The relationship of pedestrian injuries to socioeconomic characteristics in a large Southern California County

A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph Convolutional Networks

In recent years, increased attention has been given to understanding the spatial pattern of crashes in urban areas. Accurately capturing the spatial relationship between crash counts and variables requires extracting hidden information from multiple data sources. In this study, we propose a machine learning model to explore the spatial impact of activity patterns on spatially aggregated crash counts. Our paper introduces a two-step framework: (a) the Latent Dirichlet Allocation (LDA) model, an unsupervised method for mining hidden activity patterns from floating vehicle trajectory data, and (b) the Graph Convolutional Network (GCN) model, which builds the spatial relationship between multi-source data. The data and hidden activity patterns were aggregated into 175 Traffic Analysis Zones (TAZs) in San Francisco using spatial partitioning. The GCN model provided higher prediction accuracy than commonly used machine learning algorithms that did not consider combined spatial relationships and those that only considered traditional vehicle counts data. Furthermore, we used attribution algorithms to obtain the respective weight scores of each factor. Our results reveal that daily vehicle kilometers traveled, road density, population density, commercial activity during weekends, and residential activity during morning peak hours on weekdays are factors associated with crashes.

"Current incidence of injuries in Iran; findings of STEPS survey 2021"

Background

The updated epidemiology of injuries at the national and sub-national levels are required for policymakers to effectively handle the burden of injuries. This paper aimed to assess the incidence and risk factors of different injuries in Iran based on a recent national survey.

Methods

We used data from Iran Stepwise approach to surveillance (STEPS) Survey 2021, a population-based study in urban and rural areas of Iran's 31 provinces. A multistage clustered probability design and weighting adjustments were used to select eligible individuals and generate estimations. We estimated the incidence of injuries, assessed sociodemographic variables, and identified potential behavioral risk factors associated with injuries, and results were reported for sociodemographic and geographic stratifications.

Result

Data from 27,874 participants of the STEPS survey were assessed, of which 1538 (5.5 %, 95 % CI: [5.2-5.8]) reported having an injury in the past 12 months. Falls (44.4 %) were the most common cause of injury, followed by road traffic injury (21.7 %) and exposure to mechanical forces (16.5 %). Except for falls and burns, males had a higher proportion of all types of injuries. Logistic regression analysis showed that being male (OR: 1.7, [1.5, 2.0]) and being an occasional or heavy alcohol drinker (OR: 2.0, [1.3, 3.0] and OR: 2.7, [1.7, 4.1] respectively) were significant risk factors associated with road traffic injuries. Seatbelt use was 90.0 % among both drivers and front-seat passengers, while the use of safety car seats for children was as low as 9.4 %. Injury incidence varied significantly among provinces, with the highest incidence among males observed in Razavi Khorasan (11.2 %) and among females observed in Tehran (12.0 %).

Conclusion

This study investigated the updated epidemiology of injuries in Iran and revealed socioeconomic and geographic disparities across country. This epidemiological information can be used to modify injury prevention programs.

Age-specific characteristics of road traffic injuries among children and adolescents in South Korea

OBJECTIVE

Road traffic injuries (RTIs) are the leading cause of mortality among children and adolescents. This study aimed to identify and compare the age-specific epidemiology, clinical characteristics and factors related to severe RTIs among children and adolescents who had RTIs.

METHODS

This multicenter cross-sectional study was conducted using data collected between January 2011 and December 2018 in the Emergency Department-based Injury In-depth Surveillance registry in South Korea. A total of 66,632 participants younger than 19 years who presented with RTIs to emergency departments (EDs) were classified under three age groups: preschoolers (age 0-6 years, n = 18,694), elementary school student (age 7-12 years, n = 21,251), and middle and high school student (age 13-18 years, n = 26,687). Data on demographic and injury-related factors were analyzed, and multivariate logistic regression was used to determine the factors related to severe RTIs, which were defined as the Excess Mortality Ratio-based Injury Severity Score ≥16.

RESULTS

RTIs among children and adolescents were more common in boys (71.0%), during weekdays (39.7%), in the summer (31.1%), and between 12 noon and 6 pm (47.9%). The most common type of road users were passengers (preschoolers, 46.4%) and cyclists (age 7-12 years and age 13-18 years, 50.1% and 36.2%, respectively). The proportion of head injury was highest in the preschoolers group (57.3%). The length of ED stay, Excess Mortality Ratio-adjusted Injury Severity Score, and the proportion of intensive care unit admission increased with age. Nighttime (0-6 am), vulnerable road users (motorcyclists, bicyclists, and pedestrians), and use of emergency medical services were significantly associated with severe injury.

CONCLUSIONS

The three age groups of patients younger than 19 years with RTIs differed in the types of road user, proportions of injured body regions, and clinical outcomes. In an effort to reduce RTIs to children and adolescents, age-specific focused intervention should be considered. Additionally, the injury severity was found to be associated with nighttime occurrence, vulnerable road users, ED visit through emergency medical services, and nonuse of safety devices across all age group.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

Identify the key characteristics of pedestrian collisions through in-depth interviews: a pilot study

This study aimed to assess the feasibility of recruiting injured pedestrians from the emergency department of a major trauma centre, using an in-depth interview shortly post collision. Convenience sampling was used to prospectively recruit injured pedestrians from the Alfred Hospital Emergency and Trauma Centre. Of the 102 injured pedestrians, 39 met eligibility criteria and of these, 30 (77%) consented and completed the questionnaire. Over half of the collisions occurred at an intersection (57%), and of these the most common pre-impact vehicle manoeuvre was a vehicle turning into the street the pedestrian was crossing. In-depth interview during the early post-crash period was a feasible and effective method of collecting detailed data in an accessible sample. However, only 38% of patients met eligibility criteria. To enhance representativeness, supplementing interview data with police-reported crash data, recruiting from hospital wards and crash location assessment is recommended.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

Multiple-scale spatial analysis of paediatric, pedestrian road traffic injuries in a major city in North-Eastern Iran 2015-2019

Background

Paediatric, pedestrian road traffic injuries (PPRTIs) constitute a major cause of premature death in Iran. Identification of high-risk areas would be the primary step in designing policy intervention for PPRTI reduction because environmental factors play a significant role in these events. The present study aims to determine high-risk areas for PPRTIs at three different geographical scales, including the grid network, the urban neighbourhood and the street levels in Mashhad, Iran during the period 2015–2019.

Methods

This cross-sectional retrospective study was based on all pedestrian accidents with motor vehicles involving children (less than 18 years of age) between March 2015 and March 2019 in the city of Mashhad, which is the second-most populous city in Iran. The Anselin Local Moran’s I statistic and Getis-Ord Gi* were performed to measure spatial autocorrelation and hotspots of PPRTIs at the geographical grid network and neighbourhood level. Furthermore, a spatial buffer analysis was used to classify the streets according to their PPRTI rate.

Results

A total of 7390 PPRTIs (2364 females and 4974 males) were noted during the study period. The children’s mean age was 9.7 ± 5.1 years. Out of the total PPRTIs, 43% occurred on or at the sides of the streets, 25 of which labelled high-risk streets. A high-high cluster of PPRTI was discovered in the eastern part of the city, while there was a low-low such cluster in the West. Additionally, in the western part of the city, older children were more likely to become injured, while in the north-eastern and south-eastern parts, younger children were more often the victims.

Conclusions

Spatial analysis of PPRTIs in an urban area was carried out at three different geographical scales: the grid network, the neighbourhood and the street level. The resulting documentation contributes reliable support for the implementation and prioritization of preventive strategies, such as improvement of the high-risk streets and neighbourhoods of the city that should lead to decreasing numbers of PPRTIs.

Qualitative Field Observation of Pedestrian Injury Hotspots: A Mixed-Methods Approach for Developing Built- and Socioeconomic-Environmental Risk Signatures

Road traffic injuries constitute a significant global health burden; the World Health Organization estimates that they result in 1.35 million deaths annually. While most pedestrian injury studies rely predominantly on statistical modelling, this paper argues for a mixed-methods approach combining spatial analysis, environmental scans, and local knowledge for assessing environmental risk factors. Using data from the Nova Scotia Trauma Registry, severe pedestrian injury cases and ten corresponding hotspots were mapped across the Halifax Regional Municipality. Using qualitative observation, quantitative environmental scans, and a socioeconomic deprivation index, we assessed hotspots over three years to identify key social- and built-environmental correlates. Injuries occurred in a range of settings; however, clear patterns were not observed based on land use, age, or socio-economic status (SES) alone. Three hotspots revealed an association between elevated pedestrian injury and a pattern of geographic, environmental, and socio-economic factors: low- to middle-SES housing separated from a roadside attraction by several lanes of traffic, and blind hills/bends. An additional generalized scenario was constructed representing common risk factors across all hotspots. This study is unique in that it moves beyond individual measures (e.g., statistical, environmental scans, or geographic information systems (GIS) mapping) to combine all three methods toward identifying environmental features associated with pedestrian motor vehicle crashes (PMVC).

The association of material deprivation component measures with injury hospital separations in British Columbia, Canada

Background

This study examines social disparities across neighbourhood levels of income, education and employment in relation to overall injury hospital separations in the province of British Columbia, Canada. Further, the study examines the relationships of social disparities to a set of three injury prevention priorities in British Columbia, namely, transport (motor vehicle occupant, pedestrian and cyclist), falls among older adults, and youth self-harm. The goal being to better understand area-based injury incidence with a view to precision prevention initiatives, particularly for more vulnerable populations.

Methods

Acute hospital separations from the Discharge Abstract Database were identified for all causes of injury and the three BC injury prevention priorities for the period April 1, 2009 to March 31, 2014, inclusive. An ecological approach was applied where each hospital separation case was attributed with the income, education and employment level according to the injured individual’s area of residence, derived from the 2011 CensusPlus data.

Results

Injury hospital separation data were available for 191 Forward Sortation Areas in BC. Between April 1, 2009 and March 31, 2014, there was a total of 177,861 injury-related hospital separations, averaging 35,572 hospital separations per year and an annual rate of 779 injury hospital separations per 100,000 population. Injury hospital separation rates varied with the measured neighbourhood area socioeconomic status variables. Injury hospital separation rates demonstrated an inverse relationship with neighbourhood levels of income and education. Neighbourhood area socioeconomic status differences were also associated with the injury hospital separation rates for falls among older adults, motor vehicle crashes involving motor vehicle occupants, pedestrians, cyclists and young drivers, and youth self-harm.

Conclusions

The study results show that neighbourhood levels of income, education and employment are associated with the risk of injury hospital separation. In particular, low education levels in FSAs was associated with increased risk of injury hospital separation, mainly for motor vehicle occupants, pedestrians, young drivers, and youth self-harm. The results of this study provide useful information for implementing injury prevention initiatives and interventions in BC to align with the provincial public health system and road safety strategy goals, particularly for identified priorities.

Prevalence and Socio-Economic Determinants of Disabilities Caused by Road Traffic Accidents in Iran; A National Survey

Objective

To determine the prevalence and socio-economic disparity among victims with disabilities caused by RTAs in Iran as country with a high rate of accidents.

Method

The source of data was the Iranian Multiple Indicator Demographic and Health Survey, a nationwide cross-sectional study. The sampling framework was based on the population and housing census for Iran in 2006. Provincial samples ranged from 400 to 6,400 households. The target sample was 3,096 clusters consisting of 2,187 urban and 909 rural clusters. In the present study, all but a few indicators are reported at provincial levels. Mortality indicators, accident and disability rates, low birth weight rate and young age at marriage rates are presented at the national level only. Logistic regression was performed to investigate the individual and family factors influencing RTAs that lead to disability in Iran.

Results

The period prevalence (12 months) of road traffic accident disabilities (RTADs) in the total population of 111415 was 30.52 (95% CI: 21.13.41.64) per 100,000 individuals. Among those who had been injured during the year leading up to the study, the proportion of disabilities caused by RTAs was 31.67 (95% CI; 8.51.54.97) per 1000 pedestrians, 20.99 (95% CI: 13.37.30.75) per 1000 motorcyclists, 18.64 (95% CI: 7.71.29.57) per 1000 vehicle drivers. Multivariate logistic regression analysis showed that the risk of RTADs differed significantly in relation to age (AOR 50-59 vs. 0-9=10. 78, p-value:0.05); activity status (AOR unemployed vs. employed=4.72, p-value:0.001) and family income (AOR q2 vs. q1=0.37, p-value:0.048) of the victim.

Conclusion

In addition to the risks associated with socio-economic groups, particularly vulnerable groups, RTADs have consequences which can lead to further marginalization of individuals, can affect their quality of life and damage the community as a whole.

Challenges in studying statewide pedestrian injuries and drug involvement

BACKGROUND

Increasing U.S. rates of pedestrian injuries could be attributable in part to changing policies and attitudes towards drugs and associated increases in use, yet drug use has not been investigated widely as a risk factor for pedestrian injury. This study details challenges to investigating drug-involved pedestrian crashes using existing surveillance systems.

METHODS

Using California police reports from 2004 to 2016, we performed simple linear regression with the proportion of data that was missing by year for drug and alcohol use as the outcome of interest. We also explored differences in the relative proportion of missing data across sex, race, and age groups through simple logistic regression. Finally, we compared missing data for alcohol and drug use indicators for pedestrians and drivers.

RESULTS

From 2004 to 2016, 182,278 pedestrians were involved in crashes across California. Only 1.22% (n = 2219) of records indicated drug use, and 98% had missing data for drug use; the proportion of missing data did not change over time (b = - 0.040, p = 0.145, 95% CI = (- 0.095, 0.016)). The proportion of missing values for alcohol use increased each year (b = 0.49, 95% CI = (0.26, 0.72), p = 0.001). Driver drug and alcohol use indictors showed similar data missingness, and missing data did not show significant variation over time. Hispanics were more likely to have missing data for drug use compared to Whites (OR = 0.61, p < 0.001, 95% CI = (0.56, 0.67)), and Blacks were more likely to have missing data for alcohol use compared to Whites (OR = 0.87, p < 0.0001, 95% CI = (0.84, 0.91)).

CONCLUSIONS

Drug use may be a key contributing factor to pedestrian injury, but drug use remains consistently and largely unmeasured in existing surveillance systems. Without better collection of drug and alcohol data, monitoring trends in drug-involved pedestrian injury will not be feasible.

Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure

This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010-2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.

Injuries in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED)

This study examined caregiver-reported medically-attended injuries among 30-68 month old children with autism spectrum disorder (ASD) compared to general population (POP) and non-ASD developmental disorders (DD) controls in the Study to Explore Early Development. Injuries were common in ASD cases (32.3%) as well as POP (30.2%) and DD (27.8%) controls; most resulted in an emergency visit or hospitalization. After accounting for sociodemographic, health, IQ and behavior differences, odds of injury in ASD cases were significantly higher than DD controls but similar to POP controls. Attention problems mediated the relationships. Clinicians caring for children with both ASD and attention problems should consider providing targeted safety advice. Differences in injury risk between children with ASD vs. other developmental disorders need further study.

Correlates of fatality risk of vulnerable road users in Delhi

Pedestrians, cyclists, and users of motorised two-wheelers account for more than 85% of all the road fatality victims in Delhi. The three categories are often referred to as vulnerable road users (VRUs). Using Bayesian hierarchical approach with a Poisson-lognormal regression model, we present spatial analysis of road fatalities of VRUs with wards as areal units. The model accounts for spatially uncorrelated as well as correlated error. The explanatory variables include demographic factors, traffic characteristics, as well as built environment features. We found that fatality risk has a negative association with socio-economic status (literacy rate), population density, and number of roundabouts, and has a positive association with percentage of population as workers, number of bus stops, number of flyovers (grade separators), and vehicle kilometers travelled. The negative effect of roundabouts, though statistically insignificant, is in accordance with their speed calming effects for which they have been used to replace signalised junctions in various parts of the world. Fatality risk is 80% higher at the density of 50 persons per hectare (pph) than at overall city-wide density of 250 pph. The presence of a flyover increases the relative risk by 15% compared to no flyover. Future studies should investigate the causal mechanism through which denser neighborhoods become safer. Given the risk posed by flyovers, their use as congestion mitigation measure should be discontinued within urban areas.

A systematic review of socioeconomic status measurement in 13 years of U.S. injury research

OBJECTIVE

The purpose of this review was to assess the impact of socioeconomic status (SES) on injury and to evaluate how U.S. injury researchers have measured SES over the past 13years in observational research studies.

DESIGN & METHODS

This systematic review included 119 US injury studies indexed in PubMed between January 1, 2002 and August 31, 2015 that used one or more individual and/or area-level measures of SES as independent variables. Study findings were compared to the results of a previous review published in 2002.

RESULTS

Findings indicate SES remains an important predictor of injury. SES was inversely related to injury in 78 (66%) of the studies; inverse relationships were more consistently found in studies of fatal injury (77.4%) than in studies of non-fatal injury (58%). Approximately two-thirds of the studies (n=73, 61%) measured SES along a gradient and 59% used more than one measure of SES (n=70). Studies that used a gradient measure of SES and/or more than one measure of SES identified significant relationships more often. These findings were essentially equivalent to those of a similar 2002 review (Cubbin & Smith, 2002).

CONCLUSIONS

There remains a need to improve measurement of SES in injury research. Public health training programs should include best practices for measurement of SES, which include: measuring SES along a gradient, selecting SES indicators based on the injury mechanism, using the smallest geographic region possible for area-level measures, using multiple indicators when possible, and using both individual and area-level measures as both contribute independently to injury risk. Area-level indicators of SES are not accurate estimates of individual-level SES.

PRACTICAL APPLICATIONS

Injury researchers should measure SES along a gradient and incorporate individual and area-level SES measures that are appropriate to the injury outcome under study.

Spatial distribution of child pedestrian injuries along census tract boundaries: Implications for identifying area-based correlates

Census tracts are often used to investigate area-based correlates of a variety of health outcomes. This approach has been shown to be valuable in understanding the ways that health is shaped by place and to design appropriate interventions that account for community-level processes. Following this line of inquiry, it is common in the study of pedestrian injuries to aggregate the point level locations of these injuries to the census tracts in which they occur. Such aggregation enables investigation of the relationships between a range of socioeconomic variables and areas of notably high or low incidence. This study reports on the spatial distribution of child pedestrian injuries in a mid-sized U.S. city over a three-year period. Utilizing a combination of geospatial approaches, Near Analysis, Kernel Density Estimation, and Local Moran’s I, enables identification, visualization, and quantification of close proximity between incidents and tract boundaries. Specifically, results reveal that nearly half of the 100 incidents occur within roads that are also census tract boundaries. Results also uncover incidents that occur on tract boundaries, not merely near them. This geographic pattern raises the question of the utility of associating area-based census data from any one tract to the injuries occurring in these border zones. Furthermore, using a standard spatial join technique in a Geographic Information System (GIS), these points located on the border are counted as falling into census tracts on both sides of the boundary, which introduces uncertainty in any subsequent analysis. Therefore, two additional approaches of aggregating points to polygons were tested in this study. Results differ with each approach, but without any alert of such differences to the GIS user. This finding raises a fundamental concern about techniques through which points are aggregated to polygons in any study using point level incidents and their surrounding census tract socioeconomic data to understand health and place. This study concludes with a suggested protocol to test for this source of uncertainty in analysis and an approach that may remove it.

An overview of geospatial methods used in unintentional injury epidemiology

Background

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.

Methods

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.

Results

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).

Conclusion

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-016-0097-0) contains supplementary material, which is available to authorized users.

Association between trauma and socioeconomic deprivation: a registry-based, Scotland-wide retrospective cohort study of 9,238 patients

Background

Trauma remains a leading cause of morbidity and mortality in the UK and throughout the world. Socioeconomic deprivation has been linked with many types of ill-health and previous studies have shown an association with injury in other parts of the world. The aim of this study was to investigate the association between socioeconomic deprivation and trauma incidence and case-fatality in Scotland.

Methods

The study included nine thousand two hundred and thirty eight patients attending Emergency Departments following trauma across Scotland in 2011-12. A retrospective cohort study was conducted using secondary data extracted from the national trauma registry. Postcode of residence was used to generate deciles using the Scottish Index of Multiple Deprivation. The incidence rate ratio (IRR) was calculated to allow comparison of incidence of trauma across SIMD deciles. For mortality, observed: expected ratios were obtained using observed mortality in the cohort and expected deaths using probability of survival based on Trauma and Injury Severity Score (TRISS) method.

Results

Compared with the most deprived decile, the least deprived had an incidence rate ratio (IRR) for all trauma of 0.43 (95 % CI 0.32–0.58, p < 0.001). The association was stronger for penetrating trauma (IRR 0.07, 95 % CI .01–0.56, p = 0.011). There was a significant interaction between age, gender and SIMD. For case fatality, multivariate logistic regression showed that, severity of trauma (ISS > 15) OR 18.11 (95 % CI 13.91 to 23.58) and type of injury (Penetrating versus blunt injury) OR 2.07 (95 % CI 1.15 to 3.72) remain as independent predictors of case fatality in this dataset.

Discussion

Our data shows a higher incidence of trauma amongst a socioeconomically deprived population, in keeping with other areas of the world. In our dataset, outcome, as measured by in-hospital mortality, does not appear to be associated with socioeconomic deprivation.

Conclusion

In Scotland, populations living in socioeconomically deprived areas have a higher incidence of trauma, especially penetrating trauma, requiring hospital attendance. Case fatality is associated with more severe trauma and penetrating trauma, but not socioeconomic deprivation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13049-016-0275-7) contains supplementary material, which is available to authorized users.

Disability related to road traffic crashes among adults in Spain

BACKGROUND

Road traffic accidents cause substantial morbidity and disease burden; few studies have examined their impact on disability.

OBJECTIVE

To estimate the magnitude and distribution of disability due to road traffic accidents according to socio-demographic variables, and its main socioeconomic and health determinants.

METHODS

A cross-sectional study was conducted in community-dwelling participants in the "2008 Spanish National Disability Survey", a representative sample of 91,846 households with 20,425 disabled persons older than 15 years; 443 had disability due to road traffic accidents.

RESULTS

The prevalence was 2.1 per 1000 inhabitants (95% CI:1.8-2.3), with no differences by sex. Risk was highest among persons aged 31 to 64 years, and onset of disability showed a sharp inflection point at age 16 years in both sexes. Odds ratios (ORs) were higher (OR=1.3; 95% CI:1.1- 1.7) for participants with secondary education than for those with the lowest educational levels and were lower (OR: 0.5; 95% CI:0.3-0.8) for participants with the highest household income levels than for those with lowest. Only 24% of disabled participants were gainfully employed. As compared to other sources of disability, traffic crashes caused greater disability in terms of mobility (OR=3.1;p<0.001), a greater need for health/social services (OR=1.5;p=0.003), and more problems with private transportation (OR=1.6;p<0.001), moving around outside the home (OR=1.6;p<0.001) and changes in economic activity (OR=2.4;p<0.001).

CONCLUSIONS

The prevalence of disability due to road traffic accidents in Spain is lower than in other developed countries, with middle-aged and socio-economically underprivileged persons being the most affected. Disability due to road traffic accidents is related to a greater demand for social/health care support, problems of accessibility/commuting, and major changes in economic activity.

Census-based socioeconomic indicators for monitoring injury causes in the USA: a review

BACKGROUND

Unlike the UK or New Zealand, there is no standard set of census variables in the USA for characterising socioeconomic (SES, socioeconomic status) inequalities in health outcomes, including injury. We systematically reviewed existing US studies to identify conceptual and methodological strengths and limitations of current approaches to determine those most suitable for research and surveillance.

METHODS

We searched seven electronic databases to identify census variables proposed in the peer-reviewed literature to monitor injury risk. Inclusion criteria were that numerator data were derived from hospital, trauma or vital statistics registries and that exposure variables included census SES constructs.

RESULTS

From 33 eligible studies, we identified 70 different census constructs for monitoring injury risk. Of these, fewer than half were replicated by other studies or against other causes, making the majority of studies non-comparable. When evaluated for a statistically significant relationship with a cause of injury, 74% of all constructs were predictive of injury risk when assessed in pairwise comparisons, whereas 98% of all constructs were significant when aggregated into composite indices. Fewer than 30% of studies selected SES constructs based on known associations with injury risk.

CONCLUSIONS

There is heterogeneity in the conceptual and methodological approaches for using census data for monitoring injury risk as well as in the recommendations as to how these constructs can be used for injury prevention. We recommend four priority areas for research to facilitate a more unified approach towards use of the census for monitoring socioeconomic inequalities in injury risk.

Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: space-time path vs. potential path tree methods

Research on the extent to which pedestrians are exposed to road collision risk is important to the improvement of pedestrian safety. As precise geographical information is often difficult and costly to collect, this study proposes a potential path tree method derived from time geography concepts in measuring pedestrian exposure. With negative binomial regression (NBR) and geographically weighted Poisson regression (GWPR) models, the proposed probabilistic two-anchor-point potential path tree (PPT) approach (including the equal and weighted PPT methods) are compared with the deterministic space-time path (STP) method. The results indicate that both STP and PPT methods are useful tools in measuring pedestrian exposure. While the STP method can save much time, the PPT methods outperform the STP method in explaining the underlying vehicle-pedestrian collision pattern. Further research efforts are needed to investigate the influence of walking speed and route choice.

Association between neighbourhood marginalization and pedestrian and cyclist collisions in Toronto intersections

OBJECTIVE

Pedestrian and cyclist collisions comprise a significant proportion of preventable injury. In urban settings, collision rates have been linked to various socio-demographic factors. We sought to determine whether neighbourhood marginalization affects pedestrian and cyclist collisions in the Greater Toronto Area.

METHODS

For 114 intersections, pedestrian and cyclist collisions were extracted from the Toronto Traffic Data Centre database. We used a geographic information system approach to determine census Dissemination Areas and an associated Ontario Marginalization Index (ON-Marg) for each intersection. We performed a logistic regression to examine the associations between the four ON-Marg dimensions (residential instability, material deprivation, dependency, ethnic concentration) and pedestrian and cyclist collisions.

RESULTS

The odds of sustaining a collision were independently associated with residential instability for both pedestrians (OR 1.84, 95% CI 1.21-2.84, p=0.006) and cyclists (OR 2.04, 95% CI 1.34-3.16, p=0.001). Higher overall collision rates (both pedestrian and cyclist) were associated with both ethnic concentration (OR 1.56, 95% CI 1.05-2.37, p=0.033) and residential instability (OR 2.16, 95% CI 1.43-3.38, p=0.001). Material deprivation and dependency were not significant risk factors for intersection collisions in this model.

CONCLUSIONS

Collisions involving pedestrians and cyclists are more common in areas of increased residential instability and ethnic concentration in Toronto. Intersections in neighbourhoods with these characteristics could be targeted for strategies to reduce pedestrian and cyclist injury risk in urban settings.

The socioeconomic inequality in traffic-related disability among Chinese adults: the application of concentration index

Traffic crashes have become the fifth leading cause of burden of diseases and injuries in China. More importantly, it may further aggravate the degree of health inequality among Chinese population, which is still under-investigated. Based on a nationally representative data, we calculated the concentration index (CI) to measure the socioeconomic inequality in traffic-related disability (TRD), and decomposed CI into potential sources of the inequality. Results show that more than 1.5 million Chinese adults were disabled by traffic crashes and the adults with financial disadvantage bear disproportionately heavier burden of TRD. Besides, strategies of reducing income inequality and protecting the safety of poor road users, are of great importance. Residence appears to counteract the socioeconomic inequality in TRD, however, it does not necessarily come to an optimistic conclusion. In addition to the worrying income gap between rural and urban areas, other possible mechanisms, e.g. the low level of post-crash medical resources in rural area, need further studies. China is one of the developing countries undergoing fast motorization and our findings could provide other countries in similar context with some insights about how to maintain socioeconomic equality in road safety.

Disparity surveillance of nonfatal motor vehicle crash injuries

OBJECTIVE

The lack of race information for nonfatal motor vehicle crash injuries in the United States has limited the understanding of racial disparities in motor vehicle crashes (MVCs). In this article, we describe a pilot surveillance project in Nebraska that linked crash reports and driver's license records to investigate racial disparity among nonfatal MVC injuries.

METHODS

The project linked 43,157 severely and nonseverely injured drivers from crash reports between 2006 and 2010 to the corresponding state driver's license database so that drivers' race information from each MVC could be retrieved. A log rate model was used to examine the likelihood of MVC injuries by drivers' race along the dimensions of age, sex, and place of residence.

RESULTS

Black drivers had 31.6 and 87 percent more severe and nonsevere injuries, respectively, than white drivers. Rural residents were more likely than urban residents to have severe MVC injuries. Controlling for residence status, age, and sex did not alter the basic pattern that black drivers had higher rates of nonfatal MVC injuries.

CONCLUSIONS

The linkage approach provides an effective way to obtain additional information for MVC injury disparity surveillance. To reduce racial disparities in severe and nonsevere MVC injuries, race-sex-, race-age-, and race-location-specific interventions should be considered based on their significant contributions to disparity.

A geographic analysis of collisions involving child pedestrians in a large Southern California county

OBJECTIVES

The goal of this study is to explore the relationship between child pedestrian injuries and socioeconomic characteristics of neighborhoods in the context of local traffic volume.

METHODS

Child pedestrian collisions were identified in the data of the California Statewide Integrated Traffic Records System (SWITRS). Nine hundred sixty crashes over a 5-year period were identified, geocoded, and mapped to Orange County census tracts. Census data from 2000 were used to identify tracts, population, and population characteristics in the county of approximately 3,000,000 individuals. Pedestrian collision maps were merged with census characteristics and analyzed using STATA (Version 10.1, Stata Corp, College Station, IX) to determine correlations between socioeconomic factors and collision rates within census tracts.

RESULTS

The percentage of the population living in households with low income (less than 185% of the federal poverty level) was the strongest predictor of pedestrian injuries. One fourth of census tracts had less than 9 percent of residents with low income and averaged 6 per 100,000 child pedestrian crashes annually. One fourth of the census tracts had more than 32 percent of residents with low income and an average of 56 child pedestrian crashes per 100,000 annually. These data indicate an 8.8-fold increase in collision frequency in the lowest income quartile over the highest income quartile. Other socioeconomic correlates strongly associated with increased child collisions include population density, proportion of population speaking English less than very well, lack of high school education, number of multifamily residences, and Latino ethnicity.

CONCLUSIONS

Our study showed that child pedestrian collisions are nearly 9 times more frequent in the poorest quartile of neighborhoods than in the richest quartile. Other factors associated with increased pediatric collision risk include increased neighborhood crowding, low levels of education and English speaking ability, and Latino ethnicity.