Objective: This study investigated the phenomenon known as the healthy user bias by equating hormone therapy (HT) use (past or current) with healthy user status.
Methods: Data from the Survey of Midlife in the United States were used to identify the predictors of HT use. The unique Survey of Midlife in the United States data include psychological, demographic, health-related, and behavioral variables as well as history of HT use. Predictors of HT use were combined to derive propensity scores, describing the likelihood that a woman was an HT user, based on her psychological, demographic, physical, and behavioral profile (ie, likelihood of being a healthy user) as opposed to her actual use of HT. Finally, cognitive performance on an executive function test was examined in women stratified by propensity score.
Results: Using a multiple logistic regression model, nine variables emerged as predictors of HT use. The nine variables were used to estimate the propensity or conditional probability of using HT for each subject; resultant propensity scores were ranked and divided into tertiles. Women in the highest tertile demonstrated shorter median response latencies on a test of executive function than did women who did not use HT.
Conclusions: From an array of psychological, medical, and behavioral variables, nine emerged as predictors of HT use. If validated, these features may serve as a means of estimating the phenomenon known as healthy user bias. Moreover, these data suggest that the degree to which a woman fits a model of a healthy user may influence cognitive response to HT.
These data suggest that the degree to which a woman fits a model of a healthy user may influence cognitive response to hormone therapy.
From the 1Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI; 2Geriatric Research, Education, and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI; 3Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI; 4Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI; and 5Institute on Aging, University of Wisconsin-Madison, Madison, WI.
Received July 18, 2011; revised and accepted September 21, 2011.
Funding/support: This research was supported by funding from the National Institutes of Health (K23 AG024302; P50 AG033514).
Financial disclosure/conflicts of interest: None reported.
This is Geriatric Research, Education and Clinical Center manuscript number 2011-10.
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