Despite some limitations, these methods provide useful information on breast cancer risk for women who plan to participate in an annual mammographic screening program.

The majority of patients were overprescribed opioids. Significant prescribing variation exists that was not explained by patient factors. These data will guide practices to optimize opioid prescribing after surgery.

BackgroundResearch addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients.MethodsThe warehouse is based on a National Institutes of Research–funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries.ResultsWe describe the two institutions’ administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request.ConclusionThe resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information.After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2327-8) contains supplementary material, which is available to authorized users.

BackgroundLobular involution, or age-related atrophy of breast lobules, is inversely associated with breast cancer risk, and mammographic breast density (MBD) is positively associated with breast cancer risk.MethodsTo evaluate whether lobular involution and MBD are independently associated with breast cancer risk in women with benign breast disease, we performed a nested cohort study among women (n = 2666) with benign breast disease diagnosed at Mayo Clinic between January 1, 1985, and December 31, 1991 and a mammogram available within 6 months of the diagnosis. Women were followed up for an average of 13.3 years to document any breast cancer incidence. Lobular involution was categorized as none, partial, or complete; parenchymal pattern was classified using the Wolfe classification as N1 (nondense), P1, P2 (ductal prominence occupying <25%, or >25% of the breast, respectively), or DY (extremely dense). Hazard ratios (HRs) and 95% confidence intervals (CIs) to assess associations of lobular involution and MBD with breast cancer risk were estimated using adjusted Cox proportional hazards model. All tests of statistical significance were two-sided.ResultsAfter adjustment for MBD, having no or partial lobular involution was associated with a higher risk of breast cancer than having complete involution (none: HR of breast cancer incidence = 2.62, 95% CI = 1.39 to 4.94; partial: HR of breast cancer incidence = 1.61, 95% CI = 1.03 to 2.53; Ptrend = .002). Similarly, after adjustment for involution, having dense breasts was associated with higher risk of breast cancer than having nondense breasts (for DY: HR of breast cancer incidence = 1.67, 95% CI = 1.03 to 2.73; for P2: HR of breast cancer incidence = 1.96, 95% CI = 1.20 to 3.21; for P1: HR of breast cancer incidence = 1.23, 95% CI = 0.67 to 2.26; Ptrend = .02). Having a combination of no involution and dense breasts was associated with higher risk of breast cancer than having complete involution and nondense breasts (HR of breast cancer incidence = 4.08, 95% CI = 1.72 to 9.68; P = .006).ConclusionLobular involution and MBD are independently associated with breast cancer incidence; combined, they are associated with an even greater risk for breast cancer.