The Epidemiology of Community-Acquired Clostridium Difficile in the Niagara Region, Ontario, Canada, Between September 2011 and December 2013
Bibliographic record
Abstract
Clostridium difficile infections (CDIs) have historically been associated with exposure to healthcare settings. In recent years, however, the incidence of community-acquired Clostridium difficile infections (CA-CDI), along with the number of patients requiring hospitalization for it, has been increasing. This research uses a framework grounded in Complex Adaptive Systems (CAS) to reveal new and different epidemiological findings on CA-CDI to indicate novel health equity leverage points. It explores the epidemiology and established risk factors associated with CA-CDI in the Niagara Region, Ontario, and compares them with those of healthcare-associated CDI (HA-CDI) in the same area. \nThe first manuscript evaluates the literature on existing evidence of risk factors for CA-CDI by applying The Joanna Briggs Institute (JBI) Reviewers Manual 2015, Methodology for JBI Scoping Reviews. The review identifies that CA-CDI is seen more often than HA-CDI in younger and female populations. Exposure to antimicrobials is common but not as common as in HA-CDI cases. The scoping review establishes the need for further epidemiological studies on CA-CDI. The second manuscript provides a nonparametric descriptive analysis, comparing CA-CDI and HA-CDI cases in Niagara Health System (NHS) hospitals, based on a retrospective case series design. Hospitalized CA-CDI patients have a lower median age and less exposure to antimicrobials and other medications. Gender proportions are similarly distributed between the two groups. The emerging recommendation is that CA-CDI must be considered as a potential diagnosis in patients admitted to hospital with diarrhea, even in the absence of conventional CDI risk factors. The third and final manuscript evaluates the spatial and genotype features of CA-CDI and HA-CDI. It finds that geographical clustering, temporal patterns, and genotypic features are unique in each category. These studies point to the need for a better understanding of transmission routes between communities and healthcare settings; further research is required to establish community CA-CDI risk factors. \nTogether, these evaluations establish that we must develop a systems approach to explore health problems and respond effectively at a population level. The research and policy environment must be strengthened by modifying current practices, setting priorities, and providing funding for empirical studies and equitable health policies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".