title: Relationships among quality of life, severity, and control measures in asthma: an evaluation using factor analysis.
abstract: BACKGROUND
  Validated psychometric tools measuring quality of life, asthma control, and asthma severity have been developed, but their relationships with each other and with other important patient-centered outcomes have not been rigorously assessed.
  
  
  OBJECTIVE
  To use factor analysis to evaluate the relationships of these validated tools with each other and with other patient-centered outcomes.
  
  
  METHODS
  Surveys were completed by a random sample of 2854 Health Maintenance Organization members age 18 to 56 years with persistent asthma. Surveys included demographic information; validated tools measuring generic (Short Form-12; SF-12) and asthma-specific (Juniper Mini Asthma Quality of Life Questionnaire; AQLQ) quality of life, asthma control (Asthma Therapy Assessment Questionnaire), and asthma symptom severity (Asthma Outcomes Monitoring System); self-described severity, control, and course over time; and history of acute exacerbations.
  
  
  RESULTS
  Principal component analysis suggested a 5-factor model that accounted for approximately 59% of the variability. The most prominent rotated factor reflected asthma symptom frequency (19.4% of variability), was measured by the symptom subscale of the AQLQ, and was the only factor significantly related to the Asthma Therapy Assessment Questionnaire, Asthma Outcomes Monitoring System, or the self-reported assessments of severity, control, or course. Other factors included symptom bother (12.1% of variability), reflected by the environment and emotion AQLQ subscales; activity limitation (13.9% of variability), reflected by the activity AQLQ subscale and the SF-12 physical component scale; mental health (8.3% of variability), reflected by the SF-12 mental component scale; and acute exacerbations (5.0% of variability), not measured by any of the validated scales.
  
  
  CONCLUSIONS
  Distinct components of patient-reported asthma health status can be identified by factor analysis. Distinct constructs of severity versus control cannot be identified by the use of these tools alone.
authors.affiliation: Department of Allergy, Kaiser-Permanente Medical Center, San Diego, CA 92111, USA. michael.x.schatz@kp.org
authors.forename: Michael
authors.initials: M
authors.lastname: Schatz
authors.forename: David
authors.initials: D
authors.lastname: Mosen
authors.forename: Andrea J
authors.initials: AJ
authors.lastname: Apter
authors.forename: Robert S
authors.initials: RS
authors.lastname: Zeiger
authors.forename: William M
authors.initials: WM
authors.lastname: Vollmer
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Stibolt
authors.forename: Albin
authors.initials: A
authors.lastname: Leong
authors.forename: Michael S
authors.initials: MS
authors.lastname: Johnson
authors.forename: Guillermo
authors.initials: G
authors.lastname: Mendoza
authors.forename: E Francis
authors.initials: EF
authors.lastname: Cook
mesh_terms: D000293:Adolescent; D000328:Adult; D001249:Asthma / Q000517:prevention & control*; D002140:California; D004644:Emotions; D004781:Environmental Exposure; D005163:Factor Analysis, Statistical; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D012307:Risk Factors; D018434:Sickness Impact Profile; D011795:Surveys and Questionnaires