Evidence map›Paper›PMID 33062492›Full record

ArticleCureus2020

Racial Disparity Amongst Stroke Patients During the Coronavirus Disease 2019 Pandemic.

Hammad Ghanchi, Tye Patchana, James Wiginton, Jonathan D Browne, Ai Ohno, Ronit Farahmandian, Jason Duong, Vladimir Cortez, Dan E Miulli

RetractedOpen access · diamondAbstract readRetracted Publication
In one paragraph

Article in Cureus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Hammad GhanchiNeurosurgery, Riverside University Health System Medical Center, Moreno Valley, USA.
Tye PatchanaNeurosurgery, Riverside University Health System Medical Center, Moreno Valley, USA.
James WigintonNeurosurgery, Riverside University Health System Medical Center, Moreno Valley, USA.
Jonathan D BrowneNeurosurgery, California University of Science and Medicine, San Bernardino, USA.
Ai OhnoNeurosurgery, California University of Science and Medicine, San Bernardino, USA.
Ronit FarahmandianNeurosurgery, California University of Science and Medicine, San Bernardino, USA.
Jason DuongNeurosurgery, Riverside University Health System Medical Center, Rancho Cucamonga, USA.
Vladimir CortezNeurosurgery, Riverside University Health System Medical Center, Moreno Valley, USA.
Dan E MiulliNeurosurgery, Arrowhead Regional Medical Center, Colton, USA.
Arrowhead Regional Medical Center · USRiverside University Health System - Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction The global coronavirus disease 2019 (COVID-19) pandemic has had deleterious effects on our healthcare system. Lockdown measures have decreased the number of patients presenting to the hospital for non-respiratory illnesses, such as strokes. Moreover, there appears to be a racial disparity among those afflicted with the virus. We sought to assess whether this disparity also existed for patients presenting with strokes. Methods The Get with the Guidelines National Stroke Database was reviewed to assess patients presenting with a final diagnosis of ischemic stroke, transient ischemic attack (TIA), subarachnoid hemorrhage (SAH), or spontaneous/nontraumatic intraparenchymal hemorrhage (IPH). The period of February - May 2020 was chosen given the surge of patients affected with the virus and national shutdowns. Data from this same time during 2019 was used as the control population. Our hospital numbers and four additional regions were assessed (California hospitals, Pacific State hospitals, Western Region hospitals, and all hospitals in the United States). Patients were categorized by race (White, Black/African American, Asian, Native American, Hispanic) in each cohort. The primary endpoint of this study is to compare whether there was a significant difference in the proportion of patients in each reported racial category presenting with stroke during the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results A downward trend in total number of patients was noted in all five regional cohorts assessed. A statistically significant increase in the number of Black and Hispanic patients presenting with strokes was noted in California, Pacific hospitals, Western hospitals, and all hospitals in the United States during various months studied comparing 2020 to 2019. A statistically significant increase in the Hispanic population was noted in February and March in all California hospitals (p=0.005 and 0.02, respectively) and Pacific Coast hospitals (p=0.005 and 0.039, respectively). The Western region and all national hospitals noted a significant increase in strokes in the Hispanic population in April (p=0.039 and 0.023, respectively). A statistically significant increase of strokes in the Black population was noted in April in Pacific hospitals, Western region hospitals, and all national hospitals (p=0.039, 0.03, and 0.03, respectively). Conclusion The COVID-19 pandemic has adversely affected certain racial groups more than others. A similar increase is noted in patients presenting with strokes in these specific racial populations. Moreover, lack of testing for the SARS-CoV-2 virus may be missing a possible link between racial disparity for patients infected with the virus and patients presenting with stroke. The authors advocate for widespread testing for all patients to further assess this correlation.

Indexed as

covidcovid 19racial disparitystroke

Identifiers

PMID33062492
PMCPMC7549889
OpenAlexW3085114969

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.