Child and youth chronic physical health conditions: a comparison of survey data and linked administrative health data in Ontario
Bibliographic record
Abstract
BACKGROUND: Population-based studies in Canada and the United States estimate chronic physical health conditions affect between 20 to 30% of children aged 0 to 17. Challenges in measuring chronic conditions include the use of inconsistent definitions and algorithms that capture a limited number of conditions. Thus, we developed a chronic health condition (CHC) algorithm using administrative data to determine whether a child has a CHC based on (1) the diagnosis recorded for the visit, (2) the number of visits, and (3) within a specific reference period. METHODS: Data were from the cross-sectional 2014 Ontario Child Health Study, linked with Ontario Health Insurance Plan (OHIP) administrative health data. Unweighted prevalence estimates and agreement analyses (Cohen's Kappa, sensitivity, specificity) were used to compare the survey parent-reported and algorithm-based presence of a CHC. RESULTS: 31.8% and 27.1% of children and youth had a CHC based on administrative and survey data, respectively. Agreement between administrative and survey data was poor (k = 0.17). Among a few specific conditions, agreement varied depending on the type of condition (e.g., diabetes k = 0.77 vs health conditions k = 0.21). CONCLUSION: We found considerable discrepancies between administrative and survey-reported data. The results highlight the importance of using algorithms developed from multiple datasets to examine complex research questions, such as the measurement of chronicity.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".