Evidence map›Paper›PMID 33664282›Full record

ArticleScientific reports2021

A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions.

Xin Chen, Wei Hou, Sina Rashidian, Yu Wang, Xia Zhao, George Stuart Leibowitz, Richard N Rosenthal, Mary Saltz, Joel H Saltz, Elinor Randi Schoenfeld and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

11 authors at 2 institutions in 1 country.

Xin ChenDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Wei HouDepartment of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook, NY, USA.
Sina RashidianDepartment of Computer Science, Stony Brook University, Stony Brook, NY, USA.
Yu WangDepartment of Computer Science, Stony Brook University, Stony Brook, NY, USA.
Xia ZhaoSchool of Health Technology and Management, Stony Brook University, Stony Brook, NY, USA.
George Stuart LeibowitzSchool of Social Welfare, Stony Brook University, Stony Brook, NY, USA.
Richard N RosenthalDepartment of Psychiatry, Renaissance School of Medicine, Stony Brook, NY, USA.
Mary SaltzDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Joel H SaltzDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
Elinor Randi SchoenfeldDepartment of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook, NY, USA.
Fusheng WangDepartment of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA. fusheng.wang@stonybrook.edu.
Stony Brook University · USStony Brook School · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

Indexed as

AdolescentAdultAgedAge DistributionAnalgesics, OpioidDrug OverdoseEpidemicsFemaleHeroinHumansMaleMiddle AgedOpioid-Related DisordersRetrospective StudiesYoung AdultAnalgesics, OpioidHeroin

Identifiers

PMID33664282
PMCPMC7933431
OpenAlexW3135592735

What OpenQuestion holds

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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.