Abstract
Objectives:
Although cure rates for early stage anal squamous cell cancer (ASCC) are overall high, there may be racial disparities in receipt of treatment and outcome precluding favorable outcomes across all patient demographics. Therefore, we aimed to assess the time to treatment initiation and overall survival (OS) in Black and white patients receiving definitive chemoradiation for early stage ASCC.
Methods:
We identified patients diagnosed with early-stage (stage I-II) ASCC and treated with chemoradiation diagnosed between 2004–2016 in the National Cancer Database. Clinical and treatment variables were compared by race using chi-square, and OS assessed via Cox regression with 1:1 nearest neighbor propensity score matching.
Results:
Among 9,331 patients, 90.6% were white. Black patients had longer median time to treatment initiation as compared to white patients (47 vs. 36 days, p<.001), and on multivariable analysis, the Black race was associated with higher odds of > 6 weeks of time to treatment initiation. (HR 1.78 [1.53–2.08], p<.001). Furthermore, Black patients had worse OS (5-year survival 71% vs. 77%, p<.001), which persisted after propensity score matching (p=.007).
Conclusions:
Black patients had a longer time to treatment initiation and worse OS as compared to white patients with early-stage ASCC treated with chemoradiation. Further research is needed to better elucidate the etiologies of these disparities.
Keywords: Anal Neoplasms, Time to Treatment, Healthcare Disparities, Propensity Score, Race Factors
INTRODUCTION
Although anal cancer is a rare disease, with 8,300 new cases diagnosed annually in the United States, both the incidence and mortality of anal cancer has been increasing over the last two decades, with an annual percentage chance of 1.9% and 3.6%, respectively1. According to the American Cancer Society, there are approximately 1000 deaths attributed to anal cancer annually2. Anal squamous cell cancer (ASCC) is the most common histologic subtype of anal cancer, constituting about 80% of cases3,4. Although the underlying causes of the rising rates are unclear, the most common hypotheses include increased prevalence of human papillomavirus (HPV), changing sexual practices, increasing prevalence of human immunodeficiency virus (HIV), among other factors that cause immunodeficiency5. In particular, ASCC is strongly associated with HPV, with 90% of cases caused by infection with oncogenic HPV strains6,7. In addition, patients with HIV have a 40 to 80 times greater risk for developing ASCC as compared to the general population8.
The current standard treatment for ASCC is chemoradiotherapy (CRT) with 5-Fluorouracil (5-FU) and mitomycin9. Cure rates for localized ASCC are overall high, with a 5-year survival rate of approximately 81%2. Therefore, early diagnosis and treatment of ASCC are critical for achieving a good prognosis. Disparities based upon sociodemographic characteristics, including race, have been reported for several other malignancies10–13, however have not recently been studied for early stage ASCC, despite the high relative prevalence of traditionally medically disadvantaged populations diagnosed with ASCC.
As such, we assessed racial disparities in presentation and overall survival (OS) in patients undergoing definitive CRT for early-stage (stage I and II) ASCC using the National Cancer Database (NCDB).
MATERIALS AND METHODS
The NCDB is a hospital-based database, including more than 1,500 Commission on Cancer (CoC)-accredited facilities in the US and provides de-identified data on approximately 70% of cancer cases, including patient demographics, tumor characteristics, the first course of treatments and survival outcomes (https://www.facs.org/quality-programs/cancer/ncdb).
Study Population
We identified 56,426 adult patients (18–90 years) with anal cancer diagnosed between 2004 and 2016. We used the “C21.0-C21.2” ICD-O-3/WHO 2008 site recode and “8070–8078” ICD-O-3 histologic codes to limit to patients with ASCC histology14–17. We included early stage (I-II) American Joint Committee on Cancer (AJCC)18 patients with ASCC who received concurrent CRT (defined as the start date of chemotherapy and radiotherapy within 14 days of each other)17. The staging was defined using the AJCC clinical stage group, which includes the 6th and 7th editions wherein there were no changes19,20. The majority of the patients did not undergo surgery; therefore, the clinical AJCC stage was combined with the pathological AJCC stage to determine the most accurate overall AJCC stage21,22. Patients with other histologies, those who had more than one primary tumor, with unknown follow-up, non-white or Black race, and those who did not receive CRT and the first course of treatment at the reporting facility (N=46,612) were excluded (Figure 1).
Figure 1.
Flow chart showing patient selection
Statistical analysis
Chi-square test or Fisher’s exact test was used to compare baseline categorical variables by race. We used the two-sample T-test for continuous variables. Overall survival was evaluated using the Kaplan-Meier method and compared between white and Black patients with the log-rank test. Cox proportional regression method was used for multivariable survival analysis with race (Black vs. white) as the primary independent variable of interest adjusting the effect of other risk factors described below. Patients were censored if still alive at the date of the last follow-up. Multivariable logistic regression analysis was also used to assess the relationship between race and time to treatment initiation (≤ 6 weeks vs. > 6 weeks). We defined time to treatment initiation as days between diagnosis and CRT initiation19, which was stratified as ≤ 6 weeks versus > 6 weeks. Other variables included in the model were demographics, socioeconomic status, and staging [AJCC stage I-II]. Patient demographics include age [< 50, 50–64, ≥ 65 years], sex [male, female], facility type [academic, non-academic, other], Charlson-Deyo Score [0–2+], facility location [New England, Middle Atlantic, South Atlantic, East North Central, East South Central, West North Central, West South Central, Mountain, Pacific]. Comorbid conditions defined as Charlson-Deyo score, which is calculated based on disease severity23. Socioeconomic status contains rurality [metropolitan, non-metropolitan], insurance status [uninsured, private insurance, Medicaid, Medicare, other government insurance], education level [rates of patients without high school level ≥ 17.6%, 10.9% - 17.5%, 6.3% - 10.8%, < 6.3%], travel distance to treatment facility [< 12.5, 12.5–49.9, ≥ 50 miles], and median income quartiles [< $40,227, $40,227–50,353, $50,354–63,332, ≥ $63,333]. Patients with missing data for each variable were categorized as “unknown” and were included in multivariable analyses as such.
We used 1:1 nearest neighbor propensity score matching (PSM) in an attempt to reduce confounding effects24. R software version 3.6.2 with MatchIt package was performed for PSM with sex, age, year of diagnosis, comorbidity score, facility type, facility location, insurance status, income, education, rurality, travel distance, stage, and grade as matching variables25. Survival analyses were reanalyzed after propensity matched using Kaplan Meier and Cox regression methods.
P-values were two-tailed considered statistically significant if <.05. All statistical analyses were performed using SPSS version 25.0. This study was deemed exempt from the Institutional Review Board at the University of Texas Southwestern, given the use of de-identified data.
RESULTS
Baseline Characteristics
Among 9,311 patients who met inclusion criteria, 8,451 (90.6%) were white, and the median age was 58 years. White patients were more likely to be female (67% vs. 51%), older age (≥ 65 years) (31% vs. 17%), of higher education level (25% vs. 7%), have private insurance (50% vs. 39%), and report higher income (35% vs. 16%), as compared to Black patients (all p<.001). In contrast, Black patients were more likely to live in metropolitan areas (92% vs. 81%), have comorbidity scores ≥ 2 (18% vs. 6%), receive treatment at an academic center (46% vs. 28%), live in the Southern Atlantic, (32% vs. 23%), and travel shorter distance for care (80% vs. 61%) (all p<.001) (Table 1). Black patients were more likely to have Medicaid insurance as compared to white patients (19% vs. 8%, p<.001). In addition, black patients had longer median time to treatment initiation (47 vs. 36 days, p<.001) and on multivariable logistic regression, the Black race was associated with higher odds of > 6 weeks of time to treatment initiation (HR 1.78 [1.53–2.08], p<.001) (Figure 2). Other variables positively associated with ≥ 6 weeks until treatment initiation included high comorbidity score (≥ 2) and long travel distance (≥ 50 miles) (Table 2).
Table 1.
Baseline characteristics
| Characteristics | Unmatched | p-value | Propensity Matched | p-value | ||
|---|---|---|---|---|---|---|
| White (%) | Black (%) | White (%) | Black (%) | |||
| 8, 451 (90.6) | 880 (9.4) | 880 (50.0) | 880 (50.0) | |||
| Sex | <.001 | NS | ||||
| Male | 2,789 (33) | 428 (48.6) | 433 (49.2) | 428 (48.6) | ||
| Female | 5,662 (67) | 452 (51.4) | 447 (50.8) | 452 (51.4) | ||
| Age | None | <.001 | NS | |||
| < 50 years | 1,675 (19.8) | 329 (37.4) | 290 (33.0) | 329 (37.4) | ||
| 50–64 years | 4,125 (48.8) | 397 (45.1) | 447 (50.8) | 397 (45.1) | ||
| ≥ 65 years | 2,651 (31.4) | 154 (17.5) | 143 (16.3) | 154 (17.5) | ||
| Year of diagnosis | NS | NS | ||||
| 2004–2006 | 1,440 (17.0) | 148 (16.8) | 145 (16.5) | 148 (16.8) | ||
| 2007–2009 | 1,863 (22.0) | 190 (21.6) | 194 (22.0) | 190 (21.6) | ||
| 2010–2012 | 2,320 (27.5) | 241 (27.4) | 261 (29.7) | 241 (27.4) | ||
| 2013–2015 | 2,828 (33.5) | 301 (34.2) | 280 (31.8) | 301 (34.2) | ||
| Comorbidity Score | <.001 | NS | ||||
| 0 | 6,936 (82.1) | 607 (69.0) | 612 (69.5) | 607 (69.0) | ||
| 1 | 1,016 (12.0) | 115 (13.1) | 125 (14.2) | 115 (13.1) | ||
| 2+ | 499 (5.9) | 158 (18.0) | 143 (16.3) | 158 (18.0) | ||
| Facility Type | <.001 | <.001 | ||||
| Academic | 2,386 (28.2) | 402 (45.7) | 354 (40.2) | 402 (45.7) | ||
| Non-academic | 5,822 (68.9) | 398 (45.2) | 494 (56.1) | 398 (45.2) | ||
| Others | 243 (2.9) | 80 (9.1) | 32 (3.6) | 80 (9.1) | ||
| Facility Location | <.001 | <.001 | ||||
| New England | 596 (7.1) | 32 (3.6) | 53 (6.0) | 32 (3.6) | ||
| Middle Atlantic | 1,057 (12.5) | 143 (16.3) | 138 (15.7) | 143 (16.3) | ||
| South Atlantic | 1,923 (22.8) | 282 (32.0) | 210 (23.9) | 282 (32.0) | ||
| East North Central | 1,474 (17.4) | 141 (16.0) | 183 (20.8) | 141 (16.0) | ||
| East South Central | 641 (7.6) | 60 (6.8) | 70 (8.0) | 60 (6.8) | ||
| West North Central | 596 (7.1) | 37 (4.2) | 47 (5.3) | 37 (4.2) | ||
| West South Central | 508 (6.0) | 60 (6.8) | 59 (6.7) | 60 (6.8) | ||
| Mountain | 410 (4.9) | 4 (0.5) | 26 (3.0) | 4 (0.5) | ||
| Pacific | 1,003 (11.9) | 41 (4.7) | 62 (7.0) | 41 (4.7) | ||
| Unknown | 243 (2.9) | 80 (9.1) | 32 (3.6) | 80 (9.1) | ||
| Insurance status | <.001 | .03 | ||||
| Uninsured | 417 (4.9) | 67 (7.6) | 74 (8.4) | 67 (7.6) | ||
| Private | 4,197 (49.7) | 346 (39.3) | 402 (45.7) | 346 (39.3) | ||
| Medicaid | 645 (7.6) | 164 (18.6) | 128 (14.5) | 164 (18.6) | ||
| Medicare | 2,892 (34.2) | 262 (29.8) | 246 (28.0) | 262 (29.8) | ||
| Other Government | 127 (1.5) | 17 (1.9) | 9 (1.0) | 17 (1.9) | ||
| Unknown | 173 (2.0) | 24 (2.7) | 21 (2.4) | 24 (2.7) | ||
| Income | <.001 | .001 | ||||
| < $40,227 | 1,432 (16.9) | 401 (45.6) | 364 (41.4) | 401 (45.6) | ||
| $40,227 - $50,353 | 1,977 (23.4) | 206 (23.4) | 237 (26.9) | 206 (23.4) | ||
| $50,354 - $63,332 | 1,977 (23.4) | 122 (13.9) | 148 (16.8) | 122 (13.9) | ||
| ≥ $63,333 | 2,939 (34.8) | 139 (15.8) | 131 (14.9) | 139 (15.8) | ||
| Unknown | 126 (1.5) | 12 (1.4) | 0 (0) | 12 (1.4) | ||
| Education¥ | <.001 | .01 | ||||
| ≥17.6% | 1,512 (17.9) | 355 (40.3) | 342 (38.9) | 355 (40.3) | ||
| 10.9% - 17.5% | 2,197 (26.0) | 269 (30.6) | 294 (33.4) | 269 (30.6) | ||
| 6.3% - 10.8% | 2,485 (29.4) | 179 (20.3) | 177 (20.1) | 179 (20.3) | ||
| <6.3 | 2,143 (25.4) | 65 (7.4) | 67 (7.6) | 65 (7.4) | ||
| Unknown | 114 (1.3) | 12 (1.4) | 0 (0) | 12 (1.4) | ||
| Rurality | <.001 | .03 | ||||
| Metropolitan | 6,878 (81.4) | 811 (92.2) | 809 (91.9) | 811 (92.2) | ||
| Non-metropolitan | 1,368 (16.2) | 55 (6.3) | 67 (7.6) | 55 (6.3) | ||
| Unknown | 205 (2.4) | 14 (1.6) | 4 (0.5) | 14 (1.6) | ||
| Travel distance | <.001 | NS | ||||
| <12.5 miles | 5,143 (60.9) | 705 (80.1) | 694 (78.8) | 705 (80.1) | ||
| 12.5–49.9 miles | 2,654 (31.4) | 137 (15.6) | 158 (18.0) | 137 (15.6) | ||
| ≥50 miles | 630 (7.5) | 37 (4.2) | 28 (3.2) | 37 (4.2) | ||
| Unknown | 24 (0.3) | 1 (0.1) | 0 (0) | 1 (0.1) | ||
| Stage | NS | NS | ||||
| I | 2,420 (28.6) | 227 (25.8) | 228 (25.9) | 227 (25.8) | ||
| II | 6,031 (71.4) | 653 (74.2) | 652 (74.1) | 653 (74.2) | ||
| Grade | .003 | NS | ||||
| I-II | 4,253 (50.3) | 420 (47.7) | 423 (48.1) | 420 (47.7) | ||
| III-IV | 2,106 (24.9) | 197 (22.4) | 191 (21.7) | 197 (22.4) | ||
| Unknown | 2,092 (24.8) | 263 (29.9) | 266 (30.2) | 263 (29.9) | ||
| Overall Survival | <.001 | .007 | ||||
| 5-year survival rate | 77 % | 71 % | 75 % | 71 % | ||
Education: percentage of patients without high school level
Figure 2.
Histogram to display time to treatment initiation (≤ 6 weeks vs. > 6 weeks) in white and Black patients with anal squamous cell carcinoma
Table 2.
Multivariable Logistic regression analysis for time to treatment initiation (≤ 6 weeks vs. > 6 weeks)
| Characteristics | OR (95% CI) | p-value |
|---|---|---|
| Race | ||
| White | Ref | |
| Black | 1.78 (1.53–2.08) | <.001 |
| Sex | ||
| Male | Ref | |
| Female | 0.72 (0.65–0.79) | <.001 |
| Age | ||
| <50 years | Ref | |
| 50–64 years | 1.03 (0.91–1.16) | NS |
| ≥65 years | 1.06 (0.91–1.24) | NS |
| Comorbidity Score | ||
| 0 | Ref | |
| 1 | 1.07 (0.94–1.23) | NS |
| 2+ | 1.28 (1.08–1.53) | .004 |
| Facility Type | ||
| Academic | Ref | |
| Non-academic | 0.62 (0.56–0.68) | <.001 |
| Other | 0.76 (0.58–0.98) | .03 |
| Travel distance | ||
| <12.5 miles | Ref | |
| 12.5–49.9 miles | 1.02 (0.92–1.13) | NS |
| ≥50 miles | 1.25 (1.04–1.51) | .02 |
| Unknown | 1.08 (0.43–2.71) | NS |
| Income | ||
| < $40,227 | Ref | |
| $40,227 - $50,353 | 0.98 (0.85–1.12) | NS |
| $50,354 - $63,332 | 0.96 (0.84–1.10) | NS |
| ≥ $63,333 | 0.89 (0.78–1.01) | NS |
| Unknown | 1.61 (1.08–2.40) | .02 |
| Insurance status | ||
| Uninsured | Ref | |
| Private | 0.62 (0.51–0.76) | <.001 |
| Medicaid | 1.17 (0.93–1.49) | NS |
| Medicare | 0.71 (0.57–0.88) | .002 |
| Other/Unknown | 0.95 (0.71–1.27) | NS |
| Rurality | ||
| Metropolitan | Ref | |
| Non-metropolitan | 0.80 (0.70–0.93) | .003 |
| Unknown | 0.99 (0.73–1.33) | NS |
| Stage | ||
| I | Ref | |
| II | 0.53 (0.48–0.58) | <.001 |
| Grade | ||
| I-II | Ref | |
| III-IV | 0.82 (0.74–0.92) | .001 |
| Unknown | 1.02 (0.92–1.14) | NS |
Note: The multivariable logistic regression predicted the probability of time to treatment initiation: > 6 weeks
Survival analyses
Without propensity score matching, Black patients had worse OS compared to white patients (HR 1.18 [1.02–1.36], p=.03), 5-year survival rate 71% vs. 77%, p<.001) (Table 1, Figure 3A). After propensity score matching, the Black race remained associated with worse overall survival (5-year survival rate 71% vs. 75%, p=.007) (Table 1, Figure 3B). After multivariable analysis, this difference has remained as Black patients had decreased overall survival than white patients (HR 1.25 [1.02–1.52], p=.03) (Table 3).
Figure 3.
Overall survival in white and Black patients with anal squamous cell carcinoma (A) unmatched groups (B) propensity matched groups
Table 3.
Multivariable Cox regression analysis for overall survival
| Characteristics | Unmatched | Propensity matched | ||
|---|---|---|---|---|
| HR (95% CI) | p-value | HR (95% CI) | p-value | |
| Race | ||||
| White | Ref | Ref | ||
| Black | 1.18 (1.02–1.36) | .03 | 1.25 (1.02–1.52) | .03 |
| Sex | ||||
| Male | Ref | Ref | ||
| Female | 0.64 (0.58–0.70) | <.001 | 0.62 (0.50–0.76) | <.001 |
| Age | ||||
| < 50 years | Ref | Ref | ||
| 50–64 years | 1.28 (1.12–1.46) | <.001 | 1.16 (0.91–1.47) | NS |
| ≥ 65 years | 2.00 (1.71–2.35) | <.001 | 1.66 (1.21–2.28) | .002 |
| Comorbidity Score | ||||
| 0 | Ref | Ref | ||
| 1 | 1.34 (1.19–1.51) | <.001 | 1.37 (1.04–1.81) | .02 |
| 2+ | 2.27 (1.98–2.59) | <.001 | 2.44 (1.94–3.08) | <.001 |
| Facility Type | ||||
| Academic | Ref | Ref | ||
| Non-academic | 1.12 (1.01–1.24) | .03 | 0.97 (0.79–1.20) | NS |
| Unknown | 1.69 (1.28–2.24) | <.001 | 1.47 (0.95–2.28) | NS |
| Travel distance | ||||
| <12.5 miles | Ref | Ref | ||
| 12.5–49.9 miles | 0.91 (0.82–1.01) | NS | 0.87 (0.65–1.16) | NS |
| ≥50 miles | 0.91 (0.76–1.10) | NS | 0.84 (0.49–1.44) | NS |
| Unknown | 1.31 (0.50–3.50) | NS | N/A | |
| Income | ||||
| < $40,227 | Ref | Ref | ||
| $40,227 - $50,353 | 0.86 (0.76–0.98) | .02 | 1.03 (0.81–1.31) | NS |
| $50,354 - $63,332 | 0.86 (0.76–0.98) | .02 | 1.14 (0.86–1.51) | NS |
| ≥ $63,333 | 0.77 (0.68–0.87) | <.001 | 0.87 (0.64–1.18) | NS |
| Unknown | 0.79 (0.53–1.17) | NS | 0.51 (0.18–1.43) | NS |
| Insurance status | ||||
| Uninsured | Ref | Ref | ||
| Private | 0.63 (0.52–0.78) | <.001 | 0.76 (0.51–1.14) | NS |
| Medicaid | 1.10 (0.87–1.38) | NS | 1.04 (0.68–1.59) | NS |
| Medicare | 0.93 (0.76–1.15) | NS | 1.26 (0.84–1.90) | NS |
| Other/Unknown | 0.97 (0.75–1.31) | NS | 1.45 (0.84–2.53) | NS |
| Rurality | ||||
| Metropolitan | Ref | Ref | ||
| Non-metropolitan | 1.20 (1.05–1.37) | .006 | 1.65 (1.11–2.45) | .01 |
| Unknown | 1.15 (0.86–1.54) | NS | 3.18 (1.44–7.01) | .004 |
| Stage | ||||
| I | Ref | Ref | ||
| II | 1.66 (1.49–1.85) | <.001 | 1.59 (1.24–2.04) | <.001 |
| Grade | ||||
| I-II | Ref | Ref | ||
| III-IV | 0.99 (0.89–1.09) | NS | 1.00 (0.78–1.29) | NS |
| Unknown | 0.89 (0.80–0.99) | .03 | 0.79 (0.63–0.99) | .04 |
DISCUSSION
In this comprehensive nationwide study of patients with early-stage ASCC, we observed socioeconomic and racial disparities in treatment and outcome. In particular, Black patients had a worse OS. The etiologies of this disparity are likely multifactorial, including both patient and systems-level factors such as insurance status, income, education, and access to timely and high quality cancer care26–28. Indeed, we also found that the Black race was associated with greater time to treatment initiation. These findings are consistent with prior reports such as by Ramsey et al., showing that among patients with ASCC, Black patients with public insurance and lower education levels experienced greater treatment initiation delays as compared to their white counterparts19. Notably, Black patients were more likely to be treated at academic cancer centers. It is possible that some of the delays in treatment initiation for Black patients could be due to difficulties in obtaining timely work-up and care at academic centers due to longer wait times at such centers, although we controlled for facility type in our analyses. Prior studies have also demonstrated an association between racial disparities and delay to treatment initiation in breast, colorectal, prostate, lung, and head & neck cancers29–33. Accordingly, research on cancer outcomes has demonstrated that delays in the initiation of cancer treatment appear to be associated with worse survival29,30,34,35.
Several prior studies have also demonstrated a higher incidence and mortality of ASCC among Black patients14,36,37. In a study using the SEER database, Arora et al. reported that white women and Black men had the highest rate of increase in the incidence of ASCC38. Another study showed that Black patients have higher annual percentage increases in incidence and mortality of ASCC compared to whites and that 5-year relative survival was significantly lower in Black patients (56% vs. 67%)39. Indeed, multiple studies have demonstrated that Black patients with ASCC have higher mortality as compared to whites14,21,38,40. In a large cohort, Fields et al. found that the Black race was significantly associated with unfavorable survival in stage I, II, and III diseases but not for stage IV disease40. Furthermore, a combined analysis of two studies using the SEER database between 1973 to 2005 and 1988 to 2012 showed the Black race as an independent predictor of worse survival41,42. Our study adds to the literature by demonstrating this disparity among early stage ASCC patients, who have the highest chance for disease cure.
Our study has several limitations inherent to large cancer registries, including lack of information on treatment completion, details on radiotherapy fields, treatment toxicities, chemotherapy dosage and regimens, details of staging (endoscopy, biopsy, specific imaging studies, etc.), and missed appointments. In addition, there was no information on HIV status, which would have been helpful to include as a covariable. Also, NCDB only includes overall survival, and there is no information on cancer-specific outcomes.
Black patients with early stage ASCC had greater time to treatment initiation and worse OS as compared to white patients. Further research is needed to elucidate the underpinnings of these disparities.
Research Support:
NCI Cancer Center Support Grant to UT Southwestern Medical Center (5P30CA142543-07) to Muhammad Beg. The research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001105. Muhammad Beg is Designated Dedman Family Scholar in Clinical Care.
Abbreviations:
- CRT
Chemoradiation therapy
- ASCC
anal squamous cell cancer
- NCDB
National Cancer Database
- OS
Overall survival
- NS
not significant
Footnotes
Conflicts of Interest: None
This manuscript has been presented as an abstract form for the 2020 American Society of Clinical Oncology Annual Meeting.
REFERENCES
- 1.Sites A SEER cancer statistics review 1975–2011. Bethesda, MD: National Cancer Institute; 2014. [Google Scholar]
- 2.Howlader N, Noone A, Krapcho M, et al. SEER Cancer Statistics Review, 1975–2013, National Cancer Institute; Bethesda, MD: In:2016. [Google Scholar]
- 3.Nelson RA, Levine AM, Bernstein L, et al. Changing patterns of anal canal carcinoma in the United States. J Clin Oncol. 2013;31(12):1569–1575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Valvo F, Ciurlia E, Avuzzi B, et al. Cancer of the anal region. Crit Rev Oncol Hematol. 2019;135:115–127. [DOI] [PubMed] [Google Scholar]
- 5.Shiels MS, Pfeiffer RM, Chaturvedi AK, et al. Impact of the HIV epidemic on the incidence rates of anal cancer in the United States. J Natl Cancer Inst. 2012;104(20):1591–1598. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hoots BE, Palefsky JM, Pimenta JM, et al. Human papillomavirus type distribution in anal cancer and anal intraepithelial lesions. Int J Cancer. 2009;124(10):2375–2383. [DOI] [PubMed] [Google Scholar]
- 7.Alemany L, Saunier M, Alvarado-Cabrero I, et al. Human papillomavirus DNA prevalence and type distribution in anal carcinomas worldwide. Int J Cancer. 2015;136(1):98–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Melbye M, Rabkin C, Frisch M, et al. Changing patterns of anal cancer incidence in the United States, 1940–1989. Am J Epidemiol. 1994;139(8):772–780. [DOI] [PubMed] [Google Scholar]
- 9.Shridhar R, Shibata D, Chan E, et al. Anal cancer: current standards in care and recent changes in practice. CA Cancer J Clin. 2015;65(2):139–162. [DOI] [PubMed] [Google Scholar]
- 10.Khawja SN, Mohammed S, Silberfein EJ, et al. Pancreatic cancer disparities in African Americans. Pancreas. 2015;44(4):522–527. [DOI] [PubMed] [Google Scholar]
- 11.Newman LA. Disparities in breast cancer and african ancestry: a global perspective. Breast J 2015;21(2):133–139. [DOI] [PubMed] [Google Scholar]
- 12.Berger M, Lund MJ, Brawley OW. Racial disparities in lung cancer. Curr Probl Cancer. 2007;31(3):202–210. [DOI] [PubMed] [Google Scholar]
- 13.Bhardwaj A, Srivastava SK, Khan MA, et al. Racial disparities in prostate cancer: a molecular perspective. Front Biosci (Landmark Ed). 2017;22:772–782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Bojko MM, Kucejko RJ, Poggio JL. Racial Disparities and the Effect of County Level Income on the Incidence and Survival of Young Men with Anal Cancer. Health Equity. 2018;2(1):193–198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Goffredo P, Robinson TJ, Frakes JM, et al. The impact of location on the prognosis of squamous cell carcinomas of the anorectal region. journal of surgical research. 2018;231:173–178. [DOI] [PubMed] [Google Scholar]
- 16.Goffredo P, Garancini M, Robinson TJ, et al. A national-level validation of the new American Joint committee on cancer 8th edition subclassification of stage IIA and B Anal squamous cell cancer. Annals of surgical oncology. 2018;25(6):1654–1660. [DOI] [PubMed] [Google Scholar]
- 17.Youssef I, Osborn V, Lee A, et al. Survival benefits and predictors of use of chemoradiation compared with radiation alone for early stage (T1–T2N0) anal squamous cell carcinoma. Journal of Gastrointestinal Oncology. 2019;10(4):616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hauerstock D, Ennis RD, Grossbard M, et al. Efficacy and toxicity of chemoradiation in the treatment of HIV-associated anal cancer. Clinical colorectal cancer. 2010;9(4):238–242. [DOI] [PubMed] [Google Scholar]
- 19.Ramey SJ, Rich BJ, Kwon D, et al. Demographic disparities in delay of definitive chemoradiation for anal squamous cell carcinoma: a nationwide analysis. Journal of gastrointestinal oncology. 2018;9(6):1109–1126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Elson JK, Kachnic LA, Kharofa JR. Intensity‐modulated radiotherapy improves survival and reduces treatment time in squamous cell carcinoma of the anus: A National Cancer Data Base study. Cancer. 2018;124(22):4383–4392. [DOI] [PubMed] [Google Scholar]
- 21.Bilimoria KY, Bentrem DJ, Rock CE, et al. Outcomes and prognostic factors for squamous-cell carcinoma of the anal canal: analysis of patients from the National Cancer Data Base. Dis Colon Rectum. 2009;52(4):624–631. [DOI] [PubMed] [Google Scholar]
- 22.Bilimoria KY, Bentrem DJ, Ko CY, et al. Squamous cell carcinoma of the anal canal: utilization and outcomes of recommended treatment in the United States. Annals of surgical oncology. 2008;15(7):1948. [DOI] [PubMed] [Google Scholar]
- 23.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of clinical epidemiology. 1992;45(6):613–619. [DOI] [PubMed] [Google Scholar]
- 24.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research. 2011;46(3):399–424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ho DE, Imai K, King G, et al. MatchIt: nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, http://gkingharvard.edu/matchit 2011. [Google Scholar]
- 26.Williams DR, Priest N, Anderson NB. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychol. 2016;35(4):407–411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Chaudhry SI, Herrin J, Phillips C, et al. Racial disparities in health literacy and access to care among patients with heart failure. J Card Fail. 2011;17(2):122–127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Rikard RV, Thompson MS, McKinney J, et al. Examining health literacy disparities in the United States: a third look at the National Assessment of Adult Literacy (NAAL). BMC Public Health. 2016;16:975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Naghavi AO, Echevarria MI, Strom TJ, et al. Treatment delays, race, and outcomes in head and neck cancer. Cancer Epidemiol. 2016;45:18–25. [DOI] [PubMed] [Google Scholar]
- 30.Liederbach E, Sisco M, Wang C, et al. Wait times for breast surgical operations, 2003–2011: a report from the National Cancer Data Base. Ann Surg Oncol. 2015;22(3):899–907. [DOI] [PubMed] [Google Scholar]
- 31.Halpern MT, Holden DJ. Disparities in timeliness of care for U.S. Medicare patients diagnosed with cancer. Curr Oncol. 2012;19(6):e404–413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Gwyn K, Bondy ML, Cohen DS, et al. Racial differences in diagnosis, treatment, and clinical delays in a population-based study of patients with newly diagnosed breast carcinoma. Cancer. 2004;100(8):1595–1604. [DOI] [PubMed] [Google Scholar]
- 33.Elmore JG, Nakano CY, Linden HM, et al. Racial inequities in the timing of breast cancer detection, diagnosis, and initiation of treatment. Med Care. 2005;43(2):141–148. [DOI] [PubMed] [Google Scholar]
- 34.Colleoni M, Bonetti M, Coates AS, et al. Early start of adjuvant chemotherapy may improve treatment outcome for premenopausal breast cancer patients with tumors not expressing estrogen receptors. The International Breast Cancer Study Group. J Clin Oncol. 2000;18(3):584–590. [DOI] [PubMed] [Google Scholar]
- 35.Colleoni M, Gelber RD. Time to initiation of adjuvant chemotherapy for early breast cancer and outcome: the earlier, the better? J Clin Oncol. 2014;32(8):717–719. [DOI] [PubMed] [Google Scholar]
- 36.Shiels MS, Kreimer AR, Coghill AE, et al. Anal Cancer Incidence in the United States, 1977–2011: Distinct Patterns by Histology and Behavior. Cancer Epidemiol Biomarkers Prev. 2015;24(10):1548–1556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Johnson LG, Madeleine MM, Newcomer LM, et al. Anal cancer incidence and survival: the surveillance, epidemiology, and end results experience, 1973–2000. Cancer. 2004;101(2):281–288. [DOI] [PubMed] [Google Scholar]
- 38.Arora N, Gupta A, Zhu H, et al. Race- and Sex-Based Disparities in the Therapy and Outcomes of Squamous Cell Carcinoma of the Anus. J Natl Compr Canc Netw. 2017;15(8):998–1004. [DOI] [PubMed] [Google Scholar]
- 39.Baughman DM, Shah BK. Disparities in receipt of radiotherapy and survival by age, sex, and race among patients with non-metastatic squamous cell carcinoma of the anus. J Gastrointest Oncol. 2016;7(6):968–973. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Fields AC, Welten VM, Lu P, et al. Does race impact survival for patients with anal squamous cell carcinoma? J Surg Oncol. 2019;120(7):1201–1207. [DOI] [PubMed] [Google Scholar]
- 41.Kim E, Kim JS, Choi M, et al. Conditional Survival in Anal Carcinoma Using the National Population-Based Survey of Epidemiology and End Results Database (1988–2012). Dis Colon Rectum. 2016;59(4):291–298. [DOI] [PubMed] [Google Scholar]
- 42.Metildi C, McLemore EC, Tran T, et al. Incidence and survival patterns of rare anal canal neoplasms using the surveillance epidemiology and end results registry. Am Surg. 2013;79(10):1068–1074. [PMC free article] [PubMed] [Google Scholar]