Reveals the spatiotemporal distribution patterns and future risks of five major cancers worldwide

Recently, a study published in the Journal of Geo-Information Science has highlighted a significant upward trend in the mortality rates of major cancers worldwide. Conducted by Associate Professor Shen Li and his team at the School of Remote Sensing and Information Engineering, Wuhan University, the research analyzed data from the Global Burden of Disease (GBD) study and the World Bank, covering the mortality rates of lung, colorectal, stomach, liver, and pancreatic cancers in 200 countries between 2011 and 2019. The team employed a Multiscale Geographically Weighted Regression (MGWR) model to extract spatial heterogeneity between cancer mortality rates and various influencing factors—such as the proportion of the population aged 65 and above, smoking, alcohol consumption, unhealthy diets, and economic indicators—and used an ARIMA model to quantitatively analyze the temporal trends and cyclical fluctuations of these cancers.

 

Building on these analyses, the researchers developed an integrated Bayesian spatiotemporal model that combined spatial and temporal information to predict and assess global cancer mortality risks. The findings indicate that from 2011 to 2019, the mortality rate for the five major cancers increased by an average of approximately 17.2 cases per 100,000 people, with over 72.8% of countries exhibiting relatively high risk. Notably, the mortality rates in Europe, Central Asia, North America, and the East Asia-Pacific regions have risen significantly faster than those in Africa and South Asia, with high-income and upper-middle-income countries displaying particularly elevated risk levels.

 

Furthermore, the study underscores that factors such as population aging, smoking, unhealthy dietary habits (including high sugar and processed meat intake), low physical activity, and socioeconomic indicators (such as per capita GDP, per capita GNI, and healthcare expenditure) are critical drivers of global cancer mortality. By integrating diverse statistical and spatiotemporal analysis methods, the research innovatively quantifies the spatiotemporal non-stationarity of cancer mortality risk, providing strong data support and scientific evidence for public health authorities and policymakers in devising both global and regional cancer prevention and control strategies.

 

For more details, please refer to the original article:

A Spatio-Temporal Modeling Study on the Global Disease Burden of Major Types of Cancers

 

https://www.sciengine.com/JGIS/doi/10.12082/dqxxkx.2025.240528（If you want to see the English version of the full text, please click on the iFLYTEK Translation  in the article page.）

 

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