Introduction
Although five-year survival rates for multiple myeloma (MM) in the US have continued to steadily improve in recent decades, these advances have not accrued equally to all segments of the population. Sub-groups of patients differentiated by race, socioeconomic status, ethnicity, and geographic location continue to experience inferior survival rates of MM. Little is known about the interacting effects of rural-urban differences and race on the outcomes of patients with MM. The objective of this study is to examine national rural-urban trends in 5-year myeloma-specific survival and overall survival (OS) for patients with MM in a diverse sample of racial groups.
Methods
This cross-sectional study used SEER data from 1975-2019 to include patients diagnosed with MM. Patients who were 18 years or younger, had missing information regarding rural or urban designation, and had missing follow-up time were excluded. Rural-urban designation was determined using the 2013 Rural Urban Continuum Codes (RUCC). Patients in counties with codes 1 to 3 were classified as urban, while all counties with codes 4 to 9 were rural. SEER coded race and ethnicity data was used. OS was calculated as the time from diagnosis to death from any cause or censored if the patient was still alive. Myeloma-specific survival was calculated as the time from diagnosis to myeloma-related death or censored if the patient was still alive; deaths due to non-myeloma causes were censored at the date of death. These patients were excluded from analysis: missing or unknown cause of death; Non-Hispanic American Indian/Alaska Native, Non-Hispanic Asian or Pacific Islander, and Non-Hispanic Unknown Race. Kaplan-Meier methods were used to estimate 5-year survival rates. Cox proportional hazards regression was used for multivariable modeling of survival data. All survival analyses were completed using SAS 9.4. For all p-values in the model, the 0.05 alpha level was used.
Results
From 1975 to 2019, there were 40,435 MM cases, with 55.9% between ages 60 and 79 years (Table). Overall, there were 18,680 (46.2%) female, 2,815 (7%) Hispanic (all races), 6,100 (15.1%) Non-Hispanic Black (NHB), and 31,510 (77.9%) Non-Hispanic White (NHW) patients. The majority of patients were classified as urban (84.7%). Rural patients were more likely to be NHW (92.2%). Very few (1.3%) NHB patients were from rural counties. Additionally, 23,180 (57.3%) patients were diagnosed between 2000 and 2019. The 5-year OS and myeloma-specific survival rates increased substantially from 1975 to 2019 across all races (Figure). During the study period, the 5-year myeloma-specific survival rate increased from 28.1% to 62.8% for urban patients and from 26.5% to 56% for rural patients. In the multivariable model (data available after January 1, 1990) younger age, urban home address, later year of diagnosis and NHB race were significantly associated with superior myeloma-specific survival (p<0.01). Compared with NHW, NHB patients had significantly improved myeloma-specific survival (HR=0.928, 95% CI: 0.889-0.969, p=0.0007). There was no significant interaction between rurality and race (p>0.05). In multivariable model for OS, younger age, later year of diagnosis, female sex and urban home address were associated with superior OS (p<0.01), but race/ethnicity was no longer a significant predictor.
Conclusions
Over 4+ decade study period, 5-year OS and myeloma-specific survival rate increased in all racial groups regardless of rural-urban location. The survival of rural patients was consistently lower than urban patients for NHW and Hispanics. Due to the small sample size, we did not analyze this relationship for NHB patients. Although the annual percent change for myeloma-specific survival was greater for NHW than NHB patients, the latter exhibited better outcomes compared with NHW patients even after adjusting for all other factors including age, sex, year of diagnosis and rural or urban home location. We plan to expand our analyses to include SEER-Medicare linked database to understand if differences in treatment variables, demographics and socioeconomic status can explain the disparities in survival.
Disclosures
Voorhees:Sanofi: Membership on an entity's Board of Directors or advisory committees; Nervianos Medical Sciences: Research Funding; Regeneron: Consultancy; Karyopharm: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Novartis: Consultancy; GSK: Consultancy, Research Funding; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Data Safety and Monitoring; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Bhutani:Janssen Research & Development: Research Funding; Bristol-Myers Squibb/Celgene: Research Funding; Amgen: Research Funding; Adaptive Biotechnologies: Research Funding; Takeda: Research Funding.
