Oddly, there is still a lot of vaccine hesitancy and conspiracy theories even though that has all been debunked. What’s even more puzzling are the number of people who will claim that it hasn’t been tested adequately (it’s been tested far more than any other vaccine at launch), but then they will go on to take drugs like hydroxychloroquine and ivermectin, that hadn’t been tested much for COVID initially, and after they have been tested and found not to provide any benefit, they still insist that it’s a cover up. Another study was published in JAMA this week indicating that ivermectin is no better than placebo.
So are vaccines better at preventing COVID infection, hospitalizations, and death? The data paints a very cleart picture.
Infection
This graphs represents cases by vaccination status per 100,000 people.
At first glance, the green line at the bottom right doesn’t seem to any benefit from the bivalent boosters. However, this is due to the big surge of cases at the start of 2022, which changes the scale of the y-axis. This is a view of just the part of the graph when the bivalent data became available.
Of course, there were legitimate concerns about the safety of the vaccine in adolescents and children. Views of the data can be found in the link in the sources section below. This is what the case data looks like for the <5 year old cohort, suggesting efficacy among this age group as well
Hospitalization
This is hospitalizations per 100,000 among the >18 year old population by vaccination status.
Hospitalization data isn’t available in their visualization tool for the <5 age group, but this is how hospitalizations look for 5-11 year olds by vaccination status.
Deaths
This represents deaths per 100,000 among those over 18 by vaccination status.
Again, it’s useful to zoom in to see the imapact of the bivalent boosters.
2020 election outcome data for the presidential race was used to group voters by county into six strata:
80-100% Republican
60-80% Republican
50-60% Republican
50-60% Democrat
60-80% Democrat
80-100% Democrat
Each of these strata were pooled for both 2010 population per the US Census Bureau and the incidence of COVID Cases. Due to the geographical election structure not aligning with county structure in Alaska, Alaska was excluded from this analysis.
The pooled COVID cases for each strata were then calculated as an incidence rate per 100,000 per calendar day. A 7-day moving average was then applied to each strata to remove some of the normal weekday variations and each line on the graph was colored to represent the degree of dominance of a political party (dark red represents >80% Republican, dark blue represents >80% Democrat) by 2020 presidential vote.
The hypothesis was that the more strongly a county voted Republican, the more likely it was to listen to and be influenced by misinformation and disinformation channels.
Results
There is a clear gradient in case rates depending on the political leanings of counties. This has remained consistent over time with two exceptions.
When the pandemic first started, a large proportion of the spread was in New York City. This was before there was a good understanding of the mode of transmission and in a very dense population area. As the virus spread into less populous area of the country and more knowledge was gained about transmission, other factors became more important in spread, such as messaging and beliefs.
The other anomaly in this pattern is in the spring of this year. This could be an effect of college students traveling during spring break, who are at an age where they are eager to return to normal and more likely to be a source of asymptomatic community transmission.
For better clarity in just how big the differences are in the extremes, the next graph only shows the >80% wins in the election. Except during the rapid case declines that were due to the vaccination efforts that reduced spread and the previously noted exceptions, the rates of new cases in strongly republican counties were almost four times as high as those in strongly democratic.
Discussion
This becomes much more important as it related to vaccination efforts. As of this writing, the vaccination rates by state correlate well with voting. A current view of this map can be found here.
The majority of lies and disinformation seems to spread mostly in right wing media and is instigated by twelve different people. This is causing irreparable harm to health and to the economy. Sadly, as new variants come to dominate cases, the spread will be most obvious among those who have been fooled by these sources. What remains to be seen is whether the media will be held responsible for the damage they have caused.
Do your part. Get vaccinated. Encourage others to do so. As B.1.617.2 (the delta variant) gains a foothold, your health and life may depend on it.
An Epilogue
There is a similar pattern for deaths when stratified the same way, which is not surprising at all. The two big jumps for about a month in May 2020 are due to states catching up on death reports and submitting them all at once.
8/21/21 Addendum. Fox “News” carries some responsibility for what is happening by allowing lies like this.
12/4/21 The rapid drop in the 80%+ Republican counties the past few weeks has been puzzling. The data hasn’t been tracking with that of the other strata. One possible explanation might have something to do with those who move south for the winter. However, the data this week painted a darker picture. While it may be easy to skew case data by not reporting it, it’s much harder to do so with death data. While this certainly doesn’t draw any specific conclusions, it certainly supports evidence that some governors have been trying to hide the impact in their states.
12/9/21 It appears that data from a very red state has been suppressed for about a month.
Yesterday I showed the impacts that could be expected in the US by age overall. I’ve taken US census data on a deeper dive and broken that down to the state level so people could see what that could mean for each state if drastic measures are not in place.
Something interesting jumped out at me as I looked at the table. It’s pretty obvious that states that are thought of as retirement destinations are going to have proportionally bigger problems.
These numbers are using the assumptions of an attack rate of 20% and the case fatality rates for age groups reported by the China CDC.
10-19
20-29
30-39
40-49
50-59
60-69
70-79
80+
AL
261
260
240
486
1,687
4,243
6,017
5,438
AK
39
45
43
69
245
600
560
465
AZ
386
401
367
691
2,208
5,959
9,331
8,426
AR
165
161
152
289
995
2,522
3,707
3,364
CA
2,081
2,331
2,273
4,056
12,957
29,902
38,492
41,586
CO
293
329
340
594
1,832
4,559
5,606
5,129
CT
187
185
176
356
1,353
3,166
4,295
4,666
DE
49
50
48
89
348
918
1,350
1,080
DC
28
54
58
65
191
425
627
611
FL
991
1,070
1,065
2,091
7,383
19,426
31,193
33,094
GA
597
586
564
1,120
3,513
8,001
10,553
8,874
HI
65
78
77
138
447
1,285
1,699
2,092
ID
103
93
89
169
547
1,439
1,994
1,726
IL
670
695
686
1,290
4,360
10,486
13,713
14,544
IN
364
369
341
646
2,268
5,592
7,244
7,354
IA
174
169
161
285
1,043
2,712
3,575
4,249
KS
165
161
148
274
938
2,455
2,965
3,540
KY
230
244
221
446
1,546
3,887
5,093
4,989
LA
250
255
254
441
1,539
3,968
4,982
4,657
ME
60
61
64
127
522
1,445
1,878
1,962
MD
306
319
329
612
2,185
5,018
6,600
6,377
MA
340
400
368
680
2,518
5,878
7,791
8,431
MI
512
552
482
955
3,540
9,159
11,924
11,963
MN
291
294
307
534
1,961
4,690
5,928
6,574
MS
174
161
153
287
967
2,507
3,500
2,964
MO
317
331
316
576
2,086
5,321
7,364
7,372
MT
54
55
54
95
346
1,069
1,430
1,248
NE
108
104
104
179
600
1,588
1,956
2,374
NV
154
163
169
321
999
2,483
3,513
2,874
NH
67
68
65
132
552
1,324
1,700
1,673
NJ
452
454
462
931
3,296
7,445
9,955
10,835
NM
118
112
110
191
670
1,862
2,604
2,466
NY
943
1,110
1,064
1,923
6,884
16,323
22,179
24,484
NC
553
562
530
1,075
3,534
8,804
12,222
10,859
ND
37
50
42
65
235
601
722
995
OH
610
621
582
1,126
4,088
10,472
13,731
14,207
OK
220
219
210
369
1,259
3,149
4,352
4,388
OR
202
226
232
430
1,345
3,861
5,069
4,830
PA
637
672
641
1,220
4,609
11,762
15,819
17,903
PR
156
172
144
333
1,121
2,873
4,846
5,053
RI
52
61
54
99
382
949
1,247
1,429
SC
266
269
253
498
1,742
4,546
6,619
5,424
SD
49
48
44
77
284
777
957
1,114
TN
350
369
346
687
2,297
5,849
7,915
7,207
TX
1,696
1,647
1,634
2,975
8,855
19,949
25,128
23,616
UT
208
203
178
307
776
1,933
2,411
2,366
VT
30
33
29
57
232
664
871
778
VA
440
466
465
874
2,948
7,014
9,125
8,961
WA
368
425
442
752
2,458
6,254
8,266
7,434
WV
88
89
83
182
638
1,864
2,436
2,406
WI
301
307
296
545
2,098
5,284
6,615
7,232
WY
33
29
31
53
187
556
657
581
*Please note that there is insufficient data for children <10