The emergence of COVID-19 in Indonesia: analysis of predictors of infection and mortality using independent and clustered data approaches

  1. Erlina Burhan
  2. Ari Fahrial Syam
  3. Ahmad Jabir Rahyussalim
  4. Prasenohadi
  5. Navy G Lolong Wulung
  6. Agus Dwi Susanto
  7. I Gede Ketut Sajinadiyasa
  8. Dewi Puspitorini
  9. Dewi Lestari
  10. Indah Suci Widyahening
  11. Vivi Setiawaty
  12. Dwiana Ocviyanti
  13. Kartika Qonita Putri
  14. Aswin Guntara
  15. Davrina Rianda
  16. Anuraj H Shankar
  17. Rina Agustina

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Abstract

Background

Analyses of correlates of SARS-CoV-2 infection or mortality have usually assessed individual predictors. This study aimed to determine if patterns of combined predictors may better identify risk of infection and mortality

Methods

For the period of March 2 nd to 10 th 2020, the first 9 days of the COVID-19 pandemic in Indonesia, we selected all 18 confirmed cases, of which 6 died, and all 60 suspected cases, of which 1 died; and 28 putatively negative patients with pneumonia and no travel history. We recorded data for travel, contact history, symptoms, haematology, comorbidities, and chest x-ray. Hierarchical cluster analyses (HCA) and principal component analyses (PCA) identified cluster and covariance patterns for symptoms or haematology which were analysed with other predictors of infection or mortality using logistic regression.

Results

For univariate analyses, no significant association with infection was seen for fever, cough, dyspnoea, headache, runny nose, sore throat, gastrointestinal complaints (GIC), or haematology. A PCA symptom component for fever, cough, and GIC tended to increase risk of infection (OR 3.41; 95% CI 1.06-14; p=0.06), and a haematology component with elevated monocytes decreased risk (OR 0.26; 0.07-0.79; 0.027). Multivariate analysis revealed that an HCA cluster of 3-5 symptoms, typically fever, cough, headache, runny nose, sore throat but little dyspnoea and no GIC tended to reduce risk (aOR 0.048; <0.001–0.52; 0.056). In univariate analyses for death, an HCA cluster of cough, fever and dyspnoea had increased risk (OR 5.75; 1.06 − 31.3, 0.043), but no other individual predictor, cluster or component was associated. Other significant predictors of infection were age ≥ 45, international travel, contact with COVID-19 patient, and pneumonia. Diabetes and history of contact were associated with higher mortality.

Conclusions

Cluster groups and co-variance patterns may be stronger correlates of SARS-CoV-2 infection than individual predictors. Comorbidities may warrant careful attention as would COVID-19 exposure levels.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.10.20147942: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval was obtained from the Research Ethics Committee of the Faculty of Medicine, Universitas Indonesia – Dr. Cipto Mangunkusumo General Hospital, with approval number 20030331.
    Consent: After consideration of ethical issues, logistics and urgency of this work, the Committee waived the requirement for written individual informed consent and approved the sharing of anonymized data.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SPSS V.26.0 and SAS 9.4 were used for all analyses.
    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:
    There are limitations to our study. First, the sample size was small as this is a preliminary study on COVID-19 during its emergence in Indonesia, and was limited to data that were accessible from that period. We also note the lack of data on smoking and body mass index. This is may be important because ACE-2 expression has been reported to increase in smokers and persons with complications of high body mass index.(24-26) Further investigation of correlates of expression, activation, and interaction of ACE-2 with SARS-CoV-2 in the Indonesian population would be important. The likelihood of underreported cases needs to be considered given the variety of clinical manifestations and suboptimal availability of tools for diagnosis. Actions need to be taken such as a simulation to assess and verify pandemic preparedness for influx of cases at referral hospitals for proper triage, and establishment of high throughput diagnostics laboratories for COVID-19.

    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.

    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 10.1101/2020.07.10.20147942v1 on medRxiv