Covid Hospitalized Patients per 100k – 25 Most vs. 25 Least Stringent States
twitter.comI think it’s worth noting that the difference between stringent and non stringentin the USA is dramatically different then say the difference in policy between Brazil and China. It may be that the big things - lock down at certain mathematical points versus universal masking mandates makes a huge difference.
We can't honestly use China as a data point, seeing as how they have been censoring from the beginning, and arresting activists who try to report on covid deaths.
10 Most vs 10 Least Stringent: https://twitter.com/IAmTheActualET/status/139042697357133005...
Doesn't bother to account for population density, which is a huge red flag or sign that author doesn't know what the heck is up.
Nor coastal vs. interior.
Very interesting.
What conclusions can we draw from this?
Given that the author is an anti-masker conspiracy theorist, we can learn that one can score Internet brownie points in the anti-masker conspiracy theory community by plotting garbage data without providing sources or context.
The data comes from healthdata.gov[1] and sources[2] at the U.S. Census Bureau, the U.S. Bureau of Labor Statistics, the Kaiser Family Foundation, Ballotpedia, Editorial Projects in Education, Centers for Disease Control and Prevention, National Restaurant Association, Littler Mendelson, Husch Blackwell and Ogletree Deakins.
How is this garbage data? Maybe it is, but I think you may need to do more work here than just calling him names. Everyone seems to feel very strongly about this on both sides, but I get the sense he made a good-faith effort with legitimate data. Perhaps you could do the same if you feel his conclusions were incorrect?
[1] https://healthdata.gov/dataset/COVID-19-Reported-Patient-Imp...
[2] https://wallethub.com/edu/states-coronavirus-restrictions/73...
Thanks for the sources. Unfortunately [2] won't load for me, claiming "too many requests".
Things I would look for in the data would be, at least:
- Are there substantial differences in the restrictions? If the least stringent rule is "wear masks in many public spaces", and the most stringent rule is "weak masks in slightly more public spaces", that wouldn't be substantial. Substantial differences would include actual lockdowns/curfews/closure of non-essential business. A strict limit of "one customer per N square meters of shop space" might also be substantial, if N is large enough.
- Are compliance and enforcement taken into account?
- (Pointed out by another poster:) Is population density taken into account?
> I think you may need to do more work here than just calling him names.
I might. But then again, if a renegade software developer on Twitter claims to overturn the medical community's consensus in one chart, I might not.
I'd check the link again because it answers most if not all of your questions. It weighs (with gradated strictness) mask usage, restaurant closures, school re-openings, large gathering restrictions, health checks, travel, lockdowns, and more. You may have some quibbles with the methodology, and that's fine, but it really doesn't look like "garbage data".
Also, there is no medical consensus on this array of restrictions and I don't think he claims to overturn anything. There is a robust and healthy debate among scientists and medical professionals on the effectiveness of school closures, lockdowns, temperature checks, etc. The scientific certainty of mask efficacy in some circumstances simply can't be a stand-in for every pandemic restriction.
I understand this is a polarizing topic because it maps closely to political leanings, but we should all try to be more open-minded to good faith efforts to understand the data whether it confirms our biases or not.
States arent the ones Karening around without a mask in enclosed spaces. How about a randomized study looking into social media history of hospitalized patients to get the % of those actually following best practices?
Or just breaking it down by mask compliance rates per county. Masks are heavily political, but averaged across the size of a state things shift from red/blue to shades of purple. At the county level, these things seem (anecdotally) to separate much further. The hot spots around here were all in the rural areas with low mask rates.
Other than the geriatric and the obese, its the poor who are the most disproportionately hospitalized.
Its the poor who are also most harmed by the harsh economic effects of the covid response.