title: Predictive modeling of health care costs: do cardiovascular risk markers improve prediction?
abstract: BACKGROUND
  To investigate the ability of multiple cardiovascular disease (CVD) markers to predict future health care costs. CVD markers included traditional risk factors (smoking status, body mass index, waist circumference, alcohol intake, diabetes, total : high-density lipoprotein cholesterol ratio, actual hypertension, physical activity) and newer markers (carotid intima-media thickness, hemoglobin A1c, apolipoprotein B : apolipoprotein A-1 ratio, lipoprotein (a), leukocyte count, high-sensitive C-reactive protein, plasma fibrinogen, estimated glomerular filtration rate, urinary albumin : creatinine ratio).
  
  
  METHODS
  The study sample consisted of 2233 participants without history of myocardial infarction, stroke, heart failure, and angina pectoris at baseline (50.6% women; mean age 60.9 years; age range 45-81 years) from the cohort Study of Health in Pomerania, Germany (median follow-up 5 years).
  
  
  RESULTS
  Predictive modeling revealed that a basic model with sex, age, years of school education, insurance status, and income explained 0.9% in baseline total cost variation and 1.5% in total cost variation at 5-year follow-up. The incorporation of a combination of significant CVD markers resulted in an increase in the R2 for total costs of 70% at baseline and 69% after 5 years, with a final R2 of 0.030 at baseline and an R2 of 0.048 at 5-year follow-up.
  
  
  CONCLUSIONS
  Our data suggest that for individuals without history of CVD, the simultaneous addition of several CVD risk markers improves predictive modeling of future health care costs beyond that of a model that is based on established health care predictors.
authors.affiliation: Helmhotz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology, Ingolstaedter Landstrasse, Neuherberg, Germany. sebastian.baumeister@uni-greifswald.de
authors.forename: Sebastian E
authors.initials: SE
authors.lastname: Baumeister
authors.forename: Marcus
authors.initials: M
authors.lastname: Dörr
authors.forename: Dörte
authors.initials: D
authors.lastname: Radke
authors.forename: Matthias
authors.initials: M
authors.lastname: Nauck
authors.forename: Ulrich
authors.initials: U
authors.lastname: John
authors.forename: Paul
authors.initials: P
authors.lastname: Marschall
authors.forename: Steffen
authors.initials: S
authors.lastname: Flea
authors.forename: Carsten-Oliver
authors.initials: CO
authors.lastname: Schmidt
authors.forename: Dietrich
authors.initials: D
authors.lastname: Alte
authors.forename: Henry
authors.initials: H
authors.lastname: Völzke
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000191:economics* / Q000209:etiology*; D016009:Chi-Square Distribution; D004522:Educational Status; D005260:Female; D005544:Forecasting; D005858:Germany; D017048:Health Care Costs / Q000639:trends*; D006305:Health Status Indicators; D006801:Humans; D007182:Income; D019458:Insurance Coverage; D007348:Insurance, Health; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D011446:Prospective Studies; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013997:Time Factors