Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort, 2021, Westerlind et al

Abstract
Background
The impact of Covid-19 and its long-term consequences is not yet fully understood. Sick leave can be seen as an indicator of health in a working age population, and the present study aimed to investigate sick-leave patterns after Covid-19, and potential factors predicting longer sick leave in hospitalised and non-hospitalised people with Covid-19.

Methods
The present study is a comprehensive national registry-based study in Sweden with a 4-month follow-up. All people who started to receive sickness benefits for Covid-19 during March 1 to August 31, 2020, were included. Predictors of sick leave ≥1 month and long Covid (≥12 weeks) were analysed with logistic regression in the total population and in separate models depending on inpatient care due to Covid-19.

Results
A total of 11,955 people started sick leave for Covid-19 within the inclusion period. The median sick leave was 35 days, 13.3% were on sick leave for long Covid, and 9.0% remained on sick leave for the whole follow-up period. There were 2960 people who received inpatient care due to Covid-19, which was the strongest predictor of longer sick leave. Sick leave the year prior to Covid-19 and older age also predicted longer sick leave. No clear pattern of socioeconomic factors was noted.

Conclusions
A substantial number of people are on sick leave due to Covid-19. Sick leave may be protracted, and sick leave for long Covid is quite common. The severity of Covid-19 (needing inpatient care), prior sick leave, and age all seem to predict the likelihood of longer sick leave. However, no socioeconomic factor could clearly predict longer sick leave, indicating the complexity of this condition. The group needing long sick leave after Covid-19 seems to be heterogeneous, indicating a knowledge gap.

Open access, https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-11013-2

 

Was my hope for Covid studies actually looking at sufficient physiological detail etc to be able to differentiate those who will develop Long Covid symptoms and those who go on to develop ME like symptoms from those who readily recover over optimistic?

It seems prospective studies like this are not looking at the right things and the potentially interesting studies continue to be retrospective, though at least the retrospective studies involve potentially more homogeneous groups and larger patient cohorts.

 

Many long haulers could not get sick leave for a number of reasons, may have either dropped hours or stopped working entirely despite having no access to sick leave. So this is very misleading data, it misses out on a lot of relevant information, largely because of decisions made to make sick leave almost impossible for long haulers.

Not as bad but roughly in a similar category of "no reports of harm" because they are dismissed on arrival. They can't count those who were denied sick leave, the data never make it into the system.

Medicine is seriously way too narrowly focused, especially in EBM stuff, they use artificial scenarios as a proxy for real life.

 

Eh? Is it me or does that sentence not make sense.

If no socioeconomic factor predicts the length of sick leave it surely that indicates that a person's income, financial status or security doesn't seem to affect how long they need for sick leave.

If socioeconomics are playing, or can't be blamed for playing any factor then you're more likely to be looking at the effect of the infection itself. Would that not make it clearer?