Spatiotemporal mapping of major trauma in Victoria, Australia.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2022
2022
Historique:
received:
21
11
2021
accepted:
22
03
2022
entrez:
6
7
2022
pubmed:
7
7
2022
medline:
9
7
2022
Statut:
epublish
Résumé
Spatiotemporal modelling techniques allow one to predict injury across time and space. However, such methods have been underutilised in injury studies. This study demonstrates the use of statistical spatiotemporal modelling in identifying areas of significantly high injury risk, and areas witnessing significantly increasing risk over time. We performed a retrospective review of hospitalised major trauma patients from the Victorian State Trauma Registry, Australia, between 2007 and 2019. Geographical locations of injury events were mapped to the 79 local government areas (LGAs) in the state. We employed Bayesian spatiotemporal models to quantify spatial and temporal patterns, and analysed the results across a range of geographical remoteness and socioeconomic levels. There were 31,317 major trauma patients included. For major trauma overall, we observed substantial spatial variation in injury incidence and a significant 2.1% increase in injury incidence per year. Area-specific risk of injury by motor vehicle collision was higher in regional areas relative to metropolitan areas, while risk of injury by low fall was higher in metropolitan areas. Significant temporal increases were observed in injury by low fall, and the greatest increases were observed in the most disadvantaged LGAs. These findings can be used to inform injury prevention initiatives, which could be designed to target areas with relatively high injury risk and with significantly increasing injury risk over time. Our finding that the greatest year-on-year increases in injury incidence were observed in the most disadvantaged areas highlights the need for a greater emphasis on reducing inequities in injury.
Sections du résumé
BACKGROUND
Spatiotemporal modelling techniques allow one to predict injury across time and space. However, such methods have been underutilised in injury studies. This study demonstrates the use of statistical spatiotemporal modelling in identifying areas of significantly high injury risk, and areas witnessing significantly increasing risk over time.
METHODS
We performed a retrospective review of hospitalised major trauma patients from the Victorian State Trauma Registry, Australia, between 2007 and 2019. Geographical locations of injury events were mapped to the 79 local government areas (LGAs) in the state. We employed Bayesian spatiotemporal models to quantify spatial and temporal patterns, and analysed the results across a range of geographical remoteness and socioeconomic levels.
RESULTS
There were 31,317 major trauma patients included. For major trauma overall, we observed substantial spatial variation in injury incidence and a significant 2.1% increase in injury incidence per year. Area-specific risk of injury by motor vehicle collision was higher in regional areas relative to metropolitan areas, while risk of injury by low fall was higher in metropolitan areas. Significant temporal increases were observed in injury by low fall, and the greatest increases were observed in the most disadvantaged LGAs.
CONCLUSIONS
These findings can be used to inform injury prevention initiatives, which could be designed to target areas with relatively high injury risk and with significantly increasing injury risk over time. Our finding that the greatest year-on-year increases in injury incidence were observed in the most disadvantaged areas highlights the need for a greater emphasis on reducing inequities in injury.
Identifiants
pubmed: 35793336
doi: 10.1371/journal.pone.0266521
pii: PONE-D-21-36951
pmc: PMC9258853
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0266521Subventions
Organisme : HCRW_
ID : HCRW_HRG-20-1755(P)
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Epidemiology. 2015 Mar;26(2):247-54
pubmed: 25643104
Soc Sci Med. 1999 Feb;48(4):445-69
pubmed: 10075171
Inj Prev. 2020 Feb;26(1):82-84
pubmed: 31537617
J Trauma. 2007 Jan;62(1):221-5; discussion 225-6
pubmed: 17215759
Inj Epidemiol. 2016 Dec;3(1):32
pubmed: 28018997
Burns. 2015 May;41(3):437-45
pubmed: 25554260
Accid Anal Prev. 2019 Feb;123:123-131
pubmed: 30476630
J Trauma Acute Care Surg. 2014 Apr;76(4):1035-40
pubmed: 24662869
ANZ J Surg. 2004 Jun;74(6):424-8
pubmed: 15191472
Gerontology. 2005 Sep-Oct;51(5):340-5
pubmed: 16110237
BMC Geriatr. 2008 Mar 17;8:6
pubmed: 18366645
Biomed Res Int. 2014;2014:537614
pubmed: 24955360
BMJ Open. 2018 Nov 6;8(10):e023114
pubmed: 30401726
Inj Prev. 2021 Apr;27(2):166-171
pubmed: 32917743
Health Place. 2013 Jan;19:131-7
pubmed: 23220376
Rev Saude Publica. 2011 Apr;45(2):409-15
pubmed: 21344120
J Trauma Acute Care Surg. 2015 May;78(5):962-9
pubmed: 25909416
Am J Epidemiol. 2015 Apr 1;181(7):521-31
pubmed: 25700887
J Trauma Acute Care Surg. 2019 Feb;86(2):289-298
pubmed: 30531330
PLoS One. 2010 Jan 19;5(1):e8775
pubmed: 20098745
J Epidemiol Community Health. 2010 Jan;64(1):63-7
pubmed: 19692719
Accid Anal Prev. 2006 Jan;38(1):122-7
pubmed: 16139232
Annu Rev Public Health. 2012 Apr;33:107-22
pubmed: 22429160
Med J Aust. 2008 Nov 17;189(10):546-50
pubmed: 19012550
J Gerontol A Biol Sci Med Sci. 2002 Jul;57(7):M473-8
pubmed: 12084812
Am J Prev Med. 2018 Jul;55(1):72-79
pubmed: 29773489
Epidemiol Rev. 2003;25:24-42
pubmed: 12923988
Int J Environ Res Public Health. 2010 Mar;7(3):1002-17
pubmed: 20617015
Inj Prev. 2015 Aug;21(4):260-5
pubmed: 25694418
J Gerontol A Biol Sci Med Sci. 2021 Sep 13;76(10):1821-1828
pubmed: 33537735
Resuscitation. 2011 Jul;82(7):886-90
pubmed: 21481512