Inpatient Trauma Mortality after Implementation of the Affordable Care Act in Illinois
Bibliographic record
Abstract
INTRODUCTION: Illinois hospitals have experienced a marked decrease in the number of uninsured patients after implementation of the Affordable Care Act (ACA). However, the full impact of health insurance expansion on trauma mortality is still unknown. The objective of this study was to determine the impact of ACA insurance expansion on trauma patients hospitalized in Illinois. METHODS: We performed a retrospective cohort study of 87,001 trauma inpatients from third quarter 2010 through second quarter 2015, which spans the implementation of the ACA in Illinois. We examined the effects of insurance expansion on trauma mortality using multivariable Poisson regression. RESULTS: There was no significant difference in mortality comparing the post-ACA period to the pre-ACA period incident rate ratio (IRR)=1.05 (95% confidence interval [CI] [0.93-1.17]). However, mortality was significantly higher among the uninsured in the post-ACA period when compared with the pre-ACA uninsured population IRR=1.46 (95% CI [1.14-1.88]). CONCLUSION: While the ACA has reduced the number of uninsured trauma patients in Illinois, we found no significant decrease in inpatient trauma mortality. However, the group that remains uninsured after ACA implementation appears to be particularly vulnerable. This group should be studied in order to reduce disparate outcomes after trauma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".