Hospital-Based Contact Tracing of Patients With COVID-19 and Health Care Workers During the COVID-19 Pandemic in Eastern India: Cross-sectional Study

  1. Durgesh Prasad Sahoo
  2. Arvind Kumar Singh
  3. Dinesh Prasad Sahu
  4. Somen Kumar Pradhan
  5. Binod Kumar Patro
  6. Gitanjali Batmanabane
  7. Baijayantimala Mishra
  8. Bijayini Behera
  9. Ambarish Das
  10. G Susmita Dora
  11. L Anand
  12. S M Azhar
  13. Jyolsna Nair
  14. Sasmita Panigrahi
  15. R Akshaya
  16. Bimal Kumar Sahoo
  17. Subhakanta Sahu
  18. Suchismita Sahoo

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Abstract

The contact tracing and subsequent quarantining of health care workers (HCWs) are essential to minimizing the further transmission of SARS-CoV-2 infection and mitigating the shortage of HCWs during the COVID-19 pandemic situation.

Objective

This study aimed to assess the yield of contact tracing for COVID-19 cases and the risk stratification of HCWs who are exposed to these cases.

Methods

This was an analysis of routine data that were collected for the contact tracing of COVID-19 cases at the All India Institute of Medical Sciences, Bhubaneswar, in Odisha, India. Data from March 19 to August 31, 2020, were considered for this study. COVID-19 cases were admitted patients, outpatients, or HCWs in the hospital. HCWs who were exposed to COVID-19 cases were categorized, per the risk stratification guidelines, as high-risk contacts or low-risk contacts

Results

During contact tracing, 3411 HCWs were identified as those who were exposed to 360 COVID-19 cases. Of these 360 cases, 269 (74.7%) were either admitted patients or outpatients, and 91 (25.3%) were HCWs. After the risk stratification of the 3411 HCWs, 890 (26.1%) were categorized as high-risk contacts, and 2521 (73.9%) were categorized as low-risk contacts. The COVID-19 test positivity rates of high-risk contacts and low-risk contacts were 3.8% (34/890) and 1.9% (48/2521), respectively. The average number of high-risk contacts was significantly higher when the COVID-19 case was an admitted patient (number of contacts: mean 6.6) rather than when the COVID-19 case was an HCW (number of contacts: mean 4.0) or outpatient (number of contacts: mean 0.2; P=.009). Similarly, the average number of high-risk contacts was higher when the COVID-19 case was admitted in a non–COVID-19 area (number of contacts: mean 15.8) rather than when such cases were admitted in a COVID-19 area (number of contacts: mean 0.27; P<.001). There was a significant decline in the mean number of high-risk contacts over the study period (P=.003).

Conclusions

Contact tracing and risk stratification were effective and helped to reduce the number of HCWs requiring quarantine. There was also a decline in the number of high-risk contacts during the study period. This indicates the role of the implementation of hospital-based, COVID-19–related infection control strategies. The contact tracing and risk stratification approaches that were designed in this study can also be implemented in other health care settings.

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  1. Version published to 10.2196/28519
  2. Version published to 10.2196/preprints.28519
  3. ScreenIT

    SciScore for 10.1101/2020.11.01.20220475: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval to conduct this study was obtained from Institutional Ethics Committee of AIIMS, Bhubaneswar. (Reference number: T/IM-NF/CMFM/20/76) Statistical analysis was done using Microsoft excel 2013 version and Statistical Package for Social Science’s version 22.0 (SPSS 22.0).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Ethical approval to conduct this study was obtained from Institutional Ethics Committee of AIIMS, Bhubaneswar. (Reference number: T/IM-NF/CMFM/20/76) Statistical analysis was done using Microsoft excel 2013 version and Statistical Package for Social Science’s version 22.0 (SPSS 22.0).
    Microsoft excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Limitations: Categorization of risk was based on the history by the contacts which may lead to high chances of social desirability bias. There was a chance of misinformation where the hospital staffs deliberately want to be in high risk categories so that a quarantine period of 14 days could be availed by them as paid on-duty leave. There was also a chance of misinformation due to wrong recall. Sometimes the HCWs fail to remember the patient and their PPE status during patient care. Low risk contacts were not tested as a routine unless symptomatic which could heave missed some cases as many of COVID-19 patients remain asymptomatic or paucisymptomatic. Conclusion: Contact tracing and risk stratification was effective and helped in reducing the number of HCWs going for quarantine. There was a decline in high-risk contacts during study period suggesting role of implementation of hospital based COVID related infection control strategies. Findings obtained during contact tracing might also be beneficial in mounting appropriate and strategic infection control measures. This contact tracing and risk stratification approach designed in the current study can also be implemented in other healthcare settings.

    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.
    • Thank you for including a protocol registration statement.

    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.

  4. Version published to 10.1101/2020.11.01.20220475v1 on medRxiv