Abstract A004: Paid Family Leave as a Cancer Prevention Strategy? The association of state paid family leave implementation with early-onset breast and endometrial cancer incidence
Bibliographic record
Abstract
Abstract Introduction: Breastfeeding reduces later risk of hormone-sensitive cancers. In the US, longer breastfeeding durations are associated with higher socioeconomic status (SES). Paid Family Leave (PFL), implemented in California (CA) since 2004, increased breastfeeding initiation and duration. We assessed whether PFL implementation reduced early-onset (age 20-54) breast and endometrial cancer incidence and explored modification by SES. Methods: In a quasi-experimental comparative interrupted time-series approach using registry-level Surveillance, Epidemiology, and End Results (SEER) data, we assessed annual age-adjusted incidence rates (2000-2019) for early-onset first malignant breast (C50) and endometrial (C54.1) cancers for women of working and reproductive age after PFL (diagnosed age 20-54). We compared changes in trends for registries exposed to PFL (in CA) to changes in trends for registries not exposed to PFL for areas with comparable pre-PFL trends. To account for cancer’s induction period, two lags (5 and 10 years) were evaluated, with the 5-year lag considered a negative control. We examined trends by race/ethnicity, and for breast cancer, by age (pre-screening >40 vs. post-screening ≥40) and hormone receptor (HR) status. Using the same approach, we compared differences in county-level incidence across quartiles of a county-level SES index. Results: For early-onset breast cancer, 10 years after PFL implementation, non-significantly reduced incidence trends (lowered slopes) were observed in PFL-exposed registries compared to non-PFL exposed registries, with the strongest reductions for non-Hispanic Black women age 40-54. PFL was associated with significantly reduced incidence trends for women 40-54 for counties in lower SES quartiles exposed to PFL compared to lower-SES counties not exposed to PFL (e.g., ∼7 fewer annual cases per 100,000 accounting for baseline trends in the lowest SES quartile). No difference was observed comparing counties in the highest SES quartile. For early-onset endometrial cancer, incidence trends were significantly lower for non-Hispanic American Indian /Alaska Native and Asian Pacific Islander women (2.8 and 2.1 fewer annual cases per 100,000, respectively) for PFL-exposed registries compared to unexposed registries. When examining county-level trends by SES, no significant differences in trends associated with PFL were observed within any SES quartile. Using the 5-year lag, no clear or consistent associations were observed for any cancer, race/ethnicity, or SES quartile. Conclusions: In this ecological analysis, reductions in early-onset breast and endometrial cancer incidence trends emerged 10 years after PFL implementation in CA among specific race-ethnicities. For early-onset breast cancer, associations were stronger among lower SES counties and not observed among high-SES counties. This supports the hypothesis that PFL may contribute to cancer prevention through mechanisms such as increased breastfeeding particularly for people with socioeconomic barriers to accessing parental leave and breastfeeding. Citation Format: Erica J. Lee Argov, Mary Beth Terry, Wan Yang, Parisa Tehranifar. Paid Family Leave as a Cancer Prevention Strategy? The association of state paid family leave implementation with early-onset breast and endometrial cancer incidence [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A004.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".