I largely avoid politics here because it’s a much more subjective topic that is far less data-focused in nature than what I try to provide here, so the most political I’ve gotten was to provide past election results and county voting trends with no political commentary. This post will attempt to maintain that line between data and opinion, but I understand that the topic of politics and Covid-19 is already a very controversial mix, and simply sticking to the data won’t necessarily be perceived as unbiased by all who read this. With that in mind, I can only say that this post is based on existing, straightforward data, and I have made no attempts to add any personal biases to the results one way or another.
With all that said, I was curious whether there was any correlation between voting patterns and Covid cases/deaths locally, specifically with Ohio’s 88 counties. There has been some suggestion in the media that Red- or Republican-voting places- have generally had worse outcomes than Blue- or Democratic-voting counties. So we will look at the following factors to see if that holds true locally:
1. Total Cases
2. Total Cases Per Capita
3. Total Deaths
4. Total Deaths Per Capita
But we first must establish what the Red vs. Blue counties are. Ohio doesn’t register voters by party affiliation, so instead, I used county voting results for the past 6 presidential elections, or since 2000. Based on the % of voters voting for either Republican or Democrat, I created 5 different levels of political bias.
Deep Blue: Counties where Democratic voters beat Republican voters by 25 points or more.
Light Blue: Counties where Democratic voters beat Republican voters by 5-24 points.
Neutral: Counties where Democratic/Republican voter advantage falls under 5 points.
Light Red: Counties where Republican voters beat Democratic voters by 5-24 points.
Deep Red: Counties where Republican voters beat Democratic voters by 25 points or more.
The point system is taken from the average of the past 6 elections, so keep in mind that some counties may be more Red or Blue currently than the average suggests.
So based on that criteria, which Ohio counties are Red vs. Blue vs. Neutral? Well, Ohio’s overwhelming rural counties are mostly in the Deep Red category (39 of 88), followed by Light Red (34 of 88), Neutral (7 of 88), Light Blue (7 of 88) and just 1 Deep Blue county in the state. The map below breaks the counties down and gives the average voting bias across the last 6 elections.
Now we need to take those established voting biases and compare them to how the counties performed during Covid-19.
Case Numbers Through 3/31/2022
Total All Red Counties: 1,230,285
Total All Neutral Counties: 498,375
Total All Blue Counties: 944,141
Average Total Cases by County Group
Deep Red Counties: 15,138.8
Light Red Counties: 18,819.8
Neutral Counties: 71,196.4
Light Blue Counties: 96,563.6
Deep Blue County: 268,196
On first glance, it would seem that Red counties did much better on average despite having more cases overall, but the problem is that those counties have much lower populations and literally cannot have the same average totals of more populated ones. That’s why we instead have to look at per-capita cases.
Total Cases Per 100K People
All Red Counties: 23.9
All Neutral Counties: 22.8
All Blue Counties: 21.9
Deep Red Only: 23.7
Light Red Only: 24.0
Neutral Only: 22.8
Light Blue Only: 22.0
Deep Blue Only: 21.7
With per-capita, the story is exactly the opposite, with Red counties having more cases by population. If you’re wondering why the Deep Red counties had a slightly lower per-capita rate than Light Red, the answer is Holmes County. Perhaps due to its heavy Amish population that tends to avoid modern medicine and technology- and largely keep to themselves in the process- reported per-capita cases there were the lowest in the state by far, and single-handedly lowered the entire Deep Red county grouping below Light Red counties. Regardless, the outcome was still the same- Red did worse.
Top 10 Highest Per-Capita Case Rate
1. Marion: 30.5
2. Lawrence, Scioto: 30.0
3. Pickaway: 29.9
4. Muskingum: 29.3
5. Jackson: 27.4
6. Fayette: 27.3
7. Guernsey: 26.9
8. Allen, Defiance, Pike: 26.8
9. Union: 26.4
10. Clark, Erie: 26.1
Top 10 Lowest Per-Capita Case Rate
1. Holmes: 11.6
2. Geauga: 17.1
3. Carroll: 19.5
4. Wayne: 20.0
5. Meigs: 20.1
6. Ashtabula: 20.3
7. Washington: 20.6
8. Summit: 20.7
9. Ottawa: 20.9
10. Trumbull: 21
Of course, one of the caveats with this data is that many, many cases went unreported, and those cases were more likely to be in rural areas with lower access to testing and medical facilities. There’s also the issue that Covid doesn’t always present with symptoms. So, it’s possible that the per-capita discrepancy was even larger.
Deaths, however, are more concrete. Not all of them have been counted either, obviously, but it’s a lot harder to hide a death than it is a symptomless case.
Total Deaths Through 3/31/2022
All Red Counties: 18,295
All Neutral Counties: 7,219
All Blue Counties: 12,523
Average Total Deaths by County Group
Deep Red: 227.6
Light Red: 277.0
Neutral: 1,031.3
Light Blue: 1,253.3
Deep Blue: 3,750.0
Again, on the surface, the average looks bad for Blue counties despite having the lower overall total, but the truth again rests with the per-capita figures.
Total Deaths Per Capita by County Group
Deep Red: 394.4
Light Red: 390.4
Neutral: 345.4
Light Blue: 328.6
Deep Blue: 304.0
On a per-capita basis, Blue counties lost 76 fewer people per 100K than did Red counties. That’s a lot of lives.
So what can be concluded from all this? The first is that the results in Ohio were not isolated. Across almost all states, Blue-leaning states had better outcomes per-capita than Red. Speculation as to why that is runs the gamut, from better overall local policies to differing views on science to even education levels and access to harmful media sources that disseminate misinformation. Whatever the case may be, the United States has lost more people than any other nation in the world, at now over 1 million. It is by far the worst pandemic and the worst natural disaster in American history. With so much bad news these days, we can only hope that something valuable has been learned in all this terrible mess.
