The hidden complexity of measuring number of chronic conditions using administrative and self-report data: A short report
Bibliographic record
Abstract
Objective: To examine agreement between administrative and self-reported data on the number of and constituent chronic conditions (CCs) used to measure multimorbidity. Study Design and Setting: Cross-sectional self-reported survey data from four Canadian Community Health Survey waves were linked to administrative data for residents of Ontario, Canada. Agreement for each of 12 CCs was assessed using kappa ( κ) statistics. For the overall number of CCs, perfect agreement was defined as agreement on both the number and constituent CCs. Jackknife methods were used to assess the impact of individual CCs on perfect agreement. Results: The level of chance-adjusted agreement between self-report and administrative data for individual CCs varied widely, from κ = 5.5% (inflammatory bowel disease) to κ = 77.5% (diabetes), and there was no clear pattern on whether using administrative data or self-reported data led to higher prevalence estimates. Only 26.9% of participants had perfect agreement on the number and constituent CCs; 10.6% agreed on the number but not constituent CCs. The impact of each CC on perfect agreement depended on both the level of agreement and the prevalence of the individual CC. Conclusion: Our results show that measuring agreement on multimorbidity is more complex than for individual CCs and that even small levels of individual condition disagreement can have a large impact on the agreement on the number of CCs.
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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 | 0.068 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".