These findings are shared for research purposes and indications for decision makers to help them broaden perspectives and expand understanding until they materialize in a reviewed paper.

Diet, lifestyle and obesity

Very early on in the epidemic, it became obvious that co-morbidity was a significant factor in the covid impact. This was observed (but not necessarily quantified) for diabetes, obesity etc., as well as implied by the much higher covid mortality for elder people (where co-morbidity is much more common). 

To quantify the impact of lifestyle variables on the covid mortality, we turned to « Our World In Data » which provides daily, collated, open source data for Covid mortality (using sources such as the European Centre for Disease Prevention and Control, World Health Organization and Johns Hopkins). We used the latest cumulative natural log transformed total death per million and regressed it against the 2016 compilation of Central Intelligence Agency of obesity adult prevalence rate (Country Comparison: Obesity - Adult Prevalence Rate, 2016).

The first run of data, where we did a worldwide regression, showed that the obesity adult prevalence rate (which is the proportion of obese in the population per country) is a statistically significant predictor in determining the Total Deaths Per Million. For each unit of increase in obesity %, the average total death per million increases by 8.76% on a global average (with a highly significant p-value). However, as the epidemic was at different stages in different continents, we decided to do the regression by continent. This gave us the following results:








South America



North America











0.619258 ** not significant**


What this means is that for each percent increase in obesity, mortality per million goes up by 6.48% in Africa, 10.88% in South America, 9.07% in North America, 12.89% in Europe and 8.51% in Asia, all very significant impacts with highly significant p-values. The only outlier is Oceania, where the mortality rates were very low in spite of high obesity, since those countries closed the borders very early on (and the p-value is not significant).

Incidentally, in the presence of obesity data, other variables (diabetes prevalence, smokers, population aged 70 and older, life expectancy, population density) were insignificant, further demonstrating the importance of obesity as a significant indicator in epidemic course.

For example, Japan, the country with the oldest population in the world, with very high population density, with no lockdown, few restrictions and low testing outperformed most other countries; Japan’s obesity rate is the lowest in the world at 4.3%. 

further confirm this observations


Solidity of factor : Strong (Micro observation, large scale association)

Impact : 1 (Highest)


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