Search

Background: Interventions aimed at upstream factors contributing to late‐stage diagnoses could reduce disparities and improve breast cancer outcomes. This study examines the association between measures of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States. Methods: We studied 67,588 women aged 66–90 from the SEER‐Medicare linked database (2010–2015). The primary outcome was breast cancer stage at diagnosis. Multinomial regression models adjusted for individual and neighborhood socio‐economic factors were performed using a three‐category outcome (stage 0, early stage, and late stage). Key census tract‐level independent variables were residence in the same house as the previous year, owner‐occupied homes, and an index of contemporary mortgage lending bias. Results: In models adjusted for individual factors, higher levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.10, 95% CI 1.02–1.20; RR = 1.31, 95% CI 1.16–1.49; RR = 1.41, 95% CI 1.24–1.60 for least to high, respectively). In models adjusted for individual and neighborhood socio‐economic factors, moderate and high levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.16, 95% CI 1.02–1.33 for moderate and RR = 1.18, 95% CI 1.02–1.37 for high). Owner occupancy and tenure were not associated with later stage diagnosis in adjusted models. Conclusions: Contemporary mortgage lending bias demonstrated a significant gradient relationship with later stage at diagnosis of breast cancer. Policy interventions aimed at reducing place‐based mortgage disinvestment and its impacts on local resources and opportunities should be considered as part of an overall strategy to decrease late‐stage breast cancer diagnosis and improve prognosis. [ABSTRACT FROM AUTHOR]

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time‐to‐event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate‐dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user‐friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood‐borne cancer, where heteroskedasticity and nonproportional hazards are likely present. [ABSTRACT FROM AUTHOR]

Introduction: Hemodialysis (HD) patients have significant burden of cerebral ischemic pathology noted on brain imaging. These ischemic type lesions maybe due to cerebral hypoperfusion that may be occurring during blood pressure (BP) fluctuations commonly noted during HD sessions. We evaluated changes in cerebral perfusion and measured an index of cerebral autoregulation (CA index) during HD to identify potential risk factors for intradialytic decline in cerebral perfusion and impaired cerebral autoregulation. Methods: In this cross‐sectional study, we included HD patients age 50 years or older receiving conventional in‐center HD. We measured cerebral perfusion during HD, using cerebral oximetry, and calculated the correlation between cerebral perfusion and BP during HD as an index of CA. We measured the association between potential risk factors for intradialytic decline in cerebral perfusion and CA index. Findings: We included 32 participants and 118 HD sessions in our analysis. The mean ± SD decline in cerebral oxygen saturation during HD was 6.5% ± 2.9% with a relative decline from baseline values of 9.2% ± 4.4%. Greater drop in systolic BP (SBP) during HD was associated with decline in cerebral oxygen saturation, p = 0.02. Impaired CA index was noted in 37.3% of HD sessions. Having diabetes and >20 mmHg drop in SBP during HD were associated with increased (worse) CA index with an increase of 0.24 95%CI [0.06, 0.41] for diabetes and increase of 0.43 95%CI [0.27, 0.56] for a >20 mmHg drop in SBP during HD. Discussion: Cerebral perfusion can decline during HD and is associated with changes in systemic BP. This may be due to impaired cerebral autoregulation in HD patients. Risk factors for worse CA index include diabetes and >20 mmHg drop in SBP during HD. This study highlights the risk of intradialytic decline in cerebral perfusion and impaired cerebral autoregulation in HD patients. [ABSTRACT FROM AUTHOR]