Secular Trends in Breast Cancer Risk Among Women With HIV Initiating ART in North America
Bibliographic record
Abstract
BACKGROUND: Studies suggest lower risk of breast cancer in women with HIV versus without HIV. These estimates may be biased by lower life expectancy and younger age distribution of women with HIV. Our analysis evaluated this bias and characterized secular trends in breast cancer among women with HIV initiating antiretroviral therapy. We hypothesized breast cancer risk would increase over time as mortality decreased. SETTING: Women with HIV prescribed antiretroviral therapy in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) from 1997 through 2016. METHODS: We estimated breast cancer hazard (cause-specific hazard ratios) and cumulative incidence accounting for competing risks (subdistribution hazard ratios) to assess changes in breast cancer risk over time. This was assessed overall (1997-2016) and within/across calendar periods. Analyses were adjusted for race/ethnicity and inverse probability weighted for cohort. Cumulative incidence was graphically assessed by calendar period and race/ethnicity. RESULTS: We observed 11,587 women during 1997-2016, contributing 63 incident breast cancer diagnoses and 1,353 deaths [73,445 person-years (median follow-up = 4.5 years)]. Breast cancer cumulative incidence was 3.2% for 1997-2016. We observed no secular trends in breast cancer hazard or cumulative incidence. There were annual declines in the hazard and cumulative incidence of death (cause-specific hazard ratios and subdistribution hazard ratios: 0.89, 95% confidence interval: 0.87 to 0.91) which remained within and across calendar periods. CONCLUSIONS: These findings contradict the hypothesis of increasing breast cancer risk with declining mortality over time among women with HIV, suggesting limited impact of changing mortality on breast cancer risk. Additional inquiry is merited as survival improves among women with HIV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".