Linking Hospital and Tax data to support research on the economic impacts of hospitalization
Bibliographic record
Abstract
ABSTRACT ObjectivesThis project links data on acute inpatient hospitalizations from the Canadian Discharge Abstract Database (DAD) with data on income and employment from various taxation- and employment-based administrative files. The goal was to create a linked database that will support research on the labour market and financial outcomes experienced by individuals and families following acute illness requiring hospitalization. ApproachData from the 1999/00 to 2014/15 Discharge Abstract Database (DAD) were linked to the 1981-2013/14 T1 Tax filer data and the Canadian Child Tax Benefit data. We sought to create a unique association between Health Insurance Numbers (HIN) available in the DAD and Social Insurance Numbers (SIN) available in the tax data by using variables common to both data sets – date of birth, postal code and sex. Both transactional data sets were “individualized” such that unique combinations of the linkage variables were identified and eligible for linkage. The linkage was conducted using deterministic methods. ResultsApproximately 97% of combinations involving date of birth, postal code and sex in the hospitalization data were uniquely related to a single valid HIN (n=18.8 million). Similarly, approximately 96% of the keys on the Tax data file were associated with a unique person. Approximately 86% of HINs were associated with a unique identifier in the tax file and these HINs account for approximately 83% of the hospital records. The linkage was consistent over time, with linkage rates between 85% and 88% of HINs for all years. Some variation in linkage rates were observed by jurisdiction and by age. (Error estimates to be reported) ConclusionThis project has created a unique linked database that will support research on the economic consequences of ‘health shocks’ for individuals and their families, and the implications for income, labour and health policies. This database represents a new and unique resource that will fill an important national data gap, and enable a wide range of relevant research.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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".