Geospatial mapping can be used to identify geographic areas and social factors associated with intentional injury as targets for prevention efforts distinct to a given community

Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis

BACKGROUND

Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation.

OBJECTIVE

This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies.

METHODS

We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health.

RESULTS

While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health.

CONCLUSIONS

The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.

Pooled Analysis of Trauma Centers Better Predicts Risk Factors for Firearm Violence Reinjury

INTRODUCTION

Many trauma centers use the first firearm injury admission as a reachable moment to mitigate reinjury. Understanding repeat firearm violence can be difficult in metropolitan areas with multiple trauma centers and laws that prohibit sharing private health information across health systems. We hypothesized that risk factors for repeat firearm violence could be better understood using pooled data from two major metropolitan trauma centers.

METHODS

Two level I trauma center registries were queried (2007-2017) for firearm injury admissions using International Classification of Diseases, Ninth and Tenth Revision (ICD9/10) Ecodes. A pseudo encryption tool allowed sharing of deidentified firearm injury and repeat firearm injury data without disclosing private health information. Factors associated with firearm reinjury admissions including, age, sex, race, payor, injury severity, intent, and discharge, were assessed by multivariable logistic regression.

RESULTS

We identified 2145 patients with firearm injury admissions, 89 of whom had a subsequent repeat firearm injury admission. Majority of repeat firearm admissions were assaulted (91%), male (97.8%), and non-Hispanic Black (86.5%). 31.5% of repeat firearm injury admissions were admitted to a different trauma center from their initial admission. Independent predictors of repeat firearm injuries were age (adjusted odds ratio [aOR] 0.94, P < 0.001), male sex (aOR 6.18, P = 0.013), non-Hispanic Black race (aOR 5.14, P = 0.007), or discharge against medical advice (aOR 6.64, P=<0.001).

CONCLUSIONS

Nearly a third of repeat firearm injury admissions would have been missed in the current study without pooled metropolitan trauma center data. The incidence of repeat firearm violence is increasing and those at the highest risk for reinjury need to be targeted for mitigating interventions.

Trends in disparities research on trauma and acute care surgery outcomes: A 10-year systematic review of articles published in The Journal of Trauma and Acute Care Surgery

ABSTRACT

This is a 10-year review of The Journal of Trauma and Acute Care Surgery (JTACS) literature related to health care disparities, health care inequities, and patient outcomes. A retrospective review of articles published in JTACS between January 1, 2013, and July 15, 2022, was performed. Articles screened included both adult and pediatric trauma populations. Included articles focused on patient populations related to trauma, surgical critical care, and emergency general surgery. Of the 4,178 articles reviewed, 74 met the inclusion criteria. Health care disparities related to gender (n = 10), race/ethnicity (n = 12), age (n = 14), income status (n = 6), health literacy (n = 6), location and access to care (n = 23), and insurance status (n = 13) were described. Studies published on disparities peaked in 2016 and 2022 with 13 and 15 studies respectively but dropped to one study in 2017. Studies demonstrated a significant increase in mortality for patients in rural geographical regions and in patients without health insurance and a decrease in patients who were treated at a trauma center. Gender disparities resulted in variable mortality rates and studied factors, including traumatic brain injury mortality and severity, venous thromboembolism, ventilator-associated pneumonia, firearm homicide, and intimate partner violence. Under-represented race/ethnicity was associated with variable mortality rates, with one study demonstrating increased mortality risk and three finding no association between race/ethnicity and mortality. Disparities in health literacy resulted in decreased discharge compliance and worse long-term functional outcomes. Studies on disparities in JTACS over the last decade primarily focused on location and access to health care, age, insurance status, and race, with a specific emphasis on mortality. This review highlights the areas in need of further research and funding in the Journal of Trauma and Acute Care Surgery regarding health care disparities in trauma aimed at interventions to reduce disparities in patient care, ensure equitable care, and inform future approaches targeting health care disparities.

LEVEL OF EVIDENCE

Systematic Review; Level IV.

The Geography of Injuries in Trauma Systems: Using Home as a Proxy for Incident Location

INTRODUCTION

Effective trauma system organization is crucial to timely access to care and requires accurate understanding of injury and resource locations. Many systems rely on home zip codes to evaluate geographic distribution of injury; however, few studies have evaluated the reliability of home as a proxy for incident location after injury.

METHODS

We analyzed data from a multicenter prospective cohort collected from 2017 to 2021. Injured patients with both home and incident zip codes were included. Outcomes included discordance and differential distance between home and incident zip code. Associations of discordance with patient characteristics were determined by logistic regression. We also assessed trauma center catchment areas based on home versus incident zip codes and variation regionally at each center.

RESULTS

Fifty thousand one hundred seventy-five patients were included in the analysis. Home and incident zip codes were discordant in 21,635 patients (43.1%). Injuries related to motor vehicles (aOR: 4.76 [95% CI 4.50-5.04]) and younger adults 16-64 (aOR: 2.46 [95% CI 2.28-2.65]) were most likely to be discordant. Additionally, as injury severity score increased, discordance increased. Trauma center catchment area differed up to two-thirds of zip codes when using home versus incident location. Discordance rate, discordant distance, and catchment area overlap between home and incident zip codes all varied significantly by geographic region.

CONCLUSIONS

Home location as proxy for injury location should be used with caution and may impact trauma system planning and policy, especially in certain populations. More accurate geolocation data are warranted to further optimize trauma system design.

Access to care following injury in Northern Malawi, a comparison of travel time estimates between Geographic Information System and community household reports

INTRODUCTION

Injuries disproportionately impact low- and middle-income countries like Malawi. The Lancet Commission on Global Surgery's indicators include the population proportion accessing laparotomy and open fracture care, key trauma interventions, within two hours. The "Golden Hour" for receiving facility-based resuscitation also guides injury care system strengthening. Firstly, we estimated the proportion of the local population able to reach primary, secondary and tertiary facility care within two and one hours using Geographic Information System (GIS) analysis. Secondly, we compared community household-reported with GIS-estimated travel time.

METHODS

Using information from a Health and Demographic Surveillance Site (Karonga, Malawi) on road network, facility location, and local staff-estimated travel speeds, we used a GIS-generated friction surface to calculate the shortest travel time from all households to each facility serving the population. We surveyed community households who reported travel time to their preferred, closest, government secondary and tertiary facilities. For recently injured community members, time to reach facility care was recorded. To assess the relationship between community household-reported travel time and GIS-estimated travel time, we used linear regression to generate a proportionality constant. To assess associations and agreement between injured patient-reported and GIS-estimated travel time, we used Kendall rank and Cohen's kappa tests.

RESULTS

Using GIS, we estimated 79.1% of households could reach any secondary facility, 20.5% the government secondary facility, and 0% the government tertiary facility, within two hours. Only 28.2% could reach any secondary facility within one hour, 0% for the government secondary facility. Community household-reported travel time exceeded GIS-estimated travel time. The proportionality constant was 1.25 (95%CI 1.21-1.30) for the closest facility, 1.28 (95%CI 1.23-1.34) for the preferred facility, 1.45 (95%CI 1.33-1.58) for the government secondary facility, and 2.12 (95%CI 1.84-2.41) for tertiary care. Comparing injured patient-reported with GIS-estimated travel time, the correlation coefficient was 0.25 (SE 0.047) and Cohen's kappa was 0.15 (95%CI 0.078-0.23), suggesting poor agreement.

DISCUSSION

Most households couldn't reach government secondary care within recognised thresholds indicating poor temporal access. Since GIS-estimated travel time was shorter than community-reported travel time, the true proportion may be lower still. GIS derived estimates of population emergency care access in similar contexts should be interpreted accordingly.

Youth violence prevention can be enhanced by geospatial analysis of trauma registry data

INTRODUCTION

Geographic information systems (GIS) have been used to understand relationships between trauma mechanisms, locations and social determinants for injury prevention. We hypothesized that GIS analysis of trauma center registry data for assault patients ages 14-29 with census tract data would identify geospatial and structural determinants of youth violence.

METHODS

Admissions to a Level 1 trauma center from 2010 to 2019 were retrospectively reviewed to identify assaults in those 14-29 years. Prisoners were excluded. Home and injury scene addresses were geocoded. Cluster analysis was performed with the Moran I test for spatial autocorrelation. Census tract comparisons were done using American Communities Survey (ACS) data by t-test and linear regression.

RESULTS

There were 1608 admissions, 1517 (92.4%) had complete addresses and were included in the analysis. Mean age was 23 yrs ± 3.8, mean ISS was 7.5 ± 6.2, there were 11 (0.7%) in-hospital deaths. Clusters in six areas of the trauma catchment were identified with a Moran I value of 0.24 (Fig 1, Z score = 17.4, p < 0.001). Linear regression of ACS demographics showed predictors of assault were unemployment (OR 4.5, 95% CI: 2.7- 6.4, p < 0.001), Spanish spoken at home (OR 6.6, 95% CI: 3.4-9.8, p < 0.001) and poverty level (OR 1.9, 95% CI: 1.1-2.7, p < 0.001). Education level of less than high school diploma, single parent households and race were not significant predictors.

CONCLUSION

GIS analysis of registry data can identify high risk areas for youth violence and correlated social and structural determinants. Violence prevention efforts can be better targeted geographically and socioeconomically with better understanding of these risk factors.

LEVEL OF EVIDENCE

Epidemiological, level III.

Social determinants of health and patient-level mortality prediction after trauma

BACKGROUND

Social determinants of health (SDOH) impact patient outcomes in trauma. Census data are often used to account for SDOH; however, there is no consensus on which variables are most important. Social vulnerability indices offer the advantage of combining multiple constructs into a single variable. Our objective was to determine if incorporation of SDOH in patient-level risk-adjusted outcome modeling improved predictive performance.

METHODS

We evaluated two social vulnerability indices at the zip code level: Distressed Community Index (DCI) and National Risk Index (NRI). Individual variable combinations from Agency for Healthcare Research and Quality's SDOH data set were used for comparison. Patients were obtained from the Pennsylvania Trauma Outcomes Study 2000 to 2020. These measures were added to a validated base mortality prediction model with comparison of area under the curve and Bayesian information criterion. We performed center benchmarking using risk-standardized mortality ratios to evaluate change in rank and outlier status based on SDOH. Geospatial analysis identified geographic variation and autocorrelation.

RESULTS

There were 449,541 patients included. The DCI and NRI were associated with an increase in mortality (adjusted odds ratio, 1.02; 95% confidence interval, 1.01-1.03 per 10% percentile rank increase; p < 0.01, respectively). The DCI, NRI, and seven Agency for Healthcare Research and Quality variables also improved base model fit but discrimination was similar. Two thirds of centers changed mortality ranking when accounting for SDOH compared with the base model alone. Outlier status changed in 7% of centers, most representing an improvement from worse-than-expected to nonoutlier or nonoutlier to better-than-expected. There was significant geographic variation and autocorrelation of the DCI and NRI (DCI; Moran's I 0.62, p = 0.01; NRI; Moran's I 0.34, p = 0.01).

CONCLUSION

Social determinants of health are associated with an individual patient's risk of mortality after injury. Accounting for SDOH may be important in risk adjustment for trauma center benchmarking.

LEVEL OF EVIDENCE

Prognostic/Epidemiologic, level IV.

Comparing forms of neighborhood instability as predictors of violence in Richmond, VA

Violence events tend to cluster together geospatially. Various features of communities and their residents have been highlighted as explanations for such clustering in the literature. One reliable correlate of violence is neighborhood instability. Research on neighborhood instability indicates that such instability can be measured as property tax delinquency, yet no known work has contrasted external and internal sources of instability in predicting neighborhood violence. To this end we collected data on violence events, company and personal property tax delinquency, population density, race, income, food stamps, and alcohol outlets for each of Richmond, Virginia's 148 neighborhoods. We constructed and compared ordinary least-squares (OLS) to geographically weighted regression (GWR) models before constructing a final algorithm-selected GWR model. Our results indicated that the tax delinquency of company-owned properties (e.g., rental homes, apartments) was the only variable in our model (R2 = 0.62) that was associated with violence in all but four Richmond neighborhoods. We replicated this analysis using violence data from a later point in time which yielded largely identical results. These findings indicate that external sources of neighborhood instability may be more important to predicting violence than internal sources. Our results further provide support for social disorganization theory and point to opportunities to expand this framework.

Geographies of Sexual Assault: A Spatial Analyses to Identify Neighborhoods Affected by Sexual and Gender-Based Violence

Emergency departments are a common access point for survivors of sexual and gender-based violence (SGBV), but very little is known about where survivors live and the neighborhoods they return to. The objectives of this study were to describe the patient population that present for a sexual or partner-based assault and explore the geographic distribution of cases across the Ottawa-Gatineau area. Data for this study were extracted from the Sexual Assault and Partner Abuse Care Program (SAPACP) case registry (January 1 to December 31, 2015) at The Ottawa Hospital. Spatial analyses were conducted using six-digit postal codes converted into Canadian Census Tract units to identify geographic areas with concentrated cases of SGBV. Concentrated areas were defined as Census Tracts with seven or more SGBV cases within a single calendar year. In 2015, there were 406 patients seen at the SAPACP and 348 had valid postal codes and were included in the analyses. More than 90% of patients were female and 152 (43.68%) were below 24 years of age. More than 70% knew their assailant and the most common locations of the assault were at the survivors' home (31.03%), assailants' home (27.01%), or outdoors (10.92%). Eight concentrated areas were identified including three in the downtown entertainment district, three lower income areas, one high-income neighborhood, and one suburb more than 20 km from downtown. The findings from this study describe the typical clinical presentation of sexual and domestic assault survivors and also challenge geographic stereotypes of where survivors live and what areas of the city are most affected by SGBV. Using residential information provides a survivor-centric approach that highlights the widespread nature of SGBV and supports the need for population-based approaches to improve care for survivors.

Can social vulnerability indices predict county trauma fatality rates?

BACKGROUND

Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.

METHODS

We evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.

RESULTS

A total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).

CONCLUSION

While the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.

LEVEL OF EVIDENCE

Epidemiological, level IV.

Demographics and Incident Location of Gunshot Wounds at a Single Level I Trauma Center

Introduction

Kansas has seen a steady increase in the rate of firearm deaths and injuries. Little is known surrounding the demographic and geospatial factors of these firearm-related traumas. The purpose of this study was to describe the overall incidence of firearm-related traumas, identify high injury locations, and examine any racial/ethnic disparities that may exist.

Methods

A retrospective review was conducted of all patients 14 years or older who were admitted with a gunshot wound (GSW) to a Level I trauma center between 2016 and 2017.

Results

Forty-nine percent of patients were Caucasian, 26.5% African American, and 19.6% Hispanic/Latino. Hispanic/Latino patients were the youngest (25.8 ± 8.8 years) and Caucasians were the oldest (34.3 ± 14.1 years, p = 0.002). Compared to Caucasian patients, African American (42.0%) and Hispanic/Latino (54.1%) patients were more likely to be admitted to the intensive care unit (ICU; p = 0.034) and experienced longer ICU lengths of stay (2.5 ± 6.3 and 2.4 ± 4.7 days, p = 0.031, respectively). African American patients (96.0%) experienced more assaults, while Caucasians were more likely to receive gunshot wounds accidentally (26.9%, p = 0.001). More African American (86.0%) and Hispanic/Latino (89.2%) patients were injured with a handgun and Caucasians sustained the highest percentage of shotgun/rifle related injuries (16.1%, p = 0.012). Most GSWs occurred in zip codes 67202, 67203, 67213, 67211, and 67214. Geographical maps indicated that GSWs occur in neighborhoods with low-income and high minority residents and in the downtown and nightclub areas of the city.

Conclusions

Most GSW victims were older Caucasian males. Racial differences were noted and injury locations concentrated in certain locations.

Spatial analysis of elder abuse in a Brazilian municipality

OBJECTIVE

to analyze the spatial distribution of elder abuse in Ribeirão Preto-SP, according to victims' place of residence and event.

METHOD

an ecological study that analyzed 1,153 elder abuse police reports (2009 to 2013). Local gross and empirical Bayesian rates and Local Moran's I were calculated.

RESULTS

there was a heterogeneous distribution of concentration of sectors with a high incidence rate surrounded by neighbors with also high rates, considering place of residence. In contrast, analysis by place of occurrence showed a greater concentration in the central areas of the municipality. More than 80% of cases of violence occurred at their own homes and with spatial dependence on distribution by occurrence up to a 5,000 m distance from their places of residence.

CONCLUSION

data reinforce that violence is a complex phenomenon, affecting several social strata, including in central urban areas and in elderly people families' own context.

Demographics and Incident Location of Traumatic Injuries at a Single Level I Trauma Center

Introduction

Traumatic injuries are preventable and understanding determinants of injury, such as socio-economic and environmental factors, is vital. This study evaluated traumatic injuries and identified areas of high trauma incidence.

Methods

A retrospective review was conducted of all patients 14 years or older who were admitted with a traumatic injury to a Level I trauma center between 2016 and 2017. Descriptive analyses were presented and maps of high injury areas were generated.

Results

The most frequent mechanisms of injury were falls (58.3%), motor vehicle crashes (22.3%), and motorcycle crashes (5.7%). Fall patients were more likely to be female (59.6%) and were the oldest age group (72.1 ± 17.2) compared to motor vehicle and motorcycle crash patients. Severe head (22.1%, p = 0.007) and extremity (35.7%, p = 0.001) injuries were most frequent among fall patients, however, more motorcycle crash patients required mechanical ventilation (16.1%, p < 0.001) and experienced the longest intensive care unit length of stay (5.3 ± 6.8 days, p < 0.001) and mechanical ventilation days (6.6 ± 8.5, p < 0.036). Motorcycle crash patients also had the greatest number of deaths (7.5%, p < 0.001). The generated maps of all traumas suggested that most injuries occur near our hospital and are located in several of the most population-dense zip codes.

Conclusion

Patient demographics, injury severity, and hospital outcomes varied by mechanisms of injury. Traumatic injuries occurred near our hospital and were located in several of the most populationdense zip codes. Injury prevention efforts should target high incident areas.

Geospatial characteristics of non-motor vehicle and assault-related trauma events in greater Phoenix, Arizona

BACKGROUND

Injury-causing events are not randomly distributed across a landscape, but how they are associated with the features and characteristics of the places where they occur in Arizona (AZ) remains understudied. Clustering of trauma events and associations with areal sociodemographic characteristics in the greater Phoenix (PHX), AZ region can promote understanding and inform efforts to ameliorate a leading cause of death and disability for Arizonans. The outcomes of interest are trauma events unrelated to motor vehicle crashes (MVC) and the subgroup of trauma events due to interpersonal assaults.

METHODS

A retrospective, ecological study was performed incorporating data from state and national sources for the years 2013-2017. Geographically weighted regression models explored associations between the rates of non-MVC trauma events (n/10,000 population) and the subgroup of assaultive trauma events per 1000 and areal characteristics of socioeconomic deprivation (areal deprivation index [ADI]), the density of retail alcohol outlets for offsite consumption, while controlling for race/ethnicity, population density, and the percentage urban population.

RESULTS

The 63,451 non-MVC traumas within a 3761 mile2 study area encompassing PHX and 22 surrounding communities, an area with nearly 60% of the state's population and 54% of the trauma events in the AZ State Trauma Registry for the years 2013-2017. Adjusting for confounders, ADI was associated with the rates of non-MVC and assaultive traumas in all census block groups studied (mean coefficients 0.05 sd. 0.001 and 0.07 sd. 0.002 for non-MVC and assaultive trauma, respectively). Alcohol retail outlet density was also associated with non-MVC and assaultive traumas in fewer block groups compared to ADI.

CONCLUSION

Socioeconomic deprivation and alcohol outlet density were associated with injury producing events in the greater PHX area. These features persist in the environment before and after the traumas occur. Ongoing research is warranted to identify the most influential areal predictors of traumatic injury-causing events in the greater PHX area to inform and geographically target prevention initiatives.

The Relationship Between Income and Burn Incidence in Winnipeg, Manitoba, Canada: A Population Health Study

Burns continue to be a common cause of morbidity around the world, and socioeconomic status has been linked to high-burn risk in developed and developing countries. The purpose of this study was to define in Winnipeg, Manitoba, Canada: 1) demographics of adult patients with severe burns; 2) the relationship between household income and burn incidence; and 3) specific geographic areas that may benefit from targeted burn prevention strategies. We conducted a retrospective study of adult (>18 years) patients in Winnipeg, with burns severe enough to require at least 1 day of hospitalization between 2006 and 2016. Area-level median household income data at two geographic levels were collected from 2011 Census based on patient postal codes. Of 207 patients that met study criteria, 156 (75.4%) were male. Mean age was 44.5 ± 16.9 years, and the most common cause of burns was fire/flame (52.2%). The analysis of income data revealed that lower area-level income groups had a higher incidence of burns, with the lowest income quintile group having 5.4 times higher incidence than highest income quintile group. Spatial analysis software was used to map the incidence rates, and clusters of high-risk burns were identified in and around the city center region. Overall, our study showed an inverse relationship between area-level income and burn injury incidence. The identification and mapping of high incidence hotspots will allow policy makers to target groups who will benefit most from burn prevention strategies.

The relationship between geographic location and outcomes following injury: A scoping review

INTRODUCTION

Globally, injury incidence and injury-fatality rates are higher in regional and remote areas. Recovery following serious injury is complex and requires a multi-disciplinary approach to management and community re-integration to optimise outcomes. A significant knowledge gap exists in understanding the regional variations in hospital and post-discharge outcomes following serious injury. The aim of this study was to review the evidence exploring the association between the geographic location, including both location of the event and place of residence, and outcomes following injury.

MATERIALS AND METHODS

A scoping review was used to investigate this topic and provide insight into geographic variation in outcomes following traumatic injury. Seven electronic databases and reference lists of relevant articles were searched from inception to October 2018. Studies were included if they measured injury-related mortality, outcomes associated with hospital admission, post-injury physical or psychological function and analysed these outcomes in relation to geographic location.

RESULTS

Of the 2,213 studies identified, 47 studies were included revealing three key groups of outcomes: mortality (n = 35), other in-hospital outcomes (n = 8); and recovery-focused outcomes (n = 12). A variety of measures were used to classify rurality across studies with inconsistent definitions of rurality/remoteness. Of the studies reporting injury-related mortality, findings suggest that there is a greater risk of fatality in rural areas overall and in the pre-hospital phase. For those patients that survived to hospital, the majority of studies included identified no difference in mortality between rural and urban patient groups. In the small number of studies that reported other in-hospital and recovery outcomes no consistent trends were identified.

CONCLUSION

Rural patients had a higher overall and pre-hospital mortality following injury. However, once admitted to hospital, there was no significant difference in mortality. Inconsistencies were noted across measures of rurality measures highlighting the need for more specific and consistent international classification methods. Given the paucity of data on the impact of geography on non-mortality outcomes, there is a clear need to develop a larger evidence base on regional variation in recovery following injury to inform the optimisation of post-discharge care services.

Trauma registry data as a tool for comparison of practice patterns and outcomes between low- and middle-income and high-income healthcare settings

PURPOSE

There is a lack of data-driven, risk-adjusted mortality estimates for injured children outside of high-income countries (HIC). To inform injury prevention and quality improvement efforts, an upper middle-income country (UMIC) pediatric trauma registry was compared to that of a HIC.

METHODS

Clinical data, injury details, and mortality of injured children (< 18 years) hospitalized in two centers (USA and South African (SA)) from 2013 to 2017 were abstracted. Univariate and multivariable analyses evaluated risk of mortality and were expressed as odds ratios (OR) with 95% confidence intervals (CI).

RESULTS

Of 2089 patients, SA patients had prolonged transfer times (21.1 vs 3.4 h) and were more likely referred (78.2% vs 53.9%; both p < 0.001). Penetrating injuries were more frequent in SA (23.2% vs 7.4%, p < 0.001); injury severity (9 vs 4) and shock index (0.90 vs 0.80) were greater (both p < 0.001). SA utilized cross-sectional imaging more frequently (66.4% vs 37.3%, p < 0.001). In-hospital mortality was similar (1.9% SA, 1.3% USA, p = 0.31). Upon multivariable analysis, ISS > 25 [210.50 (66.0-671.0)] and penetrating injury [5.5 (1.3-23.3)] were associated with mortality, while institution [1.7 (0.7-4.2)] was not.

CONCLUSIONS

Despite transfer time, the centers demonstrated comparable survival rates. Comparison of registry data can alert clinicians to problematic practice patterns, assisting initiatives to improve trauma systems.

Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment (MIMIC): a methodology for reliably measuring prehospital time and distance to definitive care

The detailed study of prehospital injury death is critical to advancing trauma and emergency care, as circumstance and causality have significant implications for the development of mitigation strategies. Though there is no true 'Golden Hour,' the time from injury to care is a critical element in the analysis matrix, particularly in patients with severe injury. Currently, there is no standard method for the assessment of time to definitive care after injury among prehospital deaths. This article describes a methodology to estimate total prehospital time and distance for trauma patients transported via ground emergency medical services and helicopter emergency medical services using a geographic information system. Data generated using this method, along with medical examiner and field investigation reports, will be used to estimate the potential survivability of prehospital trauma deaths occurring in five US states and the District of Columbia as part of the Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment study. One goal of this work is to develop standard metrics for the assessment of total prehospital time and distance, which can be used in the future for more complex spatial analyses to gain a deeper understanding of trauma center access. Results will be used to identify high priority areas for research and development in injury prevention, trauma system performance improvement, and public health.

Spatial distribution of maxillofacial injuries caused by urban violence: An ecological analysis to identify high-risk areas

OBJECTIVES

To investigate the spatial and spatial-temporal distribution of oral and maxillofacial injuries caused by urban violence, as well as to identify underlying disparities at regional level through a geostatistical approach.

METHODS

This was a historical ecological cohort study of trauma cases caused by urban violence using aggregate data from victims assisted in a Brazilian medical-forensic service between January 2012 and December 2015. The longitudinal patterns of change observed in each geographic area (neighbourhoods) were evaluated using the finite mixture model (FMM). The spatial autocorrelation of events was investigated using the Getis-Ord Indicator (Gi*) to identify significant hot and cold spatial clusters. With a spatial regression model, it was also found when socioeconomic variables, residential infrastructure and neighbourhood infrastructure were associated with high incidence rates. The significance level was set at P ≤ 0.05.

RESULTS

The finite mixture model revealed three different patterns of longitudinal trajectory of the incidence of oral and maxillofacial trauma caused by urban violence (TP1 to TP3, P < 0.05). TP1 was characterized by an incidence that remained stable and high over time, comprising 17.4% of the city's neighbourhoods. In TP2, it was observed that the incidence was moderate, with a slightly increasing trend in the last year evaluated, representing around 41.8% of the sample. In contrast, in TP3, it was found that the incidence was relatively low and remained stable over time, accounting for about 40.8% of the sample. The Getis-Ord (Gi*) statistic identified significant high-risk clusters in the western (P < 0.05), southern (P < 0.05), and eastern regions (P < 0.05) and low risk in the northern region (P < 0.05). The spatial regression model indicated significant association between areas with unfavourable socioeconomic conditions and higher incidence of events (β = 0.178, SE = 0.046, P < 0.001).

CONCLUSIONS

Clusters demarcating areas with high socio-spatial vulnerability for urban violence and oral and maxillofacial injuries were identified. The findings highlight the need to improve living conditions in segregated urban areas and develop intersectoral actions to improve living conditions, employment, public safety, social support, health care and prevention.