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Background: Cancer contributes substantially to the life expectancy gap between US blacks and whites, and racial cancer disparities remain stubborn to eradicate. Disparities vary geographically, suggesting that they are not inevitable.Methods: The authors examined the relationship between housing discrimination and the size of cancer disparities across large US metropolitan statistical areas (MSAs). MSA-level cancer disparities were measured using data from the US Centers for Disease Control and Prevention. Mortgage discrimination for each MSA was estimated using the Home Mortgage Disclosure Act database, and MSA racial segregation was determined using US Census data. Patterns of housing discrimination and cancer disparities were mapped, and the associations between these place-based factors and cancer disparities across MSAs were measured.Results: Black-to-white cancer mortality disparities (rate ratios) varied geographically, ranging from 1.50 to 0.86; 88% of mortality ratios were >1, indicating higher mortality for blacks. In areas with greater mortgage discrimination, the gap between black and white cancer mortality rates was larger (correlation coefficient [r] = 0.32; P = .001). This relationship persisted in sex-specific analyses (males, r = 0.37; P < .001; females, r = 0.23; P = .02) and in models controlling for confounders. In contrast, segregation was inconsistently associated with disparities. Adjusting for incidence disparities attenuated, but did not eliminate, the correlation between mortgage discrimination and mortality disparities (r = 0.22-0.24), suggesting that cancer incidence and survival each account for part of the mortality disparity.Conclusions: Mortgage discrimination is associated with larger black-to-white cancer mortality disparities. Some areas are exceptions to this trend. Examination of these exceptions and of policies related to housing discrimination may offer novel strategies for explaining and eliminating cancer disparities. [ABSTRACT FROM AUTHOR]

Residential segregation is an important factor that negatively impacts cancer disparities, yet studies yield mixed results and complicate clear recommendations for policy change and public health intervention. In this study, we examined the relationship between local and Metropolitan Statistical Area (MSA) measures of Black isolation (segregation) and survival among older non-Hispanic (NH) Black women with breast cancer (BC) in the United States. We hypothesized that the influence of local isolation on mortality varies based on MSA isolation—specifically, that high local isolation may be protective in the context of highly segregated MSAs, as ethnic density may offer opportunities for social support and buffer racialized groups from the harmful influences of racism. Local and MSA measures of isolation were linked by Census Tract (CT) with a SEER-Medicare cohort of 5,231 NH Black women aged 66–90 years with an initial diagnosis of stage I-IV BC in 2007–2013 with follow-up through 2018. Proportional and cause-specific hazards models and estimated marginal means were used to examine the relationship between local and MSA isolation and all-cause and BC-specific mortality, accounting for covariates (age, comorbidities, tumor stage, and hormone receptor status). Of 2,599 NH Black women who died, 40.0% died from BC. Women experienced increased risk for all-cause mortality when living in either high local (HR = 1.20; CI = 1.08–1.33; p < 0.001) or high MSA isolation (HR = 1.40; CI = 1.17–1.67; p < 0.001). A similar trend existed for BC-specific mortality. Pairwise comparisons for all-cause mortality models showed that high local isolation was hazardous in less isolated MSAs but was not significant in more isolated MSAs. Both local and MSA isolation are independently associated with poorer overall and BC-specific survival for older NH Black women. However, the impact of local isolation on survival appears to depend on the metropolitan area's level of segregation. Specifically, in highly segregated MSAs, living in an area with high local isolation is not significantly associated with poorer survival. While the reasons for this are not ascertained in this study, it is possible that the protective qualities of ethnic density (e.g., social support and buffering from experiences of racism) may have a greater role in more segregated MSAs, serving as a counterpart to the hazardous qualities of local isolation. More research is needed to fully understand these complex relationships. National Cancer Institute. • Residential racial segregation is an important factor that influences mortality outcomes. • The impact of isolation, a measure of segregation, on survival may vary by race/ethnicity. • Racial/ethnic enclaves may provide protective health benefits for breast cancer survival. • Greater isolation/segregation negatively impacts survival outcomes for NH Black women. [ABSTRACT FROM AUTHOR]

Background Challenged by public opinion, peers and the Congressional Budget Office, medical specialty societies have begun to develop “Top Five” lists of expensive procedures that do not provide meaningful benefit to at least some categories of patients for whom they are commonly ordered. The extent to which these lists have influenced the behavior of physicians or patients, however, remains unknown. Methods We partner with a statewide consortium of health systems to examine the effectiveness of two interventions: (i) “basic” public reporting and (ii) an “enhanced” intervention, augmenting public reporting with a smart phone-based application that gives providers just-in-time information, decision-making tools, and personalized patient education materials to support reductions in the use of eight breast cancer interventions targeted by Choosing Wisely® or oncology society guidelines. Our aims are: (1) to examine whether basic public reporting reduces use of targeted breast cancer practices among a contemporary cohort of patients with incident breast cancer in the intervention state relative to usual care in comparison states; (2) to examine the effectiveness of the enhanced intervention relative to the basic intervention; and (3) to simulate cost savings forthcoming from nationwide implementation of both interventions. Discussion The results will provide rigorous evidence regarding the effectiveness of a unique all-payer, all-age public reporting system for influencing provider behavior that may be easily exportable to other states, and potentially also to large healthcare systems. Findings will be further relevant to the ACO environment, which is expected to provide financial disincentives for ineffective or unproven care. Trial Registration: ClinicalTrials.gov number pending. [ABSTRACT FROM AUTHOR]

We introduce a new approach to inference for subgroups in clinical trials. We use Bayesian model selection, and a threshold on posterior model probabilities to identify subgroup effects for reporting. For each covariate of interest, we define a separate class of models, and use the posterior probability associated with each model and the threshold to determine the existence of a subgroup effect. As usual in Bayesian clinical trial design we compute frequentist operating characteristics, and achieve the desired error probabilities by choosing an appropriate threshold(s) for the posterior probabilities. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new simulation method, auxiliary random functions is introduced. When used within a Gibbs sampler, this method enables a unified treatment of exact, right-censored, left-censored, left-truncated and interval censored data, with and without covariates in survival models. The models and methods are exemplified via illustrative analysis. [ABSTRACT FROM AUTHOR]

To develop and validate a clinically informed algorithm that uses solely Medicare claims to identify, with a high positive predictive value, incident breast cancer cases.Population-based Surveillance, Epidemiology, and End Results (SEER) Tumor Registry data linked to Medicare claims, and Medicare claims from a 5 percent random sample of beneficiaries in SEER areas.An algorithm was developed using claims from 1995 breast cancer patients from the SEER-Medicare database, as well as 1995 claims from Medicare control subjects. The algorithm was validated on claims from breast cancer subjects and controls from 1994. The algorithm development process used both clinical insight and logistic regression methods.Training set: Claims from 7,700 SEER-Medicare breast cancer subjects diagnosed in 1995, and 124,884 controls. Validation set: Claims from 7,607 SEER-Medicare breast cancer subjects diagnosed in 1994, and 120,317 controls.A four-step prediction algorithm was developed and validated. It has a positive predictive value of 89 to 93 percent, and a sensitivity of 80 percent for identifying incident breast cancer. The sensitivity is 82–87 percent for stage I or II, and lower for other stages. The sensitivity is 82–83 percent for women who underwent either breast-conserving surgery or mastectomy, and is similar across geographic sites. A cohort identified with this algorithm will have 89–93 percent incident breast cancer cases, 1.5–6 percent cancer-free cases, and 4–5 percent prevalent breast cancer cases.This algorithm has better performance characteristics than previously proposed algorithms. The ability to examine national patterns of breast cancer care using Medicare claims data would open new avenues for the assessment of quality of care. [ABSTRACT FROM AUTHOR]

Here we extend predictive method for model selection of Laud and Ibrahim to the generalized linear model. This prescription avoids the need to directly specify prior probabilities of models and prior densities for the parameters. Instead, a prior prediction for the response induces the required priors. We propose normal and conjugate priors for generalized linear models, each using a single prior prediction for the mean response to induce suitable priors for each variable-subset model. In this way, an informative prior is used to select a subset of variables. In addition to producing a ranking of models by size of the predictive criterion, the standard deviation of the criterion is used as a calibration number to produce a set of equally good models. A straightforward Markov chain Monte Carlo algorithm is used to accomplish the necessary computations. We illustrate this method with real and simulated datasets and compare results with the Bayes factors and the Akaike information and Bayes information model selection criteria. The simulation results confirm the efficacy of the method. because the correct model is known. An illustrative application demonstrates selection of important predictors of success in identifying the sentinel lymph node during surgical treatment ot breast cancer. A forward selection procedure is described to avoid a full search over the 218 possible models in this case. [ABSTRACT FROM AUTHOR]

In cost-effectiveness analysis, the incremental cost-effectiveness ratio is used to measure economic efficiency of a new intervention, relative to an existing one. However, costs and effects are seldom known with certainty. Uncertainty arises from two main sources: uncertainty regarding correct values of intervention-related parameters and uncertainty associated with sampling variation. Recently, attention has focused on Bayesian techniques for quantifying uncertainty. We computed the Bayesian-based 95% credible interval estimates of the incremental cost-effectiveness ratio of several related HIV prevention interventions and compared these results with univariate sensitivity analyses. The conclusions were comparable, even though the probabilistic technique provided additional information. [ABSTRACT FROM AUTHOR]

SUMMARY We examine the problem of specifying prior probabilities for all possible subset models in the context of variable selection in normal linear models. A solution is proposed that uses a prior prediction for the observable, an associated weight, and prior opinion regarding error precision as the only required input. Numerical examples are given to illustrate the method. [ABSTRACT FROM AUTHOR]

Presents a paper on the computation of several number theoretic functions using the application of probability. Blocks of numbers; Sum of ratio of blocks to bits.

Introduces the use of a predictive Bayesian criterion in the context of model selection problem based on the predictive density of a replicate experiment (PDRE). Provision of a calibration of the criterion; Relationships of the proposed criterion to other prevalent criteria; Illustration of the proposed methodology.

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]

Background: One-third to one-half of patients prescribed adjuvant endocrine therapy are nonadherent during the recommended 5-year endocrine therapy course. This study investigated whether poor pharmacy synchronization of medication fills (requiring refills on different days) acts as a barrier to adherence.Methods: A cohort of older women with stage 0 to III endocrine receptor-positive breast cancer in 2011 was identified from the Surveillance, Epidemiology, and End Result-Medicare claims-linked cancer registry. Women with endocrine therapy and at least 1 other medication fill were identified, and the 3-month synchronization of their fills was calculated as 1 minus the quotient of the number of pharmacy visits and the number of filled medications. Regression models were used to examine the association between synchronization (in quartiles adjusted for the number of medications) and adherence to endocrine therapy (defined as a medication possession ratio ≥80%) over the subsequent year.Results: During the 3 months after the first endocrine therapy prescription, the study cohort of 3212 women had a mean of 8.6 pharmacy visits (standard deviation, 4.7) with a mean synchronization of 0.3 (standard deviation, 0.2). Those in the third (odds ratio, 1.29; 95% confidence interval, 1.04-1.59) and fourth (most) medication number-adjusted synchronization quartiles (odds ratio, 1.49; 95% confidence interval, 1.19-1.86) were more likely to be adherent than those in the least. Multivariate model predictions showed that the proportion of patients who were adherent over 1 year varied from 68.9% in the least synchronized quartile to 76.6% in the most synchronized one.Conclusions: Prescription refill synchronization is strongly associated with adherence to endocrine therapy. Efforts to improve adherence should address this. [ABSTRACT FROM AUTHOR]

Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies. [ABSTRACT FROM AUTHOR]

The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury. The National Trauma Databank (NTDB) was used to identify patients 65+ y between 2007 and 2014. Training was performed on an enriched cohort of diverse patients. Factors included age, comorbidities, length of stay, and physiologic parameters to predict in-hospital mortality and discharge disposition (home versus skilled nursing/long-term care facility). Length of stay and discharge status were analyzed via competing risks survival analysis with Bayesian additive regression trees and a multinomial mixed model. The resulting sample size was 47,037 patients. Admission GCS and age were important in predicting mortality and discharge disposition. As GCS decreased, patients were more likely to die (risk ratio increased by average of 1.4 per 2-point drop in GCS, P < 0.001). As GCS decreased, patients were also more likely to be discharged to a skilled nursing or long-term care facility (risk ratio decreased by 0.08 per 2-point decrease in GCS, P < 0.001). The area under curve for prediction of discharge home was improved in the competing risks model 0.73 versus 0.43 in the traditional multinomial mixed model. Predicting older adult discharge disposition after trauma is improved using machine learning over traditional regression analysis. We confirmed that a nonlinear, competing risks paradigm enhances prediction on any given hospital day post injury. [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]

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]

Les auteurs considèrent des méthodes bayésiennes pour ajuster trois modèles de survie semi-paramétriques incorporant des covariables dépendant du temps. En particulier, ces modèles sont dus à Cox [Cox ( [ABSTRACT FROM AUTHOR]

PURPOSE: Donor selection practices for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) vary, and the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models has not been studied. METHODS: We trained a Bayesian ML model in 10,318 patients who underwent MUD HCT from 1999 to 2014 to provide patient- and donor-specific predictions of clinically severe (grade 3 or 4) acute graft-versus-host disease or death by day 180. The model was validated in 3,501 patients from 2015 to 2016 with archived records of potential donors at search. Donor selection optimizing predicted outcomes was implemented over either an unlimited donor pool or the donors in the search archives. Posterior mean differences in outcomes from optimal donor selection versus actual practice were summarized per patient and across the population with 95% intervals. RESULTS: Event rates were 33% (training) and 37% (validation). Among donor features, only age affected outcomes, with the effect consistent regardless of patient features. The median (interquartile range) difference in age between the youngest donor at search and the selected donor was 6 (1-10) years, whereas the number of donors per patient younger than the selected donor was 6 (1-36). Fourteen percent of the validation data set had an approximate 5% absolute reduction in event rates from selecting the youngest donor at search versus the actual donor used, leading to an absolute population reduction of 1% (95% interval, 0 to 3). CONCLUSION: We confirmed the singular importance of selecting the youngest available MUD, irrespective of patient features, identified potential for improved HCT outcomes by selecting a younger MUD, and demonstrated use of novel ML models transferable to optimize other complex treatment decisions in a patient-specific way. [ABSTRACT FROM AUTHOR]

Dans cet article nons étudions l'estimation par la méthode du maximum de vraisemblance des probabilités p [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]

Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

Introduction: Educational interventions to promote patients' self-management of chronic medical conditions can lead to better health outcomes. However, it is unclear what patient factors affect their response to such interventions in the management of hypertension. Hypothesis: We hypothesized that more activated patients would have a greater improvement in systolic blood pressure (SBP). Methods: We implemented two educational interventions in 404 veterans with hypertension. They had a similar impact on SBP, decreasing by a mean of 4.4 mm Hg and 10.1 mm Hg among those with uncontrolled BP (>/=140/90 mm Hg). We used validated scales to measure factors that might influence patient response to education, including self-efficacy, Patient Activation Measure (PAM), healthcare attitudes, hypertension knowledge and medication adherence. Our primary outcome was a decrease in SBP (ΔSBP). We first examined the association of each factor with ΔSBP. We then repeated this analysis after adjusting for baseline SBP, which was the most powerful predictor. Finally, we used stepwise linear regression to identify factors that were independently associated with ΔSBP. We did not adjust results for multiple comparisons; all p-values are 2-sided. Results: We obtained 12-month SBP data from 379 participants. Each 10 mm Hg increase in baseline SBP was associated with a 5.6 mm Hg larger ΔSBP (95% CI, 4.8-6.5). While age, education and medical comorbidities were unrelated to ΔSBP, self-efficacy and PAM were associated in bivariate analysis. Measures of healthcare attitudes, hypertension knowledge and medication adherence all lacked statistical significance. After adjusting for baseline SBP, each standard deviation (SD) change in self-efficacy was associated with a 1.6 mm Hg larger ΔSBP (95% CI, 0.25-2.9). Similarly, each SD change in PAM was associated with 0.9 mm Hg larger ΔSBP (95% CI, -0.4-2.3). In multivariate analysis, only baseline SBP and self-efficacy were associated with ΔSBP. Effect size was the same as in bivariate analysis. Conclusions: In conclusion, among this population of veterans, self-efficacy, as measured by the General Self-Efficacy scale, was associated with a greater impact of education on SBP. Strategies to enhance self-efficacy may aid in successful chronic management of hypertension. [ABSTRACT FROM AUTHOR]

Purpose: The effect of advanced practice provider (APP) involvement in oncology care on cancer-specific outcomes is unknown. We examined the association between team-based APP–physician care during chemotherapy and chemotherapy-related adverse events (AEs) among women with breast cancer. Methods: We performed separate nested case-control analyses in two national cohorts of women who received chemotherapy for incident breast cancer. Cohorts were identified from Medicare (≥ 65 years of age) and MarketScan (18 to 64 years of age) data. Cases experienced a chemotherapy-related AE (emergency room visit and/or hospitalization). Controls were matched 1:1 on the basis of each patient's age, comorbidities, census region, state's APP scope of practice regulations, and observation period from chemotherapy initiation to first AE. APP exposure (any outpatient claim billed by an APP during the observation period) was assessed for each matched pair member. Results: Among the 1,948 cases in the Medicare cohort, 225 (12%) had APP exposure before the first chemotherapy-related AE, compared with 213 controls (11%; P =.54). Among the 725 cases in the MarketScan cohort, 52 (7%) had APP exposure compared with 65 controls (9%; P =.21). In the matched case-control analysis, there was no association between outpatient APP exposure during chemotherapy and AEs in either cohort (Medicare: OR, 1.06 [95% CI, 0.87 to 1.30]; MarketScan: OR, 0.76 [95% CI, 0.50 to 1.14]). Conclusion: Our results suggest that team-based APP-physician care that includes an APP who is billing independently, at least for certain patients receiving chemotherapy, may be a viable strategy to safely leverage the scarce oncology workforce to increase access and delivery of cancer care. [ABSTRACT FROM AUTHOR]

Studies exploring the development of hypertension have traditionally been unable to distinguish which of the observed changes are underlying causes from those that are a consequence of elevated blood pressure. In this study, a custom-designed servo-control system was utilized to precisely control renal perfusion pressure to the left kidney continuously during the development of hypertension in Dahl salt-sensitive rats. In this way, we maintained the left kidney at control blood pressure while the right kidney was exposed to hypertensive pressures. As each kidney was exposed to the same circulating factors, differences between them represent changes induced by pressure alone. RNA sequencing analysis identified 1,613 differently expressed genes affected by renal perfusion pressure. Three pathway analysis methods were applied, one a novel approach incorporating arterial pressure as an input variable allowing a more direct connection between the expression of genes and pressure. The statistical analysis proposed several novel pathways by which pressure affects renal physiology. We confirmed the effects of pressure on p-Jnk regulation, in which the hypertensive medullas show increased p-Jnk/Jnk ratios relative to the left (0.79 ± 0.11 vs. 0.53 ± 0.10, P < 0.01, n = 8). We also confirmed pathway predictions of mitochondrial function, in which the respiratory control ratio of hypertensive vs. control mitochondria are significantly reduced (7.9 ± 1.2 vs. 10.4 ± 1.8, P < 0.01, n = 6) and metabolomic profile, in which 14 metabolites differed significantly between hypertensive and control medullas (P < 0.05, n = 5). These findings demonstrate that subtle differences in the transcriptome can be used to predict functional changes of the kidney as a consequence of pressure elevation. [ABSTRACT FROM AUTHOR]

Bayesian additive regression trees (BART) provide a framework for flexible nonparametric modeling of relationships of covariates to outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, and exceeding that of its competitors. Software is also readily available for such outcomes. In this article, we introduce modeling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis. Simulation studies of one-sample and two-sample scenarios, in comparison with long-standing traditional methods, establish face validity of the new approach. We then demonstrate the model's ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario with crossing survival functions and survival function estimation in a scenario where hazards are multiplicatively modified by a highly nonlinear function of the covariates. Using data from a recently published study of patients undergoing hematopoietic stem cell transplantation, we illustrate the use and some advantages of the proposed method in medical investigations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Racial health disparities continue to be a serious problem in the United States and have been linked to contextual factors, including racial segregation. In some cases, including breast cancer survival, racial disparities appear to be worsening. Using the Home Mortgage Disclosure Act (HMDA) database, we extend current spatial analysis methodology to derive new, spatially continuous indices of (1) racial bias in mortgage lending and (2) redlining. We then examine spatial patterns of these indices and the association between these new measures and breast cancer survival among Black/African American women in the Milwaukee, Wisconsin metropolitan area. These new measures can be used to examine relationships between mortgage discrimination and patterns of disease throughout the United States. [ABSTRACT FROM AUTHOR]

The article attempts to measure the continuity of care experienced by hospitalized patients and to determine predictors of inpatient physician continuity.

Background: Aromatase inhibitors (AIs) substantially reduce breast cancer mortality in clinical trials, but high rates of nonadherence to these long-term oral therapies have reduced their impact outside of trials. We examined the association of generic AI availability with AI adherence among a large national breast cancer cohort. Methods: Using a quasi-experimental prepost design, we examined the effect of generic AI introductions (7/2010 and 4/2011) on adherence among a national cohort of women with incident breast cancer in 2006 and 2007 who were enrolled in the Medicare D pharmaceutical coverage program. Medicare D claims were used to calculate AI adherence, defined as a medication possession ratio of 80% or more of eligible days, over 36 months. Multivariable logistic regression models estimated with generalized estimating equations were applied to longitudinal adherence data to control for possible confounders, including receipt of a Medicare D low-income subsidy, and to account for repeated measures. All statistical tests were two-sided. Results: Sixteen thousand four hundred sixty-two Medicare D enrollees were eligible. Adherence declined throughout the study. However, among women without a subsidy, the median quarterly out-of-pocket cost of anastrozole fell from $183 in the fourth quarter of 2009 to $15 in 2011, and declines in adherence were attenuated with generic AI introductions. Regression-adjusted adherence probabilities were estimated to be 5.4% higher after generic anastrozole was introduced in 2010 and 11% higher after generic letrozole/exemestane was introduced in 2011. Subsidy recipients had higher adherence rates throughout the study. Conclusions: The introduction of generic medications attenuated the decline in adherence to AIs over three years of treatment among breast cancer survivors not receiving low-income subsidies for Medicare D coverage. [ABSTRACT FROM AUTHOR]

Background: Aromatase inhibitors (AIs) substantially reduce breast cancer mortality in clinical trials, but high rates of nonadherence to these long-term oral therapies have reduced their impact outside of trials. We examined the association of generic AI availability with AI adherence among a large national breast cancer cohort. Methods: Using a quasi-experimental prepost design, we examined the effect of generic AI introductions (7/2010 and 4/2011) on adherence among a national cohort of women with incident breast cancer in 2006 and 2007 who were enrolled in the Medicare D pharmaceutical coverage program. Medicare D claims were used to calculate AI adherence, defined as a medication possession ratio of 80% or more of eligible days, over 36 months. Multivariable logistic regression models estimated with generalized estimating equations were applied to longitudinal adherence data to control for possible confounders, including receipt of a Medicare D low-income subsidy, and to account for repeated measures. All statistical tests were two-sided. Results: Sixteen thousand four hundred sixty-two Medicare D enrollees were eligible. Adherence declined throughout the study. However, among women without a subsidy, the median quarterly out-of-pocket cost of anastrozole fell from $183 in the fourth quarter of 2009 to $15 in 2011, and declines in adherence were attenuated with generic AI introductions. Regression-adjusted adherence probabilities were estimated to be 5.4% higher after generic anastrozole was introduced in 2010 and 11% higher after generic letrozole/exemestane was introduced in 2011. Subsidy recipients had higher adherence rates throughout the study. Conclusions: The introduction of generic medications attenuated the decline in adherence to AIs over three years of treatment among breast cancer survivors not receiving low-income subsidies for Medicare D coverage. [ABSTRACT FROM AUTHOR]

R2896 --> 626.2 --> Epigenetics may reflect the interactions between DNA and environment and lifestyle and DNA methylation marks have been implicated in the development and progression of hypertension. In an African American (AA) cohort of hypertensives and normotensives (n = 281), we have recently discovered up to 72 methylation regions (MRs) in whole blood DNA that were significantly associated with 24‐h BP phenotypes (False Discovery Rate (FDR) < 0.05) via reduced representation bisulfite sequencing (RRBS). One‐time clinic BP measurements were not associated with MRs. Upon adjustments for covariates age, sex, and body mass index, 13 MRs remained associated with 24‐h BP phenotypes (FDR < 0.05), each explaining 6.5% ‐ 9.4% of BP variance. Analysis of the chr19 MR in an independent cohort (n=117) using Bisulfite ULtrapLEx Targeted Sequencing (BULLET‐Seq), a new method that we developed, validated its association with 24‐h avg BP phenotypes (FDR < 0.05). In the current study, we functionally annotated the MRs that we identified as uniquely associated with 24‐h BP phenotypes and examined CpG sites previously reported to be associated with BP. Two MRs were near chr1 linkage disequilibrium (LD) regions within genes, PRDM16 and CASZ1, containing BP‐associated sentinel single nucleotide polymorphisms (SNPs). Although no MRs were found to directly overlap with reported BP‐relevant expression or methylation quantitative trait loci (eQTLs or meQTLs), many overlapped with enhancer regions, CTCF‐binding sites, or genes related to hypertension and stroke. To demonstrate consistency with prior studies, we found associations between reported clinic BP‐associated methylation array chip CpGs and the 24‐h BP phenotypes in our study. Of the 16 BP‐associated CpGs from two reports that were detected in both the methylation chip and our RRBS data, several CpGs were significantly associated with 24‐h and clinic BP phenotypes in our cohort (p < 0.05) but not after adjustment for both covariates and FDR. A chr7 CpG (cg06340364) showed significant negative associations with all studied BP phenotypes except nighttime and clinic DBPs (p < 0.05, unadjusted). These investigations highlight the value of regional DNA methylation and the ability to capture the overall effect of environmental and genetic factors that influence BPs over a 24‐h period which have functional epigenomic relevance. [ABSTRACT FROM AUTHOR]

Objectives. We examined whether community translation of an effective evidence-based fall prevention program via standard monetary support can produce a community-wide reduction in fall injuries in older adults and evaluated whether an enhanced version with added technical support and capacity building amplified the fall reduction effect. Methods. We completed a randomized controlled community trial among adults aged 65 and older in (1) 10 control communities receiving no special resources or guidance on fall prevention, (2) 5 standard support communities receiving modest funding to implement Stepping On, and (3) 5 enhanced support communities receiving funding and technical support. The primary outcome was hospital inpatient and emergency department discharges for falls, examined with Poisson regression. Results. Compared with control communities, standard and enhanced support communities showed significantly higher community-wide reductions (9% and 8%, respectively) in fall injuries from baseline (2007-2008) to follow-up (2010-2011). No significant difference was found between enhanced and standard support communities. Conclusions. Population-based fall prevention interventions can be effective when implemented in community settings. More research is needed to identify the barriers and facilitators that influence the successful adoption and implementation of fall prevention interventions into broad community practice. [ABSTRACT FROM AUTHOR]

Background and Aims Persons receiving haemodialysis ( HD) are at increased risk of cognitive impairment ( CI). Since blood pressure ( BP) fluctuations during HD may affect cerebral perfusion - and subsequently cognitive function - we examined the relationship between dialytic BP fluctuation and cognitive outcomes. Methods We included HD patients without diagnosed dementia who were 50 years or older. Using established methods, we classified participants' in CI categories (none to mild and moderate to severe) based on results of a neurocognitive battery. We collected demographic and laboratory data from dialysis unit records, as well as all BP measurements from 12 dialysis sessions. We tested the association between CI and BP fluctuation, adjusting for demographic and laboratory variables. Results Our study enrolled 39 patients; 25 had moderate to severe CI. The normal to mild CI group and the moderate to severe patients had similar degrees of BP fluctuation (average minimum systolic BP ( SBP): 107.6 ± 18.7 vs 110.2 ± 18.6 mmHg, maximum drop in SBP: 32.6 ± 10.2 vs 35.4 ± 15.0 mmHg; proportion of sessions with SBP < 90 mmHg: 0.2 ± 0.3 vs 0.2 ± 0.3; average change in SBP, pre to post HD: 10.2 ± 12.4 vs 11.8 ± 16.4 mmHg, all P > 0.55). There was no association between BP variables and performance on individual cognitive tests. Multivariable analysis showed that older age and non-Caucasian race were associated with a reduction in cognitive scores. Conclusions There was no cross-sectional association between dialytic BP changes and cognitive performance. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine the role of body mass and subcutaneous fat in injury severity and pattern sustained by overweight drivers. Finite element models were created to represent the geometry and properties of subcutaneous adipose tissue in the torso with data obtained from reconstructed magnetic resonance imaging data-sets. The torso adipose tissue models were then integrated into the standard multibody dummy models together with increased inertial parameters and sizes of the limbs to represent overweight occupants. Frontal crash simulations were carried out considering a variety of occupant restraint systems and regional body injuries were measured. The results revealed that differences in body mass and fat distribution have an impact on injury severity and pattern. Even though the torso adipose tissue of overweight subjects contributed to reduce abdominal injury, the momentum effect of a greater body mass of overweight subjects was more dominant over the cushion effect of the adipose tissue, increasing risk of other regional body injuries except abdomen. Through statistical analysis of the results, strong correlations (p < 0.01) were found between body mass index and regional body injuries except neck injury. The analysis also revealed that a greater momentum of overweight males leads to greater forward torso and pelvic excursions that account for higher risks (p < 0.001) of head, thorax and lower extremity injury than observed in non-overweight males. The findings have important implications for improving the vehicle and occupant safety systems designed for the increasing global obese population. [ABSTRACT FROM AUTHOR]

The goal of the present study was to narrow a region of chromosome 13 to only several genes and then apply unbiased statistical approaches to identify molecular networks and biological pathways relevant to bloodpressure salt sensitivity in Dahl salt-sensitive (SS) rats. The analysis of 13 overlapping subcongenic strains identified a 1.37 Mbp region on chromosome 13 that influenced the mean arterial blood pressure by at least 25 mmHg in SS rats fed a high-salt diet. DNA sequencing and analysis filled genomic gaps and provided identification of five genes in this region, Rfwd2, Fam5b, Astn1, Pappa2, and Tnr. A crossplatform normalization of transcriptome data sets obtained from our previously published Affymetrix GeneChip dataset and newly acquired RNA-seq data from renal outer medullary tissue provided 90 observations for each gene. Two Bayesian methods were used to analyze the data: 1) a linear model analysis to assess 243 biological pathways for their likelihood to discriminate blood pressure levels across experimental groups and 2) a Bayesian graphical modeling of pathways to discover genes with potential relationships to the candidate genes in this region. As none of these five genes are known to be involved in hypertension, this unbiased approach has provided useful clues to be experimentally explored. Of these five genes, Rfwd2, the gene most strongly expressed in the renal outer medulla, was notably associated with pathways that can affect blood pressure via renal transcellular Na+ and K+ electrochemical gradients and tubular Na+ transport, mitochondrial TCA cycle and cell energetics, and circadian rhythms. [ABSTRACT FROM AUTHOR]

Abstract: Background context: Readmissions within 30 days of hospital discharge are undesirable and costly. Little is known about reasons for and predictors of readmissions after elective spine surgery to help plan preventative strategies. Purpose: To examine readmissions within 30 days of hospital discharge, reasons for readmission, and predictors of readmission among patients undergoing elective cervical and lumbar spine surgery for degenerative conditions. Study design: Retrospective cohort study. Patient sample: Patient sample includes 343,068 Medicare beneficiaries who underwent cervical and lumbar spine surgery for degenerative conditions from 2003 to 2007. Outcome measures: Readmissions within 30 days of discharge, excluding readmissions for rehabilitation. Methods: Patients were identified in Medicare claims data using validated algorithms. Reasons for readmission were classified into clinically meaningful categories using a standardized coding system (Clinical Classification Software). Results: Thirty-day readmissions were 7.9% after cervical surgery and 7.3% after lumbar surgery. There was no dominant reason for readmissions. The most common reasons for readmissions were complications of surgery (26%–33%) and musculoskeletal conditions in the same area of the operation (15%). Significant predictors of readmission for both operations included older age, greater comorbidity, dual eligibility for Medicare/Medicaid, and greater number of fused levels. For cervical spine readmissions, additional risk factors were male sex, a diagnosis of myelopathy, and a posterior or combined anterior/posterior surgical approach; for lumbar spine readmissions, additional risk factors were black race, Middle Atlantic geographic region, fusion surgery, and an anterior surgical approach. Our model explained more than 60% of the variability in readmissions. Conclusions: Among Medicare beneficiaries, 30-day readmissions after elective spine surgery for degenerative conditions represent a target for improvement. Both patient factors and operative techniques are associated with readmissions. Interventions to minimize readmissions should be specific to surgical site and focus on high-risk subgroups where clinical trials of interventions may be of greatest benefit. [Copyright &y& Elsevier]

Abstract: Postmortem preservation conditions may be one of factors contributing to wide material property variations in brain tissues in literature. The objective of present study was to determine the effects of preservation temperatures on high strain-rate material properties of brain tissues using the split Hopkinson pressure bar (SHPB). Porcine brains were harvested immediately after sacrifice, sliced into 2mm thickness, preserved in ice cold (group A, 10 samples) and 37°C (group B, 9 samples) saline solution and warmed to 37°C just prior to the test. A SHPB with tube aluminum transmission bar and semi-conductor strain gauges were used to enhance transmitted wave signals. Data were gathered using a digital acquisition system and processed to obtain stress–strain curves. All tests were conducted within 4h postmortem. The mean strain-rate was 2487±72s−1. A repeated measures model with specimen-level random effects was used to analyze log transformed stress–strain responses through the entire loading range. The mean stress–strain curves with ±95% confidence bands demonstrated typical power relationships with the power value of 2.4519 (standard error, 0.0436) for group A and 2.2657 (standard error, 0.0443) for group B, indicating that responses for the two groups are significantly different. Stresses and tangent moduli rose with increasing strain levels in both groups. These findings indicate that storage temperatures affected brain tissue material properties and preserving tissues at 37°C produced a stiffer response at high strain-rates. Therefore, it is necessary to incorporate material properties obtained from appropriately preserved tissues to accurately predict the responses of brain using stress analyses models, such as finite element simulations. [ABSTRACT FROM AUTHOR]

BACKGROUND: The authors sought to identify socioeconomic (SES) factors associated with adjuvant hormone therapy (HT) use among a contemporary population of older breast cancer survivors. METHODS: Telephone surveys were conducted among women (ages 65-89 years) residing in 4 states (California, Florida, Illinois, and New York) who underwent initial breast cancer surgery in 2003. Demographic, SES, and treatment information was collected. RESULTS: Of 2191 women, 67% received adjuvant HT with either tamoxifen or an aromatase inhibitor (Al); 71% of those women were on an Al. When adjusting for multiple demographic and SES factors, predictors of HT use were better education (high school degree or higher), better informational/emotional support, and younger age (ages 65-79 years). Race/ethnicity, income, and insurance coverage for medication costs were not associated with receiving HT. For those on HT, when adjusting for all other factors, women were more likely to receive an Al if they had insurance coverage for some or all medication costs, if they were wealthier, if they had better informational/emotional support, and if they were younger (ages 65-69 years). CONCLUSIONS: The majority of older women in this population-based cohort received adjuvant HT, and the adoption of Als was early. The results indicted that providers should be aware that a woman's education level and support system influence her decision to take HT. Given the high cost of Als, their benefits in postmenopausal women with hormone receptor-positive breast cancer, and the current finding that women With no insurance coverage for medication costs were significantly less likely to receive an Al, we recommend that policymakers address this issue. [ABSTRACT FROM AUTHOR]

Subject recruitment for epidemiologic studies is associated with major challenges due to privacy laws now common in many countries. Privacy policies regarding recruitment methods vary tremendously across institutions, partly because of a paucity of information about what methods are acceptable to potential subjects. The authors report the utility of an opt-out method without prior physician notification for recruiting community-dwelling US women aged 65 years or older with incident breast cancer in 2003. Participants (n = 3,083) and possibly eligible nonparticipants (n = 2,664) were compared using characteristics derived from billing claims. Participation for persons with traceable contact information was 70% initially (2005–2006) and remained over 90% for 3 follow-up surveys (2006–2008). Older subjects and those living in New York State were less likely to participate, but participation did not differ on the basis of socioeconomic status, race/ethnicity, underlying health, or type of cancer treatment. Few privacy concerns were raised by potential subjects, and no complaints were lodged. Using opt-out methods without prior physician notification, a population-based cohort of older breast cancer subjects was successfully recruited. This strategy may be applicable to population-based studies of other diseases and is relevant to privacy boards making decisions about recruitment strategies acceptable to the public. [ABSTRACT FROM PUBLISHER]

Background: Men tend to have more upper body mass and fat than women, a physical characteristic that may predispose them to severe motor vehicle crash (MVC) injuries, particularly in certain body regions. This study examined MVC-related regional body injury and its association with the presence of driver obesity using both real-world data and computer crash simulation. Methods and Findings: Real-world data were from the 2001 to 2005 National Automotive Sampling System Crashworthiness Data System. A total of 10,941 drivers who were aged 18 years or older involved in frontal collision crashes were eligible for the study. Sex-specific logistic regression models were developed to analyze the associations between MVC injury and the presence of driver obesity. In order to confirm the findings from real-world data, computer models of obese subjects were constructed and crash simulations were performed. According to real-world data, obese men had a substantially higher risk of injury, especially serious injury, to the upper body regions including head, face, thorax, and spine than normal weight men (all p<0.05). A U-shaped relation was found between body mass index (BMI) and serious injury in the abdominal region for both men and women (p,0.05 for both BMI and BMI2). In the high-BMI range, men were more likely to be seriously injured than were women for all body regions except the extremities and abdominal region (all p<0.05 for interaction between BMI and sex). The findings from the computer simulation were generally consistent with the real-world results in the present study. Conclusions: Obese men endured a much higher risk of injury to upper body regions during MVCs. This higher risk may be attributed to differences in body shape, fat distribution, and center of gravity between obese and normal-weight subjects, and between men and women. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) is a T-cell mediated autoimmune disease targeting the insulin-producing pancreatic b cells. Naturally occurring FOXP3+CD4+CD25high regulatory T cells (Tregs) play an important role in dominant tolerance, suppressing autoreactive CD4+ effector T cell activity. Previously, in both recent-onset T1D patients and β cell antibodypositive at-risk individuals, we observed increased apoptosis and decreased function of polyclonal Tregs in the periphery. Our objective here was to elucidate the genes and signaling pathways triggering apoptosis in Tregs from T1D subjects. Principal Findings: Gene expression profiles of unstimulated Tregs from recent-onset T1D (n = 12) and healthy control subjects (n = 15) were generated. Statistical analysis was performed using a Bayesian approach that is highly efficient in determining differentially expressed genes with low number of replicate samples in each of the two phenotypic groups. Microarray analysis showed that several cytokine/chemokine receptor genes, HLA genes, GIMAP family genes and cell adhesion genes were downregulated in Tregs from T1D subjects, relative to control subjects. Several downstream target genes of the AKT and p53 pathways were also upregulated in T1D subjects, relative to controls. Further, expression signatures and increased apoptosis in Tregs from T1D subjects partially mirrored the response of healthy Tregs under conditions of IL-2 deprivation. CD4+ effector T-cells from T1D subjects showed a marked reduction in IL-2 secretion. This could indicate that prior to and during the onset of disease, Tregs in T1D may be caught up in a relatively deficient cytokine milieu. Conclusions: In summary, expression signatures in Tregs from T1D subjects reflect a cellular response that leads to increased sensitivity to apoptosis, partially due to cytokine deprivation. Further characterization of these signaling cascades should enable the detection of genes that can be targeted for restoring Treg function in subjects predisposed to T1D. [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]

Abstract: The objective of this study was to test the hypothesis that the human lumbosacral joint behaves differently from L1–L5 joints and provides primary moment–rotation responses under pure moment flexion and extension and left and right lateral bending on a level-by-level basis. In addition, range of motion (ROM) and stiffness data were extracted from the moment–rotation responses. Ten T12–S1 column specimens with ages ranging from 27 to 68 years (mean: 50.6±13.2) were tested at a load level of 4.0Nm. Nonlinear flexion and extension and left and right lateral bending moment–rotation responses at each spinal level are reported in the form of a logarithmic function. The mean ROM was the greatest at the L5–S1 level under flexion (7.37±3.69°) and extension (4.62±2.56°) and at the L3–L4 level under lateral bending (4.04±1.11°). The mean ROM was the least at the L1–L2 level under flexion (2.42±0.90°), L2–L3 level under extension (1.58±0.63°), and L1–L2 level under lateral bending (2.50±0.75°). The present study proved the hypothesis that L5–S1 motions are significantly greater than L1–L5 motions under flexion and extension loadings, but the hypothesis was found to be untrue under the lateral bending mode. These experimental data are useful in the improved validation of FE models, which will increase the confidence of stress analysis and other modeling applications. [Copyright &y& Elsevier]

Objectives. We examined the association between number of breast cancer operations performed in a hospital (hospital volume) and all-cause and breast cancer-specific mortality using a national database and statistical methods appropriate for clustering and reducing confounding. Methods. In a retrospective cohort study, we linked Surveillance, Epidemiology, and End Results tumor registry data with Medicare claims data. The cohort included 11225 Medicare patients who had undergone surgery for early-stage breast cancer from 1994 to 1996 in 457 different hospitals. Primary outcomes were all-cause and breast cancer-specific survival rates at a mean follow-up time of 62.5 months. Results. In comparison with treatment in a low-volume hospital, treatment in a high-volume hospital was associated with hazard ratios of 0.83 (95% confidence interval [CI] = 0.75, 0.92) for all-cause mortality and 0.80 (CI = 0.66, 0.97) for breast cancer-specific mortality. Conclusions. An association between the volume of breast cancer operations performed in a hospital and 5-year survival rates was observed for both all-cause and breast cancer-specific mortality. Further work investigating the aspects of hospital volume that contribute to increased survival is warranted. [ABSTRACT FROM AUTHOR]

The article presents a case study on the inequalities in colon cancer screening in the U.S. medicare population. The study shows that the lowest relative risk of screening colonoscopy in women was in the oldest age group and the highest income tertile as compared to men. It was found out that there remain disparities between sex and racial or ethnic groups in screening practices, despite Medicare coverage for colorectal cancer screening expansion.

Abstract: The objective of this study was to determine the bone mineral density (BMD) of cervical vertebrae and correlate with the lumbar spine. Fifty-seven young adult healthy male volunteers, ranging from 18 to 41 years of age, underwent quantitative computed tomography (QCT) scanning of C2-T1 and L2–L4 vertebrae. To account for correlations, repeated measures techniques were used to compare data as a function of spinal level and region. Linear regression methods were used (±95% CI) to compare data as a function of spinal level and region. The mean age and body height were 25.0 ± 5.8 years and 181.0 ± 7.6 cm. BMD decreased from the rostral to caudal direction along the spinal column. Grouped data indicated that the neck is the densest followed by the first thoracic vertebra and low back with mean BMD of 256.0 ± 48.1, 194.3 ± 44.2, and 172.2 ± 28.4 mg/cm3, respectively; differences were statistically significant. While BMD did not vary significantly between the three lumbar bodies, neck vertebrae demonstrated significant trends. The matrix of correlation coefficients between BMD and spinal level indicated that the relationship is strong in the lumbar (r = 0.92–0.96) and cervical (r = 0.73–0.92) spines. Data from the present study show that the trabecular bony architecture of the neck is significantly different from the low back. These quantitative BMD data from a controlled young adult healthy human male volunteer population may be valuable in establishing normative data specifically for the neck. From a trabecular bone density perspective, these results indicate that lumbar vertebrae cannot act as the best surrogates for neck vertebrae. Significant variations in densities among neck vertebrae, unlike the low back counterpart, may underscore the need to treat these bones as different structures. [Copyright &y& Elsevier]

Objectives. We examined the role of body mass index (BMI) and other factors in driver deaths within 30 days after motor vehicle crashes. Methods. We collected data for 22 107 drivers aged 16 years and older who were involved in motor vehicle crashes from the Crashworthiness Data System of the National Automotive Sampling System (1997-2001). We used logistic regression and adjusted for confounding factors to analyze associations between BMI and driver fatality and the associations between BMI and gender, age, seatbelt use, type of collision, airbag deployment, and change in velocity during a crash. Results. The fatality rate was 0.87% (95% confidence interval [CI]=0.50, 1.24) among men and 0.43% (95% CI=0.31, 0.56) among women involved as drivers in motor vehicle crashes. Risk for death increased significantly at both ends of the BMI continuum among men but not among women (P<.05). The association between BMI and male fatality increased significantly with a change in velocity and was modified by the type of collision, but it did not differ by age, seatbelt use, or airbag deployment. Conclusions. The increased risk for death due to motor vehicle crashes among obese men may have important implications for traffic safety and motor vehicle design. (Am J Public Health. 2006;96:734-739. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To measure the early adoption of bone density testing and examine the association between older age and such testing. DESIGN: Retrospective study of Medicare administrative claims. SETTING: Five states and six urban regions in the United States. PARTICIPANTS: Female Medicare recipients aged 66 to 90. MEASUREMENTS: Bone density testing in women without prior osteoporosis or fracture (osteoporosis screening) was evaluated. The association between age and osteoporosis screening was then examined while controlling for other demographic and health factors. RESULTS: Of 43,802 women eligible for osteoporosis screening, 22.9% were tested between 1999 and 2001, the first 3 full years of Medicare coverage. Receipt of bone density tests decreased with increasing age, from 27.2% of women aged 66 to 70 to fewer than 10% of women aged 86 to 90. After adjustment for race, comorbidity, fracture risk, and socioeconomic factors, bone density testing decreased significantly with each age category, so that women aged 71 to 75 were slightly less likely than (adjusted odds ratio (AOR) =0.91, 95% confidence interval (CI) =0.86–0.96), women aged 76 to 80 were less likely than (AOR =0.71, 95% CI =0.67–0.76), and women aged 81 to 85 were half as likely as (AOR =0.50, 95% CI =0.46–0.55) women aged 66 to 70 to receive a bone density test. CONCLUSION: In the 3 years after Medicare reimbursement for osteoporosis screening began, adoption of bone density testing was lowest in women in age groups at highest fracture risk. [ABSTRACT FROM AUTHOR]