College of Health and Human Sciences, Northern Illinois University, DeKalb.
Northern Illinois University, 323 Wirtz Hall, DeKalb, IL 60115 (
Prev Chronic Dis. 2024 Oct 31;21:E84. doi: 10.5888/pcd21.240046.
Despite declining cancer death rates in the US, cancer remains the second deadliest disease and disparities persist. Although research has focused on identifying risk factors for cancer deaths and associated disparities, few studies have examined how these relationships vary over time and space. The primary objective of this study was to identify cancer mortality hot spots and cold spots - areas where cancer death rates decreased less than or more than neighboring areas over time. A secondary objective was to identify risk factors of cancer mortality hot spots and cold spots.
We analyzed county-level cancer death rates from 2004 through 2008 and 2014 through 2018, exploring disparities in changes over time for socioeconomic and demographic variables. We used hot spot analysis to identify areas with larger decreases (cold spots) and smaller decreases (hot spots) in cancer death rates and random forest machine learning analysis to assess the relative importance of risk factors associated with hot spots and cold spots. We mapped spatial clustering areas.
Geospatial analysis showed hot spots predominantly in the Plains states and Midwest and cold spots in the Southeast, Northeast, 2 Mountain West states (Utah and Idaho), and a portion of Texas. Factors with the strongest influence on hot spots and cold spots were unemployment, preventable hospital stays, mammography screening, and high school education.
Geospatial disparities in changes in cancer death rates point out the critical role of access to care, socioeconomic position, and health behaviors in persistent cancer mortality disparities. Study results provide insights for interventions and policies that focus on addressing health care access and social determinants of health.
尽管美国的癌症死亡率在下降,但癌症仍是第二大致死疾病,且存在差异。尽管研究集中在确定癌症死亡和相关差异的风险因素上,但很少有研究探讨这些关系随时间和空间的变化。本研究的主要目的是确定癌症死亡率的热点和冷点——随着时间的推移,癌症死亡率下降幅度小于或大于邻近地区的区域。次要目的是确定癌症死亡率热点和冷点的风险因素。
我们分析了 2004 年至 2008 年和 2014 年至 2018 年的县级癌症死亡率,探讨了随时间推移,社会经济和人口统计学变量变化的差异。我们使用热点分析来识别癌症死亡率下降幅度较大(冷点)和较小(热点)的区域,并使用随机森林机器学习分析来评估与热点和冷点相关的风险因素的相对重要性。我们绘制了空间聚类区域。
地理空间分析显示,热点主要集中在平原州和中西部,冷点则集中在东南部、东北部、两个西部山区(犹他州和爱达荷州)以及得克萨斯州的一部分。对热点和冷点影响最大的因素是失业率、可预防的住院治疗、乳房 X 光筛查和高中学历。
癌症死亡率变化的地理空间差异表明,获得医疗保健、社会经济地位和健康行为在持续的癌症死亡率差异中起着至关重要的作用。研究结果为关注医疗保健获取和健康的社会决定因素的干预措施和政策提供了见解。