Validation of congenital anomaly coding in Canada's administrative databases compared with a congenital anomaly registry
Bibliographic record
Abstract
BACKGROUND: Congenital anomaly (CA) surveillance provides epidemiologic data that are necessary for health planning. Approaches to CA surveillance vary; however, an increasing number of jurisdictions rely on administrative health databases for case ascertainment. This study aimed to assess the validity of CA coding in three administrative databases compared with a CA registry. METHODS: A cohort of 5862 live and stillborn infants from Calgary Alberta Canada was created through linking 12 clinical and administrative databases. Diagnostic codes for all health care contacts (hospitalizations, emergency room visits, out-patient physician visits) in the first 3 months of life were examined for relevant International Classification of Disease codes. Sensitivity, positive predictive values, and kappa coefficients were calculated, and data from the Alberta Congenital Anomalies Surveillance System was used as the reference standard. RESULTS: The ability of administrative data to accurately ascertain CAs varied by data source and the specificity of the diagnosis. Consistently, hospitalization data out-performed other administrative data sources in terms of sensitivity, positive predictive values, and kappa. Kappa scores for CAs easily visible at birth ranged from moderate (0.62 for emergency room visits and 0.65 for out-patient physician claims) to good (0.83 for hospitalization data) depending on the data source. CONCLUSION: The validity of CA coding in administrative databases compared with a CA registry varies by database used and by CA studied. This has important implications for national surveillance efforts. Condition-specific validity should be assessed locally before use of these data sources for research or planning purposes.
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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 | 0.037 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".