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Evidence of gender bias in the diagnosis and management of covid-19 patients: a big data analysis of electronic health records

J. Ancochea, Jose L. Izquierdo, I. H. Medrano, A. Porras, M. Serrano, S. Lumbreras, C. del Río-Bermúdez, S. Marchesseau, I. Salcedo, I. Zubizarreta, Y. González, Joan B. Soriano

Background:

The impact of sex and gender in the incidence and severity of COVID-19 remains controversial. 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 1st to May 1st, 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 confirmed diagnosis of COVID-19 were identified. Of these, 2,443 (51%) were female, who were on average 1.5 years younger than male patients (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 age range (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.


Palabras clave: artificial Intelligence; sex differences; COVID-19; Natural Language Processing; SARS-CoV-2.


Journal of Women's Health.

Aceptado para su publicación.



Cita:
J. Ancochea, Jose L. Izquierdo, I. H. Medrano, A. Porras, M. Serrano, S. Lumbreras, C. del Río-Bermúdez, S. Marchesseau, I. Salcedo, I. Zubizarreta, Y. González, Joan B. Soriano. Evidence of gender bias in the diagnosis and management of covid-19 patients: a big data analysis of electronic health records. Journal of Women's Health.


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