Identifying and describing a cohort effect in the national database of reported cases of hepatitis C virus infection in Canada (1991-2010): an age-period-cohort analysis
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infection has a high likelihood of becoming chronic and lead to a range of conditions with poor health outcomes. Identifying birth groups highly affected by HCV infection may better focus public health interventions and ensure their cost-effectiveness. Our analysis focused on studying the association of the birth year and reporting period with rates of cases of HCV infection reported in Canada over a 20-year period. METHODS: Laboratory-confirmed acute or chronic HCV cases with information on sex, age and year of report from 6 provinces and territories that reported line-listed data to the Canadian Notifiable Diseases Surveillance System from 1991 to 2010 were used. Sex-specific infection rates for 5-year birth groups born between 1921 and 1990 were calculated. Rates of HCV infection were log-logit transformed and underwent mean polish analysis and panel linear regression. Rate ratios of HCV infection in the 5-year age groups and their 95% confidence intervals were calculated, with rates in males and females born in 1941-1945 used as references. RESULTS: Males born between 1946 and 1970 had 21%-40% higher reported rates of HCV infection, whereas females born between 1946 and 1975 had 12%-43% higher reported rates compared with rates in the respective sexes who were born in 1941-1945. INTERPRETATION: Individuals born between 1946 and 1965 contributed the most to the rates of HCV infection reported in Canada between 1991 and 2010. The cohort effect was present in male and female cases of HCV infection with birth year up to 1970 and 1975, respectively. Our findings will support the development of HCV prevention programs and policies in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".