The Impact of the Affordable Care Act on Trauma Outcomes in At-Risk Groups
Bibliographic record
Abstract
OBJECTIVE: Analyze the impact of the Affordable Care Act (ACA) on trauma outcomes at a population level and within at-risk subgroups. BACKGROUND: Trauma disproportionately affects the uninsured. Compared to the insured, uninsured patients have worse functional outcomes and increased mortality. The goal of the ACA was to increase access to insurance. METHODS: An interrupted time series was conducted using data from the National Inpatient Sample database between 2011 and 2016. Data from Alberta, Canada was used as a control group. Mortality, length of stay, and probability of discharge home with or without home health care was examined using monthly time intervals, with January 2014 as the intervention time. Single and multiple group interrupted time series were conducted. Subgroup analyses were conducted using income quartiles and race. RESULTS: After the intervention, there was a monthly reduction in mortality of 0.0148% ( P < 0.01) in the American cohort: there was no change in the Canadian cohort. The White subgroup experienced a mortality reduction: the non-White subgroup did not. There was no significant change in length of stay or discharge home rate at a population level. There was a monthly increase in the probability of discharge with home health (0.0247%: P < 0.01); this was present in the lower-income quartiles and both race groups. The White subgroup had a higher rate of utilization of home health pre-ACA, and this discrepancy persisted post-ACA. CONCLUSIONS: The ACA is associated with improved mortality and increased use of home health services. Discrepancies amongst racial groups and income quartiles are present.
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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 | 0.005 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".