Using Administrative Health Data to Define a Cohort of Youth Affected by Chronic Health Conditions: Preparing for Cross-Sectoral Data Linkage
Bibliographic record
Abstract
IntroductionIn Alberta, 2,400 youth with chronic needs transition to adulthood every year, and many are not prepared for this change. Transferring youth from pediatric to adult-oriented care is poorly managed. To improve this process, we need to know how youth patients use health services during this period. Objectives and ApproachWe used the Alberta Health Services Corporate Data Repository (CDR-9), which collects records of ambulatory visits, to define a cohort of patients with chronic disease using pediatric tertiary care; data is available from 2008 to 2016. Personal health numbers allowed for deterministic data linkage to CDR-9, registry data (e.g., death dates, moves out of province), and area deprivation indices. Eligible patients were: (a) between ages 12-15 years in 2008 (for ≥2 years observation in adulthood, after age 18), (b) involved with a Chronic Care Clinic (CCC) at Alberta Children’s Hospital, and (c) had repeated CCC visits with ≥3 months between visits. ResultsWe identified 26 Chronic Care Clinics (CCC) at Alberta Children’s Hospital (Calgary, Alberta), with stakeholder input. Using CDR-9, a total of 10,111 patients at the hospital were identified who were 12 to 15 years old at the start of the study window (in 2008), and who visited a CCC before age 18. Less than 1% (n=418) were excluded due to moving out of province or having an invalid personal heath number. Final sample sizes were captured across 3 algorithms (A1, A2, A3), based on frequency of CCC visits within a 2-year period: (i) A1: ≥2 CCC visits (N=4123); (ii) A2: ≥3 CCC visits (N=2242); (iii) A3: ≥4 CCC visits (N=1344). Conclusion/ImplicationsOur identified cohort of youth affected by chronic conditions is the first of its kind in Alberta, and can answer important questions about patterns of service utilization in other sectors of care. Our next step is to link the cohort to population-level datasets (e.g., physician claims, NACRS, CIHI-DAD).
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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 | 0.077 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".