Search

Background: U.S. women of ages 50–74 years are recommended to receive screening mammography at least biennially. Our objective was to evaluate multilevel predictors of nonadherence among screened women, as these are not well known. Materials and Methods: A cohort study was conducted among women of ages 50–74 years with a screening mammogram in 2011 with a negative finding (Breast Imaging-Reporting and Data System 1 or 2) within Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium research centers. We evaluated the association between woman-level factors, radiology facility, and PROSPR research center, and nonadherence to breast cancer screening guidelines, defined as not receiving breast imaging within 27 months of an index screening mammogram. Multilevel mixed-effects logistic regression was used to calculate odds ratios and 95% confidence intervals. Results: Nonadherence to guideline-recommended screening interval was 15.5% among 51,241 women with a screening mammogram. Non-Hispanic Asian/Pacific Islander women, women of other races, heavier women, and women of ages 50–59 years had a greater odds of nonadherence. There was no association with ZIP code median income. Nonadherence varied by research center and radiology facility (variance = 0.10, standard error = 0.03). Adjusted radiology facility nonadherence rates ranged from 10.0% to 26.5%. One research center evaluated radiology facility communication practices for screening reminders and scheduling, but these were not associated with nonadherence. Conclusions: Breast cancer screening interval nonadherence rates in screened women varied across radiology facilities even after adjustment for woman-level characteristics and research center. Future studies should investigate other characteristics of facilities, practices, and health systems to determine factors integral to increasing continued adherence to breast cancer screening. [ABSTRACT FROM AUTHOR]

Background Growing numbers of older women receive adjuvant breast cancer therapies, but little is known about the long-term effects of current therapies upon health-related quality of life outside of clinical trials. Methods A population-based cohort of postmenopausal women with incident breast cancer aged sixty-five and older was identified from Medicare claims from four states and followed over five years. General health-related quality of life (HRQOL) was assessed using the Medical Outcomes Study SF-12 Health Survey, and breast cancer-related HRQOL was assessed using the breast cancer subscale of the functional assessment of cancer therapy (FACT-B BCS). The association of HRQOL with sociodemographic variables, comorbidity, and breast cancer variables (stage, treatments, and treatment sequelae) was examined in longitudinal models. Results Among the 3083 older breast cancer survivors, general HRQOL as measured by SF-12 mental and physical component scores was similar to norms for non-cancer populations, and remained stable throughout follow-up. Breast cancer treatments, including surgery and radiation, adjuvant hormonal therapy, and cytotoxic chemotherapy were not associated with worsened general health scores. A similar pattern was seen for breast cancer-related HRQOL scores, except that chemotherapy was associated with slightly worse scores. Lymphedema occurred in 17% of the cohort, and was strongly associated with all measures of HRQOL. Reductions in general HRQOL with lymphedema development were larger than those with an age increase of 10 years. Conclusions There is little association of breast cancer treatment with HRQOL in older breast cancer patients followed for up to five years, but the development of lymphedema is associated with substantial reductions in HRQOL. [ABSTRACT FROM AUTHOR]

Breast cancer screening holds a prominent place in public health, health care delivery, policy, and women's health care decisions. Several factors are driving shifts in how population-based breast cancer screening is approached, including advanced imaging technologies, health system performance measures, health care reform, concern for 'overdiagnosis,' and improved understanding of risk. Maximizing benefits while minimizing the harms of screening requires moving from a '1-size-fits-all' guideline paradigm to more personalized strategies. A refined conceptual model for breast cancer screening is needed to align women's risks and preferences with screening regimens. A conceptual model of personalized breast cancer screening is presented herein that emphasizes key domains and transitions throughout the screening process, as well as multilevel perspectives. The key domains of screening awareness, detection, diagnosis, and treatment and survivorship are conceptualized to function at the level of the patient, provider, facility, health care system, and population/policy arena. Personalized breast cancer screening can be assessed across these domains with both process and outcome measures. Identifying, evaluating, and monitoring process measures in screening is a focus of a National Cancer Institute initiative entitled PROSPR (Population-based Research Optimizing Screening through Personalized Regimens), which will provide generalizable evidence for a risk-based model of breast cancer screening, The model presented builds on prior breast cancer screening models and may serve to identify new measures to optimize benefits-to-harms tradeoffs in population-based screening, which is a timely goal in the era of health care reform. Cancer 2014;120:2955-2964. © 2014 American Cancer Society. [ABSTRACT FROM AUTHOR]

To identify sociodemographic characteristics, numeracy level, and breast cancer risk factors that are independently associated with the accuracy of lifetime and 5-year breast cancer risk perceptions. Cross-sectional survey. A probability scale was used to measure lifetime and 5-year risk perceptions. The absolute difference between perceived risk and the Gail model risk of breast cancer was calculated. Linear regression models were built to predict lifetime and 5-year breast cancer risk estimation error. Primary care internal medicine practices ( N = 2). Two hundred fifty-four women 40 to 85 years of age. The mean lifetime and 5-year calculated breast cancer risk was 8.4% (SD [standard deviation] 6.1) and 1.5% (SD 1.3), respectively. Subjects had a mean estimation error for lifetime and 5-year risk of 29.5% (SD 22.9) and 24.8% (SD 23.9), respectively. In multivariate analyses, lower numeracy scores (0.005), higher number of previous breast biopsies (0.016), and a higher number of first-degree relatives (0.054) were predictive of larger estimation error for lifetime breast cancer risk. White race (0.014), lower educational levels (0.009), higher number of previous breast biopsies (0.008), and higher number of first-degree relatives (0.014) were predictive of larger estimation error for 5-year risk. Among a primary care population, breast cancer risk factors may be more consistently associated with pessimistic perceptions of breast cancer risk than other factors studied during a lifetime and 5-year time span. Primary care physicians should consider counseling patients about individual breast cancer risk factors and risk over time. J GEN INTERN MED 2004;19:310–315. [ABSTRACT FROM AUTHOR]

A multi-attribute utility (MAU) decision model for menopausal hormone replacement therapy (HRT) was developed using structured interviews (n=40) to identify decision factors, and a telephone survey (n=97) to ascertain utility scores. Utility scores for individual factors and composite scores reflecting the HRT decision were compared according to HRT use. Composite utility scores (range of −1.0 to 1.0, with higher values supporting HRT use) were 0.55, −0.27, and −0.19 for the 48 HRT users, 23 former users, and 26 never users, respectively (P<0.0001). Among HRT current users, the main factors supporting use were concerns about heart disease, osteoporosis, and symptoms of menopause. Among former users, side effects weighed heavily against use, and among never users breast cancer concerns weighed heavily against use. Linear regression methods were used to identify the utilities most predictive of current HRT use. The decision model provided insight regarding how personal expectations and values influence HRT use. [Copyright &y& Elsevier]