Disentangling the effects of various risk factors and trends in lung cancer mortality

Our plain-English summary of Disentangling the effects of various risk factors and trends in lung cancer mortality, by X. Wu, J. Zhang, Y. Yoshida (Scientific Reports, 2025).

The study

• Authors: X. Wu, J. Zhang, Y. Yoshida
• Journal: Scientific Reports
• Published: 2025;15(1):8719
• Cited by: 1 article, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Lung cancer is a leading cause of mortality in oncological classifications, yet the impact of various risk factors on lung cancer mortality (LCM) in non-smokers remains unclear. This study aims to weigh out the diverse impact of multiple risk factors on LCM rates and identify trends in LCM rates worldwide. We initially employed Random Forest Tree (RFT) and Gradient Boosting Regression (GBR) to identify common primary factors influencing LCM. After eliminating four common primary factors, a comparative analysis between partial and Pearson correlations was conducted to filter out significant factors in the correlations between risk factors and LCM rates across 204 countries from 2005 to 2019. The findings show that excluding the impacts of occupational exposure to arsenic, smoking, residential radon, occupational exposure to silica, occupational exposure to asbestos, high systolic blood pressure, secondhand smoke, child wasting, and alcohol use had a considerably greater impact on LCM than particular matter pollution (PM2.5). Furthermore, a Multiple Joinpoint Regression analysis identified increasing trends of LCM rates in the 142 countries (e.g., China and India); decreasing trends in 38 countries (e.g., Denmark and Norway), and stable trends in 24 countries (e.g., Sudan, Mali, and Australia). This research suggests that in addition to considering the effects of occupational exposure to arsenic, smoking, residential radon, and occupational exposure to silica on LCM rates, occupational exposure to asbestos, high systolic blood pressure, secondhand smoke, child wasting, and alcohol use should be considered in lung cancer prevention strategies, especially in countries with increasing trends of LCM rates.

© 2025. The Author(s).

In plain English

This study used Global Burden of Disease estimates for 204 countries from 2005 to 2019, comparing countries rather than individual people, to examine how 73 risk factors related to lung cancer death rates. Two machine-learning methods pointed to four main factors: occupational exposure to arsenic, smoking, residential radon and occupational exposure to silica. With those four held constant, the correlation between occupational exposure to asbestos and lung cancer death rates dropped from 0.792 to 0.478, which the authors called a moderate positive association. In a further model, occupational exposure to asbestos, high systolic blood pressure, secondhand smoke, child wasting, alcohol use and others ranked as more important than particulate matter pollution. Lung cancer death rates rose in 142 countries, fell in 38 and were stable in 24. The authors concluded these factors, including asbestos, should be considered in prevention strategies. They noted that country-level data may differ from local findings. They also noted that asbestos-related lung cancer typically has a 20 to 40 year latency, so the deaths studied reflect past exposures.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Analysis of country-level Global Burden of Disease data using machine-learning models (Random Forest Tree and Gradient Boosting Regression), partial correlation, and joinpoint trend regression
• Who was studied: Lung cancer mortality and mortality associated with 73 risk factors in 204 countries, 2005 to 2019, from Global Burden of Disease open datasets (3060 observations)
• Exposure: 73 Global Burden of Disease risk factors, each entered as the estimated mortality associated with that factor. One of these was 'occupational exposure to asbestos'. No asbestos fiber types, products, jobs or doses are specified.
• Main results:
  • Occupational exposure to arsenic, smoking, residential radon and occupational exposure to silica were the four top factors common to both the Gradient Boosting Regression and Random Forest Tree models. They were then used as control variables.
  • For occupational exposure to asbestos, the Pearson correlation with lung cancer mortality rates was 0.792. The partial correlation, controlling for the four main factors, was 0.478 (both reported with significance 0.000).
  • The authors described the partial correlation coefficients for occupational exposure to asbestos and for occupational carcinogens (about 0.5) as a moderate positive association with lung cancer mortality.
  • After controlling for the four main factors, 70.59% of the variables changed the direction of their correlation with lung cancer mortality.
  • In the Gradient Boosting Regression model of 11 selected factors, several factors ranked as more important than particulate matter pollution (PM2.5): occupational exposure to asbestos, high systolic blood pressure, secondhand smoke, child wasting, alcohol use, child stunting, diet high in red meat and high LDL cholesterol.
  • The Gradient Boosting Regression model had a mean squared error of 9.36 and an R2 of 0.98.
  • Lung cancer mortality rates showed significant increasing trends in 142 countries.
  • Lung cancer mortality rates showed decreasing trends in 38 countries.
  • Lung cancer mortality rates were stable in 24 countries.
• The authors' conclusion: The authors concluded that, in addition to arsenic, smoking, residential radon and silica, a further set of factors should be considered in lung cancer prevention strategies in countries with increasing lung cancer mortality: occupational exposure to asbestos, high systolic blood pressure, secondhand smoke, child wasting and alcohol use.
• Limitations the authors state:
  • The data were large-scale and aggregated at the country level, which the authors said caused inconsistency between local findings and global assessments.
  • Smoking appeared less important than arsenic exposure in this analysis. The authors attributed this partly to study scale, data sources and limits of machine-learning methods.
  • Demographic factors such as gender, age and race were not considered.
  • Lung cancer mortality was not divided into non-small cell and small cell lung cancer.
  • There may be a time gap between exposure and outcome. Asbestos-related lung cancer typically has a 20 to 40 year latency, so the outcomes reflect past rather than current exposures.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer.

Medical terms in this study, explained in our Lexicon: Lung Cancer, Occupational Exposure.

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