Background

Multiple myeloma is a significant health concern with varying incidence and mortality rates across different regions and ethnic groups. Previous research has indicated disparities influenced by genetic and socioeconomic factors, but comprehensive global data analysis is limited. Understanding these variations is crucial for identifying regions with inadequate diagnostic and treatment resources and for developing targeted healthcare policies. This study aims to provide a detailed analysis of multiple myeloma rates worldwide, focusing on regional and ethnic disparities.

Methods

Countries were grouped into regions based on GLOBOCAN classifications. Racial and ethnic compositions were considered using data from the CIA World Factbook. Age-Standardized Incidence Rates (ASIR) and Age-Standardized Mortality Rates (ASMR) per 100,000 population for multiple myeloma were calculated for each region. Comparative analysis of these rates was conducted among different racial and ethnic groups within the regions.

Results

Multiple myeloma incidence and mortality rates vary significantly across regions, with North America showing the highest incidence (ASR 4.8) and mortality (ASR 1.6) rates, influenced by its predominantly White, Black, and Hispanic populations. Africa and Asia, with predominantly Black and Asian populations respectively, have the lowest incidence rates (ASR 1.1 and 1.2), likely due to underdiagnosis and reporting issues. Mortality rates reflect disparities in healthcare access, with Africa and Latin America showing higher relative mortality despite lower incidence, affected by mixed ethnic groups in Latin America. Europe and Oceania display high incidence rates (ASR 2.8 and 3.7) with moderate mortality, highlighting the influence of predominantly Caucasian populations in Europe and mixed ethnic compositions in Oceania.

Conclusions

The disparities in multiple myeloma incidence and mortality rates across regions and ethnic groups highlight the significant impact of genetic predispositions, socioeconomic factors, and healthcare access on disease outcomes. Efforts to improve diagnosis, treatment, and healthcare infrastructure, particularly in under-resourced regions, are crucial to addressing these inequalities.