Population Health, Department of Public Health, Commonwealth of Massachusetts, Boston, MA, United States.
Wenjun LiCenter for Health Statistics and Department of Public Health, University of Massachusetts Lowell, Lowell, MA, United States.
Leland K AckersonCenter for Health Statistics and Department of Public Health, University of Massachusetts Lowell, Lowell, MA, United States.
Ric BaylyDepartment of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States.
Olaf DammannDepartment of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States; Department of Gynecology and Obstetrics, Hannover Medical School, Hannover, Germany.
Cici BauerDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Funding

Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-Emptive Public Health ResponsesR01DA054267 · NIDA · TUFTS UNIVERSITY BOSTON · PI Cici Bauer, Thomas J Stopka · 2022 to 2026
$3.4M
NIDA NIH HHS R01 DA054267
6 · The paper itself

Abstract

backgroundFatal opioid-related overdoses (OOD) present significant public health challenges. Intuitive and replicable analytical approaches are needed to inform targeted public health responses.

methodsWe obtained fatal OOD data for 2005-2021 from the Massachusetts Registry of Vital Records and Statistics. We conducted heatmap analyses to assess trends in fatal OOD rates per 100,000 residents, visualizing rates by death year and decedent age at one-year intervals, stratifying by race/ethnicity, sex, rurality, and involved substances. We calculated Getis-Ord Gi* statistics to identify spatial clusters of OOD rates.

resultsAmong 20,774 fatal OODs, rates were higher among males, and highly variable by race/ethnicity, age group, and rurality. While fatal OOD rates increased in urban before rural communities, rates were higher in rural communities by 2018-2019. Stimulant-related fatal OODs were elevated in 2020 and 2021. Fatal OOD rates involving fentanyl and stimulants increased precipitously and simultaneously in the non-Hispanic Black population in 2020 and 2021, with a bimodal age distribution peaking among those in their 40s and 60s. Elevated rates among 30-to-60 year old Hispanic residents were largely tied to synthetic opioids from 2015 to 2021. Spatial clusters were detected for prescription opioids, heroin, and stimulants in western Massachusetts. For synthetic opioids, hotspots became more ubiquitous across the state from 2016 to 2021, intensifying in southeastern Massachusetts.

conclusionOur novel approach uncovered new time varying and spatial patterns in fatal OOD rates not previously reported. Identified shifts in fatal OOD rates by sex, age, and race/ethnicity can inform location-specific field actions targeting subpopulations at disproportionally high risk.

Indexed as

Drug OverdoseOpiate OverdoseAdultAge DistributionAnalgesics, OpioidFentanylHumansMaleMassachusettsMiddle AgedAnalgesics, OpioidFentanylClustersDemographic variationsDrug typeHeatmapsMassachusettsOpioid overdose mortalityTrends

Identifiers

PMID36931131
PMCPMC10121848

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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.