Medicaid Pregnancy Termination Funding and Racial Disparities in Congenital Anomaly–Related Infant Deaths
Bibliographic record
Abstract
OBJECTIVE: To explore whether state restrictions on Medicaid funding for pregnancy termination of anomalous fetuses could be contributing to the black-white disparity in infant death resulting from congenital anomalies. METHODS: Data on deaths resulting from anomalies were obtained from U.S. vital statistics records (1983-2004) and the Nationwide Inpatient Sample (2003-2007). We conducted an ecological study using Poisson and logistic regression to explore the association between state Medicaid funding for pregnancy terminations of anomalous fetuses and infant death resulting from anomalies by calendar time, race, and individual Medicaid status. RESULTS: Since 1983, a gap in anomaly-related infant death has developed between states without compared with those with Medicaid funding for pregnancy termination (rate ratio in 2004 1.21, 95% confidence interval [CI] 1.18-1.24; crude risks: 146.8 compared with 121.7/100,000). Blacks were significantly more likely than whites to be on Medicaid (60.2% compared with 29.2%) and to live in a state without Medicaid funding for pregnancy termination (65.8% compared with 59.6%). The increased risk of anomaly-related death associated with lack of state Medicaid funding for pregnancy termination was most pronounced among black women on Medicaid (relative risk 1.94, 95% CI 1.52-2.36; crude risks: 245.5 compared with 129.3/100,000). CONCLUSION: States without Medicaid funding for pregnancy termination of anomalous fetuses have higher rates of infant death resulting from anomalies than those with funding, and this difference is most pronounced among black women on Medicaid. Restrictions on Medicaid funding for termination of anomalous fetuses potentially could be contributing to the black-white disparity in anomaly-related infant death. LEVEL OF EVIDENCE: II.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".