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.

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.

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.

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.

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.

Guideline-concordant treatment predicts survival: a National Cancer Database validation study of novel composite locoregional and systemic treatment scores among women with early stage breast cancer.

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

A dependent Dirichlet process model for survival data with competing risks.

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

The impact of generic aromatase inhibitors on initiation, adherence, and persistence among women with breast cancer: Applying multi-state models to understand the dynamics of adherence.

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.

Normative ranges of nasal airflow variables in healthy adults.

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

Nonparametric competing risks analysis using Bayesian Additive Regression Trees.

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.

Prevalence and scope of advanced practice provider oncology care among Medicare beneficiaries with breast cancer.

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

Factors associated with chronic opioid use after cervical spine surgery for degenerative conditions.

OBJECTIVE: Opioids 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. METHODS: The 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. RESULTS: Two 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. CONCLUSIONS: One 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.