Overweight and obesity code (E66) trends and predictors in Canada: Cross-sectional analysis of Discharge Abstract Data (DAD), 2018–2022
Bibliographic record
Abstract
INTRODUCTION: Since the adoption of billing codes for obesity, few studies have examined their use in administrative healthcare data. Of those that have, analyses have been limited to examinations of coding validity and trends among persons diagnosed with obesity (ICD-10, E66 code). This study aimed to explore the prevalence and predictors in E66 use across Canada two years prior to, and after the onset of Covid-19. METHODS: This secondary analysis used the 2018-2022 Discharge Abstract Dataset of the Canadian Institute for Health Information. The sample consists of 166,335 individuals 20 to 64 years old across all provinces/territories, excluding Québec. Prevalence of E66 was assessed for each province, and multivariable logistic regression analysis was used to estimate the odds of E66 coding. RESULTS: Regional variations were present in E66 use, with Manitoba having the highest prevalence of coding. Of those with a E66 code, 98.7 % were within the obesity BMI category. In general, females of higher age, with one or more comorbidities, and shorter length of stay had higher odds of receiving the E66 code. Odds of E66 coding were also lower in females after the onset of Covid-19, whereas in males, only those with shorter length of hospital stay had consistently higher odds of diagnosis. CONCLUSION: This study offers new insight into E66 use across Canada, and points to the need for consistent acquisition of weight and height information, and the use of E66 coding within existing electronic medical records systems to inform inter-provincial care gaps for obesity-related care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".