Socioeconomic Factors and Racial and Ethnic Differences in the Initiation of Home Dialysis
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Home dialysis has been underused in the United States, especially among minority groups. We investigated whether adjustment for socioeconomic factors would attenuate racial/ethnic differences in the initiation of home dialysis. STUDY DESIGN: Retrospective observational cohort study. SETTING & POPULATION: Adult patients in the US Renal Data System who initiated dialysis on day 1 with either in-center hemodialysis (HD), home HD (HHD), or peritoneal dialysis (PD) from 2005 to 2013. PREDICTOR: Race/ethnicity: non-Hispanic white, Hispanic, black, or Asian. OUTCOME: Initiating dialysis with PD versus in-center HD and HHD versus in-center HD for each minority group compared with non-Hispanic whites. ANALYTICAL APPROACH: Odds ratios and 95% CIs estimated by logistic regression. RESULTS: Of 523,526 patients, 55% were white, 28% were black, 13% were Hispanic, and 4% were Asian; 8% started dialysis on PD, and 0.1%, on HHD. In unadjusted analyses, blacks and Hispanics were 30% and 19% less likely and Asians were 31% more likely to start on PD than whites. The differences narrowed when fully adjusted for demographic, medical, and socioeconomic factors. Adjustment for socioeconomic factors reduced these differences between white and black, Hispanic, and Asian patients by 13%, 28%, and 1%, respectively. Blacks were just as likely and Hispanics and Asians were less likely to start on HHD than whites. This did not change appreciably when fully adjusted for demographic, medical, and socioeconomic factors. LIMITATIONS: No data for physician and patient preferences or modality education. CONCLUSIONS: Black and Hispanic patients are less likely to start on PD than white patients, attributable partly, though not completely, to socioeconomic factors. Hispanics and Asians are less likely to start on HHD than whites. This was materially unaffected by socioeconomic factors. More research is needed to determine whether urgent-start PD programs and transitional care units in socioeconomically disadvantaged areas might reduce these disparities and increase home dialysis use among all groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".