Development of a Pregnancy Cohort in Commercial Insurance Claims Data: Evaluation of Deliveries Identified From Inpatient Versus Outpatient Claims
Bibliographic record
Abstract
PURPOSE: Studies using insurance claims data to identify pregnancies are rarely able to directly assess the validity of the pregnancy/delivery. Inpatient versus outpatient delivery claims may provide different levels of evidence, but more stringent requirements could result in exclusion of true pregnancies. We identified delivery codes from the inpatient and outpatient settings and examined possible confirmatory evidence suggesting that a delivery truly occurred. METHODS: Using a US commercial insurance database (2006-2021), we identified potential pregnancies by presence of delivery claims from a provider and/or facility. We classified deliveries as inpatient (claim date during inpatient admission) or outpatient (claim date not during inpatient admission). We identified possible confirmatory evidence for each delivery including: (1) Presence of both provider and facility delivery codes; (2) presence of both diagnosis and procedure delivery codes; (3) labor and delivery revenue codes; (4) gestational age diagnosis codes; (5) pregnancy-related care codes; (6) linkage to an infant claim; and (7) infant insurance enrollment and linkage to a birthing parent. We quantified the proportion of deliveries with confirmatory evidence by delivery setting. Among deliveries with ≥ 1 piece of confirmatory evidence, we compared patient characteristics by apparent delivery setting. RESULTS: Among 4 084 474 delivery episodes, 96.4% were classified as inpatient and 3.6% outpatient. 99.9% of inpatient and 94.0% of outpatient deliveries had ≥ 1 piece of confirmatory evidence. Pregnancy-related care codes were the most common type of confirmatory evidence (99.0% inpatient, 85.7% outpatient). Deliveries classified as inpatient occurred among patients who were older and more clinically complex (i.e., more pregnancy complications, chronic diseases, and prescription medications). CONCLUSIONS: The vast majority of deliveries had confirmatory evidence regardless of apparent setting. Patient characteristics differed by delivery setting. Inclusion of apparent outpatient deliveries may increase the sample size of the study population and improve the generalizability of study results.
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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 | 0.042 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".