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:
|
Continent |
Coefficient |
P-Value |
|
Africa |
6.48 |
0.007787 |
|
South America |
10.88 |
0.000048 |
|
North America |
9.07 |
0.000049 |
|
Europe |
12.89 |
0.000041 |
|
Asia |
8.51 |
0.000565 |
|
Oceania |
-1.42 |
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%.
https://www.bmj.com/content/369/bmj.m2237
https://academic.oup.com/cid/article/doi/10.1093/cid/ciaa415/5818333
https://www.medrxiv.org/content/10.1101/2020.07.06.20147025v1
further
confirm
this observations
Solidity
of
factor : Strong (Micro observation, large scale association)
Impact
:
1 (Highest)