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预防道路交通事故的驾照后驾驶员教育

Post-licence driver education for the prevention of road traffic crashes.

作者信息

Ker K, Roberts I, Collier T, Renton F, Bunn F

机构信息

Public Health Intervention Research Unit, London School of Hygiene and Tropical Medicine, 49-51 Bedford Square, London, UK, WC1B 3DP.

出版信息

Cochrane Database Syst Rev. 2003(3):CD003734. doi: 10.1002/14651858.CD003734.

Abstract

BACKGROUND

Worldwide, each year over a million people are killed and some ten million people are permanently disabled in road traffic crashes. Post-licence driver education is used by many as a strategy to reduce traffic crashes. However, the effectiveness of post-licence driver education has yet to be ascertained.

OBJECTIVES

To quantify the effectiveness of post-licence driver education in reducing road traffic crashes.

SEARCH STRATEGY

We searched the following electronic databases: the Cochrane Injuries Group's Specialised Register, Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, TRANSPORT (NTIS, TRIS, TRANSDOC, IRRD), Road Res (ARRB), ATRI, National Research Register, PsycInfo, ERIC, SPECTR, Zetoc, SIGLE, Science (and Social Science) Citation Index. We searched the Internet, checked reference lists of relevant papers and contacted appropriate organisations. The search was not restricted by language or publication status.

SELECTION CRITERIA

Randomised controlled trials comparing post-licence driver education versus no education, or one form of post-licence driver education versus another.

DATA COLLECTION AND ANALYSIS

Two reviewers independently screened search results, extracted data and assessed methodological trial quality.

MAIN RESULTS

We found 24 trials of driver education, 23 conducted in the USA and one in Sweden. Twenty trials studied remedial driver education. The methodological quality of the trials was poor and three reported data unsuitable for meta-analysis. Nineteen trials reported traffic offences: pooled relative risk (RR) = 0.96, 95% confidence interval (95% CI) = 0.94, 0.98); trial heterogeneity was significant (p=<0.00001). Fifteen trials reported traffic crashes: pooled RR = 0.98 (95% CI 0.96, 1.01), trial heterogeneity was not significant (p=0.75). Four trials reported injury crashes: pooled RR = 1.12 (95% CI 0.88, 1.41), trial heterogeneity was significant (p=<0.00001). No one form of education (correspondence, group or individual) was found to be substantially more effective than another, nor was a significant difference found between advanced driver education and remedial driver education. Funnel plots indicated the presence of publication bias affecting the traffic offence and crash outcomes.

REVIEWER'S CONCLUSIONS: This systematic review provides no evidence that post-licence driver education is effective in preventing road traffic injuries or crashes. Although the results are compatible with a small reduction in the occurrence of traffic offences, this may be due to selection biases or bias in the included trials. Because of the large number of participants included in the meta-analysis (close to 300,000 for some outcomes) we can exclude, with reasonable precision, the possibility of even modest benefits.

摘要

背景

在全球范围内,每年有超过100万人死于道路交通事故,约1000万人永久致残。许多人将驾照后驾驶员教育作为减少交通事故的一种策略。然而,驾照后驾驶员教育的有效性尚未得到确定。

目的

量化驾照后驾驶员教育在减少道路交通事故方面的有效性。

检索策略

我们检索了以下电子数据库:Cochrane伤害组专业注册库、Cochrane对照试验中央注册库、MEDLINE、EMBASE、TRANSPORT(NTIS、TRIS、TRANSDOC、IRRD)、Road Res(ARRB)、ATRI、国家研究注册库、PsycInfo、ERIC、SPECTR、Zetoc、SIGLE、科学(及社会科学)引文索引。我们还搜索了互联网,查阅了相关论文的参考文献列表,并联系了相关组织。检索不受语言或出版状态的限制。

入选标准

比较驾照后驾驶员教育与无教育,或一种形式的驾照后驾驶员教育与另一种形式的随机对照试验。

数据收集与分析

两名评价员独立筛选检索结果,提取数据并评估试验方法学质量。

主要结果

我们找到了24项驾驶员教育试验,其中23项在美国进行,1项在瑞典进行。20项试验研究了补救性驾驶员教育。试验的方法学质量较差,3项试验报告的数据不适合进行荟萃分析。19项试验报告了交通违法情况:合并相对危险度(RR)=0.96,95%置信区间(95%CI)=0.94,0.98;试验异质性显著(p<0.00001)。15项试验报告了交通事故:合并RR=0.98(95%CI 0.96,1.01),试验异质性不显著(p=0.75)。4项试验报告了受伤事故:合并RR=1.12(95%CI 0.88,1.41),试验异质性显著(p<0.00001)。未发现任何一种教育形式(函授、团体或个人)比另一种形式更有效,高级驾驶员教育和补救性驾驶员教育之间也未发现显著差异。漏斗图表明存在影响交通违法和事故结果的发表偏倚。

评价员结论

本系统评价没有证据表明驾照后驾驶员教育在预防道路交通伤害或事故方面有效。尽管结果与交通违法发生率略有降低相符,但这可能是由于纳入试验中的选择偏倚或偏倚所致。由于荟萃分析纳入的参与者数量众多(某些结果接近30万),我们可以合理精确地排除即使是适度获益的可能性。

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