The Burden of Cancer and Precancerous Conditions Among Transgender Individuals in a Large Health Care Network: Retrospective Cohort Study
Notice bibliographique
Résumé
Background: Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research. Objective: This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals. Methods: We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023. Transgender individuals were identified using a validated, computable phenotype algorithm that used structured data and clinical notes. They matched 1:10:10 by age and calendar year of index date with cisgender women and cisgender men. The index date was the first transgender-related record for transgender individuals and a matched diagnosis date for cisgender controls. Primary outcomes included new-onset cancers associated with human papillomavirus, human immunodeficiency virus, tobacco, alcohol, lung, breast, and colorectal sites. Secondary outcomes were precancerous conditions related to the same cancer types. We calculated cumulative incidence rates and conducted time-to-event analyses using the Fine-Gray method, treating all-cause death as a competing risk, to assess associations between gender identity and the presence of cancer or precancer, adjusting for demographic and clinical covariates. Interaction analyses evaluated if associations between cancer risk factors and precancer differed by gender identity. Results: We identified 2745 transgender individuals (mean age at index date 25.1, SD 14.0 years) and matched them with 27,450 cisgender women and 27,450 cisgender men from the same health care system. The cumulative incidence of cancer did not differ significantly between transgender and cisgender cohorts (transgender n=28, 1.0% vs cisgender women, n=358, 1.3%; P=.13 and cisgender men, n=314, 1.1%; P=.64). However, transgender individuals exhibited significantly higher risks for precancerous conditions compared to cisgender women (subdistribution hazard ratios [sHRs] 1.1, 95% CI 1.0-1.3) and cisgender men (sHR 1.3; 95% CI 1.2-1.5). Specifically, transgender individuals were more likely to develop colorectal precancer (sHR 1.2; 95% CI 1.1-1.4) compared to cisgender women, as well as human papillomavirus-related precancer (sHR 1.8; 95% CI 1.4-2.3) and colorectal precancer (sHR 1.4; 95% CI 1.2-1.6) compared to cisgender men. Subgroup analyses showed similar patterns in both female-to-male and male-to-female individuals compared with their matched cisgender counterparts. Interaction analyses revealed stronger protective effects of private insurance or Medicare against precancers in transgender individuals than in cisgender peers, while being non-Hispanic Black or having substantial comorbidities were stronger risk factors among transgender individuals. Conclusions: Transgender individuals showed a similar cancer incidence yet significantly higher precancer incidence compared with cisgender individuals, suggesting underdiagnosis or delayed detection. These findings highlight the need for tailored preventive care strategies, including targeted screenings and risk reduction interventions, to address cancer disparities in the transgender population.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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