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Purpose: Over 50% of breast cancer patients prescribed a 5-year course of daily oral adjuvant endocrine therapy (ET) are nonadherent. We investigated the role of costs and cancer medication delivery mode and other medication delivery factors on adherence. Methods: We conducted a retrospective cohort study of commercially insured and Medicare advantage patients with newly diagnosed breast cancer in 2007–2015 who initiated ET. We examined the association between 12-month ET adherence (proportion of days covered by fills ≥ 0.80) and ET copayments, 90-day prescription refill use, mail order pharmacy use, number of pharmacies, and synchronization of medications. We used regression models to estimate nonadherence risk ratios adjusted for demographics (age, income, race, urbanicity), comorbidities, total medications, primary cancer treatments, and generic AI availability. Sensitivity analyses were conducted using alternative specifications for independent variables. Results: Mail order users had higher adherence in both commercial and Medicare-insured cohorts. Commercially insured patients who used mail order were more likely to be adherent if they had low copayments (< $5) and 90-day prescription refills. For commercially insured patients who used local pharmacies, use of one pharmacy and better synchronized refills were also associated with adherence. Among Medicare patients who used mail order pharmacies, only low copayments were associated with adherence, while among Medicare patients using local pharmacies both low copayments and 90-day prescriptions were associated with ET adherence. Conclusion: Out-of-pocket costs, medication delivery mode, and other pharmacy-related medication delivery factors are associated with adherence to breast cancer ET. Future work should investigate whether interventions aimed at streamlining medication delivery could improve adherence for breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Patient contextual data (PCD) are often missing from electronic health records, limiting the opportunity to incorporate preferences and life circumstances into care. Engaging patients through tools that collect and summarize such data may improve communication and patient activation. However, differential tool adoption by race might widen health care disparities. Objective: Determine if a digital tool designed to collect and present PCD improves communication and patient activation; secondarily, evaluate if use impacts outcomes by race. Design, Setting, and Participants: A pragmatic, two-armed, non-blinded, randomized controlled trial conducted during 2019 in a primary care setting. Intervention: The PCD tool (PatientWisdom) invited patients to identify preferences, values, goals, and barriers to care. Patients were randomized to a standard pre-visit email or facilitated enrollment with dedicated outreach to encourage use of the tool. Main Outcomes and Measures: Outcomes of interest were post-visit patient communication and patient activation measured by the Communication Assessment Tool (CAT) and Patient Activation Measure (PAM), respectively. Outcomes were evaluated using treatment-on-the-treated (TOT) and intention-to-treat (ITT) principles. Key Results: A total of 301 patients were enrolled. Facilitated enrollment resulted in a five-fold increase in uptake of the PCD tool. TOT analysis indicated that the PCD tool was associated with notable increases in specific CAT items rated as excellent: "treated me with respect" (+ 13 percentage points; p = 0.04), "showed interest in my ideas" (+ 14 percentage points; p = 0.03), "showed care and concern" (+ 16 percentage points; p = 0.02), and "spent about the right amount of time with me" (+ 11 percentage points; p = 0.05). There were no significant pre/post-visit differences in PAM scores between arms (− 4.41 percentage points; p = 0.58). ITT results were similar. We saw no evidence of the treatment effect varying by race in ITT or TOT analyses. Conclusions and Relevance: The inclusion of PCD enhanced essential aspects of patient-provider communication but did not affect patient activation. Outcomes did not differ by race. Trial Registration: Clincaltrials.gov identifier: NCT03766841 [ABSTRACT FROM AUTHOR]

Background: The aim of this large nationwide study was to validate two novel composite treatment scores that address guideline-concordant locoregional and systemic breast cancer care. We examined the relationship between these two scores and their association with survival. Methods: Women with Stage I–III unilateral breast cancer were identified within the National Cancer Database. For each woman, a locoregional and a systemic treatment score (0, 1, 2) was assigned based on receipt of guideline-concordant care. Multivariable Cox regression models evaluated the association between the scores and survival. Results: 623,756 women were treated at 1,221 different American College of Surgeons Commission on Cancer (CoC) facilities. Overall, 86% had a locoregional treatment score of 2 (most guideline-concordant), 75% had a systemic treatment score of 2, and 72% had both scores of 2. Median follow-up was 4.5 years. Compared to women with a locoregional treatment score of 2, those with a score of 1 or 0 had a 1.7-fold and 2.0-fold adjusted greater risk of death. Compared to women with a systemic treatment score of 2, those with a score of 1 or 0 had a 1.5-fold and 2.1-fold adjusted greater risk of death. Risk-adjusted 5-year overall survival was 91.6% when both scores were 2 compared to 73.4% when both scores were 0. Conclusions: In this large national study of CoC facilities, two composite scores capturing guideline-concordant breast cancer care had independent and combined robust effects on survival. These clinically constructed novel scores are promising tools for health services research and quality-of-care studies. [ABSTRACT FROM AUTHOR]

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models' performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package "DPWeibull" implementing all of the proposed methods is available at CRAN. [ABSTRACT FROM AUTHOR]

In Caucasian and Asian populations, evidence suggests that 24-h blood pressures (BP) are more predictive of long-term cardiovascular events than clinic BP. However, few long-term studies have evaluated the predictive value of 24-h BP phenotypes (24-h, daytime, nighttime) among African Americans (AA). The purpose of this study is to evaluate the added value of 24-h BP phenotypes compared to clinic BP in predicting the subsequent fatal and non-fatal cardiovascular/renal disease events in AA subjects. AA subjects (n = 270) were initially studied between 1994 and 2006 and standardized clinic BP measurements were obtained during screening procedures for a 3-day inpatient clinical study during which 24-h BP measurements were obtained. To assess the subsequent incidence of cardiovascular and renal disease events, follow-up information was obtained and confirmed by review of paper and electronic medical records between 2015 and 2017. During a mean follow-up of 14 ± 4 years, 50 subjects had one or more fatal or non-fatal cardiovascular/renal disease events. After adjustment for covariates, clinic systolic and diastolic BP were strongly associated with cardiovascular/renal disease events and all-cause mortality (p < 0.0001). Twenty-four-hour BP phenotypes conferred a small incremental advantage over clinic BP in predicting cardiovascular/renal events, which was limited to making a difference of one predicted event in 250–1,000 predictions depending on the 24-h BP phenotype. Nocturnal BP was no more predictive than the other 24-h BP phenotypes. In AA, 24-h BP monitoring provides limited added value as a predictor of cardiovascular/renal disease events. Larger studies are needed in AA to confirm these findings. [ABSTRACT FROM AUTHOR]

Purpose: Virtual surgery planning based on computational fluid dynamics (CFD) simulations of nasal airflow has the potential to improve surgical outcomes for patients with nasal airway obstruction (NAO). Virtual surgery planning requires normative ranges of airflow variables, but few studies to date have quantified inter-individual variability of nasal airflow among healthy subjects. This study reports CFD simulations of nasal airflow in 47 healthy adults. Methods: Anatomically accurate three-dimensional nasal models were reconstructed from cone beam computed tomography scans and used for steady-state inspiratory airflow simulations with a bilateral flowrate of 250 ml/s. Normal subjective sensation of nasal patency was confirmed using the nasal obstruction symptom evaluation and visual analog scale. Healthy ranges for several CFD variables known to correlate with subjective nasal patency were computed, including unilateral airflow, nasal resistance, airspace minimal cross-sectional area (mCSA), heat flux (HF), and surface area stimulated by mucosal cooling (defined as the area where HF > 50 W/m2). The normative ranges were targeted to contain 95% of the healthy population and computed using a nonparametric method based on order statistics. Results: A wide range of inter-individual variability in nasal airflow was observed among healthy subjects. Unilateral airflow varied from 60 to 191 ml/s, airflow partitioning ranged from 23.8 to 76.2%, and unilateral mCSA varied from 0.24 to 1.21 cm2. These ranges are in good agreement with rhinomanometry and acoustic rhinometry data from the literature. A key innovation of this study are the normative ranges of flow variables associated with mucosal cooling, which recent research suggests is the primary physiological mechanism of nasal airflow sensation. Unilateral HF ranged from 94 to 281 W/m2, while the surface area stimulated by cooling ranged from 27.4 to 64.3 cm2. Conclusions: These normative ranges may serve as targets in future virtual surgery planning for patients with NAO. [ABSTRACT FROM AUTHOR]

Purpose: Advanced practice providers (APPs) have increasingly become members of the oncology care team. Little is known about the scope of care that APPs are performing nationally. We determined the prevalence and extent of APP practice and examined associations between APP care and scope of practice regulations, phase of cancer care, and patient characteristics. Methods: We performed an observational study among women identified from Medicare claims as having had incident breast cancer in 2008 with claims through 2012. Outpatient APP care included at least one APP independently billing for cancer visits/services. APP scope of practice was classified as independent, reduced, or restricted. A logistic regression model with patient-level random effects was estimated to determine the probability of receiving APP care at any point during active treatment or surveillance. Results: Among 42,550 women, 6583 (15%) received APP care, of whom 83% had APP care during the surveillance phase and 41% during the treatment phase. Among women who received APP care during a given year of surveillance, the overall proportion of APP-billed clinic visits increased with each additional year of surveillance (36% in Year 1 to 61% in Year 4). Logistic regression model results indicate that women were more likely to receive APP care if they were younger, black, healthier, had higher income status, or lived in a rural county or state with independent APP scope of practice. Conclusions: This study provides important clinical and policy-relevant findings regarding national practice patterns of APP oncology care. Among Medicare beneficiaries with incident breast cancer, 15% received outpatient oncology care that included APPs who were billing; most of this care was during the surveillance phase. Future studies are needed to define the degree of APP oncology practice and training that maximizes patient access and satisfaction while optimizing the efficiency and quality of cancer care. [ABSTRACT FROM AUTHOR]

Purpose: To calculate tract-level estimates of liver cancer mortality in Wisconsin and identify relationships with racial and socioeconomic variables.Methods: County-level standardized mortality ratios (SMRs) of liver cancer in Wisconsin were calculated using traditional indirect adjustment methods for cases from 2003 to 2012. Tract-level SMRs were calculated using adaptive spatial filtering (ASF). The tract-level SMRs were checked for correlations to a socioeconomic advantage index (SEA) and percent racial composition. Non-spatial and spatial regression analyses with tract-level SMR as the outcome were conducted.Results: County-level SMR estimates were shown to mask much of the variance within counties across their tracts. Liver cancer mortality was strongly correlated with the percent of Black residents in a census tract and moderately associated with SEA. In the multivariate spatially-adjusted regression analysis, only Percent Black composition remained significantly associated with an increased liver cancer SMR.Conclusions: Using ASF, we developed a high-resolution map of liver cancer mortality in Wisconsin. This map provided details on the distribution of liver cancer that were inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables. [ABSTRACT FROM AUTHOR]

Purpose: Although users of aromatase inhibitors have higher total fracture risk in some randomized trials, little is known about their risk outside of clinical trials or in older higher-risk cohorts.Methods: In a population-based retrospective cohort study, we identified all older US Medicare D prescription drug insurance plan-enrolled women who had initial breast cancer surgery in 2006-2008 and began hormonal therapy (an aromatase inhibitor (AI) or tamoxifen) within the subsequent year. Total nonvertebral and hip fractures through 2012 were identified using a validated algorithm. The association of fracture outcomes with hormonal therapy type was assessed using competing risk regression models that accounted for differences in measured baseline covariates. Treatment assignment bias was reduced using inverse probability of treatment weighting computed from propensity scores.Results: Among 23,378 women taking hormonal therapy (23.2% aged 80 or over), there were 3000 total and 436 hip fractures. Although AI users were younger and had lower comorbidity, after propensity score weighting, these and other covariates were balanced. Total nonvertebral risk was higher for users of AIs compared with tamoxifen, HR 1.11 (1.02-1.21), but the small increase in risk for hip fracture was not statistically significant, HR 1.04 (0.84-1.30).Conclusions: Although total nonvertebral fracture risk was higher among AI users, differences in hip fractures were not significant in a large population-based cohort of older women.Implications For Cancer Survivors: Use of aromatase inhibitors by older women is associated with high risk for nonvertebral fracture that is increased compared with use of tamoxifen. Fracture risk should be assessed among patients taking these medications. [ABSTRACT FROM AUTHOR]

Purpose: Population-based studies have revealed higher mortality among breast cancer patients treated in low-volume hospitals. Other studies have demonstrated disparities in race and socioeconomic status (SES) in breast cancer survival. The purpose of our study was to determine whether nonwhite or low-SES patients are disproportionately treated in low-volume hospitals. Methods: A population-based cohort of 2,777 Medicare breast cancer patients who underwent breast cancer surgery in 2003 participated in a survey study examining breast cancer outcomes. Information was obtained from survey responses, Medicare claims, and state tumor registry data. Results: On univariate analysis, patients treated at low-volume hospitals were less likely to be white, less likely to live in an urban location, and more likely to have a low SES with less social support and live a greater distance from a high-volume hospital. Education, marital status, total household income, having additional insurance besides Medicare, population density of primary residence, and tangible support were associated with distance to the nearest high-volume hospital. On multivariate analysis, the independent predictors of treatment at a low-volume hospital were being nonwhite ( P = 0.003), having a lower household income ( P < 0.0001), residence in a rural location ( P = 0.01), and living a greater distance from a high-volume hospital ( P < 0.0001). Conclusions: In this large population-based cohort, women who were poorer, nonwhite, and who lived in a rural location or at a greater distance from a high-volume hospital were more likely to be treated at low-volume hospitals. These differences may partially explain racial and SES disparities in breast cancer outcomes. [ABSTRACT FROM AUTHOR]

Health numeracy is associated with increased understanding of cancer risk reduction information and improved control of chronic disease. A cross-sectional survey was conducted among a primary care population to evaluate the effect of health numeracy on breast, cervical, and colorectal cancer screening. No association was found between health numeracy and cancer screening. However, at a baseline screening rate of 85%, increased knowledge (RR 1.06, 95% CI 1.02-1.08) and decreased perceived barriers (RR 0.93, 95% CI 0.92-0.95) were associated with increased screening rates. In conclusion, health numeracy was not predictive of cancer screening among a primary care population. [ABSTRACT FROM AUTHOR]

Background: Racial and socioeconomic disparities have been identified in osteoporosis screening.Objective: To determine whether racial and socioeconomic disparities in osteoporosis screening diminish after hip fracture.Design: Retrospective cohort study of female Medicare patients.Setting: Entire states of Illinois, New York, and Florida.Participants: Female Medicare recipients aged 65-89 years old with hip fractures between January 2001 and June 2003.Measurements: Differences in bone density testing by race/ethnicity and zip-code level socioeconomic characteristics during the 2-year period preceding and the 6-month period following a hip fracture.Results: Among all 35,681 women with hip fractures, 20.7% underwent bone mineral density testing in the 2 years prior to fracture and another 6.2% underwent testing in the 6 months after fracture. In a logistic regression model adjusted for age, state, and comorbidity, women of black race were about half as likely (RR 0.52 [0.43, 0.62]) and Hispanic women about 2/3 as likely (RR 0.66 [0.54, 0.80]) as white women to undergo testing before their fracture. They remained less likely (RR 0.66 [0.50, 0.88] and 0.58 [0.39, 0.87], respectively) to undergo testing after fracture. In contrast, women residing in zip codes in the lowest tertile of income and education were less likely than those in higher-income and educational tertiles to undergo testing before fracture, but were no less likely to undergo testing in the 6 months after fracture.Conclusions: Racial, but not socioeconomic, differences in osteoporosis evaluation continued to occur even after Medicare patients had demonstrated their propensity to fracture. Future interventions may need to target racial/ethnic and socioeconomic disparities differently. [ABSTRACT FROM AUTHOR]

Within the context of non-parametric Bayesian inference, Dykstra and Laud (1981) define an extended gamma (EG) process and use it as a prior on increasing hazard rates. The attractive features of the extended gamma (EG) process, among them its capability to index distribution functions that are absolutely continuous, are offset by the intractable nature of the computation that needs to be performed. Sampling based approaches such as the Gibbs Sampler can alleviate these difficulties but the EG processes then give rise to the problem of efficient random variate generation from a class of distributions called D-distributions. In this paper, we describe a novel technique for sampling from such distributions, thereby providing an efficient computation procedure for non-parametric Bayesian inference with a rich class of priors for hazard rates. [ABSTRACT FROM AUTHOR]