The MDHHS Region 2 South Healthcare Coalition area is the most populous region in Michigan and comprises Monroe, Washtenaw, and Wayne (including the city of Detroit) Counties. MDDHS Region 2 North Healthcare Coalition includes Macomb, Oakland, and St. Clair Counties. The Healthcare Coalitions coordinated with 27 hospitals and 7 MDHHS Medical Control Authorities (MCAs), which supervise and coordinate an emergency medical services (EMS) system for a geographic region, to invite employees to participate. The protocol was reviewed by CDC human subjects research officials, who determined the activity to be public health surveillance and exempt from full institutional review board review (2).

Figure 1

Figure 1. Seropositivity for SARS-CoV-2 among healthcare, first response, and public safety personnel, by hospital and Medical Control Authority agency location, Detroit metropolitan area, Michigan, USA, May–June 2020. Centroid: Detroit city center....

Of 16,397 participants, 6.9% (95% CI 6.5%–7.3%) were positive for SARS-CoV-2 IgG, indicating previous infection. In contrast, 2.7% (95% CI 2.5%–3.0%) reported having previously tested positive by RT-PCR using a nasal, throat, or saliva sample. Participant age ranged from 19 to 82 years (mean 42.1, SD 12.2). Seroprevalence was lower among those >65 years of age (3.2% of the sample; seroprevalence 3.5%, 95% CI 2.1%–5.4%) compared with all younger age groups (Table 1). Women, 68.6% of participants, had a similar seroprevalence to men. Non-Hispanic Black participants made up 7.3% of the sample and had the highest seroprevalence (16.3%, 95% CI 14.2%–18.5%) compared with other race/ethnic groups, including Non-Hispanic White participants (6.0%, 95% CI 5.6%–6.4%), who made up 78.4% of the sample. Seroprevalence among participants by facility ranged from 0.5% to 17.9% and was inversely related to distance of the facility from the Detroit geographic center. Seroprevalence was highest (11.0%, 95% CI 10.3%–11.7%) among participants at facilities within 15 km of Detroit’s center and lowest (1.8%, 95% CI 1.4%–2.2%) at locations 30–55 km away (Figure 1). Higher seroprevalence with closer proximity to Detroit was observed among most participants, regardless of occupation and healthcare setting (Appendix Figure 1). Among participants who reported close contact (within 6 feet) with a person with confirmed COVID-19 for >10 minutes, seroprevalence was highest among those with exposure to a household member (34.3%, 95% CI 30.2%–38.6%). Participants living in multiunit housing had higher seroprevalence compared with those living in single-family housing (8.4%, 95% CI 7.2%–9.8% vs. 6.7%, 95% CI 6.3%–7.1%).

Figure 2

Figure 2. Seropositivity for SARS-CoV-2 among healthcare personnel by selected occupation and hospital work location, Detroit metropolitan area, Michigan, USA, May–June 2020. Red bars: lower 95% CI for percent positive is >6.9%...

By work location, seroprevalence was highest among participants who worked in hospital wards (8.8%, 95% CI 8.0%–9.7%) and lowest among those working in police departments (3.9%, 95% CI 2.5%–5.8%) (Table 2). Within hospitals, lower seroprevalence was found among persons working in intensive care units (ICUs; 6.1%) and operating rooms or surgical units (4.5%) compared with participants working in wards (8.8%) and emergency departments (EDs; 8.1%). By occupation, the highest seroprevalence was found among nurse assistants (12.8%) and physical therapists (10.6%) and the lowest among laboratory technicians (3.4%) and police officers (4.0%). Seroprevalence also varied by hospital-based work locations and occupation (Figure 2). Among participants working on hospital wards, the lower bound of the 95% CI for seropositivity was higher than overall seroprevalence (6.9%) for nurse assistants (13.6%, 95% CI 10.2%–17.5%), administration/clerks (11.9%, 95% CI 7.0%–18.5%), respiratory therapists (10.8%, 95% CI 7.1%–15.6%), and nurses (9.8%, 95% CI 8.5%–11.2%). Nurse assistants who worked in an “other hospital location” (15.9%, 95% CI 8.2%–26.7%) and nurses who worked in EDs (9.9%, 95% CI 8.3%–11.7%) had similarly elevated seroprevalence.

Other occupational risk factors are included in the Appendix. Participants reported the average number of times per shift since March 1, 2020, in which they had participated in aerosol-generating procedures (Appendix Figure 2) and were given a list of examples for reference (Appendix Table 1). Seroprevalence generally increased with increasing procedure frequency (p = 0.04 by test for trend), with the highest percent positivity among those who participated in such procedures >25 times per shift on average (9.1%, 95% CI 7.4%–11.0%). Participants also reported how frequently they used each component of PPE, using a Likert scale. Overall, there was no pattern seen in percent antibody positivity with frequency of use with any PPE component (Appendix Table 2). Among those reporting ideal frequency of use (“all the time”) for a specific PPE component, seroprevalence was similar to the overall seroprevalence (Table 2).

Figure 3

Figure 3. Adjusted odds ratios and 95% CIs for seropositivity for SARS-CoV-2 among healthcare, first response, and public safety personnel, Detroit metropolitan area, Michigan, USA, May–June 2020. Adjusted model was estimated using...

Multivariable adjustment using generalized estimating equations was performed (Figure 3; Appendix Table 3). Factors most strongly associated with likelihood of seropositivity were exposure to a household member with confirmed COVID-19 (adjusted odds ratio [aOR] 6.18, 95% CI 4.81–7.93) and working within 15 km of the Detroit center (aOR 5.60, 95% CI 3.98–7.89 compared with 30–55 km). Compared with physicians, occupations more likely to be seropositive included nurse assistant (aOR 1.88, 95% CI 1.24–2.83) and nurse (aOR 1.52, 95% CI 1.18–1.95). Working in a hospital ED was the sole location with increased adjusted odds of seropositivity (aOR 1.16, 95% CI 1.00–1.35). Consistently wearing an N95 respirator (aOR 0.83, 95% CI 0.72–0.95) or surgical facemask (vs. using them less than “all the time”) lowered the likelihood of being seropositive (aOR 0.86, 95% CI 0.75–0.98).

Healthcare, first response, and public safety personnel in 27 hospitals and 7 MCA areas were surveyed in the Detroit metropolitan area. Among these facilities, seroprevalence of SARS-CoV-2 IgG ranged from 0.5% to 17.9%, indicating wide variation in seroprevalence. A major role for community acquisition of SARS-CoV-2 infection is suggested by the strong association between seropositivity and working closer to the Detroit center and exposure to a household member with confirmed COVID-19. Workers remain vulnerable at home, where social distancing and PPE use may be difficult and likelihood of exposure during presymptomatic or asymptomatic periods is high. Similar patterns have been found in other studies of healthcare worker infections, in which community or household exposure to persons who tested positive for SARS-CoV-2 was the primary predictor of seroconversion (5,6). The geographic distribution of seroprevalence among first responders in this study was related to population-based cumulative case reporting in Michigan through April 30, 2020: higher percentage positivity for RT-PCR testing was reported for counties near the Detroit center (Oakland, Macomb, and Wayne [0.48%–1.04%]) versus lower levels for the outlying counties (Washtenaw, St. Clair, and Monroe [0.22%–0.32%]) (7).

Although 6.9% of participants were seropositive, only 2.7% reported a history of a positive RT-PCR test. This finding of higher seroprevalence compared with confirmed active infection is similar to other serology surveys of the general population (8–10). A study by Havers et al. estimated 6–24 times as many infections as the number of reported cases detected by RT-PCR (8). Our study revealed »2.5 times more infections than cases based on self-reported RT-PCR results. The 2.7% positivity for RT-PCR may be higher in the healthcare and first responder population compared with the general public (which ranged from 0.22% to 1.04% in the 6 Michigan counties) as a result of targeted and repeated testing of personnel in hospitals and emergency medical services settings (11). Even so, surveillance of these occupational groups in Detroit based on self-reported RT-PCR testing results would have identified a minority of infections.

Healthcare workers are known to be at occupational risk for SARS-CoV-2 exposure (12). Participants in occupations that may involve frequent and prolonged patient contact, such as nurse assistants and nurses (13,14), were more likely to be seropositive than physicians. Multivariable analysis revealed a weak association between lower seropositivity and consistent use of N95 respirators and surgical facemasks. Lower seroprevalence was observed among participants who reported high use of PPE despite shortages and reuse/extension protocols that could be hypothesized to lower the observed effectiveness of PPE. These and other confounding factors may obscure the role PPE plays in preventing infection, and it may be necessary to account for multiple factors in studies assessing the effect of PPE. The lower likelihood of seroprevalence associated with working in the controlled environments of a hospital ICU or surgical ward may reflect the impact of additional mitigation measures, including clear identification of infected persons and environmental and engineering controls (15). This pattern of lower seropositivity among staff in higher-risk versus lower-risk hospital settings has been described previously (16). However, even within healthcare work settings, some workers such as nurse assistants had a higher risk of infection than those in other roles. This finding highlights the concern that certain occupations may require additional focus on assessing and controlling factors related to transmission.

Together, these analyses of community and workplace factors show the contribution of community acquired infection to seropositivity among Detroit area healthcare workers. For 3 hospital settings (hospital ward, ED, and ICU) that could be compared across healthcare occupations, seropositivity rose with closer proximity of the facility to the Detroit center. This pattern suggests that regardless of occupation or work location, community acquisition was a common underlying factor of infection risk. There are 2 related implications. First, the observed impact of PPE may be reduced given the background impact of community acquisition of SARS-CoV-2 infection. Second, reducing community spread through population-based measures may directly protect healthcare workers on 2 fronts: reduced occupational exposure as a result of fewer infected patients in the less controlled workplace settings such as the ED, and reduced exposure in their homes and communities.

After adjusting for other factors, we found that women were less likely than men to be seropositive. This pattern was seen only in adjusted analysis; women’s lower risk may have been obscured by their disproportionate representation in the occupations at higher risk of infection. Women represented 69% of the sample but made up 86% of those in nursing and nurse assistance.

Participants >65 years of age were less likely to be infected than younger workers. This pattern may be the result of measures to protect older workers from high-risk situations or from greater precautions taken among this group. A population study that also observed lower seroconversion among older persons found that older persons were less likely to live with a household contact (17). Seroconversion may also diminish with age in general (17), although other studies showed no pattern by age or higher seroprevalence among older persons (18,19). Participants of non-Hispanic Black race/ethnicity remained more likely to be seropositive than non-Hispanic white participants, even after adjustment. Community-level surveillance of COVID-19 infection and SARS-CoV-2 infection has demonstrated overrepresentation of minority groups in population-adjusted analyses (20,21). One hypothesis for the higher risk of infection among Black and Hispanic persons is employment in jobs without possibility of working remotely (22). Unfortunately, the survey did not collect information about occupation and workplace of household members. We speculate that the higher risk of exposure/infection among non-Hispanic Black versus non-Hispanic White participants in our study likely reflects uncontrolled confounding by factors for which data were not available.

Some limitations must be considered. The survey was a convenience sample with unknown representativeness: 80% of the 20,650 employees anticipated by MCA and hospital contacts to be eligible participated but agency participation varied, with highest participation among hospital personnel. The cross-sectional design precluded determining the source of exposure. In addition, comprehensive exposure data (e.g., travel, commuting, social exposures) were not collected. Because of the limited questionnaire length, PPE questions did not probe donning and doffing training, participant familiarity with PPE use, or reuse or extension protocols that may have affected effectiveness (11). No additional questions were asked about other workplace infection control practices. Another potential source of bias is the healthy worker effect, in which persons with prolonged COVID-19 infection or sequelae would not have been onsite to participate. Seroprevalence may be underestimated, given that the sensitivity of the antibody test was less than 100%. It is also possible that participants who were infected did not seroconvert (23; F. Gallais et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.06.21.20132449v1), but it is unknown whether lack of seroconversion may have occurred systematically between occupations (e.g., those exposed more intensely or with more severe illness may be more likely to develop antibodies) (24). Although more recent infections may have not been detected, it is unlikely that this varied systematically across groups. Strengths included coverage of a large number of personnel at hospitals and first response/public safety facilities and pairing antibody testing with questionnaire data to enable focus on a high-risk population.

Key implications for the risk of SARS-CoV-2 infection among healthcare, first response, and public safety personnel include the impact of community acquisition, increased odds of exposure associated with specific healthcare occupations, and the protection provided by PPE. Effects of interventions that could be further studied and implemented include providing alternative housing to healthcare workers during times or in areas of high community prevalence and ensuring that workers in high-risk occupations are given adequate PPE, specifically N95 respirators and surgical facemasks, as well as infection control training.