Stanford Study on Santa Clara County: Very questionable conclusions

My last post discussed a study from Stanford that suggested 50-85 times greater Infection Rate (IR) compared to the Case Rate (CR) in Santa Clara County. The Wall Street Journal published a discussion of this Study (which has not yet been peer reviewed) claiming that it was good evidence of a much lower fatality rate for COVID-19. Turns out that study was deeply flawed. The test used likely had a false positive rate of 13%, not 0.5% assumed by the authors. That alone makes the conclusions completely bogus. In addition, the study population was not truly a random sample and likely had significant selection bias. For a complete expose watch this:

One would expect something better from Stanford, but like I said, this was not yet peer reviewed.

“This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.”

But the Wall Street Journal reported on it in a favorable way, not revealing that one of the authors of the study was also the author who wrote the WSJ article!

A brief note about false positives and false negatives.

Suppose you are looking at a population of 1,000,000 people with an infection rate of 1% (990,000 do not have the disease)

Assume a sensitivity of 93% (the test is positive in 93% of true positives)

Assume a specificity of 96% (false positive rate of 4%)

If you test everyone, 9300 of the 10,000 true cases will be detected, 700 of the cases will not be detected.

BUT 40,000 false positives will be found for a total of 49,300 positives. You will publish an infection rate of 4.93% while the real infection rate is only 1%.

Statistics are tricky. The sensitivity and specificity of a test are extremely important.

Be careful about what you read. We all would like to be reassured that it would be safe to relax restrictions but we still do not yet know  the true IFR. The true infection rate depends on widespread testing with an accurate test and we have not yet done that.

Besides the economic downturn associated with shelter in place, there are valid clinical concerns about the damage being caused (depression, anxiety, suicide, spousal abuse, child abuse, reluctance to call 911 for a real emergency, etc..) We will need to return to less restrictions in an incremental way based on regional circumstances (NYC not the same as Northern California).

For a detailed discussion about how and when we should relax restrictions read this.

There has been allot of comparing apples with oranges in the social media. People keep trying to compare COVID-19 to the flu. They are very different with respect to the fatality rate and ease of transmission. (In addition, whereas we have had a vaccine for Influenza A and B, we do not have one for COVID-19 or any other Corona Virus)


Case Rate (CR) is the # of known cases based on nasal swab PCR test divided by population.

Infection Rate (IR) is the actual # of cases divided by population. This is estimated by performing a reliable serology test on a large random sample of people, testing for infection by measuring antibodies (there are a few tests available but their sensitivity and specificity remain controversial and crucial)

A recent analysis comparing the 2009 H1N1 influenza A pandemic to COVID 19 suggested this:

Case Fatality Rate Infection Fatality Rate
2009 H1N1 Virus (flu) 0.1% to 0.2% 0.02%
COVID-19 New York 8% 0.50%

Some folks on social media have been comparing the CFR of the flu to the IFR of COVID-19. That is comparing apples to oranges.

The data in the table above are based on what appears to be the most recent and reliable information from New York City. The data on 2009 H1N1 is reported here.

In the old news clip below, 2920 adult deaths associated with 12 million cases of H1N1 calculates out to a 0.02% IFR which is exactly the same IFR described in the study linked above..

In this same report and in other discussions of H1N1 it was clear that children were more severely effected compared to COVID-19.

The table above would indicate that the IFR (infection fatality rate) of COVID-19 IS 25 TIMES GREATER than the IFR of the 2009 H1N1 Influenza A pandemic. The CFR of COVID-19 IN NEW YORK CITY is 40 times greater. This represents a much greater difference than the relative fatality rates suggested by the highly questionable conclusions of the Stanford Study of Santa Clara County.

There is a possibility that the New York City strain of COVID-19 might be more lethal than the strain of COVID-19 on the West Coast. That suggestion is PURELY SPECULATIVE and so far there is no data to support it. This possibility has been suggested because  NYC and New Jersey hospitals are much closer to capacity with COVID-19 compared to the West Coast experience and there are portable refrigerator truck morgues outside of hospitals in NYC and New Jersey where the local morgues filled up weeks ago. Again I would point to the major differences of the apparent CFRs between various countries and regions within countries which have not yet been explained (as discussed in my last post).

We have much more to learn, we need more testing (both nasal PCR and blood serology) to understand the spread and lethality of this disease. Those in the social media who claim we already have herd immunity are spewing nonsense. Herd immunity requires > 80% infection rate. Our measured IRs are highest in NYC (about 15%) and much lower in other areas where “reliable” serology has been performed.

One great failure in our country has been the prolonged lack of adequate testing. Shelter-in-place should have been a time-out to collect data and access where we are. That can only happen with reliable wide-spread testing. To AVOID overwhelming our hospitals and health care workers we must identify cases, trace contacts, isolate positives and isolate contacts. Isolation would ideally not be at home where the disease could easily spread to the entire household. Isolation at home is only reasonable when that home has a separate bedroom and bathroom for the infected person AND the household follows strict isolation and hygiene.

We must all recognize that the primary objective of shelter in place is to avoid overwhelming the health care system. Eventually, unless a treatment or vaccine becomes available, the disease will infect most of our population before we reach herd immunity. To return to economic activity and a more “normal life” we will necessarily accept a large number of deaths, primarily but not exclusively amongst the elderly and infirm. Generally it would seem reasonable to begin incrementally relaxing restrictions in areas of low impact, wearing masks, working from home where possible, avoiding public gatherings especially in confined spaces, and following good personal hygiene. So far the best information on risk (of death) appears to be in the table above, stratifying for age and risk factors.


Eat clean, drink filtered water, love, laugh, exercise outdoors in a greenspace, get some morning sunlight, block the blue light before bed, engage in meaningful work, find a sense of purpose, spend time with those you love, AND sleep well tonight.

Doctor Bob



2 thoughts on “Stanford Study on Santa Clara County: Very questionable conclusions

  1. Pingback: COVID 19 UPDATE: What have we learned? | Practical Evolutionary Health

  2. Pingback: COVID 19 Fatality Rate vs Flu, the social media incorrect comparisons persist despite the data demonstrating a large difference. | Practical Evolutionary Health

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