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. 2019 Nov;19(11):699-708.e5.
doi: 10.1016/j.clml.2019.06.009. Epub 2019 Jun 26.

Characterizing Lymphoma Incidence and Disparities for a Cancer Center Catchment Region

Amy A Ayers  1 , Lin Lyu  2 , Kaylin Dance  2 , Kevin C Ward  3 , Christopher R Flowers  4 , Jean L Koff  5 , Lauren E McCullough  2
Affiliations

Characterizing Lymphoma Incidence and Disparities for a Cancer Center Catchment Region

Amy A Ayers et al. Clin Lymphoma Myeloma Leuk. 2019 Nov.

Abstract

Background: Racial disparities in non-Hodgkin lymphoma (NHL) are not well-elucidated for specific catchment areas, which can influence outcomes. Leveraging regional data from a population-based cancer registry may provide unique opportunities to quantify NHL disparities.

Materials and methods: Using Surveillance, Epidemiology, and End Results (SEER) data for NHL cases diagnosed in Georgia from 2001 to 2015, we examined NHL incidence rates by lymphoma subtype and racial differences in baseline characteristics and outcomes for diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). Cox regression models identified predictors of overall survival (OS).

Results: SEER documented 38,504 NHL cases in Georgia from 2001 to 2015. The age-adjusted incidence rate for NHL in Georgia increased 1.03% per year, and the annual percentage change was 1.72 in blacks compared with 0.84 in whites. Compared with whites, blacks with DLBCL and FL were more likely to be diagnosed at a younger age (DLBCL, 54.1 vs. 65.5 years; P < .0001; FL, 58.4 vs. 64.0 years; P < .0001) and with B symptoms (DLBCL, 44.4% vs. 33.4%; P < .0001; FL, 28.5% vs. 21.4%; P = .004). Across racial categories, age at diagnosis > 60 years, advanced stage, and B symptoms predicted worse OS in DLBCL and FL. Blacks with DLBCL more commonly were diagnosed with stage III/IV disease (55.5% vs. 48.1%; P < .0001) and had worse 5-year relative survival (58.8% vs. 62.3%; P = .01).

Conclusions: Regional cancer registry data can be used to define incidence patterns and disparities in outcomes across NHL subtypes to help define key targets for interventions in a catchment area.

Keywords: Diffuse large B-cell lymphoma; Follicular lymphoma; Non-Hodgkin lymphoma; Racial differences; SEER.

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Conflict of interest statement

Disclosure

Dr Flowers has served as a consultant for: Abbvie, Astra Zeneca, Bayer, BeiGene, Celgene, Denovo Biopharma, Genentech/Roche (unpaid), Gilead, OptumRx, Karyopharm, MEI Pharmaceuticals, Pharmacyclics/Janssen, and Spectrum. Related to Dr Flowers’ research, Emory University has received research funding from: Abbvie, Acerta, BeiGene, Celgene, Gilead, Genentech/Roche, Janssen Pharmaceutical, Millennium/Takeda, M2Gen, Pharmacyclics, TG Therapeutics, Burroughs Wellcome Fund, Eastern Cooperative Oncology Group, National Cancer Institute, and the V Foundation. The remaining authors have stated that they have no conflicts of interest.

Figures

Figure 1
Figure 1. Selection of DLBCL and FL Study Cohorts. A, Selection of DLBCL Cohort. B, Selection of FL Cohort
Abbreviations: DLBCL = diffuse large B-cell lymphoma; FL = follicular lymphoma.
Figure 2
Figure 2
Age-specific Incidence Rate of Diffuse Large B-cell Lymphoma (A) and Follicular Lymphoma (B) by Gender and Race in Georgia (2001–2015)
See this image and copyright information in PMC

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