key: cord-0697121-iasqozza
authors: Doernberg, Sarah B; Holubar, Marisa; Jain, Vivek; Weng, Yingjie; Lu, Di; Bollyky, Jenna B; Sample, Hannah; Huang, Beatrice; Craik, Charles S; Desai, Manisha; Rutherford, George W; Maldonado, Yvonne
title: Incidence and prevalence of COVID-19 within a healthcare worker cohort during the first year of the SARS-CoV-2 pandemic
date: 2022-03-12
journal: Clin Infect Dis
DOI: 10.1093/cid/ciac210
sha: 7b34a7cec606c3231558451742792bf1f535d83b
doc_id: 697121
cord_uid: iasqozza

BACKGROUND: Preventing SARS-CoV2 infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of COVID-19 in a US HCW cohort and to identify risk factors associated with infection. METHODS: We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. RESULTS: 2435 HCWs contributed 768 person years of follow-up time. We identified 21/2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% CI, 0.53% to 1.32%). We identified 70/2414 (2.9%) incident infections yielding a cumulative incidence rate of 9.11 cases per 100 person years (95% CI 7.11 to 11.52). Community contact with a known COVID-19 case most strongly correlated with increased hazard for infection (HR 8.1, 95% CI, 3.8, 17.5). High-risk work-related exposures (i.e., breach in protective measures) drove an association between work exposure and infection (HR 2.5, 95% CI, 1.3-4.8). More cases were identified in HCW when community case rates were high. CONCLUSION: We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections but contact at work was not unless accompanied by high-risk exposure.

Many assume that healthcare workers (HCWs) acquire COVID-19 at work. [1] [2] [3] While early studies supported work-related risks, more recent studies have shown that factors including communitybased exposures, race/ethnicity, and residential zip code, are associated with SARS-CoV2 acquisition and may be more consequential than workplace exposures. [4] [5] [6] Existing literature and media reports have noted widely varying estimates of prevalence, incidence, and risk factors for infection. [7] [8] [9] Few studies have assessed HCW infection and risk longitudinally despite changes in community prevalence of COVID-19 over time, workplace infection prevention efforts, and dynamic individual adherence to public health measures outside of work. Two European groups reported data from longitudinal HCW screening programs and estimated prevalence and incidence of infection 2,10 but to date, a similar granular approach to describing prevalence and incidence of COVID-19 in United States HCWs has not been reported.

Additionally, most seroprevalence studies of HCWs-both cross-sectional and longitudinal-have used a single unconfirmed serology test without orthogonal confirmation (i.e., using a different test) as their main outcome measure. Most studies have also not reported neutralizing antibody titers. [11] [12] [13] [14] Orthogonal antibody testing increases specificity, which is critical when testing populations with low disease prevalence. 15 Further, neutralizing antibody titers provides a functional assessment of immune responses.

To address these current gaps in our understanding of SARS-CoV-2, we sought to estimate and characterize the prevalence and incidence of COVID-19 using both reverse-transcription polymerase A c c e p t e d M a n u s c r i p t 5 chain reaction (RT-PCR) and orthogonal antibody testing in a large longitudinal cohort of HCW during a dynamic phase of the US epidemic and to identify risk factors associated with HCW infection.

The COVID-19 Healthcare Worker Antibody and RT-PCR Tracking (CHART) Study was approved by the 

From May-September 2020, we recruited HCW from Stanford Health (SHC), UCSF Health (UCSF), and Zuckerberg San Francisco General Hospital (ZSFGH) for this longitudinal, prospective cohort study.

These three medical centers serve large, mostly non-overlapping catchment populations in the San Francisco Bay Area and implemented similar mitigation policies over time (Supplementary Table 1 ).

Recruitment included medical center-wide email and verbal announcements, targeted email notifications to department leaders, and recruitment flyers.

HCWs completed an electronic screening questionnaire (Supplementary Material). Inclusion criteria were (1) age ≥18 years old, (2) employment at one of the three medical centers, and (3) did not anticipate ending employment or taking leave in the next 6 months. Eligible HCWs provided consent electronically. We collected study data using REDcap electronic data capture tools hosted at Stanford University. 16, 17 A c c e p t e d M a n u s c r i p t 6

The study was conducted from July 2020 to January 2021. Participants completed up to 10 visits: 7 visits at two-week intervals (±7 days) followed by 3 visits at four-week intervals (±7 days) up to completion or end of the study.. At all visits, participants completed an electronic survey and study staff collected nasopharyngeal (NP) swabs; swabs were optional for the final 3 visits. Participants underwent phlebotomy monthly for anti-SARS-CoV-2 antibody testing. For individuals who tested positive by either RT-PCR or serology, additional visits were scheduled weekly for four visits for serology testing only. Participants received no incentives or compensation for joining the study.

The UCSF Clinical Laboratories and Chan-Zuckerberg Biohub analyzed samples from the UCSF and ZSFGH sub-cohorts. Serology was performed using an assay to detect anti-nucleocapsid IgG (antinucleocapsid Ab; Abbott Architect, Abbott Laboratories, Abbott Park, IL) 18 . The Stanford Clinical Virology Laboratory analyzed samples using an assay to detect anti-spike IgG (anti-spike Ab;

Eurimmune Medizinische Labordiagnostika AG, Lübeck, Germany) 19 as well as a laboratory-derived assay to detect anti-receptor-binding-domain IgG (anti-RBD Ab) 20 . Samples that were positive at one laboratory underwent confirmatory testing at the other laboratory. Serum samples that were positive for antibodies to either spike, nucleocapsid, or both proteins were assayed for the presence of neutralizing antibodies at UCSF or at Vitalant Research Institute (San Francisco, CA) by optimizing a lentivirus-based pseudotype neutralization assay 21 .

A c c e p t e d M a n u s c r i p t 7 At UCSF, RT-PCR testing was performed using either (1) the M2000 Abbott RealTime Sars-CoV2 assay 22 

We defined a low-risk work exposure as providing direct care to, being within 6 feet of, directly interacting with the environment in which a COVID-19 patient received care, or processing laboratory samples from a COVID-19 patient. We defined a high-risk exposure at work as ever interacting with a COVID-19 patient without full PPE-the institutionally recommended PPE for care of patients with COVID-19-or having a breach in PPE (e.g., tears, accidental removal).

We defined an RT-PCR result as positive if the result was (1) detected or (2) indeterminate (positive RT-PCR followed by negative subsequent confirmatory RT-PCR test(s) done according to medical center occupational health protocols).

We defined positive confirmed serology as having an initial positive serology (anti-nucleocapsid Ab or anti-spike Ab) followed by confirmation with a second positive serology using a different target We defined baseline prevalent cases as participants with positive RT-PCR or positive confirmed serology at their initial visit. Participants who did not have baseline infection entered an incident cohort. We defined incident cases among this cohort as participants with a positive RT-PCR or a positive confirmed serology at any subsequent visit. The date of incident infection was the first date on which either the RT-PCR or the first serology test was positive (if confirmatory testing occurred within 4 weeks).

We estimated the prevalence as the proportion of cases at baseline out of total number of enrolled participants who completed baseline visits. We estimated the cumulative incidence as the number of incident cases divided by the total follow-up time per 100 person-years and assumed a uniform incidence distribution across the 6-month follow-up time. We censored person-time when a participant met the case definition, completed or withdrew from the study, or received a first dose of any COVID-19 vaccine. We calculated the confidence intervals (CI) using a non-parametric bootstrapping method. We conducted a sensitivity analysis to assess the impact of different case definitions on estimates considering 1) all unconfirmed positive serology results as cases, 2) all individuals with a single positive RT-PCR, no positive serology result, and at least one serology measurement ≥4 weeks after the positive RT-PCR as potential false positives and removing them from case counts. We obtained community-wide data on COVID-19 incidence in the six Bay Area counties from the California Department of Public Health. 28 A c c e p t e d M a n u s c r i p t 9 We compared characteristics of prevalent and incident cases to non-cases. For binary time-varying exposures, we used participant self-report at the most recent visit prior to censoring. For continuous time-varying exposures, we computed median responses across all visits prior to censoring. We reported symptoms using the most recent reported status at the visit at which infection was identified. We reported standardized mean difference (SMD) to describe magnitude of difference in characteristics between incident cases and non-cases. Magnitude of effect is considered small if SMD=0.2, medium if SMD=0.5, and large if SMD=0.8.

In the incident cohort, we first assessed associations between time to infection and baseline characteristics using multivariable Cox proportional hazards models. We evaluated the impact of pre-specified time-varying exposures on time to infection via marginal structural models (MSM). [29] [30] [31] [32] We implemented a 2-step MSM model for each time-varying exposure by first estimating inverse Overall, demographic and behavior characteristics of participants with prevalent and incident COVID-19 reflected overall cohort characteristics (Table 1) were both stable (Figures 2A and 2B ).

We identified 21/2435 individuals with evidence of COVID-19 at baseline and estimated a prevalence of 0.86% (95% CI, 0.53% to 1.32%). We identified 70/2414 (2.9%) individuals with incident COVID-19 during follow-up and estimated a cumulative incidence rate of 9.11 cases per 100 person years (95% CI 7.11 to 11.52). The number of incident cases increased with rising prevalence of COVID-19 in the 8-county region in which the study was conducted (Figure 2 ). Incidence rate estimates did not differ by sub-groups of gender, race/ethnicity, or job role (Supplementary Figure 1) . M a n u s c r i p t We performed a sensitivity analysis using an alternative incident COVID-19 case definition that included all unconfirmed positive serology results as cases, resulting in 26 prevalent and 71 incident cases. This slightly increased the baseline prevalence to 1.07% (95% CI, 0.79% to 1.56%) and

increased the cumulative incidence rate to 9.26 cases per 100 person-years (95% CI, 7.24 to 11.69).

To examine the impact of potential false positive RT-PCRs, we performed a second sensitivity analysis using a second alternative case definition that excluded 7 cases meeting this definition. This decreased the cumulative incidence rate to 8.18 cases per 100 person-years (95% CI, 6.29 to 10.4).

Overall, the testing yield of the incident cohort was relatively low: only 30/12,007 (0.25%) RT-PCR tests performed on asymptomatic participants were positive, and 7/30 (23%) of these met the false positive case definition. Among the 1170 participants who reported symptoms at any visit, 58 (5%) were confirmed as prevalent or incident cases. Among 1252 participants who never reported symptoms, 32 (3%) were confirmed as prevalent or incident cases.

While incident cases more commonly reported ever having symptoms (48/70, 69%), many non-cases (1112/2344, 48%) reported symptoms at least once (Supplementary Table 2 ). The most common symptoms reported by non-cases were fatigue (326, 14%), headache (466, 20%), nasal congestion (325, 14%), and rhinorrhea (412, 17%). Non-cases infrequently reported fever, chills, or decreased taste/smell while cases reported them more commonly.

In a multivariable Cox proportional hazards model, we did not find an association of incident COVID-19 with fixed variables including baseline age, gender, race, ethnicity, household size, role, or work A c c e p t e d M a n u s c r i p t 13 category (Supplementary Table 3 ). In marginal structural models of self-reported time-varying variables, community contact with a known COVID-19 case strongly correlated with increased hazard for COVID-19 (HR 8.1, 95% CI, 3.8, 17.5; Table 2 ). Self-reported exposure to a COVID-19 patient at work was associated with infection (p=0.013), but this appeared to be primarily driven by high-risk exposures (i.e., a PPE failure or breach or an exposure to patient biological material; HR 2.5, 95% CI, 1.3-4.8). Increasing community COVID-19 case rate showed a trend towards elevated adjusted hazard of HCW infection, but this finding did not reach statistical significance (HR 1.3, 95% CI 0.97-1.8). Neither time spent in the healthcare workplace, time spent providing direct patient-facing care, or adherence to community mitigation strategies were associated with COVID-19 infection.

In this large observational cohort of healthcare workers, we observed modest COVID-19 infection rates despite consistent COVID-19 exposure at work. Changes in COVID-19 incidence tracked most closely with community infection rates and self-reported community contact with known COVID-19 cases rather than work-related factors, except when breaches in standard safety protocols or PPE occurred. 5 Our data provide evidence of the overall safety of standard healthcare work environment protocols and PPE guidelines, and are concordant with emerging literature showing that the main COVID-19 related risks to HCW are those coming from home and community-based factors. 33 By combining longitudinal and orthogonal RT-PCR and serology testing, our study allowed for a robust granular estimation of the true incidence of COVID-19 infection among HCW. Unlike many studies based on a single serologic test, we used confirmatory serology testing and also measured neutralizing antibody responses. 34 As our data show, the serological response to infection is A c c e p t e d M a n u s c r i p t 14 multifaceted and evolves over time; measuring a single antibody response to one target may result in inaccurate estimates of true infection rates. 35 By testing serially and confirming antibody responses, we captured some COVID-19 cases that would have likely been missed with a single test in time and excluded others that were likely false positives.

In our sensitivity analysis accounting for potential false positive COVID-19 cases, our incidence estimates were 10% lower. This may have been an underestimate because many cases were Our study is subject to several limitations. We enrolled volunteer participants and had a high fraction of MD, MD-equivalent, and RN practitioners. This cohort composition did not comprehensively reflect the occupational diversity within our medical centers. Thirty-eight percent of those screened did not enroll in the study; because we did not assess reasons for nonparticipation, it is unclear to what degree this may have introduced any bias in our study population.

We relied on self-reporting of COVID-19 related risks both at work and home, which may have resulted in overreporting of adherence to protective measures. Additionally, our institutional PPE recommendations changed over time; as such, not all breaches in PPE are considered equivalent.

However, unlike many studies that have used information from employee health and safety offices, our study was independent of the medical centers in order to foster confidential no-fault reporting.

Because sequencing of virus was beyond the scope of the study, the association between self-A c c e p t e d M a n u s c r i p t 15 reported breach in PPE and incident COVID-19 cases remains solely an association and not proof that the breach itself led to the incident infection. Additionally, we did not perform orthogonal SARS-CoV-2 antibody testing on samples that were initially antibody negative, and thus could have missed certain incident cases. We also did not include confirmatory testing of RT-PCR results so could have inadvertently included false-positive RT-PCR results in incidence rate estimates. We addressed this with a sensitivity analysis and found that incidence rates were minimally impacted. Finally, our study was conducted before more recent variants of concern with increased transmissibility and immune escape emerged. One key strength of our study was our use of marginal structural modeling using detailed longitudinal data to better estimate risks.

Within a large group of frontline healthcare workers, our data indicate that healthcare workplaces pursuing comprehensive mitigation strategies can operate safely despite facing sequential waves of COVID-19 cases. However, HCW do face community-based risks for acquiring COVID-19. Medical center infection control practices, vaccination programs, and community mitigation approaches should be sustained and maximized to protect HCW and health systems during periods of future risk related to rising caseloads and emerging SARS-CoV-2 variants. 

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We sincerely thank all participants for generously volunteering their time and effort to this study.We also thank all study staff, our participating health systems including occupational health and infection control and prevention teams, medical center staff and university and medical center leadership.