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Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests.

ABSTRACT: Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States-Indiana and Connecticut-have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.

SUBMITTER: Kline D

PROVIDER: S-EPMC8255994 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesFirst responders, including firefighters, emergency medical technicians (EMTs), paramedics, and law enforcement officers, are working on the front lines to fight the COVID-19 pandemic and facing an increased risk of infection. This study assessed the seroprevalence of SARS-CoV-2 infection among first responders in northeastern Ohio.MethodsA survey and immunoglobulin G antibody test against SARS-CoV-2 nucleocapsid protein were offered to University Hospitals Health System-affiliated first-responder departments during May to September 2020. The survey contained questions about demographic characteristics and history of SARS-CoV-2 infection. A total of 3080 first responders with diverse job assignments from more than 400 fire and police departments participated in the study.ResultsOf 3080 participants, 73 (2.4%) were seropositive and 26 (0.8%) had previously positive real-time polymerase chain reaction results. Asymptomatic infection accounted for 46.6% (34 of 73) of seropositivity. By occupation, rates of seropositivity were highest among administration/support staff (3.8%), followed by paramedics (3.0%), EMTs (2.6%), firefighters (2.2%), and law enforcement officers (0.8%). Work-associated exposure rates to COVID-19 patients were: paramedics (48.2%), firefighters (37.1%), EMTs (32.3%), law enforcement officers (7.7%), and administration/support staff (4.4%). Self-reported community exposure was positively correlated with self-reported work-associated exposure rate (correlation coefficient = 0.99). Neither self-reported community nor work-associated exposure was correlated with SARS-CoV-2 seroprevalence. We found no significant difference in seroprevalence among sex/gender or age groups; however, Black participants had a higher positivity rate than participants of other racial groups despite reporting lower exposure.ConclusionsDespite the high work-associated exposure rate to SARS-CoV-2 infection, first responders with various roles demonstrated seroprevalence no higher than their administrative/supportive colleagues, which suggests infection control measures are effective in preventing work-related infection.

Project description:The diagnosis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection relies on the detection of viral RNA by real-time reverse transcription polymerase chain reaction (rRT-PCR) performed with respiratory specimens, especially nasopharyngeal swabs. However, this procedure requires specialized medical personnel, centralized laboratory facilities, and time to provide results (from several hours up to 1 d). In addition, there is a non-negligible risk of viral transmission for the operator who performs the procedure. For these reasons, several studies have suggested the use of other body fluids, including saliva, for the detection of SARS-CoV-2. The use of saliva as a diagnostic specimen has numerous advantages: it is easily self-collected by the patient with almost no discomfort, it does not require specialized health care personnel for its management, and it reduces the risks for the operator. In the past few months, several scientific papers, media, and companies have announced the development of new salivary tests to detect SARS-CoV-2 infection. Posterior oropharyngeal saliva should be distinguished from oral saliva, since the former is a part of respiratory secretions, while the latter is produced by the salivary glands, which are outside the respiratory tract. Saliva can be analyzed through standard (rRT-PCR) or rapid molecular biology tests (direct rRT-PCR without extraction), although, in a hospital setting, these procedures may be performed only in addition to nasopharyngeal swabs to minimize the incidence of false-negative results. Conversely, the promising role of saliva in the diagnosis of SARS-CoV-2 infection is highlighted by the emergence of point-of-care technologies and, most important, point-of-need devices. Indeed, these devices can be directly used in workplaces, airports, schools, cinemas, and shopping centers. An example is the recently described Rapid Salivary Test, an antigen test based on the lateral flow assay, which detects the presence of the virus by identifying the spike protein in the saliva within a few minutes.

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Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests.

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