The Interplay Between Acute and Long-Term Care Clostridium difficile Incidence in the United States Veterans Health Administration: A Retrospective Cohort Study of 169 Facilities
Bibliographic record
Abstract
Background. Although most acute and long-term care facilities in the same region are coupled by patient sharing and many experience high rates of C. difficile infection (CDI), the inter-facility spread of C. difficileis understudied. Our objective was to consider the determinants of CDI incidence across acute care (AC) and long-term care (LTC) facilities, with a specific interest in the role of patient sharing. Methods. We conducted a retrospective cohort study of CDI from 2006-2012 across the Veterans Affairs Healthcare System. Our outcome was defined as a health-care-associated lab-identified C. difficileevent, defined as a positive lab test in a patient having at least 3 days of facility exposure in the prior 8 weeks, and occurring at least 8 weeks from a previous positive test. Individual-level risk variables included (1) age, (2) antibiotic use, and history of (3) acute or (4) long-term care stay in the prior 56 days. Facility-level predictors included (1) antibiotic use (days with therapy per 1000 person-days), the proportion of persons with an (2) acute or (3) long-term care stay in prior 56 days, and importation of CDI cases from (4) acute or (5) long-term care per 10 000 person-days. Results. Eighty-seven LTC and 82 AC facilities met our inclusion criteria. The incidence of CDI in LTC was 3.8 per 10,000 patient-days (n = 6766 cases) and was 17.9 per 10,000 patient-days (n = 26,113 cases) in AC. LTC patients with a recent AC stay were more likely to develop CDI than those without a recent AC stay (IRR = 4.81, 95% CI: 4.56, 5.07). Similarly, AC patients with a recent LTC stay were also more likely to develop CDI than those without, but to a lesser degree (IRR = 1.88, 95% CI: 1.79, 1.98). Imported CDI cases were more prevalent in LTC (median = 75 per 10,000 patient-days, range: 1.5-355) compared to AC (median = 39, range: 0-115, p < 0.001). In bivariate weighted linear regression models, importation of AC cases was a strong predictor of increased CDI incidence in LTC (R2 = 0.62), but importation of cases from LTC was not as strong a predictor of AC rates (R2 = 0.21). Conclusion. Acute care and long-term care facilities are both impacted by importation of CDI. This research suggests that improved regional communication, and an inter-facility, coordinated approach to infection control, could help reduce CDI spread. Disclosures. All authors: No reported disclosures
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".