Rural-Dwelling Patients With Rheumatoid Arthritis and Risk of Myocardial Infarction Hospitalization: An Observational Study Using the National Inpatient Sample
Bibliographic record
Abstract
Objective To assess whether there is a rural-urban and income-related disparity in the incidence and outcomes of hospitalization for myocardial infarction (MI) in patients with rheumatoid arthritis (RA). Methods We used the 2016-2019 US National Inpatient Sample (NIS) data and selected all patients with RA. Rural vs urban residential status was identified using NIS classifications. We categorized median household income based on patient ZIP code by quartile. We assessed the multivariable-adjusted odds ratios (aORs) to examine the association of rural residence with MI hospitalization. Results The crude rate of MI hospitalization per 100,000 area-specific hospitalizations in people with RA was significantly higher for rural vs urban residents, 2441 vs 1878 (P< 0.001). In multivariable-adjusted models, compared to urban residents, rural-residing residents with RA were almost twice as likely to be hospitalized with MI (aOR 1.70;P< 0.001). Rural residence was not significantly associated with higher hospital charges or MI hospitalization costs (P> 0.05). Compared to the lowest quartile, the 2 highest income quartiles were significantly associated with lower odds of MI hospitalization in patients with RA; aORs were 0.87 (P< 0.001) and 0.92 (P= 0.01). Female sex, African American race, elective admission, Medicare payer, government hospital ownership, rural hospital location, and small hospital bed size were significantly associated with lower odds of MI hospitalization. Conclusion Our study findings of rural-urban and socioeconomic status disparities for MI hospitalizations in patients with RA provide policymakers with data and information for action. Policy decisions based on these data can potentially reduce these disparities and improve outcomes for rural residents.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".