Bijan A. Niknam

Characteristics Associated With Differences in Survival Among Black and White Women With Breast Cancer

IMPORTANCE Difference in breast cancer survival by race is a recognized problem among Medicare beneficiaries. OBJECTIVE To determine if racial disparity in breast cancer survival is primarily attributable to differences in presentation characteristics at diagnosis or subsequent treatment. DESIGN, SETTING, AND PATIENTS Comparison of 7375 black women 65 years and older diagnosed between 1991 to 2005 and 3 sets of 7375 matched white control patients selected from 99,898 white potential controls, using data for 16 US Surveillance, Epidemiology and End Results (SEER) sites in the SEER-Medicare database. All patients received follow-up through December 31, 2009, and the black case patients were matched to 3 white control populations on demographics (age, year of diagnosis, and SEER site), presentation (demographics variables plus patient comorbid conditions and tumor characteristics such as stage, size, grade, and estrogen receptor status), and treatment (presentation variables plus details of surgery, radiation therapy, and chemotherapy). MAIN OUTCOMES AND MEASURES 5-Year survival. RESULTS The absolute difference in 5-year survival (blacks, 55.9%; whites, 68.8%) was 12.9% (95% CI, 11.5%-14.5%; P < .001) in the demographics match. This difference remained unchanged between 1991 and 2005. After matching on presentation characteristics, the absolute difference in 5-year survival was 4.4% (95% CI, 2.8%-5.8%; P < .001) and was 3.6% (95% CI, 2.3%-4.9%; P < .001) lower for blacks than for whites matched also on treatment. In the presentation match, fewer blacks received treatment (87.4% vs 91.8%; P < .001), time from diagnosis to treatment was longer (29.2 vs 22.8 days; P < .001), use of anthracyclines and taxols was lower (3.7% vs 5.0%; P < .001), and breast-conserving surgery without other treatment was more frequent (8.2% vs 7.3%; P = .04). Nevertheless, differences in survival associated with treatment differences accounted for only 0.81% of the 12.9% survival difference. CONCLUSIONS AND RELEVANCE In the SEER-Medicare database, differences in breast cancer survival between black and white women did not substantially change among women diagnosed between 1991 and 2005. These differences in survival appear primarily related to presentation characteristics at diagnosis rather than treatment differences.

Racial disparities in colon cancer survival: a matched cohort study.

Context Black patients have decreased colon cancer survival compared with white patients. Contribution In a model that sequentially matched patients with colon cancer by demographic characteristics, then presentation, and then treatment, little of the racial difference in colon cancer survival was found to be due to differences in treatment. Caution Only patients covered by Medicare were studied. Implication Efforts to decrease racial disparities in colon cancer survival may be best focused on prevention and early detection of disease. The Editors With nearly 100000 new cases each year, colon cancer is the fourth-most common cancer in the United States and is also responsible for the second-highest number of deaths with approximately 50000 per year (1). The incidence of colon cancer is highest among black persons (2), and racial disparities in survival among patients with colon cancer have long existed (36). Numerous reports have not only identified and documented worse outcomes in black patients with colon cancer but have suggested potential reasons for the disparity based on differences in screening (7, 8), comorbid conditions on presentation (9), stage (1012), treatment (1315), and socioeconomic status (16). In the Medicare population as a whole, life tables indicate a disparity between black and white patients in 5-year survival at age 65 years of 3.6% (17), but this widens substantially when a patient develops a serious illness, such as colon cancer (6). Although we examined the extent of the racial disparity in colon cancer survival in the Medicare population, the main purpose is to understand the nature of the disparity. We asked whether white patients who present like black patients are treated as black patients are treated, and if not, to what extent a disparity in treatment explains the disparity in survival. We assessed the magnitude of the disparity; examined whether the disparity has changed from 1998 and before (1991 to 1998) to 1999 and after (1999 to 2005), determined the relative contributions of presentation at diagnosis (and treatment after presentation) to differences in survival experienced by these groups, and explored how socioeconomic variables relate to the overall disparity. Our goal was to assist in determining which paths should be pursued to eliminate the persistent racial disparity in colon cancer survival. Methods Patient Population This research protocol was approved by the Institutional Review Board of The Childrens Hospital of Philadelphia (Philadelphia, Pennsylvania). We obtained the Survey, Epidemiology, and End Results (SEER)Medicare database for the years 1991 to 2005 for 16 SEER sites throughout the United States, including all sites except the Alaska Native Tumor Registry. There were 88858 patients aged 65 years or older with newly diagnosed invasive colon cancer. For each patient, the SEER Patient Entitlement and Diagnosis Summary File (18, 19) was merged with Medicare Parts A and B, outpatient claims, and the beneficiary summary file (which was updated to 31 December 2009 for this data set), providing a minimum of 4 years of follow-up for all patients. For all analyses of trends over time, we examined the 12 SEER sites that were collecting data during the entire study. For analyses that did not consider trends over time, we used all 16 sites. Defining Patient Characteristics We defined race by using the SEER algorithm (20) and compared black with white non-Hispanic and white Hispanic patients for the primary analysis. Patient comorbid conditions, such as congestive heart failure, diabetes, past acute myocardial infarction, stroke, hypertension, and 21 other conditions noted in the Supplement, were defined with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes (2124) and collected from Medicare claims (inpatient, outpatient, and physician bills) during a 3-month period before diagnosis. Supplement. Racial Disparities in Colon Cancer Survival: Extended Analyses Tumor Biology Characteristics of the patients tumor, including stage, grade, number of nodes dissected, and number of positive nodes, were obtained from the SEER Patient Entitlement and Diagnosis Summary File. For patients with stage II colon cancer, we used SEER and Medicare data to define 2 strata (high and low) of risk for recurrence (2527), on the basis of the presence of 1 of the following prognostic indicators: T4 tumor status, perforation, and fewer than 10 nodes removed (Supplement). Treatment Variables We defined treatment on the basis of information from both SEER and Medicare data. Surgery was defined by billing codes in the Medicare files. Evidence of chemotherapy was also determined by Medicare billing codes. Radiation therapy was determined by Medicare billing codes and information from the SEER Patient Entitlement and Diagnosis Summary File (Supplement). Statistical Analysis Similar to our previously published work (28), this analysis used tapered multivariate matching (2830) to compare the entire population of black patients in SEER-Medicare with 3 white populations individually paired to the black population to answer various questions about the origins of the racial disparity. We used all black patients for each match, so the black population was unchanged and fully representative of black patients in the SEER population. The white population changed according to the variables used in the match. We created 3 overlapping (30) matched analyses: a demographic characteristics match, which matched white to black patients by SEER site, age, sex, and year of diagnosis; a presentation match, which matched black and white patients by demographic characteristics as well as presentation characteristics, comorbid conditions, and tumor biology (stage, including high- and low-risk stage II, grade, and nodes); and a treatment match, which included matching variables from demographic characteristics and presentation as well as relevant treatment variables, including surgery, chemotherapy, radiation therapy, and individual types of chemotherapy. The hazards of adjustments made by models rather than matching are discussed by Rubin (31), Hansen (32), Stuart (33), and Lu and colleagues (34). As suggested by Rubin and Rosenbaum (3537), matching was performed first without viewing outcomes. The PROC ASSIGN (38) function in SAS, version 9.2 (SAS Institute), was used for all matching, providing optimal matches that minimized the total distance within matched pairs (37). We used near-fine balance for the SEER site in the presentation and treatment matches (28, 39, 40). This meant that each site contributed nearly identical numbers of white and black patients to each matched analysis. Matching on patient covariates in the presentation and treatment matches also included a score predicting black race (a propensity score) and a risk score based on a Charlson score (4144). The propensity scores for the presentation and treatment matches came from a logistic regression of white-versus-black race using all of the variables to be controlled in the specific match. Matching on the propensity score tends to balance variables making up the propensity score (37, 4547). For each matching variable, we checked similarities between black and white patients using the standardized difference in means before and after matching, which is the mean difference between groups in units of the before matching SDs (22, 48, 49). A conventional rule of thumb aims for mean standardized differences below 0.2 of an SD (22, 48, 49), although we aimed for standardized differences below 0.1. We also assessed how closely we achieved balance using 2-sample randomization tests, specifically the Wilcoxon rank-sum test for each continuous covariate, the Fisher exact test for each binary covariate, and a single cross-match test for all covariates in a given match (28, 5053). When testing the hypothesis that there were no differences in outcomes between the matched patients, the Wilcoxon sign-rank statistic (54) was calculated for continuous variables and the McNemar statistic (55) was used for binary outcomes. When modeling survival differences over time, we used the paired version of the Cox proportional hazards model (56). When comparing paired survival distributions, we used the PrenticeWilcoxon test (57). We obtained SEs for the paired differences in survival probabilities using the bootstrap method as described by Efron and Tibshirani (58). Differences among white patients were tested using the exterior match that removed overlap in the white control groups (28, 30), again testing for differences in survival using the PrenticeWilcoxon test (57). For all tests of outcomes and matching quality, differences were considered statistically significant if the Pvalue was less than 0.05. For analyses that compared survival in the 2 time periods, we used only the 12 SEER sites that collected data for the full duration of these intervals. Role of the Funding Source The Agency for Healthcare Research and Quality and National Science Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Results Quality of the Matches: Matching Results In Table 1, we report characteristics of the entire black population and 3 white populations matched sequentially to the black population. Table 1. Quality of Matches* The 3 matches sequentially removed aspects of the disparity while leaving other aspects in place so we could develop an understanding of how the disparity occurs. In each match, the variables controlled in that match were closely balanced, with no standardized difference exceeding 0.07 SD. Complete matching tables are provided in the Supplement. In a given match, unmatched variables exhibit differences that reveal aspects of the disparity. For example, among all black patients with colon cancer,

Comparison of the Value of Nursing Work Environments in Hospitals Across Different Levels of Patient Risk

IMPORTANCE The literature suggests that hospitals with better nursing work environments provide better quality of care. Less is known about value (cost vs quality). OBJECTIVES To test whether hospitals with better nursing work environments displayed better value than those with worse nursing environments and to determine patient risk groups associated with the greatest value. DESIGN, SETTING, AND PARTICIPANTS A retrospective matched-cohort design, comparing the outcomes and cost of patients at focal hospitals recognized nationally as having good nurse working environments and nurse-to-bed ratios of 1 or greater with patients at control group hospitals without such recognition and with nurse-to-bed ratios less than 1. This study included 25 752 elderly Medicare general surgery patients treated at focal hospitals and 62 882 patients treated at control hospitals during 2004-2006 in Illinois, New York, and Texas. The study was conducted between January 1, 2004, and November 30, 2006; this analysis was conducted from April to August 2015. EXPOSURES Focal vs control hospitals (better vs worse nursing environment). MAIN OUTCOMES AND MEASURES Thirty-day mortality and costs reflecting resource utilization. RESULTS This study was conducted at 35 focal hospitals (mean nurse-to-bed ratio, 1.51) and 293 control hospitals (mean nurse-to-bed ratio, 0.69). Focal hospitals were larger and more teaching and technology intensive than control hospitals. Thirty-day mortality in focal hospitals was 4.8% vs 5.8% in control hospitals (P < .001), while the cost per patient was similar: the focal-control was -

Examining Causes of Racial Disparities in General Surgical Mortality: Hospital Quality Versus Patient Risk.

163 (95% CI = -

Comparing Outcomes between Antibody Induction Therapies in Kidney Transplantation

542 to

Twelve-Month Outcomes After Transplant of Hepatitis C-Infected Kidneys Into Uninfected Recipients: A Single-Group Trial.

215; P = .40), suggesting better value in the focal group. For the focal vs control hospitals, the greatest mortality benefit (17.3% vs 19.9%; P < .001) occurred in patients in the highest risk quintile, with a nonsignificant cost difference of

Failure-to-Rescue After Acute Myocardial Infarction

941 per patient (

Adjustment for atherosclerosis diagnosis distorts the effects of percutaneous coronary intervention and the ranking of hospital performance

53 701 vs

A Framework for the Assessment of Graduate Medical Education

52 760; P = .25). The greatest difference in value between focal and control hospitals appeared in patients in the second-highest risk quintile, with mortality of 4.2% vs 5.8% (P < .001), with a nonsignificant cost difference of -

Defining Multimorbidity in Older Surgical Patients

862 (