ArticlePloS one2022
Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- The rising tide of opioid use disorder: analyzing 30 years of epidemiological trends and socioeconomic factors in the U.S.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Spatio-Temporal Modeling for Multi-County Opioid Overdose Surveillance: A Unified Graph Convolutional Framework.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Review
- A spatiotemporal analysis of opioid prescriptions in Indiana from 2015 to 2019.Substance abuse treatment, prevention, and policy · 2025Article
- Spatiotemporal forecasting of opioid-related fatal overdoses: towards best practices for modeling and evaluation.American journal of epidemiology · 2025Article
- Understanding the use of telemedicine across different opioid use disorder treatment models: A scoping review.Journal of telemedicine and telecare · 2025Article
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Time-space characteristics of emergency medical service attendance and layperson naloxone administration during non-fatal opioid overdoses in Rhode Island: A retrospective, event-level analysis.Annals of epidemiology · 2025Article
- Clinical risk prediction using language models: benefits and considerations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Reductions in sustained prescription opioid use within the US between 2017 and 2021.Scientific reports · 2024Article
- Leveraging the Full Continuum of Care to Prevent Opioid Use Disorder.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?World journal of clinical cases · 2023Review
- Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.
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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.