| Literature DB >> 32684119 |
Anna-Karin Lidström1, Fredrik Sund1, Bo Albinsson2, Johan Lindbäck3, Gabriel Westman1.
Abstract
BACKGROUND: During the Covid-19 pandemic, the protection of healthcare workers has been in focus throughout the world, but the availability and quality of personal protective equipment has at times and in some settings been suboptimal.Entities:
Keywords: Covid-19; IgG; SARS-CoV-2; healthcare workers; transmission
Mesh:
Substances:
Year: 2020 PMID: 32684119 PMCID: PMC7594729 DOI: 10.1080/03009734.2020.1793039
Source DB: PubMed Journal: Ups J Med Sci ISSN: 0300-9734 Impact factor: 2.384
Figure 1.Density plot illustrating the distribution of IgG anti-SARS-CoV-2. All values shifted 0.01 to allow logarithmic transformation. All subjects included in the analysis.
Characteristics of IgG anti-SARS-CoV-2 positive and negative study subjects.
| IgG positive | IgG negative | |
|---|---|---|
| Age, years | 42 (18–78) | 45 (18–85) |
| Sampling time from project start, days | 12 (0–28) | 12 (0–29) |
| Male sex | 164/577 (28.4%) | 1855/8102 (22.9%) |
| Working in health care | 495/577 (85.8%) | 6799/8102 (83.9%) |
| Working in primary health care | 72/577 (12.5%) | 1351/8060 (16.8%) |
| Working with outpatient care | 186/465 (40.0%) | 3301/6311 (52.3%) |
| Working in Covid-19 specific unit | 39/577 (6.8%) | 387/8060 (4.8%) |
| Working in Covid-19 possible unit | 111/577 (19.2%) | 1682/8060 (20.9%) |
Data presented as medians (range) or proportions.
Figure 2.Forest plot of IgG anti-SARS-CoV-2 prevalence. Subgroup prevalence and confidence intervals of IgG anti-SARS-CoV-2 positivity. N is the total number of subjects in each category. All subjects included in the analysis.
Predictors of IgG anti-SARS-CoV-2 serostatus.
| Odds ratio | 95% confidence interval | ||
|---|---|---|---|
| Age, years | 0.984 | 0.978–0.991 | <0.001 |
| Time from study start, days | 1.005 | 0.992–1.019 | 0.41 |
| Male sex | 1.334 | 1.104–1.612 | 0.003 |
| Working in health care | 1.175 | 0.918–1.505 | 0.2 |
Multivariable logistic regression model. All study subjects included in the analysis.
Figure 3.Univariable logistic modelling of relation between age (Panel A, left) and time to sampling (Panel B, right) and risk of IgG anti-SARS-CoV-2 positivity. All subjects included in analysis.
Predictors of IgG anti-SARS-CoV-2 serostatus.
| Odds ratio | 95% confidence interval | ||
|---|---|---|---|
| Age at sampling, years | 0.988 | 0.980–0.995 | 0.001 |
| Time from study start, days | 1.019 | 1.004–1.035 | 0.014 |
| Male sex | 1.107 | 0.879–1.394 | 0.387 |
| Working in primary health care | 0.711 | 0.493–1.026 | 0.068 |
| Working with outpatient care | 0.631 | 0.497–0.801 | <0.001 |
| Working in Covid-19 specific unit | 1.114 | 0.766–1.619 | 0.572 |
| Working in Covid-19 possible unit | 1.275 | 0.945–1.721 | 0.112 |
Multivariable logistic regression model. Only healthcare staff included in the analysis.