Evidence map›Paper›PMID 29329015›Full record

ArticleAccident; analysis and prevention2018

Modelling of road traffic fatalities in India.

Rahul Goel

Open access · hybridAbstract read
In one paragraph

Article in Accident; analysis and prevention, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.9field-weighted citation impact, top 8% of its field
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

3 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

1 author at 1 institution in 1 country.

Rahul GoelMRC Epidemiology Unit, University of Cambridge, UK. Electronic address: rg574@medschl.cam.ac.uk.
University of Cambridge · GB

Funding

Medical Research Council MR/K023187/1Medical Research Council MR/P024408/1Wellcome Trust
6 · The paper itself

Abstract

Passenger modes in India include walking, cycling, buses, trains, intermediate public transport modes (IPT) such as three-wheeled auto rickshaws or tuk-tuks, motorised two-wheelers (2W) as well as cars. However, epidemiological studies of traffic crashes in India have been limited in their approach to account for the exposure of these road users. In 2011, for the first time, census in India reported travel distance and mode of travel for workers. A Poisson-lognormal mixture regression model is developed at the state level to explore the relationship of road deaths of all the road users with commute travel distance by different on-road modes. The model controlled for diesel consumption (proxy for freight traffic), length of national highways, proportion of population in urban areas, and built-up population density. The results show that walking, cycling and, interestingly, IPT are associated with lower risk of road deaths, while 2W, car and bus are associated with higher risk. Promotion of IPT has twofold benefits of increasing safety as well as providing a sustainable mode of transport. The mode shift scenarios show that, for similar mode shift across the states, the resulting trends in road deaths are highly dependent on the baseline mode shares. The most worrying trend is the steep growth of death burden resulting from mode shift of walking and cycling to 2W. While the paper illustrates a limited set of mode shift scenarios involving two modes at a time, the model can be applied to assess safety impacts resulting from a more complex set of scenarios.

Indexed as

Accidents, TrafficAutomobilesBayes TheoremBicyclingCluster AnalysisHumansIndiaMotor VehiclesRisk FactorsTransportationWalkingAccident prediction modelDistance-decay functionsEcological modelIndiaTraffic fatalities

Identifiers

PMID29329015
PMCPMC5792624
OpenAlexW2790026884

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.