Predictive modeling of health care costs: do cardiovascular risk markers improve prediction?
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.
Baumeister
Sebastian E
SE
Helmhotz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology, Ingolstaedter Landstrasse, Neuherberg, Germany. sebastian.baumeister@uni-greifswald.de
Dörr
Marcus
M
Radke
Dörte
D
Nauck
Matthias
M
John
Ulrich
U
Marschall
Paul
P
Flea
Steffen
S
Schmidt
Carsten-Oliver
CO
Alte
Dietrich
D
Völzke
Henry
H
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