Age-Stratified Cost of Care in CKD: Real-World Data from a Retrospective Cohort Study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects an estimated 14% of US adults, and is more common in those aged 65 years or older, than younger age groups. Little is known regarding the costs of CKD by age. In a large cohort of patients with prevalent CKD, this study described the mean total and direct medical costs accrued in the overall and age-stratified cohorts. Methods: A retrospective longitudinal cohort study was conducted using Optum® Market Clarity electronic health record-linked claims data. The study cohort included adults with 2 estimated glomerular filtration rates <60 ml/min/1.73m2 (3-12 months apart) followed by an ICD 9/10 code for CKD, and 1-year of continuous enrolment pre- and post-CKD diagnosis. These included patients diagnosed before January 1, 2020 plus new cases arising in 2020 (prevalent cohort); subjects who died or disenrolled in 2020 were excluded upon censoring. Total costs including direct medical costs (inpatient, outpatient, emergency department) as well as medications were calculated for the prevalent CKD cohort for the year 2020. Mean costs per patient were computed for the overall cohort and by age groups. Results: In 2020, there were 214,631 patients with CKD, consisting of: 7,270 (3.4%) aged 18-49 y; 44,879 (20.9%) aged 50-64 y; 115,070 (53.6%) aged 65-80 y; and 47,412 (22.1%) aged >81 y. The mean (SD) total and medical costs were $68,095 (139,099) and $48,379 (104,185), respectively. Cost breakdown by age groups showed annual mean total costs of: $82,958 (203,481), $76,073 (170,039), $69,675 (135,308), and $54,432 (95,669), among the 18-49, 50-64, 65-80, and >81 y subgroups, respectively. Total medical costs were $53,388 (139,247), $50,826 (126,435), $49,614 (101,644), and $42,297 (76,868), respectively, ranging from 64% in those aged 18-49 to 78% among those >81 y. Conclusion: In a large cohort of insured patients with prevalent CKD, medical costs accounted for nearly two-thirds of the total costs, and this proportion increased in older age groups. Better understanding of the drivers of medical costs by age along with other sociodemographic and clinical characteristics can provide critical insights on surveillance of CKD, and consequently, informing optimal strategies for reducing the burden of CKD. Funding: Commercial Support - Boehringer Ingelheim
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".