Inside CKD: Cost-Effectiveness of Multinational Screening for CKD
Bibliographic record
Abstract
Introduction: Early detection of chronic kidney disease (CKD) could slow its progression; however, most patients in earlier stages remain undiagnosed. Our study objective was to assess the cost-effectiveness of multinational CKD screening strategies from the payer perspective across general and higher-risk populations. Methods: Using the published Inside CKD microsimulation, we projected virtual closed populations to assess CKD screening strategies in 31 countries or regions over a lifetime horizon. We considered people aged ≥ 65 or ≥ 45 years in the general population and in high-risk subgroups (type 2 diabetes [T2D], hypertension, or cardiovascular disease [CVD]). Simulated populations could receive 2 serum creatinine (SCr) tests assessing estimated glomerular filtration rate (eGFR), "2 eGFR only", or an additional urinary albumin-to-creatinine ratio test (UACR), "2 eGFR and 1 UACR", versus current practice. Eligible patients received renin-angiotensin system inhibitors (RASi). Results: Screening the general population aged ≥ 45 years for CKD was cost-effective versus current practice in all countries or regions using the "2 eGFR and 1 UACR" strategy, and cost-effective in all but 1 country using the 2 eGFR only strategy. The 2 eGFR and 1 UACR strategy showed consistently higher cost-effectiveness. Screening general populations aged ≥ 45 years increased projected CKD diagnosis rates per 100,000 persons eligible for screening from 459 by current practice to 7475 patients using 2 eGFR only, or 14,392 using 2 eGFR and 1 UACR. Similar trends in cost-effectiveness and diagnosis rates were observed in persons aged ≥ 65 years. Conclusion: CKD screening may be cost-effective in general populations worldwide, including in populations aged ≥ 45 years. Our analysis corroborates global guideline recommendations for simultaneous eGFR and UACR testing if considered in the context of local factors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".