title: Modeling asthma exacerbations through lung function in children.
abstract: BACKGROUND
  Formal economic evaluation using a model-based approach is playing an increasingly important role in health care decision making.
  
  
  OBJECTIVE
  To develop a model by using an objective measure of lung function-- prebronchodilator FEV(1) as a percent of predicted (FEV(1)% predicted)--as the primary independent factor to predict the frequency of adverse events related to the exacerbation of asthma on a population level.
  
  
  METHODS
  We developed a Markov simulation model of childhood asthma by using data from the Childhood Asthma Management Program. The primary outcomes were the result of asthma exacerbations defined as hospitalizations, emergency department (ED) visits, and the need for oral corticosteroid therapy. Predicted monthly frequencies for each acute event were based on negative binomial regression equations estimated from the placebo arm of the Childhood Asthma Management Program with covariates of age, prebronchodilator FEV(1)% predicted, time in study, prior hospitalizations, and prior nocturnal awakenings.
  
  
  RESULTS
  Simulated versus observed mean number of acute events were similar within the placebo and treatment groups. While the trial demonstrated treatment effects of 48% reduction in hospitalizations, 46% reduction in ED visits, and 44% reduction in the need for oral corticosteroid therapy at 48 months, the model simulated similar reductions of 49% in hospitalizations, 41% in ED visits, and 46% in the need for oral corticosteroid therapy.
  
  
  CONCLUSIONS
  Our findings suggest that longitudinal intervention effects may be modeled through FEV(1)% predicted to estimate hospitalizations, ED visits, and need for oral corticosteroid therapy in childhood asthma for planning and evaluation purposes.
authors.affiliation: Department of Population Medicine, Center for Child Health Care Studies, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA. ann.wu@childrens.harvard.edu
authors.forename: Ann Chen
authors.initials: AC
authors.lastname: Wu
authors.forename: Martin
authors.initials: M
authors.lastname: Gregory
authors.forename: Steven
authors.initials: S
authors.lastname: Kymes
authors.forename: Dennis
authors.initials: D
authors.lastname: Lambert
authors.forename: Joshua
authors.initials: J
authors.lastname: Edler
authors.forename: Dustin
authors.initials: D
authors.lastname: Stwalley
authors.forename: Anne L
authors.initials: AL
authors.lastname: Fuhlbrigge
mesh_terms: D000284:Administration, Oral; D000305:Adrenal Cortex Hormones / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy* / Q000503:physiopathology*; D002648:Child; D003198:Computer Simulation; D003661:Decision Support Techniques; D018450:Disease Progression; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008168:Lung / Q000503:physiopathology*; D008297:Male; D008954:Models, Biological; D015337:Multicenter Studies as Topic; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic; D012129:Respiratory Function Tests; D014481:United States
chemical_list: D000305:Adrenal Cortex Hormones