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Estimating mortality burden attributable to short-term PM_2.5 exposure: A national observational study in China.

ABSTRACT: Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM_2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM_2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM_2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM_2.5 exposure in China. Here, we explored the non-linear association between short-term PM_2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM_2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM_2.5 exposure in China. The pooled PM_2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m³ and decreased risk from 62 to 250 μg/m³. We estimated a total of 169,862 additional deaths from short-term PM_2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM_2.5-mortality association estimated 32,186 deaths attributable to PM_2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM_2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM_2.5-related mortality estimations when considering the disease burden attributable to PM_2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM_2.5.

SUBMITTER: Li T

PROVIDER: S-EPMC6548716 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivePM2.5, which is a major contributor to air pollution, has large effects on lung cancer mortality. We want to analyse the long-term trends in lung cancer burden attributable to PM2.5 exposure and provide evidence that can be used for preventive measures and health resource planning.MethodsMortality data related to lung cancer were obtained from the Global Burden of Disease (GBD) 2019 project. A joinpoint regression analysis was used to assess the magnitude and direction of the trends in mortality from 1990 to 2019, and the age-period-cohort method was used to analyse the temporal trends in the mortality rate of lung cancer attributable to PM2.5 exposure by age, period, and cohort.ResultsFrom 1990 to 2019, the age-standardized mortality rate (ASMR) attributable to PM2.5 exposure trended slowly upwards, and the ASMR due to ambient PM2.5 exposure (APE) increased significantly, that due to household PM2.5 exposure (HPE) decreased. The longitudinal age curves show that the mortality rates due to PM2.5 exposure among younger individuals were low, and they significantly increased from their levels among those in the 45-49 age group to their levels among those in the over-85 age group. From 1990 to 2019, the period RRs due to APE increased, but those due to HPE decreased. Similar trends were observed in the cohort RRs. The overall net drift per year attributable to PM2.5 exposure was below 0. The local drift values increased with age and were above 0 for the over-80 age groups. The overall net drifts per year were above zero for APE and below zero for HPE. The corresponding results among males were higher than those among females.ConclusionsIn China, the type of air pollution responsible for lung cancer has changed from household air pollution to ambient air pollution. PM2.5 exposure is more harmful among males and older people. Ambient air pollution should be emphasized, and China should strengthen its implementation of effective public policies and other interventions.

Project description:Background: Ambient particulate matter is a public health concern in East Asia as it contributes to a growing number of all-cause and cancer deaths. This study aimed to estimate lung cancer death attributable to ambient particulate matter (PM) < 2.5 μm (PM2.5) in East Asia countries. Methods: The attributable death rates of lung cancer were estimated based on the calculation of population attributable fraction. We performed joinpoint regression analysis and age-period-cohort (APC) model to estimate temporal trends of the attributable death to PM2.5. Results: In 2019, PM2.5 was estimated to have caused 42.2% (nearly 0.13 million) of lung cancer deaths in East Asia men. During 1990-2019, the increase in age-standardized death rates of lung cancer attributable to PM2.5 was highest in China, which increased by 3.50% in males and 3.71% in females. The death rate caused by PM2.5 also significantly increased in the Democratic People's Republic of Korea (2.16% in males; 3.06% in females). Joinpoint analysis showed that the rates generally increased in younger and older people in both the Democratic People's Republic of Korea and Mongolia, while it only increased in elderly people in other countries'. Age effect from APC analysis demonstrated the risk of lung cancer death attributable to PM2.5 generally increased from young to old age. Period effect indicated that from 1994-1998 to 2019-2023 period risk continuously increased by 1.77, 1.68, and 1.72 times in China, the Democratic People's Republic of Korea, and Japan, respectively. The period risk decreased from 1999 to 2009 and subsequently increased from 2009 to 2019 in both the Republic of Korea and Mongolia. Conclusions: The death rate of lung cancer attributable to PM2.5 is increasing in the Democratic People's Republic of Korea, Mongolia, and China. In East Asia, China is facing the highest attributable death rate in recent decades. The period effect suggested a remarkably increased risk of lung cancer death caused by PM2.5 in China, the Democratic People's Republic of Korea, and Japan during the long-term period. It is recommended that the governments of these countries should continuously concentrate on particulate matter pollution governance and improvement.

Project description:It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

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Estimating mortality burden attributable to short-term PM_2.5 exposure: A national observational study in China.

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Dataset Information

Estimating mortality burden attributable to short-term PM2.5 exposure: A national observational study in China.

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OmicsDI is part of the ELIXIR infrastructure

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Estimating mortality burden attributable to short-term PM_2.5 exposure: A national observational study in China.