Official websites use. Share sensitive information only on official, secure websites. To characterize the impact of uterine leiomyomas fibroids in a racially diverse sample of women in the United States. A total of women White, African-American, other races aged 29—59 with self-reported symptomatic uterine leiomyomas participated in a national survey. We assessed diagnosis, information seeking, attitudes about fertility, impact on work, and treatment preferences.
Frequencies and percentages were summarized. Chi-square test was used to compare age groups. Women waited an average of 3. Uterine leiomyomas cause significant morbidity. When considering treatment, women are most concerned about surgical options, especially women under 40 who want to preserve fertility. Leiomyoma symptoms include heavy or prolonged menstrual bleeding, menstrual pain or cramping, passing blood clots, bloating, bowel or bladder dysfunction, and fatigue.
Few studies have attempted to gauge symptomatology of women with leiomyomas, assess the way that women obtain information about the disease, or reveal how women use this information to make treatment choices. A recently conducted international survey confined its assessment to only bleeding and pain symptomology. The purpose of this study is to provide a comprehensive assessment of the burden and impact of uterine leiomyomas in a diverse group of women. Because of the potentially higher impact of uterine leiomyomas on women of childbearing age, we attempted to quantitate the frequency and magnitude of morbidity in women under the age of 40 compared to older age groups: 40—49 and 50— We conducted a cross-sectional survey of United States U.
Our target sample was 1, participants. The Focused Ultrasound Foundation was not included or named in any part of the survey. No ads or links to the Foundation were included in the survey. The goal of HPOL is to provide a representative sample of the general population while identifying and reaching under-represented populations of interest.
Harris Interactive uses a weighting algorithm with propensity score adjustment to minimize the nonrandom selection bias inherent in internet-based surveys. Each recruitment source is carefully vetted through a rigorous interviewing and testing process and then monitored for response quality on an ongoing basis.
