Evidence map›Paper›PMID 38188231›Full record

ArticleCampbell systematic reviews2024

Effectiveness of road safety interventions: An evidence and gap map.

Rahul Goel, Geetam Tiwari, Mathew Varghese, Kavi Bhalla, Girish Agrawal, Guneet Saini, Abhaya Jha, Denny John, Ashrita Saran, Howard White and 1 more

Abstract read
In one paragraph

Article in Campbell systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. A cyclist-centric 360Scientific data · 2026
    Article
  3. Article
  4. Article
  5. 'Road safety is no accident': building efficient road safety lead agencies, strategies and targets in the world, 2009-2023.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2025
    Article
  6. Article
  7. Article
  8. Pattern of road traffic fatalities in India: a case study of Chhattisgarh State.International journal of injury control and safety promotion · 2025
    Article
  9. Review
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Rahul GoelTransportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India.
Geetam TiwariTransportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India.
Mathew VargheseSt. Stephen's Hospital Orthopaedic Delhi India.
Kavi BhallaDepartment of Public Health Sciences University of Chicago Chicago Illinois USA.
Girish AgrawalTransportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India.
Guneet SainiTexas A & M University College Station Texas USA.
Abhaya JhaTransportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India.
Denny JohnFaculty of Life and Allied Health Sciences M S Ramaiah University of Applied Sciences, Bangalore Karnataka India.
Ashrita SaranCampbell Collaboration New Delhi India.
Howard WhiteCampbell Collaboration New Delhi India.
Dinesh MohanTransportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge. Objectives: The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels. Search Methods: The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019. Selection Criteria: The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes. Data Collection and Analysis: The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2. Main Results: The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors ( Authors' Conclusions: The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

Identifiers

PMID38188231
PMCPMC10765170

What OpenQuestion holds

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LicenceCC BY
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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.