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

Aiello, Allison E.

Boyce, Ross M.

Shook-Sa, Bonnie E.

Carolina Digital Repository

Accepted ManuscriptA tale of two studies: Study design and our understanding of SARS-CoV-2 seroprevalence Ross M. Boyce, MD, MSc1, Bonnie E. Shook-Sa, DrPH2, and Allison E. Aiello, PhD3 1Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA 2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA 3Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA Corresponding Author: Ross M. Boyce, MD, MSc Division of Infectious Diseases University of North Carolina at Chapel Hill CB# 7030, Bioinformatics Building 130 Mason Farm Road, 2nd Floor Chapel Hill, NC 27599-7030 (919) 966-2537ross_boyce@med.unc.edu Accepted Manuscript2 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 Accepted Manuscript3 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. Therefore, the article presented here by Bajema et al. works towards filling a key methodological gap in the SARS-CoV-2 seroprevalence literature. The authors attempted to estimate and compare the prevalence of SARS-CoV-2 antibodies among residents of the Atlanta metropolitan area using two approaches: (i) a representative community survey and (ii) a convenience sample of remnant blood from a commercial laboratory. Samples wereobtained over roughly the same time period, while serological testing was performed in a single laboratory using a validated assay; minimizing the potential causes of variability. The Accepted Manuscript4 two methods, after statistical adjustment, arrived at similar estimates of seroprevalence: 4.94% (95% confidence interval [CI]: 3.34 - 6.64%) for the clinical study versus 3.18% (95% CI: 1.49 - 6.67%) for the community study, with a difference in seroprevalence of 1.76% (95% CI: -1.52 - 5.06%).  The study does have limitations related to seroprevalence comparisons between the two cohorts. First, there was a mismatch in target populations between the clinical and community-based studies: the Atlanta Metropolitan Statistical Area, comprised of a 29-county region (2019 population estimate: 6 million), was used for the clinical study, whereas the community-study focused on DeKalb and Fulton Counties (2019 population estimate: 1.8 million). The authors note in a previous publication that DeKalb and Fulton Counties had the highest number of reported COVID-19 cases in Georgia at the time the community study began [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 Accepted Manuscript5 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. Accepted Manuscript6 Funding All authors (RMB, BES, AEA) report funding for seroprevalence studies from the North Carolina Department of Health and Human Services. RMB is supported by the National Institutes of Health (K23AI141764). BES receives support from the the University of North Carolina at Chapel Hill Center For AIDS Research (CFAR), a National Institutes of Health funded program P30 AI050410. AEA received research support from NICHD and NIA of the National Institutes of Health under award numbers P2C HD050924 and P30 AG066615, respectively. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Conflicts of Interest All authors have completed the ICMJE uniform disclosure form and declare: no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Accepted Manuscript7 References 1. Medicine JHU. Coronavirus Resource Center. Available at:https://coronavirus.jhu.edu. Accessed December 7.2. Czeisler ME, Marynak K, Clarke KEN, et al. Delay or Avoidance of Medical CareBecause of COVID-19-Related Concerns - United States, June 2020. MMWR MorbMortal Wkly Rep 2020; 69(36): 1250-7.3. Oran DP, Topol EJ. Prevalence of Asymptomatic SARS-CoV-2 Infection : A NarrativeReview. Ann Intern Med 2020; 173(5): 362-7.4. Binnicker MJ. Challenges and Controversies to Testing for COVID-19. J ClinMicrobiol 2020; 58(11).5. Bendavid E, Mulaney B, Sood N, et al. COVID-19 Antibody Seroprevalence in SantaClara County, California. medRxiv 2020: 2020.04.14.20062463.6. Bogan A. New Data Suggest the Coronavirus Isn't as Deadly as We Thought; Astudy finds 50 to 85 times as many infections as known cases—meaning a far lowerfatality rate. Wall Street Journal (Online). 2020 04/17/2020.7. Menachemi N, Yiannoutsos CT, Dixon BE, et al. Population Point Prevalence ofSARS-CoV-2 Infection Based on a Statewide Random Sample - Indiana, April 25-29,2020. MMWR Morb Mortal Wkly Rep 2020; 69(29): 960-4.8. Shook-Sa BE, Boyce RM, Aiello AE. Estimation Without Representation: EarlySevere Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Studies and thePath Forward. J Infect Dis 2020; 222(7): 1086-9.9. Biggs HM, Harris JB, Breakwell L, et al. Estimated Community Seroprevalence ofSARS-CoV-2 Antibodies - Two Georgia Counties, April 28-May 3, 2020. MMWRMorb Mortal Wkly Rep 2020; 69(29): 965-70.10. Munoz-Price LS, Nattinger AB, Rivera F, et al. Racial Disparities in Incidence andOutcomes Among Patients With COVID-19. JAMA Netw Open 2020; 3(9): e2021892.Accepted Manuscript8 Table 1. Advantages and disadvantages of seroprevalence study designs Type of StudyRecruitment ChallengesNon-participation/loss to follow-upLevel of RepresentativenessEfficiency & TimelinessCostsRemnant Studies (e.g. blood donation, outpatients, inpatients, etc.)None or low Low Unclear or unknown High LowCommunity Studies (e.g. convenience samples, targeted community samples without weighting or sampling methods for ensuring representativeness)Moderate Moderate to high Moderate Moderate Moderate/HighPopulation-based Studies with rigorous population weighted designs and adequate participationHigh Moderate to high High Low High

A tale of two studies: Study design and our understanding of SARS-CoV-2 seroprevalence

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A tale of two studies: Study design and our understanding of SARS-CoV-2 seroprevalence

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