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Med Care. 2009 Apr;47(4):440-7. doi: 10.1097/MLR.0b013e318190b716.

Health care expenditure prediction with a single item, self-rated health measure.

Author information

1
Division of General Internal Medicine, Tulane University School of Medicine, New Orleans, Louisiana 70112, USA. kdesalv@tulane.edu

Abstract

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.

STUDY DESIGN:

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.

DATA SOURCE:

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.

PMID:
19238099
DOI:
10.1097/MLR.0b013e318190b716
[Indexed for MEDLINE]

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