Evidence map›Paper›PMID 34037960›Full record

ArticleDrugs - real world outcomes2021

A Large-Scale Observational Study on the Temporal Trends and Risk Factors of Opioid Overdose: Real-World Evidence for Better Opioids.

Jianyuan Deng, Wei Hou, Xinyu Dong, Janos Hajagos, Mary Saltz, Joel Saltz, Fusheng Wang

Open access · goldAbstract read
In one paragraph

Article in Drugs - real world outcomes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% of its field
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, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. The Tsetlin Machine: A "Third Way" in QSAR Modeling.Journal of chemical information and modeling · 2026
    Article
  3. Article
  4. 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 at 1 institution in 1 country.

Jianyuan DengDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Wei HouDepartment of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA.
Xinyu DongDepartment of Computer Science, Stony Brook University, Stony Brook, NY, USA.
Janos HajagosDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Mary SaltzDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Joel SaltzDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Fusheng WangDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA. fusheng.wang@stonybrook.edu.ORCID http://orcid.org/0000-0002-9369-9361
Stony Brook University · US

Funding

Stony Brook University OVPR Seed Grant 1158484-63845-6.
6 · The paper itself

Abstract

backgroundThe USA is in the midst of an opioid overdose epidemic. To address the epidemic, we conducted a large-scale population study on opioid overdose.

objectivesThe primary objective of this study was to evaluate the temporal trends and risk factors of inpatient opioid overdose. Based on its patterns, the secondary objective was to examine the innate properties of opioid analgesics underlying reduced overdose effects.

methodsA retrospective cross-sectional study was conducted based on a large-scale inpatient electronic health records database, Cerner Health Facts

resultsA total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts

conclusionsThe significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 suggested an ongoing need for efforts to combat the opioid overdose epidemic in the USA. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Moreover, there are physicochemical, pharmacokinetic, and pharmacodynamic properties underlying reduced overdose effects, which can be utilized to develop better opioids.

Identifiers

PMID34037960
PMCPMC8324607
OpenAlexW3165219598

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.