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Background: Epigenetic marks (eg, DNA methylation) may capture the effect of gene-environment interactions. DNA methylation is involved in blood pressure (BP) regulation and hypertension development; however, no studies have evaluated its relationship with 24-hour BP phenotypes (daytime, nighttime, and 24-hour average BPs). Methods: We examined the association of whole blood DNA methylation with 24-hour BP phenotypes and clinic BPs in a discovery cohort of 281 Black participants using reduced representation bisulfite sequencing. We developed a deep and region-specific methylation sequencing method, Bisulfite ULtrapLEx Targeted Sequencing and utilized it to validate our findings in a separate validation cohort (n=117). Results: Analysis of 38 215 DNA methylation regions (MRs), derived from 1 549 368 CpG sites across the genome, identified up to 72 regions that were significantly associated with 24-hour BP phenotypes. No MR was significantly associated with clinic BP. Two to 3 MRs were significantly associated with various 24-hour BP phenotypes after adjustment for age, sex, and body mass index. Together, these MRs explained up to 16.5% of the variance of 24-hour average BP, while age, sex, and BMI explained up to 11.0% of the variance. Analysis of one of the MRs in an independent cohort using Bisulfite ULtrapLEx Targeted Sequencing confirmed its association with 24-hour average BP phenotype. Conclusions: We identified several MRs that explain a substantial portion of variances in 24-hour BP phenotypes, which might be excellent markers of cumulative effect of factors influencing 24-hour BP levels. The Bisulfite ULtrapLEx Targeted Sequencing workflow has potential to be suitable for clinical testing and population screenings on a large scale.

Background Yearly influenza vaccination is strongly recommended at age 65 and reimbursed by Medicare without copays or deductibles at pharmacies and clinical settings. Uptake is low among patients with a high risk for influenza complications and good access to specialist care, such as recent cancer survivors. We hypothesized that more accessible pharmacies could be associated with higher immunization uptake in such patients. Objectives To determine whether pharmacy access is associated with influenza vaccination in subjects recently diagnosed with breast cancer, and whether this association differs by additional risk factors for influenza complications. Methods We examined a cohort of patients with stage 0-III breast cancer diagnosed 2011-2015 from the Surveillance, Epidemiology, and End Results–Medicare cancer registry. All retail pharmacies in the United States were identified, and pharmacy access was measured by assessing supply and demand in each census tract using a 2-stage floating catchment area approach that accounted for pharmacy driving distances recommended by the Centers for Medicare and Medicaid Services. We examined the association of pharmacy access with influenza vaccination after breast cancer diagnosis in regression models. Results More than 11% of 45,722 patients with breast cancer lived in census tracts where no pharmacies were within recommended driving distances from the population-weighted tract center. Vaccination in the year after diagnosis was less likely for patients in these very low–access tracts (adjusted odds ratio 0.92 [95% CI 0.86–0.96]), black (0.55 [0.51–0.60]) and Hispanic (0.76 [0.70–0.83]) women, and Medicaid recipients (0.74 [0.69–0.79]). Vaccination was inversely associated with per capita income in the subject’s census tract, but there was no difference in the pharmacy effect by race, ethnicity, or census tract income. Conclusion Very low pharmacy access is associated with modest reductions in vaccination that could be useful for policy and planning regarding vaccinator resources and outreach.

PURPOSE The objective was to examine the relationship between contemporary redlining (mortgage lending bias on the basis of property location) and survival among older women with breast cancer in the United States. METHODS A redlining index using Home Mortgage Disclosure Act data (2007-2013) was linked by census tract with a SEER-Medicare cohort of 27,516 women age 66-90 years with an initial diagnosis of stage I-IV breast cancer in 2007-2009 and follow-up through 2015. Cox proportional hazards models were used to examine the relationship between redlining and both all-cause and breast cancer–specific mortality, accounting for covariates. RESULTS Overall, 34% of non-Hispanic White, 57% of Hispanic, and 79% of non-Hispanic Black individuals lived in redlined tracts. As the redlining index increased, women experienced poorer survival. This effect was strongest for women with no comorbid conditions, who comprised 54% of the sample. For redlining index values of 1 (low), 2 (moderate), and 3 (high), as compared with 0.5 (least), hazard ratios (HRs) (and 95% CIs) for all-cause mortality were HR = 1.10 (1.06 to 1.14), HR = 1.27 (1.17 to 1.38), and HR = 1.39 (1.25 to 1.55), respectively, among women with no comorbidities. A similar pattern was found for breast cancer–specific mortality. CONCLUSION Contemporary redlining is associated with poorer breast cancer survival. The impact of this bias is emphasized by the pronounced effect even among women with health insurance (Medicare) and no comorbid conditions. The magnitude of this neighborhood level effect demands an increased focus on upstream determinants of health to support comprehensive patient care. The housing sector actively reveals structural racism and economic disinvestment and is an actionable policy target to mitigate adverse upstream health determinants for the benefit of patients with cancer.

BACKGROUND: Dietary sodium intake affects blood pressure (BP) and vascular function. Emerging studies in animal models suggest epigenetic changes such as DNA methylation might be involved. We hypothesized that dietary sodium restriction induces DNA methylation changes in T-cells and arterioles in humans. METHODS: Fifty subjects (46% African American, 49% women) were placed on a 1200mg sodium/day diet for 2 weeks. All food was prepared and provided in accordance with subjects’ dietary preferences. BP and flow-mediated dilation (FMD) were evaluated. Methylation sequencing (pre- and post- low-sodium diet) was performed on DNA obtained from T-cells (n=50) and biopsied arterioles (n=10) using reduced representation bisulfite sequencing. RNA-seq was also performed on arterioles (n=11). RESULTS: During the low-sodium diet period, subjects consumed an average of 946mg of sodium/day. The average systolic and diastolic BP reductions following the low-sodium intake were −8±13/−4±9 mmHg (p

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.

Objective To adapt the two-step floating catchment area approach to account for urban–rural differences in pharmacy access in the United States. Methods The urban–rural two-step floating catchment area method was described mathematically. To calculate urban–rural-two-step floating catchment area measure, census tracts and pharmacies within the study area (Southeastern Wisconsin) were classified as urban, suburban or rural, and then different catchment area sizes (2, 5 and 15 miles) were applied, based on the Centers for Medicare & Medicaid Services (CMS)’ criteria for Medicare Part D service access within urban, suburban and rural areas. The urban–rural-two-step floating catchment area measures were compared to traditional two-step floating catchment area measures computed using three fixed catchment area sizes (2, 5, and 15 miles) by visually examining their spatial distributions. Associations between the four pharmacy accessibility measures and selected socio-demographics are calculated using Spearman’s rank-order correlation and further compared. Key findings The urban–rural two-step floating catchment area measure outperforms all the fixed catchment size measures and has the strongest Spearman correlations with the selected census variables. It also reduces the number of census tracts characterized as ‘no access’ when compared to the original measures. The spatial distribution of urban–rural two-step floating catchment area pharmacy access exhibits a more granular variation across the study area. Conclusions The results support our hypothesis that spatial access to pharmacies should account for urbanicity/rurality patterns within a region.

PURPOSE Clinical trials have clearly documented the survival benefit of aromatase inhibitors (AIs); however, many women fail to initiate (primary nonadherence) or remain adherent to AIs (secondary nonadherence). Prior studies have found that costs impact secondary nonadherence to medications but have failed to examine primary nonadherence. The purpose of this study is to examine primary and secondary adherence following the reduction in copays due to the introduction of generic AIs. METHODS Using Surveillance, Epidemiology, and End Results-Medicare data, we identified 50 054 women diagnosed with incident breast cancer between 2008 and 2013. We compare women whose copays would change and those whose would not, due to the receipt of cost-sharing subsidies before and after generics were introduced using a difference-in-difference (DinD) analysis. To examine primary and secondary nonadherence, we rely on a multistate model with four states (Not yet initiated, User, Not Using, and Death). We adjusted for baseline factors using inverse probability treatment weights and then simulated adherence for 36 months following diagnosis. RESULTS The generic introduction of AIs resulted in patients initiating AIs faster (DinD = -4.7%, 95%CI = -7.0, -2.3; patients not yet initiating treatment at 6-months), being more adherent (DinD ranging in absolute increase of 8.1%-10.4%) and being less likely to not be using the therapy (DinD range in absolute decrease of 1.2% at 6 months to 8.8% at 24 months) for women that do not receive a subsidy after generics were available. CONCLUSIONS Introduction of generic alternatives to AIs significantly reduced primary and secondary nonadherence.

OBJECTIVEOpioids are commonly prescribed after surgery for painful spinal conditions, yet little is known about postoperative opioid use. The relationship between chronic opioid use and patient-reported outcomes and satisfaction with surgery is also unclear. The purpose of this study was to evaluate factors associated with opioid use 1 year after elective cervical spine surgery for degenerative conditions causing radiculopathy and myelopathy. The authors hypothesized that patients with preoperative opioid use would be more likely to report postoperative opioid use at 1 year, and that postoperative opioid use would be associated with patient-reported outcomes and dissatisfaction with surgery.METHODSThe authors performed a retrospective study of a prospective cohort of adult patients who underwent elective cervical spine surgery for degenerative changes causing radiculopathy or myelopathy. Patients were prospectively and consecutively enrolled from a single academic center after the decision for surgery had been made. Postoperative in-hospital pain management was conducted using a standardized protocol. The primary outcome was any opioid use 1 year after surgery. Secondary outcomes were the Neck Disability Index (NDI); 36-Item Short-Form Health Survey (SF-36) physical function (PF), bodily pain (BP), and mental component summary (MCS) scores; the modified Japanese Orthopaedic Association (mJOA) score among myelopathy patients; and patient expectations surveys. Patients with and without preoperative opioid use were compared using the chi-square and Student t-tests, and multiple logistic regression was used to study the associations between patient and surgical characteristics and postoperative opioid use 1 year after surgery.RESULTSTwo hundred eleven patients were prospectively and consecutively enrolled, of whom 39 were lost to follow-up for the primary outcome; 43.6% reported preoperative opioid use. Preoperative NDI and SF-36 PF and BP scores were significantly worse in the preoperative opioid cohort. More than 94% of both cohorts rated expectations of pain relief as extremely or somewhat important. At 1 year after surgery, 50.7% of the preoperative-opioid-use cohort reported ongoing opioid use, and 17.5% of patients in the no-preoperative-opioid-use cohort reported ongoing opioid use. Despite this, both cohorts reported similar improvements in NDI as well as SF-36 PF, BP, and MCS scores. More than 70% of both cohorts also reported being extremely or somewhat satisfied with pain relief after surgery. Predictors of 1-year opioid use included preoperative opioid use, duration of symptoms for more than 9 months before surgery, tobacco use, and higher comorbidity index.CONCLUSIONSOne year after elective cervical spine surgery, patients with preoperative opioid use were significantly more likely to report ongoing opioid use. However, patients in both groups reported similar improvements in patient-reported outcomes and satisfaction with pain relief. Interventions targeted at decreasing opioid use may need to focus on patient factors such as preoperative opioid use or duration of symptoms before surgery.

DNA methylation, an epigenetic mark, may reflect the interactions between DNA, environment, and lifestyle. It has been implicated in the development and progression of hypertension, a risk factor for cardiovascular disease. We hypothesize that regions of DNA methylation in blood cells can explain 24h BP phenotypes in African Americans. We performed Reduced Representation Bisulfite Sequencing (RRBS) in a discovery cohort of 281 African Americans. Several DNA methylation regions (MRs) were significantly associated with continuously monitored 24-h, daytime, or nighttime SBP, DBP, PP, and MAP after adjustments for covariates age, sex, and body mass index (False Discovery Rate (FDR) = 0.013 - 0.050). Each of these MRs explained a substantial portion of 24h BP variance, ranging from 6.5% - 9.4%. After FDR adjustment, there were no MRs significantly associated with clinic BPs (FDR > 0.1374), calculated by the average of 4 resting measurements (2 per arm) by sphygmomanometer. To interrogate specific regions of DNA methylation, our lab developed a potentially clinically applicable, deep, and targeted methylation sequencing method called Bisulfite-Specific PCR ULtrapLEx Targeted Sequencing (BULLET-Seq), and tested it in two reference samples for three MRs of interest. BULLET-Seq is able to accurately quantify the 10% changes in the dilution series when methylation rates ranged from ~40% to 90% (a chr19 methylation region; R 2 = 0.95 - 0.97) and can modestly measure these changes when rates range from ~2% to 4% (a chr5 region, R 2 = 0.82), and is questionable when methylation rates are below 2% (a chr13 region, R 2 = 0.03 - 0.27). Validation of the chr19 MR in an independent cohort (n=117) was performed in a single BULLET-Seq run. After covariate adjustment, the chr19 region was significantly associated with 24h BPs (SBP, DBP, and MAP; FDR < 0.05), confirming the findings from the discovery cohort. The MR accounted for up to 1.75% of BP variance in the 24h phenotypes. In conclusion, the reported DNA MRs have potential to be excellent markers for the cumulative effect of factors that influence 24h BPs and the BULLET-Seq workflow can be applied in clinical and population settings to screen up to thousands of patients.

Adjuvant endocrine therapy (AET) for breast cancer reduces mortality, but one-third to one-half of patients discontinue it early or are nonadherent.We developed a pilot single-site study of patients with evidence of early nonadherence to AET to assess the feasibility of a novel, clinical pharmacist-led intervention targeting symptom and medication management.Patients with prescription fill records showing nonadherence were enrolled in a single-arm feasibility study. Automated reminders were sent by e-mail or text with a link to symptom monitoring assessments weekly for 1 month and monthly until 6 months. Clinical oncology pharmacists used guideline-based symptom management and other medication management tools to support adherence and ameliorate symptoms reported on the assessments. Patient-reported outcome assessments included physical, mental, and social health domains and self-efficacy to manage symptoms and medications. Feasibility outcomes included completion of symptom reports and pharmacist recommendations.Of 19 participants who were nonadherent who enrolled and completed initial assessments, 18 completed all final study procedures, with 14 completing all assessments and no patient missing more than 3 assessments. All 18 participants reported at least one of 3 symptom types, and the majority reported attempting pharmacist recommendations. Patient-reported measures of physical, mental, and social health and self-efficacy improved, and 44% of the patients became adherent.An intervention using pharmacists in an oncology practice to systematically monitor and manage symptoms shows promise to reduce symptoms, enhance support and self-efficacy, and improve adherence to AET.

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.

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 2003–2012. 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 was inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables.

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.

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

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.

Survivorship Care Plans (SCPs) may facilitate long-term care for cancer survivors, but their effectiveness has not been established in hematopoietic cell transplantation recipients. We evaluated the impact of individualized SCPs on patient-reported outcomes among transplant survivors. Adult (≥18 years at transplant) survivors who were 1-5 years post transplantation, proficient in English, and without relapse or secondary cancers were eligible for this multicenter randomized trial. SCPs were developed based on risk-factors and treatment exposures using patient data routinely submitted by transplant centers to the Center for International Blood and Marrow Transplant Research and published guidelines for long-term follow up of transplant survivors. Phone surveys assessing patient-reported outcomes were conducted at baseline and at 6 months. The primary end point was confidence in survivorship information, and secondary end points included cancer and treatment distress, knowledge of transplant exposures, health care utilization, and health-related quality of life. Of 495 patients enrolled, 458 completed a baseline survey and were randomized (care plan=231, standard care=227); 200 (87%) and 199 (88%) completed the 6-month assessments, respectively. Patients’ characteristics were similar in the two arms. Participants on the care plan arm reported significantly lower distress scores at 6 months and an increase in the Mental Component Summary quality of life score assessed by the Short Form 12 (SF-12) instrument. No effect was observed on the end point of confidence in survivorship information or other secondary outcomes. Provision of individualized SCPs generated using registry data was associated with reduced distress and improved mental domain of quality of life among 1-5 year hematopoietic cell transplantation survivors. Trial registered at clinicaltrials.gov 02200133.

e18557 Background: The purpose of this study was to examine the association between measures of housing quality, stability, and access on breast cancer stage at diagnosis among older women living in the United States. Methods: This study included 67,588 women aged 66-90 with data from the SEER-Medicare linked database. The primary outcome was breast cancer stage at diagnosis. Multinomial regression models were performed using a three-category outcome (stage 0, early-stage (I-II), late-stage (III-IV)). The key independent variables were median housing value, percentage living in the same house as the previous year, percentage owner occupied homes, and an index of contemporary mortgage lending bias (redlining). Results: In adjusted models, higher contemporary mortgage lending bias was significantly associated with later-stage diagnosis (RR = 1.10 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). Median housing value was inversely associated with later-stage diagnosis, but to a lesser degree than mortgage lending bias (RR = 0.88, 95%CI 0.80-0.96; RR = 0.77, 95% CI 0.68-0.88 for second and third tertiles, respectively). Owner occupancy and tenure were not significantly associated with late-stage diagnosis in adjusted models. Conclusions: Contemporary mortgage lending bias demonstrated a significant dose-response relationship with later stage at diagnosis of breast cancer in this cohort of elderly women. Policy interventions aimed at reducing the effects of redlining with the goal of decreasing late-stage breast cancer diagnosis to improve prognosis should be considered. Table. Relative risk of late stage breast cancer diagnosis based on measures of housing quality and stability, as well as redlining. Risk is relative to the base outcome, stage 0. Values for the first tertile of housing quality and stability as well as the “least” category for redlining are not shown in this table as they are the base outcome which the other values are compared to. Standard error was adjusted for MSA clustering effects in all models.[Table: see text]

Background Theoretically, the estrogen deprivation induced by aromatase inhibitors (AIs) might cause ischemic heart disease, but empiric studies have shown mixed results. We aimed to compare AIs and tamoxifen with regard to cardiovascular events among older breast cancer patients outside of clinical trials. We hypothesized that AIs increase the risk of myocardial infarction. Methods We identified women age ≥67 years diagnosed with breast cancer from June 30, 2006 to June 1, 2008 in the surveillance, epidemiology, and end results (SEER)-Medicare database, treated with either tamoxifen or an AI, and followed through December 31, 2012. To compare myocardial infarction (MI) risk for the treatment groups of AIs vs tamoxifen, we developed and assigned stabilized probability of treatment weights and used the Fine and Gray model for time to MI with death not related to MI as a competing risk. Results Of the cohort of 5648 women, 4690 were treated with AIs and 958 with tamoxifen; a total of 251 patients developed MI, and 22 patients died of MI during the study period while 476 died of other causes. The hazard for MI was not significantly different between AI vs tamoxifen groups (HR = 1.01, 95% CI 0.72-1.42), after adjusting for the following known MI risk factors at the start of adjuvant therapy: diabetes, ischemic heart disease, congestive heart failure, MI, and peripheral vascular disease. Conclusions In this SEER-Medicare-based population study, there were no significant differences in the risk of MI between AI and tamoxifen users after adjustment for known risk factors.

Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book …is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments…are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course…" -Thomas Louis, Johns Hopkins University "The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems….Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems." - Peter Mueller, University of Texas With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests. Key Features Applies a Bayesian perspective to applications in biomedical science Highlights advances in clinical trial design Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in general Authors Gary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is professor Emeritus in the Department of Statistics as the University of California, Irvine.

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, P0.001). 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, P0.001). 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.

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 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-020-06583-7.

The SARS-CoV-2 outbreak prompted public health interventions and changes in public behavior that may have affected the 2019-2020 influenza season.Using data from a laboratory in southeastern Wisconsin, we compared the number of weekly influenza tests and their positivity rates during the 2019-2020 influenza season with the previous 4 seasons.The number of influenza tests per week at the outset of the SARS-CoV-2 outbreak was higher than the average the previous 4 years, and positivity rates declined to 0% earlier than any of the previous 4 seasons.The testing trajectory and positivity rate for influenza differed during the part of the 2019-2020 season coinciding with the SARS-CoV-2 outbreak as compared to similar periods during the previous 4 seasons.

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.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.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.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.

Key Points Question Which patient and clinician factors are associated with a successful or failed video visit? Findings This quality improvement study of 137 846 video visits showed an overall 90% success rate. Patient rather than clinician factors were more systematically associated with successful completion of video visits, and clinician comfort with technology was associated with successful video visits or conversion to telephone visits. Meaning The findings suggest that, as policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should be kept in the forefront., Importance Telemedicine provides patients access to episodic and longitudinal care. Policy discussions surrounding future support for telemedicine require an understanding of factors associated with successful video visits. Objective To assess patient and clinician factors associated with successful and with failed video visits. Design, Setting, and Participants This was a quality improvement study of 137 846 scheduled video visits at a single academic health system in southeastern Wisconsin between March 1 and December 31, 2020, supplemented with patient experience survey data. Patient information was gathered using demographic information abstracted from the electronic health record and linked with block-level socioeconomic data from the US Census Bureau. Data on perceived clinician experience with technology was obtained using the survey. Main Outcomes and Measures The primary outcome of interest was the successful completion of a scheduled video visit or the conversion of the video visit to a telephone-based service. Visit types and administrative data were used to categorize visits. Mixed-effects modeling with pseudo R2 values was performed to compare the relative associations of patient and clinician factors with video visit failures. Results In total, 75 947 patients and 1155 clinicians participated in 137 846 scheduled video encounters, 17 190 patients (23%) were 65 years or older, and 61 223 (81%) patients were of White race and ethnicity. Of the scheduled video encounters, 123 473 (90%) were successful, and 14 373 (10%) were converted to telephone services. A total of 16 776 patients (22%) completed a patient experience survey. Lower clinician comfort with technology (odds ratio [OR], 0.15; 95% CI, 0.08-0.28), advanced patient age (66-80 years: OR, 0.28; 95% CI, 0.26-0.30), lower patient socioeconomic status (including low high-speed internet availability) (OR, 0.85; 95% CI, 0.77-0.92), and patient racial and ethnic minority group status (Black or African American: OR, 0.75; 95% CI, 0.69-0.81) were associated with conversion to telephone visits. Patient characteristics accounted for systematic components for success; marginal pseudo R2 values decreased from 23% (95% CI, 21.1%-26.1%) to 7.8% (95% CI, 6.3%-9.4%) with exclusion of patient factors. Conclusions and Relevance As policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should guide decisions. In particular, this quality improvement study suggests that underserved patients may become disproportionately vulnerable by cuts in coverage for telephone-based services., This quality improvement study evaluates patient and clinician factors to assess which factors are associated with the successful completion or failure of telemedicine video appointments.

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. 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. 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). 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. 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.