Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling.

ABSTRACT:

Background

Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.

Methods

During first wave of the SARS-Cov-2 epidemic (April 23rd to June 24th, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an indepen

SUBMITTER: Lei J 

PROVIDER: S-EPMC9374214 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature