A99 HIGHER HOSPITAL INPATIENT RACIAL DIVERSITY IS ASSOCIATED WITH BETTER OUTCOMES AMONG HISPANIC AND INDIGENOUS AMERICAN PATIENTS FOR FIVE COMMON GASTROINTESTINAL DIAGNOSES
Bibliographic record
Abstract
Abstract Background There is evidence that gastrointestinal disease (GI) outcomes are poorer among patients from underrepresented backgrounds. However, the impact of hospital patient racial diversity on GI outcomes is understudied. Aims We aimed to investigate the impact of hospital patient racial diversity on GI outcomes Methods Using the 2019 National Inpatient Sample (NIS), racial diversity was defined by the percentage of Hispanic or Indigenous American patients discharged from each hospital. GI discharge diagnoses were defined by diagnostic related group. We included GI bleeding, inflammatory bowel diseases, GI obstruction, cirrhosis, and alcoholic hepatitis. Logistic regression was used to predict major complication rates or comorbidity (MCC), long length of stay (LOS), and high total charges. Control variables included age, gender, payer type, patient location, area-associated income quartile, hospital characteristics including size, urban vs. rural, teaching vs. nonteaching, region, and the interaction of the percentage of Hispanic and Indigenous Americans with patient race. Results were validated with the 2018 NIS dataset. Results Our analysis cohort of 537,830 hospitalizations included 252,225 GI bleeding discharges, 59,310 gastrointestinal obstruction discharges, 106,820 cirrhosis and alcoholic hepatitis discharges, and 119,475 inflammatory bowel diseases discharges. In the unadjusted analyses, MCC rates were higher among Hispanic (24.8%) and Indigenous American patients (30.4%), compared to Whites (18.3%). In adjusted analyses, compared to white patients, Hispanic patients had higher MCC rates [adjusted odds ratio (OR) 1.21, 95% Confidence Interval (CI) 1.15-1.28]. The same trend was seen among Indigenous American patients [OR 1.25, (95% CI) 1.09-1.43]. However, as hospital Hispanic diversity increased, inpatient MCC outcomes for Hispanics improved [OR 0.93, (95% CI) 0.87-1.14], and were even better among Indigenous American patients as hospital inpatient Indigenous American diversity increased [OR 0.83, (95% CI) 0.73-0.94] (Table 1). A similar improvement in MCC with increasing Hispanic and Indigenous American diversity was observed in the 2018 validation cohort. Table 1 highlights impact of increasing diversity on LOS and total charges. Conclusions Increasing hospital inpatient Hispanic and Indigenous American diversity is associated with better outcomes for these underrepresented minority groups. More research is needed on the impact of cultural competence and linguistic concordance on patient outcomes. Funding Agencies None
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".