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Examining Road Traffic Mortality Status in China: A Simulation Study

Overview of attention for article published in PLOS ONE, April 2016
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Article details
Title
Examining Road Traffic Mortality Status in China: A Simulation Study
Published in
PLOS ONE, April 2016
DOI 10.1371/journal.pone.0153251
Pubmed ID
Authors
Abstract

Data from the Chinese police service suggest substantial reductions in road traffic injuries since 2002, but critics have questioned the accuracy of those data, especially considering conflicting data reported by the health department. To address the gap between police and health department data and to determine which may be more accurate, we conducted a simulation study based on the modified Smeed equation, which delineates a non-linear relation between road traffic mortality and the level of motorization in a country or region. Our goal was to simulate trends in road traffic mortality in China and compare performances in road traffic safety management between China and 13 other countries. Chinese police data indicate a peak in road traffic mortalities in 2002 and a significant and a gradual decrease in population-based road traffic mortality since 2002. Health department data show the road traffic mortality peaked in 2012. In addition, police data suggest China's road traffic mortality peaked at a much lower motorization level (0.061 motor vehicles per person) in 2002, followed by a reduction in mortality to a level comparable to that of developed countries. Simulation results based on health department data suggest high road traffic mortality, with a mortality peak in 2012 at a moderate motorization level (0.174 motor vehicles per person). Comparisons to the other 13 countries suggest the health data from China may be more valid than the police data. Our simulation data indicate China is still at a stage of high road traffic mortality, as suggested by health data, rather than a stage of low road traffic mortality, as suggested by police data. More efforts are needed to integrate safety into road design, improve road traffic management, improve data quality, and alter unsafe behaviors of pedestrians, drivers and passengers in China.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 11 22%
Student > Ph. D. Student 7 14%
Student > Master 4 8%
Lecturer 3 6%
Other 2 4%
Other 10 20%
Unknown 12 24%
Readers by discipline
Readers by discipline Count As %
Engineering 14 29%
Medicine and Dentistry 5 10%
Biochemistry, Genetics and Molecular Biology 2 4%
Business, Management and Accounting 2 4%
Nursing and Health Professions 2 4%
Other 8 16%
Unknown 16 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 October 2018.
All research outputs
#14,682,700
of 23,509,253 outputs
Outputs from PLOS ONE
#122,911
of 201,328 outputs
Outputs of similar age
#162,947
of 302,419 outputs
Outputs of similar age from PLOS ONE
#3,060
of 5,320 outputs
Altmetric has tracked 23,509,253 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 201,328 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 302,419 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,320 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.