Evidence map›Paper›PMID 33165560›Full record

ArticleJAMA internal medicine2021

Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada.

Kevin A Brown, Aaron Jones, Nick Daneman, Adrienne K Chan, Kevin L Schwartz, Gary E Garber, Andrew P Costa, Nathan M Stall

Open access · bronzeAbstract read
In one paragraph

Article in JAMA internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed, 3 pooled it
51.5field-weighted citation impact, top 1% 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

120 citing papers in PubMed, 3 syntheses or guidelines pooled it, 240 citations in OpenAlex.

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  18. Age, gender and household infrastructural inequality in COVID-19: Contextual analysis of Mamelodi.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2024
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60 more citing papers are in PubMed but not listed here.

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

8 authors at 7 institutions in 2 countries.

Kevin A BrownPublic Health Ontario, Toronto, Ontario, Canada.
Aaron JonesDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Nick DanemanPublic Health Ontario, Toronto, Ontario, Canada.
Adrienne K ChanDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Kevin L SchwartzPublic Health Ontario, Toronto, Ontario, Canada.
Gary E GarberPublic Health Ontario, Toronto, Ontario, Canada.
Andrew P CostaDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Nathan M StallThe Institute for Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada.
McMaster University · CAOttawa Hospital · CASt. Joseph Health System · USSunnybrook Hospital · CASunnybrook Research InstituteUniversity of Toronto · CAWomen's College Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Nursing home residents have been disproportionately affected by coronavirus disease 2019 (COVID-19). Prevention recommendations emphasize frequent testing of health care personnel and residents, but additional strategies are needed. Objective: To develop a reproducible index of nursing home crowding and determine whether crowding was associated with COVID-19 cases and mortality in the first months of the COVID-19 epidemic. Design, Setting, and Participants: This population-based retrospective cohort study included more than 78 000 residents across more than 600 nursing homes in Ontario, Canada, and was conducted from March 29 to May 20, 2020. Exposures: The nursing home crowding index equaled the mean number of residents per bedroom and bathroom. Main Outcomes and Measures: The cumulative incidence of COVID-19 cases confirmed by a validated nucleic acid amplification assay and mortality per 100 residents; the introduction of COVID-19 into a home (≥1 resident case) was a negative tracer. Results: Of 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78 607 residents (women, 54 160 [68.9%]; age ≥85 years, 42 919 [54.6%]). A total of 5218 residents (6.6%) developed COVID-19 infection, and 1452 (1.8%) died of COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes; 4496 infections (86%) occurred in 63 homes (10%). The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 homes (50%) had a high crowding index (≥2). Incidence in high crowding index homes was 9.7% vs 4.5% in low crowding index homes (P < .001), while COVID-19 mortality was 2.7% vs 1.3%, respectively (P < .001). The likelihood of COVID-19 introduction did not differ (high = 31.3% vs low = 30.2%; P = .79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with an increased incidence of infection (relative risk [RR] = 1.73, 95% CI, 1.10-2.72) and mortality (RR, 1.69; 95% CI, 0.99-2.87). A propensity score analysis yielded similar conclusions for infection (RR, 2.09; 95% CI, 1.30-3.38) and mortality (RR, 1.83; 95% CI, 1.09-3.08). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 998 COVID-19 cases (19.1%) and 263 deaths (18.1%). Conclusions and Relevance: In this cohort of Canadian nursing homes, crowding was common and crowded homes were more likely to experience larger and deadlier COVID-19 outbreaks.

Indexed as

CrowdingNursing HomesAged, 80 and overCOVID-19Disease OutbreaksFemaleHumansIncidenceMaleOntarioRetrospective StudiesRisk FactorsSARS-CoV-2

Identifiers

PMID33165560
PMCPMC7653540
OpenAlexW3098620210

What OpenQuestion holds

Textmetadata
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.