Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database
Bibliographic record
Abstract
BACKGROUND: Administrative data are commonly used to study clinical outcomes in renal disease. Race is an important determinant of renal health delivery and outcomes in Canada but is not validated in most administrative data, and the correlation with census-based definitions of race is unknown. OBJECTIVES: Validation of self-reported race (SRR) in a Canadian provincial renal administrative database (Patient Records and Outcome Management Information System [PROMIS]) and comparison with the Canadian census categories of race. DESIGN: Prospective patient survey study to validate SRR in PROMIS. SETTING: British Columbia, Canada. PATIENTS: Adult patients registered in PROMIS. MEASUREMENTS: Survey SRR was used as gold standard to validate SRR in PROMIS. Self-reported race in PROMIS was compared with census race categories. METHODS: This is a cross-sectional telephone survey of a random sample of all adults in PROMIS conducted between February 2016 and November 2016. Responders selected a race category from PROMIS and from the Canadian census. Sensitivity (Sn) and specificity (Sp) were calculated with 95% confidence intervals (CIs). RESULTS: A total of 21 039 patients met inclusion criteria, 1677 were selected for the survey and 637 participated (38% response rate). There were no differences between the PROMIS, sampled, and responder populations. PROMIS SRR had an accuracy of 95.3% (95% CI: 94.2%-97.0%) when validated against the survey SRR with Sn and Sp ≥90% in all race groups except in Aboriginals (Sn 87.5%). The positive and negative predictive values were ≥95%, except in very low and high-prevalence groups, respectively. The Canadian census had an accuracy of 95.7% (95% CI: 94.4%-97.6%) when validated against PROMIS SRR with Sn and Sp ≥90%. The results did not differ in subgroups based on age, sex, birth outside Canada, or renal group (glomerulonephritis, chronic kidney disease, hemodialysis, peritoneal dialysis, transplant recipients, or live donors). LIMITATIONS: Analysis of minority groups and lower prevalence groups is limited by sample size. Results may not be generalizable to other administrative databases. CONCLUSIONS: We have shown high accuracy of PROMIS SRR that validates its use in the secondary analysis of administrative data for research. There is high correlation between PROMIS and census race categories which allows linkage with other data sources that use census-based definitions of race.
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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 | 0.022 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".