A state-level history of opioid overdose deaths in the United States: 1999-2021

We examined a natural history of opioid overdose deaths from 1999-2021 in the United States to describe state-level spatio-temporal heterogeneity in the waves of the epidemic. We obtained overdose death counts by state from 1999-2021, categorized as involving prescription opioids, heroin, synthetic opioids, or unspecified drugs. We developed a Bayesian multivariate multiple change point model to flexibly estimate the timing and magnitude of state-specific changes in death rates involving each drug type. We found substantial variability around the timing and severity of each wave across states. The first wave of prescription-involved deaths started between 1999 and 2005, the second wave of heroin-involved deaths started between 2010 and 2014, and the third wave of synthetic opioid-involved deaths started between 2014 and 2021. The severity of the second and third waves was greater in states in the eastern half of the country. Our study highlights state-level variation in the timing and severity of the waves of the opioid epidemic by presenting a 23-year natural history of opioid overdose mortality in the United States. While reinforcing the general notion of three waves, we find that states did not uniformly experience the impacts of each wave.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2024-09-06
DOI
https://doi.org/10.1371/journal.pone.0309938
Citations
16
Primary Topic
Opioid Use Disorder Treatment
Type
article
Field-Weighted Citation Impact
8.69

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A state-level history of opioid overdose deaths in the United States: 1999-2021

Staci A. Hepler, David Kline, Lance Allyn Waller, Noa Krawczyk et al.
16 citations
PLoS ONE
Opioid Use Disorder Treatment
8.69
article

A state-level history of opioid overdose deaths in the United States: 1999-2021

Staci A. Hepler, David Kline, Lance Allyn Waller, Noa Krawczyk, Magdalena Cerdá, Ariadne Rivera‐Aguirre
article en
16 citations

Abstract

We examined a natural history of opioid overdose deaths from 1999-2021 in the United States to describe state-level spatio-temporal heterogeneity in the waves of the epidemic. We obtained overdose death counts by state from 1999-2021, categorized as involving prescription opioids, heroin, synthetic opioids, or unspecified drugs. We developed a Bayesian multivariate multiple change point model to flexibly estimate the timing and magnitude of state-specific changes in death rates involving each drug type. We found substantial variability around the timing and severity of each wave across states. The first wave of prescription-involved deaths started between 1999 and 2005, the second wave of heroin-involved deaths started between 2010 and 2014, and the third wave of synthetic opioid-involved deaths started between 2014 and 2021. The severity of the second and third waves was greater in states in the eastern half of the country. Our study highlights state-level variation in the timing and severity of the waves of the opioid epidemic by presenting a 23-year natural history of opioid overdose mortality in the United States. While reinforcing the general notion of three waves, we find that states did not uniformly experience the impacts of each wave.

PLoS ONEVol. 19(9)
Emory University (US), Wake Forest University (US), New York University (US)
Wake Forest University, National Institutes of Health, National Institute on Drug Abuse
Good health and well-being
Openalex Percentile: Top 2%
Opioid Use Disorder Treatment
8.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.