Health Care Expenditure Prediction With a Single Item, Self-Rated Health Measure
Bibliographic Data
| ID | 9104820 |
|---|---|
| Authors | Karen B Desalvo (Tulane University, corresponding author), Tiffany M Jones (0000-0002-7884-1155, Tulane University, corresponding author), John Peabody (0000-0002-0210-9232, Demoiselle 2 Femme), Jay McDonald, Jay R McDonald (0000-0001-8208-9085, Tulane University, corresponding author), Stephan D Fihn (University of Washington), Stephan Fihn, Vincent S Fan (0000-0002-6178-1117, University of Washington), Vincent Fan, Jiang He (0000-0002-3292-9370, Tulane University), Paul Muntner (0000-0002-4711-5492, Icahn School of Medicine at Mount Sinai, corresponding author) |
| Year | 2009 |
| Volume | 47 |
| Issue | 4 |
| Pages | 440-447 |
| Publication date | 2009-04-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e318190b716 |
| PMID | 19238099 |
| OpenAlex | W2094522856 |
| Language | EN |
| Citations received | 36 |
| References cited | 34 |
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
Cohort · Comorbidity · Decile · Family medicine · Health care · Health insurance · Medical Expenditure Panel Survey · Percentile · Percentile rank · Statistics · Demography · Global Health Care Issues · Healthcare Policy and Management · Healthcare Systems and Reforms · Internal Medicine · Medicine · Pharmacy
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| Unique citing works | 36 |
|---|---|
| Citations per year | 2,25 |
| Citation span | 2010 - 2025 (16) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 32 |