Abstract
Background:
This study aims to examine the relationship between race and rural-urban context in head and neck cancer (HNC) survival and determine factors that potentially drive this disparity.
Methods:
Using the National Cancer Database from 2004 to 2015, we identified a retrospective cohort of 146,256 HNC patients. Kaplan-Meier survival curves and the Cox proportional hazards regression were used to calculate adjusted hazard ratios (aHR).
Results:
Median survival by patient subgroup was as follows: white urban (67 mo.; 95% Confidence Interval [CI]: 66.0-67.9), white rural (59.1 mo.; 95% [CI]: 57.2-60), black urban (43.1 mo.; 95% [CI]: 41.1-44.5) and black rural (35.1 mo.; 95% [CI]: 31.9-39.0). The difference in five-year survival, stratified rural-urban context, was greater among black patients (ΔRMST 0.18; 95% [CI]: 0.10-0.27) than white patients (ΔRMST 0.08; 95% [CI]: 0.06-0.11).
In the univariable Cox proportional hazards analysis with white urban patients as reference group, black rural patients had the worst survival (HR: 1.45; 95% [CI]: 1.43-1.48; p<.001), followed by black urban patients (HR: 1.29; 95% [CI]: 1.28-1.30; p<.001), and white rural patients (HR: 1.08 95% [CI]: 1.07-1.09; p<.001). This disparity persisted when controlling for demographic, socioeconomic, and clinical factors.
Conclusion:
Black HNC patients, specifically those living in rural areas, have decreased survival. Survival differences by rural-urban status are greater among black patients than white patients.
Impact:
We have shown that race and rural-urban status impact HNC survival outcomes. Our findings will help future researchers to better frame approaches to address this disparity.
Introduction
Head and neck cancer (HNC) encompasses cancers of the oral cavity, oropharynx, hypopharynx, larynx, paranasal sinuses, and nasal cavity. In 2020, the American Cancer Society estimates 53,260 new diagnoses of HNC and 10,750 HNC related deaths.1 According to the Surveillance, Epidemiology, and End Results (SEER) database, the overall incidence rate of HNC between 1992-2014 was 14.3 per 100,000 black patients compared to 12.2 per 100,000 white patients.2 Additionally, the mean five-year survival among black patients with HNC has been reported between 29-31% compared to 55-59% in whites.3 Factors contributing to racial outcome disparities among HNC patients have been debated in the literature.
In addition to race, the geographical location of the patient’s residence (rural-urban context) is another important factor that has been shown to influence patient outcomes. Prior research has demonstrated associations between rural-urban context and survival outcomes in breast, lung, and colorectal cancers.4 In HNC patients, rural context has been associated with advanced disease stage at diagnosis and worse survival as compared to urban.2 Differences by rural-urban context are often attributed to travel barriers and decreased access to care among rural patients.5,6 However, previous findings have not been consistent with respect to the impact of rural-urban status on survival.5,7-9
Although the impacts of race and rural-urban context in HNC survival have been addressed separately, there has not been adequate research into the combined effect of these factors.10-12 We aimed to examine the relationship between race and residential rural-urban context and its impact on overall survival in HNC patients. We hypothesized that the combined impact of both race and rural-urban status in non-oropharyngeal HNC would disproportionately influence black-rural patients.
Materials and Methods
Study Population:
The National Cancer Database (NCDB) is maintained by the American College of Surgeons and American Cancer Society. It was established in 1989 and includes 34 million records from cancer registries of over 1,500 participating hospitals. The database contains more than 70% of newly diagnosed cancer cases nationwide.13 The NCDB includes data on baseline patient demographics, staging, and survival.
We evaluated HNC cases from 2004 to 2015, filtering the data for adult patients with International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) codes for cancers of the oral cavity, larynx, or hypopharynx (Table 1). We excluded non-squamous cell cases, palliative care patients, cases missing variables, and those diagnosed after the reference date. Patients with oropharyngeal cancer were excluded due to the association between human papillomavirus (HPV) and improved survival, and because of unequal distribution by race and geographical location.14,15 Additionally, we only included non-Hispanic black and white patients due to small sample size of other races/ethnicities. After applying inclusion and exclusion criteria, our study cohort consisted of 146,256 patients with cancers involving hypopharynx, larynx and oral cavity subsites (Figure 1).
Table 1:
ICO-0-3 codes used for site classification
| Category | ICD-O-3 codes |
|---|---|
| Oral Cavity | C02.0-C02.3, C02.8, C03.0-C03.9, C04.0-C04.9, C05.0, C05.2, C05.8, C06.0-C06.8 |
| Larynx | C32.0-C32.9 |
| Hypopharynx | C12.9-C12.9 |
| Squamous Cell Histology | 8052, 8083, 8078-8070 |
Figure 1: Exclusion Criterion for National Cancer Database Cohort.
This figure depicts our exclusion criteria for filtering the original 383,778 patients found in the NCDB to our final cohort of 146,256.
Exposure and Outcome Assessment:
Our primary exposures were race and rural-urban context. We defined rural-urban context using Rural-Urban Continuum Codes (RUCC) published by the United States Department of Agriculture Economic Research Service to distinguish counties by population size, adjacency to a metropolitan areas, and degree of urbanization.16 RUCC were determined based on patient county of residence recorded at diagnosis. We used RUCC from 2003 to classify patients into urban (RUCC 1-3) and rural subgroups (RUCC 4-9). To study the interaction between race and rural-urban context, patients were further grouped as black urban, black rural, white urban, and white rural patients.
Death was our primary outcome and was ascertained using the NCDB PUF vital status variable. All patients alive after the study ended in 2015 were censored in our analysis.
Covariates:
Covariates were classified into demographic (age and gender), socioeconomic (insurance status and distance traveled to primary treatment center), and clinical (primary tumor site and stage) factors. Age was treated as a continuous variable. The NCDB categorizes the patient’s primary insurance carrier at the time of diagnosis and treatment into the following groups: not insured, private insurance, Medicaid, Medicare, and other government funding entities. Distance traveled was reported in the NCDB as crowfly. This variable was calculated by measuring the longitudinal distance between the center of the patient’s zip code – the center of the patient’s city was used when zip code was unavailable – and the address of the facility where they received treatment. We categorized the crowfly variable into quartiles 1 (<4.8 mi), 2 (4.8 to 11.4 mi), 3 (11.5 to 28.7 mi), and 4 (>28.8mi). Stage was classified using the pathologic stage of the patient. Clinical stage was used where pathologic stage was not available. The Commission on Cancer Accreditation program categorizes cancer treatment facilities into the following types: Community Cancer Program, Comprehensive Community Cancer Program, Academic/Research Program, Integrated Network Cancer Program, and Other or unknown types of cancer program. Each case was assigned a facility type based on the facility where it was first reported.
Statistical Analysis:
The descriptive analysis compared covariates across race and rural-urban context and was tested by two-sided Pearson chi-square tests for categorical variables. For continuous variables, means and standard deviations (SD) were calculated and t-tests were conducted for normally distributed variables. Kaplan-Meier overall survival curves were constructed and log-rank p-values were calculated. Cox proportional hazards regressions were used to estimate both unadjusted hazard ratios (HR) and adjusted hazard ratios (aHR). The proportional hazards and linearity assumptions were tested and satisfied. We also estimated restricted mean survival time (RMST) and differences in RMST. Restricted mean survival time is the area under the Kaplan-Meier curve and is interpreted as the “life expectancy” between diagnosis and five years. We also conducted exploratory analyses to investigate changes in rural-urban disparities overtime and within each treatment facility for each subgroup (black urban, black rural, white urban, and white rural). To increase the size sample size, we used only two groups: cases diagnosed between 2004-2007 and cases diagnosed between 2008-2011. All statistical analyses were implemented using the R studio version 3.5.1 using the survival, survminer, and survRM2 packages.17-20
Results
Descriptive Statistics:
Our cohort was comprised of 80.1% (n = 117,081) urban patients and 19.9% (n = 29,175) rural patients (Table 2). Most of our cohort was made up of white patients. There was a modest increase in private insurance in urban compared to rural areas. The urban and rural cohorts had comparable numbers in each stage. Approximately 9.8% (n = 14,320) of patients had cancer of the hypopharynx, 54.6% (n = 79,902) of the larynx, and 35.6% (n = 52,034) of the oral cavity.
Table 2:
Descriptive Statistics of Population
| Black Rural* (N=2272) |
Black Urban (N=16053) |
White Urban (N=101028) |
White Rural (N=26903) |
P-value | Overall (N=146256) |
|
|---|---|---|---|---|---|---|
| Gender | ||||||
| Female | 465 (20.5%) | 4003 (24.9%) | 29743 (29.4%) | 7429 (27.6%) | <0.001 | 41640 (28.5%) |
| Male | 1807 (79.5%) | 12050 (75.1%) | 71285 (70.6%) | 19474 (72.4%) | 104616 (71.5%) | |
| Age | ||||||
| Mean (SD) | 61.2 (10.6) | 62.1 (10.7) | 64.8 (12.2) | 64.2 (11.7) | <0.001 | 64.4 (11.9) |
| Median [Min, Max] | 60.0 [21.0, 90.0] | 61.0 [18.0, 90.0] | 65.0 [18.0, 90.0] | 64.0 [18.0, 90.0] | 64.0 [18.0, 90.0] | |
| Insurance | ||||||
| Medicaid | 524 (23.1%) | 3485 (21.7%) | 8033 (8.0%) | 2802 (10.4%) | <0.001 | 14844 (10.1%) |
| Medicare | 984 (43.3%) | 6784 (42.3%) | 49515 (49.0%) | 14117 (52.5%) | 71400 (48.8%) | |
| Other Government | 46 (2.0%) | 356 (2.2%) | 1816 (1.8%) | 728 (2.7%) | 2946 (2.0%) | |
| Private | 414 (18.2%) | 4051 (25.2%) | 37288 (36.9%) | 7834 (29.1%) | 49587 (33.9%) | |
| Uninsured | 304 (13.4%) | 1377 (8.6%) | 4376 (4.3%) | 1422 (5.3%) | 7479 (5.1%) | |
| Stage | ||||||
| 0 | 60 (2.6%) | 538 (3.4%) | 5307 (5.3%) | 1012 (3.8%) | <0.001 | 6917 (4.7%) |
| 1 | 391 (17.2%) | 3145 (19.6%) | 32252 (31.9%) | 7795 (29.0%) | 43583 (29.8%) | |
| 2 | 298 (13.1%) | 2234 (13.9%) | 15720 (15.6%) | 4625 (17.2%) | 22877 (15.6%) | |
| 3 | 432 (19.0%) | 2892 (18.0%) | 15295 (15.1%) | 4450 (16.5%) | 23069 (15.8%) | |
| 4 | 1091 (48.0%) | 7244 (45.1%) | 32454 (32.1%) | 9021 (33.5%) | 49810 (34.1%) | |
| Site | ||||||
| Hypopharynx | 302 (13.3%) | 2154 (13.4%) | 9570 (9.5%) | 2294 (8.5%) | <0.001 | 14320 (9.8%) |
| Larynx | 1466 (64.5%) | 10450 (65.1%) | 53037 (52.5%) | 14949 (55.6%) | 79902 (54.6%) | |
| Oral Cavity | 504 (22.2%) | 3449 (21.5%) | 38421 (38.0%) | 9660 (35.9%) | 52034 (35.6%) | |
Univariate Survival Analysis
Survival was assessed through Kaplan Meier Survival Curves and five-year overall survival. The highest median survival time was observed in white-urban patients (67.0 mo.; 95% Confidence Interval [CI]: 66.0-67.9), followed by white rural (59.1 mo.; 95% CI: 57.2-60.8), black urban (43.1 mo.; 95% CI: 41.1-44.5) and black rural (35.1 mo.; 95% [CI]: 31.9-39.0) (Figure 1, Table 3). Five-year survival differences by rural-urban context were greater among black patients (ΔRMST 0.18; 95% CI: 0.10-0.27) than white patients (ΔRMST 0.08; 95% CI: 0.06-0.11).
Table 3:
Cumulative Deaths
| Time from Diagnosis (years) |
||||||
|---|---|---|---|---|---|---|
| Subgroup | 0 | 1 | 2 | 3 | 4 | 5 |
| Black Rural | 0 | 556 | 899 | 1076 | 1181 | 1248 |
| Black Urban | 1 | 3538 | 5779 | 6997 | 7689 | 8143 |
| White Rural | 7 | 4724 | 7962 | 9776 | 10951 | 11835 |
| White Urban | 28 | 16968 | 28518 | 34877 | 39171 | 42145 |
In our univariable Cox proportional hazard regression model, using white urban patients as the reference group, black rural patients (hazard ratio [HR]: 1.5; 95% CI: 1.43-1.48; p<.001) had the worst survival, followed by black urban (HR: 1.29; 95% CI: 1.28-1.30; p<.001), and white rural (HR: 1.08 95% CI: 1.07-1.09; p<.001) (Table 4).
Table 4:
Unadjusted and Adjusted Hazard Ratios
| Variable | HR (95% CI) | aHR* (95% CI) |
|---|---|---|
| Subgroup | ||
| White Urban | Reference | Reference |
| White Rural | 1.08 (1.07 - 1.09) | 1.11 (1.10 - 1.12) |
| Black Urban | 1.29 (1.28 - 1.30) | 1.11 (1.10 - 1.12) |
| Black Rural | 1.45 (1.43 - 1.48) | 1.30 (1.27 - 1.33) |
Multivariable Survival Analysis:
With demographic, socioeconomic and clinical factors included in the Cox regression model, a significant difference in survival outcome was observed between racial and geographic groups (Table 4). Using white urban patients as the reference group, black rural patients had the highest risk of mortality (adjusted HR (aHR): 1.30, 95% CI: 1.27 - 1.33) followed by black urban (aHR: 1.11, 95% CI: 1.10-1.12), and white rural (aHR: 1.04, 95% CI: 1.10 - 1.12) (Table 4). However, the interaction between race and rural-urban context was not statistically significant(p=.18).
Year of Diagnosis Analyses:
There were no significant differences observed by year of diagnosis in any of the subgroups (Supplemental Figure S1; Supplemental Table S1). However, black urban, white urban, and white rural patients diagnosed from 2008 – 2011 trended towards increased survival compared to 2004 – 2007.
Facility Type Analyses:
In our next survival analysis, we stratified by race and rural-urban status (Supplemental Table S1, Supplemental Figure S2). In all subgroups, except for black rural, community hospitals had the lowest survival. However, the 95% confidence interval of median survival in months of all facility types overlapped in both the black rural and black urban subgroups. Among both urban- and rural-white patients, patients treated in academic facilities had the longest medial survival.
Discussion
Our analysis demonstrates differences in survival of HNC patients according to racial-geographic status, with worse overall survival in the black rural population. This disparity persists after adjusting for potential mediating covariates in a multivariable model. Additionally, when comparing rural-urban disparities by race, we found that the effect of rural-urban context is greater among black patients compared to white patients. Interestingly, we found that there is no synergistic interaction between race and rural-urban context.
Studies reporting HNC survival differences according to rural-urban context have shown inconsistent results.6,7 Similar to our study, a prior Canadian study limited to oral cavity cancer patients showed improved disease-free survival in patients living in urban compared to rural geographical locations.7 A more recent Canadian study by Kim et al. showed no association between rural-urban context and survival, however their study included patients with oropharyngeal cancer which is closely associated with HPV.5 HPV-positive oropharyngeal cancer represents a biologically distinct disease with improved prognosis over HPV-negative HNC. While HPV-positive disease has become more prevalent, Black Americans bear a disproportionate burden of HPV-negative HNC.2 Indeed, a recent meta-analysis showed that oropharyngeal survival disparities by race disappear after controlling for HPV status.12 Therefore, HNC racial disparity research that has traditionally included oropharyngeal cases has been obscured by HPV status. To avoid this, our current study did not include oropharyngeal cases. In the future, we plan to perform an additional analysis addressing the differences in the impact of rural urban status and race on oropharyngeal cancer. Contrary to the before mentioned studies, we used a US data source, which may have different factors driving disparity than the Canadian data sources used in the other studies. Additionally, our study uses rural-urban definitions based on the US RUCC, which differ from the definitions used by these previous studies. The Zhang et al. study defined rural as any community smaller than 10,000 people. The Kim et. Al study classified urban rural status based on postal code into four categories: rural (< 1,000 residents), small urban centers (1,000 - 29,999 residents), medium urban centers (30,000 - 99,999 residents), and large urban centers (100,000 + residents). By contrast, the RUCC used by our study classify population centers into metro urban counties (RUCC 1 - 3), nonmetro urban counties (RUCC 4 - 6), and nonmetro completely rural counties (RUCC 7 – 9). Because there were relatively few cases in rural areas alone (8,164 cases; 5.58%), we included all non-metro areas in our rural subgroup (29,175 cases; 20%). We believe this cutpoint is more relevant and helped our study to better identify trends that were missed in the Kim et. Al paper, which only had a total sample size of 3,036 patients.
Several factors associated with rural residence may contribute to our findings. Rural residents have less access to tertiary care centers, specialized medical professionals, and public transportation, all of which can cause significant delays in diagnosis and treatment.21 One study highlighted the importance of timely care by demonstrating that even a two-month delay in treatment contributes to a higher hazard of death in pharyngeal cancers.22 To address these disparities, researchers should focus on geographical distribution of treatment services as well as potential solutions to increase access and transportation in rural areas.
Our findings are consistent with many previous studies that have suggested a difference in survival between black and white HNC patients.3,23-25 Recent studies show that black patients are more likely to distrust healthcare professionals, which may stem from a fear of discrimination or from misinformation about health-related procedures.26,27 Black patients are also more likely to be underinsured, have lower income, and have lower medical literacy.28,29 These barriers may decrease their number of clinical visits and cancer screenings. Ultimately, these racial disparities result in later HNC diagnosis and worse survival outcomes.3
Previous studies have found that rural, racial-minority cancer patients have more difficulty accessing care.30 Our study highlights this problem, showing a greater difference in survival between black rural and urban patients than between white rural and urban groups. Survival disparities by race and rural-urban context may also be influenced by differences in smoking rates. People living in rural communities have a higher prevalence of cigarette smoking, and their health is also negatively impacted more so than patients living in urban areas.31 Additionally, there are known differences in smoking levels by race.32 In fact, it has been shown that minority patients experiencing racial discrimination have higher risk for smoking.33 In addition to smoking, rural areas have higher rates obesity and physical inactivity.34 Although we did not find a statistical interaction between race and rural-urban context, these factors clearly increase the risk of death among rural black HNC patients.35
Both of our analyses showed no statistical significance by date of diagnosis when groups were subclassified by rural-urban status. This indicates that racial-geographic disparities did not significantly change from 2004 to 2011. However, survival in all groups except black rural patients trended towards increased survival. The lack of trend in the black urban group is likely due to a smaller sample size and insufficient power.
In our survival analysis by facility type, our results showed that academic hospitals had the highest median survival time followed by comprehensive, integrated network, and community hospitals. When further classifying by race and rural-urban status, community-treated patients trended towards having the lowest survival in all subgroups except black rural. Additionally, white patients treated at academic facilities had improved survival over all other facilities. This finding matches previous studies suggesting that patients at non-teaching hospitals had lower survival outcomes.36 These outcome differences by facility type may be attributed, in part, to case volume at different centers. Academic centers have a higher case volume compared to other cancer centers, which is strongly associated with improved survival.37 However, this difference was not observed among black patients. Black-rural patients had their best outcomes when treated at integrated-network facilities, and black-urban patients survived longest when treated at comprehensive facilities.
There are several limitations to this study. Although the NCDB is comprehensive for oncological variables and outcomes, large database studies have some inherent limitations. Due to the nature of this database, there may be some errors present in coding. Furthermore, the data is retrospective and incomplete in some areas. The NCDB does not capture smoking status and alcohol consumption and therefore we were unable to control for these known risk factors. Another limitation is our inability to estimate true distances traveled to receive care since the crowfly variable in NCDB represents a straight line. Future prospective studies should incorporate smoking and alcohol consumption, as well as true distance traveled to better evaluate the effects of race and rural-urban context on HNC patient outcomes.
We used the NCDB database, which allows for a large, generalizable study with a diverse population. With this large sample size, we can study the cross-classification of rural-urban context with race. Our results demonstrate a disparity in survival in the context of rural-urban context with black rural patients exhibiting the worst survival followed by black urban, white rural, and white urban patients. In order to enhance survival among burdened HNC patients, further studies should be performed in small community settings to determine effective and specific solutions.
Supplementary Material
Figure 2: Kaplan-Meier Curve Stratified by Race and Rural-Urban Status.
This figure depicts survival probability over five years from the original date of diagnosis. Black rural patients are depicted by a dotted, black line (n=2,272), black urban patients by a solid, black line (n=16,053), white rural by a dotted, gray line (n=101,028), and white urban by a solid, gray line (n=26,903).
Acknowledgements
We make no additional acknowledgements.
Footnotes
The authors declare no potential conflicts of interest.
References
- 1.Cancer statistics, 2020 - Siegel - 2020 - CA: A Cancer Journal for Clinicians - Wiley Online Library. Accessed February 4, 2020. https://acsjournals.onlinelibrary.wiley.com/doi/full/10.3322/caac.21590 [Google Scholar]
- 2.Fakhry C, Krapcho M, Eisele DW, D’Souza G. Head and Neck Squamous Cell Cancers in the United States are Rare and Risk is Now Higher Among Whites than Blacks for the First Time. Cancer. 2018;124(10):2125–2133. doi: 10.1002/cncr.31322 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Daraei P, Moore CE. Racial Disparity Among the Head and Neck Cancer Population. J Cancer Educ. 2015;30(3):546–551. doi: 10.1007/s13187-014-0753-4 [DOI] [PubMed] [Google Scholar]
- 4.Groome PA, Schulze KM, Keller S, Mackillop WJ. Demographic Differences between Cancer Survivors and those who Die Quickly of their Disease. Clin Oncol. 2008;20(8):647–656. doi: 10.1016/j.clon.2008.05.006 [DOI] [PubMed] [Google Scholar]
- 5.Kim JD, Firouzbakht A, Ruan JY, et al. Urban and rural differences in outcomes of head and neck cancer: Urban vs. Rural Head and Neck Cancer Survival. The Laryngoscope. 2018;128(4):852–858. doi: 10.1002/lary.26836 [DOI] [PubMed] [Google Scholar]
- 6.Allareddy V, Konety BR. Characteristics of patients and predictors of in-hospital mortality after hospitalization for head and neck cancers: Mortality after Head and Neck CA. Cancer. 2006;106(11):2382–2388. doi: 10.1002/cncr.21899 [DOI] [PubMed] [Google Scholar]
- 7.Zhang H, Dziegielewski PT, Jean Nguyen TT, et al. The effects of geography on survival in patients with oral cavity squamous cell carcinoma. Oral Oncol. 2015;51(6):578–585. doi: 10.1016/j.oraloncology.2015.03.012 [DOI] [PubMed] [Google Scholar]
- 8.Zuniga SA, Lango MN. Effect of rural and urban geography on larynx cancer incidence and survival. The Laryngoscope. 2018;128(8):1874–1880. doi: 10.1002/lary.27042 [DOI] [PubMed] [Google Scholar]
- 9.Totadri S, Trehan A, Kaur A, Bansal D. Effect of socio-economic status & proximity of patient residence to hospital on survival in childhood acute lymphoblastic leukaemia. Indian J Med Res. 2019;149(1):26–33. doi: 10.4103/ijmr.IJMR_579_17 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Farquhar DR, Masood MM, Lenze NR, et al. Travel time to provider is associated with advanced stage at diagnosis among low income head and neck squamous cell carcinoma patients in North Carolina. Oral Oncol. 2019;89:115–120. doi: 10.1016/j.oraloncology.2018.12.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Gaubatz ME, Bukatko AR, Simpson MC, et al. Racial and socioeconomic disparities associated with 90-day mortality among patients with head and neck cancer in the United States. Oral Oncol. 2019;89:95–101. doi: 10.1016/j.oraloncology.2018.12.023 [DOI] [PubMed] [Google Scholar]
- 12.Lenze NR, Farquhar DR, Mazul AL, Masood MM, Zevallos JP. Racial disparities and human papillomavirus status in oropharyngeal cancer: A systematic review and meta-analysis. Head Neck. 2019;41(1):256–261. doi: 10.1002/hed.25414 [DOI] [PubMed] [Google Scholar]
- 13.National Cancer Database: Tools, Reports, and Resources . American College of Surgeons. Accessed December 17, 2019. https://www.facs.org/~/media/files/quality%20programs/cancer/ncdb/ncdb_2016_booklet_v11.ashx [Google Scholar]
- 14.Worsham MJ, Stephen JK, Chen KM, et al. Improved Survival with HPV among African Americans with Oropharyngeal Cancer. Clin Cancer Res. 2013;19(9):2486–2492. doi: 10.1158/1078-0432.CCR-12-3003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ragin C, Liu JC, Jones G, et al. Prevalence of HPV Infection in Racial-Ethnic Subgroups of Head and Neck Cancer Patients. Carcinogenesis. 2017;38(2):218–229. doi: 10.1093/carcin/bgw203 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.USDA ERS - Rural-Urban Continuum Codes. Accessed July 23, 2019. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/ [Google Scholar]
- 17.Therneau TM. A Package for Survival Analysis in S.; 2015. https://CRAN.R-project.org/package=survival [Google Scholar]
- 18.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2019. https://www.R-project.org/ [Google Scholar]
- 19.Kassambara A, Kosinski M, Biecek P. Survminer: Drawing Survival Curves Using “Ggplot2.”; 2019. https://CRAN.R-project.org/package=survminer [Google Scholar]
- 20.Uno H, Tian L, Cronin A, Battioui C, Horiguchi M. SurvRM2: Comparing Restricted Mean Survival Time.; 2017. https://CRAN.R-project.org/package=survRM2 [Google Scholar]
- 21.Rural residence and cancer outcomes in the United States: issues and challenges. - PubMed - NCBI. Accessed February 4, 2020. https://www.ncbi.nlm.nih.gov/pubmed/24097195 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Koivunen P, Rantala N, Hyrynkangas K, Jokinen K, Alho O-P. The impact of patient and professional diagnostic delays on survival in pharyngeal cancer. Cancer. 2001;92(11):2885–2891. doi: [DOI] [PubMed] [Google Scholar]
- 23.Gourin CG, Podolsky RH. Racial Disparities in Patients With Head and Neck Squamous Cell Carcinoma: The Laryngoscope. 2006;116(7):1093–1106. doi: 10.1097/01.mlg.0000224939.61503.83 [DOI] [PubMed] [Google Scholar]
- 24.Mahal BA, Inverso G, Aizer AA, Bruce Donoff R, Chuang S-K. Impact of African-American race on presentation, treatment, and survival of head and neck cancer. Oral Oncol. 2014;50(12):1177–1181. doi: 10.1016/j.oraloncology.2014.09.004 [DOI] [PubMed] [Google Scholar]
- 25.Moore RJ, Doherty DA, Do K-A, Chamberlain RM, R. Khuri F. Racial Disparity in Survival of Patients with Squamous Cell Carcinoma of the Oral Cavity and Pharynx. Ethn Health. 2001;6(3-4):165–177. doi: 10.1080/13557850120078099 [DOI] [PubMed] [Google Scholar]
- 26.Ragin CC, Langevin SM, Marzouk M, Grandis J, Taioli E. Determinants of head and neck cancer survival by race. Head Neck. 2011;33(8):1092–1098. doi: 10.1002/hed.21584 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Income Inequality and the Persistence of Racial Economic Disparities ∣ Sociological Science. Accessed February 4, 2020. https://www.sociologicalscience.com/articles-v5-8-182/ [Google Scholar]
- 28.Health Insurance Literacy: Disparities by Race, Ethnicity, and Language Preference - PubMed. Accessed February 4, 2020. https://pubmed.ncbi.nlm.nih.gov/30875174-health-insurance-literacy-disparities-by-race-ethnicity-and-language-preference/?from_term=health+insurance+racial+difference&from_pos=9 [PubMed] [Google Scholar]
- 29.Blumberg LJ, Holahan J, Karpman M, Elmendorf C. Characteristics of the Remaining Uninsured: An Update. Urban Institute. Published July 12, 2018. Accessed February 5, 2020. https://www.urban.org/research/publication/characteristics-remaining-uninsured-update [Google Scholar]
- 30.Mollica MA, Weaver KE, McNeel TS, Kent EE. Examining urban and rural differences in perceived timeliness of care among cancer patients: A SEER-CAHPS study. Cancer. 2018;124(15):3257–3265. doi: 10.1002/cncr.31541 [DOI] [PubMed] [Google Scholar]
- 31.Horn K Cutting Tobacco’s Rural Roots: Tobacco Use in Rural Communities. Accessed February 21, 2020. https://www.lung.org/assets/documents/research/cutting-tobaccos-rural-roots.pdf [Google Scholar]
- 32.Caraballo RS, Sharapova S, Asman KJ. Differences in Current Cigarette Smoking Between Non-Hispanic Whites and Non-Hispanic Blacks by Gender and Age Group, United States, 2001 – 2013. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2016;18(Suppl 1):S41–S48. doi: 10.1093/ntr/ntv150 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Agunwamba AA, Kawachi I, Williams DR, Finney Rutten LJ, Wilson PM, Viswanath K. Mental Health, Racial Discrimination, and Tobacco Use Differences Across Rural-Urban California. J Rural Health Off J Am Rural Health Assoc Natl Rural Health Care Assoc. 2017;33(2):180–189. doi: 10.1111/jrh.12182 [DOI] [PubMed] [Google Scholar]
- 34.Lundeen EA. Obesity Prevalence Among Adults Living in Metropolitan and Nonmetropolitan Counties — United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67. doi: 10.15585/mmwr.mm6723a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Rural Health Reform Policy Research Center. Accessed February 5, 2020. https://ruralhealth.und.edu/projects/health-reform-policy-research-center [Google Scholar]
- 36.Boakye EA, Osazuwa-Peters N, Chen B, et al. Multilevel Associations Between Patient- and Hospital-Level Factors and In-Hospital Mortality Among Hospitalized Patients With Head and Neck Cancer. JAMA Otolaryngol Neck Surg. 2020;146(5):444–454. doi: 10.1001/jamaoto.2020.0132 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Divi V, Ma Y, Rhoads KF. Regional variation in head and neck cancer mortality: Role of patient and hospital characteristics. Head Neck. 2016;38(S1):E1896–E1902. doi: 10.1002/hed.24343 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.