The virus that causes COVID-19 was present in New York City long before the city’s first case of the disease was confirmed on March 1, researchers at the Icahn School of Medicine at Mount Sinai report.
Their study found that more than 1.7 million New Yorkers – about 20 percent of the city’s population – have already been infected with the virus, known as SARS-CoV-2, and that the infection fatality rate of the virus is close to 1 percent, ten times deadlier than the flu.
Results of the retrospective surveillance study of more than 10,000 plasma samples taken from the beginning of February to July will be published in Nature on Tuesday, November 3.
A sharp rise in infections in New York City occurred in the week ending March 8, followed by a significant increase of COVID-19 deaths during the week ending March 15. New York State implemented a stay-at-home order March 22, after which daily case numbers in New York City started to plateau and then decreased in April and May.
Very little testing capacity was available at the beginning of the local epidemic in early March, but, “We now know there were many asymptomatic and mild to moderate cases that likely went undetected,” said Emilia Mia Sordillo, MD, Ph.D., Associate Professor of Pathology, Molecular and Cell Based Medicine, Director of Clinical Microbiology, an attending physician in Infectious Diseases at the Icahn School of Medicine and the Mount Sinai Health System, and a senior author on the paper.
“In this study, we aimed to understand the dynamics of infection in the general population and in people seeking urgent care.”
The study findings are based on a dataset of 10,691 plasma samples from Mount Sinai Health System patients obtained and tested between the weeks ending February 9 and July 5.
The first group included 4,101 samples from patients seen in Mount Sinai’s emergency departments and from patients that were admitted to the hospital for urgent care.
This group, termed the “urgent care” group, served as a positive control group designed to detect increasing SARS-CoV-2 infections in individuals with moderate to severe COVID-19 as the local epidemic progressed.
The second group of 6,590 samples, termed the “routine care” group, were obtained from patients at OB/GYN visits, labor and deliveries, oncology-related visits, hospitalizations due to elective surgeries and transplant surgeries, preoperative medical assessments and related outpatient visits, cardiology office visits, and other regular office/treatment visits.
Researchers reasoned that these samples might resemble the general population more closely because the purposes for these scheduled visits were unrelated to acute SARS-CoV-2 infection. The urgent care group comprised 45.5 percent females while the routine care group included 67.6 percent females.
The majority of individuals in the urgent care group were over 61 years of age while the routine care group had a more balanced age distribution that more closely resembled the general population adult population.
To estimate true infection rates, researchers measured the presence of antibodies to past SARS-CoV-2 infections, rather than the presence of the virus, in weekly intervals.
The antibody test used in this research – an enzyme-linked immunosorbent assay (ELISA) – was developed and launched at Mount Sinai and is able to detect the presence or absence of antibodies to SARS-CoV-2, as well as the titer (level) of antibodies an individual has.
The high sensitivity and specificity of this test—meaning that the rate of false negatives and false positives is low – allowed it to be among the first to receive emergency use authorization from New York State and the U.S. Food and Drug Administration.
“Our two-step ELISA test confirms the presence and level antibodies. The use of two sequential tests reduces the false positive rate and favors high specificity resulting in a sensitivity of 95 percent and a specificity of 100 percent,” said Viviana Simon, MD, Ph.D., Professor of Microbiology, and Medicine; a member of the faculty of the Global Health and Emerging Pathogens Institute at the Icahn School of Medicine; and a senior author on the paper.
Seroprevalence increased at different rates in both groups, rising sharply in the urgent care group. Notably, seropositive samples were found as early as mid-February (several weeks before the first official cases) and leveled out at slightly above 20 percent in both groups after the epidemic wave subsided by the end of May. From May to July, seroprevalence and antibody titers stayed stable, suggesting lasting antibody levels in the population.
“Our data suggests that antibody titers are stable over time, that the seroprevalence in the city is around 22 percent, that at least 1.7 million New Yorkers have been infected with SARS-CoV-2 so far, and that the infection fatality rate is 0.97 percent after the first epidemic wave in New York City,” said Florian Krammer, Ph.D., Mount Sinai Professor in Vaccinology at the Icahn School of Medicine and corresponding author on the paper.
“We show that the infection rate was relatively high during the first wave in New York but is far from seroprevalence that might indicate community immunity (herd immunity). Knowing the detailed dynamics of the seroprevalence shown in this study is important for modeling seroprevalence elsewhere in the country.”
The first cases of COVID-19 were identified in New York State (NYS) in early March, 2020, and since then NYS, particularly the metropolitan New York City (NYC) area, has become one of the most-impacted communities in the United States [1,2]. As of June 2, 2020, over 370,000 laboratory-confirmed diagnoses have been made, accounting for approximately 25% of diagnoses in the United States [2,3].
As with most infections, laboratory-confirmed diagnoses undercount the true population-level burden of infections; with SARS-CoV-2, the virus that causes COVID-19, key factors that contribute to underdiagnosis include absent or mild symptoms and access to testing [4].
Thus, although NYS has tested more residents for COVID-19 than any other state (over 2,229,000 persons tested through June 2, 2020), it is likely that laboratory-confirmed cases represent a relatively small portion of the total number of persons with a history of infection in NYS [3].
Estimates of COVID-19 cumulative incidence (i.e., prevalence of previous or current infection) can inform the extent of epidemic spread and the number of persons still susceptible and progress toward herd immunity, which are critical for parameterizing simulation models and informing policies, including those for altering societal restrictions [5].
Furthermore, such data provide needed denominators for understanding the extent of diagnosis, rates of hospitalization, morbidity, and mortality, and geographic differences.
Antibody testing for SARS-CoV-2 has emerged as an important tool for understanding infection history. Although a several-week window period for development of IgG antibodies and evidence that not all persons with infection develop an antibody response limit their utility for diagnostics, and their interpretation for short- and long-term immunity remain uncertain, as with other infections, antibody prevalence serostudies with validated assays can assess population-level cumulative incidence in the recent past [[6], [7], [8], [9], [10], [11]].
Antibody serostudies for SARS-CoV-2 are being conducted in other countries and in the United States are occurring on the national and county levels, but none have been conducted at the state level, and only one population-based serostudy has been peer-reviewed [[12], [13], [14], [15]].
The current array of recommendations against individual movement and business operation during the pandemic complicates study specimen collection. A recent RNA survey in Iceland and serosurveys in two California counties conducted sampling at centralized testing sites, which offer ease of execution particularly in small geographies, with potentially large self-selection biases [13,15,16].
Alternative approaches include random at-home mail-in testing and community-intercept studies in high-traffic locations that remain open [14].
To provide a statewide picture of COVID-19 infection through late-March and diagnoses by early-April 2020, during April 19–28, 2020, the NYS Department of Health (NYSDOH) conducted a community-based serostudy throughout NYS.
Cumulative incidence among non-institutionalized adults, by geographic and demographic features, was estimated from weighted reactivity rates that were adjusted for validated test characteristics. Combining these findings with cumulative diagnoses enabled estimation of the percent of infections diagnosed.
Discussion
From the largest U.S. SARS-CoV-2 serosurvey to date, we estimated that over 2 million adult NYS residents were infected through the end of March. Our findings estimate the extent of transmission of and community experience with SARS-CoV-2, particularly in the NYC metropolitan region.
Despite large numbers of persons acquiring SARS-CoV-2, this represents only 14.0% of adult residents, suggesting that, even in this COVID-19 epicenter, the epidemic is substantially less than the estimated ~70% U.S. herd immunity threshold [25].
Against this remaining epidemic potential, ongoing vigilance through rigorous and extensive epidemic monitoring, testing, and contact tracing is a necessary component for predicting, preventing, and/or mitigating a second epidemic wave, consistent with state and federal guidance for reopening [5,26].
This vigilance is needed even in the rest of NYS outside the metropolitan region, which are in the first phases of reopening in NYS, and where lowest cumulative incidence suggests the highest proportion susceptible.
Our finding of higher cumulative incidence in the regions of the NYC metropolitan area, particularly NYC, is consistent with the known distribution of diagnoses. Furthermore, in these regions of high urbanicity, significant racial/ethnic disparities in infection history were found, with minority communities experiencing disproportionate risk.
The drivers of greater COVID-19 risk and disparities in urban areas continue to be studied, but may relate to population density and the mechanisms by which transportation, employment, housing, and other socioeconomic or environmental factors shape opportunities for transmission [[27], [28], [29]]. A recent NYS study on a random sample of COVID-19 hospitalizations showed limited racial/ethnic differences in clinical outcomes, suggesting that observed differences in mortality by race and ethnicity may be in large part driven by different infection histories in the community [3,[30], [31], [32]]. Research is needed to understand the drivers of increased COVID-19 risk experienced by minority communities, followed by actions to improve health equity.
The finding that over 8.9% of adults were diagnosed reveals both the opportunities for further expansion of diagnostic testing in NYS, yet in the context of far higher diagnosis and testing levels than other U.S. settings suggests substantial progress to date [1,13].
Compared to all persons with infection history, there was a higher representation of males and those older than 55 years among diagnosed persons. Given the lower reactivity rates observed among this age group, our results expand observations from previous studies that older adults may be more likely to exhibit symptoms or illness or be more likely to seek care [30,[33], [34], [35]].
Although not an aim of this analysis, we note that in conjunction with 12,822 publicly reported COVID-19 deaths for NYS through April 17 (reflecting median 19 days-post-infection to death), our findings suggest an infection fatality ratio of 0.6%. This estimate is in line with estimates of 0.5%–1.0% observed in other countries; however, additional analyses are needed to more precisely estimate the infection fatality ratio in NYS [36,37].
Strengths of our study include a large sample, which contained 0.1% of the adult NYS population, and a systematic sampling approach in one of the only open public venues in the state, where a necessary commodity is purchased. Although a convenience sample, survey weights adjusted for biased demographic/geographic representation, noting that the general agreement of unweighted and weighted results suggests demographic representativeness of the study sample, and we further adjusted results for assay performance, under varied scenarios.
Our study may nevertheless be limited by residual nonrepresentativeness of the underlying population. This includes potential undersampling of persons from vulnerable groups who might be less likely to go grocery shopping. For this to impact our findings, those remaining home would need to have differential antibody prevalence compared with their age/sex/racial-ethnic/regional group peers.
If persons staying at home had lower prevalence because of self-isolation, our study’s cumulative incidence would be a slight overestimate. Furthermore, our sample did not include those who have died from COVID-19 or those who reside in long-term care facilities, which have been differentially impacted, causing a slight underestimate, nor those in the hospital or at home due to COVID-19 illness, some of whom would be expected to have detectable antibodies [38,39].
Such actively symptomatic persons would be expected to be a small portion of the cumulative infection burden since the outbreak’s commencement, and given most would have been infected after March 29, their exclusion also likely causes observed values to be overestimated.
Although data are limited on the potential for self-selection to alter our results, a recent Icelandic study found comparable prevalence when participants were tested after online self-registration versus random invitation [16]. This finding, in conjunction with our systematic community intercept approach, suggests that this bias may be small, outside of outright nonresponse.
We note that although every effort was made to ensure unbiased sampling through a DOH staff-led recruitment process, patron-initiated requests for testing were honored, and in some sites, accounted for a significant percentage of total tests performed. It is possible that customers who seek out testing may be more likely to have been exposed to SARS-CoV-2.
If true, our estimate of cumulative incidence would be overestimated. Another source of potential recruitment bias comes from patron refusal to be tested, either on initial request or after agreeing to participate. Although not systematically collected, nightly report outs by testing leads indicated that most persons approached agreed to be tested and that few persons left after agreeing to be tested, regardless of wait time, supporting low nonresponse.
Results presented may differ from publicly discussed preliminary estimates, given both our inclusion of more participants and analytic adjustments for test characteristics. Timeframes used for cumulative infections and diagnoses are approximate, being based on the evolving SARS-CoV-2 immunological and testing literature, with the 10-day sampling period during a linear growth phase of the epidemic.
The findings of this study suggest extensive SARS-CoV-2 transmission in NYS and highlight the remaining opportunities for prevention and diagnosis. As the epidemic grows in other regions of the country, this study offers a potential model for other jurisdictions to monitor their epidemic.
Estimates of cumulative incidence can be combined with diagnostic totals, or other epidemic markers such as mortality, to provide a holistic epidemic view during a time of unprecedented pandemic and to best craft high-impact approaches to prevention, containment, treatment, and mitigation.
reference link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297691/
More information: Daniel Stadlbauer et al, Repeated cross-sectional sero-monitoring of SARS-CoV-2 in New York City, Nature (2020). DOI: 10.1038/s41586-020-2912-6
