Effects of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

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

Optimal Donor Selection Across Multiple Outcomes For Hematopoietic Stem Cell Transplantation By Bayesian Nonparametric Machine Learning.

Allogeneic hematopoietic cell transplantation (HCT) is one of the only curative treatment options for patients suffering from life-threatening hematologic malignancies; yet, the possible adverse complications can be serious even fatal. Matching between donor and recipient for 4 of the HLA genes is widely accepted and supported by the literature. However, among 8/8 allele matched unrelated donors, there is less agreement among centers and transplant physicians about how to prioritize donor characteristics like additional HLA loci (DPB1 and DQB1), donor sex/parity, CMV status, and age to optimize transplant outcomes. This leads to varying donor selection practice from patient to patient or via center protocols. Furthermore, different donor characteristics may impact different post transplant outcomes beyond mortality, including disease relapse, graft failure/rejection, and chronic graft-versus-host disease (components of event-free survival, EFS). We develop a general methodology to identify optimal treatment decisions by considering the trade-offs on multiple outcomes modeled using Bayesian nonparametric machine learning. We apply the proposed approach to the problem of donor selection to optimize overall survival and event-free survival, using a large outcomes registry of HCT recipients and their actual and potential donors from the Center for International Blood and Marrow Transplant Research (CIBMTR). Our approach leads to a donor selection strategy that favors the youngest male donor, except when there is a female donor that is substantially younger.

Effect of Implementing a Commercial Electronic Early Warning System on Outcomes of Hospitalized Patients.

Despite the widespread adoption of early warning systems (EWSs), it is uncertain if their implementation improves patient outcomes. The authors report a pre-post quasi-experimental evaluation of a commercially available EWS on patient outcomes at a 700-bed academic medical center. The EWS risk scores were visible in the electronic medical record by bedside clinicians. The EWS risk scores were also monitored remotely 24/7 by critical care trained nurses who actively contacted bedside nurses when a patient's risk levels increased. The primary outcome was inpatient mortality. Secondary outcomes were rapid response team calls and activation of cardiopulmonary arrest (code-4) response teams. The study team conducted a regression discontinuity analysis adjusting for age, gender, insurance, severity of illness, risk of mortality, and hospital occupancy at admission. The analysis included 53,229 hospitalizations. Adjusted analysis showed no significant change in inpatient mortality, rapid response team call, or code-4 activations after implementing the EWS. This study confirms the continued uncertainty in the effectiveness of EWSs and the need for further rigorous examinations of EWSs.

Isolation and survival: The impact of local and MSA isolation on survival among non-Hispanic Black women diagnosed with breast cancer in the United States using a SEER-Medicare cohort.

BACKGROUND: 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. METHODS: 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). FINDINGS: 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. INTERPRETATION: 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. FUNDING: National Cancer Institute.

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

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.

Centralization of Initial Care and Improved Survival of Poor Patients With Breast Cancer.

PURPOSE: Poor women with breast cancer have worse survival than others, and are more likely to undergo surgery in low-volume facilities. We leveraged a natural experiment to study the effectiveness of a policy intervention undertaken by New York (NY) state in 2009 that precluded payment for breast cancer surgery for NY Medicaid beneficiaries treated in facilities performing fewer than 30 breast cancer surgeries annually. METHODS: We identified 37,822 women with stage I-III breast cancer during 2004-2008 or 2010-2013 and linked them to NY hospital discharge data. A multivariable difference-in-differences approach compared mortality of Medicaid insured patients with that of commercially or otherwise insured patients unaffected by the policy. RESULTS: Women treated during the postpolicy years had slightly lower 5-year overall mortality than those treated prepolicy; the survival gain was significantly larger for Medicaid patients (P = .018). Women enrolled in Medicaid had a greater reduction than others in breast cancer-specific mortality (P = .005), but no greater reduction in other causes of death (P = .50). Adjusted breast cancer mortality among women covered by Medicaid declined from 6.6% to 4.5% postpolicy, while breast cancer mortality among other women fell from 3.9% to 3.8%. A similar effect was not observed among New Jersey Medicaid patients with breast cancer treated during the same years. CONCLUSION: A statewide centralization policy discouraging initial care for breast cancer in low-volume facilities was associated with better survival for the Medicaid population targeted. Given these impressive results and those from prior research, other policymakers should consider adopting comparable policies to improve breast cancer outcomes.[Media: see text].

Medication delivery factors and adjuvant endocrine therapy adherence in breast cancer.

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

Persistence of mortgage lending bias in the United States: 80 years after the Home Owners' Loan Corporation security maps.

Housing discrimination and racial segregation have a long history in the United States. The 1930's Home Owners' Loan Corporation (HOLC) "residential security maps," recently digitized, have become a popular visualization of Depression era mortgage lending risk patterns across American cities. Numerous housing policies have since been instituted, including the Home Mortgage Disclosure Act (HMDA), but mortgage lending bias persists. The degree to which detailed spatial patterns of bias have persisted or changed along with urban change is not well understood. We compare historic HOLC grades and contemporary levels of mortgage lending bias using spatially detailed HMDA data. We further examine the relationship between HOLC risk grades and contemporary racial and ethnic settlement patterns. Results suggest that historical mortgage lending risk categorizations and settlement patterns are associated with contemporary mortgage lending bias and racial and ethnic settlement patterns. Concerted and deliberate efforts will be needed to change these patterns.

Hospitalization Outcomes Among Patients With COVID-19 Undergoing Remote Monitoring.

Importance: Health care systems have implemented remote patient monitoring (RPM) programs to manage patients with COVID-19 at home, but the associations between participation and outcomes or resource utilization are unclear. Objective: To assess whether an RPM program for COVID-19 is associated with lower or higher likelihood of hospitalization and whether patients who are admitted present earlier or later for hospital care. Design, Setting, and Participants: This retrospective, observational, cohort study of RPM was performed at Froedtert & Medical College of Wisconsin Health Network, an academic health system in southeastern Wisconsin. Participants included patients with internal primary care physicians and a positive SARS-CoV-2 test in the ambulatory setting between March 30, 2020, and December 15, 2020. Data analysis was performed from February 15, 2021, to February 2, 2022. Exposures: Activation of RPM program. Main Outcomes and Measures: Hospitalizations within 2 to 14 days of a positive test. Inverse propensity score weighting was used to account for differences between groups. Sensitivity analyses were performed looking at usage of the RPM among patients who activated the program. Results: A total of 10 660 COVID-19-positive ambulatory patients were eligible, and 9378 (88.0%) had email or mobile numbers on file and were invited into the RPM program; the mean (SD) age was 46.9 (16.3) years and 5448 patients (58.1%) were women. Patients who activated monitoring (5364 patients [57.2%]) had a mean (SD) of 35.3 (33.0) check-ins and a mean (SD) of 1.27 (2.79) (median [IQR], 0 [0-1]) free-text comments. A total of 878 patients (16.4%) experienced at least 1 alert; 128 of 5364 activated patients (2.4%) and 158 of 4014 inactivated patients (3.9%) were hospitalized (χ21 = 18.65; P < .001). In weighted regression analysis, activation of RPM was associated with a lower odds of hospitalization (odds ratio, 0.68; 95% CI, 0.54-0.86; P = .001) adjusted for demographics, comorbidities, and time period. Monitored patients had a longer mean (SD) time between test and hospitalization (6.67 [3.21] days vs 5.24 [3.03] days), a shorter length of stay (4.44 [4.43] days vs 7.14 [8.63] days), and less intensive care use (15 patients [0.3%] vs 44 patients [1.1%]). Conclusions and Relevance: These findings suggest that activation of an RPM program is associated with lower hospitalization, intensive care use, and length of stay among patients with COVID-19.

Predicting outcomes after traumatic brain injury: A novel hospital prediction model for a patient reported outcome.

BACKGROUND: Estimation of long-term quality of life in patients sustaining Traumatic brain injuries is a difficult but important task during the early hospitalization. There are very limited tools to assess these outcomes, therefore we aimed to develop a predictive model for quality-of-life that could be used in hospitalized adults with TBIs. METHODS: The TRACK-TBI dataset was used to identify adult patients with TBI from 2014 to 2018. Multiple variables were assessed to predict favorable versus unfavorable scores on the Quality of Life after Brain Injury-Overall Scale (QOLIBRI-OS). RESULTS: We included 1549 subjects. 57% had a favorable outcome, and were more likely to have private insurance, higher GCS scores, and fewer comorbidities. A model (TBI-PRO) for 3, 6, and 12-month QOLIBRI score was created. The AUROCs for predicting 3, 6 and 12-month favorable QOLIBRI scores were 0.81, 0.79, and 0.76, respectively. CONCLUSION: The TBI-PRO model adequately estimates long-term outcomes in patients with TBI.

Feasibility of a pharmacist-led symptom monitoring and management intervention to improve breast cancer endocrine therapy adherence.

BACKGROUND: Adjuvant endocrine therapy (AET) for breast cancer reduces mortality, but one-third to one-half of patients discontinue it early or are nonadherent. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Unique Associations of DNA Methylation Regions With 24-Hour Blood Pressure Phenotypes in Black Participants.

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 Blacks 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.

Risk factors for intradialytic decline in cerebral perfusion and impaired cerebral autoregulation in adults on hemodialysis.

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.

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

BACKGROUND: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Analysis of Clinician and Patient Factors and Completion of Telemedicine Appointments Using Video.

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.

Ecological Study of Variability in the Relationship between Liver Cancer Mortality and Racial Residential Segregation.

Racial segregation has been identified as a predictor for the burden of cancer in several different metropolitan areas across the United States. This ecological study tested relationships between racial segregation and liver cancer mortality across several different metropolitan statistical areas in Wisconsin. Tract-level liver cancer mortality rates were calculated using cases from 2003-2012. Hotspot analysis was conducted and segregation scores in high, low, and baseline mortality tracts were compared using ANOVA. Spatial regression analysis was done, controlling for socioeconomic advantage and rurality. Black isolation scores were significantly higher in high-mortality tracts compared to baseline and low-mortality tracts, but stratification by metropolitan areas found this relationship was driven by two of the five metropolitan areas. Hispanic isolation was predictive for higher mortality in regression analysis, but this effect was not found across all metropolitan areas. This study showed associations between liver cancer mortality and racial segregation but also found that this relationship was not generalizable to all metropolitan areas in the study area.

Pharmacy deserts and patients with breast cancer receipt of influenza vaccines.

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.

Mortgage Lending Bias and Breast Cancer Survival Among Older Women in the United States.

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.

Dietary Sodium Restriction Results in Tissue-Specific Changes in DNA Methylation in Humans.

[Figure: see text].

Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning.

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.