Gender, socioeconomic status and emergency department visits among cancer survivors in the USA: a population-based study
Bibliographic record
Abstract
Aim: To assess patterns of emergency department visits and subsequent hospitalization in relation to gender and socioeconomic status among a cohort of cancer survivors in the USA. Materials & methods: National Health Interview Survey datasets (2011–2017) were reviewed and participants with a history of cancer and complete information about emergency department visits in the past 12 months were included. Multivariable logistic regression analyses were used to assess factors associated with emergency department visits and subsequent hospitalization after the most recent emergency department visit. Results: A total of 22,240 cancer survivors were included in the current analysis; of which 16,133 participants (72.5%) who have not visited an emergency department in the past 12 months and 6107 participants (27.5%) who have visited an emergency department in the past 12 months. Multivariable logistic regression analysis suggested the following factors are associated with emergency department visits; younger age (odds ratio [OR] with increasing age: 0.98; 95% CI: 0.98–0.99), female gender (OR: 1.07; 95% CI: 1.00–1.15), African American race (OR: 1.26; 95% CI: 1.13–1.40), unmarried status (OR for married vs unmarried: 0.79; 95% CI: 0.74–0.84), lower yearly earnings (OR: 1.36; 95% CI: 1.20–1.54), poor health status (OR: 7.02; 95% CI: 6.02–8.18) and incomplete health insurance coverage (OR for complete coverage vs incomplete coverage: 0.66; 95% CI: 0.54–0.80). On the other hand, the following factors were associated with subsequent hospitalization: older age (OR: 1.004; 95% CI: 1.000–1.008), male gender (OR for female vs male: 0.86; 95% CI: 0.78–0.94), unmarried status (OR for married vs unmarried status: 0.80; 95% CI: 0.73–0.88), not working (OR: 1.44; 95% CI: 1.23–1.68), lower yearly earnings (OR: 1.31; 95% CI: 1.07–1.60), poor health status (OR: 8.43; 95% CI: 6.76–10.51) and lack of health insurance coverage (OR for complete coverage vs incomplete coverage: 0.71; 95% CI: 0.55–0.93). Conclusion: Female cancer survivors were more likely to visit the emergency department, whereas they were less likely to be subsequently hospitalized. Cancer survivors with lower socioeconomic status were more likely to visit emergency departments and to be subsequently hospitalized.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".