Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records
Abstract
Background
It remains unknown whether the frequency and severity of COVID-19 affect women differently than men. Here, we aim to describe the characteristics of COVID-19 patients at disease onset, with special focus on the diagnosis and management of female patients with COVID-19.
Methods
We explored the unstructured free text in the electronic health records (EHRs) within the SESCAM Healthcare Network (Castilla La-Mancha, Spain). The study sample comprised the entire population with available EHRs (1,446,452 patients) from January 1 st to May 1 st , 2020. We extracted patients’ clinical information upon diagnosis, progression, and outcome for all COVID-19 cases.
Results
A total of 4,780 patients with a test-confirmed diagnosis of COVID-19 were identified. Of these, 2,443 (51%) were female, who were on average 1.5 years younger than males (61.7±19.4 vs. 63.3±18.3, p=0.0025). There were more female COVID-19 cases in the 15-59 yr.-old interval, with the greatest sex ratio (SR; 95% CI) observed in the 30-39 yr.-old interval (1.69; 1.35-2.11). Upon diagnosis, headache, anosmia, and ageusia were significantly more frequent in females than males. Imaging by chest X-ray or blood tests were performed less frequently in females (65.5% vs. 78.3% and 49.5% vs. 63.7%, respectively), all p<0.001. Regarding hospital resource use, females showed less frequency of hospitalization (44.3% vs. 62.0%) and ICU admission (2.8% vs. 6.3%) than males, all p<0.001.
Conclusion
Our results indicate important sex-dependent differences in the diagnosis, clinical manifestation, and treatment of patients with COVID-19. These results warrant further research to identify and close the gender gap in the ongoing pandemic.
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