Vaccine

MicPaPa said:
This one's a real head scratcher - can anyone figure out the common threads here?

View attachment 13252
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That chart does not appear in the NBER report. The obvious question, then, is where did you copy it from?

"NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications."

The authors are well-known wingnut cranks.

 
More sad news from my favorite Thai place. 14 years in business and she looks like she will close before summer she says. True story. Before the Rona, place was packed, line outside to get in for dinner and lunch specials all day for $8.95 and everyone talking and having a great time. Today, it;s just the mom and her son. I took my wife for lunch today and will not be going back. It's just sad and depressing. You order at counter and she brings food in plastic dishes and plastic cups. Lunch Special is now $13.95. No one to wash the dishes and cook and bus tables and wait on table. I gave her a big hug and thanked her. She said people only order take out now and I dont have enough business like before. Gas triple and food cost are going up up up. She said people come by now to say goodbye. I gave her funny look and she says, "Ye, Goodbye, I move Texas, I move to Florida." No joke.
ลาก่อนไปเท็กซัส
Lā k̀xn pị thĕksạs̄
 
Is your church getting paid by the government to convince the flock to get injected?

They reportedly get $10 for each person-to-person outreach including direct phone calls and text messages, direct social media messages, door-knocking campaigns, “and anything that involves one-on-one dialogue promoting the COVID-19 shot.”

The church or other nonprofit gets another $10 for each person who actually goes and rolls up his or her sleeve for the shot.

Plus, if you can covert them to Christ, you get 10% of their income, if they want to be in good standing with the church and the Lord. Pay to play even at church. Wow, what a wonderful world.
 
espola said:
That chart does not appear in the NBER report. The obvious question, then, is where did you copy it from?

"NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications."

The authors are well-known wingnut cranks.

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If you read the report you would have located the data-appendix link. But I get your MO, ad hominem the authors instead of addressing the data. Check.
 
dad4 said:
Your list says: to avoid epidemics, you should live far away from other people.

FL is the only high pop density state in the left column. There are no low pop density states in the right column.

Did you think it meant something else?
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Yeah, that's what it says.
 
MicPaPa said:
Yeah, that's what it says.
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Look at the list of states.

In what way are Vermont, Maine, Montana, and Idaho different from New York, New Jersey, DC, Los Angeles and Chicago?

You've definitely proven that respiratory disease spreads better when you are nearer to other people.

Congratulations.
 
dad4 said:
Look at the list of states.

In what way are Vermont, Maine, Montana, and Idaho different from New York, New Jersey, DC, Los Angeles and Chicago?

You've definitely proven that respiratory disease spreads better when you are nearer to other people.

Congratulations.
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There are plenty of low density states that received a D grade or worse and their are plenty of high density states that received a C or better. Again your only considering Covid results, where this study is looking at multiple factors that determines successful health policy.
 
watfly said:
There are plenty of low density states that received a D grade or worse and their are plenty of high density states that received a C or better. Again your only considering Covid results, where this study is looking at multiple factors that determines successful health policy.
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If you look carefully at the study mechanism, it has the earmarks of choosing factors and analysis equations to give the desired result.
 
watfly said:
For example?
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To adjust a pandemic outcome from the industry composition of its economy, we use the following multivariate linear regression equation. ys=α+xsβ+es where β is vector of coefficients, one coefficient for each of the share variables in xs. Because the share variables and the regression residual have mean zero among the fifty states and DC, α is the national average outcome y. We interpret xsβ as the part of the outcome explained by industry composition and ys - xsβ =α+es as the outcome adjusted for industry (or health) composition. We estimate α and β using ordinary least squares in the pre-pandemic data for the fifty states and DC.

For our GDP by state component, we used the same regression method with the vector elements Mining, Oil and Gas, Accommodations and Food, and Arts and Entertainment.

Because COVID infection mortality risk is extremely age-related -- 8700 times higher in age 85+ than in 5 to 17, according to the CDC – we applied an age-adjustment to the number of observed deaths in each age group to bring the numbers in line with a standard U.S. population. Because CDC suppresses totals of less than 10, we combined ages less than 35, but because there are few deaths in that age range it should not affect the accuracy of the adjustment.

To further adjust these numbers for substantial differences in metabolic health across states, we applied the same regression methodology we used in the economic section to the age-standardized rates above using CDC-reported prevalence of obesity and diabetes, the conditions most strongly correlated with COVID-associated deaths.

etc, etc, etc
 
espola said:
To adjust a pandemic outcome from the industry composition of its economy, we use the following multivariate linear regression equation. ys=α+xsβ+es where β is vector of coefficients, one coefficient for each of the share variables in xs. Because the share variables and the regression residual have mean zero among the fifty states and DC, α is the national average outcome y. We interpret xsβ as the part of the outcome explained by industry composition and ys - xsβ =α+es as the outcome adjusted for industry (or health) composition. We estimate α and β using ordinary least squares in the pre-pandemic data for the fifty states and DC.

For our GDP by state component, we used the same regression method with the vector elements Mining, Oil and Gas, Accommodations and Food, and Arts and Entertainment.

Because COVID infection mortality risk is extremely age-related -- 8700 times higher in age 85+ than in 5 to 17, according to the CDC – we applied an age-adjustment to the number of observed deaths in each age group to bring the numbers in line with a standard U.S. population. Because CDC suppresses totals of less than 10, we combined ages less than 35, but because there are few deaths in that age range it should not affect the accuracy of the adjustment.

To further adjust these numbers for substantial differences in metabolic health across states, we applied the same regression methodology we used in the economic section to the age-standardized rates above using CDC-reported prevalence of obesity and diabetes, the conditions most strongly correlated with COVID-associated deaths.

etc, etc, etc
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Not sure how that skews it to a desired outcome. It appears that the Covid adjustments may have actually worked against some low density states which dispels Dad4's theory. See Montana and South Dakota ranking in the bottom 10 on Covid factors.

To be perfectly honest with you, the math is way above my head, but the factors they adjusted for seem perfectly reasonable. It seems fair to Hawaii to adjust for industry composition and not overly penalize them because they rely on tourism. Why do you believe the adjustments are not reasonable? They appear to work both ways regardless of a red or blue state.
 
espola said:
To adjust a pandemic outcome from the industry composition of its economy, we use the following multivariate linear regression equation. ys=α+xsβ+es where β is vector of coefficients, one coefficient for each of the share variables in xs. Because the share variables and the regression residual have mean zero among the fifty states and DC, α is the national average outcome y. We interpret xsβ as the part of the outcome explained by industry composition and ys - xsβ =α+es as the outcome adjusted for industry (or health) composition. We estimate α and β using ordinary least squares in the pre-pandemic data for the fifty states and DC.

For our GDP by state component, we used the same regression method with the vector elements Mining, Oil and Gas, Accommodations and Food, and Arts and Entertainment.

Because COVID infection mortality risk is extremely age-related -- 8700 times higher in age 85+ than in 5 to 17, according to the CDC – we applied an age-adjustment to the number of observed deaths in each age group to bring the numbers in line with a standard U.S. population. Because CDC suppresses totals of less than 10, we combined ages less than 35, but because there are few deaths in that age range it should not affect the accuracy of the adjustment.

To further adjust these numbers for substantial differences in metabolic health across states, we applied the same regression methodology we used in the economic section to the age-standardized rates above using CDC-reported prevalence of obesity and diabetes, the conditions most strongly correlated with COVID-associated deaths.

etc, etc, etc
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If you want to make group A look better than group B, all you need to do is correct for the factors which put group A at a disdvantage.

So, if you want to make blue state policies look good, you correct for population density and timing of first wave.

If you want to make red state policies look good, you correct for obesity rates and population age.

Actual researchers won’t be fooled. But it works if your goal is to have a nice headline in NYT or WSJ.
 
watfly said:
Not sure how that skews it to a desired outcome. It appears that the Covid adjustments may have actually worked against some low density states which dispels Dad4's theory. See Montana and South Dakota ranking in the bottom 10 on Covid factors.

To be perfectly honest with you, the math is way above my head, but the factors they adjusted for seem perfectly reasonable. It seems fair to Hawaii to adjust for industry composition and not overly penalize them because they rely on tourism. Why do you believe the adjustments are not reasonable? They appear to work both ways regardless of a red or blue state.
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It would take someone trained and skilled in the field to review their methods properly. The disclaimer on the first page more or less declares that this is just an unreviewed "working paper" being passed around for comments. From the results found in simple internet searches, it appears that it has become a favorite among certain political circles, which I must assume was the authors' intent.
 
dad4 said:
If you want to make group A look better than group B, all you need to do is correct for the factors which put group A at a disdvantage.

So, if you want to make blue state policies look good, you correct for population density and timing of first wave.

If you want to make red state policies look good, you correct for obesity rates and population age.

Actual researchers won’t be fooled. But it works if your goal is to have a nice headline in NYT or WSJ.
Click to expand...

You said it better than I did.
 
dad4 said:
If you want to make group A look better than group B, all you need to do is correct for the factors which put group A at a disdvantage.

So, if you want to make blue state policies look good, you correct for population density and timing of first wave.

If you want to make red state policies look good, you correct for obesity rates and population age.

Actual researchers won’t be fooled. But it works if your goal is to have a nice headline in NYT or WSJ.
Click to expand...
Again your only looking at one factor, there are economic and education factors. The great irony is you want to adjust for population density because it favors blue states.
 
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