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Help Me Understand the Testing Percent Positive Benchmark….
I mostly lurk here, but I think this might be a good place to get help with a question. Here in Arizona, one of the three benchmarks for re-opening schools, businesses, gyms, etc. is the percent of positive tests. It is often reported that a high percentage (above 5% is the benchmark in AZ) shows that there isn’t enough testing available and that the virus is not under control. I could understand the purpose of this benchmark if the state were either a) testing everyone they could get their hands on and/or b) testing a random sample in a community, not just people who self-select to test.
In Arizona, there is surely not a lack of available testing. That may have been true six weeks ago, but not now. This is widely reported (not enough people showing up for tests), but I’ll share my own anecdote.
Over the weekend I was ill, similar to a stomach virus, and since pretty much every common symptom is linked to COVID I figured, well, why not get a test? Arizona State University has developed a saliva test, which sounded much less uncomfortable to me so I went to their site and noticed that they had many open appointments. There were multiple spots open every five minutes up until closing time. I made an appointment, drove over to the site and the huge stadium parking lot set up with serpentine lanes was all but empty. Imagine a university stadium parking lot with four cars. I pulled up at 9:25, was handed the vial, did my spit thing, and drove away at 9:35. Easy-peasy. Although they estimated a 48-hour turnaround, at 8 pm that evening I received a text that my results were in and I was negative. Obviously, they are not overwhelmed. (As a side note, I highly recommend the saliva test. It is thought to be more accurate and was by far more comfortable than a nasal swab!).
We are at about a 10% positive test rate right now. Nothing can open until we are at or below 5%. I really didn’t think I had COVID, but I thought: hey, maybe I’ll help lower the percent positives! My kids really want to go to school! If testing sites are empty like this one, which was in a very busy metro area, I assume people who do not have symptoms are not getting tested. Oh ….and it’s all free, too, so that’s not an obstacle. How in the world are we supposed to ever lower that percentage by much if only symptomatic people get tested? I suppose it will go down as the virus wanes, but should this really be a benchmark? Isn’t this statistic somewhat meaningless? Even if everyone is encouraged to get tested, why would one do that willingly? Why would I get tested when I am feeling well? It tells me nothing other than the fact that at that moment in time, I do not have COVID. Tomorrow is a different day.
Am I missing something here? I just don’t get the constant harping on the percent positive of tests benchmark and why it is being used to guide all reopenings.
Published in General
I think the OP might be suffering from a mild version of the nirvana fallacy. Clearly this positivity stat isn’t a be all and end all. It requires some interpretation, in conjunction with other information, to assess whether the virus situation is getting better or worse. The problem is we don’t have a “be all and end all” stat. There is no nirvana. About all we can do is assess the information we can get. And higher positivity rates (I suspect and believe) do positively correlate with a worsening outbreak. So it’s something to consider (among other things) in making public policy vis-a-vis the virus.
I’m not suggesting your state is necessarily using the stat responsibly. Maybe it’s exaggerating its importance. I don’t know. But I will defend the notion that it’s one piece of information that’s probably worth keeping an eye on.
https://ricochet.com/791487/do-the-covid-goals-make-sense/
It’s hilarious that so many of us are questioning the same thing. I just wrote a similar post posing the same question. Without the government asking more people to test w/o symptoms, I don’t think there’s much change of getting the % lower. I think the testing numbers have peaked. People will be testing if they think they might have been in contact with someone who has the virus, but otherwise I think the number of tests will continue to drop and the positive rate will continue to bounce around between 5% and 10%.
Excellent question. One I’ve been asking ever since our own state imposed similar benchmarks for reopening.
Locally, you had to qualify to get a test; that is, you needed to claim one or more of a list of symptoms. That pretty much guarantees a high positivity rate.
It’s crap. It’s a way of keeping us imprisoned. The percent positive tells you more about who is getting tested than about the progress of the disease.
The only important metric is deaths. By that metric, the epidemic is over. It was over in May.
That’s pretty much the way it was a few weeks ago when I got tested for covid-19. Twice. For the free Google-RiteAid test I had to fill out an online form telling what symptoms I had. For the test in our local health care system I talked to a woman who did a phone interview to determine if my symptoms warranted a test. (She agreed that waiting 10 days for the Google-RiteAid results was ridiculous, and said our local health care system would be much faster. It was.) It didn’t seem like they had set the bar very high, or that they were having trouble dealing with the volume in either system.
Both turned out negative, and I’m now waiting impatiently for Lyme disease test results to come back from the Mayo Clinic.
A real life example of using the ratio is provided by the colleges. UNC Chapel Hill has had in person classes for 1 week. They kept dorms <60% full and utilized less than 30% of their classroom seats. But in that one week the positivity rate jumped from 2.8% to 13.6% (approx 1000 students tested). So they have shifted to all virtual classes.
https://www.dailytarheel.com/article/2020/08/breaking-remote
https://carolinatogether.unc.edu
Did they report on how many of those positive tests had any symptoms?
Well, now we have to wait 14-28 days to see if they get really ill or die.
We are in a kind of laboratory.
I agree. It’s not perfect but it’s a useful metric, and it helps answer the question as to whether an increase in positive cases is the result of the spread truly getting worse, or just an increase in the overall number of tests. Same if there is a decrease.
It helps tell you the scale is the problem, even if it’s imprecise. Look at it this way: if you could have tested for it in Nov or Dec of ‘19, you would have had a 0% positive rate (excluding false positives) because no one had it. All symptoms would have been symptoms of something else. Now, if 10% test positive, you can get some idea of the spread. If you got 20%, 30%, 40% or worse you know you have a problem on an entirely different scale, and can react accordingly.
It’s like guessing your home value from recent sales of similar homes in the area. It may be off for any number of reasons, but it gives something reasonable, and usually fairly accurate, to go by when you need to make decisions.
Well, that’s the problem, isn’t it? False positives lead us into very bad choices, especially when the rate of false positives is near or over the rate of actual positives.
“Sentence first, verdict afterwards!”
It strikes me that a great deal of the disagreement/anger over testing policies can be attributed to a difference in belief over a simple question:
Is the pandemic already “over”?
There are quite a few people – including on this thread – who seem convinced that there will not be a major second wave of Covid anywhere in the US even if we were to rescind all extraordinary distancing/testing/tracing/hygiene measures tomorrow. And indeed, there are several well-reasoned theories suggesting that the level of population immunity in the US might already be high enough to preclude any major second wave.
On the other hand, every other significant respiratory viral pandemic in recorded history had a major second (and sometimes third) wave, which was often heavier than the first in many places. Given the other similarities between the Covid pandemic and historical influenza pandemics, it’s far too early to rule out the possibility of a second wave based on hypothetical (albeit solid) calculations.
In other words, nobody knows whether there is going to be a second wave – not Anthony Fauci, not Michael Levitt, not me, not you.
What we do know, though, is that the areas hit hard by the first wave did not get much warning in the form of a slow, obvious increase in symptomatic cases. By the time they realized something was amiss, it was too late. So asymptomatic/”unwittingly symptomatic” transmission is clearly a major driver of the largest outbreaks, and screening for asymptomatic cases is necessary if we want to avoid them.
So if we postulate a simple 50/50 chance of the pandemic not being over, then screening for asymptomatic spread is not only logical but highly advisable given its relatively low cost.
The real problem at hand here is not the actual collection of information, but what policymakers, the media, and the public do with that information.
We know that the major outbreaks were always preceded by undetected spread among low-risk populations, but we also know that outbreaks among low-risk populations (like college kids) don’t inevitably lead to major outbreaks among high-risk populations. So how do we handle detection of an outbreak in a low-risk population when it could be a prelude to a major outbreak but could just as easily be a false positive?
It’s like hurricane forecasting: we monitor the region in the equatorial eastern Atlantic because that’s where devastating hurricanes originate, and having that early warning gives us time to prepare. Of course, if we battened up the entire East Coast of the US every time a tropical depression was detected off the coast of west Africa the country would freeze up every fall. But the answer isn’t to stop all hurricane forecasting and wait until the winds on the East Coast exceed 60 mph to board up the windows – by that point, it’s too late.
What’s urgently needed is a rational way of handling such “early warning” signs that doesn’t involve taking drastic measures every time we detect an incipient trend. It’s not any easy task by any means, but simply refusing to collect information because we don’t know how to handle it is definitely not the solution.
As Scott Atlas pointed out, cases are not important. The critical metrics are deaths and hospitalizations.
That was the point of 15 days to flatten the curve.
I believe in a typical flu season 45 to 60 million Americans get it. And somehow civilization survived
Atlas is correct that death and long-term disability are the only outcomes that matter in terms of assessing the damage caused by the virus.
But if we agree that deaths count, and that we want to prevent deaths, it makes sense to look for warning signs of an impending death wave. And currently, the only sign we know of that predicts death waves is infections among the low-risk population. It’s not a very good predictor, but if lowering deaths is a priority, there’s currently no alternative to testing low-risk people and looking for asymptomatic infections.
Even if we shift to a “shelter the vulnerable” strategy, we can’t place senior citizens and nursing homes under indefinite quarantine. We would still need some type of warning sign to know when to isolate the vulnerable from the rest of society. Again, the only warning sign we currently have is population-wide testing. That might change in the future, but we’re just not there yet.
Again, testing itself is not the problem. It’s the over-reaction to test results that’s the problem.
Data on deaths and hospitalizations help us know whether we’ve flattened the curve. Data on cases help us know whether another wave is imminent.
Oh, you are asking for a rational policy.
What would such a protocol look like?
Sorry, not quite sure what you mean here. Do you mean what a protocol would look like for detecting impending waves of Covid that didn’t involve testing lots of low-risk people?
Case in point: the University of North Carolina sent everybody home after a week when positivity rates went up.
What would a protocol for testing low risk people look like?
How would subjects be selected? From what area? How many? Over how long a period of time? S separate inquiry for each state, each county?
thank you for that clarification.
I should revise a previous comment.
It’s not about cases.
It’s not about deaths.
It’s about hospitalizations/ICU patients and their age.
I don’t know of any “established” protocols yet, since most places are still making up their policies as they go.
A nationwide policy for a country the size of the US would be completely impractical and counterproductive. At most a policy would be sensible for the state level, but breaking it down to county level would probably be even more useful since the virus tends to cause local outbreaks within limited geographical areas (like a metropolitan area).
As to how to choose people, the sky’s the limit. As I mentioned in a previous comment, some countries have managed to implement truly random testing, with a new group tested every week/month.
Since that is difficult to imagine in the US, another option would be to look for people in positions that are very exposed to the virus – like public transit employees, food processing facility workers, bartenders, etc. – and basically use them as sentinels.
There are also a few early-warning concepts that don’t involve direct testing. For example, the receptor in the body for the coronavirus is actually more highly concentrated in the gut than the lungs, and so many people also get a gastointestinal infection. This means the virus can be easily detected in municipal wastewater. It’s not yet clear how good a bellwether this metric is. There are also attempts to monitor the spread through tools like Google searches – if the number of people searching for “What are the symptoms of Covid” spikes in a certain geographical region, that might be a good sign of “silent” spread.
Basically, the sky’s the limit in terms of strategies that could be used for early detection/mitigation of large outbreaks.
The more important hurdle is for a region to pick a strategy that actually makes sense and is feasible for their situation, and then to carry though with it. That’s where most places in the US seem to be stumbling.
All of these different testing approaches (and even threads like this one) don’t mean anything if they’re not implemented appropriately as part of a larger, coherent strategy with a sensible goal. Heck, some places have kept most of their society open without much if any testing at all – but they did have a coordinated strategy.
All of the places that have quickly re-opened and stayed open so far had a coherent plan – in many cases quite different plans, yet in each case a plan that faced considerable pushback when it was first made. Even if your preferred option is “do nothing” or “shelter the elderly”, that still requires fleshing out precisely what those vague platforms mean, and then selling them to a very skeptical public.
Without a rudimentary consensus of how we want to approach the pandemic, all this talk of specific testing strategies is beside the point. But that consensus remains far away – there may be a great deal of consensus among closed groups like the Ricochet user base, but in the public at large nobody has closed the deal yet.
Thanks for your response. It answered my question very well.
Bingo! I would think a “low prevalence” baseline could be found measuring certain locations and then comparing current levels over time to hospitalizations.
I haven’t heard much more about this:
Coronavirus traces found in March 2019 sewage sample, Spanish study shows
But, um . . . in Spain, in March of 2019? I can’t find any sort of follow-up to the initial reports.