摘要
【目的】癌症是全球绝大多数国家的主要致病死因,对人类寿命和公共卫生构成了严重威胁。本文探讨了全球五类主要癌症死亡率的时空分布特征,并给出了未来发展趋势预测。【方法】本文针对2011—2019年全球200个国家五类主要癌症(肺癌、结直肠癌、胃癌、肝癌与胰腺癌),采用GBD数据与世界银行数据库资料,基于MGWR模型提取各类癌症死亡率影响因素的空间异质性特征,利用ARIMA模型提取各类癌症死亡率的时间变化趋势特征,并将该时空信息作为参数输入构建贝叶斯时空模型,对全球主要类型癌症死亡风险进行预测评估。【结果】研究发现,全球五类癌症死亡率均持续增加,2019年各类癌症死亡率较2011年平均上升了17.2/100000人。全球超过72.8%的国家癌症死亡相对风险较高(RR>1),呈现出明显的空间聚集性。【结论】相比非洲与南亚地区,欧洲、中亚、北美、东亚及太平洋地区癌症死亡率增速较快。相比中低收入和低收入国家,高收入和中高收入国家各类癌症死亡率上升趋势明显,相对风险更高。65岁及以上人口占比、吸烟、酒精、低运动强度、高糖加工饮食、人均GDP、人均GNI和人均医疗卫生支出成为全球主要类型癌症死亡风险的关键影响因素。本研究通过集成不同地理时空分析方法优势,创新性构建了涵括时空分组变量和不同影响因素的疾病风险时空预测模型,灵活度高,可解释性强,更适用于量化时空非平稳性关系,能够有效评估全球不同地区主要类型癌症死亡的相对风险,加深了地理空间建模技术与流行病研究的交叉融合,对严峻的全球癌症防控规划具有重大科学意义。
[Objectives]Cancer is the leading cause of death in most countries worldwide,posing a significant threat to human longevity and public health.This study explores the spatiotemporal distribution characteristics of mortality rates for five major types of cancer worldwide and provides predictions for future trends.[Methods]Aiming at five major cancer types(lung,colorectal,gastric,liver,and pancreatic cancer)in 200 countries from 2011 to 2019,this study used GBD and World Bank data to extract spatial heterogeneity of the factors affecting cancer mortality using the MGWR model.The ARIMA model was used to extract temporal trend characteristics of various cancer mortality rates.Such spatial-temporal information was integrated into a Bayesian spatial-temporal model to predict and evaluate the global mortality risk for the five types of cancer.[Results]Results revealed that global death rate for all five cancer types increased,with an average rise of 17.2 deaths per 100000 people in 2019 compared to 2011.Over 72.8% of countries exhibited a high relative risk of cancer death(RR>1),indicating significant spatial clustering.[Conclusions]Regions such as Europe,Central Asia,North America,and East Asia and the Pacific experienced faster increases in cancer death rates compared to Africa and South Asia.Compared to low-and middle-income countries,middle-high-and high-income countries showed a more pronounced upward trend in cancer mortality and a higher relative risk.Key factors influencing global cancer mortality included the percentage of the population aged 65 years and older,smoking,alcohol consumption,low physical activity,high sugar diets,GDP per capita,GNI per capita,and health expenditure per capita.By integrating the advantages of different geographical spatial-temporal analysis methods,this study developed an innovative spatiotemporal prediction model of disease risk that integrates spatial-temporal grouping variables and multiple influencing factors.This proposed model is highly flexible,interpretable,and better suited for quantifying non-stationarity spatial-temporal relationships.While the structured spatial and temporal effects increase computational demands,the model effectively assesses cancer mortality risk across regions,offering robust insights into the spatiotemporal dynamics of disease.This approach deepens the integration of geospatial modeling technology and epidemiological research,providing significant scientific contributions to global cancer research,prevention,and control planning.
作者
申力
徐瑱梵
艾明耀
卢宾宾
SHEN Li;XU Zhenfan;AI Mingyao;LU Binbin(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430072,China)
出处
《地球信息科学学报》
北大核心
2025年第3期698-715,共18页
Journal of Geo-information Science
基金
国家自然科学基金项目(42201448、42071368)
中央高校自主科研项目项目(2042022dx0001)。
关键词
全球疾病负担
癌症死亡率
多尺度地理加权回归
时间序列预测
贝叶斯时空模型
global burden of disease
cancer mortality rate
Multiscale Geographically Weighted Regression
time series forecasting
Bayesian spatiotemporal model
作者简介
申力(1986-),女,河北张家口人,博士,副教授,主要从事时空大数据挖掘应用研究。E-mail:shenli1986@whu.edu.cn;通信作者:卢宾宾(1984-),男,河南周口人,博士,副教授,主要从事空间统计和数据科学研究。E-mail:binbinlu@whu.edu.cn。
