Risk factors affecting COVID-19 case fatality rate: A quantitative analysis of top 50 affected countries

  1. Poh Hui Goh
  2. Wafiah Ilyani Mahari
  3. Norhadyrah Izazie Ahad
  4. Li Ling Chaw
  5. Nurolaini Kifli
  6. Bey-Hing Goh
  7. Siang Fei Yeoh
  8. Khang Wen Goh
  9. Long Chiau Ming

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Abstract

Background: Latest clinical data on treatment on coronavirus disease 2019 (COVID-19) indicated that older patients and those with underlying history of smoking, hypertension or diabetes mellitus might have poorer prognosis of recovery from COVID-19. We aimed to examine the relationship of various prevailing population-based risk factors in comparison with mortality rate and case fatality rate (CFR) of COVID-19.Methods: Demography and epidemiology data were used, which have been identified as verified or postulated risk factors for mortality of adult inpatients with COVID-19. The number of confirmed cases and the number of deaths until April 16, 2020 for all affected countries were extracted from Johns Hopkins University COVID-19 websites. Datasets for indicators that are prevailing or postulated factors of COVID-19 mortality were extracted from the World Bank database. Out of 185 affected countries, the top 50 countries were selected for analysis in this study. The following seven variables were included in the analysis, based on data availability and completeness: 1) proportion of people aged 65 above, 2) proportion of male in the population, 3) smoking prevalence, and 4) number of hospital beds. Linear regression analysis was carried out to determine the relationship between CFR and the aforementioned risk factors.Results: United States shows approximately 0.20% of confirmed cases and it has about 4.85% of CFR. Luxembourg shows the highest percentage of confirmed cases of 0.55% but a low 2.05% of CFR, showing that a high percentage of confirmed cases does not necessarily lead to high CFR. There is a significant association between CFR, people aged 65 and above (β=4.70; p = 0.035).Conclusion: Countries with high proportion of older people above 65 years old have a significant risk of having high CFR from COVID-19. Nevertheless, gender differences and smoking prevalence failed to prove a significant relationship with COVID-19 mortality rate and CFR.

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  1. Version published to 10.36877/pmmb.a0000171
  2. ScreenIT

    SciScore for 10.1101/2020.05.20.20108449: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe following seven variables were included in the analysis, based on data availability and completeness: 1) proportion of people aged 65 above, 2) proportion of male in the population, 3) diabetes prevalence, 4) smoking prevalence, 5) current health expenditure, 6) number of hospital beds and 7) number of nurses and midwives.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were conducted using Microsoft Excel and R (ver. 3.6.0).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 a number of limitations in this study that need to be acknowledged. Firstly, some factors had to be excluded due to incomplete data such as malaria prevalence and BCG vaccination. Secondly, the years from which the data was collected were not consistent for all indicators. Thirdly, the data collected were not from the same year for one indicator such as the number of hospital beds. Lastly, some required data were unavailable to sufficiently make an overall conclusion for some of the factors, including comorbidities. There were 4 other proposed comorbidities to be analyzed but only two indicators’ datasets were available in World Bank Data, which are diabetes and smoking prevalence. Therefore, more research should be conducted to further understand the relationship between comorbidities and CFR. This would help to identify and to better understand other possible factors that may also affect CFR.

    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.

  3. Version published to 10.1101/2020.05.20.20108449v1 on medRxiv