Dataset Information

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

ABSTRACT:

Background

Post COVID-19 condition (PCC) can lead to considerable morbidity, including prolonged sick-leave. Identifying risk groups is important for informing interventions. We investigated heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave and identified subgroups at higher risk.

Methods

We conducted a hybrid survey and register-based retrospective cohort study of Danish residents who tested positive for SARS-CoV-2 between November 2020 and February 2021 and a control group who tested negative, with no known history of SARS-CoV-2. We estimated the causal risk difference (RD) of long-term sick-leave due to PCC and used the causal forest method to identify individual-level heterogeneity in the effect of infection on sick-leave. Sick-leave was defined as >4 weeks of full-time sick-leave from 4 weeks to 9 months after the test.

Results

Here, in a cohort of 88,818 individuals, including 37,482 with a confirmed SARS-CoV-2 infection, the RD of long-term sick-leave is 3.3% (95% CI 3.1% to 3.6%). We observe a high degree of effect heterogeneity, with conditional RDs ranging from -3.4% to 13.7%. Age, high BMI, depression, and sex are the most important variables explaining heterogeneity. Among three-way interactions considered, females with high BMI and depression and persons aged 36-45 years with high BMI and depression have an absolute increase in risk of long-term sick-leave above 10%.

Conclusions

Our study supports significant individual-level heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave, with age, sex, high BMI, and depression identified as key factors. Efforts to curb the PCC burden should consider multimorbidity and individual-level risk.

SUBMITTER: Jakobsen KD

PROVIDER: S-EPMC10733276 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

Jakobsen Kim Daniel KD O'Regan Elisabeth E Svalgaard Ingrid Bech IB Hviid Anders A

Communications medicine 20231220 1

<h4>Background</h4>Post COVID-19 condition (PCC) can lead to considerable morbidity, including prolonged sick-leave. Identifying risk groups is important for informing interventions. We investigated heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave and identified subgroups at higher risk.<h4>Methods</h4>We conducted a hybrid survey and register-based retrospective cohort study of Danish residents who tested positive for SARS-CoV-2 between November 2020 and February 2021 ...[more]

PMID: 38123739

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Dataset Information

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

Background

Methods

Results

Conclusions

Publications

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

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