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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».