Geographical distribution of road traffic deaths in England and Wales: place of accident compared with place of residence

Haynes, Robin; Jones, Andrew; Harvey, Ian; Jewell, Tony; Lea, David A. M.

doi:10.1093/pubmed/fdh212

Cited by 15 publications

(10 citation statements)

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“…However, some studies conducted in cities of southern Europe have not found these inequalities (Esnaola, et al, 2006). In addition, some studies have found associations between mortality due to traffic injuries and physical and socioeconomic characteristics of the area where the injury occurred, which might be different from the area of residence (Haynes, et al, 2005,Jones, et al, 2008. In the case of drug overdose, some studies conducted in cities of southern Europe have found that areas with greater socioeconomic deprivation present higher mortality risks (Gotsens, et al, 2011a,Michelozzi, et al, 1999,Esnaola, et al, 2006.…”

Section: Resultsmentioning

confidence: 99%

Socioeconomic inequalities in injury mortality in small areas of 15 European cities

et al. 2013

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This study analysed socioeconomic inequalities in mortality due to injuries in small areas of 15 European cities, by sex, at the beginning of this century. A cross-sectional ecological study with units of analysis being small areas within 15 European cities was conducted. Relative risks of injury mortality associated with the socioeconomic deprivation index were estimated using hierarchical Bayesian model. The number of small areas varies from 17 in Bratislava to 2666 in Turin. The median population per small area varies by city (e.g. Turin had 274 inhabitants per area while Budapest had 76,970). Socioeconomic inequalities in all injury mortality are observed in the majority of cities and are more pronounced in men. In the cities of northern and western Europe, socioeconomic inequalities in injury mortality are found for most types of injuries. These inequalities are not significant in the majority of cities in southern Europe among women and in the majority of central eastern European cities for both sexes. The results confirm the existence of socioeconomic inequalities in injury related mortality and reveal variations in their magnitude between different European cities.

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Section: Resultsmentioning

confidence: 99%

Socioeconomic inequalities in injury mortality in small areas of 15 European cities

et al. 2013

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“…More deprived areas might have poorer built environment and more infrastructure problems which can explain the higher risk of being injured for pedestrians (Steinbach et al, 2010). On the other hand, some studies have found association between mortality due to traffic injuries and physical and socio-economic characteristics of the area where the accident occurred, which might be different from the place of residence (Haynes et al, 2005;Jones et al, 2008). In this sense, when mortality due to traffic injuries is analysed at the ecological level we should consider that the place of the accident usually does not coincide with place of residence (Eksler and Lassarre, 2008).…”

Section: Discussionmentioning

confidence: 99%

Socio-economic inequalities in mortality due to injuries in small areas of ten cities in Spain (MEDEA Project)

Gotsens

Marí-Dell’Olmo

Martínez-Beneito³

et al. 2011

Accident Analysis & Prevention

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“…The majority of studies were concerned with road traffic injuries ( n = 36) (Chakravarthy et al 2010; Cinnamon et al 2011; DiMaggio 2015; Dissanayake et al 2009; Durkin et al 2005; Eksler and Lassarre 2008; Eksler et al 2008; Erdogan 2009; Haynes et al 2005; Haynes et al 2008; Hijar et al 2003; Hosking et al 2013; Hu et al 2008; Huff et al 2012; Jones et al 2008; La Torre et al 2007; Lassarre and Thomas 2005; Lateef 2011; Lawrence et al 2015; Mohan et al 2015; Morency and Cloutier 2006; Nagata et al 2011; Nunes and Nascimento 2012; Nunn and Newby 2015; Paulozzi 2006; Poulos et al 2012; Razzak et al 2011; Schuurman et al 2009; Silva et al 2011; Slaughter et al 2014; Spoerri et al 2011; Statter et al 2011; Sukhai et al 2009; Unni et al 2012; Weiner and Tepas 2009; Yan-Hong et al 2006). Other studies considered falls ( n = 11) (Bamzar and Ceccato 2015; Chan et al 2012; de Pina et al 2008; Dey et al 2010; Lai et al 2009a; Lai et al 2009b; Lai et al 2011; Morency et al 2012; Towne et al 2015; Turner et al 2009; Yiannakoulias et al 2003), burns ( n = 9) (Edelman et al 2010; Fouillet et al 2006; Goltsman et al 2014; Harlan et al 2013; Heng et al 2015; Mian et al 2014; Niekerk et al 2006; Stylianou et al 2015; Williams et al 2003), drowning ( n = 4) (Dai et al 2013; Maples and Tiefenbacher 2009; Sharif et al 2012; Shenoi et al 2015), occupational ( n = 2) (Breslin et al 2007; Forst et al 2015), aviation-related ( n = 2) (Grabowski et al 2002a, 2002b), poisoning ( n = 1) (Nkhoma et al 2004), natural disaster ( n = 1) (Peek-Asa et al 2000) and dog-bite ...…”

Section: Resultsmentioning

confidence: 99%

An overview of geospatial methods used in unintentional injury epidemiology

et al. 2016

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BackgroundInjuries 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.MethodsNine 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.ResultsFrom 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).ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s40621-016-0097-0) contains supplementary material, which is available to authorized users.

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Geographical distribution of road traffic deaths in England and Wales: place of accident compared with place of residence

Cited by 15 publications

References 3 publications

Socioeconomic inequalities in injury mortality in small areas of 15 European cities

Socioeconomic inequalities in injury mortality in small areas of 15 European cities

Socio-economic inequalities in mortality due to injuries in small areas of ten cities in Spain (MEDEA Project)

An overview of geospatial methods used in unintentional injury epidemiology

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