Evidence map›Paper›PMID 31315282›Full record

ArticleInternational journal of environmental research and public health2019

Exploring the Effect of Driving Factors on Traffic Crash Risk among Intoxicated Drivers: A case Study in Wujiang.

Zeyang Cheng, Zhenshan Zu, Jian Lu, Yunxuan Li

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zeyang ChengJiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, 211189, China.
Zhenshan ZuTraffic Police Brigade of Wujiang Public Security Bureau, Suzhou, 215200, China.
Jian LuJiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, 211189, China. lujian_1972@seu.edu.cn.
Yunxuan LiJiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, 211189, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intoxicated driving is a threat to both drivers and other road users. Exploring the association between intoxicated driving factors and traffic crashes is essential for taking effective countermeasures. Most previous works have studied the relation between intoxicated driving and traffic crash based on some large-sized cities. The current study aims to evaluate the effect of driving factors on traffic crashes among intoxicated drivers in a small-sized city in China. Descriptive statistics and binary logistic regression analysis are performed to guide the study, and the data (N=1010) for the period 2016-2017 in Wujiang (i.e., a small-sized city in China) are employed as the target samples. The results demonstrate age, years of driving experience, road position, week, hour and blood alcohol concentration (BAC) are associated with traffic crashes in Wujiang. Specifically, the age of "18-25", the years of driving experience of "≤2", the "road intersection", the "weekend", the period of "0:00-6:59" and the BAC of "above 150 mg/100 mL" are more likely to cause traffic crashes among intoxicated drivers. The findings can be referred to make some targeted policies or measures to relieve Wujiang's intoxicated driving situation and reduce the number of crashes caused by intoxicated driving.

Indexed as

Accidents, TrafficAdolescentAdultAlcoholic IntoxicationAutomobile DrivingBlood Alcohol ContentChinaCitiesFemaleHumansMaleMiddle AgedRisk FactorsYoung AdultBlood Alcohol Contentbinary logistic regressionIntoxicated drivingtargeted policiestraffic crashes

Identifiers

PMID31315282
PMCPMC6678633

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.