Abstract
Objective
To evaluate how patient circumstances that may influence access to care, including area deprivation index (ADI), distance to care center, and rural/urban status affect stage of presentation for HNSCC.
Study Design
Retrospective, single‐institution study.
Setting
Urban, academic tertiary care center.
Methods
Patients with a history of HNSCC of the oral cavity, oropharynx, hypopharynx, larynx who presented to our institution between 2018 and 2023 were included. Demographic factors including age, race, sex, ADI, insurance status, distance to tertiary care center, and rural‐urban continuum codes were collected. Primary outcome of interest was clinical stage at presentation.
Results
1170 patients met inclusion criteria, of which 675 (57.7%) presented with early‐stage disease and 495 (42.3%) presented with late‐stage disease. On multivariable analysis, closer proximity to care center, insurance status, current or former smoking status, p16 negativity, and tumor subsite of the oral cavity or hypopharynx were associated with late‐stage diagnosis. After sub‐stratification for patients within the closest distance quartile, female sex, p16 negativity, non‐white and non‐black race, and insurance status remained associated with late‐stage disease presentation.
Conclusion
Patient characteristics often associated with socioeconomic disadvantage including minority race, Medicaid, or lack of insurance were associated with a later stage of presentation for HNSCC. Individuals living within the closest distance to our tertiary care center were more likely to present with late‐stage disease, highlighting the need for improved access to care for socioeconomically disadvantaged urban populations.
Keywords: head and neck cancer, health care disparities, geographic location, neighborhood deprivation index, stage of presentation
Head and neck squamous cell carcinoma (HNSCC) is a complex and heterogeneous malignancy that poses a significant global health burden, with an incidence rate of 890,000 worldwide. 1 Despite advances in diagnostic and therapeutic strategies, outcomes for patients with HNSCC remain largely dependent on the stage at diagnosis, with late‐stage presentation being associated with poorer prognosis and reduced survival. 2 Identifying factors that influence stage at diagnosis is critical to improve early detection rates and develop targeted interventions.
Data has shown that demographic and socioeconomic factors such as sex, race, smoking and alcohol history, neighborhood deprivation index, access to care, and rural/urban residence can predict outcomes across various cancer types, including head and neck cancer (HNC). 3 , 4 , 5 , 6 , 7 , 8 The impact of sociodemographic factors on cancer stage at diagnosis has been explored in various cancer types including breast, lung, colorectal, cervical, and prostate. 9 , 10 , 11 , 12 These studies have suggested that certain patient populations, including ethnic minorities, those of lower socioeconomic class, and the uninsured are at greater risk of advanced‐stage cancer diagnosis and represent vulnerable groups who potentially face significant barriers to early detection and timely treatment. The specific interplay between demographic, socioeconomic, and geographic factors and stage at diagnosis in HNSCC remains relatively understudied, often with conflicting results. Further research is needed to identify at‐risk populations and their unique barriers to care. Understanding these barriers can inform targeted interventions to reduce disparities and improve cancer outcomes.
In this study, we retrospectively reviewed a large cohort of patients diagnosed with HNSCC at a single, metropolitan, tertiary care center to investigate the demographic and socioeconomic factors associated with stage at diagnosis in an urban setting. By identifying predictors of late‐stage presentation, we aim to guide strategies and interventions for addressing disparities in HNSCC care.
Methods
Patient Cohort
Electronic medical records (EMR) of patients who presented to our tertiary care center between 2018 and 2023 with head and neck squamous cell carcinoma (HNSCC) were reviewed. Patients were included if they had a confirmed diagnosis of HNSCC of the oropharynx, oral cavity, larynx, or hypopharynx, and available data to determine their American Joint Committee on Cancer (AJCC) 8th Edition clinical stage upon presentation. This study was approved by the Thomas Jefferson University Institutional Review Board (IRB).
Demographics and Measures of Socioeconomic Status
Basic demographics including age, sex, race (White/Caucasian, Black/African American, and Other), and insurance status were collected. Social factors such as smoking and alcohol history (never, current, and former) were reported.
Area deprivation was measured using the Area Deprivation Index (ADI). This is a validated measure of socioeconomic neighborhood deprivation that is measured as a national percentile between 0 and 100 (higher scores indicate higher deprivation) based on factors such as poverty, housing, employment, and education. 13 , 14 The ADI was derived by entering a patient's home zip code into a publicly available website sponsored by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health. 15 Distance to the tertiary care center was calculated as the distance (in miles) between the patient's home zip code and the zip code of our tertiary care center. ADI and distance to our tertiary care center were stratified into quartiles within this patient population. Rural‐urban continuum codes (RUCC) from the US Department of Agriculture were used to characterize the rural or urban composition of the residential county for each patient based on home zip code. 16 The RUCC categories are defined as follows: (1) counties in metro areas of 1 million population or more, (2) counties in metro areas of 250,000 population to 1 million population, (3) counties in metro areas of fewer than 250,000 population, (4) urban population of 20,000 or more, adjacent to a metro area, (5) urban population of 20,000 or more, not adjacent to a metro area, (6) urban population of 2500 to 19,999, adjacent to a metro area, (7) urban population of 2500 to 19,999, not adjacent to a metro area, (8) completely rural or less than 2500 urban population, adjacent to a metro area and (9) completely rural or less than 2500 urban population, not adjacent to a metro area. 17 RUCC was categorized into 3 groups: large metropolitan areas (RUCC 1), small metropolitan areas (RUCC 2,3), and nonmetropolitan or rural areas (RUCC 4‐9). Year of presentation was clustered into 2 groups (2018‐2019, 2020‐2023) to determine the effect of the COVID‐19 pandemic on stage of presentation.
Outcome Variable
The primary outcome variable was HNSCC stage at diagnosis, which was grouped into 2 categories based on 8th Edition of the American Joint Committee on Cancer (AJCC) clinical Staging of Head and Neck Cancer: Early‐stage (I/II) and Late‐stage (III/IV).
Statistical Analysis
Numerical variables were reported as mean (standard deviation) and categorical variables were reported as frequency (percent). Chi‐square and Wilcoxon rank sum tests were used to compare categorical and continuous variables, respectively, between patients who presented with early‐stage and late‐stage HNSCC. Demographic, socioeconomic, social, and clinical factors that were significantly different between the 2 cohorts on univariable analysis were subsequently included in a multivariable binary logistic regression analysis that was performed to identify independent predictors of late‐stage presentation. Based on the results of this analysis, a separate subgroup analysis was performed separately based on p16 status and for patients within the first quartile for distance to our tertiary care center. An α level of 0.05 was used for all tests. All statistical analyses were performed using R Studio Version 2023.03.1.
Results
Demographics, Socioeconomic, and Baseline Characteristics
1170 patients met inclusion criteria, of which 675 (57.7%) presented with early‐stage disease and 495 (42.3%) presented with late‐stage disease. The average age across the cohort was 65.5 years (SD = 11.4). Patients with early‐stage disease were more likely to be male (81% vs 69%, P < .001), White/Caucasian (89% vs 79%, P < .001), never smoke (38% vs 17%, P < .001), and never drink alcohol (72% vs 62%, P < .001). A greater proportion of patients with early‐stage disease had p16+ HNSCC (83% vs 22%) and disease of the oropharynx (67% vs 21%, P < .001). Patients presenting with late‐stage disease had a greater national ADI percentile compared to those who presented with early‐stage disease (48.6 vs 42.3, P < .001). Percentile quartiles of 25th, 50th, 75th, and 100th, for distance to tertiary care center were defined at 10.4, 19.9, 48.4, and 938.4 miles, respectively, and were unevenly distributed between patients with early‐stage and late‐stage disease (P = .007). Among the entire population, 855 (73%) patients came from large metropolitan areas, whereas 250 (21%) came from small metropolitan and 65 (5.5%) came from nonmetro or rural areas, respectively. There was no difference in RUCC distribution between those with early and late‐stage presentation. Patients with private insurance more frequently presented with early‐stage disease (N = 331, 49%) compared to those with Medicaid, Medicare, or no insurance (Table 1). There was no significant difference in the percentage of patients presenting with late‐stage disease before (2018‐2019, 42%) the COVID‐19 era versus during (2020‐2023, 42%) the COVID‐19 era.
Table 1.
Univariate Analysis of Demographic Characteristics Between Patients With Early Stage (I/II) and Late Stage (III/IV) Presentation
| Characteristic | N | Overall N = 1170a | Early Stage (I/II) N = 675 | Late Stage (III/IV) N = 495 | P‐valueb |
|---|---|---|---|---|---|
| Age | 1170 | 65.50 (11.40) | 65.11 (11.28) | 66.03 (11.55) | .15 |
| Gender | 1170 | <.001 | |||
| Female | 282 (24%) | 131 (19%) | 151 (31%) | ||
| Male | 887 (76%) | 543 (81%) | 344 (69%) | ||
| Race | 1170 | <.001 | |||
| White | 992 (85%) | 600 (89%) | 392 (79%) | ||
| Black | 106 (9.1%) | 47 (7.0%) | 59 (12%) | ||
| Other | 7.2 (6.2%) | 28 (4.1%) | 44 (8.9%) | ||
| ADI (avg, SD) | 1100 | 44.95 (23.26) | 42.25 (22.84) | 48.59 (23.36) | <.001 |
| Distance | 1170 | .007 | |||
| Q1 | 293 (25%) | 144 (21%) | 149 (30%) | ||
| Q2 | 293 (25%) | 179 (27%) | 114 (23%) | ||
| Q3 | 293 (25%) | 172 (25%) | 120 (24%) | ||
| Q4 | 293 (25%) | 180 (27%) | 112 (23%) | ||
| RUCC | 1170 | ||||
| 1 | 855 (73%) | 483 (72%) | 372 (75%) | .2 | |
| 2‐3 | 250 (21%) | 128 (19%) | 94 (19%) | ||
| 4‐9 | 64 (5.5%) | 290 (43%) | 29 (5.9%) | ||
| Insurance | 1161 | ||||
| Private | 496 (43%) | 331 (49%) | 165 (34%) | <.001 | |
| Medicare | 607 (52%) | 328 (49%) | 279 (57%) | ||
| Medicaid | 36 (31%) | 7 (1.0%) | 29 (5.9%) | ||
| Uninsured | 22 (1.9%) | 6 (0.9%) | 16 (3.3%) | ||
| Smoking | 1167 | <.001 | |||
| Never | 342 (29%) | 256 (38%) | 86 (17%) | ||
| Current | 324 (28%) | 128 (19%) | 196 (40%) | ||
| Former | 501 (43%) | 290 (43%) | 211 (43%) | ||
| Heavy EtOH | 1163 | <.001 | |||
| Never | 786 (68%) | 480 (72%) | 306 (62%) | ||
| Current | 317 (27%) | 170 (25%) | 147 (30%) | ||
| Former | 60 (5.2%) | 21 (3.1%) | 39 (7.9%) | ||
| Subsite | 1170 | <.001 | |||
| Oral cavity | 305 (26%) | 99 (15%) | 207 (42%) | ||
| Oropharynx | 556 (48%) | 452 (67%) | 104 (21%) | ||
| Hypopharynx | 63 (5.4%) | 5 (0.7%) | 58 (12%) | ||
| Larynx | 245 (21%) | 119 (18%) | 126 (25%) | ||
| P16 status | 890 | <.001 | |||
| Negative | 364 (41%) | 95 (17%) | 269 (78%) | ||
| Positive | 526 (59%) | 449 (83%) | 77 (22%) |
Mean (SD); n (%).
Wilcoxon rank sum test; Pearson's Chi‐squared test.
Multivariable Logistic Regression to Identify Predictors of Late‐Stage Presentation
On multivariable logistic regression, Medicaid insurance (aHR 14.7, 95% CI 4.00, 64.9), lack of insurance (aHR 5.54, 95% CI 1.37, 25.7), current smoking status (aHR 3.79, 95% CI 2.16‐6.6.77), former smoking status (aHR 2.46, 95% CI 1.45‐4.23) disease of the oral cavity (aHR 3.28, 95% CI 1.78‐6.06), and disease of the hypopharynx (aHR 22.1, 95% CI 7.59, 83.7) were associated with late‐stage diagnosis. Greater distance to tertiary care center (aHR 0.83, 95% CI 0.70, 1.00) and p16+ disease (aHR 0.14, 95% CI 0.08‐0.23) were associated with reduced odds of late‐stage disease (Table 2).
Table 2.
Predictors of Late‐Stage Presentation on Multivariate Analysis
| Characteristic | aHR | 95% CI | P‐value |
|---|---|---|---|
| Gender | |||
| Female | ‐ | ‐ | |
| Male | 1.05 | 0.66, 1.67 | .8 |
| Race | <.001 | ||
| White | ‐ | ‐ | |
| Black | 0.88 | 0.43, 1.79 | .7 |
| Other | 1.75 | 0.78, 4.04 | .2 |
| ADI (avg, SD) | 1.01 | 1.00, 1.02 | .11 |
| Distance Quartile (increasing) | 0.83 | 0.70, 1.00 | .04 |
| Insurance | |||
| Private | ‐ | ‐ | |
| Medicaid | 14.7 | 4.00, 64.9 | <.001 |
| Medicare | 1.34 | 0.90, 1.99 | .20 |
| Uninsured | 5.54 | 1.37, 25.7 | .02 |
| Smoking | |||
| Never | ‐ | ‐ | |
| Current | 3.79 | 2.16, 6.77 | <.001 |
| Former | 2.46 | 1.45, 4.23 | <.001 |
| Heavy EtOH | |||
| Never | ‐ | ‐ | |
| Current | 1.18 | 0.77, 1.81 | .5 |
| Former | 2.19 | 0.98, 5.02 | .061 |
| Subsite | <.001 | ||
| Oropharynx | ‐ | ‐ | ‐ |
| Oral Cavity | 3.28 | 1.78, 6.06 | <.001 |
| Hypopharynx | 22.2 | 7.59, 83.7 | <.001 |
| Larynx | 1.29 | 0.70, 2.36 | .4 |
| P16 status | |||
| Negative | ‐ | ||
| Positive | 0.14 | 0.08, 0.23 | <.001 |
Abbreviations: aHR, odds ratio; CI, confidence interval.
Multivariable logistic regression was repeated separately for patients with p16‐positive and p16‐negative disease, respectively. Among patients with p16+ disease, other race (aHR 5.30, 95% CI 1.75, 16.4), higher ADI national percentile (aHR 1.01, 95% CI 1.00, 1.03), current smoking status (aHR 5.00, 95% CI 2.45,10.6), and former smoking status (aHR 2.29, 95% CI 1.17, 4.67) were associated with late‐stage diagnosis. For patients with p16‐ disease, greater distance to tertiary care center was associated with lower odds of late‐stage presentation (aHR 0.70, 95% CI 0.56, 0.87).
Predictors of Late‐Stage Presentation for Patients Within the First Distance Quartile
Given the apparent association between closer distance to our tertiary care center and late‐stage disease, a subgroup analysis was performed to identify risk factors for late‐stage presentation among patients within the first distance quartile (Tables 3 and 4). On univariable analysis, female sex (35% vs 14%, P < .001), other race (15% vs 4.9%, P = .003), higher average ADI (57.4 vs 47.6, P = .001), Medicare, Medicaid, or uninsured status (P < .001) were associated with late‐stage disease. In contrast, p16‐positive and oropharyngeal tumors were associated with an earlier presentation (Table 3). On multivariable analysis, other race (aHR 5.68, 95% CI 1.33, 33.7) and current smoking status (aHR 3.16, 95% CI 1.10, 9.28) were associated with increased odds of late‐stage disease. Male sex (aHR 0.31, 95% CI 0.11, 0.81), p16‐positive tumors (aHR 0.10, 95% CI 0.05, 0.22), and private or Medicare insurance (aHR 0.04, 95% CI 0.00, 0.25) were, in contrast, associated with reduced odds of late‐stage disease. ADI was not significantly associated with stage of diagnosis in this cohort (Table 4). Tumor subsite was removed from the final multivariable model due to model non‐convergence and infinite confidence intervals for patients with hypopharyngeal tumors.
Table 3.
Univariate Analysis of Demographic Characteristics Between Patients With Early Stage (I/II) and Late Stage (III/IV) Presentation for Patients Within the First Quartile for Distance to Tertiary Care Center
| Characteristic | N | Overall N = 293a | Early Stage (I/II) N = 144 | Late Stage (III/IV) N = 149 | P‐valueb |
|---|---|---|---|---|---|
| Age | 293 | 65.10 (10.28) | 64.69 (9.46) | 65.50 (11.04) | .3 |
| Gender | 292 | <.001 | |||
| Female | 72 (25%) | 20 (14%) | 52 (35%) | ||
| Male | 220 (75%) | 128 (86%) | 98 (65%) | ||
| Race | 293 | .003 | |||
| White | 195 (67%) | 108 (75%) | 87 (58%) | ||
| Black | 69 (24%) | 29 (20%) | 40 (27%) | ||
| Other | 29 (9.9%) | 7 (4.9%) | 22 (15%) | ||
| ADI (avg, SD) | 276 | 52.62 (25.42) | 47.62 (25.83) | 57.40 (24.16) | .001 |
| Insurance | 290 | <.001 | |||
| Private | 113 (39%) | 73 (51%) | 40 (27%) | ||
| Medicare | 153 (53%) | 67 (47%) | 86 (59%) | ||
| Medicaid | 15 (5.2%) | 2 (1.4%) | 13 (8.9%) | ||
| Uninsured | 9 (3.1%) | 2 (1.4%) | 7 (4.8%) | ||
| Smoking | 292 | <.001 | |||
| Never | 81 (28%) | 55 (38%) | 26 (18%) | ||
| Current | 93 (32%) | 26 (18%) | 67 (45%) | ||
| Former | 118 (40%) | 63 (44%) | 55 (37%) | ||
| Heavy EtOH | 293 | .4 | |||
| Never | 186 (63%) | 93 (65%) | 93 (62%) | ||
| Current | 80 (27%) | 41 (28%) | 39 (26%) | ||
| Former | 27 (9.2%) | 10 (6.9%) | 17 (11%) | ||
| Subsite | 293 | <.001 | |||
| Oral Cavity | 60 (20%) | 22 (15%) | 38 (26%) | ||
| Oropharynx | 132 (45%) | 92 (64%) | 40 (27%) | ||
| Hypopharynx | 25 (8.5%) | 1 (0.7%) | 24 (16%) | ||
| Larynx | 76 (26%) | 29 (20%) | 47 (32%) | ||
| P16 status | 235 | <.001 | |||
| Negative | 120 (51%) | 24 (21%) | 96 (79%) | ||
| Positive | 116 (49%) | 91 (79%) | 25 (21%) |
Mean (SD); n (%).
Wilcoxon rank sum test; Pearson's Chi‐squared test.
Table 4.
Predictors of Late‐Stage Presentation on Multivariate Analysis (for Patients Within First Distance Quartile)
| Characteristic | aHR | 95% CI | P‐value |
|---|---|---|---|
| Gender | |||
| Female | ‐ | ‐ | ‐ |
| Male | 0.31 | 0.11, 0.81 | .020 |
| Race | |||
| White | ‐ | ‐ | 0 |
| Black | 1.34 | 0.55, 3.32 | .5 |
| Other | 5.68 | 1.33, 33.7 | .032 |
| ADI (avg, SD) | 1.00 | 0.98, 1.01 | .8 |
| Smoking | |||
| Never | ‐ | ‐ | ‐ |
| Current | 3.16 | 1.10, 9.28 | .033 |
| Former | 1.97 | 0.78, 5.17 | .2 |
| Insurance | |||
| Medicaid/Uninsured | ‐ | ‐ | ‐ |
| Private/Medicare | 0.04 | 0.00, 0.25 | .004 |
| P16 status | |||
| Negative | ‐ | ‐ | ‐ |
| Positive | 0.10 | 0.05, 0.22 | <.001 |
Abbreviations: aHR, odds ratio; CI, confidence interval.
Discussion
To our knowledge, this study represents the largest single‐institution study to date that evaluates the role of both patient and neighborhood‐level sociodemographic factors on the initial stage of presentation of all site HNSCC. Several expected patient and disease‐related characteristics were associated with a late stage of diagnosis, as patients who were former smokers/drinkers or had oral cavity, hypopharynx and/or p16‐negative tumors presented with more advanced disease. Furthermore, sociodemographic factors including minority race, Medicaid insurance and lack of insurance, and closer travel distance were associated with late‐stage disease. Here, we add to the growing body of literature that demonstrates socioeconomic disadvantage, both at an individual and community level, is associated with late‐stage diagnosis, and consequently worse outcomes, for HNSCC.
Advanced stage at presentation is a well‐established, negative prognostic factor for HNSCC. 2 Social disparities are widely prevalent among the HNSCC patient population and often limit access to health care from initial diagnosis through treatment. 18 Social determinants of health can negatively impact timely cancer care, patient quality of life, and overall survival. 18 , 19 , 20 Numerous studies have demonstrated that low socioeconomic status is associated with a later stage of diagnosis and worse overall survival for HNSCC. 21 , 22 , 23 , 24 , 25 , 26 , 27 The definition of socioeconomic status, however, varies greatly between studies and often relies on proxies such as race, education level, and insurance status. Likewise, in our study, several factors typically associated with a vulnerable patient population, including minority race, lack of insurance, or Medicaid were associated with a late stage of diagnosis. The ADI, on the other hand, is a composite measure of neighborhood‐level disadvantage that may provide a more comprehensive approach to understanding health care disparities. 15 , 28 ADI captures several critical measures of socioeconomic disadvantage across the domains of income, education, employment, and housing quality associated with a given neighborhood. Few studies to date have correlated ADI with delayed HNSCC diagnosis and treatment. A recent study by Malik et al found patients with a higher ADI were more likely to present with late‐stage nasopharyngeal carcinoma and subsequently, poor local control and decreased overall survival. 21 In contrast, although univariate analysis in our cohort showed a trend towards later stage presentation among patients with higher ADI, this association was not statistically significant in multivariable analysis, both for the overall cohort and within the closest distance quartile.
Individuals from disadvantaged neighborhoods may face substantial obstacles to care including (but not limited to) language barriers, low education level, poor health care literacy, decreased number of health care providers in their area and lack of available transportation to treating facilities. However, the ADI is inherently limited by a lack of granularity on an individual level, particularly in heterogenous urban neighborhoods where substantial variation in access to care may exist within the same neighborhood. Individual‐level factors such as race, insurance status, health care literacy, and transportation access may play a more substantial role than neighborhood‐level deprivation alone. As such, the ADI may better serve as a population‐level risk marker of socioeconomic disadvantage rather than a surrogate for individual or community‐level deprivation in a single‐institution urban setting.
There is conflicting evidence in the literature regarding the impact of geographic location, including rural/urban status and access to tertiary care centers on head and neck cancer diagnosis and treatment. 24 , 25 , 29 , 30 , 31 , 32 Prior research has demonstrated an increased incidence of HNSCC in urban populations, often attributed to increased tobacco and alcohol use. 33 Despite this, multiple studies have demonstrated an association between increased travel time and greater distance to academic care centers with an increased risk of late‐stage diagnosis, treatment delay, and worse survival outcomes. 24 , 29 , 30 In line with these findings, rurality has been associated with advanced‐stage diagnosis, often attributed to increased travel time and lack of access to tertiary care providers. 34 However, a recent systematic review found conflicting evidence regarding the role of rurality and HNSCC outcomes with 2 studies demonstrating a correlation between rurality and late‐stage diagnosis, 3 studies with urban status and late‐stage diagnosis, and 3 studies with no correlation between rural/urban status and stage at diagnosis. 32 In contrast, our results differ from the existing literature in that living within a shorter distance to our tertiary care center was associated with advanced stage at diagnosis on multivariate analysis, suggesting an increased vulnerability for urban patients directly adjacent to our center. The largely metropolitan composition of our patient population, however, limits our ability to analyze the effect of rural/urban status, and as such our results may not be generalizable to centers with a larger rural patient population.
These findings must be interpreted within the context of our distinct location in Philadelphia, Pennsylvania. Our tertiary care center is based in a major metropolitan city that faces some of the highest poverty rates (25.8%) in the country. 35 , 36 There are vast differences in socioeconomic status among neighborhoods, with poverty rates approaching 40% in the area directly adjacent to our care center. Subgroup analysis for patients within the closest distance to our tertiary care center showed that the average ADI was 52.6 as compared to 45.0 for the entire patient population, highlighting the degree of socioeconomic disadvantage directly adjacent to our care center. Despite the high density of tertiary care centers present in Philadelphia, decreased access to healthcare for its residents has been consistently documented. Lack of health care literacy, language barriers, housing insecurity, and limited access to transportation disproportionately affects socioeconomically disadvantaged populations within the city. 37 , 38 , 39 Our results support these findings; and highlight that despite proximity to healthcare systems, many patients face insurmountable barriers to adequate cancer diagnosis and treatment.
As demonstrated by our results, barriers to cancer diagnosis and treatment often begin upstream, where systemic inequalities such as poverty, racial disparities, inadequate housing, transportation, insurance coverage, and implicit biases result in poor access to healthcare. 40 These population‐level factors can have substantial downstream effects at the individual level, with high levels of healthcare mistrust and avoidance documented amongst disadvantaged populations. 41 It is critical that health care systems examine the needs of their unique patient populations to identify barriers to care and develop patient‐centered approaches to alleviate the socioeconomic, environmental, cultural, and physical barriers to care amongst their most at‐risk populations. A wide range of programs and interventions to improve access to cancer care have been evaluated. A recent systematic review identified 7 main types of interventions to mitigate barriers to cancer care, including patient navigation, education, virtual health, service‐redesign, financial support and improving geographic accessibility. 42 Their results suggest that several of these interventions may improve access to care, particularly at the screening and diagnosis stage. However, only 1 study evaluated interventions for head and neck cancer patients specifically. Most research on improving access to care for HNSCC has focused on screening and early detection programs. A 2023 systematic review of HNC screening programs found evidence of improved survival for oral cavity and nasopharyngeal cancer 43 ; however, most studies were based in Asia, where disease prevalence is high. Results of screening programs for HNC in the US are mixed, where knowledge about HNC and its associated risk factors amongst the general population are low. 44 A retrospective cohort study of an annual HNC screening clinic over 10 years found that only 2.5% of individuals presented with findings suspicious for malignancy, and 0.2% had a diagnosis confirmed on biopsy. 45 Further, only 0.7% of their participants fell below the poverty line. Recent focus has shifted to screening in high‐risk populations such as those identified in our study, where late‐stage presentation is more common. Studies have shown that community‐based recruitment efforts, rather than hospital‐based, are more successful at recruiting patients of low socioeconomic status and those with significant smoking and/or drinking history. 46 , 47 However, poor follow‐up is consistently documented amongst participants, particularly those in the higher‐risk category. 47 Overall, community‐based screening approaches may be more effective at early detection and diagnosis for high‐risk populations, however, further efforts are needed to identify and eliminate barriers to subsequent follow‐up.
There are limitations to this study. First, there are limitations inherent to the retrospective nature including selection bias, data availability, and inability to control all confounding variables. Further, our results are limited to a single academic institution which may be impacted by regional competition and may not be representative of patients presenting to other institutions. The largely urban population of our study also may limit the generalizability of our results to other non‐urban centers. However, our results provide important insight into how sociodemographic factors influence the stage of presentation for HNSCC in an urban setting.
Conclusion
At our urban‐based academic center, patient‐related sociodemographic factors and proximity to our care center influenced the stage of presentation for HNSCC. It is critical to understand disparities unique to a given health care system to develop targeted interventions. Community‐based screenings and educational events in high‐risk populations may help improve outreach, however, further research is necessary to improve subsequent health care follow up, timely diagnosis, and cancer treatment.
Author Contributions
Kelly Bridgham, concept/design, data acquisition, data analysis and interpretation, manuscript preparation and approval; Praneet Kaki, data acquisition, data analysis and interpretation, manuscript preparation; Annie Moroco, concept/design, data analysis and interpretation, manuscript preparation and approval; Bryce Hambach, data acquisition, data analysis and interpretation, manuscript preparation; Eric Mastrolonardo, concept/design, data analysis and interpretation, manuscript preparation and approval; David Cognetti, concept/design, data analysis and interpretation, manuscript preparation and approval; Meghan Crippen, concept/design, data analysis and interpretation, manuscript preparation and approval; Arielle Thal, concept/design, data analysis and interpretation, manuscript preparation and approval.
Disclosures
Competing interests
None.
Funding source
None.
This article was presented at the AAO‐HNSF 2024 Annual Meeting & OTO EXPO, September 28‐October 1, 2024, Miami Beach, Florida
References
- 1. Barsouk A, Aluru JS, Rawla P, Saginala K, Barsouk A. Epidemiology, risk factors, and prevention of head and neck squamous cell carcinoma. Med Sci. 2023;11(2):42. 10.3390/medsci11020042 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Medow MA, Weed HG, Schuller DE. Simple predictors of survival in head and neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg. 2002;128(11):1282‐1286. 10.1001/archotol.128.11.1282 [DOI] [PubMed] [Google Scholar]
- 3. Le H, Ziogas A, Lipkin SM, Zell JA. Effects of socioeconomic status and treatment disparities in colorectal cancer survival. Cancer Epidemiol Biomark Prev. 2008;17(8):1950‐1962. 10.1158/1055-9965.EPI-07-2774 [DOI] [PubMed] [Google Scholar]
- 4. Sitenga JL, Aird G, Ahmed A, Walters R, Silberstein PT. Socioeconomic status and survival for patients with melanoma in the United States: an NCDB analysis. Int J Dermatol. 2018;57(10):1149‐1156. 10.1111/ijd.14026 [DOI] [PubMed] [Google Scholar]
- 5. Polani F, Hwang MW, Yu E, Wang H, Aragon‐Ching JB. Analyzing disparities in socioeconomic factors, clinical characteristics, and outcomes in bladder cancer: insights from the National Cancer Database (NCDB). J Clin Oncol. 2024;42(16_suppl):4572. 10.1200/JCO.2024.42.16_suppl.4572 [DOI] [Google Scholar]
- 6. Finke I, Behrens G, Weisser L, Brenner H, Jansen L. Socioeconomic differences and lung cancer survival—systematic review and meta‐analysis. Front Oncol. 2018;8:536. 10.3389/fonc.2018.00536 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. 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. 10.1016/j.oraloncology.2018.12.023 [DOI] [PubMed] [Google Scholar]
- 8. Choi SH, Terrell JE, Fowler KE, et al. Socioeconomic and other demographic disparities predicting survival among head and neck cancer patients. PLoS ONE. 2016;11(3):e0149886. 10.1371/journal.pone.0149886 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Yu XQ. Socioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and race. BMC Cancer. 2009;9(1):364. 10.1186/1471-2407-9-364 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Forrest LF, Sowden S, Rubin G, White M, Adams J. Socio‐economic inequalities in stage at diagnosis, and in time intervals on the lung cancer pathway from first symptom to treatment: systematic review and meta‐analysis. Thorax. 2017;72(5):430‐436. 10.1136/thoraxjnl-2016-209013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Patel A, Gantz O, Zagadailov P, Merchant AM. The role of socioeconomic disparity in colorectal cancer stage at presentation. Updat Surg. 2019;71(3):523‐531. 10.1007/s13304-019-00632-5 [DOI] [PubMed] [Google Scholar]
- 12. Schwartz KL, Crossley‐May H, Vigneau FD, Brown K, Banerjee M. Race, socioeconomic status and stage at diagnosis for five common malignancies. Cancer Causes Control. 2003;14(8):761‐766. 10.1023/A:1026321923883 [DOI] [PubMed] [Google Scholar]
- 13. Messer LC, Laraia BA, Kaufman JS, et al. The Development of a Standardized Neighborhood Deprivation Index. J Urban Health. 2006;83(6):1041‐1062. 10.1007/s11524-006-9094-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Singh GK, Lin CCC. Area deprivation and inequalities in health and health care outcomes. Ann Intern Med. 2019;171(2):131‐132. 10.7326/M19-1510 [DOI] [PubMed] [Google Scholar]
- 15. Kind AJH, Buckingham WR. Making neighborhood‐disadvantage metrics accessible—the Neighborhood Atlas. N Engl J Med. 2018;378(26):2456‐2458. 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Adamo MDL, Ruhl J. SEER Program Coding and Staging Manual 2018. Bethesda, MD: National Cancer Institute, 20892. U.S. Department of Health and Human Services National Institutes of Health National Cancer Institute; 2018.
- 17. Rural‐Urban Continuum Code—SEER Datasets . SEER. https://seer.cancer.gov/seerstat/variables/countyattribs/ruralurban.html.
- 18. Rogers SN, Lowe D, Kanatas A. Social determinants of health‐related quality of life outcomes for head and neck cancer patients. Oral. 2021;1(4):313‐325. 10.3390/oral1040031 [DOI] [Google Scholar]
- 19. Ioerger P, Mills K, Wagoner SF, et al. Inequities associated with advanced stage at presentation of head and neck cancer: a systematic review. JAMA Otolaryngol Head Neck Surg. 2024;150(8):727‐740. 10.1001/jamaoto.2024.1180 [DOI] [PubMed] [Google Scholar]
- 20. Menezes FdS, Verzinhasse Peres S, de Castro Junior G, Antunes JLF, Latorre M, Toporcov TN. Survival inequalities in head and neck cancers: a hospital‐based cohort study. Head Neck. 2023;45(9):2377‐2393. 10.1002/hed.27433 [DOI] [PubMed] [Google Scholar]
- 21. Malik NH, Montero M, Chen JJ, Sinha S, Yom SS, Chan JW. Higher area deprivation index is associated with poorer local control and overall survival in non‐metastatic nasopharyngeal carcinoma. Head Neck. 2024;46(6):1468‐1474. 10.1002/hed.27751 [DOI] [PubMed] [Google Scholar]
- 22. Brody‐Camp S, McCoul ED, Lefante JJ, Aslam R. Socioeconomic status and survival in nasopharyngeal carcinoma: a population‐based study. Laryngoscope. 2021;131(12):2719‐2723. 10.1002/lary.29702 [DOI] [PubMed] [Google Scholar]
- 23. Farquhar DR, Lenze NR, Masood MM, et al. Access to preventive care services and stage at diagnosis in head and neck cancer. Head Neck. 2020;42(10):2841‐2851. 10.1002/hed.26326 [DOI] [PubMed] [Google Scholar]
- 24. 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. 10.1016/j9oraloncology.2018.12.02 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Jafari A, Shen SA, Qualliotine JR, et al. Socioeconomic factors affect presentation stage and survival in sinonasal squamous cell carcinoma. Laryngoscope. 2021;131(11):2421‐2428. 10.1002/lary.29568 [DOI] [PubMed] [Google Scholar]
- 26. Lebo NL, Khalil D, Balram A, et al. Influence of socioeconomic status on stage at presentation of laryngeal cancer in the United States. Otolaryngol Head Neck Surg. 2019;161(5):800‐806. 10.1177/0194599819856305 [DOI] [PubMed] [Google Scholar]
- 27. Lenze NR, Farquhar D, Sheth S, et al. Socioeconomic status drives racial disparities in hpv‐negative head and neck cancer outcomes. Laryngoscope. 2021;131(6):1301‐1309. 10.1002/lary.29252 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Hassan AM, Nguyen HT, Corkum JP, et al. Area deprivation index is associated with variation in quality of life and psychosocial well‐being following breast cancer surgery. Ann Surg Oncol. 2023;30(1):80‐87. 10.1245/s10434-022-12506-z [DOI] [PubMed] [Google Scholar]
- 29. Ringstrom MJ, Christian J, Bush ML, Levy JE, Huang B, Gal TJ. Travel distance: impact on stage of presentation and treatment choices in head and neck cancer. Am J Otolaryngol. 2018;39(5):575‐581. 10.1016/j.amjoto.2018.06.020 [DOI] [PubMed] [Google Scholar]
- 30. Ambroggi M, Biasini C, Del Giovane C, Fornari F, Cavanna L. Distance as a barrier to cancer diagnosis and treatment: review of the literature. Oncologist. 2015;20(12):1378‐1385. 10.1634/theoncologist.2015-0110 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Gadkaree SK, McCarty JC, Feng AL, et al. Role of physician density in predicting stage and survival for head and neck squamous cell carcinoma. Head Neck. 2021;43(2):438‐448. 10.1002/hed.26495 [DOI] [PubMed] [Google Scholar]
- 32. Tomescu A, Sullivan N, Davies C, et al. Effect of rurality in head and neck cancer: a scoping review. Laryngoscope. 2024;134(11):4441‐4457. 10.1002/lary.31509 [DOI] [PubMed] [Google Scholar]
- 33. Sharp L, Donnelly D, Hegarty A, et al. Risk of several cancers is higher in urban areas after adjusting for socioeconomic status. results from a two‐country population‐based study of 18 common cancers. J Urban Health. 2014;91(3):510‐525. 10.1007/s11524-013-9846-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Lawrence LA, Heuermann ML, Javadi P, Sharma A. Socioeconomic status and rurality among patients with head and neck cancer. Otolaryngol Head Neck Surg. 2022;166(6):1028‐1037. 10.1177/01945998211019278 [DOI] [PubMed] [Google Scholar]
- 35. Diez‐Roux AV, Moore KA, Melly SK, Wang X, Joshi R. Neighborhood poverty and health in Philadelphia. Philadelphia, PA: Dornsife School of Public Health, Drexel University; 2017.
- 36. Shusted CS, Kane GC. Linkage between poverty and smoking in Philadelphia and its impact on future directions for tobacco control in the city. Popul Health Manag. 2020;23(1):68‐77. 10.1089/pop.2019.0006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Brown EJ, Polsky D, Barbu CM, Seymour JW, Grande D. Racial disparities in geographic access to primary care in Philadelphia. Health Aff. 2016;35(8):1374‐1381. 10.1377/hlthaff.2015.1612 [DOI] [PubMed] [Google Scholar]
- 38. Hudgins A, Uzwiak B, Pizzicato L, Viner K. Barriers to effective care: speciality drug treatment in Philadelphia. J Subst Abuse Treat. 2021;131:108639. 10.1016/j.jsat.2021.108639 [DOI] [PubMed] [Google Scholar]
- 39. Stey AM, Byskosh A, Etkin C, et al. Describing the density of high‐level trauma centers in the 15 largest US cities. Trauma Surg Acute Care Open. 2020;5(1):e000562. 10.1136/tsaco-2020-000562 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Ruiz‐Pérez I, Rodríguez‐Gómez M, Pastor‐Moreno G, Escribá‐Agüir V, Petrova D. Effectiveness of interventions to improve cancer treatment and follow‐up care in socially disadvantaged groups. Psycho‐Oncology. 2019;28(4):665‐674. 10.1002/pon.5011 [DOI] [PubMed] [Google Scholar]
- 41. Bourgeois A, Horrill T, Mollison A, Stringer E, Lambert LK, Stajduhar K. Barriers to cancer treatment for people experiencing socioeconomic disadvantage in high‐income countries: a scoping review. BMC Health Serv Res. 2024;24(1):670. 10.1186/s12913-024-11129-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Santos Salas A, Bassah N, Pujadas Botey A, et al. Interventions to improve access to cancer care in underserved populations in high income countries: a systematic review. Oncol Rev. 2024;18:1427441. 10.3389/or.2024.1427441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Sykes EA, Weisbrod N, Rival E, Haque A, Fu R, Eskander A. Methods, detection rates, and survival outcomes of screening for head and neck cancers: a systematic review. JAMA Otolaryngol Head Neck Surg. 2023;149(11):1047‐1056. 10.1001/jamaoto.2023.3010 [DOI] [PubMed] [Google Scholar]
- 44. Luryi AL, Yarbrough WG, Niccolai LM, et al. Public awareness of head and neck cancers: a cross‐sectional survey. JAMA Otolaryngol Head Neck Surg. 2014;140:639‐646. [DOI] [PubMed] [Google Scholar]
- 45. Shuman AG, McKiernan JT, Thomas D, et al. Outcomes of a head and neck cancer screening clinic. Oral Oncol. 2013;49(12):1136‐1140. 10.1016/j.oraloncology.2013.09.007. [DOI] [PubMed] [Google Scholar]
- 46. Harris MS, Phillips DR, Sayer JL, Moore MG. A comparison of community‐based and hospital‐based head and neck cancer screening campaigns: identifying high‐risk individuals and early disease. JAMA Otolaryngol Head Neck Surg. 2013;139(6):568‐573. 10.1001/jamaoto.2013.3153 [DOI] [PubMed] [Google Scholar]
- 47. Freiser ME, Cohen ER, Szczupak M, et al. Recruitment of underserved, high‐risk participants to a head and neck cancer screening program. Laryngoscope. 2016;126(12):2699‐2704. 10.1002/lary.26035 [DOI] [PubMed] [Google Scholar]