key: cord-0956345-ttma2djc
authors: Boyce, Ross M; Shook-Sa, Bonnie E; Aiello, Allison E
title: A tale of two studies: Study design and our understanding of SARS-CoV-2 seroprevalence
date: 2020-12-18
journal: Clin Infect Dis
DOI: 10.1093/cid/ciaa1868
sha: 1e6f18b33870906bd8bbc4318a0a866162bc8c8f
doc_id: 956345
cord_uid: ttma2djc

nan

M a n u s c r i p t 2

The COVID-19 pandemic is arguably the most important public health crisis of the last century. To date, infections with the SARS-CoV-2 virus have caused nearly 300,000 deaths in the United States alone [1] , while also contributing to substantial excess morbidity and mortality from delayed and deferred care [2] . In addition to the direct and indirect health impacts, policies intended to limit the spread of the disease have resulted in large-scale disruptions to education systems, economic activity, and social networks. Put simply, the COVID-19 pandemic has impacted the daily lives of nearly all Americans in a way that no other health crisis has in our lifetimes.

One of the most challenging aspects of SARS-CoV-2 surveillance and prevention is the high proportion of individuals who remain asymptomatic or experience only mild symptoms and therefore never seek care [3] . Frequent testing can help identify individuals who harbor asymptomatic infections, but there are numerous logistical barriers associated with screening a large population [4] . An alternative method for quantifying the number of asymptomatic infections and characterizing the overall prevalence of SARS-CoV-2 in the population is the use of seroprevalence studies that measure antibody response to infection.

A positive serological test provides evidence of past exposure to the virus and can be coupled with data on symptoms to assess whether the individual experienced symptomatic or asymptomatic infection.

Thus, we should not be surprised that serological studies of SARS-CoV-2 prevalence among the general population have assumed an outsized role in the public square and are frequently highlighted in major news outlets, often in support of disparate policy aims. For example, early in the course of the US pandemic, the now widely-critiqued Santa Clara County study [5] was cited as evidence that the "COVID-19 death toll would be closer to that of seasonal flu" and used to question the wisdom of public health mandates [6] . In contrast, the authors of a more rigorous study in Indiana, which was conducted in the same month and found a similarly low proportion of seropositive individuals as the Santa Clara County A c c e p t e d M a n u s c r i p t 3 study, concluded that "many persons in Indiana remain susceptible" and that "adherence to evidence-based public health mitigation measures is needed to reduce surge in hospitalizations and prevent morbidity and mortality from COVID-19 [7] ."

As the pandemic has evolved, more seroprevalence studies have been published. Most of these studies have employed convenience samples that reflect very different populations across a wide range of study designs, serological assays, and statistical methods.

Undoubtedly, these studies have provided critical data and substantially advanced our understanding of transmission dynamics. Yet results across these differing study designs are frequently interpreted interchangeably and are extrapolated to the general population, despite important questions regarding representativeness, generalizability, and methodological consistency [8] . Comparisons across studies are made more challenging by the rapidly changing dynamics of the pandemic and geographic differences in infection patterns. What is urgently needed, however, are studies that compare seroprevalence estimates derived from multiple approaches covering the same geographic areas and timeframes, while using similar laboratory techniques. Such "studies of studies" can highlight the potential trade-offs between factors such as accuracy, efficiency, and cost (Table 1) .

Moreover, these comparison studies can provide the critical data to allow modelers to more accurately estimate the direction of the pandemic and subsequently impacts on the health system. [9] , creating some doubt about the comparability of these estimates. Second, the clinical sample was only standardized by age and sex and did not account for differences in socioeconomic status or race/ethnicity between the participants and the population, even as disparities in SARS-CoV-2 infection and outcomes have been associated with these factors [10] . Third, while the authors find no significant difference between the seroprevalence estimates, we must remind ourselves that a failure to reject a null hypothesis of no difference is not an acceptance of the null. In other words, the finding of no statistical difference between approaches does not indicate that no differences exist. We would also highlight that seemingly small differences can have substantial impacts when applied to a large population. For example, the 1.76% absolute difference reported between the approaches represent over 100,000 infections in the Atlanta metropolitan area; a number that certainly could affect contingency plans for hospital beds.

Despite the limitations of the study, it is generally reassuring that the seroprevalence estimates based on community and clinical samples were "pretty close." Unfortunately, this is no guarantee that community and clinical estimates will align for different time periods or A c c e p t e d M a n u s c r i p t 5 geographic areas, as factors driving selection bias can vary across populations and can change over time. Thus, we cannot say that the two methods should be used

interchangeably. Instead, we should leverage the advantages of each approach to draw a more complete picture of the current state of SARS-CoV-2 transmission in the community.

Convenience samples, and particularly those that employ remnant clinical specimens, provide timely estimates at relatively low cost compared to community seroprevalence studies. Clinical studies may be particularly useful when repeated in the same target population over multiple time points. In contrast, more resource-intensive, prospective studies that seek to enroll a representative sample of the population are likely the only way to accurately estimate seroprevalence. This is particularly the case for sub-groups such as historically marginalized populations that may not be well-represented in samples relying on engagement with existing health systems. Given the well-established disparities related to socioeconomic status and race/ethnicity, getting the numbers right is critically important to achieving health equity and ultimately ending the pandemic.

Delay or Avoidance of Medical Care Because of COVID-19-Related Concerns -United States

Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review

Challenges and Controversies to Testing for COVID-19

COVID-19 Antibody Seroprevalence

New Data Suggest the Coronavirus Isn't as Deadly as We Thought; A study finds 50 to 85 times as many infections as known cases-meaning a far lower fatality rate

Population Point Prevalence of SARS-CoV-2 Infection Based on a Statewide Random Sample -Indiana

Estimation Without Representation: Early Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Studies and the Path Forward

Estimated Community Seroprevalence of SARS-CoV-2 Antibodies -Two Georgia Counties

Racial Disparities in Incidence and Outcomes Among Patients With COVID-19

A c c e p t e d M a n u s c r i p t 7 A c c e p t e d M a n u s c r i p t 8