Nass overreaches on virus test data claim
A growing list of Wisconsin communities are mandating mask use in public amid rising COVID-19 case counts.
But are these politicians and decision-makers relying on bad data?
That was the claim from state Sen. Steve Nass, R-Whitewhater in a recent appearance on a conservative radio show.
“Local governments are making these (COVID-19) decisions on inaccurate data because the negatives are not being entered,” Nass told host Vicki McKenna in a July 24 interview on Madison’s WIBA. “They’re sitting on these things across the state of Wisconsin.”
The longtime lawmaker took aim at Dane County in particular, asserting 17,000 negative COVID-19 tests “have not been reported” by Public Health Madison & Dane County, which when factored in lowered the occurrence of positive tests to 2.1%. That would be drastically below the statewide average of around 10% and the 5.5% figure Dane County had most recently reported for a two-week average.
Nass then went a step further to claim the tests were delayed intentionally “so they could get their mask mandate and they can restrict restaurants and bars even more.”
For this check we’ll set aside Nass’s claims about Dane County’s motivations and focus in on his initial claim:
Is he right that negative tests aren’t being reported across the state, skewing data used by decision-makers?
Some health departments are backlogged
We’ll start with the claim of missing tests. There have, indeed, been delays reporting negative test results.
The day before Nass’s interview, state Health Services Secretary Andrea Palm acknowledged to the Milwaukee Journal Sentinel that health departments around the state were behind in
Coronavirus data
State Sen. Steve Nass
R-Whitewater
“Local governments are making these (COVID-19) decisions on inaccurate data because the negatives are not being entered … across the state of Wisconsin.”
Reporting was delayed, but impact on decisions likely minimal processing and reporting negative tests. She said the delays were generally one to three days, but Dane County officials were about 10 days behind at that point.
Palm said that certainly impacted the percent positive stats on a “day-to-day basis” and encouraged the public to rely on the seven-day average for a more accurate picture.
Why there is/was a backlog
To better understand why this happens, let’s zoom in on Dane County.
People who take tests are notified as soon as results are available — whether results are positive or negative. But those two groups were not reported in the same way, Public Health Madison & Dane County said in a news release and Facebook post.
All tests must be manually verified to ensure the person taking the test lives in Dane County and that information like the address is properly filled out. This happens immediately on positive tests, for which officials must conduct contact tracing.
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But this manual verification caused a backlog among negative tests as testing volume increased. Dane County didn’t report those numbers until the locations were verified, which was taking as long as 10 days. So while long-term averages were in the ballpark, daily counts could be significantly skewed.
Other health departments had similar delays while the negative test results were in what officials call a “staging area” awaiting verification, but delays elsewhere weren’t as long.
On July 24, Dane County changed its methodology and began reporting negative test results as soon as results were available. It now adjusts those tallies down later to account for tests from people who live outside Dane County — typically about one-third of the tests.
But the county had acknowledged a delay in negative tests starting almost a month earlier. The weekly data releases starting June 29 said the backlog was affecting the reported numbers. And as the backlog grew, the weekly release for July 20 didn’t even include a percent positive calculation.
Putting it all together
This brings us back to Nass’s fundamental claim — that this skewed data is especially problematic because it is being used by local decision-makers.
The percentage of COVID-19 tests with a positive result has been a key indicator of virus prevalence, but Nass’s assumption that the negative test delays skewed local decisions has a couple of problems.
First, the delays outside of Dane County have been described as limited to a few days at most. That will affect daily counts, but not the seven- or 14day averages that health officials have long pointed to as more reliable indicators. And that’s just one of many indicators that could factor into decisionmaking, along with the number of positive tests, hospitalizations and deaths.
Second, this assumes decisionmakers wouldn’t be aware of the negative test backlog. The restrictions put in place there the first week of July came from the health department, which was of course aware of its own backlog of negative tests.
It’s important to note that mask mandates in places around the state have been imposed by county boards and city councils which would not have such intimate knowledge of testing data. But it seems reasonable to presume such decisions would typically be made in consultation with local health officials, who would know that data.
The statement
The verdict
Our ruling
Nass said local governments across the state are making decisions based on inaccurate COVID-19 data “because the negatives are not being entered.”
Nass is right that a delay reporting negative tests led to the release of inaccurate data, which local decisionmakers could have partially relied on while deciding to implement additional restrictions. But his description overreaches on several fronts.
The backlogs outside Dane County were generally just a couple of days, so they wouldn’t have much effect on the more trusted seven and 14-day rolling averages of how many tests were positive. And percent positive is just one of several key indicators that guide local decision-making.
For the longer backlog in Dane County, it makes no sense to claim local decision-makers were unaware of the 10day backup, since the orders came from the health department itself.
Factoring in those elements, we have a statement that is partially accurate but leaves out important details. That’s our definition of Half True.