Effect of Chronic Kidney Disease and Comorbid Conditions on Health Care Costs: A 10-Year Observational Study in a General Population
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is common, but the longitudinal effects of CKD and associated comorbidities on health care costs in the general population are unknown. METHODS: Population-based cohort study of 2,988 subjects in Germany, aged 25-74 years at baseline, who participated both in the baseline and 10-year follow-up examination (1994/95-2004/05). Presence of CKD was based on serum creatinine and defined as an estimated glomerular filtration rate of <60 ml/min/1.73 m(2). Self-reported health services utilization was used to estimate costs. RESULTS: Health care costs at baseline and follow-up were higher for subjects with CKD. Controlling for socio-economics, lifestyle factors and comorbid conditions, subjects with baseline CKD, in comparison to those without, exhibited 65% higher total costs 10 years after baseline examination, corresponding to a difference in adjusted costs of EUR 743. Incident CKD was related to 38% higher total costs. Costs for inpatient treatment and drug costs were the major costs components, while CKD revealed no effect on outpatient costs. The effect of CKD was strongly modified by angina, myocardial infarction, diabetes, and anemia. CONCLUSIONS: The direct effect of CKD on costs is modified by comorbid conditions. Therefore, early treatment of CKD and its precipitous factors may save future health care costs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".