Epidemiological Patterns and Hospital Characteristics Associated with Increased Incidence of<i>Clostridium difficile</i>Infection in Quebec, Canada, 1998–2006
Bibliographic record
Abstract
OBJECTIVE: To explore epidemiological patterns of the incidence of Clostridium difficile infection (CDI) and hospital characteristics associated with increased incidence during nonepidemic and epidemic years. DESIGN: Retrospective and prospective ecological study. SETTING: Eighty-three acute care hospitals participating in CDI surveillance in the province of Quebec, Canada. METHODS: A Serfling-type regression model applied to data obtained from an administrative database (1998-2006) and prospective Quebec CDI surveillance (2004-2006) was used to calculate expected CDI baseline incidence and to detect incidence exceeding the defined epidemic threshold at the provincial and hospital level. Multivariable Poisson regression was used to determine hospital characteristics associated with increased incidence during nonepidemic (1998-2001) and epidemic (2003-2005) periods. RESULTS: During the study period (1998-2006), 4,525,847 discharges, including 45,508 with a CDI in any diagnosis field, were reported by 83 hospitals. During 1998-2001, the average Quebec incidence of CDI was 10,304 cases in 1,775,822 discharges (5.8 cases per 1,000 discharges) and presented a pattern of seasonality, with similar patterns at the hospital level for some hospitals. The Quebec epidemic started in October-November 2002 and peaked in March 2004 at 845 cases in 40,852 discharges (20.7 cases per 1,000 discharges). In multivariable analysis, higher incidence was associated with location in Montreal and surrounding regions, greater hospital size, larger proportion of hospitalized elderly patients, longer length of stay, and greater proportion of comorbidities in patients, whereas teaching profile was associated with decreased incidence during both nonepidemic and epidemic periods. The effect of geographical location on incidence was greater during the epidemic. CONCLUSION: Baseline incidence from nonepidemic years and hospital characteristics associated with CDI incidence should be taken into account when estimating the efficacy of interventions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".