Home Monitoring for Fever: An Inexpensive Screening Method to Prevent Household Spread of COVID-19

  1. Justin Kim
  2. Marcus A. Threadcraft
  3. Wei Xue
  4. Sijie Yue
  5. Richard P. Wenzel
  6. Frederick S. Southwick

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Abstract

The COVID-19 pandemic surge has exceeded testing capacities in many parts of the world. We investigated the effectiveness of home temperature monitoring for early identification of COVID-19 patients.

Study Design

We compared home temperature measurements from a convenience sample of 1180 individuals who reported being test positive for SARS-CoV-2 to an age, sex, and location matched control group of 1249 individuals who had not tested positive.

Methods

All individuals monitored their temperature at home using an electronic smartphone thermometer that relayed temperature measurements and symptoms to a centralized cloud based, de-identified data bank.

Results

Individuals varied in the number of times they monitored their temperature. When temperature was monitored for over 72 hours fever (> 37.6°C or 99.7°F or a change in temperature of > 1°C or 1.8°F) was detected in 73% of test positive individuals, a sensitivity comparable to rapid SARS-CoV-2 antigen tests. When compared our control group the specificity of fever for COVID-19 was 0.70. However, when fever was combined with complaint s of loss of taste and smell, difficulty breathing, fatigue, chills, diarrhea, or stuffy nose the odds ratio of having COVID-19 was sufficiently high as to obviate the need to employ RTPCR or antigen testing to screen for and isolate coronavirus infected cases.

Conclusions

Our findings suggest that home temperature monitoring could serve as an inexpensive convenient screen for the onset of COVID-19, encourage earlier isolation of potentially infected individuals, and more effectively reduce the spread of infection in closed spaces.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.21.21268203: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    These analyses were performed using Prism 9.0 by GraphPad LLC.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations to our findings. First, we analyzed a convenience sample that may not be applicable to all socioeconomic groups and these data cannot infer cause-effect due to selection bias factors; however, the survey did include a broad geographic distribution, a wide age range and a prolonged home monitoring period. Secondly COVID-19 positive tests were self-reported and may underestimate the number of positive tests. We suspect our control populations may have contained some SARS-CoV-2 infected individuals, and this condition would be expected to underestimate the specificity of fever for detecting the onset of COVID-19. A third concern is the low specificity of fever. In comparison to a matched control population, the specificity of fever for detecting COVID-19 ranged from 0.62-0.73. However, the low specificity of fever is to be expected, and determining the etiology of fever is one of the most frequent reasons for Infectious Disease consultation.41 Fever serves as a nonspecific warning of possible infection and should trigger a more complete history, exam, and the ordering of specific tests to clarify the etiology. However, in the setting of a high incidence of COVID-19 the presence of fever has a higher likelihood of reflecting the onset of this disease. As shown in Figure 3, we propose a simple management algorithm for management of individuals home monitoring for fever. First and most important for preventing spread in the workplace, school or househo...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.12.21.21268203v1 on medRxiv