By Kent R. Kroeger (Source: NuQum.com, April 2, 2020)
According to recent a Pew Research Poll, 59 percent of Democrats (and those Democrat-leaning) said the coronavirus outbreak is a major national threat, but only one-third of Republicans (and those Republican-leaning) had the same view.
Partisans in the U.S can’t even leave politics out of a worldwide pandemic. Perhaps German writer Thomas Mann was right when he told a German audience in 1930 that “politics is everything.”
Still, we can’t all agree the coronavirus pandemic is a national crisis?
Nope, apparently not.
And our hyper-partisan political culture is a having a tangible impact on coronavirus suppression and mitigation strategies at the state-level.
For example, 37 governors have issued statewide stay-at-home orders, according to CNN. Notable, however, are those states that not issued such a statewide order: Alabama, Arkansas, Iowa, Missouri, Nebraska, North Dakota, Oklahoma, South Carolina, South Dakota, Texas, Utah and Wyoming.
Every one of the stay-at-home laggard states was won by Donald Trump in the 2016 election. Furthermore, among governors in Trump-states that have implemented stay-at-home orders, many have Democratic governors: Kansas, Michigan, North Carolina, Pennsylvania and Wisconsin.
The state that surprises me the most is Iowa — my home state. While the Hawkeye State is not immune to partisan rancor, the state has a long history of leading on important socially progressive policies (public schools, racial integration, Head Start programs, marriage equality, etc.).
“I can’t lock the state down,” Iowa Governor Kim Reynolds told reporters last Tuesday. “I can’t lock everybody at home.”
But, actually, Iowa law says she can — she just chooses not to.
According to the Iowa governor, the “internal data” she is looking at concludes that a stay-at-home order is not necessary.
What “internal data” is she talking about? She isn’t clear on that. Is it a super secret data modeling method that forecasts coronavirus transmission? Who knows?
And, while I’m picking on Iowa, a big ‘Boo’ on the Iowa press corps for not pressing Reynolds on exactly what “internal data” she is using to make this life-and-death decision. By the time Reynold’s secret data source signals a stay-at-home order is necessary, it will be too late for hundreds of Iowans who will die because her short-sighted response to the crisis.
Says Reynolds about her reluctance to issue a stay-at-home order, such a policy would put undue strain on the supply chain and essential workforce.
Is Iowa’s economy more important than its retiree population? Reynolds might not put it exactly that way, but by delaying the stay-at-home order (and she will issue such an order at some point), that is precisely what she is saying.
With one of the oldest populations in the U.S., Iowa is particularly susceptible to a coronavirus outbreak in nursing home and long-term care facilities. In fact, Reynolds shared some of her coronavirus data when she admitted a Cedar Rapids (Linn County) long-term care facility — Heritage Specialty Care — is already experiencing an “outbreak” with 30 confirmed cases.
As the tragic experience at the Life Care Center in Kirkland, Washington attests, once an outbreak starts within a vulnerable population living in close quarters, the virus is impossible to stop and the results are deadly.
If Reynolds’ internal data isn’t telling her that “Iowa is a ticking time-bomb,” then it is junk data.
Now that I’ve vented my frustration about Iowa, here is the really distressing thing I’ve discovered in the U.S. data regarding the coronavirus: There is clear evidence that the extent to which states are testing for the coronavirus is significantly impacted by partisan politics.
I get it that some people believe the ‘coronavirus panic’ is more hype than reality. Why didn’t the world lock-down during the 2009 Swine Flu pandemic that killed 150,000–575,000 worldwide, including approximately 12,000 Americans, according to a Centers for Disease Control (CDC) estimate?
It is a legitimate question, though Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, aptly responds with three reasons the coronavirus is different from the 2009 Swine Flu: (1) The 2009 swine flu pandemic was spread out over many months and didn’t place nearly the level of strain on the world’s health care system that the coronavirus has done in just a matter of a weeks; (2) the medical community has extensive experience developing vaccines for influenza viruses and, in the 2009 pandemic, a vaccine was developed within a year of the outbreak (There is no such assurance that a vaccine will be developed as quickly for the 2019 coronavirus); and, finally, (3) we don’t know the contagiousness and lethality of the coronavirus yet — only research and time will settle that question. In the meantime, prudent levels of action (such as lockdowns) are well-advised, according to Dr. Fauci.
Yet, there will always be people suspicious of the news media and politicians anytime drastic measures such as lockdowns and stay-at-home orders become common, particularly when it has a dramatic effect on the economy. At times during this pandemic, I have included myself in that skeptical crowd.
But not when it comes to testing.
Broad-based, systematic testing is a critical element of any national effort to contain a highly contagious and deadly virus, according to health organizations like the CDC and the World Health Organization (WHO). A country can’t address a problem effectively if it doesn’t understand the breadth of the problem.
As former NASA Flight Director Gene Kranz might say: ”Let’s work the problem people. Let’s not make things worse by guessing.”
Broad-based, systematic testing for the coronavirus removes some of the guesswork out of policy making during a pandemic.
Unfortunately, coronavirus testing is not being consistently implemented by U.S. states, as of 30 March. As Figure 1 (below) shows, the states that have conducted the fewest coronavirus tests per 100,000 people tend to be states that Trump won in 2016 — red states like Oklahoma, South Carolina, Texas, Alabama, Mississippi, Kentucky, Georgia and, of course, Iowa.
Figure 1: State-level coronavirus testing per capita (Data source: The COVID Tracking Project)
Republicans will rightfully point out that many of the states doing the most testing are also red states: Louisiana, Utah, North Dakota, Alaska, South Dakota and Montana. All have tested, so far, more than the national average of 359 coronavirus tests per 100,000 people.
Before concluding that there is a genuine political bias in the application of coronavirus testing, I tested a simple regression model to explain levels of coronavirus testing at the state-level. Included in my model were the following control variables: (1) the number of people hospitalized in the state for the coronavirus, (2) percentage of tests coming back negative (a proxy for states with broader testing procedures that include people without symptoms), and (3) an indicator variable for states where large (>50) infection clusters occurred (NY, WA, LA).
Additionally, I tested numerous political variables, two of which were significant predictors of state-level testing: (1) Trump’s statewide share of the 2016 vote, and (2) an indicator variable for states with a Republican governor but a Democrat-controlled state legislature.
According to the model, the relative importance of each variable in explaining state-level coronavirus testing are as follows (in ranked order; numbers in parentheses indicated standardized coefficients and the direction of the relationship):
- States with large-cluster infections (+0.65)
- Number of coronavirus patients hospitalized (-0.56)
- Percent of tests coming back negative (0.33)
- Trump’s share of 2016 statewide vote (-0.29)
- A Republican governor in a Democrat-controlled state (0.21).
The finding that states with the most coronavirus hospitalizations tend to have lower rates of testing, all else equal, is not as surprising as it sounds. Its significance is driven by the fact that the states with the most hospitalizations tend to be bigger states, which will also tend to have lower per capita levels of testing given their large populations.
More intriguing is the finding that Trump states, all else equal, are testing for the coronavirus at lower per capital levels. For every 10 percent of Trump’s 2016 vote share, the state tests 41 fewer people (per 100,000 people), all else equal.
Likewise, in Democrat-controlled states and holding other factors constant, the presence of a Republican governor adds 148 tests per 100,000 people.
See, not all Republicans are bad.
This may be the most important statistic that has so far emerged during the coronavirus pandemic: Twenty-five percent of those infected with the coronavirus may be asymptomatic, according to the CDC. These are people who carry the virus, show none of its COVID-19 symptoms, yet may still be contagious.
Super-spreaders are a likely source of many cases of the coronavirus worldwide.
To my mind, the super-spreader is the scariest aspect of the coronavirus. Given this virus kills between 0.1 and 3 percent of those who have it, identifying asymptomatic coronavirus carriers — or, at least, the prevalence of these people in the general population — should be among the highest priorities for government agencies charged with controlling the coronavirus.
Without large-scale levels of testing, including the systematic testing of people without any COVID-19 symptoms, it is impossible to know the extent of the super-spreader problem.
The time is long over for Republican governors in red states to operate under the fiction that the coronavirus pandemic is a phantasm of the liberal (Trump-hating) media. The time is now to lock-down and test.
To Iowa Governor Kim Reynolds: If your “internal data” is not telling you to lock-down, stay-at-home and do far more testing of Iowans, you need new “internal data.”
Requests for the data used in this analysis can be sent to: firstname.lastname@example.org (It is also available at www.COVIDtracking.org)
A Linear Model of State-level Coronavirus Testing