Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index
Bibliographic record
Abstract
Background: Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI). Methods: We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012. Results: The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample. Conclusion: A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".