Back

SARS-CoV-2 SEROPREVALENCE AMONG ALL WORKERS IN A TEACHING HOSPITAL IN SPAIN: UNMASKING THE RISK.

Galan, I.; Velasco, M.; Casas, M. L.; Goyanes, M. J.; Rodriguez-Caravaca, G.; Losa, J. E.; Noguera, C.; Castilla, V.; Working Group Alcorcon COVID-19 investigators,

2020-05-29 infectious diseases
10.1101/2020.05.29.20116731 medRxiv
Show abstract

BackgroundHealth-care workers (HCW) are at increased risk for SARS-CoV-2 infection, but few studies have evaluated prevalence of antibodies against SARS-CoV-2 among them. ObjectiveTo determine the seroprevalence against SARS-CoV-2 in all HCW. MethodsCross-sectional study (April 14th- 27th, 2020) of all HCW at Hospital Universitario Fundacion Alcorcon, a second level teaching hospital in Madrid, Spain. SARS-CoV-2 IgG was measured by ELISA. HCW were classified by professional category, working area, and risk for SARS-CoV-2 exposure. ResultsAmong 2919 HCW, 2590 (90.5%) were evaluated. Mean age was 43.8 years (SD 11.1) and 73.9% were females. Globally, 818 (31.6%) workers were IgG positive, with no differences for age, sex or previous diseases. Among them, 48.5% did not report previous symptoms. Seropositivity was more frequent in high (33.1%) and medium (33.8%) than in low-risk areas (25.8%, p = 0.007), but no difference was found for hospitalization areas attending COVID-19 and non-COVID-19 patients (35.5 vs 38.3% p = NS). HCW with a previous SARS-CoV2 PCR positive test were IgG seropositive in 90.8%. By multivariate logistic regression analysis, seropositivity was associated with being physicians (OR 2.37, CI95% 1.61-3.49), nurses (OR 1.67, CI95% 1.14-2.46), or nurse- assistants (OR 1.84, CI95% 1.24-2.73), HCW working at COVID-19 hospitalization areas (OR 1.71, CI95% 1.22-2.40), non-COVID-19 hospitalization areas (OR 1.88, CI95% 1.30-2.73), and at the Emergency Room (OR 1.51, CI95% 1.01-2.27) ConclusionsSeroprevalence uncovered a high rate of infection previously unnoticed among HCW. Patients not suspected of having COVID-19 as well as asymptomatic HCW may be a relevant source for nosocomial SARS-CoV-2 transmission.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.