Multipronged Intervention Strategy to Control an Outbreak of <i>Clostridium difficile</i> Infection (CDI) and Its Impact on the Rates of CDI from 2002 to 2007
Bibliographic record
Abstract
OBJECTIVE: At the end of 2002, a new, more virulent strain of Clostridium difficile, designated BI/NAP1, was the cause of a massive outbreak of infection in the province of Quebec. This particular strain was associated with a dramatic increase in morbidity and mortality among affected patients in 2003-2004. We tested and implemented a multipronged infection control approach to curtail the rate of C. difficile infection (CDI). DESIGN: Five-year observational study. SETTING: A 554-bed, acute care tertiary hospital, the largest single medical center in Quebec, Canada. METHODS: To curtail the magnitude of the outbreak, we implemented a global strategy consisting of rapid C. difficile testing for all hospitalized patients who had at least 1 occurrence of liquid stool, the rapid isolation of patients infected with C. difficile in a dedicated ward with a specially trained housekeeping team, a global hand hygiene program, and the hiring of infection control practitioners. Antibiotic consumption at the institutional level was also monitored during the 5-year surveillance period. Cases of hospital-acquired CDI per 1,000 admissions were continuously monitored on a monthly basis during the entire surveillance period. RESULTS: The highest recorded CDI rate was 42 cases per 1,000 admissions in January 2004. Once additional infection control resources were put in place, the rate decreased significantly during the period from April 2005 to March 2007. During the 2003-2004 period, there were 762 cases of CDI (mean annual rate, 37.28 cases per 1,000 admissions) recorded in our study, compared with 292 cases of CDI (14.48 cases per 1,000 admissions) during the 2006-2007 period (OR, 0.379 [95% CI, 0.331-0.435]; p< .001), a 61% reduction. In March 2007, the equivalent of 4 full-time equivalent infection control practitioners were in place, which gave a ratio of 0.96 infection control practitioners per 133 beds in use, compared with the ratio of 0.24 infection control practitioners per 133 beds in use in 2003, and the total number of hours dedicated to cleaning and housekeeping increased by 26.2%. The total amount of antibiotics used in the hospital did not vary significantly from 2002 to 2007, although there were changes in the classes antibiotic used. CONCLUSION: The implementation of a multipronged intervention strategy to control the outbreak of CDI significantly improved the overall situation at the hospital and underlined the importance of investing in stringent infection control practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".