Evidence map›Paper›PMID 32791492›Full record

Observational studyJournal of medical Internet research2020

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis.

Joann G Elmore, Pin-Chieh Wang, Kathleen F Kerr, David L Schriger, Douglas E Morrison, Ron Brookmeyer, Michael A Pfeffer, Thomas H Payne, Judith S Currier

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Learning Health System in Crisis: Lessons From the COVID-19 Pandemic.Mayo Clinic proceedings. Innovations, quality & outcomes · 2021
    Review
  6. 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

9 authors.

Joann G ElmoreDepartment of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States.ORCID 0000-0002-7311-6835
Pin-Chieh WangDepartment of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States.ORCID 0000-0003-1465-9933
Kathleen F KerrDepartment of Biostatistics, UW School of Public Health, Seattle, WA, United States.ORCID 0000-0002-6438-9583
David L SchrigerDepartment of Emergency Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States.ORCID 0000-0003-0242-1127
Douglas E MorrisonDepartment of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA, United States.ORCID 0000-0002-7195-830X
Ron BrookmeyerDepartment of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA, United States.ORCID 0000-0002-8639-3399
Michael A PfefferDepartment of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States.ORCID 0000-0002-8958-0843
Thomas H PayneDepartment of Medicine, UW School of Medicine, Seattle, WA, United States.ORCID 0000-0002-3416-5474
Judith S CurrierDepartment of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States.ORCID 0000-0003-4279-4737

Funding

Scientific and Technical CoreP2CHD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HIRAM BELTRAN-SANCHEZ · 2016 to 2026
$6.9M
NICHD NIH HHS P2C HD041022
6 · The paper itself

Abstract

backgroundAccurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks.

objectiveThe aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years.

methodsA retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons.

resultsThe percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure.

conclusionsA significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

Indexed as

Acute DiseaseAdultAmbulatory Care FacilitiesBetacoronavirusCaliforniaCoronavirus InfectionsCoughCOVID-19Electronic Health RecordsEmergency Service, HospitalFemaleHospitalizationHumansMaleMiddle AgedPandemicsCOVID-19electronic health recordforecastpandemicpredictiontime-series analysis

Identifiers

PMID32791492
PMCPMC7485935

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.