Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
Cancer Epidemiol. 2023 Apr;83:102343. doi: 10.1016/j.canep.2023.102343. Epub 2023 Feb 24.
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.
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.
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.
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.
本研究旨在分析 2013 年至 2017 年美国多发性骨髓瘤（MM）的发病空间模式，以提高对 MM 潜在环境风险因素的认识。
本研究利用美国癌症统计公共数据库分析了 50 个州和哥伦比亚特区的年龄调整发病率（ASR）。我们首先将 ASR 分为五分位数，并使用面域图描述空间模式。为了识别 ASR 的全局和局部聚类，我们进行了空间自相关（全局 Moran's I）分析和 Anselin 的局部空间自相关（LISA）分析。我们使用 Welch 单侧 t 检验比较了整个美国聚类和各县的选定人口统计学和社会经济因素的均值。
本研究发现，美国各县的 ASR 存在明显的空间差异。美国东南部以及纽约州首府地区（奥尔巴尼周边的大都市区）和纽约市的县 ASR 较高，而美国西南部和西部的县 ASR 较低。ASR 呈显著正空间自相关。我们在佐治亚州和南卡罗来纳州发现了两个主要的高-高局部 ASR 聚类，在亚利桑那州、阿拉巴马州、亚利桑那州、新罕布什尔州、俄亥俄州、俄勒冈州和田纳西州发现了五个主要的低-低局部 ASR 聚类。种族人口分布可能部分解释了美国 MM 发病的空间分布。
本研究结果表明，美国 MM 的发病存在明显的空间分布，存在两个高-高和五个低-低的局部聚类。MM 的非随机分布提示特定区域的环境暴露可能对 MM 的发病风险具有重要作用。
