title: Using county-level public health data to prioritize medical education topics.
abstract: BACKGROUND
  Medical education topics might be locally prioritized using public health data on health outcomes and risk factors unrelated to quality of care.
  
  
  METHODS
  The Missouri Information for Community Assessment (MICA) supplied preventable hospitalization rates (PHRs) for asthma, chronic obstructive pulmonary disease (COPD), diabetes, heart failure, and hypertension in 114 counties from 1998 to 2002. For each disease, a linear regression model predicted PHR from behavior, access, and disease prevalence data from MICA and other public data sources. For each disease in each county, the residual, unexplained PHR should include effects of local medical practices. Variation in relative priority of diseases between counties was estimated from raw PHR and unexplained PHR.
  
  
  RESULTS
  The raw values of the five PHRs varied geographically in different patterns. Regression models explained between 46% and 83% of the variability. The medical education priorities implied by unexplained PHR values differ from priorities inferred from unadjusted PHR or disease prevalence.
  
  
  CONCLUSIONS
  Patient behavior and poor health care access contribute to PHR but do not fully explain variation in PHR. If county-level unexplained PHR values identify high priority medical education topics, then other measures of importance, notably disease prevalence and PHR, are poor identifiers of high value topics. Although available predictor and outcome variables constrain the current analysis, unexplained variation in health outcome measures might identify educational opportunities. These observations suggest strategies for balancing and evaluating controlled trials of knowledge dissemination efforts and eventually for deploying educational activities.
authors.affiliation: Washington University School of Medicine, St Louis, MO 63110, USA. wsumner@im.wustl.edu
authors.forename: Walton
authors.initials: W
authors.lastname: Sumner
authors.forename: Mario
authors.initials: M
authors.lastname: Schootman
authors.forename: Philip
authors.initials: P
authors.lastname: Asaro
authors.forename: Yan
authors.initials: Y
authors.lastname: Yan
authors.forename: Michael D
authors.initials: MD
authors.lastname: Hagen
mesh_terms: D004502:Education, Medical, Continuing; D006301:Health Services Needs and Demand; D006760:Hospitalization / Q000639:trends; D006801:Humans; D016014:Linear Models; D008923:Missouri; D011159:Population Surveillance; D011634:Public Health / Q000706:statistics & numerical data*; D019984:Quality Indicators, Health Care; D012307:Risk Factors