The first round of antibody testing in L.A country reveals approximately 4.1% of the adult population has COVID-19 antibodies


USC and the Los Angeles County Department of Public Health on Monday released preliminary results from a collaborative scientific study that suggests infections from the new coronavirus are far more widespread — and the fatality rate much lower — in L.A. County than previously thought.

The results are from the first round of an ongoing study by USC researchers and county health officials.

They will be conducting antibody testing over time on a series of representative samples of adults to determine the scope and spread of the pandemic across the county.

Based on the results of the first round of testing, the research team estimates that approximately 4.1% of the county’s adult population has an antibody to the virus. A

djusting this estimate for the statistical margin of error implies about 2.8% to 5.6% of the county’s adult population has an antibody to the virus — which translates to approximately 221,000 to 442,000 adults in the county who have been infected.

That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county at the time of the study in early April.

The number of COVID-related deaths in the county has now surpassed 600.

“We haven’t known the true extent of COVID-19 infections in our community because we have only tested people with symptoms, and the availability of tests has been limited,” said lead investigator Neeraj Sood, professor of public policy at the USC Price School for Public Policy and senior fellow at the USC Schaeffer Center for Health Policy and Economics.

“The estimates also suggest that we might have to recalibrate disease prediction models and rethink public health strategies.”

What do the antibody testing results mean for controlling COVID-19?

The results have important implications for public health efforts to control the local epidemic.

According to the study, 6% of adult men had been infected while 2% of women had been infected in the county. Researchers said they could not determine at this time whether the difference was by chance or statistically significant.

The study also showed that 7% of African Americans in the county had been infected, 6% of whites had been infected, 4.2% of Asians had been infected and 2.5% of Latinos had been infected.

The breakdown of those infected by age was 2.4% of adults ages 18 to 34 compared to 5.6% of adults ages 35 to 54 and 4.3% of adults 55 and older.

“These results indicate that many persons may have been unknowingly infected and at risk of transmitting the virus to others,” said Barbara Ferrer, director of the L.A. County Department of Public Health.

“These findings underscore the importance of expanded polymerase chain reaction (PCR) testing to diagnose those with infection so they can be isolated and quarantined while also maintaining the broad social distancing interventions.”

She added that the data suggests the county’s mortality rate is around 0.1% or 0.2% of true infections, but added that this was no consolation to the friends and families of the people who have died. The number of deaths related to COVID-19 in the county has surpassed 600.

The antibody test is helpful for identifying past infection, but a PCR test is required to diagnose a current infection.

“Though the results indicate a lower risk of death among those with infection than was previously thought, the number of COVID-related deaths each day continues to mount, highlighting the need for continued vigorous prevention and control efforts,” said Paul Simon, chief science officer at the L.A. County Department of Public Health and co-lead on the study.

The study’s results have not yet been peer-reviewed by other scientists. The researchers plan to test new groups of participants every few weeks in the coming months to gauge the pandemic’s trajectory in the region.

More on USC/L.A. County testing

With help from medical students from the Keck School of Medicine of USC, USC researchers and public health officials conducted drive-thru antibody testing on April 10 and 11 at six sites.

Participants were recruited via a proprietary database that is representative of the county population. The database is maintained by LRW Group, a market research firm.

The researchers used a rapid antibody test for the study. The FDA allows such tests for public health surveillance to gain greater clarity on actual infection rates.

The test’s accuracy was further assessed at a lab at Stanford University using blood samples that were positive and negative for COVID-19.

In addition to Sood and Simon, other authors and institutions contributing to the study include Peggy Ebner of the Keck School of Medicine, Daniel Eichner of the Sports Medicine Research and Testing Laboratory, Jeffrey Reynolds of LRW Group and Eran Bendavid and Jay Bhattacharya of the Stanford University School of Medicine.

There are two broad issues worth understanding for Covid-19 diagnostic testing.

The first issue relates to who gets tested. Due to an extreme paucity of kits in the United States, testing has been done mainly on people with convincing symptoms or who have been in contact with people diagnosed with Covid-19. If testing is performed on a wider population sample, the incidence of Covid-19 will likely prove to be much higher than currently reported.

That would be both bad news and good news. The bad news is that it could mean that the pandemic is more advanced than we thought. The good news is that a higher number of Covid-19 infections would make the case fatality rate lower than current estimates because we would be dividing by a larger number of cases.

The second issue relates to the test itself. There is already a growing concern that the diagnostic test for Covid-19 is not perfect. It may come back positive in some people who are not infected with SARS-CoV-2 and negative in some people who are.

Diagnostic tests are developed with both sensitivity and specificity in mind. The greater the sensitivity, the less likely it will miss real cases. The greater the specificity, the more likely uninfected individuals will be correctly deemed negative.

The problem is that tests almost never have 100% sensitivity and 100% specificity. The test and the truth together create four possibilities: true positives, true negatives, false positives and false negatives.

There’s a trade-off involved because an increasingly liberal test (more sensitive) will include more and more individuals in the population who do not actually have the disease (less specific).

This trade-off has important implications for interpreting Covid-19 population trends based on testing to date and going forward.

For Covid-19 testing, the threshold for calling a test positive should not be set too high. Failing to isolate someone who actually has Covid-19 and sending her back to a nursing home would be put many people in harm’s way.

But if the bar for calling a test positive is set too low, then a subset of patients who do not have Covid-19 will test positive for it.

Tests for Covid-19 are developed under idealized conditions with test tube samples from positive cases and negative controls. New ones are approved by the FDA under an emergency use authorization based on analytic validity, meaning they performed appropriately on test tube samples. Demonstration of clinical validity is not required.

Real-world performance could be worse. For example, the test could be cross-reactive with another virus. Or it could detect the presence of the novel coronavirus even after an individual is no longer infectious — so while it accurately detected the virus, it did not correctly identify the disease.

Coronavirus is present in secretions in such abundance that it is easy to detect, potentially too easy: Even the most minuscule cross-contamination while samples are handled creates the risk of a false positive.

Think of a hospital environment where personal protective equipment like masks are being reused due to shortages.

Poor techniques in sample collection could also lead to false negatives.

Many diagnostic tests, even routine ones, are not rigorously validated against an external, real-world gold standard. The myriad new tests emerging for Covid-19 include at-home tests and rapid tests for point of service testing.

They are produced by multiple vendors, each with different and as-yet-unmeasured accuracy.

As serology testing for Covid-19 exposure and immunity is offered to the public, its false positive rates and false negative rates may be markedly different from the viral detection tests that have dominated to date.

When applied to a broad swath of the population, a test’s performance can be surprisingly counterintuitive. It can perform worse than expected, producing a potentially large proportion of false positives in populations less likely to have the disease.

Consider a scenario with Covid-19 testing in an asymptomatic or mild population with 1 in 51 people infected (about 2%, the lower estimate for the NBA). Assume the test is always positive in individuals with the disease but falsely positive 10% of the time (which would be superior to many medical tests in use). As shown in the figure, the chance that someone with a positive test result is actually infected is under 20% (1 in 6).

As systematic testing is performed in the general population, patients less likely to have the disease — including asymptomatic individuals without known exposures — will be tested. A large number of false positive results would lead to an overestimate of the number of asymptomatic cases in many regions.

A large number of false positives could also overestimate the contribution of asymptomatic spread to the dynamics of the pandemic. False positives could also decimate the health care workforce if workers were inadvertently and unnecessarily quarantined and kept from seeing patients.

The magnitude of the false positive and false negative problem remains unclear. Policymakers are aware of this potential issue, but early data on the sensitivity and specificity of tests in Wuhan, China, have been retracted.

There is also a counting problem. If the virus is widespread, which it may be, the more Covid-19 testing that is done the more cases of Covid-19 we will find. When the World Health Organization states that “it took 67 days from the first reported case to reach the first 100,000 cases of Covid-19, it took only 11 days for the second 100,000 cases, and just four days for the third 100,000 cases,” we must recognize that part of that rise may be due to increased testing.

There will soon be a drastically increased number of tests and testing platforms in the United States. Germany and South Korea have implemented drive-through testing as an approach.

In Iceland, deCODE genetics recently released the results of a population sample of 5,571 Covid-19 tests, of which 48 were positive, enabling what is perhaps a more robust population estimate of 0.86% prevalence. An important caveat, though, is potential false positives and false negatives.

Back to the NBA. The discrepancy between the incidence of Covid-19 among professional basketball players versus the incidence in China and Italy can be explained in at least two ways: false positive test results in the NBA or basketball players’ privileged access to early Covid-19 testing, much of which was performed by private companies.

It is not hard to imagine some false positive test results among NBA players. There has been considerable ire directed at the NBA for moving asymptomatic players to the head of the line while critically ill suspected cases go untested in the U.S. But if many more Americans than we have counted have mild or asymptomatic cases of Covid-19 and simply cannot get tested, perhaps the NBA experience might actually be teaching us about the true prevalence of Covid-19 in the general population.

Now that testing is coming on line at scale, a critical next step is to design a population-based sampling approach that includes accurate, individual-level, de-identified information about whether tested patients had clinical courses consistent with Covid-19, their comorbidities, medications, and, of course, survival.

The clinical validity of the testing strategy is paramount. Before this information emerges, strategies to reduce spread including social distancing, ceasing nonessential activity, and closing schools remain essential.

But we should rapidly assess the sensitivity and specificity of Covid-19 diagnostics and serology testing as well as their performance across different populations.



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