This is a numerical & narrative thread looking at the relationship between NPI (mandate) stringency and state level COVID outcomes in the USA.
Contents: 1) discuss motivation 2) present methods and sources 3) present results and implications
2
Motivation:
NPIs are disruptive. Nowhere in life do we entertain harm unless there is potential benefit. We are 16 months into this and have completed a full "epidemic cycle". We are in a period of COVID-19 quiescence nationally.
It's a good time to examine the results.
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So why examine the USA in isolation?
1) We have a heterogenous response (Oxford Stringency Avg 41.6 SDEV 9.2)
2) We have diverse demographics in key variables
3) '57, '68, 2009 Influenza pandemic outcomes suggest wide inter-country variation and 2+ years to "mature"
There was a 25-70 fold geographic variation in mortality with the '68 Influenza A H2N2 epidemic.
In line with 1918 and 2009 Influenza pandemic analyses.
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In '57, there was substantial residual variation even within a region after adjusting for development indicators. Interestingly, the singled out Scandinavia as a notable example where Finland experienced substantially higher mortality than Denmark or Sweden. 🤨
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The inter-country variation may be attributable to prior exposure to H1N1 thereby reducing the severity of the H2N2 infection (UK, Canada, NE USA in 1951). In some regions, preceding severe Influenza episodes may have led to the death of more frail before the advent of H2N2.
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And the mortality impact might manifest over the course of the entire pandemic. This was seen in the ’57 and ’68 Influenza pandemics.
9/ We see a light mortality year in Sweden in 2019. In 2019, Swedish excess mortality was 1-4 standard deviations below normal which was not demonstrated in the rest of Scandinavia (UK excess for comparison). But Scandinavia C19 Twitter is very 2020. #TimeToMoveOn
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All of this emphasizes the benefits of looking at one nation when assessing NPIs. So let’s dive in.
Factors examined:
-Oxford NPI Stringency Index
-C19 and Excess Deaths/M
-% Obesity
-% population >65yo
-Crowding Index
-% Multigen Households
-Annual Precipitation
1) Deaths were normalized to count per million of population
2) Linear and Quadratic regression was used. Higher order regression was not used to avoid over-fitting.
3) Log transformation considered, but NOT used
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*Raw* Deaths/M showed weak reduction with higher stringency and correlation was weaker than with obesity rates: 2.8% vs. 7.1%
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% population >65yo did not correlate with a *Raw* Deaths/M difference (<<1%).
This is due to the fact that the % >65yo is ~ similar nationally (16.5% STDEV 1.9%). Therefore, predictably, adjusting for age distribution to varying degrees was not a notable differentiator.
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The impact of BMI on relative risk of severe C19 is higher when younger and therefore more relevant for 80-85% of the population of a state. Consequently, when we adjust for Deaths/M for obesity rates, the already weak effect of NPI stringency disappears.
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Annual precipitation and multigenerational households also demonstrated weak effects.
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You’ll notice how the regression is heavily biased by Hawaii. 5.5h flight from CA, it would be disingenuous to suggest Hawaii is not in a unique situation. If we remove Hawaii from the multigenerational HH regression, a correlation even stronger than obesity emerges.
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Even in the “Mask Havens” of East Asia, people do not mask at home. That said, there is evidence from studies of NPIs in Influenza that NPIs might increase the % of infections from aerosols (>50%) and aerosol mediated infections may be more severe: ncbi.nlm.nih.gov/pmc/articles/P…
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I discuss the paper and implications in detail here:
So what benefits did we accrue with business closures, school closures, mask mandates? Nothing really.
Indeed, on top of divisiveness, acrimony, school closures, stay-at-home orders may have increased spread in multigenerational households (disproportionately ethnic).
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This disproportionate impact on low wage essential worker ethnic households manifested quite clearly:
When you arrive at the same conclusions looking at the data in multiple ways, you’ve arrived at a version of truth.
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Interestingly, none of this was particularly surprising. How differently would matters have gone if the respected Dr. DA Henderson of Smallpox eradication was alive? liebertpub.com/doi/pdfplus/10…
Despite the above, “experts” continue to pine away about mitigations and engage in campaigns of fear about the latest variant citing “our race to herd immunity”.
Our cases didn’t plummet in mid-Jan '21 because we hit herd immunity & the UK is not imploding from “Delta”.
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Functional definition of an “expert”: someone who is more right than wrong in the face of uncertainty.
By any measure, “experts” repeating refrains from last year should be disqualified.
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Hopefully you can see that there is data to be analyzed robustly and application of rigor would suggest that they should change their tune.
They have not.
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Indeed, some act as if they have discovered a powerful tool in NPIs and they are propped up by what can only be described as officially sanctioned misinformation.
How much more will it take for you to reject what they have to offer?
We should have never allowed this to happen. All we can do now is make sure that it never happens again
/end
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Meanwhile, colleagues lost jobs, some required letters from me, nurses were banned from eating in the nursing lounge, children/families were harassed, and @GavinNewsom required boosters.
Multiple studies in process or under review show risk reduction by 3+ doses falling down to 2 doses, controlling for prior-infection, within 90 days. Can't share those.
Even in this *nationwide Qatari study*, differences in risk reduction were so small, that confidence intervals for protection from severe disease overlap for almost all mixes of prior infection and vaccination for BA.1 and BA.2.
Quantitatively review a national test and vaccine provider’s ongoing test positivity data.
Sources: Walgreen’s, 2020 US Census, Our World in Data
Yes, I’m embarrassed that @Walgreens has more comprehensive testing data than the @CDCgov, but that’s not my fault.
All proportions are a 3d moving average.
Without any age considerations, is Walgreen’s testing over-sampling a particular cohort?
We start with age cohort vaccination rates from the CDC:
For parsimony, I treat CDC 2 dose completed and Walgreen’s 2 dose or booster completed in the same category collapsing Walgreen’s 3 dose regimens into the 2 dose category.
1st Question: is Walgreen's data biased by "over-sampling" one group or the other (i.e., more vaccinated)?
He points out 3 important issues that many of us have been concerned about and pointing out for quite a while.
1) contrary to fraudulent claims, the vaccines were never trialed to stop transmission and claims to this fell apart quickly. Search my TL for a year plus on this.
2) boosting in perpetuity has real risks beyond imprinting (“OAS”) and no clear demonstrable benefit for large swaths of the population