Estimating health-selective migration in patients with systemic lupus erythematosus or Sjogren's from administrative data
Bibliographic record
Abstract
Canadian public health agencies have a mandate to monitor the prevalence, incidence and patterns of chronic disease. These agencies are increasingly using administrative health data for these purposes. However, valid use of administrative data for chronic disease surveillance requires an understanding of some inherent limitations. Health-selective migration, which occurs when people migrate differentially by health status, is a limitation that has not been estimated in administrative data sources. To investigate this issue, we estimated health-selective migration in a cohort of systemic lupus erythematosus (SLE) and Sjogren's patients, identified from physician and hospital claims databases in Quebec and compared them to rates in an age and sex frequency-matched sample from Montreal, Quebec using hierarchical logistic regression. The association between disease and migration was modified by both age and disease duration. Both SLE and Sjogren's patients migrated less than controls when young. For example, 30-year-old SLE (OR 0.54, 95% CrI 0.45-0.64) and Sjogren's (OR 0.41, 95% CrI 0.28-0.56) patients with two years of disease duration had lower odds of moving than frequency-matched controls. Above age 50, the odds of migration in SLE and Sjogren's patients was comparable or slightly higher than in controls. Patients at age 70 with two years of disease duration had an OR of moving of 1.29 (95% CrI 1.04-1.58) in SLE and 1.09 (95% CrI 0.81-1.42) in Sjogren's. The associations between migration and disease duration in SLE and Sjogren's were qualitatively different. One year of SLE duration was associated with an OR of 0.96 (95% CrI 0.93-0.98) and one year of Sjogren's duration was associated with an OR of 1.05 (95% CrI 1.00-1.10). Results were similar when using SLE and Sjogren's patients pre-diagnosis as the control and when looking at migration on a regional scale. Overall, SLE and Sjogren's have an impact on migration rates which varies by age, disease and disease duration.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".