| Literature DB >> 33780664 |
Sylvain Ladoire1, Vincent Goussot2, Emilie Redersdorff3, Adele Cueff4, Elise Ballot5, Caroline Truntzer5, Siavoshe Ayati6, Leila Bengrine-Lefevre6, Nathalie Bremaud6, Bruno Coudert6, Isabelle Desmoulins6, Laure Favier6, Cléa Fraisse6, Jean-David Fumet6, Roxana Hanu6, Audrey Hennequin6, Alice Hervieu6, Silvia Ilie6, Courèche Kaderbhai6, Aurélie Lagrange6, Nils Martin6, Irina Mazilu6, Didier Mayeur6, Rémi Palmier6, Anne-Laure Simonet-Lamm6, Julie Vincent6, Sylvie Zanetta6, Laurent Arnould7, Charles Coutant8, Aurélie Bertaut4, François Ghiringhelli9.
Abstract
BACKGROUND: In view of the potential gravity of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection for patients with cancer, epidemiological data are vital to assess virus circulation among patients and staff of cancer centres. We performed a prospective study to investigate seroprevalence of SARS-CoV-2 antibodies among staff and patients with cancer at a large cancer centre, at the end of the period of first national lockdown in France and to determine factors associated with the risk of SARS-CoV-2 infection.Entities:
Keywords: Antibody; COVID-19; Cancer center; Cancer patients; Healthcare workers; SARS-COV-2; Serodiagnosis; Serology; Seroprevalence; Staff
Year: 2021 PMID: 33780664 PMCID: PMC7914029 DOI: 10.1016/j.ejca.2021.02.027
Source DB: PubMed Journal: Eur J Cancer ISSN: 0959-8049 Impact factor: 9.162
Employees characteristics (N = 663).
| Characteristics | Positive serology (N = 12) | Negative serology (N = 651) | P value | Test | N (%) (N = 663) |
|---|---|---|---|---|---|
| 1 | Fisher | ||||
| Female | 10 (83.3%) | 512 (78.6%) | 522 (78.7%) | ||
| Male | 2 (16.7%) | 139 (21.4%) | 141 (21.3%) | ||
| 0.3182 | Wilcoxon | ||||
| N | 12 | 649 | 661 | ||
| Mean (std) | 35.3 (12.3) | 38.6 (11.6) | 38.5 (11.6) | ||
| Median [min - max] | 33.5 [21.0–55.0] | 37.0 [3.0–81.0] | 37.0 [3.0–81.0] | ||
| 0.5436 | Chi-square | ||||
| Dijon | 4 (33.3%) | 273 (42.1%) | 277 (41.9%) | ||
| Outside of Dijon | 8 (66.7%) | 376 (57.9%) | 384 (58.1%) | ||
| Missing values | 0 | 2 | 2 |
Fig. 1Working conditions among employees. (A–D) Pie chart for professional categories of employees (A), working conditions during lockdown (B) and contact with patients during work within (C) or outside of (D) the COVID-19 sector. Light (dark) colours represent employees with positive (negative) COVID-19 tests at the time of study. (E) Bar chart showing the proportion of time employees spent in contact with patients per day, according to whether they had a positive (light) or negative (dark) test. ns: Wilcoxon test was not significant. (F) Pie chart showing whether employees wore a mask when in contact with patients. Light (dark) colours represent employees with positive (negative) COVID-19 tests at the time of study. COVID-19, coronavirus disease 2019.
Fig. 2Employees' living conditions and symptoms. (A–B) Bar chart showing comorbidities (A) and symptoms (B) among staff, according to whether they had positive (light) or negative (dark) tests. ∗Fisher's exact test p < 0.05, ∗∗Fisher's exact test p < 0.01. (C) Bar chart showing the number of symptoms per employee according to whether they had positive (light) or negative (dark) tests. (D) Pie chart for employees with positive tests during lockdown. Light (dark) colours represent employees with positive (negative) COVID-19 tests at the time of study. COVID-19, coronavirus disease 2019.
Patients characteristics (N = 1011).
| Characteristic | Positive serology (N = 17) | Negative serology (N = 994) | P value | Test | N (%) (N = 1011) |
|---|---|---|---|---|---|
| 0.3234 | Chi-square | ||||
| Female | 10 (58.8%) | 695 (69.9%) | 705 (69.7%) | ||
| Male | 7 (41.2%) | 299 (30.1%) | 306 (30.3%) | ||
| 0.4192 | Wilcoxon | ||||
| N | 17 | 994 | 1011 | ||
| Mean (std) | 65.2 (12.3) | 63.1 (13.1) | 63.1 (13.0) | ||
| Median [min - max] | 68.0 [36.0–81.0] | 65.0 [24.0–95.0] | 65.0 [24.0–95.0] | ||
| 0.095 | Fisher | ||||
| Dijon | 4 (25.0%) | 107 (11.0%) | 111 (11.2%) | ||
| Outside of Dijon | 12 (75.0%) | 864 (89.0%) | 876 (88.8%) | ||
| Missing values | 1 | 23 | 24 | ||
| 1 | Fisher | ||||
| No | 2 (11.8%) | 132 (13.3%) | 134 (13.3%) | ||
| Yes | 15 (88.2%) | 860 (86.7%) | 875 (86.7%) | ||
| Missing values | 0 | 2 | 2 | ||
| 0.7413 | Chi-square | ||||
| No | 6 (35.3%) | 390 (39.2%) | 396 (39.2%) | ||
| Yes | 11 (64.7%) | 604 (60.8%) | 615 (60.8%) | ||
| 0.4273 | Fisher | ||||
| No systemic treatment | 2 (11.8%) | 132 (13.3%) | 134 (13.3%) | ||
| Chemotherapy | 7 (41.2%) | 357 (35.9%) | 364 (36%) | ||
| Targeted therapy | 1 (6%) | 162 (16.3%) | 163 (16%) | ||
| Immunotherapy | 3 (17.5%) | 88 (8.9%) | 91 (9%) | ||
| Endocrine therapy | 0 (0%) | 108 (10.9%) | 108 (10.7%) | ||
| Chemotherapy and targeted therapy | 3 (17.5%) | 97 (9.7%) | 100 (9.9%) | ||
| Chemotherapy and Immunotherapy | 1 (6%) | 31 (3.1%) | 32 (3.2%) | ||
| Radiotherapy | 0 (0%) | 12 (1.2%) | 12 (1.2%) | ||
| Other | 0 (0%) | 7 (0.7%) | 7 (0.7%) |
Fig. 3Patients' living conditions and symptoms. (A) Bar chart showing the percentage of patients with positive (light) or negative (dark) tests according to the type of primary tumor. (B) Pie chart for patients' location at the time of blood test. (C–D) Bar chart showing the percentage of comorbidities (C) and symptoms (D) among patients according to whether they had positive (light) or negative (dark) tests. ∗∗Fisher's exact test p < 0.01, ∗∗∗Fisher's exact test p < 0.001. (E) Bar chart showing the number of symptoms per patient. (F) Pie chart for patients with positive tests during confinement. Light (dark) colours represent patients with positive (negative) COVID-19 tests at the time of study. COVID-19, coronavirus disease 2019.
Fig. 4Follow-up of patients during lockdown. (A–C) Pie chart for patient visits to the center during lockdown based on whether they had positive (light) or negative (dark) tests (A), cancellation and/or postponement of treatment (B) or of consultations (C) during containment. Light (dark) colours represent patients with positive (negative) COVID-19 tests at the time of study. COVID-19, coronavirus disease 2019.