Defining maternal obesity in studies of birth outcomes: Comparing ICD‐9 codes at delivery and measures on the birth certificate
Bibliographic record
Abstract
BACKGROUND: Using ICD-9 codes underestimates the prevalence of obesity in adults; however, the validity of these codes in studies of pregnancy-related outcomes is not known. OBJECTIVES: To compare classification of maternal obesity based on ICD-9 codes in hospital discharge records versus data from birth certificates in the same women, examine predictors of agreement, and assess how associations between obesity and two birth outcomes differ by source of weight data. METHODS: This population-based study included 2 329 145 California births between 2007 and 2012. We compared data on obesity from childbirth hospital discharge records (ICD-9 codes for obesity) and birth certificates (pre-pregnancy body mass index (BMI) calculated from weight and height) and identified predictors of agreement between the two sources. Logistic regression models assessed whether the two definitions of obesity resulted in different estimates of the associations of obesity with caesarean birth and large-for-gestational age. RESULTS: Overall, 464 754 women (20.0%) had obesity based on their pre-pregnancy BMI while only 100 002 (4.3%) had an obesity-related ICD-9 code. The sensitivity of ICD-9-based obesity was low at 16.2%; however, obesity codes were highly specific at 98.7%, with a negative predictive value of 82.5% and a positive predictive value of 75.2%. Among women with obesity identified by the birth certificate, those with pre-pregnancy and pregnancy-related complications (eg diabetes and hypertension) were more likely to have an obesity-related diagnosis in their delivery hospital discharge record. Using ICD-9 codes overestimated the association of obesity with caesarean birth and newborn large-for-gestational age. CONCLUSIONS: ICD-9 codes in childbirth discharge records captured only one in five women with pre-pregnancy obesity. Sensitivity varied by maternal characteristics and conditions. This misclassification resulted in bias when examining the association of obesity and pregnancy-related outcomes.
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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 | 0.054 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".