Gender differences in treatment of Coronavirus Disease-2019

Immacolata Ambrosino1, Elena Barbagelata2, Graziamaria Corbi3

  • 1Local Healthcare Unit of Bari, Health District 10. imma-ambrosino@libero.it.

Insights

COVID-19 disproportionately affects men, especially those with underlying conditions. Analyzing treatment data by sex is crucial for developing effective, personalized therapies to reduce severe illness and mortality.

Area of Science:

  • Infectious Diseases
  • Immunology
  • Pharmacology

Background:

  • Coronavirus Disease-2019 (COVID-19) became a global pandemic with significant mortality.
  • Men exhibited higher infection rates and severe outcomes, particularly with comorbidities like diabetes and hypertension.
  • Biological factors, including hormonal influences and X-linked gene activity, may explain gender-based disparities in immune response to viral infections.

Purpose of the Study:

  • To review existing studies on COVID-19 treatments, assessing available data for gender-specific analysis.
  • To highlight the importance of sex-disaggregated data in understanding COVID-19 outcomes and treatment efficacy.
  • To emphasize the need for customized treatment strategies based on gender, age, and race.

Main Methods:

  • Systematic review of published studies on various COVID-19 drugs: chloroquine/hydroxychloroquine, remdesivir, favipiravir, lopinavir-ritonavir, tocilizumab, plasma, and immunoglobulins.
  • Evaluation of gender analysis within the selected studies.
  • Analysis of factors contributing to gender disparities in disease severity and mortality.

Main Results:

  • Limited studies reported gender-specific data for COVID-19 treatments.
  • Observed disparities in infection rates and disease severity between men and women.
  • Underlying health conditions in men correlated with increased risk of severe COVID-19 and higher mortality.

Conclusions:

  • Sex-disaggregated data reporting is essential for understanding COVID-19.
  • Knowledge of gender differences is fundamental for developing effective, personalized treatments.
  • Tailoring treatments based on sex, age, and race can reduce hospitalizations, ICU admissions, and mortality.

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