Access to Care and the Incidence of Endstage Renal Disease Due to Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Persons with low socioeconomic status have an increased risk of endstage renal disease (ESRD) due to systemic lupus erythematosus (SLE), possibly because of limited access to care. We examined if the incidence of ESRD due to SLE was higher in geographic areas with poorer access to care. METHODS: In this population-based ecological study, we tested associations between the incidence of ESRD due to SLE and the proportion of hospitalizations with no insurance, Medicaid or managed care insurance, residence in a primary care-provider shortage area or rural area, and rate of hospitalizations for ambulatory care-sensitive conditions, by ZIP code in California in 1999-2004. RESULTS: The incidence of ESRD due to SLE was higher in ZIP codes with higher proportions of hospitalizations with no insurance (r = 0.22, p < 0.0001) or Medicaid (r = 0.21, p < 0.0001), and in ZIP codes with higher rates of hospitalizations for ambulatory care-sensitive conditions (r = 0.23, p < 0.0001). In multivariate analyses, incidences were higher in ZIP codes with higher proportions of hospitalizations with Medicaid (p < 0.0001) and higher rates of hospitalizations for ambulatory care-sensitive conditions (p = 0.06), independent of the socioeconomic status of the ZIP code residents. CONCLUSION: The incidence of ESRD due to SLE is higher in areas with higher proportions of residents who have public insurance and higher rates of avoidable hospitalizations, suggesting that limited access to care may contribute to this complication of SLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".