Prevalence, Mortality, and Access to Care for Chronic Kidney Disease in Medicaid-Enrolled Adults With Sickle Cell Disease in California: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a significant complication in patients with sickle cell disease (SCD), leading to increased mortality. OBJECTIVE: This study aims to investigate the burden of CKD in Medicaid-enrolled adults with SCD in California, examine differences in disease burden between male and female individuals, and assess mortality rates and access to specialized care. METHODS: This retrospective cohort study used the California Sickle Cell Data Collection program to identify and monitor individuals with SCD. Medicaid claims, vital records, emergency department, and hospitalization data from 2011 to 2020 were analyzed. CKD prevalence was assessed based on ICD (International Classification of Diseases) codes, and mortality rates were calculated. Access to specialized care was examined through outpatient encounter rates with hematologists and nephrologists. RESULTS: Among the 2345 adults with SCD, 24.4% (n=572) met the case definition for CKD. The SCD-CKD group was older at the beginning of this study (average age 44, SD 14 vs 34, SD 12.6 years) than the group without CKD. CKD prevalence increased with age, revealing significant disparities by sex. While the youngest (18-29 years) and oldest (>65 years) groups showed similar CKD prevalences between sexes (female: 12/111, 10.8% and male: 12/101, 11.9%; female: 74/147, 50.3% and male: 34/66, 51.5%, respectively), male individuals in the aged 30-59 years bracket exhibited significantly higher rates than female individuals (30-39 years: 49/294, 16.7%, P=.01; 40-49 years: 52/182, 28.6%, P=.02; and 50-59 years: 76/157,48.4%, P<.001). During this study, of the 2345 adults, 435 (18.5%) deaths occurred, predominantly within the SCD-CKD cohort (226/435, 39.5%). The median age at death was 53 (IQR 61-44) years for the SCD-CKD group compared to 43 (IQR 33-56) years for the SCD group, with male individuals in the SCD-CKD group showing significantly higher mortality rates (111/242, 45.9%; P=.009) than female individuals (115/330, 34.9%). Access to specialist care was notably limited: approximately half (281/572, 49.1%) of the SCD-CKD cohort had no hematologist visits, and 61.9% (354/572) did not see a nephrologist during this study's period. CONCLUSIONS: This study provides robust estimates of CKD prevalence and mortality among Medicaid-enrolled adults with SCD in California. The findings highlight the need for improved access to specialized care for this population and increased awareness of the high mortality risk and progression associated with CKD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".