Forecasting the incidence of salmonellosis in seniors in Canada: A trend analysis and the potential impact of the demographic shift
Bibliographic record
Abstract
Salmonella infections remain an important public health issue in Canada and worldwide. Although the majority of Salmonella cases are self-limiting, some will lead to severe symptoms and occasionally severe invasive infections, especially in vulnerable populations such as seniors. This study was performed to assess temporal trends of Salmonella cases in seniors over 15 years (2014-2028) and assess possible impact of demographic shift on national incidence; taking into account of trends in other age groups. The numbers of reported Salmonella cases in seniors (60 years and over) in eight provinces and territories for a period of fifteen years were analysed (1998-2013) using a time-adjusted Poisson regression model. With the demographic changes predicted in the age-structure of the population and in the absence of any targeted interventions, our analysis showed the incidence of Salmonella cases in seniors could increase by 16% by 2028 and the multi-provincial incidence could increase by 5.3%. As a result, the age distribution amongst the Salmonella cases is expected to change with a higher proportion of cases in seniors and a smaller proportion in children (0-4 years old). Over the next decades, cases of infection, hospitalizations and deaths associated with Salmonella in seniors could represent a challenge to public health due to an aging population in Canada. As life expectancy increases in Canada, identification of unique risk factors and targeted prevention in seniors should be pursued to reduce the impact of the demographic shift on disease incidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".