Time trends and predictors of laboratory-confirmed recurrent and severe <i>Clostridioides difficile</i> infections in Manitoba: a population-based study
Bibliographic record
Abstract
Background: Many previous studies of Clostridioides difficile infection (CDI) epidemiology have used hospital discharge data codes, which can have limited accuracy. We used a data set of laboratory-confirmed cases of CDI in the province of Manitoba, Canada, to describe the epidemiology of CDI over a decade. Methods: We conducted a population-based historical cohort study using Manitoba Health’s population-wide laboratory-based CDI data set linked to administrative health databases. All individuals living in Manitoba and experiencing a CDI episode between 2005 and 2015 were included (n = 8471) and followed up from CDI diagnosis. We assessed time trends of CDI, incidence and predictors of recurrence and severe outcomes, and health care encounters after CDI diagnosis. CDI episodes were stratified by community versus hospital site of acquiring CDI. Results: Between 2005 and 2009, overall CDI diagnoses decreased by an average of 12.6% per year (95% confidence interval [CI] −4.4 to −20.0), with no statistically significant change from 2010 to 2015. In stratified analysis, incident and recurrent CDI had a similar decrease in the initial study time period and then stabilized. The proportion of community-associated CDI cases increased by an average of 4.8% per year (95% CI 2.8 to 6.8) during the study period. CDI acquired in a health care facility had a higher recurrence rate and more severe outcomes. Recurrence of CDI increased the likelihood of admission to hospital. Interpretation: Between 2005 and 2015, the rates of overall laboratory-confirmed CDI, incident CDI, recurrent CDI and severe outcomes following CDI initially decreased before stabilizing, and an increasing proportion of CDI cases were community-associated. There is an increasing need to test for CDI among outpatients with diarrhea and to increase efforts to prevent recurrent CDI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".