Risk factors for acute gastrointestinal illness in a Canadian population-based linkage cohort
Bibliographic record
Abstract
Acute gastrointestinal illness (AGI) remains a significant public health issue and differences in risk based on a comprehensive set of sociodemographic characteristics remain poorly understood. Thus, this retrospective cohort study was conducted to identify the risk of incurring an AGI-related emergency department (ED) visit or inpatient hospitalization based on various sociodemographic factors. Linked respondents of Canadian Community Health Survey cycles 2.1, 3.1, and 2007-2015 were followed from their interview date until 31 December 2017, using the National Ambulatory Care Reporting System (NACRS) and the Discharge Abstract Database (DAD) to capture emergency ED visits and hospitalizations due to AGI, respectively. Effects of identified potential risk factors for the incidence of AGI-related ED visits or hospitalizations were estimated Cox proportional hazards regression to generate hazard ratios (HRs) with 95% confidence intervals (CIs). A total of 190,700 respondents were linked to NACRS and 470,700 were linked to DAD. Six per cent of respondents visited an ED and 2% were hospitalized for AGI. Fully-adjusted estimates revealed that high-risk groups with the strongest effects were people with poor self-perceived health (ED visits: HR 1.47 (95% CI 1.40-1.54), hospitalizations: HR 1.92 (95% CI 1.82-2.02)), and people living with at least one chronic condition (ED visits: HR 1.54 (95% CI 1.47-1.61), hospitalizations: HR 1.65 (95% CI 1.57-1.73)). This study identified risk factors for requiring hospital care for AGI in the Canadian context. Additional research is needed to investigate mechanisms for differential exposure to pathogens by sociodemographic characteristics that might lead to increased risks of AGI.
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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) | 0.001 | 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 | 0.001 | 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".