Lung cancer (LC) has become the third leading cause of deaths in China, leading to 626,000 deaths annually. Ambient particulate matter with a diameter of 2.5μm or less (PM2.5) has been identified as one of the risk factors of LC by the International Agency for Research of Cancer (IARC). With the acceleration of industrialization and urbanisation, China has experienced severe PM2.5 pollution in the past decades and was one of the most polluted regions in the world. The influence of PM2.5 on LC has received wide attention among Chinese residents.
Environmental data such as PM2.5 are usually aggregated at a certain spatial level or a certain time interval. Spatial and temporal analyses that are capable of dealing with these aggregated data have been extensively used in environmental epidemiology studies. Spatial analysis involves spatial autocorrelation and thus produces less-biased estimates than traditional statistical methods that assume observations are independent. In addition, spatial and temporal analyses would facilitate policy makers to identify the temporal trends and high-risk spatial locations of diseases, which provide crucial information for public health policies. However, spatial and temporal analyses have been rarely used in the studies on PM2.5-LC relationship.
This project aims to systematically illustrate the spatial and temporal patterns of lung cancer mortality (LCM) and quantify the short- and long-term associations between PM2.5 and LCM in China using integrated spatial and temporal methods.
Data of LCM were retrieved from GBD Results Tool and the Chinese National Death Surveillance System. The concentrations of PM2.5 were collected from the National Urban Air Quality Real-time Publishing Platform and a satellite-based dataset. To control for possible confounding effects, data on daily weather factors were collected from the National Centres for Environmental Information. Data on smoking prevalence were collected from the National noncommunicable disease surveillance. Data on socioeconomic factors were collected from the National census.
We compared the temporal trends of LCM between China and other regions/countries. We explored the association between the trend of LC age-standardized death rate and the Socio-demographic Index (SDI) of countries. The spatial and temporal trends of LCM among subpopulations within China were identified. A generalised linear model and a distributed lag non-linear model were applied to evaluate the association between daily PM2.5 concentration and daily LCM. A geographically weighted Poisson regression model was used to quantify the long-term association between historical exposure to PM2.5 and LCM at the county level.
- LC age-standardized death rates increased significantly in China among both sexes, although LC age-standardised death rate among men started to decrease globally. Such increase was faster than the average level in countries with the same SDI. The increase in the number of LC deaths in China accounted for more than 50% of global increase.
- The trend of LCM differed between subpopulations by age, sex, and region in China. The upward trends of LCM in the west and the south of China among young population deserve particular attention.
- LC age-standardised death rates attributed to PM5 ranked second among 195 countries/territories globally and it was still increasing.
- Short-term exposure to PM5 would increase the risk of LC death. Such effects varied by season, city, and demographic characteristics of the population.
- The relationship between long-term PM5 exposure and LCM had spatial heterogeneity at the county level and enhanced with the extension of PM2.5 exposure. More than 30% of LC deaths in people aged 65 or older could be attributed to historical PM2.5 exposure of more than a decade ago.
- The results urge the government to improve comprehensive LC control policies and interventions. More resources should be invested in the west and south of China. Stricter PM5 control measures should be implemented, especially in areas with a higher burden of LCM attributable to PM2.5. Meantime, an early warning system for heavily polluted weather should be established to guide LC patients to strengthen personal protection against PM2.5.
- Ning Wang, PhD candidate
- Professor Wenbiao Hu
- Distinguished Professor Kerrie Mengersen
- Adjunct Professor Shilu Tong
- Adjunct Professor Michael Kimlin
- Professor Maigeng Zhou, Chinese Centre for Diseases Control and Prevention.