Pankratz, V. Shane

Abstract Background Objective cost estimates and source of cost differences are needed across the spectrum of cognition, including cognitively normal (CN), mild cognitive impairment (MCI), newly discovered dementia, and prevalent dementia. Methods Subjects were a subset of the Mayo Clinic Study of Aging stratified‐random sampling of Olmsted County, MN, residents aged 70 to 89 years. A neurologist reviewed provider‐linked medical records to identify prevalent dementia (review date = index). Remaining subjects were invited to participate in prospective clinical/neuropsychological assessments; participants were categorized as CN, MCI, or newly discovered dementia (assessment date = index). Costs for medical services/procedures 1‐year pre‐index (excluding indirect and long‐term care costs) were estimated using line‐item provider‐linked administrative data. We estimated contributions of care‐delivery site and comorbid conditions (including and excluding neuropsychiatric diagnoses) to between‐category cost differences. Results Annual mean medical costs for CN, MCI, newly discovered dementia, and prevalent dementia were $6042, $6784, $9431, $11,678, respectively. Hospital inpatient costs contributed 70% of total costs for prevalent dementia and accounted for differences between CN and both prevalent and newly discovered dementia. Ambulatory costs accounted for differences between CN and MCI. Age‐, sex‐, education‐adjusted differences reached significance for CN versus newly discovered and prevalent dementia and for MCI versus prevalent dementia. After considering all comorbid diagnoses, between‐category differences were reduced (e.g., prevalent dementia minus MCI (from $4842 to $3575); newly discovered dementia minus CN (from $3578 to $711)). Following the exclusion of neuropsychiatric diagnoses from comorbidity adjustment, between‐category differences tended to revert to greater differences. Conclusions Cost estimates did not differ significantly between CN and MCI. Substantial differences between MCI and prevalent dementia reflected high inpatient costs for dementia and appear partly related to co‐occurring mental disorders. Such comparisons can help inform models aimed at identifying where, when, and for which individuals proposed interventions might be cost‐effective.

AbstractIntroductionThe feasibility and validity of brief computerized cognitive batteries at the population‐level are unknown.MethodsNondemented participants (n = 1660, age 50–97 years) in the Mayo Clinic Study on Aging completed the computerized CogState battery and standard neuropsychological battery. The correlation between tests was examined and comparisons between CogState performance on the personal computer (PC) and iPad (n = 331), and in the clinic vs. at home (n = 194), were assessed.ResultsWe obtained valid data on greater than 97% of participants on each test. Correlations between the CogState and neuropsychological tests ranged from −0.462 to 0.531. Although absolute differences between the PC and iPad were small and participants preferred the iPad, performance on the PC was faster. Participants performed faster on Detection, One Card Learning, and One Back at home compared with the clinic.DiscussionThe computerized CogState battery, especially the iPad, was feasible, acceptable, and valid in the population.

Abstract Background: Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent mammographic density (PMD) with larger and node-positive tumors across all ages, and estrogen receptor (ER)–negative status among women ages <55 years. To provide insight into these associations, we examined the components of PMD [dense area (DA) and nondense area (NDA)] with breast cancer subtypes. Methods: Data were pooled from six studies including 4,095 breast cancers and 8,558 controls. DA and NDA were assessed from digitized film-screen mammograms and standardized across studies. Breast cancer odds by density phenotypes and age according to histopathologic characteristics and receptor status were calculated using polytomous logistic regression. Results: DA was associated with increased breast cancer risk [OR for quartiles: 0.65, 1.00 (Ref), 1.22, 1.55; Ptrend <0.001] and NDA was associated with decreased risk [ORs for quartiles: 1.39, 1.00 (Ref), 0.88, 0.72; Ptrend <0.001] across all ages and invasive tumor characteristics. There were significant trends in the magnitude of associations of both DA and NDA with breast cancer by increasing tumor size (Ptrend < 0.001) but no differences by nodal status. Among women <55 years, DA was more strongly associated with increased risk of ER+ versus ER− tumors (Phet = 0.02), while NDA was more strongly associated with decreased risk of ER− versus ER+ tumors (Phet = 0.03). Conclusions: DA and NDA have differential associations with ER+ versus ER− tumors that vary by age. Impact: DA and NDA are important to consider when developing age- and subtype-specific risk models. Cancer Epidemiol Biomarkers Prev; 24(5); 798–809. ©2015 AACR.

Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

Purpose Recent advances in DNA sequencing have led to the development of breast cancer susceptibility gene panels for germline genetic testing of patients. We assessed the frequency of mutations in 17 predisposition genes, including BRCA1 and BRCA2, in a large cohort of patients with triple-negative breast cancer (TNBC) unselected for family history of breast or ovarian cancer to determine the utility of germline genetic testing for those with TNBC. Patients and Methods Patients with TNBC (N = 1,824) unselected for family history of breast or ovarian cancer were recruited through 12 studies, and germline DNA was sequenced to identify mutations. Results Deleterious mutations were identified in 14.6% of all patients. Of these, 11.2% had mutations in the BRCA1 (8.5%) and BRCA2 (2.7%) genes. Deleterious mutations in 15 other predisposition genes were detected in 3.7% of patients, with the majority observed in genes involved in homologous recombination, including PALB2 (1.2%) and BARD1, RAD51D, RAD51C, and BRIP1 (0.3% to 0.5%). Patients with TNBC with mutations were diagnosed at an earlier age (P < .001) and had higher-grade tumors (P = .01) than those without mutations. Conclusion Deleterious mutations in predisposition genes are present at high frequency in patients with TNBC unselected for family history of cancer. Mutation prevalence estimates suggest that patients with TNBC, regardless of age at diagnosis or family history of cancer, should be considered for germline genetic testing of BRCA1 and BRCA2. Although mutations in other predisposition genes are observed among patients with TNBC, better cancer risk estimates are needed before these mutations are used for clinical risk assessment in relatives.