Geospatial analysis of population-based incidence of multiple myeloma in the United States
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- Title: Geospatial analysis of population-based incidence of multiple myeloma in the United States
- Author: Jason T.-H. Cheung a, Wei Zhang b, Brian C.-H. Chiu a,*
- Publisher: Elsevier Inc
- Publication: 2023
- Language: English
- Pages: 6
- Format: PDF
- File size: 3.579 KB
- DOI: 10.1016/j.canep.2023.102343
Abstract
Background
We investigated the spatial patterns of multiple myeloma (MM) incidence in the United States (US) between 2013 and 2017 to improve understanding of potential environmental risk factors for MM.
Methods
We analyzed the average county-level age-adjusted incidence rates (“ASR”) of MM between 2013 and 2017 in 50 states and the District of Columbia using the U.S. Cancer Statistics Public Use Databases. We firstly divided the ASR into quintiles and described spatial patterns using a choropleth map. To identify global and local clusters of the ASR, we performed the Spatial Autocorrelation (Global Moran’s I) analysis and the Anselin’s Local Indicator of Spatial Autocorrelation (LISA) analysis. We compared the means of selected demographic and socioeconomic factors between the clusters and counties of the whole US using Welch one-sided t-test.
Results
We identified distinct spatial dichotomy of the ASR across counties. High ASR were observed in counties in the Southeast of the US as well as the Capital District (metropolitan areas surrounding Albany) and New York City in the state of New York, while low ASR were observed in counties in the Southwest and West of the US. The ASR showed a significant positive spatial autocorrelation. We identified two major high-high local clusters of the ASR in Georgia and Southern Carolina and five major low-low local clusters of the ASR in Alabama, Arizona, New Hampshire, Ohio, Oregon, and Tennessee. The racial population distribution may partly explain the spatial distribution of MM incidence in the US.
Conclusion
Findings from this study showed distinct spatial distribution of MM in the US and two high-high and five low-low local clusters. The non-random distribution of MM suggests that environmental exposures in certain regions may be important for the risk of MM.
Highlights
- • A distinct spatial dichotomy of the MM ASR across counties in the US was present.
- • The MM ASR showed a significant positive spatial autocorrelation.
- • 2 major local high-high clusters and 5 major local low-low clusters were identified.
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• The racial population distribution may partly explain the spatial distribution of MM incidence in the US.
Introduction
Multiple myeloma (MM) is a malignancy characterized by clonal expansion of malignant plasma cells in the bone marrow, with a median survival of about 6 years . It is the second most common hematologic malignancies in the United States (US), affecting 32,270 estimated new cases and 12,830 estimated deaths in 2022 . MM is more common in males than females, and is twice as common in Blacks than Whites . MM remains mostly incurable, highlighting a critical need to identify risk factors for appropriate prevention.
Despite extensive epidemiology research in the past two decades, including works from the International Lymphoma Epidemiology Consortium (InterLymph) Myeloma Working Group, risk factors for MM, and racial/ethnic disparities in particular, remain poorly understood . To date, known risk factors for MM include genetic susceptibility, a family history of MM , older age , monoclonal gammopathy of undetermined significance (MGUS) , and obesity . Findings are inconsistent on risk factors such as occupational exposure to ionizing radiation , pesticides , and occupations such as firefighter, hairdresser and metal processer . Importantly, these risk factors do not fully explain the excess risk of MM in Blacks.
To improve our understanding of potential environmental risk factors for MM, we conducted a geospatial analysis of population-based MM incidence between 2013 and 2017 in the US. We hypothesized that the distribution of MM incidence in the US is not random. Our goal is to identify both global clusters and local outliers of MM incidence that could provide insights on etiologic factors.
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