An urban-rural and sex differences in cancer incidence and mortality and the relationship with PM2.5 exposure: An ecological study in the southeastern side of Hu line

Wang, Hong; Gao, Zhiqiu; Ren, Jingzheng; Liu, Yibo; Chang, Lisa T.-C.; Cheung, Kevin K. W.; Feng, Yun; Li, Yubin

doi:10.1016/j.chemosphere.2018.10.183

Cited by 55 publications

(32 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3a presents the observed and estimated population density from the results of the 10-fold CV of the RF model at the County level in 2013. The population intensities estimated from the RF model are in good agreement with the actual averaged-population intensity (logarithmic scale); and the R 2 and RMSE values of the CV results are 0.83 and 0.5people/km 2 , respectively. Furthermore, half of the total Counties exhibit predictions with RE < 20% (Figure 3b).…”

Section: Spatial Population Intensitysupporting

confidence: 53%

“…Particulate matter of aerodynamic diameter ≤ 2.5 µm (PM 2.5 ) is extremely harmful to public health due to the small particle size, and its physical properties and complex chemical composition [1][2][3][4]. Population exposure to PM 2.5 can result in an increased risk of various diseases, such as those affecting the respiratory, cardiovascular, and reproductive systems, leading to premature deaths or reduced cognitive performance [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Since the urban areas of the YRD region are densely distributed, NTL satellite data are more reliable for the estimation of the spatial distribution of population [47]. Previous studies on PM 2.5 exposure in the YRD region have mainly used the total-census-population data sourced from administrative divisions or the total population of public health data around sampling sites with PM 2.5 observations [2,[48][49][50][51], leaving large spatial gaps in the seasonal differences in population-exposure risks to PM 2.5 pollution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

Wang

Gao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

show abstract

Section: Spatial Population Intensitysupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

Wang

Gao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…They also found that females have a higher risk of leukemia incidence in rural areas compared to males (Table ). However, there was no significant sex difference when assessing lung cancer–––both males and females increased in risk (Wang et al, ).…”

Section: Other Particles Of Interestmentioning

confidence: 99%

The role of sex in particle‐induced inflammation and injury

Ray

Fletcher

Burmeister

et al. 2019

WIREs Nanomed Nanobiotechnol

View full text Add to dashboard Cite

The use of engineered nanomaterials within various applications such as medicine, electronics, and cosmetics has been steadily increasing; therefore, the rate of occupational and environmental exposures has also increased. Inhalation is an important route of exposure to nanomaterials and has been shown to cause various respiratory diseases in animal models. Human lung disease frequently presents with a sex/gender‐bias in prevalence or severity, but investigation of potential sex‐differences in the adverse health outcomes associated with nanoparticle inhalation is greatly lacking. Only ~20% of basic research in the general sciences use both male and female animals and a substantial percentage of these do not address differences between sexes within their analyses. This has prevented researchers from fully understanding the impact of sex‐based variables on health and disease, particularly the pathologies resulting from the inhalation of particles. The mechanisms responsible for sex‐differences in respiratory disease remain unclear, but could be related to a number of variables including sex‐differences in hormone signaling, lung physiology, or respiratory immune function. By incorporating sex‐based analysis into respiratory nanotoxicology and utilizing human data from other relevant particles (e.g., asbestos, silica, particulate matter), we can improve our understanding of sex as a biological variable in nanoparticle exposures. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials

show abstract

“…A growing body of epidemiological studies showed that particulate matter (PM), especially fine particulate matter of aerodynamic diameter < 2.5μm (PM 2.5 ), has an adverse effect on human health (Fajersztajn et al 2013;Kampa and Castanas 2008), especially for lung cancer (Wang et al 2019;Dehghani et al 2017) as well as cardiovascular diseases (Zhang et al 2014a, b;Beelen et al 2014). What's more, the International Agency for Research on Cancer (IARC) concluded that exposure to PM from outdoor air pollution is carcinogenic to humans (IARC group 1) and causes lung cancer (Loomis et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Zhou

et al. 2020

Environ Sci Pollut Res

View full text Add to dashboard Cite

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.

show abstract

An urban-rural and sex differences in cancer incidence and mortality and the relationship with PM2.5 exposure: An ecological study in the southeastern side of Hu line

Cited by 55 publications

References 61 publications

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

The role of sex in particle‐induced inflammation and injury

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

Contact Info

Product

Resources

About