Time trends in the association of <scp>ESRD</scp> incidence with area‐level poverty in the <scp>US</scp> population
Bibliographic record
Abstract
The objective of this study was to examine the temporal trends of the association between area-level poverty status and end-stage renal disease (ESRD) incidence. We hypothesized that the association between area-level poverty status and ESRD incidence has increased significantly over time. Patient data from the United States Renal Data System were linked with data from the 2000 and 2010 US census. Area-level poverty was defined as living in a zip code-defined area with ≥20% of households living below the federal poverty line. Negative binomial regression models were created to examine the association between area-level poverty status and ESRD incidence by time period in the US adult population while simultaneously adjusting for the distribution of age, sex, and race/ethnicity within a zip code. Time was categorized as January 1, 1995 through December 31, 2004 (Period 1) and January 1, 2005 through December 31, 2010 (Period 2). The percentage of adults initiating dialysis with area-level poverty increased from 27.4% during Period 1 to 34.0% in Period 2. After accounting for the distribution of age, sex, and race/ethnicity within a zip code, area-level poverty status was associated with a 1.24 (95% confidence interval [CI] 1.22, 1.25)-fold higher ESRD incidence. However, this association differed by time period with 1.04-fold (95% CI 1.02, 1.05) higher ESRD incidence associated with poverty status for Period 2 compared with the association between ESRD and poverty status in Period 1. Area-level poverty and its association with ESRD incidence is not static over time.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".