Emergency Department Utilization in the Texas Medicaid Emergency Waiver
Bibliographic record
Abstract
OBJECTIVE: To estimate the enrollment and emergency department (ED) utilization in TexKat, the Texas Medicaid emergency waiver implemented following Hurricane Katrina. DATA SOURCES: Individual-level enrollment and utilization data from the 2005 Medicaid Analytic Extract. STUDY DESIGN: Descriptive analysis is performed on variables that describe enrollment levels, the demographic characteristics of enrollees, and the most common diagnoses in ED visits. A Poisson regression model is also employed to quantify the factors related to an enrollee's probability of having an ED visit and the average number of ED visits. PRINCIPAL FINDINGS: There were 44,246 individuals enrolled in TexKat in 2005. Roughly 13% of these enrollees had at least one ED visit during the sample period, with one quarter of these individuals having more than one visit. Across all enrollees the most common diagnosis was "other upper respiratory infection," but there were significant differences in diagnosis patterns across racial/ethnic groups. The regression analysis suggests little difference in ED utilization across genders, but significant contrasts across racial/ethnic and age groups. CONCLUSIONS: As very little is known about Medicaid emergency waivers, our analysis may provide important information to policymakers who have to react quickly following a disaster. Our findings may help providers estimate potential increases in ED utilization and prepare for relatively common diagnoses. Furthermore, the analysis across racial/ethnic groups may help government officials identify important areas for outreach among vulnerable populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".