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Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities

Overview of attention for article published in Environmental Pollution, January 2016
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Title
Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities
Published in
Environmental Pollution, January 2016
DOI 10.1016/j.envpol.2015.09.011
Pubmed ID
Authors

Lina Madaniyazi, Yuming Guo, Renjie Chen, Haidong Kan, Shilu Tong

Abstract

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 21%
Researcher 8 19%
Student > Ph. D. Student 5 12%
Other 4 10%
Professor 3 7%
Other 4 10%
Unknown 9 21%
Readers by discipline Count As %
Environmental Science 10 24%
Nursing and Health Professions 4 10%
Medicine and Dentistry 4 10%
Agricultural and Biological Sciences 3 7%
Social Sciences 3 7%
Other 7 17%
Unknown 11 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2015.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Environmental Pollution
#10,055
of 13,433 outputs
Outputs of similar age
#341,806
of 399,662 outputs
Outputs of similar age from Environmental Pollution
#125
of 186 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,433 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 186 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.