Racial Disparities in C. difficile Infection
Bibliographic record
Abstract
Introduction:C. difficile infection (CDI) is the leading health care-associated infection in the United States and a major public health threat. Racial inequities exist in the United States through many pathways ranging from true biologic differences to barriers to accessing health care. There is a paucity of data exploring the link between race and CDI outcomes. Methods: We obtained data from the 2011 nationwide inpatient sample (NIS) database, the largest source of all-payer hospital discharge information in the United States, including data from 1,049 hospitals within 46 states. Standard international classification of disease, 9th edition (ICD-9) was used to identify all patients with CDI (008.45) and other salient factors. Controlling for relevant confounders including socioeconomic variables and insurance, multivariate logistic regression was utilized to assess if race was an independent predictor of mortality, intensive care unit (ICU) admission, length of stay >14 days (LOS), and colectomy among patients with CDI. Results: There were 336,943 hospitalizations identified with an associated CDI diagnosis among Whites (74.5%), Blacks (12.8%), Hispanics (7.9%), Asian/Pacific Islanders (1.8%), and Others (2.9%). On adjusted analysis, all non-white racial groups had more severe CDI-associated outcomes compared to Whites. Asian/Pacific Islanders have the most severe CDI-associated outcomes, with a significant increase in the odds of death (odds ratio (OR) 1.28; p=0.039), ICU admission (OR 1.85; p<0.001), and LOS (OR 1.32; p<0.001) compared to Whites. Hispanics also had a significant increase in mortality (OR 1.22, p<0.001), ICU admission (OR 1.34; p<0.001), and LOS (OR 1.17; p=0.035) compared to Whites. Similarly, Blacks had a significant increase in the odds of death (OR 1.19, p<0.001), ICU admission (OR 1.36; p<0.001), and LOS (OR 1.27, p<0.001) compared to Whites. Interestingly, all non-white groups had lower odds of colectomy (Asian/Pacific OR 0.57; p=0.055, Hispanics OR 0.78; p=0.030, Blacks OR 0.8; p=0.022) when compared to Whites though the difference in mortality persisted after adjusting for surgery in a multivariate model. Conclusion: We identified racial disparities among CDI-associated outcomes in the United States with non-white racial groups having high rates of severe CDI outcomes. Further studies examining the mechanisms for such an association are warranted. Disclosure - Dr. Ananthakrishnan - Scientific advisory board of Cubist Pharmaceuticals.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".