Unknown

Dataset Information

0

Patterns and predictors of sick leave among Swedish non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic.


ABSTRACT: Healthcare and residential care workers represent two occupational groups that have, in particular, been at risk of Covid-19, its long-term consequences, and related sick leave. In this study, we investigated the predictors of prolonged sick leave among healthcare and residential workers due to non-hospitalized Covid-19 in the early period of the pandemic. This study is based on a patient register (n = 3209) and included non-hospitalized healthcare or residential care service workers with a positive RT- PCR for SARS-CoV-2 (n = 433) between March and August 2020. Data such as socio-demographics, clinical characteristics, and the length of sick leave because of Covid-19 and prior to the pandemic were extracted from the patient's electronic health records. Prolonged sick leave was defined as sick leave ≥ 3 weeks, based on the Swedish pandemic policy. A generalized linear model was used with a binary distribution, adjusted for age, gender, and comorbidity in order to predict prolonged sick leave. Of 433 (77% women) healthcare and residential care workers included in this study, 14.8% needed longer sick leave (> 3 weeks) due to Covid-19. Only 1.4% of the subjects were on sick leave because of long Covid. The risk of sick leave was increased two-fold among residential care workers (adjusted RR 2.14 [95% CI 1.31-3.51]). Depression/anxiety (adjusted RR 2.09 [95% CI 1.31-3.34]), obesity (adjusted RR 1.96 [95% CI 1.01-3.81]) and dyspnea at symptom onset (adjusted RR 2.47 [95% CI 1.55-3.92]), sick leave prior to the pandemic (3-12 weeks) (adjusted RR 2.23 [95% CI 1.21-4.10]) were associated with longer sick leave. From a public health perspective, considering occupational category, comorbidity, symptoms at onset, and sick leave prior to the pandemic as potential predictors of sick leave in healthcare may help prevent staff shortage.

SUBMITTER: Kisiel MA 

PROVIDER: S-EPMC8659339 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC3918999 | biostudies-literature
| S-EPMC8164957 | biostudies-literature
| S-EPMC8530010 | biostudies-literature
| S-EPMC9884157 | biostudies-literature
| S-EPMC8173283 | biostudies-literature
| S-EPMC8961940 | biostudies-literature
| S-EPMC10512969 | biostudies-literature
| S-EPMC10275433 | biostudies-literature
| S-EPMC6341039 | biostudies-literature
| S-EPMC8152250 | biostudies-literature