Sociodemographic Variables Associated with Self-Reported Emergency Department Utilization: A Cross-Sectional, Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Current evidence has established disparities in ED utilization across various demographic and socioeconomic groups. However, there is a paucity of recent evidence exploring the interaction of multiple sociodemographic variables and their influence on emergency department (ED) utilization. OBJECTIVES: To evaluate the influence and interaction of multiple sociodemographic variables on ED utilization. METHODS: Cross-sectional, population-based logistic regression analyses were conducted using the 2023 National Health Interview Survey (NHIS) data. RESULTS: Adjusted odds of ED utilization were significantly higher among female (OR 1.31, 95% CI 1.21-1.41, p < 0.001), Black (OR 1.16, 95% CI 1.03-1.31, p = 0.014) and American Indian/Alaskan (OR 1.37, 95% CI 1.04-1.81, p = 0.026) individuals. Compared to ages 18-25, individuals aged 50-74 (p < 0.05) had lower odds. Compared to participants >5x poverty threshold, those with income 2-5x poverty threshold, 1-2x poverty threshold, and <1x poverty threshold had 19% (p < 0.001), 40% (p < 0.001) and 52% (p < 0.001) increased odds of ED utilization, respectively. Individuals with Medicaid/public insurance (OR 1.43, 95% CI 1.29-1.59, p < 0.001) and high school education (OR 1.12, 95% CI 1.01-1.23, p = 0.025) had higher odds of ED utilization compared to those with private insurance and college education. Those without access to a usual place of care (OR 0.71, 95% CI 0.60-0.82, p < 0.001) had lower odds. Compared to participants in good health, those in fair (OR 2.03, 95% CI 1.84-2.25, p < 0.001) or poor (OR 4.4, 95% CI 3.77-5.24, p < 0.001) health had higher odds of ED visits. CONCLUSIONS: Sex, age, race, income, insurance coverage, education, self-reported health status and access to care are significant predictors of ED visits.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".