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Record W4387505532 · doi:10.9778/cmajo.20220230

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

2023· article· en· W4387505532 on OpenAlexvenueaboutno aff
Ioana Nicolau, Rahim Moineddin, Tony Antoniou, Jennifer D. Brooks, Jennifer Gillis, Claire Kendall, Curtis Cooper, Michelle Cotterchio, Kate Salters, Marek Smieja, Abigail Kroch, Joanne Lindsay, Colleen Price, Anthony Mohamed, Ann N. Burchell

Bibliographic record

VenueCMAJ Open · 2023
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDemographyHazard ratioPopulationCohortCancer registryIncidence (geometry)Confidence intervalCohort studyCumulative incidenceCancerInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.353
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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