Evidence map›Paper›PMID 39788487›Full record

ArticleJournal of the American Medical Directors Association2025

Evaluation of COVID-19 Diagnosis Codes for Identification of SARS-CoV-2 Infections in a Nursing Home Cohort, 2022-2023.

Arshiya Patel, Amanda B Payne, Dustin W Currie, Thomas Franceschini, Amber Gensheimer, Joseph D Lutgring, Sujan C Reddy, Kelly M Hatfield

Abstract read
In one paragraph

Article in Journal of the American Medical Directors Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Arshiya PatelDivision of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Professional Services Business Unit, Chenega Enterprise, Systems, and Solutions, Anchorage, AK, USA. Electronic address: TVP2@cdc.gov.
Amanda B PayneCoronavirus and Other Respiratory Viruses Division, National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Dustin W CurrieDivision of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Thomas FranceschiniSignature Healthcare, Louisville, KY, USA.
Amber GensheimerSignature Healthcare, Louisville, KY, USA.
Joseph D LutgringDivision of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Sujan C ReddyDivision of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Kelly M HatfieldDivision of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

objectivesThis study aimed to evaluate the utility of electronic health record (EHR) diagnosis codes for monitoring SARS-CoV-2 infections among nursing home residents.

designA retrospective cohort study design was used to analyze data collected from nursing homes operating under the tradename Signature Healthcare between January 2022 and June 2023. SETTING AND

participantsData from 31,136 nursing home residents across 76 facilities in Kentucky, Tennessee, Indiana, Ohio, North Carolina, Georgia, Alabama, and Virginia were included.

methodsResident demographics, diagnosis codes associated with clinical diagnoses (including COVID-19), and SARS-CoV-2 testing information were collected from the EHR and supplemental testing data sources. We described the rates of infection and the clinical characteristics of residents with incident-positive SARS-CoV-2 tests and new-onset COVID-19 diagnoses. Positive predictive values (PPVs) of COVID-19 diagnosis codes were calculated for residents stratified by whether a resident was continuously present in a facility for ±3 days from the diagnosis onset date listed in EHRs, using positive SARS-CoV-2 tests to confirm infection.

resultsA total of 4876 incident-positive SARS-CoV-2 tests and 6346 new-onset COVID-19 diagnoses were recorded during the study period. Weekly rates of new-onset diagnoses were significantly higher than positive test rates, although trends followed similar trajectories. Among residents continuously present in the nursing home ±3 days from the diagnosis onset date, the PPV of COVID-19 diagnosis codes was high (3395 of 3685 = 92%; 95% CI, 91%-93%). The PPV among this group significantly varied by study quarter (P < .001). The PPV was substantially lower for 2661 diagnoses among residents not continuously present in the nursing home (24%; 95% CI, 22%-26%). CONCLUSIONS AND IMPLICATIONS: This study demonstrates the utility of diagnosis codes for assessment of COVID-19 epidemiology and trends when testing data are unavailable for residents during their stay in a nursing home. Future research should explore strategies to evaluate the utility of diagnosis codes at admission and discharge to nursing homes to enhance surveillance efforts.

Indexed as

COVID-19Nursing HomesAgedAged, 80 and overCOVID-19 TestingElectronic Health RecordsFemaleHumansMaleRetrospective StudiesSARS-CoV-2United StatesCOVID-19electronic health recordICD-10nursing homerespiratory infectionsSARS-CoV-2

Identifiers

PMID39788487
PMCPMC11890922

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LicenceCC BY
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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.