Geographic Variation of Pediatric Burn Injuries in a Metropolitan Area

Williams, K.; Schootman, Mario; Quayle, Kimberly S.; Struthers, Jim; Jaffe, David M.

doi:10.1111/j.1553-2712.2003.tb00069.x

Cited by 24 publications

(10 citation statements)

References 19 publications

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“…14 After targeting a smoke-alarm-giveaway program to the high-risk area identified through surveillance, fire-injury rates significantly decreased, demonstrating the efficacy of interventions targeted at discrete geographic areas with elevated injury incidence. 14 Similar disease mapping methods have been successfully implemented in studies of pediatric burn injuries and pediatric pedestrian injury, 15,16 supporting the utility of these methods in contributing to reductions in morbidity and mortality from pediatric injuries.…”

Section: Introductionmentioning

confidence: 93%

The spatial epidemiology of pediatric trauma

Ertl

Beyer

Tarima

et al. 2017

Journal of Trauma and Acute Care Surgery

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Introduction Despite significant advances in the prevention and treatment of pediatric trauma, preventable injuries continue to burden the lives of millions of children. In order to target prevention strategies, it is critical to identify areas with high burdens of pediatric trauma. Therefore, this study analyzed statewide data from the Ohio Trauma Registry (OTR) from 2007–2012 to identify geographical patterns in pediatric injury. Methods Data from the first hospital of care for 16,330 pediatric trauma patients under 16 years old were analyzed using the disease mapping method adaptive spatial filtering to estimate a series of maps that display age- and sex-adjusted rates of pediatric trauma, severe trauma, and standardized mortality ratios (SMR) while controlling for population size to create stable estimates throughout the study area. The locations of all trauma centers were mapped to highlight access to trauma care. Results Areas with significantly higher than expected rates of severe injury were identified in non-urban areas, where children lacked timely access to a Pediatric Trauma Center (PTC) or Level 1 Adult Trauma Center (ATC). Although highest SMRs were in urban areas, non-urban areas experienced elevated mortality with rates over 4 times higher than expected. Conclusion Areas with higher than expected age- and sex-adjusted rates of severe injury and mortality should be further explored to identify opportunities for injury prevention and appropriate access to timely care.

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Section: Introductionmentioning

confidence: 93%

The spatial epidemiology of pediatric trauma

Ertl

Beyer

Tarima

et al. 2017

Journal of Trauma and Acute Care Surgery

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“…2 Understanding the spatial distribution and types of burn injuries can give important insights to risks associated with burns. 3 Furthermore, socioeconomic characteristics of the population for a given injury can provide additional insight about the population at risk. 4 By taking proactive measures through disaster management and planning, this knowledge can also help mitigate fatalities resulting from burn.…”

mentioning

confidence: 99%

Urban–Rural Dichotomy of Burn Patients in Georgia and South Carolina

Mian

Haque

Mullins

et al. 2015

Journal of Burn Care & Research

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This study uses a 4-year (2006-2009) cross-section of epidemiological burn injury data from Georgia and South Carolina. The results from the study show that the burn patients from rural areas differ from their urban counterparts in terms of relative burn injury incidence. Younger population groups that live in lower socioeconomic status communities especially in the urban areas are at a higher risk than other population groups. The differences in the types of burns in the urban-rural communities can give us further insights to the patients' association with injury sites. The presence of fewer burn injury treatment and care facilities in rural areas and the high incidence of burn in low-income communities in the urban areas should carry important policy implications for health planners. This study will enable researchers to understand the epidemiology of burn injuries at the local and national levels in the United States. It also carries important implications for using Geographic Information Systems for studying spatial distribution of burn injuries for disaster planning and mitigation of burn injuries.

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“…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 ( n = 1) (Raghavan et al 2014). …”

Section: Resultsmentioning

confidence: 99%

“…The choropleth and classed symbol maps represented different types of summary measures: incidence rate ( n = 27), relative risk ( n = 10), frequency ( n = 8), and standardised mortality ratios ( n = 6). One study mapped more than one summary measure, namely, incidence rate and relative risk (Williams et al 2003), so again, the sum by summary measures ( n = 48) does not equal the total number of studies ( n = 47) presented choropleth maps.…”

Section: Resultsmentioning

confidence: 99%

“…In thirteen studies, different smoothing techniques were applied to address small number problem. Methods used were an empirical Bayes model ( n = 5) (de Pina et al 2008; Erdogan 2009; Lassarre and Thomas 2005; Silva et al 2011; Yiannakoulias et al 2003), Bayesian model ( n = 4) (Eksler and Lassarre 2008; Eksler et al 2008; Turner et al 2009; Williams et al 2003), BYM ( n = 3) (DiMaggio 2015; Heng et al 2015; Poulos et al 2012) and Poisson regression model ( n = 1) (Spoerri et al 2011). The most commonly used empirical Bayes method (Clayton and Kaldor 1987) determines the extent of smoothing from the underlying structure of data including the crude standardised mortality ratio, its precision and the underlying relative risk distribution.…”

Section: Resultsmentioning

confidence: 99%

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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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Geographic Variation of Pediatric Burn Injuries in a Metropolitan Area

Cited by 24 publications

References 19 publications

The spatial epidemiology of pediatric trauma

The spatial epidemiology of pediatric trauma

Urban–Rural Dichotomy of Burn Patients in Georgia and South Carolina

An overview of geospatial methods used in unintentional injury epidemiology

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