Utility of Birth Certificate Data for Evaluating Hospital Variation in Admissions to NICUs
Bibliographic record
Abstract
OBJECTIVES: Efforts to study potential overuse of NICU admissions and hospital variation in practice are often hindered by a lack of an appropriate data source. We examined the concordance of hospital-level NICU admission rates between birth certificate data and California Children’s Services (CCS) data to inform the utility of birth certificate data in studying hospital variation in NICU admissions. METHODS: We analyzed birth certificate data from California in 2012 and hospital-specific summary data from CCS regarding NICU admissions. NICU admission rates were calculated for both data sets while using CCS data as the gold standard. The difference between birth certificate–based and CCS-based NICU admission rates was assessed by using the Wilcoxon signed rank test, and concordance between the 2 rates was evaluated by using Lin’s concordance correlation coefficient and Kendall’s W concordance coefficient. RESULTS: Among a total of 103 hospitals that were linked between the 2 data sets, birth certificate data generally underreported NICU admission rates compared with CCS data (median = 7.72% vs 11.51%; P < .001). However, in a subset of 35 hospitals where the difference in NICU admission rates between the 2 data sets was small, the birth certificate–based NICU admission rate showed good concordance with the rate from CCS data (Lin’s concordance correlation coefficient = 0.91; 95% confidence interval: 0.84–0.95; Kendall’s W concordance coefficient = 0.99; P < .001). Hospitals with good-concordance data did not differ from other hospitals in the institutional characteristics assessed. CONCLUSIONS: For a selected subset of hospitals, birth certificate data may offer a reasonable means to investigate hospital variation in NICU admissions.
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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 | 0.045 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".