If the President Wants to Improve the Fatality Rate for the USA, He Just Needs to Improve Testing
When we look at the cumulative confirmed cases and fatality data for close to two hundred countries, it seems quite remarkable that some countries seem to do 10 times
better than other countries in terms of case fatality rate. We might conclude that the novel coronavirus
discriminates by country or that certain countries manage their pandemics much
better than others. The truth is more
complicated.
USA testing completeness and efficiency is very poor and needs to improve by a factor of 2.5 to match Germany’s efficiency. Testing kits should not be distributed randomly to achieve the same percentage of the population tested, but rather more to those regions that are hotter than others and returning more than 10% positive results. States like NJ that continue to return near 50% positive test results must increase their testing by more than 5 fold to achieve a more accurate measure of their infection. If the President wants a lower fatality rate for the USA, he simply needs to improve testing.
When examining
mortality rates two issues are important: how we count deaths and how we test patients. The case fatality rate is a straightforward statistic calculated by
dividing number of confirmed deaths by the number of confirmed cases for any
group or subgroup of patients. For an
ongoing pandemic, this statistic is almost always understated. Firstly, because it generally takes several
days for a confirmed case to resolve itself into death or a cure, so the
denominator using the current case count is overstated. Secondly,
many deaths occur outside the healthcare system and although the patient was never
tested positive for coronavirus they often show many signs that they probably died of
COVID-19. The cause of death assignment is
often subjective, especially when there are comorbidities involved. The most probable ones should be counted in
both the numerator and denominator to determine the true COVID-19 fatality
rate.
The other
major problem is that the total number of infectees is rarely known while the pandemic
is raging since we are most likely testing only a subpopulation of all
infectees. Often the testing protocol
stipulates that we test only the most seriously ill and most at-risk. This protocol is actually not the most
efficient way to test the population given limited resources because you wind
up testing patients that are most likely to test positive anyway and miss a large
number of asymptomatic and mildly symptomatic cases, vastly underestimating the
denominator of the fatality rate. This
is one reason it is so important to test early and widely to get an accurate
picture of how the infection is spreading within the general population. The other major impetus for wide testing is,
of course, to ascertain that every infectee is identified and isolated as soon
as possible and all their potential contacts tracked and isolated as well to
contain the spread of the disease. Once
we are able to do this we can more confidently calculate the COVID-19 fatality
rate as the total confirmed and probable deaths due to COVID-19 divided by the
total number of deaths (confirmed and probable) plus cures (including all those
infectees that never showed any symptoms). While the pandemic is raging we can only estimate
this number.
We need to
do this so we can identify those segments of the population that are most at
risk early. The table below lists the top
countries in the world in terms of confirmed cases and fatalities plus a few
countries that have been identified as doing well in terms of managing their
pandemics. We had already examined the age
and gender effect in Spain and Italy and found them to be very similar in
form with mortality doubling every 7yrs in the range 35-75yrs old. Both countries tested nearly 2.9% of their
population (as of 4/27) which seems like a reasonable amount but since both countries were
highly infected 11% to 17% of the tests returned positive. When a high percentage of tests return
positive results it means that we are only testing those that are most suspect
and not really testing the full
population adequately. South Korea is
now held up as the best practice example in early and widespread testing and
contact tracing. While they have only
tested 1.2% of their population, they tested to the point where only 2% of
their tests returned positive. Interestingly
enough when we look at their age-dependent data, we see that they follow a steeper
exponential form (mortality doubling every 5.9yrs) that is lower overall due to more complete
sampling of the full population. The
steepening we believe is due to the fact that most countries test their at-risk
(older male) population better than they do the younger population which tends
to have milder and sometimes asymptomatic cases. When we look at Germany which is often
considered the best practice country in Europe, we see a very similar
graph to that of South Korea. Germany
has tested more than South Korea (2.5% vs 1.2% of their population,
respectively) but not as thoroughly as South Korea with 8% of their tests
returning positive results.
USA testing completeness and efficiency is very poor and needs to improve by a factor of 2.5 to match Germany’s efficiency. Testing kits should not be distributed randomly to achieve the same percentage of the population tested, but rather more to those regions that are hotter than others and returning more than 10% positive results. States like NJ that continue to return near 50% positive test results must increase their testing by more than 5 fold to achieve a more accurate measure of their infection. If the President wants a lower fatality rate for the USA, he simply needs to improve testing.
Because age
is such a strong effect, we see that the apparent difference in fatality rates
between Germany (3.8%) and South Korea (2.3%) may be entirely explained by the older population of Germany (47.1 vs 41.8yrs).
The difference between South Korea and New Zealand could also be
explained by age distribution differences in their population. As testing becomes more complete and efficient
in the seven countries with double-digit percentage fatalities, we predict that
their fatality rates will decline by more than a factor of 2. The fatality rate differential now seen among
all countries will narrow significantly and we may then search for other comorbidity
risk and structural factors that could better explain any residual differences in
how each country is impacted by the COVID-19 pandemic.
Country
|
Infections
|
Infections
|
Deaths
|
Fatality
|
Death
|
Median
|
tests
|
Test %
|
/million
|
/million
|
Age
|
/million
|
postive
|
||||
Spain
|
4,905
|
229,422
|
23,521
|
10.3%
|
502.9
|
42.7
|
28,779
|
17%
|
Belgium
|
4,028
|
46,687
|
7,207
|
15.4%
|
621.8
|
41.9
|
18,468
|
22%
|
Switzerland
|
3,409
|
29,164
|
1,665
|
5.7%
|
194.6
|
42.4
|
28,343
|
12%
|
Italy
|
3,289
|
199,414
|
26,977
|
13.5%
|
445.0
|
45.5
|
29,600
|
11%
|
USA
|
3,059
|
1,010,356
|
56,797
|
5.6%
|
172.0
|
38.1
|
17,211
|
18%
|
France
|
2,546
|
165,842
|
22,293
|
13.4%
|
342.3
|
41.4
|
7,103
|
36%
|
Netherlands
|
2,232
|
38,245
|
4,518
|
11.8%
|
264.0
|
41.2
|
11,319
|
20%
|
UK
|
2,319
|
157,149
|
21,092
|
13.4%
|
311.2
|
40.5
|
10,605
|
22%
|
Germany
|
1,890
|
158,213
|
6,021
|
3.8%
|
71.9
|
47.1
|
24,738
|
8%
|
Sweden
|
1,877
|
18,926
|
2,274
|
12.0%
|
225.5
|
41.2
|
9,357
|
20%
|
Norway
|
1,396
|
7,554
|
205
|
2.7%
|
37.9
|
39.2
|
30,310
|
5%
|
S. Korea
|
208
|
10,738
|
243
|
2.3%
|
4.7
|
41.8
|
11,869
|
2%
|
Taiwan
|
18
|
429
|
6
|
1.4%
|
0.2
|
40.7
|
2,590
|
1%
|
N. Zealand
|
299
|
1,470
|
18
|
1.2%
|
3.7
|
37.9
|
26,143
|
1%
|
Australia
|
264
|
6,720
|
83
|
1.2%
|
3.3
|
38.7
|
20,277
|
1%
|
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