Abstract P71: The Accuracy of Coded Obesity in an Administrative Database
Bibliographic record
Abstract
Objective: Obesity is becoming an increasingly prevalent problem. Gathering information on the adiposity of a population is difficult, so being able to take advantage of existing data, such as that in administrative databases, is appealing. The objective of our study was to assess the validity of obesity coding in administrative databases. Methods: This study was conducted using the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) database and the Discharge Abstract Database (DAD) for Calgary. BMI was calculated within APPROACH; BMI ≥30kg/m2 defined obesity. In the DAD obesity was defined by diagnosis codes 278 (ICD-9-CM) and E65-E68 (ICD-10). Databases were linked using provincial health numbers. The sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of a diagnosis of obesity in the DAD was determined using the obesity diagnosis in APPROACH as the referent. The accuracy of coding obesity was compared across demographic categories and diagnoses. Results: A total of 17,380 subjects included in the analysis. The study population was largely male (68.8%) and had a mean BMI of 26.96 kg/m2. The overall sensitivity of a diagnosis of obesity in the administrative data was 7.75%. However, it was highly specific at 98.98%, with a NPV of 80.84% and a PPV of 65.94%. When considered by year, there were minor variations in the sensitivity of obesity coding in the administrative data, but it remained poor at under 10% throughout. The prevalence of obesity and the PPV was higher amongst those subjects with conditions associated with obesity, including diabetes and hypertension. Of those coded obese in DAD, the majority (72.89%) were Class I obese; of those not coded obese, 84.31% were Class I obese. Conclusions: Obesity coding in the DAD is poor, as reflected in the low sensitivity of the diagnostic code. However, once obesity is coded in this database, it is coded highly accurately. At present, using administrative databases to define cohorts of obese subjects for surveillance is not a viable option.
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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 | 0.046 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".