News junkies beware: Three of the most widely reported statistics during this pandemic have proven as useful as “Covid-19 Cough Syrup” or “Coronavirus Herbal Boneset Tea,” which is to say not useful at all.
Although the following figures don’t constitute lies or fake news, they are all misleading and continue to lead the United States toward ineffective solutions. As a result, Americans should view them with a healthy dose of skepticism.
1. The number of confirmed cases
Late last month, the United States reached what CBS News called a “grim milestone,” topping 6 million confirmed Covid-19 cases. The number, compiled by Johns Hopkins University, is both accurate and misleading.
The key word here is “confirmed” cases (positive tests), which pundits and the public often confuse with the number of actual Covid-19 infections. The latter figure is much, much higher, according to researchers.
In July, a study from MIT concluded that the number of Covid-19 cases could be 12 times higher than reported. Further, recent scenario planning from the Centers for Disease Control and Prevention (CDC) included a “current best estimate” that 40% of people infected with Covid-19 are asymptomatic and, therefore, unlikely to be screened or counted among the population of confirmed cases.
Based on serological testing data and this new research on asymptomatic carriers, health experts estimate that tens of millions of cases still have not yet been recorded.
That’s a major problem, one that’s proving hazardous to our nation’s health.
Knowing the actual number of infected individuals, and whether the rate is increasing or declining, helps health experts predict pending hospitalizations and deaths. The true number also tells officials whether the nation is effectively containing the virus or on the brink of disaster. The issue with the more commonly cited statistic of “confirmed cases” is that politicians and news outlets use it as a surrogate for actual cases, tethering Americans to a distorted view of the pandemic.
There’s a huge difference between confirming 6 million cases and dealing with the reality of 20, 30 or 40 million Americans who may have been infected with the coronavirus. Six million may be an accurate statistic, but it fails to reflect the real rate of transmission, and it helps to explain the ineffectiveness of public health policies to date.
2. The number of Covid-19 tests completed vs. needed
Amid ongoing concerns that Americans are being under-screened, healthcare pundits have joined with immunologists and test-kit manufacturers to call for an aggressive ramp up in Covid-19 testing.
According to a report in The New York Timeson Monday: “An average of 758,000 tests per day were performed over the past week, according to the Covid Tracking Project, well below the current nationwide target of 1.1 million daily tests.”
This target, based on a methodology developed by Harvard Global Health Institute, leaves reporters at the Times to conclude, “The number of daily coronavirus tests being conducted in the United States is 72 percent of the level considered necessary to mitigate the spread of the virus.”
From the earliest days of the outbreak in March, the World Health Organization insisted the only way to beat the virus is to “test, test, test.” And therein lies the problem. Counting the number of completed tests is, by itself, a futile exercise. The United States could conduct 200 million Covid-19 tests each month (per recommendations from the Rockefeller Foundation) or even 600 million per month (at the urging of a cross-disciplinary group of experts from Harvard), and still the impact on transmission and deaths would stay relatively minimal.
Effectively containing the spread of Covid-19 requires that screening be both quick and accurate. At present, testing in the United States is neither. The average wait time for test results is still four days to a week. Meanwhile, “false negatives” (tests that wrongly indicate no infection) occur anywhere from 2% to 37% of the time.
Getting a positive test result after a week of waiting is like receiving a hurricane warning several days after the storm has passed. People can still spread the disease while awaiting their results.
Moreover, epidemiological research shows that an infected person will make contact with an average of 36 other people. That’s a major health concern given that Covid-19 carriers are often most contagious before the onset of symptoms.
Experts at Harvard acknowledge that testing 600 million Americans every month will only prove useful if leaders also scale up contact tracing and properly enforce isolation and quarantine measures. That’s what happened in the countries that successfully contained the virus early on. There are no such plans at present in the United States on a national level.
Although testing numbers are not entirely useless, they distract from a far more significant (and still unresolved) issue: What should we do with the results? Put another way: How far is our nation willing to go to mitigate the spread of Covid-19? Officials have not been willing to force those who test positive to provide contact details or undergo mandatory quarantining. And thus, even with more testing, the pandemic will persist.
3. The case fatality rate of Covid-19
Through hundreds of thousands of years of evolution, humans have become fairly good at detecting threats. Most people know to avoid things that slither, sting or snap their jaws. But when it comes to invisible enemies, like viruses, humans have to rely on science to understand how deadly they may be.
There are a number of different ways to measure the severity of a viral threat. One of those measures, the Case Fatality Rate (CFR), is based on factual numbers but is both inaccurate and misleading. It’s derived from a simple equation: the ratio between confirmed deaths (based on death certificates) and confirmed cases (based on positive Covid-19 tests). The CFR is only as accurate as those two data points.
Since the number of cases is grossly undercounted, the mortality rate is significantly overstated. Previous estimates have placed the mortality rate as high as 4% but, with more frequent testing in recent months, that number has declined. The current mortality estimate is closer to 3%.
Even that lower number assumes there have been fewer than 7 million U.S. cases and that asymptomatic people are all being tested. Neither assumption is possible. In fact, worldwide mortality from the coronavirus could be as low as 0.3%, based on highly controlled data from Iceland. What’s the point? A ten-fold difference (3% versus 0.3%) is both massive and highly consequential.
Officials use mortality rates to determine the most appropriate response to infectious diseases. Ebola, for example, kills 50% of the people it infects on average, which is why the doctors who treat it wear hazmat suits. Seasonal flu, meanwhile, only kills around 0.1%. Thus, there are no public lockdown orders during flu season. In fact, half of all Americans don’t even bother getting vaccinated.
Though the exact mortality rate of the coronavirus isn’t yet known, it is unlike Ebola and influenza in one important way: They are both “equal opportunity killers,” posing a relatively equal threat to the youngest and oldest populations. Not so with this coronavirus. Covid-19 spares approximately 99.99% of people under 24. By contrast, it claims 35% of people 85 years or older, the majority of whom have at least one chronic illness.
Therefore, focusing on just one number—an overall mortality rate—does no one any good. Using it, policymakers have implemented a one-size-fits-none set of public health measures that over-restrict younger people who are relatively safe and under-support those at gravest danger, all while reaping economic and societal damage on all Americans.
Had health experts and lawmakers made decisions based on mortality by age and existing health status, they might have adopted a segmented national health policy, one designed to save the most lives possible without inflicting undue psychological harm on those who are at minimal risk.
Instead, they acted on the wrong set of data, underscoring a dangerous truth: Statistics can be both factual and misleading.