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Education Predicts Quality of Life Among Men With Prostate Cancer Cared for in the Department of Veterans Affairs A Longitudinal Quality of Life Analysis From CaPSURE
Sara J. Knight, PhD1,2,3 David M. Latini, PhD4,5 Stacey L. Hart, PhD1,2 Natalia Sadetsky, MD, MPH3 Christopher J. Kane, MD1,3 Janeen DuChane, PhD6 Peter R. Carroll, MD1 the CaPSURE Investigators* 1 San Francisco VA Medical Center, San Francisco, California. 2
Department of Psychiatry, University of California, San Francisco, California. 3 Department of Urology, University of California, San Francisco, California. 4 Scott Department of Urology, Baylor College of Medicine, Houston, Texas. 5 Houston Center for Quality of Care and Utilization Studies, Michael E. DeBakey VA Medical Center, Houston, Texas. 6 TAP Pharmaceutical Products Inc., Lake Forest, Illinois.
Sara J. Knight was supported in by a VA Health Services Research and Development Career Development Award (RCD 98-33-72) and a VA IIR Award (02-142-1). Research was performed using the resources and facilities at the Program to Improve Care for Veterans with Complex Comorbid Conditions at the San Francisco VA Medical Center. David M. Latini was supported by the American Cancer Society under Mentored Research Scholars Grant 06-083-01 CPPB. Research was performed using the resources and facilities at the Houston Center for Quality of Care and Utilization Studies at the Michael E. DeBakey VA Medical Center. *The current CaPSURE investigators are: Peter R. Carroll, MD (University of California, San Francisco, San Francisco, CA), James S. Cochran, MD (Urology Clinics of North Texas, Dallas, TX), Chris-
ª 2007 American Cancer Society
BACKGROUND. Previous findings have suggested that patient educational attainment is related to cancer stage at presentation and treatment for localized prostate cancer, but there is little information on education and quality of life outcomes. Patient education level and quality of life were examined among men diagnosed with prostate cancer and cared for within an equal-access health care system, the Department of Veterans Affairs Veterans Health Administration (VA).
METHODS. Participants were 248 men with prostate cancer cared for in the VA and enrolled in CaPSURE. Repeated-measures analysis of variance was used to
topher J. Kane, MD (Veterans Administration Medical Center, San Francisco, CA), Donald P. Finnerty, MD (PAPP Clinic, Newnan, GA), Eugene V. Kramolowsky, MD (The Virginia Urology Center, Richmond, VA), Robert M. Segaul, MD (Urology Associates of West Broward Belle Terre, Sunrise, FL), Paul Sieber, MD (Urological Associates of Lancaster, Lancaster, PA), Stanley A. Brosman, MD (Pacific Clinical Research, Santa Monica, CA), Lynn W. Conrad, MD (The Conrad Pearson Clinic, Germantown, TN), Ronald A. Chee-Awai, MD (Urologic Institute of New Orleans, Marrero, LA), Michael Flanagan, MD (Urology Specialists, Waterbury, CT), Jeffrey K. Cohen, MD (Triangle Urology Group, Pittsburgh, PA), Jerrold Sharkey, MD (Urology Health Center, New Port Richey, FL), Thomas W. Coleman, MD (Mobile Urology Group, Mobile, AL), Elliott C. Silbar, MD (Clinic of Urology, Milwaukee, WI), Paul S. Ray, DO (Cook County Hospital, Chicago, IL), David Noyes, MD (Berkshire Urological Associates, P.C., Pittsfield, MA), Mohammed Mostafavi, MD (Urology Group of Western New England, Springfield, MA), Louis Keeler, III, MD (Delaware Valley Urology, LLC-Voorhees, Voorhees, NJ), James Gottesman, MD (Seattle Urological, Seattle, WA), Bhupendra M. Tolia, MD (Associated Advanced Adult & Pediatric Urology, Bronx, NY), Patrick P. Daily, MD (Mississippi Urology, Jackson, MS), Glen Wells, MD (Alabama Urology, Birmingham, AL), Richard J. Kahnoski, MD (Michigan Medical, Grand Rapids, MI), Sheldon J. Freedman, MD (Las Vegas, NV), Randil Clark, MD (North Idaho Urology, Coeur D’Alene, ID), Daniel W. Lin, M.D (Veterans Administration Puget Sound HCS, Seattle, WA), Mark Austenfeld, MD (Kansas City Urology
DOI 10.1002/cncr.22597 Published online 22 March 2007 in Wiley InterScience (www.interscience.wiley.com).
Care, Kansas City, MO), Henri P. Lanctin, MD (Adult & Pediatric Urology, Sartell, MN), J. Brantley Thrasher, MD (University of Kansas, Adult Urology and Urologic Oncology, Kansas City, KS), and David W. Bowyer, MD (Snake River Urology, Twin Falls, ID). Former CaPSURE investigators are: John Forrest, MD (1995–99, Urologic Specialists of Oklahoma, Tulsa, OK), William Schmeid, MD (1995–99, Metro Urology, Jeffersonville, IN), Glen Brunk, MD (1995–99, Urology of Indiana, Indianapolis, IN), Jay Young, MD (1995–2001, South Orange County Medical Research Center, Laguna Woods, CA), Gary Katz, MD (1996–2000, Medical College of Virginia and Veterans Administration Medical Center, Richmond, VA), Stacy J. Childs, MD (1999–2000, Cheyenne Urological, Cheyenne, WY), Kevin Tomera, MD (1999–2001, Alaska Urological Associates, Anchorage, AK), Clayton Hudnall, MD (1995–2002, Urology San Antonio Research, San Antonio, TX), David Penson, MD, MPH (2000–2003, Veterans Administration Puget Sound HCS, Seattle, WA); W. Lamar Weems, MD (1996–2005, Mississippi Urology, Jackson, MS); Joseph N. Macaluso, Jr., MD (1995–2005, Urologic Institute of New Orleans, Gretna, LA). From the Mental Health and Urology Services and the Interdisciplinary Research Program to Improve Care for Older Veterans, a Research Enhancement Award Program of the Health Services Research & Development, Department of Veteran Affairs, San Francisco Veterans Affairs Medical Center, and the Department of Psychiatry and Urology, University of California at San Francisco, San Francisco, California.
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May 1, 2007 / Volume 109 / Number 9 examine quality of life over time according to education level, controlling for age, ethnicity, income, site of clinical care, and year of diagnosis.
RESULTS. Patients with lower levels of education tended to be younger, nonwhite, and have lower incomes. Controlling for age, ethnicity, income, year of diagnosis, and site, men with less formal education, compared with those with more, had worse functioning in the physical (P ¼ .0248), role physical (P ¼ .0048), role emotional (P ¼ .0089), vitality (P ¼ .0034), mental health (P ¼ .0054), social function (P ¼ .0056), and general health (P ¼ .0002) domains and worse urinary (P ¼ .003) and sexual (P ¼ .0467) side effects.
CONCLUSIONS. Men with less education experienced worse health-related quality of life across a wide range of domains and greater urinary and sexual symptoms than their peers who had more education. Clinicians should be aware that, even within an equal access to health care system, men with less education are vulnerable, having greater difficulty functioning in their daily lives after their prostate cancer treatment. Cancer 2007;109:1769–76. 2007 American Cancer Society.
KEYWORDS: prostate cancer, education, health related quality of life, socioeconomic status, disparities, VA health care system.
E
ducational attainment and health literacy have been related to stage of prostate cancer at diagnosis and patterns of care.1,2 For example, Kane et al.2 found that men with lower levels of education, at older ages, were less likely to have received definitive treatments such as radical prostatectomy and external beam radiation therapy than were older men with higher levels of education. Whereas it is not clear that education level is related to prostate cancer outcomes, such as health-related quality of life (HRQOL), apart from its impact on the treatment choice, previous work has found that men with less education have less knowledge of prostate cancer before diagnosis3,4 and have poor understanding of prostate cancer and its treatments after diagnosis even after participation in an educational program on prostate cancer.5 Because poor understanding of prostate cancer and its treatment potentially could contribute to poor management of symptoms and greater disruption to lifestyle, it is important to understand the influence of education on HRQOL and on the specific symptoms associated with prostate cancer and its treatments. In this study we examined the relation between education level and early prostate cancer outcomes. We focused on HRQOL and prostate cancer-specific
CaPSURE is supported by TAP Pharmaceutical Products Inc. (Lake Forest, IL). This research was additionally funded by National Institutes of Health/National Cancer Institute University of California-San Francisco SPORE Special Program of Research Excellence P50 C89520.
symptoms among men diagnosed with prostate cancer and cared for in the Department of Veterans Affairs Veterans Health Administration (VA). We examined the symptoms and HRQOL of men in the VA for several reasons. First, because the VA cares for veterans of the lowest socioeconomic strata, we would be able to obtain a sample where a large proportion of the men would have lower education levels. Second, the VA is an equal access to a health care system that reduces financial and geographic barriers to medical care.6,7 This provides an excellent opportunity to examine the impact of patient factors, such as educational attainment, that may influence posttreatment symptom burden and recovery, even when financial and other structural barriers to care are reduced.
MATERIALS AND METHODS Patient Population Study participants were men diagnosed with prostate cancer between 1989 and 2002 and participating in the CaPSURE study database registry. The CaPSURE study is a longitudinal observational study of men with prostate cancer diagnosed by biopsy and recruited consecutively from participating urology clinics and centers.
The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the Department of Veterans Affairs. Address for reprints: Sara J. Knight, PhD, Health Services Research and Development (151R), San
Francisco VA Medical Center, San Francisco, CA 94121; Fax: (415) 379-5614; E-mail: sara.
[email protected] Received October 5, 2006; revision received December 21, 2006; accepted January 17, 2007.
Education Predicts Prostate CA QOL/Knight et al.
All men recruited to the study were informed of the research methods and consented to participation according to a protocol approved by the Committee on Human Research at the University of California, San Francisco, and the individual sites, where applicable. Detailed descriptions of the sample characteristics and methods have been published previously.8–10 The analytic population for this study consisted of those patients cared for in the 3 VA CaPSURE sites (Richmond, Va; San Francisco, Calif; Seattle, Wash) who were in the database as of July 2003 and had completed a baseline questionnaire (n ¼ 259). Of these, the 248 patients who had completed both a baseline questionnaire and at least 1 posttreatment questionnaire (6 or 12 months) were included in the analyses. Of these, 112 were recruited from Richmond, 38 from San Francisco, and 98 from Seattle.
Data Collection and Instruments We asked patients to complete self-report forms that included questions about age, marital status, ethnic background, education level, and annual household income. Clinical data including year of diagnosis, PSA at diagnosis, Gleason score, stage, number of comorbid conditions, treatment choice, and body mass index were obtained by experienced medical record abstractors. Outcome variables included generic HRQOL and disease-specific HRQOL (ie, urinary, sexual, and bowel symptoms). Measures included the Medical Outcomes Study Short Form-36 (SF-36) v 1.0 and the UCLA Prostate Cancer Index (PCI). Both instruments are widely used and substantial evidence has been accumulated to support their validity. The SF-36 is a measure of generic HRQOL that assesses physical, functional, emotional, and social domains of function and provides summary scores for physical and mental health. The PCI is a disease-specific measure that assesses urinary, sexual, bowel function, and bother. Each instrument was administered and scored according to standard instructions with subscale scores ranging from 0 to 100 where higher scores indicated better function. Physical Component Summary and Mental Component Summary scores of the SF-36 were derived from the subscale scores using a norm-based algorithm and where higher scores indicate better function.11 Questionnaires were administered at baseline, immediately after study enrollment, and at 6 and 12 months posttreatment. Baseline questionnaires collected before 1995 were administered after the commencement of treatment.
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Statistical Analyses The association between education level and clinical and sociodemographic variables including age at diagnosis, ethnicity, annual household income, relation status, site of clinical care, prostate-specific antigen level, Gleason total, T stage, number of comorbid conditions, and body mass index were explored using chi-square analysis. In the analyses, educational attainment was treated as a categorical variable with 4 levels, including 1) less than a high school diploma, 2) high school diploma, 3) some college, and 4) college graduate or higher education. These categories were selected based on the reasoning that these are clearly defined groups that differ in recognizable educational accomplishments and would be comparable to the categories used in similar studies. Repeated measures analyses were conducted to determine the association between education level and HRQOL and prostate cancer-specific function and bother over time. This approach to the analysis was used because it takes into account the correlation of the recurring outcomes within patients. In addition, it handles missing values and truncation in an optimal way, by taking into account the time patterns of the available data. The repeated-measure model included education level and adjusted for age, ethnicity, annual household income, site of clinical care, timing of HRQOL assessment, and the interaction between education and the timing of the HRQOL assessment. We included the interaction term in the model to determine whether patterns of HRQOL differed by education level over the course of disease. We considered including treatment type in the final model because of a trend toward association of treatment type with education and conducted a multinomial logit analysis to assist with this decision. Because the outcome of this analysis (primary treatment) has 5 possible categories, we fit a multinomial logistic regression model to predict primary treatment. In multinomial regression, 1 outcome category serves as the referent for the others. In this analysis, radical prostatectomy is the referent category because it was the most common primary treatment and would provide the most statistical power. Independent variables were selected after preliminary analysis of bivariate relations and included age at diagnosis used as a categorical variable (ie, less than 55, 55-64, 65-74, 75 and older), number of comorbid conditions (0, 1-2, 3-5), ethnicity, annual household income, and year of diagnosis. Education level was forced into the model. Only year of diagnosis was significantly associated with type of treatment received in the final model (P ¼ .0025). For this reason, subsequent analyses were not adjusted according to treatment type.
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TABLE 1 Demographic and Clinical Characteristics According to Education Level*
Age
Relationshipy Ethnicity
Annual household income
Year of Diagnosis Number of comorbid conditions PSA
Gleason grade
Stage
BMI
Treatment
