Evidence map›Paper›PMID 32211912›Full record

ArticleSingapore medical journal2021

Geospatial analysis of severe road traffic accidents in Singapore in 2013-2014.

Choon Peng Jeremy Wee, Daniel Xin-Peng He, Wah Win, Marcus Eng Hock Ong

Abstract read
In one paragraph

Article in Singapore medical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Choon Peng Jeremy WeeDepartment of Emergency Medicine, Singapore General Hospital, Singapore.
Daniel Xin-Peng HeDepartment of Emergency Medicine, Singapore General Hospital, Singapore.
Wah WinDepartment of Emergency Medicine, Singapore General Hospital, Singapore.
Marcus Eng Hock OngDepartment of Emergency Medicine, Singapore General Hospital, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInjury is a significant cause of mortality and morbidity. We aimed to investigate which areas in Singapore have a significantly higher incidence of road traffic accidents (RTA) resulting in severe injuries (Tier 1), which is defined as an Injury Severity Score (ISS) greater than 15, and to develop a spatiotemporal model.

methodsData was obtained from the National Trauma Registry. The RTA locations were geomapped onto the Singapore map, and spatial statistical techniques were used to identify hotspots with the Getis-Ord Gi* algorithm.

resultsFrom 1 January 2013 to 31 December 2014, there were 35,673 people who were injured as a result of RTAs and 976 Tier 1 RTA victims. A total of 920 people were included in the geospatial analysis. Another 56 were involved in RTAs that did not occur within Singapore or had missing location data and thus were not included. 745 (81.0%) were discharged alive, whereas 175 (19.0%) did not survive to discharge (median ISS 38.00, interquartile range 30.00-48.00). Most of the Tier 1 RTA victims were motorcycle riders (50.1%, n = 461), pedestrians (21.8%, n = 201) and cyclists (9.9%, n = 91). The majority were male and aged 20-40 years, and there was a peak occurrence at 0600-0759 hours. Nine hotspots were identified (p < 0.01).

conclusionInformation from studying hotspots of RTAs, especially those resulting in severe injuries, can be used by multiple agencies to direct resources efficiently.

Indexed as

PedestriansWounds and InjuriesAccidents, TrafficAdultFemaleHumansInjury Severity ScoreMaleRegistriesSingaporeYoung Adultgeospatial analysisSingaporetrauma

Identifiers

PMID32211912
PMCPMC8801850

What OpenQuestion holds

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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.