Assessing the validity of health administrative data compared to population health survey data for the measurement of low back pain
Bibliographic record
Abstract
Low back pain (LBP) is a high-burden condition that lacks routine surveillance data. Health administrative data may be used for surveillance, but their validity for measuring LBP in the general population has not been established. We aimed to (1) determine the validity of health administrative data to measure LBP compared to self-reported LBP in a population-based sample of Ontario adults; and (2) describe the differences in characteristics of LBP cases based on data sources. Adult respondents (≥18 years) of the Canadian Community Health Survey (CCHS) from 2003 to 2012 were included (N = 150,695). Canadian Community Health Survey data were individually linked to health administrative data, including Ontario Health Insurance Plan and hospitalization data. The reference standard was defined as self-reported back problem diagnosed by a health professional in the CCHS. Measurement of LBP from billing records was defined as ≥1 physician billing or procedural code for LBP during the year preceding CCHS interview date. We measured concurrent validity by comparing prevalence, agreement (kappa), and accuracy (sensitivity, specificity, and positive and negative predictive values [PV]) of administrative data to measure LBP. Prevalence of LBP was higher using self-reported (21.2%) than administrative data (10.2%), and agreement was low (kappa = 0.21). Administrative data had sensitivity 23.9% (95% CI 23.1-24.6), specificity 93.4% (95% CI 93.2-93.7), positive PV 50.4% (95% CI 49.1-51.7), and negative PV 82.0% (95% CI 81.7-82.3). Characteristics of LBP cases based on data sources differed in sex, health/behaviour characteristics, and allied health care utilization. Using health administrative data significantly underestimates the prevalence of LBP. This can lead to misclassification bias that is likely nondifferential in epidemiological studies.
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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 | 0.122 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".