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An overview of geospatial methods used in unintentional injury epidemiology

Overview of attention for article published in Injury Epidemiology, December 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Title
An overview of geospatial methods used in unintentional injury epidemiology
Published in
Injury Epidemiology, December 2016
DOI 10.1186/s40621-016-0097-0
Pubmed ID
Authors

Himalaya Singh, Lauren V. Fortington, Helen Thompson, Caroline F. Finch

Abstract

Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.

X Demographics

X Demographics

The data shown below were collected from the profiles of 37 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 17%
Researcher 9 14%
Student > Postgraduate 7 11%
Student > Ph. D. Student 7 11%
Student > Bachelor 5 8%
Other 17 26%
Unknown 10 15%
Readers by discipline Count As %
Medicine and Dentistry 17 26%
Nursing and Health Professions 5 8%
Biochemistry, Genetics and Molecular Biology 4 6%
Business, Management and Accounting 3 5%
Computer Science 3 5%
Other 18 27%
Unknown 16 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 September 2018.
All research outputs
#1,594,909
of 25,587,485 outputs
Outputs from Injury Epidemiology
#88
of 409 outputs
Outputs of similar age
#31,880
of 423,208 outputs
Outputs of similar age from Injury Epidemiology
#3
of 7 outputs
Altmetric has tracked 25,587,485 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 409 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 423,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.