title: Do variations in disease prevalence limit the usefulness of population-based hospitalization rates for studying variations in hospital admissions?
abstract: BACKGROUND
  Studies of geographic variation in hospitalizations commonly examine age- and gender-adjusted population-based hospitalization rates (ie, the numbers of persons hospitalized relative to what is expected given the age/gender distributions in the area population).
  
  
  OBJECTIVE
  To determine whether areas identified as extreme using population-based hospitalization rates remain extreme when ranked by disease-based hospitalization rates (the numbers of persons hospitalized relative to what is expected given the amount of disease in the area).
  
  
  METHODS
  The authors examined 1997 Medicare data on both inpatient admissions and outpatient visits of patients 65 years and older in each of 71 small areas in Massachusetts for 15 medical conditions. For each area, the number of people having each condition was calculated as the sum of those hospitalized plus those treated as outpatients only. The authors used hierarchical Bayesian modeling to estimate area-specific population-based hospitalization rates, disease-based hospitalization rates (DHRs), and disease prevalence.
  
  
  METHODS
  The extent to which the same areas were identified as extreme based on population-based hospitalization rates versus DHRs.
  
  
  RESULTS
  Area-specific population-based hospitalization rates, DHRs, and disease prevalence varied substantially. Areas identified as extreme using population-based hospitalization rates often were not extreme when ranked by DHRs. For 11 of the 15 conditions, 5 or more of the 14 areas ranked in top and bottom deciles by population-based hospitalization rates were more likely than not (ie, with probability > or = 0.50) to be at least 2 deciles less extreme when ranked by DHRs.
  
  
  CONCLUSIONS
  Differences in disease prevalence can limit the usefulness of population-based hospitalization rates for studying variations in hospital admissions.
authors.affiliation: School of Management, Boston University, MA 02215, USA. mshwartz@bu.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Shwartz
authors.forename: Erol A
authors.initials: EA
authors.lastname: Peköz
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.forename: Michael A
authors.initials: MA
authors.lastname: Posner
authors.forename: Joseph D
authors.initials: JD
authors.lastname: Restuccia
authors.forename: Lisa I
authors.initials: LI
authors.lastname: Iezzoni
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D016000:Cluster Analysis; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008404:Massachusetts; D006278:Medicare; D015995:Prevalence; D014600:Utilization Review