The burden of cancer among people living with HIV in Ontario, Canada, 1997–2020: a retrospective population-based cohort study using administrative health data
Bibliographic record
Abstract
BACKGROUND: With combination antiretroviral therapy (ART) and increased longevity, cancer is a leading cause of morbidity among people with HIV. We characterized trends in cancer burden among people with HIV in Ontario, Canada, between 1997 and 2020. METHODS: We conducted a population-based, retrospective cohort study of adults with HIV using linked administrative health databases from Jan. 1, 1997, to Nov. 1, 2020. We grouped cancers as infection-related AIDS-defining cancers (ADCs), infection-related non-ADCs (NADCs) and infection-unrelated cancers. We calculated age-standardized incidence rates per 100 000 person-years with 95% confidence intervals (CIs) using direct standardization, stratified by calendar period and sex. We also calculated limited-duration prevalence. RESULTS: Among 19 403 adults living with HIV (79% males), 1275 incident cancers were diagnosed. From 1997-2000 to 2016- 2020, we saw a decrease in the incidence of all cancers (1113.9 [95% CI 657.7-1765.6] to 683.5 [95% CI 613.4-759.4] per 100 000 person-years), ADCs (403.1 [95% CI 194.2-739.0] to 103.8 [95% CI 79.2-133.6] per 100 000 person-years) and infection-related NADCs (196.6 [95% CI 37.9-591.9] to 121.9 [95% CI 94.3-154.9] per 100 000 person-years). The incidence of infection-unrelated cancers was stable at 451.0 per 100 000 person-years (95% CI 410.3-494.7). The incidence of cancer among females increased over time but was similar to that of males in 2016-2020. INTERPRETATION: Over a 24-year period, the incidence of cancer decreased overall, largely driven by a considerable decrease in the incidence of ADC, whereas the incidence of infection-unrelated cancer remained unchanged and contributed to the greatest burden of cancer. These findings could reflect combination ART-mediated changes in infectious comorbidity and increased life expectancy; targeted cancer screening and prevention strategies are needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".