title: Measuring morbidity: disease counts, binary variables, and statistical power.
abstract: OBJECTIVE
  This study compares the use of the binary disease variables with counts of the same conditions in models of self-rated health to better understand the advantages and disadvantages of each approach. In particular, the analysis seeks to determine if statistical power is adequate for the binary variable approach.
  
  
  METHODS
  Morbidity measures from adults in 2 large national surveys were used in both cross-sectional and longitudinal analyses.
  
  
  RESULTS
  Although differences across the approaches are modest, the binary variable approach offers greater explanatory power and slightly higher R2 values. Despite these advantages, statistical power is insufficient in some cases, especially for conditions that are relatively rare and/or that manifest modest differences on the outcome variable.
  
  
  CONCLUSIONS
  Statistical power estimates are advisable when using the binary variable approach, especially if the list of diseases and health conditions is extensive. Although a simple count of diseases may be useful in some research applications, separate counts for serious and nonserious conditions should be more useful in many research projects while avoiding the risk of inadequate statistical power.
authors.affiliation: Department of Sociology, Purdue University, West Lafayette, Indiana 47907-1365, USA. ferraro@purdue.edu
authors.forename: K F
authors.initials: KF
authors.lastname: Ferraro
authors.forename: J M
authors.initials: JM
authors.lastname: Wilmoth
mesh_terms: D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology*; D003430:Cross-Sectional Studies; D005260:Female; D015577:Geriatric Assessment / Q000706:statistics & numerical data*; D006306:Health Surveys; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D014481:United States / Q000453:epidemiology