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- Wolters Kluwer Health
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- Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc.
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- 1661-7657
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- 1661-7657
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- 10.1097/cu9.0000000000000107
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Abstract
Objectives: To describe and compare the incidence, stage at diagnosis, and survival for genitourinary cancers in the border regions and in Hispanic-Americans. Materials and methods: A population-based search was performed using the Surveillance, Epidemiology, and End Results Program 18 database and the Texas Cancer Registry from 2000 to 2017. Cox regression models were performed with adjusted for age, gender, race, cancer type, cancer stage, insurance status, and cause of death were used to compare cancer-specific survival. Results: A total of 63,236 kidney and renal pelvis, 38,398 bladder, 170,640 prostate, 24,313 testicular cancer cases were identified. Cancer-specific survival was found to be improved in Hispanic-Americans in kidney and renal pelvis (hazard ratio [HR], 0.903, 95% con- fidence interval [CI], 0.856–0.952, p = 0.0001), and bladder cancers (HR, 0.817, 95% CI, 0.743–0.898, p < 0.001), despite a more ad- vanced stage at diagnosis in Hispanics with bladder cancer ( p < 0.0074). Testicular cancer has a survival disadvantage for individuals living in the border region (HR, 1.315, 95% CI, 1.124–1.539, p = 0.0006). Conclusions: Disparities exist between Hispanic-Americans and Non-Hispanic White and also between individuals living in the border counties when compared to other regions. This is most significant in individuals with testicular cancer residing in the border region who demonstrate worse overall survival. Keywords: Bladder cancer; Border region; Kidney cancer; Prostate cancer; Testicular cancer 1. Introduction have experienced the greatest increase in incidence from 1998 to [8] 2011. Although these studies provide insight into the epidemio- Approximately 57.5 million Hispanic-Americans (HA) live in the logical aspects of GU cancers in HA, the majority do not account continental United States (17.8% of the total population). More for the border region with Mexico. [1] than one-third resides in the US-Mexico border region. There The current study aims to describe and compare the incidence, are many differences in incidence, survival, age at diagnosis, and stage at diagnosis, and cancer specific survival (CSS) for GU can- staging for genitourinary (GU) cancers between HA and other eth- cers in the US-Mexico border region by primarily comparing resi- [2–5] nic groups. To account for these differences, studies have pro- dents in border counties to residents outside of the border counties. posed variations in allelic distribution of oncogenes, obesity, and Outcomes were derived using the Surveillance, Epidemiology, and [2,6,7] insurance status among others, to explain cancer specific dis- End Results Program (SEER) database within the following parities, such as increased incidence or decreased survival, or ben- groups: border counties, non-border counties in border states, [2–5,8,9] efits such as lower stage at diagnosis. For instance, HA are and non-border states. We also stratify each of these geographic re- more likely to have clinically advanced-stage prostate cancer on gions by HA and NHW. Up-to-date, no studies have utilized the initial evaluation and present with more poorly differentiated can- SEER database to compare GU cancer outcomes between border [5] cer (Gleason scores 8–10) than non-Hispanic Whites (NHW). counties and other geographic regions. Conversely, for other cancers such as testicular germ cell tumors, HA have lower incidence as compared to NHW although they 2. Materials and methods *Corresponding Author: Zachariah D. Taylor, 130 Bryn Mawr Ave., Bryn Mawr, City, PA 19010, USA. E-mail address: taylorz@mlhs.org (Z.D. Taylor). Data was collected from the SEER and the Texas Cancer Registry Current Urology, (2023) 17, 2, 118–124 for the years 2000–2017 for bladder (BCa), prostate (PCa), kidney [10] Received July 26, 2021; Accepted March 9, 2022. and renal pelvis (KCa), and testis (TCa) cancers. The border re- http://dx.doi.org/10.1097/CU9.0000000000000107 gion was defined using the La Paz classification set forth by the Ba- Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. This is ker Institute for Public Health to include any county within 100km [11] an open-access article distributed under the terms of the Creative Commons of the Mexican border. Using this classification, the data was Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it sorted into border counties, non-border counties in border states, is permissible to download and share the work provided it is properly cited. The and non-border states, based on the individual's residence at the work cannot be changed in any way or used commercially without permission from the journal. time of diagnosis. Arizona is not included in the SEER database 118 Taylor et al. Volume 17 Issue 2 2023 www.currurol.org and was therefore not included in the study. Much of the border re- counterparts (AAPC, 2.3, 95% CI, 1.6–3.0). Conversely, BCa gion is graded in the most severe health professional shortage area demonstrated a significant decrease in IR in all groups except group with the major exception of San Diego County. Addition- for NHW in border counties. ally, San Diego County has a significantly higher median house- Genital system cancers demonstrated generally increasing IR for hold income, significantly lower poverty, higher average education, TCa but decreasing for PCa. No significant difference was observed be- and much greater internet access. For these reasons, San Diego tween HA and NHW for PCa. Testis cancers demonstrated a consistent County was not included in the analysis. The SEER database deter- increase in IR with AAPC ranging from 0.4 to 2.9. Additionally, HA mines residency based on where most of the time during the year is with TCa in non-border states (AAPC, 2.9, 95% CI, 1.2–4.5) and spent, thus eliminating snowbirds who spend most of their time non-border counties (AAPC, 2.2, 95% CI, 1.8–2.6) are increasing at a significantly higher rate than their NHW counterparts. away from the border region. Inclusion criteria for the study were under the age of 65, who were alive at the end of the study or had known causes of death, insurance type, and race/ethnicity. 3.2. Stage at diagnosis A direct standardization age adjustment was performed using 3.2.1. Bladder cancer Hispanic-Americans with BCa demonstrated the 2000 U.S. standard population from the census to obtain significantly higher rates of advanced disease at diagnosis than age-adjusted incidence. Incidence were further evaluated using NHW in non-border states (odds ratio [OR], 1.32, 95% CI, the Joinpoint Regression Program, version 4.0.4, using the log-linear 1.08–1.61, p < 0.0074), non-border counties (OR, 1.28, 95% CI, model. Average annual percent change (AAPC) was determined with 1.15–1.42, p < 0.0001), and border counties (OR, 1.86, 95% CI, 95% confidence interval (CI) and was compared to an AAPC of 1.08–3.20, p < 0.0260), as demonstrated in Figure 2. However, only zero. Statistically significant differences in AAPCs were determined non-border counties NHW demonstrated more advanced cancer by non-overlapping 95% CI. at diagnosis when compared to non-border states NHW (OR, Staging data were stratified based on the SEER Summary stage 1.12, 95% CI, 1.05–1.19, p <0.0004). into the following groups: in-situ (BCa only), localized, regional, 3.2.2. Kidney and renal pelvis cancer Hispanic-Americans did and distant spread, and were compared using odds-ratio analysis. not have significantly different stages at diagnosis when compared The regional and distant individuals were combined to form the to NHW. However, border region did demonstrate more advanced advanced disease group. stage at diagnosis in the following comparisons: non-border counties Individual-level data were used for Cox regression to model time versus non-border states NHW (OR, 1.05, 95% CI, 1.01–1.09, to death among cancer cases for each cancer type adjusting for age, p < 0.0241), border counties HA versus non-border states HA gender, race/ethnicity, stage at diagnosis, and insurance status. (OR, 1.39, 95% CI, 1.21–1.60, p < 0.0001), and border counties Only individuals who died from the cancer were included in the HA versus non-border counties HA (OR, 1.26, 95% CI, 1.13–1.41, survival analysis to determine CSS. Cases were censored if alive p < 0.0001). at the end of the study period or dead from other causes. In each 3.2.3. Prostate cancer Hispanic-Americans in non-border model, an interaction between race/ethnicity and border status counties demonstrated more advanced staging at diagnosis was tested but dropped due to lack of statistical significance. when compared to their NHW counterparts (OR, 1.22, 95% Analyses were conducted using R (version 3.5.1; R Foundation CI, 1.17–1.27, p < 0.0001). Additionally, non-border counties HA for Statistical Computing, Vienna, Austria) and SAS (version 9.4, demonstrated more advanced cancer than non-border states HA SAS Institute, Cary, NC). An alpha level of 0.05 was used to deter- (OR, 1.31, 95% CI, 1.21–1.42, p < 0.0001), but more advanced mine statistical significance for all analyses. than border counties HA (OR, 0.83, 95% CI, 0.74–0.93, p = 0.0018). The study was considered exempt research by the Institutional 3.2.4. Testicular cancer Hispanic-Americans with TCa consistently Review Board of Burrell College of Osteopathic Medicine and it demonstrated more advanced cancer at diagnosis when compared to was conducted in adherence with a Data Use Agreement from the their NHW counterparts as follows: non-border states HA versus SEER program. non-border states HNW (OR, 1.29, 95% CI, 1.13–1.47, p = 0.0002), non-border counties HA versus non-border counties NHW (OR, 1.44, 95% CI, 1.34–1.55, p < 0.0001), and border counties HA 3. Results versus border counties NHW (OR, 2.11, 95% CI, 1.33–3.35, p = 0.0016). There were no significant differences in any group A total of 305,265 individuals were included in the study (Table 1), when comparing between regions. with 144,690 in the non-border states, 154,622 in non-border counties, and 5953 in border counties. The border counties region 3.3. Cancer specific survival was found to have the highest percentage of HA, with 76.3% of the 3.3.1. Kidney and renal pelvis cancer There were 64,948 KCa population being HA. The large majority of the individuals in each identified in the database, of which 63,236 were included in the group were found to be insured. The border county region had the Cox regression after censoring. Of these 63,236 cases, 9775 largest percentage of uninsured (16.3%) and Medicaid (11.7%). experienced the event. In adjusted analysis, it was found that HA had better CSS when compared to NHW (HR, 0.903, 95% CI, 3.1. Incidence 0.856–0.952, p = 0.0001), as did females (HR, 0.906, 95% CI, Age-adjusted incidence rates (IR) demonstrate that HA generally 0.870–0.950, p < 0.0001). Additionally, individuals with Medicaid have a lower IR than NHW. The only instances in which HA have insurance were found to have worse CSS when compared with a higher IR than NHW is KCa in non-border counties (16.8 vs. uninsured (HR, 1.11, 95% CI, 1.027–1.201, p =0.0085). 15.2 per 100,000) and border counties (19.1 vs. 13.8 per 100,000). 3.3.2. Bladder cancer There were 39,453 BCa cases identified, AAPC in GU cancers that range from −4.5% to 2.9% per year, with 38,398 being included in the Cox regression, with 5102 as demonstrated in Figure 1. Kidney and renal pelvis cancers dem- experiencing the event. Like KCa, BCa adjusted analysis of onstrated a significant increase in IR across the study period for all HA versus NHW demonstrated that HA have better CSS groups except border counties NHW. Additionally, HA in border when compared to NHW (HR, 0.817, 95% CI, 0.743–0.898, counties with KCa are increasing at a faster rate than their NHW p < 0.0001). 119 Taylor et al. Volume 17 Issue 2 2023 www.currurol.org Table 1 Urologic cancer patient demographics and clinical characteristics based on residence during diagnosis. Total Non-border states Non-border counties in border states* Border counties (n = 305,265) (n = 144,690) (n = 154,622) (n = 5953) Race, n (%) Non-Hispanic White 252,105 (82.6) 136,452 (94.3) 114,240 (73.9) 1413 (23.7) Hispanics 53,160 (17.4) 8,238 (5.7) 40,382 (26.1) 4540 (76.3) Gender, n (%) Male 273,440 (89.6) 130,171 (90.0) 138,154 (89.3) 5115 (85.9) Female 31,825 (10.4) 14,519 (10.0) 16,468 (10.7) 838 (14.1) Cancer type Bladder Count, n (%) 39,453 (12.9) 20,272 (14.0) 18,652 (12.1) 529 (8.9) Age at Dx, yr, mean ± SD 55.9 ± 7.6 56.0 ± 7.3 55.7 ± 7.9 55.0 ± 8.7 Survival time, mo 48.0 (IQR 19–83) 46.0 (IQR 18–80) 50.0 (IQR 20–87) 59.0 (IQR 22–94) Stage at Dx In situ 21,985 (55.7) 11,807 (58.2) 9931 (53.2) 247 (46.7) Loc 12,315 (31.2) 5980 (29.5) 6128 (32.9) 207 (39.1) Reg 3226 (8.2) 1589 (7.8) 1593 (8.5) 44 (8.3) Dist 1927 (4.9) 896 (4.4) 1000 (5.4) 31 (5.9) Kidney Count, n (%) 64,948 (21.3) 28,658 (19.8) 34,418 (22.6) 1,872 (31.4) Age at Dx, yr, mean ± SD 51.4 ± 11.4 51.9 ± 10.9 51.3 ± 11.7 50.6 ± 11.7 Survival time, mo 43.0 (IQR 16–79) 41.0 (IQR 15–76) 45.0 (IQR 17–81) 47.0 (IQR 20–86.8) Stage at Dx Loc 45,728 (70.4) 20,392 (71.2) 24,118 (70.1) 1218 (65.1) Reg 10,139 (15.6) 4463 (15.6) 5354 (15.6) 322 (17.2) Dist 9081 (14.0) 3803 (13.3) 4946 (14.4) 332 (17.7) Prostate Count, n (%) 175,689 (57.6) 85,760 (59.3) 87,201 (56.4) 2728 (45.8) Age at Dx, yr, mean ± SD 57.9 ± 4.9 57.9 ± 4.9 57.8 ± 4.9 58.1 ± 4.8 Survival time, mo 65.0 (IQR 33–94) 63.0 (IQR 31–91) 68.9 (IQR 35–98) 66.0 (IQR 32–99) Stage at Dx Loc 137,329 (78.2) 67,594 (78.8) 67,597 (77.5) 2138 (78.4) Reg 31,727 (18.1) 15,484 (18.1) 15,874 (18.2) 369 (13.5) Dist 6633 (3.8) 2682 (3.1) 3730 (4.3) 221 (8.1) Testis Count, n (%) 25,175 (8.2) 10,000 (6.9) 14,351 (9.3) 824 (13.8) Age at Dx, yr, mean ± SD 33.3 ± 11.0 34.5 ± 11.1 32.6 ± 10.9 30.0 ± 10.4 Survival time, mo 54.0 (IQR 23–88) 52.0 (IQR 23–85) 54.0 (IQR 23–90) 63.0 (IQR 28–101) Stage at Dx Loc 17,190 (68.3) 6973 (69.7) 9694 (67.5) 523 (63.5) Reg 4702 (18.7) 1862 (18.6) 2697 (18.8) 143 (17.4) Dist 3283 (13.0) 1165 (11.7) 1960 (13.7) 158 (19.2) Insurance status Privately insured 266,723 (87.4) 129,821 (89.7) 132,618 (85.8) 4284 (72.0) Uninsured 15,450 (5.1) 5894 (4.1) 8588 (5.6) 968 (16.3) Medicaid 23,092 (7.6) 8975 (6.2) 13416 (8.7) 701 (11.7) Dist = Distant; Dx = Diagnosis; IQR = Interquartile range; Loc = Localized; Reg = Regional; SD = standard deviation. Data is from SEER 18 and Texas Cancer Registry for cancers diagnosed from 2000 to 2017. For categorical variables: count (%); for survival time: median months (IQR 1st quartile–3rd quartile). For age at diagnosis: mean years±standard deviation. *This category excludes border counties located in border states defined by the La Paz classification. 3.3.3. Prostate cancer There were 175,689 PCa identified, with 1.124–1.539, p = 0.0006). Additionally, non-border counties displayed 170,640 included in the Cox regression, with 5645 events occurring. comparable, but not significantly worse CSS when compared to No significant findings were observed between HA and HNW, non-border states (HR, 1.357, 95% CI, 0.986–1.869, p = or when accounting for border status as is detailed in Table 2. 0.0611). No significant findings were observed when HA were However, individuals insured with Medicaid demonstrated worse compared to NHW. CSS when compared to uninsured individuals (HR, 1.15, 95% CI, 1.06–1.276, p = 0.0088). 3.3.4. Testicular cancer There were 25,175 TCa identified, with 4. Discussion 24,313 being included in the Cox regression, with 883 events recorded. Testis cancers in border counties had a significantly worse This study demonstrates significant differences in IR, staging, and CSS when compared to non-border states (HR, 1.315, 95% CI, CSS in HA and the border region. Primarily, individuals residing 120 Taylor et al. Volume 17 Issue 2 2023 www.currurol.org Figure 1. Forest plot of urologic cancer incidence rate trends in the US and Border region from 2000 to 2017. Error bars indicate upper and lower 95% confidence intervals for each corresponding AAPC. * Indicates that AAPC is statistically different from 0 ( p <0.05). Incidence rates are per 100,000 and are age-adjusted to the 2000 US standard population (19 age groups, Census P25-1130). AAPC = average annual percent change; CI = confidence interval; NHW = non-Hispanic Whites in the border counties demonstrated worse CSS for TCa when ethnicity. There are significant disparities along the border region compared to the non-border region. This was the only cancer to resulting in poorer outcomes including: access to care, language demonstrate a CSS association with the border counties. Our study barriers, low socioeconomic status, low levels of education, rate [15–17] also demonstrated that this TCa IR is increasing in nearly all of uninsured, and high levels of poverty. Access to care is groups, but fastest amongst HA in all regions, and NHW in the particularly difficult as Texas and New Mexico are among the border counties. These results seem to imply a strong association lowest in number of urologists per 100,000 persons with 3.00 [18] between TCa and the border region than other GU cancers. and 3.02, respectively. From 2008 to 2016, on average, 13.3% Secondly, HA demonstrated better CSS in urinary system can- people were without health insurance. In that same period, 16.8% cers (KCa and BCa) and no significant difference in genital system of the population residing in New Mexico, 15.0% in California, cancers (PCa and TCa) when compared to NHW without geo- and 21.3% in Texas were uninsured, all above the national [17] graphic stratification. This is surprising due to the generally more average. Difficulty in access to care and the disparities along the advanced stage at diagnosis in the HA cohort. border would lead us to expect worse CSS along the border for all Cancer specific survival between HA and NHW has been docu- malignancies. However, we only see this reflected in TCa. mented previously with generally no CSS difference between the 2 Environmental and occupational exposures such as pesticides [12] groups. This was consistent with our study regarding PCa and and heavy metals may be contributing to the increased incidence TCa, despite the more advanced disease in HA at diagnosis. However, and worse CSS of TCa in the border region. Pesticides are par- our study demonstrated a CSS advantage for KCa and BCa in HA. In- ticularly important in the evaluation of possible risk factors in terestingly, HA with BCa had more advanced disease at diagnosis the border region due to the high agricultural production in [19,20] when compared to NHW, which did not result in poorer CSS. California and Texas. Furthermore, the border region It is unclear why HA have generally more advanced disease at diag- may have a higher rate of aggressive TCa histology. Ghazarian nosis, but no CSS disadvantage when compared to their NHW counter- et al. demonstrated higher rates of nonseminoma TCa in the parts. Regarding KCa, Batai et al. described the incidence and clinical south (including TX) and west (including NM and CA). The characteristics of HA. Those living in border states demonstrated higher higher rate of nonseminoma TCa may very likely contribute [5] prevalence of risk factors including obesity and chronic kidney dis- to the decreased CSS demonstrated in the border region. [13] [13] ease. Additionally, these individuals received suboptimal care. It has been well established that insured individuals have better [21] Thus, our results are surprising as we found that HA have better CSS survival outcomes when compared to uninsured individuals. Ad- than their NHW counterparts. Older SEER studies have found there ditionally, Medicaid has been linked to poorer outcomes when com- [14] [22] to be no difference in CSS when comparing HA to NHW. pared to non-Medicaid. It is generally thought that any insurance While our study did not demonstrate CSS differences between is better than no insurance, however, our study demonstrated that in HA and NHW for TCa, there is a significant CSS disadvantage regard to Medicaid insurance, individuals with PCa and KCa have a [21,23] for all individuals living in the border county regardless of race/ CSS disadvantage when compared to uninsured individuals. 121 Taylor et al. Volume 17 Issue 2 2023 www.currurol.org Figure 2. Urologic cancers stage at diagnosis and odds ratios 2000 to 2017. Percentages for each stage of cancer were obtained from SEER 18 and Texas Cancer Registry using the SEER Summary Stage query. Percentages were calculated by dividing the number of cases of each stage by all cases of the specific cancer. Localized stage percentages include in situ (BCa only) and localized summary stages. Advanced stage percentages include regional and distant summary stages. BC = border counties; CI = confidence interval; NBC = non-border counties in border states; NBS = non-border states; NHW = non-Hispanic White; OR = odds ratio. 122 Taylor et al. Volume 17 Issue 2 2023 www.currurol.org Table 2 Cox regression analysis of overall survival by cancer type. KCa BCa PCa TCa HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p Race/Ethnicity Hispanic 0.903 0.856–0.952 0.0001 0.817 0.743–0.898 <0.0001 0.941 0.875–1.012 0.1031 1.06 0.901–1.247 0.4824 Border status vs. non-border states Non-border counties 1.013 0.902–1.136 0.8296 1.087 0.878–1.345 0.445 1.042 0.888–1.222 0.6145 1.357 0.986–1.869 0.0611 Border counties 0.982 0.940–1.026 0.4145 0.953 0.900–1.009 0.0994 1.039 0.982–1.099 0.1875 1.315 1.124–1.539 0.0006 Sex Female 0.906 0.870–0.950 <0.0001 1.026 0.961–1.095 0.4503 NA NA Age % increase per year Each year older 1.028 1.026–1.030 <0.0001 1.017 1.013–1.021 <0.0001 1.01 1.004–1.015 0.0002 1.025 1.019–1.031 <0.0001 Insurance status vs. uninsured Medicaid 1.11 1.027–1.201 0.0085 1.089 0.982–1.207 0.1067 1.15 1.036–1.276 0.0088 1.147 0.954–1.379 0.1438 Insured 0.744 0.697–0.794 <0.0001 0.618 0.566–0.674 <0.001 0.607 0.556–0.663 <0.001 0.5 0.419–0.596 <0.0001 BCa = bladder cancer; CI = confidence interval; HR = hazard ratio; KCa = kidney and renal pelvis cancer; NA = not applicable; PCa = prostate cancer; TCa = testis cancer. Adjusted for age at diagnosis (years), race/ethnicity, gender, insurance status, and stage at diagnosis. Results of Cox regression demonstrating overall survival. Variables with significant survival advantage or disadvantage are marked in bold. The results of this study should be interpreted with caution. De- Funding source spite the robust representation of SEER 18, it does have a propen- sity to skew towards more metropolitan areas. Furthermore, SEER Charlotte Gard was partially supported by the National Institute 18 does not include data from Arizona, one of the four border states. of General Medical Sciences of the National Institutes of Health To accurately demonstrate the association of insurance, Medicare under Award Number U54GM104944. The content is solely the eligible individuals were removed from the study thus skewing the responsibility of the authors and does not necessarily represent population to be younger than typical, potentially adding a greater the official views of the National Institutes of Health. effect of genetic and other heritable forms of these malignancies. Unfortunately, any individual who does not have a US social Author contributions security number is not included in the SEER database, eliminat- ing undocumented individuals. While it is uncertain what the All authors have significantly contributed to the submitted manu- effect of including this population would be, it is generally script. Specific contributions are as follows: thought that it would exacerbate any discrepancies noted in the CCG: Conceptualization, software, statistical support, writing, editing, border region due to unreported deaths. supervision; LC: Data curation, writing-original draft preparation; MEW: Conceptualization, writing-reviewing, editing, and supervision; 5. Conclusions TT: Conceptualization, data collection, editing of manuscript; ZDT: Conceptualization, methodology, data collection, writing, editing. This study is the first to thoroughly examine the IR, staging, and CSS of GU cancers in the US-Mexico border region. While not Data and code availability universal across all GU cancers, HA living in the border region demonstrated more advanced disease than their NHW counter- All data is freely available at https://seer.cancer.gov/ and at https:// parts. However, this did not result in worse CSS. Only TCa dem- www.dshs.texas.gov/tcr/home.aspx. onstrated worse CSS in the border region. Although further Analyses were conducted using R (version 3.5.1; R Foundation research is needed to fully detail the reasons for the observed dis- for Statistical Computing, Vienna, Austria) and SAS (version crepancies in the border region and between HA and NHW, it is 9.4, SAS Institute, Cary, NC). Seer*Stat and Joinpoint software paramount that this geographic region and group of individuals are available for from https://seer.cancer.gov/. continue to be taken into consideration. References Acknowledgments [1] Bureau UC. The Nation's Older Population Is Still GrowingCensus Bureau Reports. The United States Census Bureau. Available at: https://www.census. None. gov/newsroom/press-releases/2017/cb17-100.html. Accessed April 8, 2020. [2] Suarez-Sarmiento A, Yao X, Hofmann JN, et al. Ethnic disparities in renal Statement of ethics cell carcinoma: An analysis of Hispanic patients in a single-payer healthcare system. Int J Urol 2017;24(10):765–770. 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Journal
Current Urology – Wolters Kluwer Health
Published: Jun 2, 2023