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Keywords:

  • socioeconomic status;
  • race;
  • positive surgical margin;
  • biochemical recurrence;
  • prostate cancer;
  • equal access

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

BACKGROUND:

The impact of race and socioeconomic status (SES) in prostate cancer (CaP) outcomes has been well-studied, but controversy remains. The associations of race/SES with intermediate CaP outcomes, including positive surgical margin (PSM) and biochemical recurrence (BCR), were explored in an equal-access setting.

METHODS:

Data were retrospectively collected from 2502 men in the Shared Equal Access Regional Cancer Hospitals (SEARCH) database who underwent radical prostatectomy from 1989 to 2010. SES (income, education, employment, and poverty) was estimated from linkage of home ZIP code to census data. Logistic regression with adjustment for pre- and postoperative covariates estimated risk for associations between race/SES and pathologic outcomes. Cox proportional hazards models estimated risk for associations between race/SES and time to BCR.

RESULTS:

Black men were more likely to have lower SES than white men (P < .001). On multivariate analysis, race was not associated with PSM, but higher SES was associated with less PSM and fewer Gleason sum ≥ 7 pathologic tumors when SES was assessed by education, employment, or poverty (P trend ≤ .051) and income, employment, or poverty (P trend ≤ 0.059), respectively. Crude Cox models showed black men had higher BCR risk (hazards ratio = 1.20, 95% confidence interval = 1.05-1.38, P = .009) that persisted after adjustment for covariates including SES (hazards ratio ≥ 1.18, P ≤ .040). Higher SES measured by income and poverty were associated with less BCR, but only for black men (P trend ≤ .048).

CONCLUSIONS:

Even in an equal-access setting, higher SES predicted lower PSM risk, and race persisted in predicting BCR despite adjustment for SES. Low SES black patients may be at greatest risk for postprostatectomy BCR. Cancer 2012. © 2012 American Cancer Society.

Racial and socioeconomic disparities in prostate cancer (CaP) outcomes have been well-documented. Specifically, black men and men of lower socioeconomic status (SES) are more likely than white men or those of higher SES, respectively, to die from CaP.1-4 Despite efforts to limit the extent of these racial and socioeconomic disparities, they remain significant health care concerns.

To this day, much controversy continues to exist over race and SES due to their overlapping effects on CaP outcomes. Numerous studies report greater disease-specific mortality for blacks compared to whites,1, 3-7 even after adjustment for SES. On the contrary, other studies note the effects of race disappear after adjustment for SES and suggest SES may play a more significant role than race in determining survival.2, 8-10 Importantly, very few studies have examined the interplay of race and SES on intermediate nonmortality outcomes, such as positive surgical margin (PSM) rates or biochemical progression (BCR).

Further confounding the controversy is the difficulty in separating the effects of race/SES, which are patient-level factors, from those of system-level factors such as unequal access to quality health care.3, 11 Indeed, access to even a single urologist per county in the United States has been shown to improve CaP-specific survival.12 To date, however, the vast majority of recent studies on racial/SES disparities in CaP outcomes, particularly those analyzing the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, derive from private health care settings where financial constraints may lead to biases in diagnosis and treatment, and thereby affect survival. In addition, claims-based studies commonly have not assessed for nonmortality outcomes, such as surgical margin status or BCR. As such, we sought to explore an equal-access health care setting to account for variation in access to care, at least in theory, and examine the relationships between race/SES and pathologic and biochemical outcomes.

To do this, we used patient data from the Shared Equal Access Regional Cancer Hospital (SEARCH) database, which is the largest radical prostatectomy dataset in the United States based solely on equal-access Veteran Affairs Medical Centers (VAMC).13 This database presents an ideal setting for our study, due to its equal-access nature and detailed postprostatectomy outcomes data. Although race has been previously analyzed within SEARCH,14 it has not been evaluated with adjustment for SES. Given the ongoing debate over SES and race, we therefore reanalyzed our database with adjustment for 4 different measures of SES. We hypothesized that, within the context of the equal-access VAMC health care system, SES would not significantly affect CaP outcomes.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Patient Data

After obtaining institutional review board approval from each institution, data from patients undergoing radical prostatectomy between 1989 and 2010 at 5 VAMCs (West Los Angeles and Palo Alto, California; Augusta, Georgia; Durham and Asheville, North Carolina) were retrospectively combined into the SEARCH database. Patients treated with preoperative androgen deprivation or radiation therapy were excluded. Although patients could have received care outside the VAMC, all underwent prostatectomy at the VAMC as primary therapy for their newly diagnosed CaP. The database includes demographic and clinical information, including age at surgery, home ZIP code at time of last follow-up, year of surgery, height, weight, race, preoperative prostate-specific antigen (PSA) level, clinical stage, biopsy Gleason scores, surgical specimen pathology (tumor grade, stage, and surgical margin status), and follow-up PSA. We calculated body mass index (BMI) as weight in kilograms divided by height in meters squared (kg/m2).

Four variables at the ZIP code level15 from the 2000 census were used to define SES and were divided into quartiles: 1) Income. Median household income: first quartile (<$31,319), second ($31,319-$37,042), third ($37,043-$43,022), and fourth (>$43,022); 2) Education. Percent of adults age ≥25 who had high school or higher education: first quartile (<68.3%), second (68.3%-75.7%), third (75.8%-83.7%), and fourth (>83.7%); 3) Employment. Percent of civilian labor force who were employed: first quartile (<91.5%), second (91.5%-94.0%), third (94.1%-95.7%), and fourth (>95.7%); 4) Poverty. Percent of individuals living ≥poverty level: first quartile (<81.2%), second (81.2%-86.5%), third (86.6%-90.5%), and fourth (>90.5%). Thus, higher quartiles signified higher SES.

Mean and median follow-up for BCR were 67 and 57 months, respectively. All patients were followed with serial PSA determinations and clinical visits at intervals according to the discretion of the treating attending physician. Additional treatment after surgery was at the judgment of the patient and treating physician. Biochemical recurrence was defined as a single PSA > 0.2 ng/mL, 2 concentrations at 0.2 ng/mL, or secondary treatment for an elevated PSA. Men who received adjuvant therapy within 6 months postoperatively for an undetectable PSA were considered as not having recurrence at the time of adjuvant therapy, and their follow-up was censored at that point.

Of 2892 total patients in SEARCH, 200 were missing ZIP codes and therefore missing SES measures. We also excluded 87 men for missing follow-up data and 103 for missing margin status, extracapsular extension, and/or seminal vesicle invasion. Missing values for all other variables were categorized into dummy categories for analysis. These exclusions resulted in a final study population of 2502 men (87%).

Statistical Analysis

We categorized BMI as normal weight (<25 kg/m2), overweight (25-29.9 kg/m2), and obese (≥30 kg/m2). Nonblack, nonwhite races constituted 6% of the total cohort and were grouped with the white patients. Baseline variables were subsequently compared across race (black vs white) using chi-square (categorical) and Wilcoxon rank-sum (continuous) tests. Because the 4 SES measures were highly correlated, we constructed separate multivariate logistic regression models for each SES measure to analyze odds ratios and 95% confidence intervals (CIs) for associations between race/SES and pathologic outcomes including high-grade pathologic Gleason sum (≥7 vs <7), PSM, extracapsular extension (ECE), and seminal vesicle invasion (SVI), with adjustment for preoperative covariates including age (continuous), year (continuous), BMI, PSA, clinical stage, center, and biopsy Gleason sum (all categorical). We assessed trends across SES quartiles by incorporating median values for each SES quartile into the models as a continuous variable. Based on our results for PSM, we further adjusted for postoperative covariates including pathologic Gleason sum (categorical), ECE, SVI, and prostate weight (continuous) to distinguish biologic mechanisms from surgical technique for risk of PSM.

To assess associations between race/SES and biochemical outcomes, we constructed separate Cox proportional hazards models for each SES measure and analyzed time from surgery to BCR in crude and adjusted models, with adjustment for preoperative covariates in one set of models, and pre- and postoperative covariates in a second set of models. Based on our findings for recurrence, we explored whether the results differed by race, first with interaction terms between SES and race, then with stratification by race. Trends across SES quartiles were analyzed as above. The proportional hazards assumption for each Cox model was tested and met with visual inspection of log-log plots, smoothing splines, and residual plots. All statistical analyses were performed with Stata, version 11 (StataCorp, College Station, Tex.) and a 2-tailed alpha of 0.05.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Median age was 63 years, with 71% of the cohort having PSA < 10 ng/mL (Table 1). Black men constituted 35% of the cohort and were younger, had surgery more recently, and had more PSM but fewer ECEs than did white men (all P ≤ .002). Of men with available data, black men were more likely to be obese, have PSA ≥ 20 ng/mL, have biopsy and pathologic Gleason sum 7 diseases, and have clinical T1 stage disease (all P ≤ .032) compared to white men.

Table 1. Comparison of Demographic and Clinicopathologic Characteristics by Race
CharacteristicTotalWhiteBlackPa
  • Percentages may not sum to 100 due to rounding. BMI indicates body mass index; ECE, extracapsular extension; IQR, interquartile range; PSA, prostate-specific antigen; PSM, positive surgical margin; SVI, seminal vesicle invasion.

  • a

    P values compare white and black patients, excluding unknown values.

  • b

    Wilcoxon rank-sum test.

  • c

    Chi-square test.

No. of patients (%)2502 (100)1627 (65)875 (35)
Age (y), median (IQR)63 (58-67)63 (59-68)61 (56-66).0001b
Year of surgery, median (IQR)2002 (1996-2005)2001 (1995-2005)2003 (1998-2006).0001b
BMI, n (%)    
 <25514 (21)316 (25)198 (25).032c
 25-29.9942 (38)604 (48)338 (43)
 ≥30592 (24)340 (27)252 (32)
 Unknown454 (18)36787
PSA, n (%)    
 <101772 (71)1177 (76)595 (69).001c
 10-19.9468 (19)285 (18)183 (21)
 ≥20179 (7)97 (6)82 (10)
 Unknown83 (3)6815
Clinical stage, n (%)    
 T11278 (51)727 (55)551 (69)<.001c
 >T1837 (33)592 (45)245 (31)
 Unknown387 (15)30879
Biopsy Gleason sum, n (%)    
 <71544 (62)1031 (65)513 (60).010c
 7680 (27)409 (26)271 (32)
 >7211 (8)139 (9)72 (8)
 Unknown67 (3)4819
Pathologic Gleason sum, n (%)    
 <71081 (43)738 (46)343 (39)<.001c
 71135 (45)680 (42)455 (52)
 >7273 (11)196 (12)77 (9)
 Unknown13 (0.5)130
PSM, n (%)1025 (41)629 (39)396 (45)0.001c
ECE, n (%)552 (22)390 (24)162 (19)0.002c
SVI, n (%)241 (10)148 (9)93 (11)0.215c

The distribution of SES was significantly race-dependent (Table 2). Black patients were consistently more likely to belong to the lowest SES quartile in all 4 SES measures compared to white patients with >30% of black men in the lowest quartile and <20% in the highest quartile for all SES measures (all P < .001).

Table 2. Comparison of Socioeconomic Status by Race
SES (Quartiles From Lowest to Highest)Race 
WhiteBlackPa
  • Percentages may not sum to 100 due to rounding. Quartiles reflect division of study population socioeconomic status (SES) measures at the 25th, 50th, and 75th percentiles, regardless of race.

  • a

    Chi-square test.

Income, n (%)   
 1316 (19)308 (35)<.001
 2413 (25)213 (24)
 3419 (26)207 (24)
 4479 (29)147 (17)
Education, n (%)   
 1352 (22)271 (31)<.001
 2375 (23)248 (28)
 3434 (27)190 (22)
 4466 (29)166 (19)
Employment, n (%)   
 1291 (18)333 (38)<.001
 2410 (25)255 (29)
 3437 (27)147 (17)
 4489 (30)140 (16)
Poverty, n (%)   
 1268 (16)358 (41)<.001
 2415 (26)214 (24)
 3454 (28)171 (20)
 4490 (30)132 (15)

Stratification of SES by center showed a significant geographic variation in patient demographics (Table 3). Patients from center 2 were mostly white with higher SES than patients from other centers. Stratification of SES by year of surgery showed no significant changes in SES over time (all P > .08, data not shown), although a slight decline was noted at all centers through 2000 before stabilizing for the past decade.

Table 3. Comparison of Socioeconomic Status and Race by Center
Race/SES (Quartiles From Lowest to Highest)Center 
12345Pa
  • Percentages may not sum to 100 due to rounding. SES indicates socioeconomic status.

  • a

    Chi-square test.

Race, n (%)
 White319 (57)332 (85)218 (47)328 (55)430 (86)<.001
 Black243 (43)57 (15)242 (53)264 (45)69 (14)
Income, n (%)
 1170 (30)34 (9)154 (33)154 (26)112 (22)<.001
 289 (16)50 (13)138 (30)134 (23)215 (43)
 399 (18)68 (17)115 (25)202 (34)142 (28)
 4204 (36)237 (61)53 (12)102 (17)30 (6)
Education, n (%)
 1204 (36)82 (21)146 (32)101 (17)90 (18)<.001
 293 (17)49 (13)113 (25)194 (33)174 (35)
 3107 (19)74 (19)134 (29)158 (27)151 (30)
 4158 (28)184 (47)67 (15)139 (23)84 (17)
Employment, n (%)
 1273 (49)99 (25)126 (27)97 (16)29 (6)<.001
 2160 (28)68 (17)156 (34)189 (32)92 (18)
 381 (14)96 (25)100 (22)119 (20)188 (38)
 448 (9)126 (32)78 (17)187 (32)190 (38)
Poverty, n (%)
 1238 (44)62 (16)152 (33)131 (22)33 (7)<.001
 2114 (20)61 (16)159 (35)167 (28)128 (26)
 379 (14)94 (24)90 (20)159 (27)203 (41)
 4121 (22)172 (44)59 (13)135 (23)135 (27)

There were no significant associations between race and high-grade pathologic Gleason sum or SVI (Table 4). Black men were significantly less likely to have ECE after adjustment for preoperative covariates, including SES measures (all P ≤ .010). For the SES measures of income and poverty, patients with higher SES were significantly less likely to have pathologic Gleason sum ≥ 7 disease compared to patients in the lowest SES quartile (all P trend ≤ .017), with a near-significant trend for employment as the SES measure (P trend = .059). SES was not significantly associated with ECE or SVI.

Table 4. Multivariate-Adjusted OR and 95% CI for Associations Between Race/SES and Pathologic Gleason Sum, ECE, and SVI
Race/SES (Quartiles From Lowest to Highest)Pathologic Gleason Sum≥7aECESVI
OR (95% CI)PbOR (95% CI)PbOR (95% CI)Pb
  • Each logistic regression model adjusted for age, year of surgery, SES measure, body mass index, prostate-specific antigen, clinical stage, biopsy Gleason sum, race, and center. Analysis excluded missing values for pathologic Gleason sum (0.5% of total values).

  • CI indicates confidence interval; ECE, extracapsular extension; OR, odds ratio; SES, socioeconomic status; SVI, seminal vesicle invasion.

  • a

    Compared to men with pathologic Gleason sum<7 disease.

  • b

    P values for SES measures are for trends across SES quartiles.

Black vs white1.08 (0.88-1.33).4470.72 (0.57-0.92).0071.16 (0.84-1.60).377
Income .017 .211 .291
 11.0 1.0 1.0 
 21.02 (0.79-1.32) 1.05 (0.79-1.41) 0.75 (0.50-1.12) 
 30.98 (0.76-1.27) 0.91 (0.68-1.22) 0.77 (0.52-1.15) 
 40.73 (0.56-0.97) 0.85 (0.62-1.16) 0.77 (0.50-1.18) 
Black vs white1.10 (0.89-1.35).3720.73 (0.57-0.93).0101.17 (0.85-1.62).328
Education .259 .322 .551
 11.0 1.0 1.0 
 21.09 (0.84-1.42) 0.76 (0.57-1.03) 0.72 (0.48-1.08) 
 30.89 (0.69-1.15) 0.89 (0.67-1.19) 0.63 (0.41-0.97) 
 40.91 (0.70-1.17) 0.82 (0.61-1.10) 0.90 (0.61-1.34) 
Black vs white1.07 (0.87-1.32).5190.71 (0.56-0.90).0051.17 (0.84-1.62).344
Employment .059 .190 .421
 11.0 1.0 1.0 
 20.81 (0.62-1.04) 0.74 (0.55-1.00) 0.90 (0.60-1.36) 
 30.77 (0.58-1.01) 0.78 (0.57-1.07) 1.04 (0.67-1.61) 
 40.79 (0.60-1.04) 0.83 (0.61-1.13) 0.78 (0.50-1.21) 
Black vs white1.05 (0.85-1.30).6240.73 (0.57-0.93).0101.18 (0.85-1.63).327
Poverty .006 .546 .512
 11.0 1.0 1.0 
 20.84 (0.64-1.09) 1.15 (0.85-1.55) 1.05 (0.70-1.59) 
 30.78 (0.60-1.02) 0.97 (0.71-1.32) 0.92 (0.60-1.40) 
 40.68 (0.52-0.90) 0.92 (0.68-1.26) 0.88 (0.57-1.35) 

Black men were at no different risk for PSM than white men after adjustment for pre- and postoperative covariates (Table 5). However, for all SES measures except income, patients with higher SES were significantly less likely to have PSM than patients with lower SES after adjusting for only preoperative covariates (all P trend ≤ .021). After further adjustment for prostate weight and markers of disease aggressiveness such as pathologic Gleason sum, ECE, and SVI, patients who had lower SES remained at higher risk for PSM for the SES measures of education and poverty (all P trend ≤ .034) with a near-significant trend for employment (P trend = .051).

Table 5. Multivariate-Adjusted OR and 95% CI for Associations Between Race/SES and PSM
Race/SES (Quartiles From Lowest to Highest)Model 1Model 2
OR (95% CI)PaOR (95% CI)Pa
  • Model 1 is a logistic regression model adjusted for age, year of surgery, SES measure, body mass index (BMI), prostate-specific antigen (PSA), clinical stage, biopsy Gleason sum, race, and center. Model 2 is a logistic regression model adjusted for age, year of surgery, SES measure, BMI, PSA, pathologic Gleason sum, race, center, extracapsular extension, seminal vesicle invasion, and prostate weight. Analyses excluded missing values for pathologic Gleason sum (0.5% of total values).

  • CI indicates confidence interval; OR, odds ratio; PSM, positive surgical margin; SES, socioeconomic status.

  • a

    P values for SES measures are for trends across SES quartiles.

Black vs white0.89 (0.73-1.08).2450.94 (0.76-1.16).573
Income .169 .463
 11.0 1.0 
 20.76 (0.59-0.97) 0.72 (0.55-0.94) 
 30.77 (0.60-0.98) 0.76 (0.58-0.99) 
 40.80 (0.62-1.03) 0.85 (0.64-1.13) 
Black vs white0.90 (0.74-1.09).2820.94 (0.76-1.16).560
Education .021 .032
 11.0 1.0 
 20.76 (0.59-0.97) 0.72 (0.55-0.94) 
 30.74 (0.58-0.95) 0.70 (0.54-0.92) 
 40.75 (0.58-0.95) 0.74 (0.56-0.97) 
Black vs white0.87 (0.72-1.06).1760.92 (0.74-1.14).423
Employment .019 .051
 11.0 1.0 
 20.68 (0.53-0.86) 0.72 (0.55-0.94) 
 30.82 (0.63-1.07) 0.85 (0.64-1.14) 
 40.71 (0.55-0.93) 0.72 (0.54-0.97) 
Black vs white0.87 (0.72-1.06).1750.92 (0.74-1.13).421
Poverty .011 .034
 11.0 1.0 
 20.88 (0.69-1.12) 0.85 (0.65-1.12) 
 30.79 (0.61-1.02) 0.84 (0.64-1.11) 
 40.73 (0.57-0.94) 0.73 (0.55-0.97) 

Overall, 888 of 2502 (35%) men experienced BCR. Compared to white men, black patients had 20% greater risk of BCR after prostatectomy on crude analysis (hazard ratio [HR] = 1.20, 95% CI = 1.05-1.38, P = .009; Table 6). This increased risk did not change dramatically after adjustment for preoperative covariates, including SES (all HR ≥ 1.19, all P ≤ .029), nor after further adjusting for postoperative covariates (all HR ≥ 1.18, all P ≤ .040). On crude analysis, only income as the SES measure reflected a significant trend for increased risk of BCR after prostatectomy (P trend = .001), but this trend was attenuated after adjusting for clinical and pathologic covariates (all P trend ≥ .264).

Table 6. Crude and Multivariate-Adjusted HR and 95% CI for Associations Between Race/SES and Time to Biochemical Recurrence (n = 888 Events)
Race/SES (Quartiles From Lowest to Highest)Crude ModelModel 1Model 2
HR (95% CI)PaHR (95% CI)PaHR (95% CI)Pa
  • Model 1 is a Cox proportional hazards model adjusted for age, year of surgery, SES measure, body mass index (BMI), prostate-specific antigen (PSA), clinical stage, biopsy Gleason sum, race, and center. Model 2 is a Cox proportional hazards model adjusted for age, year of surgery, SES measure, BMI, PSA, pathologic Gleason sum, race, center, positive surgical margin, extracapsular extension, and seminal vesicle invasion.

  • CI indicates confidence interval; HR, hazards ratio; SES, socioeconomic status.

  • a

    P values for SES measures are for trends across SES quartiles.

Black vs white1.20 (1.05-1.38).0091.21 (1.04-1.40).0161.19 (1.02-1.39).023
Income .001 .264 .795
 11.0 1.0 1.0 
 20.96 (0.80-1.15) 1.00 (0.83-1.20) 1.01 (0.84-1.22) 
 30.92 (0.77-1.10) 0.97 (0.80-1.17) 1.01 (0.84-1.22) 
 40.74 (0.61-0.89) 0.90 (0.73-1.10) 0.98 (0.80-1.20) 
Black vs white1.20 (1.05-1.38).0091.23 (1.05-1.42).0081.22 (1.04-1.42).012
Education .592 .564 .101
 11.0 1.0 1.0 
 21.05 (0.88-1.27) 0.94 (0.78-1.14) 1.08 (0.89-1.31) 
 30.98 (0.81-1.18) 1.00 (0.83-1.22) 1.18 (0.97-1.43) 
 40.97 (0.81-1.17) 1.04 (0.86-1.26) 1.15 (0.95-1.40) 
Black vs white1.20 (1.05-1.38).0091.20 (1.03-1.39).0221.19 (1.02-1.39).030
Employment .356 .208 .549
 11.0 1.0 1.0 
 20.95 (0.79-1.14) 0.92 (0.76-1.11) 1.02 (0.84-1.23) 
 30.93 (0.77-1.12) 0.94 (0.77-1.15) 0.96 (0.78-1.18) 
 40.92 (0.77-1.11) 0.87 (0.71-1.06) 0.94 (0.77-1.16) 
Black vs white1.20 (1.05-1.38).0091.19 (1.02-1.39).0291.18 (1.01-1.37).040
Poverty .070 .169 .562
 11.0 1.0 1.0 
 20.89 (0.74-1.06) 0.88 (0.73-1.07) 0.87 (0.72-1.06) 
 30.95 (0.79-1.14) 0.93 (0.76-1.13) 0.97 (0.80-1.19) 
 40.82 (0.68-0.99) 0.86 (0.71-1.05) 0.93 (0.76-1.13) 

Upon stratification by race, patients with higher SES who were black had significantly less risk of BCR than black patients of lower SES, using income and poverty as SES measures (all P trend ≤ .048; Table 7). Employment as the SES measure gave a similar trend nearing significance (P trend = .075). Among white patients, recurrence risk did not vary across quartiles in all SES measures (all P trend ≥ .330). However, the interaction terms between SES and race were not significant (all P interaction > .05).

Table 7. Preoperative Multivariate-Adjusted HR and 95% CI for Associations Between SES and Time to Recurrence, Stratified by Race
SES (Quartiles From Lowest to Highest)Black (328 events)White (560 events)
HR (95% CI)PaHR (95% CI)Pa
  • Each Cox proportional hazards model adjusted for age, year of surgery, SES measure, body mass index, prostate-specific antigen, clinical stage, biopsy Gleason sum, and center.

  • CI indicates confidence interval; HR, hazards ratio; SES, socioeconomic status.

  • a

    P values are for trends across SES quartiles.

Income .039 .453
 11.0 1.0 
 20.89 (0.66-1.20) 1.09 (0.85-1.40) 
 30.73 (0.53-0.99) 1.11 (0.87-1.42) 
 40.72 (0.50-1.03) 0.94 (0.72-1.23) 
Education .221 .330
 11.0 1.0 
 20.73 (0.54-0.99) 1.04 (0.81-1.34) 
 30.95 (0.69-1.31) 1.05 (0.82-1.35) 
 40.76 (0.55-1.06) 1.13 (0.88-1.44) 
Employment .075 .355
 11.0 1.0 
 20.82 (0.61-1.10) 0.91 (0.70-1.17) 
 30.77 (0.54-1.10) 0.97 (0.75-1.26) 
 40.76 (0.53-1.09) 0.86 (0.66-1.12) 
Poverty .048 .408
 11.0 1.0 
 20.95 (0.70-1.28) 0.78 (0.60-1.02) 
 30.71 (0.51-0.98) 0.95 (0.74-1.23) 
 40.77 (0.54-1.08) 0.84 (0.65-1.09) 

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

We found that, even in an equal-access health care setting, both race and SES affect pathologic and biochemical outcomes. Patients with lower SES in our cohort were more likely than patients with higher SES to have high-grade disease and PSM. Conversely, even after adjustment for SES, black patients were more likely to have BCR after prostatectomy than white patients. Risk of recurrence was influenced by SES, but only for black patients. The implications of our findings suggest that whereas SES may play a role in adverse pathologic outcomes, black race remains the stronger risk factor in predicting recurrence.

To our knowledge, we are the first to show the impact of SES on surgical margin status in the United States. One previous study from Europe used insurance status as a surrogate for SES and found PSM and BCR to vary among Italian and German men, with publicly insured patients having worse outcomes (higher PSM and worse BCR) than privately insured patients.16 However, this study did not incorporate insurance status into a multivariate model to adjust for potential confounders.16 In the current study, we ultimately do not know why SES correlated with PSM. However, PSM reflects both disease biology and surgical technique. Thus, one possibility is that men with a lower SES are receiving a less-than-ideal operation. This could be due to more resident-driven than attending-driven operations, with surgical experience proving to be a significant predictor of PSM.17 Alternatively, patients with lower SES tend to have more central adiposity18 and therefore may present with anatomical barriers to efficient resection and margin-free status.

Another hypothesis may be that patients with lower SES had more aggressive tumors that were more unlikely to be organ-confined and therefore harder to resect with clean margins. Consistent with this idea is our finding that patients with lower SES were more likely than patients with higher SES to have high-grade pathologic disease. One previous study in an equal-access center used military rank as a surrogate for SES and found enlisted men, versus officers, were more likely to harbor Gleason sum ≥ 7 pathologic disease, despite adjustment for race.19 Furthermore, analyses of different disadvantaged patient cohorts in the United States showed disproportionately high CaP severity relative to the rest of the US population.20, 21 In the current study, we attempted to distinguish between disease biology and surgical technique by adjusting for aggressive disease characteristics, including pathologic Gleason sum, ECE, and SVI, in our multivariate models. Although we still found patients with lower SES had higher odds of PSM, suggesting that technique explained at least some of the excess PSM among men with lower SES, ultimately, whether the higher risk of PSM is due to surgeon-level or disease-specific factors remains to be determined.

Importantly, although SES affected risk of PSM, race had no significant impact on margin status. This observation is consistent with those of previous studies.14, 22 Despite having no greater risk of PSM than white patients, however, and even despite having lower risk of ECE, black patients in our study were at significantly greater risk for BCR, even after adjustment for SES as well as pathologic features known to be associated with BCR, such as PSM, ECE, and SVI. This suggests that the elevated BCR risk in black men may be due more to biologic mechanisms than surgical technique. Indeed, our results may possibly corroborate mortality studies that note worse outcomes with black patients regardless of SES.3-6 Our results that show black men to be at higher risk for BCR on multivariate analysis are in line with a previously published report from SEARCH.14 In the prior study, black race was associated with a nonsignificant 19% higher risk of recurrence (P = .09). Here, we found an identical increased risk, but with longer follow-up (median 57 vs 39 months) and an expanded cohort size (2502 vs 1556 patients), these results are now statistically significant. Moreover, our results are also in line with a recent meta-analysis of race on CaP outcomes that showed a similar 20% increased risk of BCR for black patients, after adjustment for SES when available.6

Of note, we found no significant associations between SES and BCR. This observation corroborates the results of a prior study that found no association between military rank, used as a surrogate for SES, and BCR.19 Conversely, a study from the CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor) database found suggestion of a significant association between education and BCR, with more education having a protective effect on risk for BCR (HR = 0.73, P = .08).10 A crucial difference, however, between the 2 previously published studies is that the former examined patients seen at equal-access military medical centers,19 whereas the latter included patients mostly seen at the private clinics that comprise the bulk of the CaPSURE database.10 This suggests that equal access to health care may at least partially attenuate any socioeconomic disparity in biochemical outcomes. In the current study of men who were all treated at equal-access medical centers, we found that SES did not influence BCR risk, consistent with the prior study of men treated within the military. Interestingly, when stratified by race, higher SES was associated with lower recurrence risk for black patients, but not white patients, although all interaction terms between SES and race were not significant, and therefore these results stratified by race should be interpreted with caution. If confirmed in future studies, our findings would suggest that this specific patient population, namely, disadvantaged black men, may require stronger efforts to be targeted, treated, and followed aggressively for their CaP.

The results of our study have significant implications for future studies of SES, health care equity, and racial disparities. Access to health care is a system-level factor that can lead to health care disparity.11 In an equal-access health care system, which presumably provides coverage to several ZIP codes, SES at the ZIP code level should ideally not affect clinical outcomes at all. However, it did in our study. Equal access to health care may therefore not technically be “equal access” but still be dependent on system-level, regional influences such as availability of public transportation. In addition, neighborhood SES at the ZIP code level may reflect geographic distribution of resources known to affect health, such as nutrition, schools and libraries to improve health literacy, and jobs to provide the money to pay for health care. Equal-access health care also does not seem to be immune to racial disparities, because blacks in our study had higher risk of BCR despite adjustment for any differences in disease grade. One future direction may thus be to estimate the cost of health care required to provide the same quality of health care as measured by clinical outcomes, stratified by race. Consequently, further exploration of equal-access health care settings is needed with assessment of the influences of SES and race.

Our study certainly has limitations to consider. First, ZIP code level estimates of SES may not be as ideal as individual-level measures specific to each patient7 nor reflect homogenous population demographics such as the smaller geographic scales of census tract or census block.15 Nonetheless, various studies besides ours have found neighborhood SES to influence cancer outcomes.2, 3, 9 Second, although we analyzed 4 different measures of SES individually for thoroughness, no single measure may adequately encompass or define SES. However, poverty level, which we included in our study, has been suggested to perform equally well as more complex measures of SES.15 Third, although identification of the surgeon or amount of surgical experience would yield valuable information regarding rates of PSM and variation in health care practices at different locations, these data are unfortunately not currently available in the SEARCH database. Despite this, we have shown, for the first time in a US cohort, that patient SES may possibly increase the risk of PSM. Fourth, although BCR is an important CaP endpoint to consider, ultimately long-term oncologic outcomes such as time to metastases or CaP-specific death, which are currently underpowered in SEARCH, may shed greater value on the impact of race and SES on prognosis. Finally, due to a nonstandardized follow-up protocol and the inability to evaluate CaP screening outside the Veterans Administration system, the retrospective nature of our study may lead to selection bias of our results, which clearly need prospective validation.

Conclusions

In summary, even in equal-access settings, race and SES had significant interplay in influencing adverse pathologic and biochemical outcomes after prostatectomy for CaP. Patients from lower SES neighborhoods were at significantly higher risk for PSM and high-grade disease. In addition, black men regardless of SES were at greater risk than white men for BCR. The subpopulation at greatest risk for recurrence, however, may be black men from lower SES neighborhoods. Our findings have significant implications for future health care disparities research, and need external and prospective validation. Ultimately, whether increasing the quality, equality, and/or quantity of health care erases the racial and socioeconomic survival disparities in CaP remains to be seen.

FUNDING SOURCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Research support provided by the Department of Veterans Affairs, National Institute of Health (NIH) 5T32CA93245-8 (to D.I. Chu), NIH R01CA100938 (to W.J. Aronson), NIH Specialized Programs of Research Excellence grant P50CA92131-01A1 (to W.J. Aronson), the Georgia Cancer Coalition (to M.K. Terris), the Department of Defense, Prostate Cancer Research Program, (to S.J. Freedland), and the American Urological Association Foundation/Astellas Rising Star in Urology Award (to S.J. Freedland).

CONFLICT OF INTEREST DISCLOSURE

The authors made no disclosure.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES