Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study

Abstract

The serious ambient fine particulate matter (PM 2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM 2.5 in China were less considered the regional transport of PM 2.5 concentration. In this study, we aim to explore the association between lung cancer and PM 2.5 and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR), model tree-based (MT) regression, regression tree (RT) approach, and the combined forecasting model (CFM) were alternative forecasting models. The result of the Pearson correlation analysis showed that both local and regional scale PM 2.5 concentration had a significant association with lung cancer mortality and morbidity and compared with the local lag and regional lag exposure to ambient PM 2.5 ; the regional lag effect (0.172~0.235 for mortality; 0.146~0.249 for morbidity) was not stronger than the local lag PM 2.5 exposure (0.249~0.294 for mortality; 0.215~0.301 for morbidity). The overall forecasting lung cancer morbidity and mortality were 47.63, 47.86, 39.38, and 39.76 per 100,000 population. The spatial distributions of lung cancer morbidity and mortality share a similar spatial pattern in 2015 and 2016, with high lung cancer morbidity and mortality areas mainly located in the central to east coast areas in China. The stakeholders would like to implement a cross-regional PM 2.5 control strategy for the areas characterized as a high risk of lung cancer.

Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand

“…On the other hand, the time-updated residential concentration of NO 2 and O 3 are not significantly associated with death from lung cancer, which is different from most other studies [22,23,28], but similar to [56] for O 3 . In the multivariate analyses, we found that all of the input risk factors (male, older age, stage of cancer, early enrollment time, and residential concentration of PM2.5) are associated with a higher risk of death in lung cancer patients, and this is in agreement with the findings from other studies [8,20,22,23,[57][58][59].…”

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

“…The World Health Organization (WHO) believes that environmental pollution is the biggest threat to human health. Epidemiology has also confirmed the relationship between health problems such as tumors, respiratory diseases, cardiovascular and cerebrovascular diseases, and environmental pollution ( 32 – 34 ). From the perspective of social factors, the length of education and education funding are also closely related to the health of residents.…”

Benign‐malignant classification of pulmonary nodules by low‐dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

“…In recent years, with the popularization of data mining technology and the development of deep learning methods such as convolutional neural network, the ability to predict the risk of lung cancer in patients using clinical data has become more frequent. Many studies focus on the prediction of treatment response and prognosis in patients with confirmed lung cancer 39–43 . Some studies have predicted the risk of lung cancer through well‐known clinical indicators such as age, smoking history, past tumor history, asbestos exposure, COPD, weight, physical activity, and fasting blood glucose level 44 .…”

“…Many studies focus on the prediction of treatment response and prognosis in patients with confirmed lung cancer. 39 , 40 , 41 , 42 , 43 Some studies have predicted the risk of lung cancer through well‐known clinical indicators such as age, smoking history, past tumor history, asbestos exposure, COPD, weight, physical activity, and fasting blood glucose level. 44 Based on the deep learning of image data, 45 , 46 , 47 , 48 , 49 or with the help of a tracer, 50 , 51 , 52 , 53 , 54 , 55 , 56 it has become easier to determine whether pulmonary nodules are benign or malignant.…”