The Drivers of Acute and Long-term Care Clostridium difficile Infection Rates: A Retrospective Multilevel Cohort Study of 251 Facilities
Bibliographic record
Abstract
Background: Drivers of differences in Clostridium difficile incidence across acute and long-term care facilities are poorly understood. We sought to obtain a comprehensive picture of C. difficile incidence and risk factors in acute and long-term care. Methods: We conducted a case-cohort study of persons spending at least 3 days in one of 131 acute care or 120 long-term care facilities managed by the United States Veterans Health Administration between 2006 and 2012. Patient (n = 8) and facility factors (n = 5) were included in analyses. The outcome was the incidence of facility-onset laboratory-identified C. difficile infection (CDI), defined as a person with a positive C. difficile test without a positive test in the prior 8 weeks. Results: CDI incidence in acute care was 5 times that observed in long-term care (median, 15.6 vs 3.2 per 10000 person-days). History of antibiotic use was greater in acute care compared to long-term care (median, 739 vs 513 per 1000 person-days) and explained 72% of the variation in C. difficile rates. Importation of C. difficile cases (acute care: patients with recent long-term care attributable infection; long-term care: residents with recent acute care attributable infection) was 3 times higher in long-term care as compared to acute care (median, 52.3 vs 16.2 per 10000 person-days). Conclusions: Facility-level antibiotic use was the main factor driving differences in CDI incidence between acute and long-term care. Importation of acute care C. difficile cases was a greater concern for long-term care as compared to importation of long-term care cases for acute care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".