Comparison of ICD code-based diagnosis of obesity with measured obesity in children and the implications for health care cost estimates
Bibliographic record
Abstract
BACKGROUND: Administrative health databases are a valuable research tool to assess health care utilization at the population level. However, their use in obesity research limited due to the lack of data on body weight. A potential workaround is to use the ICD code of obesity to identify obese individuals. The objective of the current study was to investigate the sensitivity and specificity of an ICD code-based diagnosis of obesity from administrative health data relative to the gold standard measured BMI. METHODS: Linkage of a population-based survey with anthropometric measures in elementary school children in 2003 with longitudinal administrative health data (physician visits and hospital discharges 1992-2006) from the Canadian province of Nova Scotia. Measured obesity was defined based on the CDC cut-offs applied to the measured BMI. An ICD code-based diagnosis obesity was defined as one or more ICD-9 (278) or ICD-10 code (E66-E68) of obesity from a physician visit or a hospital stay. Sensitivity and specificity were calculated and health care cost estimates based on measured obesity and ICD-based obesity were compared. RESULTS: The sensitivity of an ICD code-based obesity diagnosis was 7.4% using ICD codes between 2002 and 2004. Those correctly identified had a higher BMI and had higher health care utilization and costs. CONCLUSIONS: An ICD diagnosis of obesity in Canadian administrative health data grossly underestimates the true prevalence of childhood obesity and overestimates the health care cost differential between obese and non-obese children.
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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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".