Association of Medicaid Expansion With Neuraxial Labor Analgesia Use in the United States: A Retrospective Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: The Affordable Care Act has been associated with increased Medicaid coverage for childbirth among low-income US women. We hypothesized that Medicaid expansion was associated with increased use of labor neuraxial analgesia. METHODS: We performed a cross-sectional analysis of US women with singleton live births who underwent vaginal delivery or intrapartum cesarean delivery between 2009 and 2017. Data were sourced from births in 26 US states that used the 2003 Revised US Birth Certificate. Difference-in-difference linear probability models were used to compare changes in the prevalence of neuraxial labor analgesia in 15 expansion and 11 nonexpansion states before and after Medicaid expansion. Models were adjusted for potential maternal and obstetric confounders with standard errors clustered at the state level. RESULTS: The study sample included 5,703,371 births from 15 expansion states and 5,582,689 births from 11 nonexpansion states. In the preexpansion period, the overall rate of neuraxial analgesia in expansion and nonexpansion states was 73.2% vs 76.3%. Compared with the preexpansion period, the rate of neuraxial analgesia increased in the postexpansion period by 1.7% in expansion states (95% CI, 1.6–1.8) and 0.9% (95% CI, 0.9–1.0) in nonexpansion states. The adjusted difference-in-difference estimate comparing expansion and nonexpansion states was 0.47% points (95% CI, −0.63 to 1.57; P = .39). CONCLUSIONS: Medicaid expansion was not associated with an increase in the rate of neuraxial labor analgesia in expansion states compared to the change in nonexpansion states over the same time period. Increasing Medicaid eligibility alone may be insufficient to increase the rate of neuraxial labor analgesia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".