Health care expenditure prediction with a single item, self-rated health measure.
BACKGROUND
Prediction models that identify populations at risk for high health expenditures can guide the management and allocation of financial resources.


OBJECTIVE
To compare the ability for identifying individuals at risk for high health expenditures between the single-item assessment of general self-rated health (GSRH), "In general, would you say your health is Excellent, Very Good, Good, Fair, or Poor?," and 3 more complex measures.


METHODS
We used data from a prospective cohort, representative of the US civilian noninstitutionalized population, to compare the predictive ability of GSRH to: (1) the Short Form-12, (2) the Seattle Index of Comorbidity, and (3) the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score. The outcomes were total, pharmacy, and office-based annualized expenditures in the top quintile, decile, and fifth percentile and any inpatient expenditures.


METHODS
Medical Expenditure Panel Survey panels 8 (2003-2004, n = 7948) and 9 (2004-2005, n = 7921).


RESULTS
The GSRH model predicted the top quintile of expenditures, as well as the SF-12, Seattle Index of Comorbidity, though not as well as the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score: total expenditures [area under the curve (AUC): 0.79, 0.80, 0.74, and 0.84, respectively], pharmacy expenditures (AUC: 0.83, 0.83, 0.76, and 0.87, respectively), and office-based expenditures (AUC: 0.73, 0.74, 0.68, and 0.78, respectively), as well as any hospital inpatient expenditures (AUC: 0.74, 0.76, 0.72, and 0.78, respectively). Results were similar for the decile and fifth percentile expenditure cut-points.


CONCLUSIONS
A simple model of GSRH and age robustly stratifies populations and predicts future health expenditures generally as well as more complex models.
DeSalvo
Karen B
KB
Division of General Internal Medicine, Tulane University School of Medicine, New Orleans, Louisiana 70112, USA. kdesalv@tulane.edu
Jones
Tiffany M
TM
Peabody
John
J
McDonald
Jay
J
Fihn
Stephan
S
Fan
Vincent
V
He
Jiang
J
Muntner
Paul
P
D000328:Adult; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D018570:Risk Assessment; D014481:United States