Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data.

ABSTRACT:

Objective

To examine sex and gender roles in COVID-19 test positivity and hospitalisation in sex-stratified predictive models using machine learning.

Design

Cross-sectional study.

Setting

UK Biobank prospective cohort.

Participants

Participants tested between 16 March 2020 and 18 May 2020 were analysed.

Main outcome measures

The endpoints of the study were COVID-19 test positivity and hospitalisation. Forty-two individuals' demographics, psychosocial factors and comorbidities were used as likely determinants of outcomes. Gradient boosting machine was used for building prediction models.

Results

Of 4510 individuals tested (51.2% female, mean age=68.5±8.9 years), 29.4% tested positive. Males were more likely to be positive than females (31.6% vs 27.

SUBMITTER: Azizi Z 

PROVIDER: S-EPMC9118360 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature