An observational study in Menopause, sponsored by University of Pittsburgh. Completed at 1 site in United States. Open to female participants aged 40 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2014-05-20.
Sponsored by University of Pittsburgh · Observational
The purpose of this study is to follow a woman's progression through menopause in order to examine the effects on health related quality of life and use of health care resources, and to understand how women are using alternative therapies.
Menopause affects every woman as she ages, yet every woman's experience is different. We are seeking to enroll 720 women from the University of Pittsburgh's Division of General Internal Medicine Outpatient practice (GIMO) at all stages of menopause (pre-, peri-, and post-menopausal), between 40 and 65 years old. They will be followed for 5 years as they progress through menopause.
Women will complete yearly questionnaires during their usual doctor's visit (or by phone or online if necessary) regarding general health, menopause and menopausal symptoms, health related quality of life, traditional and alternative therapy use, social support, and attitudes towards menopause and aging.
We will combine this information with information from women's medical charts to look at how menopause and health related quality of life impact the use of health care resources. Additionally, some women may be asked about the use of health care resources, such as doctor's visits, hospitalizations and lab tests.
University of Pittsburgh is the lead sponsor of 1,385 studies on the registry; 167 are open to participants now.
Of its 8 completed or terminated interventional studies of FDA-regulated products, 4 (50%) have results posted.
Counted across the registry records on this site, refreshed daily.
Women from the University of Pittsburgh's Division of General Internal Medicine Outpatient practice (GIMO)
Exclusion Criteria:
Health Related Quality of Life Score
The average enrollment scores of the dependent variable HRQOL \[the physical and mental component summaries (PCS and MCS) of the SF-36\] will be compared among women at different stages of menopause using ANOVA. If the HRQOL scores are not normally distributed on the original scale, a transformation is necessary. We will examine the association between both the baseline presence and severity of symptoms (0-4) and HRQOL using regression techniques. The association between the use of HT (yes/no) and HRQOL will be analyzed by ANOVA. The association between the use of CAM and HRQOL will be analyzed by ANOVA. The association between attitudes towards menopause and HRQOL will be analyzed by linear regression. Confounders, including age, comorbid medical conditions, and social support, will be included in the models. A final model examining the impact of all factors on HRQOL will be created using stepwise linear regression.
Time frame: Baseline survey
Menopause Management
We will record all methods used by women in the cohort to manage menopause. Methods will be categorized as use of: no method, HT, and CAM. A frequency table will quantify use of each method as well as type of CAM. We will analyze the relationship between both menopausal symptoms and attitudes towards menopause and methods used by Chi-square or contingency table tests.
Time frame: Menopausal Status Schema designed from STRAW and SWAN At least yearly Study Questions Menopausal Symptoms Vaginal dryness and hot flashes At least yearly Study Questions HRQOL SF-36 At least yearly CIF Social Support ISEL Yearly Study Questions Attitudes
Relationship between HSU and our independent variables, HRQOL and menopausal stage at assessment.
Using regression analysis techniques, we will examine the relationship between HSU and our independent variables, HRQOL and menopausal stage at assessment. The model will also adjust for confounders such as age and the number and type of comorbidities at the time of analysis. Because all women in the study cohort will have utilization data for at least one physician visit, we will use a linear regression model. If cost data are not normally distributed, we will apply appropriate transformations. We will calculate the concordance correlation coefficient to investigate correlation between HSU data extracted from MARS and that obtained by direct patient interview to uncover under-reporting in the MARS database.
Time frame: Physician visits Hospitalizations Prescriptions With each HRQOL assessment Electronic Medical Record (MARS) abstraction as well as subject self-report Intrusiveness of Menopausal Symptoms Year 2 assessment and yearly Study Questions Sleep
This study is completed, as verified in May 2014. You cannot join it, but the record below documents what was studied.
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University of Pittsburgh