Gender gap in cystic fibrosis mortality

Am J Epidemiol. 1997 May 1;145(9):794-803. doi: 10.1093/oxfordjournals.aje.a009172.


The authors conducted the largest study to date of survival in cystic fibrosis. The study cohort consisted of all patients with cystic fibrosis seen at Cystic Fibrosis Foundation-accredited care centers in the United States between 1988 and 1992 (n = 21,047), or approximately 85% of all US patients diagnosed with cystic fibrosis. Cox proportional hazards regression analysis was used to compare the age-specific mortality rates of males and females and to identify risk factors serving as potential explanatory variables for the gender-related difference in survival. Among the subjects 1-20 years of age, females were 60% more likely to die than males (relative risk = 1.6, 95% confidence interval 1.4-1.8). Outside this age range, male and female survival rates were not significantly different. The median survival for females was 25.3 years and for males was 28.4 years. Nutritional status, pulmonary function, and airway microbiology at a given age were strong predictors of mortality at subsequent ages. Nonetheless, differences between the genders in these parameters, as well as pancreatic insufficiency, age at diagnosis, mode of presentation, and race, could not account for the poorer survival among females. Even after adjustment for all these potential risk factors, females in the age range 1-20 years remained at greater risk for death (relative risk = 1.6, 95% confidence interval 1.2-2.1). The authors concluded that in 1- to 20-year-old individuals with cystic fibrosis, survival in females was poorer than in males. This "gender gap" was not explained by a wide variety of potential risk factors.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Anthropometry
  • Child
  • Child, Preschool
  • Cohort Studies
  • Cystic Fibrosis / mortality*
  • Female
  • Humans
  • Infant
  • Male
  • Proportional Hazards Models
  • Risk Factors
  • Sex Factors
  • Survival Analysis