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The opinions expressed in this article are those of the authors and do not necessarily reflect the position of the US Agency for Healthcare Research and Quality, the National Institutes of Health, or the US Department of Health and Human Services.
Differences in quality of care may contribute to racial variation in outcomes of bladder cancer (BCa). Quality indicators in patients undergoing surgery for BCa include the use of high-volume surgeons and high-volume hospitals, and, when clinically indicated, receipt of pelvic lymphadenectomy, receipt of continent urinary diversion, and undergoing radical cystectomy instead of partial cystectomy. The authors compared these quality indicators as well as adverse perioperative outcomes in black patients and white patients with BCa.
The Healthcare Cost and Utilization Project State Inpatient Databases for New York, Florida, and Maryland (1996-2009) were used, because they consistently included race, surgeon, and hospital identifiers. Quality indicators were compared across racial groups using regression models adjusting for age, sex, Elixhauser comorbidity sum, insurance, state, and year of surgery, accounting for clustering within hospital.
Black patients were treated more often by lower volume surgeons and hospitals, they had significantly lower receipt of pelvic lymphadenectomy and continent diversion, and they experienced higher rates of adverse outcomes compared with white patients. These associations remained significant for black patients who received treatment from surgeons and at hospitals in the top volume decile.
Although bladder cancer (BCa) incidence is twice as high among whites compared with blacks, blacks suffer a disproportionally high rate of BCa mortality, even when accounting for race differences in stage at presentation.[1-3] Variability in quality of care (QOC) may contribute to differences in BCa mortality, particularly in the definitive surgical management of aggressive disease (ie, cystectomy or bladder removal). Such procedures are technically demanding, and a successful outcome depends on specific processes of care and provider competence.[4-6]
Several indicators are suitable for evaluating the quality of BCa care.[7, 8] First, partial cystectomy (PC) provides inferior cancer control compared with radical cystectomy (RC), unless strict selection criteria are met.[9, 10] Therefore, higher receipt of PC may indicate inferior QOC. Second, the use of high-volume surgeons (HVS) and high-volume hospitals (HVH) is associated with lower complication rates, perioperative mortality, length of stay (LOS), and readmission rates.[11-13] Third, receipt of pelvic lymphadenectomy (PLND) in conjunction with RC for muscle-invasive BCa is considered the standard of care[14, 15] because of its prognostic and therapeutic value. Finally, the proportion of RC patients who undergo continent diversion also may reflect QOC, because some observers consider continent diversion more desirable for urinary function and cosmesis, and the operation is more technically demanding than noncontinent diversion.[8, 16] These structural (use of HVH and HVS) and process (receipt of RC vs PC, PLND, and continent diversion) measures affect treatment outcomes and provide a means to measure differences in QOC.
Thus, the objective of this study was to determine whether a QOC gap exists between black patients and white patients who undergo cystectomy for BCa. We hypothesized that black patients would have lower use of high-quality structural resources and processes of care than white patients and that this would be associated with more adverse events (prolonged LOS, blood transfusions, surgical complications, and in-hospital mortality). Furthermore, we hypothesized that black patients and white patients who were treated at HVH and by HVS would have similar processes of care use and perioperative outcomes.
MATERIALS AND METHODS
The State Inpatient Databases (SIDs) are encounter-level administrative data compiled through the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The SIDs contain greater than 100 uniformly recorded data elements on virtually all discharges from non-Federal hospitals in HCUP-participating states. Data elements include principal and secondary discharge diagnoses and procedures, patient demographics, expected payment source, and LOS.
We restricted our analysis to states that met all of the following criteria: 1) race/ethnicity coding was sufficiently complete for the state to be included in the HCUP disparities analytic file; 2) surgeon identifier was uniform across hospitals in the state, so that interhospital procedure volumes could be calculated; and 3) data were available through the HCUP Central Distributor. Three states (Florida, New York, and Maryland) met these criteria. We excluded data from hospitals with suspect coding of race, based on the following criteria, which reflect methods used to assemble the AHRQ disparities analytic file: 1) >30% of discharges had race reported as “other”; 2) race was missing in >50% of discharges; 3) all discharges had race coded as white, other, or missing; or 4) 100% of discharges had race coded as white. Data were available from 1996 to 2009.
We identified all cases with PC or RC listed as the principal procedure by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes (57.6, 57.7, 57.71, and 57.79). We restricted the cohort to patients with BCa (ICD-9-CM principal diagnosis codes 188, 1880, 1889, 2337, 2376, and 2394) and included all patients ages 19 to 94 years. We excluded procedures that were performed during admissions coded as “urgent” or “emergency.”
We used the uniformly coded “race” variable to classify patients as “white,” “black,” or “all others.” The number of patients identified as “Hispanic,” “Asian/Pacific Islander,” or “Native American” and “other” was too small to permit meaningful separate analyses, so we grouped them together and focused our analysis on white patients and black patients. Our use of the terms “white” and “black” throughout this report reflect the terminology used in the SIDs. Other independent variables included patient age, sex, payer (private insurance, Medicare, Medicaid, or other), the number of comorbidities, the state in which the procedure was performed, and the year of surgery.
The outcomes of interest were receipt of RC versus PC, receipt of PLND, hospital volume, surgeon volume, in-hospital mortality (yes or no), blood transfusion (yes or no), any complications (yes or no), diversion type (continent or incontinent), and LOS. On univariate and multivariable analyses, receipt of RC versus PC was assessed in the entire analytic cohort. For analyses of the remaining measures, we included only patients who underwent RC, because these measures have been validated to an extent in RC, but not in PC. Use of PLND and continent diversion was determined from ICD-9 procedure codes (403 and 578.7, respectively.) Surgeon volume (SV) was calculated as the number of RC and PC procedures performed by a particular surgeon in a particular state in each calendar year, based on the surgeon identifier. Hospital volume (HV) was similarly calculated. Both SV and HV were evaluated as outcome variables as well as predictors of the other outcomes.
In-hospital mortality and LOS are reported for all discharges in the SIDs, and blood transfusion was identified by ICD-9-CM procedure code (99.04) and/or Uniform Billing (UB-92) codes. On the basis of previous studies, we composed a binary variable indicating whether the patient had any complications according to the presence of specific secondary discharge diagnoses.
We used Pearson chi-square tests and Kruskal-Wallis tests to compare outcomes across racial groups and across SV and HV strata. To simplify univariate comparisons, annual RC volumes were classified as low, medium, and high, with high volume (≥5 cases per surgeon and ≥12 cases per hospital) representing the top volume decile, computed as a weighted average of the top volume decile for all states and all years. However, SV and HV were entered into multivariable models as continuous variables.
We examined the relation between race and each quality indicator using different types of regression models, adjusting for correlation among observations within the same hospital. To assess the associations between race and SV and HV, we fit Poisson regression models with the standard errors estimated through cluster bootstrap resampling to account for clustering within hospital. We fit logistic regression models predicting PLND, death in hospital, any complications, homologous blood transfusion, type of urinary diversion, and receipt of RC versus PC.
For the LOS analysis, we excluded inpatient deaths (n = 345), because the interpretation of LOS is different in these patients and patients with LOS <4 days (n = 49), because these probably represent coding errors. We fit a generalized least squares regression model, assuming an exchangeable correlation structure, using the log length of stay to correct for non-normality in the residuals.
In each model, we included interaction terms to allow the effect of race on outcome to vary by stratum of other independent variables. Because of the flexibility allowed by the interaction terms, there is no single effect of race to estimate; rather, the effect of race can be estimated for any combination of values of the independent variables. Therefore, we presented estimated effects of race for specific scenarios of patient characteristics. To determine whether there was an overall effect of race on each outcome, we performed F tests of the null hypothesis that parameters for all model terms involving race are equal to zero. Restricted cubic splines were used with age and year of surgery to allow for nonlinear relations with the outcome variable. Analyses were performed using R version 2.14.2 (R Core Team, Vienna, Austria). We obtained an Institutional Review Board review exemption at Vanderbilt University and signed a data-use agreement with AHRQ for use of the public-access versions of the SID.
After excluding 506 patients from hospitals with suspect coding of race and 31 with missing race, we identified 16,864 patients for the current analysis, including 14,834 (88%) white patients, 795 (5%) black patients, and 1235 (7%) other patients (Table 1). From 3% to 16% of surgeons performed RC in >1 hospital in each state per year combination, demonstrating that the identifier did indeed cross hospitals. Most surgeons performed 1 or 2 surgeries per year (73.7%; 341 of 463 surgeons on average each year), and 48.4% of patients underwent RC by surgeons who performed <5 cases in that year. The median annual hospital volume ranged from 2 to 4 RCs, depending on the state and year, and 42.9% of patients underwent their surgery at hospitals that performed <12 RCs that year.
Percentages may not sum to 100% because of rounding.
Age: Median (IQR), y
Black patients consistently used lower volume hospitals and surgeons than white patients (Table 2), despite an increase in SV and HV over time for both groups. Unadjusted analyses demonstrated that black patients also experienced lower use of evidence-based processes of care and had more adverse outcomes compared with white patients (Table 2).
We also examined the associations between SV and HV and each process and outcome measure (Table 3). SV and HV were directly associated with receipt of PLND and continent diversion and were inversely associated with LOS, complications, blood transfusions, and perioperative mortality.
Shaded rows indicate the number of patients available for analysis in the rows below; note that all quality measures other than cystectomy type were studied only in patients who underwent radical cystectomy.
In total, 2094 and 2112 patients were missing data for diversion type in the SV and HV analyses, respectively.
In the multivariable models, black race was a significant predictor of poorer performance on each quality indicator except receipt of RC versus PC and receipt of blood transfusion (Table 4, Fig. 1). However, interaction terms for black race × SV and black race × HV were nonsignificant in each model, suggesting that the QOC deficit was independent of volume. Representative figures demonstrate differences in performance on quality measures in black patients versus white patients, across strata of other independent variables, among patients who were treated by surgeons and in hospitals at the 90th percentile for volume (Fig. 1A-C). All remaining independent variables are set to the median for all patients (age, 70 years; sex, men; number of comorbidities, 1; state, New York; year, 2003; payer, Medicare) to exemplify a “typical” patient. For example, “typical” black patients who were treated at HVHs had lower odds of undergoing PLND than typical white patients over a broad range of patient ages (Fig. 1A). Black patients who were treated at HVHs by HVSs had significantly lower odds of undergoing continent diversion, but the association became nonsignificant in those aged >68 years, beyond which few patients in either group underwent continent diversion (Fig. 1B). Black patients who were treated by HVSs had markedly higher odds of in-hospital mortality compared with white patients, regardless of HV (Fig. 1C).
Table 4. Overall Effect of Black Race on Quality Measures
P values for the overall effect of black race on outcome were calculated using an F test of the null hypothesis that coefficients for all terms involving race were zero, including main effect and all interaction terms. Note that the overall effect of race may be significant even when it is not significant in a specific scenario.
Models predicting surgeon and hospital volume do not include surgeon or hospital volume as independent variables, whereas other models do include such terms.
Odds ratios for the specific scenario in which all independent variables were set to the median, except for SV and HV (which were set to the 90th percentile), are provided in Table 5. Note that, in the models for continent diversion and complications, black race was not a significant predictor in this particular scenario, although the overall effect of black race was significant (Table 4). This is typical of models that include multiple interaction terms, because the magnitude and significance of the effect may vary over strata of another independent variable, as in the interaction between black race × age on receipt of continent diversion (Fig. 1B.)
Table 5. Specific Scenario for a “Typical” Patient Treated in a High-Volume Hospital by a High-Volume Surgeona
Independent variables are adjusted to median values for all patients (age, 70 years; sex, man; comorbidity count, 1; state, New York; year, 2003; payer, Medicare), except for surgeon and hospital volume, which are set to the 90th percentile (5 patients per year for surgeon volume and 12 patients per year for hospital volume).
The estimate for the length-of-stay model represents the multiplicative effect of black race compared with white race (R indicates ratio).
In this study, we observed that black patients undergoing cystectomy experienced lower QOC than white patients, as evidenced by use of lower volume surgeons and hospitals, lower use of evidence-based processes of care, and higher incidence of adverse outcomes. Past studies have reported similar racial variation in QOC and outcomes for BCa.[3, 22-27] However, beyond confirming those previous studies, we observed that black race was associated significantly with lower QOC even among patients who were treated by providers and in hospitals at the 90th percentile for volume.
Limitations in health care access may explain why black patients were treated by lower volume surgeons and at lower volume hospitals. This finding pervades studies of health care disparities and may be influenced by factors that include socioeconomic status, insurance, transportation, employment, and social support.[4, 28] Decreased access to care can lead to inferior BCa outcomes through a delay in diagnosis, resulting more advanced disease at presentation22; or through the use of less experienced providers and hospitals, resulting in inferior outcomes because of lack of specialty care, lower use of clinical care pathways, and deviation from evidence-based practices; or by use of comparable volume but inferior quality surgeons and facilities, such as those that “fail to rescue” deteriorating patients.
It is noteworthy that black patients in our study experienced lower receipt of PLND and continent diversion and had more adverse outcomes, even when treated in an HVH and by an HVS. It is possible that patients who presented to HVSs and HVHs differed with respect to the severity of their comorbidities, disease characteristics, or surgical indications, leading to different treatment recommendations. However, we could not address this because of the limited clinical information in our data set. Black patients may not advocate for their health care as effectively as white patients, which may result in less aggressive (no PLND) or complex (incontinent) therapy. It is also possible that providers tend to recommend less aggressive or less complex therapy to black patients, because the provider may be appropriately weighing factors like comorbidity and the lower life expectancy of black individuals compared with white individuals. Alternatively, providers may inappropriately allow personal bias to guide their treatment recommendations. Although differential health care recommendations or preferences cannot be ruled out by our findings, the increased incidence of adverse outcomes among black patients suggests that other factors are at play. Despite receiving less aggressive and less complex care, black patients suffered more adverse outcomes, even in the hands of HVSs and at HVHs, suggesting that unmeasured confounders, such as differences in disease characteristics and patient comorbidity, contribute to both treatment decisions and adverse outcomes.
Whereas Finks et al demonstrated a 14% decline in perioperative mortality in the Medicare RC population between 1999 and 2008, which was associated with an increase in hospital RC volume during this period, we observed that black patients had consistently higher inpatient mortality compared with white patients regardless of SV or HV. Therefore, it may be important to examine policies that are intended to ensure that all patients benefit from volume-associated improvements in QOC, as market forces, payment reform, and other factors lead toward further concentration of care.
These findings must be interpreted in the context of study design. Our data set lacks important clinical information, such as disease characteristics, treatments, and cancer outcomes; patient-level socioeconomic data; and patient and surgeon preferences. Although there are known race differences in stage at presentation, it is unlikely that these explain the totality of the observed effects of race on LOS, transfusion, mortality, and complications. In addition, higher stage at presentation should be associated with higher receipt of RC versus PC and higher receipt of PLND, which we did not observe. It is important to note that we could not ascertain the severity of comorbidities, and it is possible that individuals who have decreased access to care have a greater number of untreated chronic conditions or fewer comorbidities coded. In addition, diversion type was missing in nearly 20% of patients. Although we did categorize SV and HV to facilitate presentation of the univariate statistics, our multivariable models did not depend on arbitrary volume cutoffs. Instead, we presented the case in which a patient is treated at a hospital and by a surgeon in the 90th of cystectomy volume to illustrate the scenario of a patient treated in a high-volume setting. Our analysis of 3 states may limit its generalizability; however, we were guided by previously established criteria for selecting states and hospitals with the most complete reporting of race. The race variable in the SID has 2 major disadvantages: 1) it captures Hispanic ethnicity in the same variable as race, making it impossible to separate the effects of racial group from those of Hispanic ethnicity; and 2) because race is based on hospital coding, it is subject to variable procedures for collecting this information at different hospitals. Unfortunately, there were too few patients in each of the other racial and ethnic groups to permit meaningful individual analyses of those groups. Many barriers to access reflect underlying socioeconomic disparities between blacks and whites for which we cannot completely control. Finally, our findings are based on patients who underwent cystectomy and do not necessarily reflect the magnitude of racial variation in QOC for all BCa patients.
These limitations notwithstanding, this data set is extremely robust, because it includes all payers and age ranges, without which it would be difficult to accumulate enough cases to compare meaningful outcomes, such as in-hospital mortality. Furthermore, the data set and modeling techniques enabled us to demonstrate the effect of race on each quality metric at different strata of SV and HV. It also may allow for subsequent analyses of sex disparities in care and the interplay between race and sex, because black women are known to have poor oncologic outcomes.
In summary, we have demonstrated that black patients with BCa are less likely to undergo cystectomy with an HVS or HVH and have lower receipt of PLND and continent diversion and more adverse perioperative events than their white counterparts, even when they are treated by HVSs and at HVHs. Further research efforts should focus on improving access to high-quality care for all patients with BCa and identifying the factors responsible for the apparent QOC gap observed in high-volume settings.
This study was supported American Cancer Society Internal Review grant 58-0009-51, administered by the Vanderbilt Ingram Cancer Center, and by the National Center for Research Resources/National Institutes of Health through Vanderbilt Clinical and Translational Science Award UL1TR000445.
CONFLICT OF INTEREST DISCLOSURES
Dr. Barocas reports personal fees from GE Healthcare, Janssen, and Dendreon outside the submitted work.