Covid-19 does not look like what you are looking for: Clustering symptoms by nation and multi-morbidities reveal substantial differences to the classical symptom triad
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Abstract
COVID-19 is by convention characterised by a triad of symptoms: cough, fever and loss of taste/smell. The aim of this study was to examine clustering of COVID-19 symptoms based on underlying chronic disease and geographical location. Using a large global symptom survey of 78,299 responders in 190 different countries, we examined symptom profiles in relation to geolocation (grouped by country) and underlying chronic disease (single, co- or multi-morbidities) associated with a positive COVID-19 test result using statistical and machine learning methods to group populations by underlying disease, countries, and symptoms. Taking the responses of 7980 responders with a COVID-19 positive test in the top 5 contributing countries, we find that the most frequently reported symptoms differ across the globe: For example, fatigue 4108(51.5%), headache 3640(45.6%) and loss of smell and taste 3563(44.6%) are the most reported symptoms globally. However, symptom patterns differ by continent; India reported a significantly lower proportion of headache (22.8% vs 45.6%, p<0.05) and itchy eyes (7.0% vs. 15.3%, p<0.05) than other countries, as does Pakistan (33.6% vs 45.6%, p<0.05 and 8.6% vs 15.3%, p<0.05). Mexico and Brazil report significantly less of these symptoms. As with geographic location, we find people differed in their reported symptoms, if they suffered from specific underlying diseases. For example, COVID-19 positive responders with asthma or other lung disease were more likely to report shortness of breath as a symptom, compared with COVID-19 positive responders who had no underlying disease (25.3% vs. 13.7%, p<0.05, and 24.2 vs.13.7%, p<0.05). Responders with no underlying chronic diseases were more likely to report loss of smell and tastes as a symptom (46%), compared with the responders with type 1 diabetes (21.3%), Type 2 diabetes (33.5%) lung disease (29.3%), or hypertension (37.8%). Global symptom ranking differs markedly from the well-known and commonly described symptoms for COVID-19, which are based on a few localised studies. None of the five countries studied in depth recorded cough or temperature as the most common symptoms. The most common symptoms reported were fatigue and loss of smell and taste. Amongst responders from Brazil cough was the second most frequently reported symptom, after fatigue. Moreover, we find that across countries and based on underlying chronic diseases, there are significant differences in symptom profiles at presentation, that cannot be fully explained by the different chronic disease profiles of these countries, and may be caused by differences in climate, environment and ethnicities. These factors uncovered by our global comorbidity survey of COVID-19 positive tested people may contribute to the apparent large asymptotic COVID-19 spread and put patients with underlying disease systematically more at risk.
Executive Summary
Evidence before this work
An early meta-analysis of epidemiological variation in COVID-19 inside and outside China studied patient characteristics including, gender, age, fatality rate, and symptoms of fever, cough, shortness of breath and diarrhoea in COVID-19 patients. They found that important symptom differences existed in patients in China compared to other countries and recommended that clinical symptoms of COVID-19 should not be generalized to fever, shortness of breath and cough only, but other symptoms such as diarrhoea are also shown to be prevalent in patients with COVID-19.
Added value of this work
W e find that across countries and based on underlying chronic diseases, there are significant differences in symptom profiles at presentation, that cannot be fully explained by the different chronic disease profiles of these countries, and may be caused by differences in climate, environment and ethnicities.
Implications of the evidence
These factors, uncovered by our global comorbidity survey of COVID-19 positive tested people may contribute to the apparent large asymptotic COVID-19 spread and put patients with underlying disease systematically more at risk.
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SciScore for 10.1101/2021.04.02.21254818: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: MD website all participants provided informed consent at the start of the online questionnaire for their data to be used for research purposes, and had to agree to the corresponding Your. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The gender variable was coded as 0 for male and 1 for female. Table 2: Resources
Software and Algorithms Sentences Resources (5) For clustering, we excluded symptoms experienced by less than 5% of the individuals in each group to avoid chaining.(6) All statistical analyses were performed using custom programs in the MATLAB R2019b (MathWorks) environment. Ethics: The … SciScore for 10.1101/2021.04.02.21254818: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: MD website all participants provided informed consent at the start of the online questionnaire for their data to be used for research purposes, and had to agree to the corresponding Your. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The gender variable was coded as 0 for male and 1 for female. Table 2: Resources
Software and Algorithms Sentences Resources (5) For clustering, we excluded symptoms experienced by less than 5% of the individuals in each group to avoid chaining.(6) All statistical analyses were performed using custom programs in the MATLAB R2019b (MathWorks) environment. Ethics: The Covid-10 Symptom Mapper data was provided to us by Your. MATLABsuggested: (MATLAB, RRID:SCR_001622)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:Strengths & limitations: One of the strengths of this study was the ability to look globally at symptoms with a specific breakdown by nationality, allowing geolocation and culture/behavioural aspects to be investigated. Symptom reports were conducted in local languages (e.g. Portugese, Hindi, etc) thus increasing accessibility, however translations may not match exactly within cultural contexts, e.g. “pain” in Brazil is “joint ache” in UK (but not stomach pain). Given the data for this analysis came from an Internet based survey, there will be differential access, however only a very low effort was needed to partake given the questionnaire was accessed via a simple website and not an app. Given the widespread use of smartphones globally, this should facilitate participation, however we acknowledge that those who are younger or in wealthier countries may be more likely to partake thus skewing the results, equally educational factors may have played a role and we do not have any socioeconomic or ethnicity information. Whilst we acknowledge that the data used are self-reported, we do not think this undermines the accuracy of underlying disease or symptom reporting. For those who report a COVID-19 positive test, we do not distinguish between type of tests and thus cannot account for differences in accuracy. Clinical and Public Health impact: Our information may be utilised in a clinical setting as an additional triage tool and for target testing, especially to better inform decis...
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: Please consider improving the rainbow (“jet”) colormap(s) used on page 7. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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