Geographical variation in kidney function testing and associations with health care costs among patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
OBJECTIVES: Clinical practice guidelines recommend at least annual testing of estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (uACR) for patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). This study assessed the adequacy of eGFR and uACR testing in this patient population across the United States. STUDY DESIGN: Observational real-world study. METHODS: Adults with CKD and T2D were identified from the Optum Clinformatics database (2015-2019). The eGFR and uACR tests were assessed nationally and by state. The proportions of tested patients and patients receiving adequate monitoring per clinical practice guidelines were analyzed during the 1-year period after T2D and CKD diagnosis, along with all-cause health care costs. RESULTS: Among 101,057 adults with CKD and T2D, 94.1% had at least 1 eGFR test and 38.7% had at least 1 uACR test over 1 year. Only 20.3% of patients had adequate uACR monitoring; this was much lower than observed for adequate eGFR monitoring (86.6%). The eGFR testing rates were high across states (range, 79.5% [Colorado] to 97.3% [Alabama]); conversely, uACR testing rates were uniformly lower and showed wider variation (range, 14.0% [Maine] to 58.9% [Hawaii]). Mean annual all-cause health care costs were $28,636 and increased with CKD GFR stage. Lower uACR testing rates were associated with higher health care costs at the state level (Pearson r = -0.55; P < .01). CONCLUSIONS: In the United States, uACR testing is underutilized, with large geographical variations in testing rates noted between states. Lower uACR testing rates were associated with higher health care costs. The lack of sufficient uACR testing raises concerns about CKD management in patients with T2D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".