Evidence map›Paper›PMID 35047657›Full record

ArticleGerontology & geriatric medicine

Short-Stay Admissions Associated With Large COVID-19 Outbreaks in Maryland Nursing Homes.

T Joseph Mattingly, Alison Trinkoff, Alison D Lydecker, Justin J Kim, Jung Min Yoon, Mary-Claire Roghmann

Abstract read
In one paragraph

Article in Gerontology & geriatric medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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. Article
  2. Article
  3. Review
  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

6 authors.

T Joseph MattinglyUniversity of Maryland School of Pharmacy, Baltimore, MD, USA.
Alison TrinkoffDepartment of Family and Community Health, University of Maryland School of Nursing, Baltimore, MD, USA.
Alison D LydeckerDepartment of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
Justin J KimDepartment of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
Jung Min YoonUniversity of Maryland School of Nursing, Baltimore, MD, USA.
Mary-Claire RoghmannDepartment of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0003-1063-9257

Funding

ACL HHS U01CK000556NCEZID CDC HHS U01 CK000556
6 · The paper itself

Abstract

At the beginning of the COVID-19 pandemic, some nursing homes (NHs) in Maryland suffered larger outbreaks than others. This study examined how facility characteristics influenced outbreak size. We conducted a retrospective analysis of secondary data from Maryland NHs to identify characteristics associated with large outbreaks, defined as when total resident cases exceeded 10% of licensed beds, from January 1, 2020, through July 1, 2020. Our dataset was unique in its inclusion of short-stay residents as a measure of resident type and family satisfaction as a measure of quality. Facility characteristics were collected prior to 2020. Like other studies, we found that large outbreaks were more likely to occur in counties with high cumulative incidence of COVID-19, and in NHs with more licensed beds or fewer daily certified nursing assistant (CNA) hours. We also found that NHs with a greater proportion of short-stay residents were more likely to have large outbreaks, even after adjustment for other facility characteristics. Lower family satisfaction was not significantly associated with large outbreaks after adjusting for CNA hours. Understanding the characteristics of NHs with large COVID-19 outbreaks can guide facility re-structuring to prevent the spread of respiratory infections in future pandemics.

Indexed as

COVID-19epidemiologynursing homesshort-stay admissions

Identifiers

PMID35047657
PMCPMC8762488

What OpenQuestion holds

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Registered trials

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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.