Frequency of testing for COVID 19 infection and the presence of higher number of available beds per country predict outcomes with the infection, not GDP of the country – A descriptive statistical analysis

  1. Binayak Sinha
  2. Sumit Sengupta
  3. Samit Ghosal

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Abstract

Introduction

The novel coronavirus epidemic which originated in late 2019 from China has wreaked havoc on millions across the world with illness, death and socioeconomic recession. As of now no valid treatment or preventative strategy has evolved worldwide and governments across the world have been forced to take the draconian step of social isolation in communities by enforcing “lockdowns”.

Aim of this Study

This study aims to correlate the rates of infection with the novel coronavirus and total deaths as the primary output variable. In addition the strength of association between infection rates and total death in comparison to GDP share of the respective countries, physicians, hospital beds and rates of testing for COVID 19 infection per thousand patients, is being assessed, in a bid to develop a model which would help to develop tools to reduce the impact of this disease.

Material & Methods

Data relating to number of cases, severity, cases recovered and deaths worldwide and specifically for the top six countries affected was collected from the WHO COVID-19 situation report which is being updated on a daily basis till 22 nd March 2020, the date of analysis. Additional data related to GDP, physician and hospital bed per 1000 patients were procured from the World Bank database. All data were collected in a file in CSV format. Analysis was conducted in Jupyter notebook with Python 3.8.2 software and also with XL-Stat statistical software for excel. The analytical strategy was descriptive with no inferential overtones.

Results

COVID 19 infection strongly correlates with total deaths (r : 0.89), with a predicted death rate of 25 patients per 1000 affected. There was no correlation between the GDP growth of the country and number of treating physicians/1000 patient population with any COVID 19 related outcome. However there was a negative correlation between COVID 19-related deaths and the number of beds available per 1000 population [r=-0.34]. Importantly there is an inverse correlation between the number of tests conducted per million population with the rates of active infections [r=-0.12], new cases [r=-0.38] and new deaths [r=-0.28] in COVID 19.

Conclusion

This is the first study to assess parameters other than age and sex and sets out a robust dataset which indicates an increased risk of worsening outcomes with lesser number of beds and testing, suggesting that the need of the hour is to increase available bed numbers and to increase rates of testing.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.01.20047373: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analysis was conducted in Jupyter notebook with Python 3.8.2 software and also with XL-Stat statistical software for excel.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    5.1 Study limitations: Firstly, correlation studies do not mean causality. The associations mentioned above are indicative of certain trends only. However, at an early stage of a novel disease trends can definitely be a good approximation for a large data-related analysis. Secondly, due to extreme paucity of data from many countries, they were excluded in certain analysis for example the one on testing frequency and outcomes. This weakness was overcome by including the top 15 countries wherefrom the majority of the data were more homogenous and devoid of significant outliers. Thirdly, the correlation values are not indicative strong associations-both positive and negative. However, this is due to the fluidity of the situation. We do not desire the numbers to go up to give us opportunity for a robust analysis. This is precisely why we attempted to identify trends from the updated data and guide our health care system to gear up for a better assessment of the situation. Fourthly, the use of GDP as a measure of disease outcomes cuts both ways. If it is an indicator of surplus wealth, then it is expected that a large proportion of the same could be channelized into the health care facility and hence the outcomes. Hence, we choose to include both GDP as well as share of the World’s GDP in this analysis to overcoming this confounding. 5.2 Strength of the study: In contrast to the association data related to mortality rates and risk factors for the same like sex age, other co-morbid...

    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

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  2. Version published to 10.1101/2020.04.01.20047373 on medRxiv