Evidence map›Paper›PMID 36625234›Full record

ArticleInfluenza and other respiratory viruses2023

Developing a sampling methodology for timely reporting of population-based COVID-19-associated hospitalization surveillance in the United States, COVID-NET 2020-2021.

Alissa O'Halloran, Michael Whitaker, Kadam Patel, A Elizabeth Allen, Kennon R Copeland, Carrie Reed, Sue Reynolds, Christopher A Taylor, Fiona Havers, Lindsay Kim and 2 more

Abstract read
In one paragraph

Article in Influenza and other respiratory viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

12 authors.

Alissa O'HalloranCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0002-7397-7870
Michael WhitakerCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Kadam PatelCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
A Elizabeth AllenNORC, The University of Chicago, Chicago, Illinois, USA.
Kennon R CopelandNORC, The University of Chicago, Chicago, Illinois, USA.
Carrie ReedCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Sue ReynoldsCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Christopher A TaylorCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Fiona HaversCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Lindsay KimCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Kirk WolterNORC, The University of Chicago, Chicago, Illinois, USA.
Shikha GargCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) required a sampling methodology that allowed for production of timely population-based clinical estimates to inform the ongoing US COVID-19 pandemic response.

methodsWe developed a flexible sampling approach that considered reporting delays, differential hospitalized case burden across surveillance sites, and changing geographic and demographic trends over time. We incorporated weighting methods to adjust for the probability of selection and non-response, and to calibrate the sampled case distribution to the population distribution on demographics. We additionally developed procedures for variance estimation.

resultsBetween March 2020 and June 2021, 19,293 (10.4%) of all adult hospitalized cases were sampled for chart abstraction. Variance estimates for select variables of interest were within desired ranges.

conclusionsCOVID-NET's sampling methodology allowed for reporting of robust and timely, population-based data on the clinical epidemiology of COVID-19-associated hospitalizations and evolving trends over time, while attempting to reduce data collection burden on surveillance sites. Such methods may provide a general framework for other surveillance systems needing to quickly and efficiently collect and disseminate data for public health action.

Indexed as

COVID-19AdultHospitalizationHumansPandemicsPopulation SurveillancePublic HealthUnited StatesCOVID-19hospitalizationspopulation-based surveillancesamplingsurvey methods

Identifiers

PMID36625234
PMCPMC9835436

What OpenQuestion holds

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LicenceCC BY
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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.