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Ann Epidemiol ; 23(5): 286-90, 2013 May.
Artículo en Inglés | MEDLINE | ID: mdl-23621994

RESUMEN

PURPOSE: To examine the importance of self-reported family history of uterine leiomyoma (fibroids) as a marker of risk. METHODS: Women, aged 35 to 49, were randomly selected from the membership of a large, urban health plan. Participants completed a self-administered questionnaire about family history of fibroids. Ultrasound screening for fibroids followed, regardless of whether participants had been previously diagnosed (660 black, 412 white). Data for each ethnic group were analyzed separately using Poisson regression. RESULTS: In both ethnic groups, women who reported a family history of fibroids had an elevated risk of fibroids compared with those without family history. However, no elevated risk was apparent for cases who did not know they had fibroids when they reported the family history information. CONCLUSIONS: Many women may first learn about their family history of fibroids when discussing their own clinical diagnosis with family members. Such bias would invalidate self-reported family history as a predictor of fibroid risk. As new pharmacologic treatments for fibroids are developed, women at high risk of fibroids would benefit from early screening and pharmacologic treatment to delay development of large fibroids and reduce the need for invasive treatments. Self-reported family history is not useful for identifying high-risk women.


Asunto(s)
Leiomioma/diagnóstico por imagen , Autoinforme , Neoplasias Uterinas/diagnóstico por imagen , Adulto , Negro o Afroamericano/estadística & datos numéricos , Población Negra/estadística & datos numéricos , Estudios Transversales , Femenino , Predisposición Genética a la Enfermedad , Humanos , Leiomioma/etnología , Leiomioma/genética , Persona de Mediana Edad , North Carolina/epidemiología , Análisis de Regresión , Factores de Riesgo , Encuestas y Cuestionarios , Ultrasonografía Intervencional , Neoplasias Uterinas/etnología , Neoplasias Uterinas/genética , Población Blanca/estadística & datos numéricos
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