Patterns of multiple enteric infections in a 10-year longitudinal study in British Columbia, Canada
Bibliographic record
Abstract
Purpose: Enteric infections are a global public health issue, in part because long-term sequelae can occur post-infection.Complicating risk estimation of sequela, such as acute kidney injury and inflammatory bowel diseases, after infections is the fact that individuals may have more than one infection including from different pathogens, concurrently or sequentially.Here, we investigated patterns of multiple infections within a retrospective, populationbased, longitudinal cohort study of enteric infections. Methods & Materials:We used provincial reportable disease data on laboratory-confirmed enteric infections ( Campylobacter, Cryptosporidium, Cyclospora, Giardia, hepatitis A , Listeria, nontyphoidal Salmonella spp , Salmonella Typhi , Salmonella Paratyphi , STEC , Shigella, Yersinia (excluding pestis) , and Vibrio parahaemolyticus ) from 2005 to 2014 inclusive.infections were considered incident if a subsequent infection caused by the same pathogen had an onset date > 90 days after the prior infection ( > 730 days for Giardia ; no recurrence for hepatitis A) .Population data were obtained from the provincial health insurance program, and included demographics and the number of days individuals were registered in the program.Results: Our cohort included 5,819,344 individuals followed for an average of 7.6 years.Within the cohort, 41,229 individuals had 43,308 infections (96.4 infections per 10 0,0 0 0 person-years), of whom 1,641 (4.0%) experienced more than one infection.Most individuals with multiple infections had two incident infections from two different pathogens; 308 (25.1%) had them concurrently.The most frequent concurrent pathogen pairs were Campylobacter and non-typhoidal Salmonella (n = 118) and the most frequent pairs at different times were Campylobacter and Yersinia (n = 142).An additional 18.5% (303/1,641) of individuals had between three and five infections across the study period, all from the same pathogen; the most frequent were Campylobacter (n = 122), Yersinia (n = 58), and Giardia (n = 58).The time to the same subsequent incident infection varied by pathogen and was shortest for non-typhoidal Salmonella (median = 288 days) and longest for Giardia (median = 1281 days). Conclusion:The potential for these multiple infections to increase the risk of sequelae compared with single infections should be explored when assessing post-infection sequelae risk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".