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PURPOSE: Higher mortality after a breast cancer diagnosis has been observed among women who are obese. We investigated the relationships between body mass index (BMI) and all-cause or breast cancer-specific mortality after a diagnosis of locoregional breast cancer. METHODS: Women diagnosed in 2004 with AJCC Stage I, II, or III breast cancer (n = 5394) were identified from a population-based National Program of Cancer Registries (NPCR) patterns of care study (POC-BP) drawing from registries in seven U.S. states. Differences in overall and breast cancer-specific mortality were investigated using Cox proportional hazards regression models adjusting for demographic and clinical covariates, including age- and stage-based subgroup analyses. RESULTS: In women 70 or older, higher BMI was associated with lower overall mortality (HR for a 5 kg/m(2) difference in BMI = 0.85, 95% CI 0.75–0.95). There was no significant association between BMI and overall mortality for women under 70. BMI was not associated with breast cancer death in the full sample, but among women with Stage I disease; those in the highest BMI category had significantly higher breast cancer mortality (HR for BMI ≥ 35 kg/m(2) vs. 18.5–24.9 kg/m(2) = 4.74, 95% CI 1.78–12.59). CONCLUSIONS: Contrary to our hypothesis, greater BMI was not associated with higher overall mortality. Among older women, BMI was inversely related to overall mortality, with a null association among younger women. Higher BMI was associated with breast cancer mortality among women with Stage I disease, but not among women with more advanced disease.

Although not guideline recommended, studies suggest 50% of locoregional breast cancer patients undergo systemic imaging during follow-up, prompting its inclusion as a Choosing Wisely measure of potential overuse. Most studies rely on administrative data that cannot delineate scan intent (prompted by signs/symptoms vs. asymptomatic surveillance). This is a critical gap as intent is the only way to distinguish overuse from appropriate care. Our aim was to assess surveillance systemic imaging post-breast cancer treatment in a national sample accounting for scan intent. A stage-stratified random sample of 10 women with stage II–III breast cancer in 2006–2007 was selected from each of 1217 Commission on Cancer-accredited facilities, for a total of 10,838 patients. Registrars abstracted scan type (computed tomography [CT], non-breast magnetic resonance imaging, bone scan, positron emission tomography/CT) and intent (cancer-related vs. not, asymptomatic surveillance vs. not) from medical records for 5 years post-diagnosis. Data were merged with each patient’s corresponding National Cancer Database record, containing sociodemographic and tumor/treatment information. Of 10,838 women, 30% had one or more, and 12% had two or more, systemic surveillance scans during a 4-year follow-up period. Patients were more likely to receive surveillance imaging in the first follow-up year (lower proportions during subsequent years) and if they had estrogen receptor/progesterone receptor-negative tumors. Locoregional breast cancer patients undergo asymptomatic systemic imaging during follow-up despite guidelines recommending against it, but at lower rates than previously reported. Providers appear to use factors that confer increased recurrence risk to tailor decisions about systemic surveillance imaging, perhaps reflecting limitations of data on which current guidelines are based. ClinicalTrials.gov Identifier: NCT02171078.

Simulations play an increasingly important role in the evaluation of osteoporosis interventions. Existing evaluations have been based on "reduced-form" cohort simulations that do not reflect the complexity and heterogeneity of osteoporosis and its outcomes. Such simplified models offer parsimony and ease of use, but they also are limited in their ability to explain and extrapolate outcomes in a way that is most useful for both clinical and health policy decision makers. Alternatively, evaluations could be based on "structural" microsimulations, which explicitly model the underlying biology of osteoporosis at the individual level. The structural approach presents technical challenges, including the need to obtain more-detailed data and the requirement that underlying biological models be validated. However, evaluations based on structural microsimulation may ultimately provide substantially more useful information, resulting in improved decision making.

We characterize employer-sponsored health insurance offering strategies in light of benefit non-discrimination and minimum wage regulation when workers have heterogeneous earnings and partially unobservable demand for (and cost of) insurance. We then empirically examine how earnings and expected medical expenses are associated with low wage workers' ability to obtain insurance before and after enactment of federal benefit non-discrimination rules. We find no evidence that the non-discrimination rules helped low wage workers (especially those with high own or children's expected medical expenses) to obtain insurance.