Back

Sex differences in COVID-19 infection and mortality in Hong Kong

Law, A. H. T.; Wong, J. Y.; Lin, Y.; Cowling, B. J.; Wu, P.

2026-03-09 infectious diseases
10.64898/2026.03.07.26347844 medRxiv
Show abstract

BackgroundVariation in COVID-19 mortality rates by sex could have several explanations. We aimed to determine sex differences in infection and mortality patterns across different COVID-19 epidemics in Hong Kong, and to evaluate potential hypotheses. MethodsWe estimated age- and sex-specific incidence rates of cases, hospitalizations, and deaths per 100,000 population. Case-hospitalization, case-fatality risks (CFRs), and hospital-fatality risks of the COVID-19 pandemic were also estimated. Adjusted and unadjusted risks were estimated and compared to explore the relationships between mortality and health-related variables. We also explored the sex ratio of COVID-19 mortality rates of respiratory diseases from 2000 to 2019. ResultsHong Kong recorded 2876110 COVID-19 cases and 12737 deaths between January 2020 and January 2023, with 1317368 cases (45.8%) and 7523 (59.1%) fatal cases occurring in males. The incidence rate of cases was similar by sex across waves. The CFRs and hospital-fatality risks were higher in men across all waves. Males had a significantly higher mortality risk after adjusting for sex, COVID-19 vaccination status, and pre-existing chronic diseases. The ratio of COVID-19 mortality rates in men versus women from 2020 to 2023 was similar to the mortality ratio for other respiratory diseases in the pre-pandemic period. ConclusionsWhile infection rates were similar for males and females, males experienced higher mortality risks even after adjusting for differences in other known risk factors. COVID-19 shares a similar sex ratio of mortality with respiratory diseases excluding COVID-19.

Published in International Journal of Infectious Diseases (predicted rank #3) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.