Socioeconomic Position and Incidence of Glomerular Diseases
Bibliographic record
Abstract
Background and objectives Social deprivation is a recognized risk factor for undifferentiated CKD; however, its association with glomerular disease is less well understood. We sought to investigate the relationship between socioeconomic position and the population-level incidence of biopsy-proven glomerular diseases. Design, setting, participants, & measurements In this retrospective cohort study, a provincial kidney pathology database (2000–2012) was used to capture all incident cases of membranous nephropathy ( n =392), IgA nephropathy ( n =818), FSGS ( n =375), ANCA-related GN (ANCA-GN, n =387), and lupus nephritis ( n =389) in British Columbia, Canada. Quintiles of area-level household income were used as a proxy for socioeconomic position, accounting for regional differences in living costs. Incidence rates were direct standardized to the provincial population using census data for age and sex and were used to generate standardized rate ratios. For lupus nephritis, age standardization was performed separately in men and women. Results A graded increase in standardized incidence with lower income was observed for lupus nephritis ( P <0.001 for trend in both sexes) and ANCA-GN ( P =0.04 for trend). For example, compared with the highest quintile, the lowest income quintile had a standardized rate ratio of 1.7 (95% confidence interval, 1.19 to 2.42) in women with lupus nephritis and a standardized rate ratio of 1.5 (95% confidence interval, 1.09 to 2.06) in ANCA-GN. The association between income and FSGS was less consistent, in that only the lowest income quintile was associated with a higher incidence of disease (standardized rate ratio, 1.55; 95% confidence interval, 1.13 to 2.13). No significant associations were demonstrated for IgA nephropathy or membranous nephropathy. Conclusions Using population-level data and a centralized pathology database, we observed an inverse association between socioeconomic position and the standardized incidence of lupus nephritis and ANCA-GN.
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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 | 0.000 | 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 | 0.001 | 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".