Geographical And Racial Disparities In Head And Neck Cancer Diagnosis In South-Eastern United States: Using Real-World Electronic Medical Records Data

Background: Rurality, race, and age at diagnosis are important predictors in head and neck cancer (HNC) prognosis. However, literature on the associations of rurality and race with age at HNC diagnosis is limited. Data on geographical, racial, and gender disparities in young HNC patients (diagnosed <= 45 years) are also scarce.Materials and Methods: This retrospective study assesses rural-urban, racial, and gender disparities in age at HNC diagnosis, using electronic medical records (Cerner) data of 4258 HNC patients (1538 residing in rural counties and 2720 in urban counties) from National Cancer Institute-designated cancer center in Alabama. Rurality was defined based on 2010 U.S. Census Bureau's rural-urban classification. Logistic regression was used to assess the association of young HNC diagnosis with demographical, behavioral, and clinical variables. ArcGIS 10.2 was used to map geospatial distribution of age and population-adjusted HNC case across rural and urban counties.Results: Patients from rural counties were less likely to be diagnosed at younger age (<= 45 years) compared with urban counties (odds ratio [OR] [95% confidence interval (CI)]: 0.74 [0.58-0.93]). Most patients present at stage III/IV (64.9% in rural and 60.2% in urban). Compared with white patients, black patients were 70% more likely to get diagnosed at a young age (95% CI: 1.23-2.35). Young patients were more likely to be females and blacks compared with older patients (p < 0.0001). Among oral cavity cancer patients, rural patients were 51% less likely to get diagnosed at young age compared with urban patients (95% CI: 0.27-0.89).Conclusions: Head and neck cancer screening is not routinely conducted so most show up at later stage of cancer. There is also evidence of disparities in age at HNC diagnosis based on rurality, race, and gender; targeted screening can help in reducing these disparities.