No abstract

Background Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear. Methods We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007-2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV) and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. Results In cross-sectional analysis, BMI was positively associated with DV (β=2.95 cm3, 95% CI 2.69, 3.21) and inversely associated with PDV (β=-2.03%, 95% CI -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β=-1.01 cm3, 95% CI -1.59, -0.42) and PDV (β=-1.17%, 95% CI -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. Conclusion Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. Impact Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.

Purpose Women diagnosed with ductal carcinoma in situ (DCIS) of the breast are at greater risk of dying from cardiovascular disease and other causes than from breast cancer, yet associations between health-related behaviors and mortality outcomes after DCIS have not been well studied. Methods We examined the association of body mass index, physical activity, alcohol consumption, and smoking with mortality among 1,925 women with DCIS in the Wisconsin In Situ Cohort study. Behaviors were self-reported through baseline interviews and up to three follow-up questionnaires. Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for mortality after DCIS, with adjustment for patient socio-demographic, comorbidity, and treatment factors. Results Over a mean of 6.7 years of follow-up, 196 deaths occurred. All-cause mortality was elevated among women who were current smokers one year prior to diagnosis (HR=2.17 [95% CI: 1.48, 3.18] vs. never smokers) and reduced among women with greater physical activity levels prior to diagnosis (HR=0.55 [95% CI: 0.35, 0.87] for ≥5 hours per week vs. no activity). Moderate levels of post-diagnosis physical activity were associated with reduced all-cause mortality (HR=0.31 [95% CI: 0.14, 0.68] for 2–5 hours per week vs. no activity). Cancer-specific mortality was elevated among smokers and cardiovascular disease mortality decreased with increasing physical activity levels. Conclusions There are numerous associations between health-related behaviors and mortality outcomes after a DCIS diagnosis. Implications for Cancer Survivors Women diagnosed with DCIS should be aware that their health-related behaviors are associated with mortality outcomes.

This pilot study investigated nurse practitioner students' communication skills when utilizing the electronic health record during history taking. The nurse practitioner students (n = 16) were videotaped utilizing the electronic health record while taking health histories with standardized patients. The students were videotaped during two separate sessions during one semester. Two observers recorded the time spent (1) typing and talking, (2) typing only, and (3) looking at the computer without talking. Total history taking time, computer placement, and communication skills were also recorded. During the formative session, mean history taking time was 11.4 minutes, with 3.5 minutes engaged with the computer (30.6% of visit). During the evaluative session, mean history taking time was 12.4 minutes, with 2.95 minutes engaged with the computer (24% of visit). The percentage of time individuals spent changed over the two visits: typing and talking, -3.1% (P = .3); typing only, +12.8% (P = .038); and looking at the computer, -9.6% (P = .039). This study demonstrated that time spent engaged with the computer during a patient encounter does decrease with student practice and education. Therefore, students benefit from instruction on electronic health record-specific communication skills, and use of a simple mnemonic to reinforce this is suggested.