Sociodemographic disparities among Floridians diagnosed with oropharyngeal cancer.

BACKGROUND

Oropharyngeal cancer (OPC) involves the base of tongue, palatine tonsils, lingual tonsil, and soft palate. Established risk factors for OPC include tobacco usage, alcohol usage, and human papillomavirus infection. Although white populations tend to have the highest risk of developing OPC, Black patients are more likely to experience distant stage disease.

METHODS

The aim of this study was to elucidate epidemiological factors that are associated with regional and distant stage disease at OPC diagnosis. We performed a retrospective cross-sectional analysis utilizing the Florida Cancer Data System (FCDS) from 2010 to 2017. Sociodemographic factors among Black and white patients were compared using chi-square analysis. Multivariable logistic regression analysis determined risk factors associated with distant stage diagnosis. Geographical mapping of census tract levels was performed to illustrate prevalence of distant stage disease at diagnosis in Florida.

RESULTS

Among 8,908 OPC cases, 7,534 (84.6%) patients were white non-Hispanic, 834 (9.4%) were white Hispanic, and 540 (6.1%) were Black. Multivariable logistic regression revealed increased distant stage disease compared to local stage among those who were Black (compared to white non-Hispanic and white Hispanic; OR = 1.55 [95% CI:1.12-2.13], p = 0.007), separated/divorced/widowed (OR = 1.36 [95% CI:1.11-1.68], p = 0.004) (compared to married), and lack insurance (OR = 1.67 [95% CI:1.16-2.41], p = 0.006) or have public insurance (OR = 1.26 [95% CI:1.04-1.53], p = 0.017) (compared to those with private insurance). There was decreased regional stage disease compared to local stage among females (OR = 0.57 [95% CI:0.49-0.66], p < .001) and older patients (OR = 0.975, [95% CI:0.968-0.982], p < 0.001). Mapping revealed higher percent of distant stage diagnoses in census tracts with lower median income.

CONCLUSIONS

Distant stage at OPC diagnosis is influenced by many risk factors, including race, sex, age, marital, and insurance status. Geographical mapping analysis can help direct screening efforts to high-risk communities.