Trends in infection-related and infection-unrelated cancer incidence among people with and without HIV infection in Ontario, Canada, 1996–2020: a population-based matched cohort study using health administrative data
Bibliographic record
Abstract
Background: People with HIV infection are at higher risk for certain cancers than the general population. We compared trends in infection-related and infection-unrelated cancers among people with and without HIV infection. Methods: We conducted a retrospective population-based matched cohort study of adults with and without HIV infection using linked health administrative databases in Ontario, Canada. Participants were matched on birth year, sex, census division (rurality), neighbourhood income quintile and region of birth. We followed participants from cohort entry until the earliest of date of cancer diagnosis, date of death, Nov. 1, 2020, or date of loss to follow-up. Incident cancers identified from Jan. 1, 1996, to Nov. 1, 2020, were categorized as infection-related or-unrelated. We examined calendar periods 1996–2003, 2004–2011 and 2012–2020, corresponding to the early combination antiretroviral therapy (cART), established cART and contemporary cART eras, respectively. We used competing risk analyses to examine trends in cumulative incidence by calendar period, age and sex, and cause-specific hazard ratios (HRs). Results: We matched 20 304 people with HIV infection to 20 304 people without HIV infection. A total of 2437 cancers were diagnosed, 1534 (62.9%) among infected people and 903 (37.0%) among uninfected people. The risk of infection-related cancer by age 65 years for people with HIV infection decreased from 19.0% (95% confidence interval [CI] 15.6%–22.3%) in 1996–2011 to 10.0% (95% CI 7.9%–12.1%) in 2012–2020. Compared to uninfected people, those with HIV infection had similar HRs of infection-unrelated cancer but increased rates of infection-related cancer, particularly among younger age groups (25.1 [95% CI 13.2–47.4] v. 1.9 [95% CI 1.0–3.7] for age 18–39 yr v. ≥ 70 yr); these trends were consistent when examined by sex. Interpretation: We observed significantly higher rates of infection-related, but not infection-unrelated, cancer among people with HIV infection than among uninfected people. The elevated rate of infection-related cancer in 2012–2020 highlights the importance of early and sustained antiretroviral therapy along with cancer screening and prevention measures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".