Importance:Emergency department (ED) pediatric readiness and pediatric inpatient services have changed over time in many hospitals, but the impact of these changes on pediatric outcomes is unclear. Objective:To evaluate pediatric mortality associated with changes to ED pediatric readiness and pediatric inpatient services over a 10-year period. Design, Setting, and Participants:This cohort study included data from January 1, 2012, through December 31, 2021, for 759 hospitals in 11 states that completed the 2013 and 2021 National Pediatric Readiness Program assessments. Participants were children aged 0 to 17 years who received care in an ED resulting in hospital admission, interhospital transfer, or death. Data analysis was performed from May 2025 to May 2026. Exposure:Changes in ED pediatric readiness and inpatient pediatric services, as measured through national assessments in 2013 and 2021. ED readiness change groups were characterized based on the weighted Pediatric Readiness Score (wPRS, range 0-100) associated with survival (wPRS ≥88 vs wPRS <88): sustained high readiness, gained, lost, or never had. Changes in pediatric inpatient services were defined as sustained, gained, lost, or never had. Main Outcomes and Measures:In-hospital mortality, including ED and inpatient deaths. Results:There were 2 416 030 children, including 337 167 who were injured (median [IQR] age, 10 [4-15] years; 4642 deaths [1.38%]) and 2 078 863 who were medically ill (median [IQR] age, 6 [1-14] years; 18 576 deaths [0.89%]). Of the 759 hospitals, the median (IQR) wPRS in 2013 vs 2021 was 71 (58-86) and 72 (62-88), respectively. Ninety-nine EDs (13.0%) had sustained high readiness, 85 (11.2%) gained readiness, 78 (10.3%) lost readiness, and 497 (65.5%) never had high readiness. For inpatient services, 225 hospitals (29.6%) sustained inpatient services, 36 (4.7%) gained services, 120 (15.8%) lost services, and 378 (49.8%) never had services. After risk adjustment, EDs that lost or never had high ED readiness were associated with 1727 (95% CI, 751-2646) and 3776 (95% CI, 2327-5143) excess deaths, respectively. Hospitals that lost or never had inpatient services were associated with 1657 (95% CI, 1214-2067) and 3745 (95% CI, 3127-4328) excess deaths, respectively. Conclusions and Relevance:This study found that the loss or persistent lack of high ED pediatric readiness and pediatric inpatient services were independently associated with excess mortality in children. Increasing ED readiness and adding inpatient services may augment pediatric survival in the US health care system.

OBJECTIVE:We used machine learning (ML) to develop firearm risk prediction models for injured children and adolescents admitted to U.S. trauma centers, including prediction of death after discharge among survivors of firearm injury. METHODS:We used a retrospective cohort of injured patients 0-17 years admitted to 982 trauma centers participating in the National Trauma Data Bank from 1/1/2014 to 12/31/2021, stratified by children (0-10 years) versus adolescents (11-17 years). We followed a subset of patients to one year (through 12/31/2022). We used ML to analyze 119 predictors and a primary outcome of firearm injury, and then 141 predictors to model a secondary outcome of death after discharge. RESULTS:There were 260,098 children (2785 [1.1 %] with firearm injuries) and 242,669 adolescents (22,318 [9.2 %] with firearm injuries). For children, the high specificity model had an area under the curve (AUC) 0.787 with 31.2 % sensitivity, specificity 95.3 %, and positive predictive value (PPV) 6.9 %. For adolescents, the model had an AUC 0.763, sensitivity 34.7 %, specificity 95.0 %, and PPV 41.3 %. High-yield predictors included Black race, Child Opportunity Index, and median household income. Post-discharge mortality among those surviving a firearm injury was 0.38 %, but deaths often occurred within one week of discharge, frequently from a repeat firearm injury, and were predicted by home neighborhood characteristics. CONCLUSIONS:Pediatric firearm injury risk stratification can be performed using information available during admission, which could guide injury prevention efforts. The first week after discharge following a firearm injury is high-risk for mortality, with risk influenced by neighborhood characteristics.

Paratransit, a demand-responsive transit mode serving passengers with mobility challenges, is increasingly electrified to enhance urban transportation sustainability. However, high investments required for dedicated charging infrastructure and the scarcity of public charging resources remain significant hurdles to large-scale deployment. This study investigates a shared charging scheme that integrates paratransit electric vehicles (EVs) into existing electric bus (EB) charging networks. A fuzzy multi-objective optimization framework is proposed to identify optimal charging co-hub locations and EV assignments by balancing supply-demand dynamics. The framework incorporates two-step floating catchment area (2SFCA) and inverted 2SFCA (i2SFCA) methods to formulate objectives and constraints for EB and paratransit systems, respectively. Through fuzzy programming, trade-offs among supply-demand dynamics are resolved, yielding efficient shared-charging plans. The framework is validated with Utah Transit Authority data, demonstrating improved charging accessibility and operational efficiency while offering actionable insights for transit agencies in planning shared charging schemes among various public transport modes.

BACKGROUND:Timely emergency medical services (EMS) response is critical to improving survival after trauma. However, concordance between EMS dispatch level and on-scene patient acuity remains poorly understood. This study evaluated the association between racial and ethnic residential segregation and concordance between EMS response level and on-scene acuity among critically injured trauma patients. METHODS:Using 2018-2022 National EMS Information System data, we analyzed trauma patient entries meeting CDC field triage criteria for transport to a trauma center. Concordance was defined as alignment between dispatch classification of response (emergent vs. nonemergent) and EMS providers' subsequent on-scene clinical assessment of acuity (critical/emergent vs. noncritical/low acuity). Racial and ethnic residential segregation at the ZIP Code level was measured using a multigroup dissimilarity index comparing neighborhood composition to county distribution. χ2 tests and multivariable logistic regression were used to assess the associations between segregation and under-triage, adjusting for region (Northeast, Midwest, South, West). RESULTS:Among 34.7 million critically injured patients over 5 years, 69% had concordant EMS responses, 6% were under-triaged, and 26% over-triaged. Concordance was highest in the Midwest (74%) and lowest in the Northeast (62%). Under-triage was most frequent in the West (10%) and least in the South (4%). Neighborhoods with medium and high segregation had twice the under-triage rates than low-segregation areas (8% and 7% vs. 4%, p<0.001). In adjusted analyses, medium and high segregation were 60% more likely to be associated with increased odds of under-triage (odds ratio: 1.61, 95% confidence: 1.60-1.61). CONCLUSIONS:This is the largest study to date demonstrating that racial and ethnic residential segregation was significantly associated with meaningfully and significantly increased risk of under-triage among critically injured trauma patients. Furthermore, structural inequities in neighborhood segregation may delay access to definitive trauma care. Equity-driven EMS policy reform, standardized dispatch protocols, and targeted training are needed to mitigate disparities in prehospital trauma response. (J Trauma Acute Care Surg 2026;00:000-000. Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.). LEVEL OF EVIDENCE:Prognostic and Epidemiological; Level IV.

This study compares US sociodemographic and geographic trends in heat-related and non-heat-related emergency medical services (EMS) activations from 2019 to 2024.

BACKGROUND:Timely transport of critically injured patients by Emergency Medical Services to verified trauma centers significantly reduces morbidity and mortality. Prior studies demonstrate that undertriage in the prehospital setting impacts outcomes, with rural communities facing additional geographic and systemic barriers to timely trauma care. The area deprivation index, a validated measure of neighborhood-level socioeconomic disadvantage, is associated with poorer health outcomes and may further influence access to trauma centers. Yet, the association between socioeconomic deprivation, rurality, and trauma center transport remains poorly defined. This study aimed to evaluate the extent of urban-rural inequities in Emergency Medical Services transport of critically injured patients to verified trauma centers across all regions of the United States and to assess the association between area deprivation index and likelihood of transport to a trauma center. METHODS:We identified all Emergency Medical Services transported critically injured patients meeting Centers for Disease Control and Prevention field triage criteria for trauma center transport in the National Emergency Medical Services Information System from 2018 to 2022 and mapped Zone Improvement Plan (ZIP) Codes containing verified trauma centers (Levels I-V) using data from the American College of Surgeons, the Trauma Center Association of America, and the American Trauma Society. The cohort was stratified by regions in the United States: Northeast, Midwest, South, and West. The incident scene area deprivation index was obtained from the Neighborhood Atlas at the census block group level. The total number and percentage of patients located in urban and rural Zone Improvement Plan (ZIP) codes transported either to a confirmed trauma center (via the National Emergency Medical Services Information System data) or to a Zone Improvement Plan (ZIP) code that contains a trauma center and the area deprivation index distribution in tertiles (low area deprivation index, moderate area deprivation index, and high area deprivation index) within regions in the United States were calculated with their statistical significance derived from t tests and analyses of variance with post hoc Tukey tests. RESULTS:A total of 36,897,269 critically injured patients met the inclusion criteria, of which 19,874,008 (53.86%) were brought to a trauma center. When stratified by rurality, 7,608,704 (54.01%) and 12,265,304 (53.77%) of critically injured patients within rural and urban areas, respectively, were transported to a trauma center. When comparing across regions, the Northeast region of the United States had the lowest percentage of critically injured patients being transported to a trauma center, whereas the Midwest region had the highest percentage (44.04% vs 67.40%; P < .001). When stratified by rurality, 35.33% vs 46.92% of critically injured patients within rural versus urban areas of the Northeast were transported to a trauma center, whereas 65.47% vs 68.57% of critically injured patients within rural versus urban areas of the Midwest were transported to a trauma center (P < .001). When evaluating area deprivation index, critically injured patients who were injured in more disadvantaged versus advantaged Zone Improvement Plan (ZIP) codes had a higher percentage of patients being transported to a trauma center even when controlling for rurality (56% vs 47%; P < .001). CONCLUSION:Substantial geographic inequities in the Emergency Medical Services transport of critically injured adult patients to verified trauma centers, varied by geographic region, rurality, and neighborhood-level socioeconomic disadvantage that exist. These findings highlight the complex and regionally variable landscape of trauma access in the United States and underscore the need for targeted, equity-focused strategies to optimize prehospital triage and ensure timely, trauma-informed care across diverse communities.

INTRODUCTION:Delayed Emergency Medical Services (EMS) response and transport (time from injury occurrence to hospital arrival) are associated with increased injury mortality. Inequities in accessing EMS care for injured patients are not well characterized. We sought to evaluate the association between the area deprivation index (ADI), a measure of geographic socioeconomic disadvantage, and timely access to EMS care within the United States. METHODS:The Homeland Infrastructure Foundation Level Data open-source database from the National Geospatial Intelligence Agency was used to evaluate the location of EMS stations across the United States using longitude and latitude coordinates. The ADI was obtained from Neighborhood Atlas at the census block group level. An ambulance desert (AD) was defined as populated census block groups with a geographic center outside of a 25-minute ambulance service area. The total population (urban and rural) located within an AD and outside an AD (non-ambulance desert [NAD]) and the ADI index distribution within those areas were calculated with their statistical significance derived from χ 2 testing. Spearman correlations between the number of EMS stations available within 25-minutes service areas and ADI were calculated, and statistical significance was derived after accounting for spatial autocorrelation. RESULTS:A total of 42,472 ground EMS stations were identified. Of the 333,036,755 people (current US population), 2.6% are located within an AD. When stratified by type of population, 0.3% of people within urban populations and 8.9% of people within rural populations were located within an AD ( p < 0.01). When compared with NADs, ADs were more likely to have a higher ADI (ADI AD , 53.13; ADI NAD , 50.41; p < 0.01). The number of EMS stations available per capita was negatively correlated with ADI ( rs = -0.25, p < 0.01), indicating that people living in more disadvantaged neighborhoods are likely to have fewer EMS stations available. CONCLUSION:Ambulance deserts are more likely to affect rural versus urban populations and are associated with higher ADIs. The impact of inequities in access to EMS care on outcomes deserves further study. LEVEL OF EVIDENCE:Prognostic and Epidemiological; Level IV.

Objective:Among children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data sources.Methods:We included children and adolescents 0-17 years transported by ambulance to acute care hospitals in 47 states from January 1, 2014 through December 31, 2022. We used 96 predictors, including basic demographic information and neighborhood measures matched to home ZIP code from 5 data sources: EMS records, American Community Survey, Child Opportunity Index, County Health Rankings, and Social Vulnerability Index. We separated children into 0-10 years (preadolescent) and 11-17 years (adolescent) cohorts and used machine learning to develop high-specificity risk prediction models for each age group to minimize false positives.Results:There were 6,191,909 children transported by ambulance, including 21,625 (0.35%) with firearm injuries. Among children 0-10 years (n = 3,149,430 children, 2,840 [0.09%] with firearm injuries), the model had 95.1% specificity, 22.4% sensitivity, area under the curve 0.761, and positive predictive value 0.41% for identifying children with firearm injuries. Among adolescents 11-17 years (n = 3,042,479 children, 18,785 [0.62%] with firearm injuries), the model had 94.8% specificity, 39.0% sensitivity, area under the curve 0.818, and positive predictive value 4.47% for identifying patients with firearm injury. There were 7 high-yield predictors among children and 3 predictors among adolescents, with little overlap.Conclusions:Among pediatric patients transported by ambulance, basic demographic information and neighborhood measures can identify children and adolescents at elevated risk of firearm injuries, which may guide focused injury prevention resources and interventions.

Importance:Inequities in rapid access to emergency medical services (EMS) represent a critical gap in prehospital care and the first system-level milestone for critically injured patients. As delays in EMS response are associated with increased mortality and known disparities within historically redlined areas are prevalent, this study sought to examine disparities in rapid access to EMS across the United States. Objective:To assess the association between historically redlined areas and rapid EMS access (defined as ≤5-minute response time) across the United States. Design, Setting, and Participants:This retrospective, cross-sectional study analyzed the geographic distribution of EMS centers in relation to 2020 US Census block groups and Home Owners' Loan Corporation (HOLC) residential security maps, classified by grades (A-D). Populations of 236 US cities with publicly available redlining data were included. Travel distance radius (5-minute drive times) was centered on population-weighted block group centroids. Redlining grades include A ("most desirable," green), B ("still desirable," blue), C ("declining," yellow), and D ("hazardous," red). Exposure:HOLC grade classification (A-D). Main Outcomes and Measures:The primary outcome was the proportion of the population with rapid EMS access. Secondary outcomes included the socioeconomic and demographic profiles of populations without rapid access. Results:Of the total US population (N = 333 036 755), 41 367 025 (12.42%) lived in cities with redlining data. Among these, 2 208 269 (5.34%) lacked rapid access to 42 472 EMS stations. Grade D areas had a higher proportion of residents without rapid EMS access compared with grade A areas (7.06% vs 4.36%; P < .001). The odds of having no rapid access to EMS in grade D areas were 1.67 (95% CI, 1.66-1.68) times higher than in grade A areas. Compared with grade A, grade D areas had a lower percentage of non-Hispanic White residents (65.21% [95% CI, 59.43%-70.99%] vs 39.36% [95% CI, 36.99%-41.73%]; P < .001), a higher percentage of non-Hispanic Black residents (10.38% [95% CI, 7.14%-13.62%] vs 27.85% [95% CI, 25.4%-30.3%]; P < .001), and greater population density (7500.72 [95% CI, 4341.26-10 660.18] persons/km2 vs 15 277.87 [95% CI, 13 281.7-17 274.04] persons/km2; P < .001). Conclusions and Relevance:In this cross-sectional study, structural disparities in rapid EMS access were associated with historically redlined areas. Strategic resource allocation and system redesign are warranted to address these inequities in prehospital emergency care.

The delineation of school attendance zones (SAZs) is a critical aspect of educational policy, influencing student distribution, resource allocation, and educational equity. SAZs determine which students attend specific schools based on geographic boundaries, shaping the demographic composition of schools and the opportunities available to students. Designing SAZs is a complex and challenging task, as it requires balancing multiple, often conflicting objectives and constraints. These include minimizing student travel time, promoting diversity and reducing segregation, balancing school capacities to prevent overcrowding or underutilization, and maintaining zone contiguity. In this paper, we present a multi-objective spatial optimization model to address these competing priorities. We applied this method to redraw the attendance boundaries of elementary schools in the Riverside Unified School District, California. The results demonstrate that our model can significantly reduce school segregation while minimizing student travel time and maintaining contiguous attendance zones. By generating a set of Pareto-optimal solutions, this approach equips policymakers with the tools to make informed decisions on SAZ delineation that improve both equity and logistical efficiency.

OBJECTIVES:We evaluated spatial clustering of pediatric firearm injuries using national 9-1-1 emergency medical services (EMS) responses, locations where these events occurred, and geographic changes over time. METHODS:This was a cross-sectional study from January 1, 2012 through December 31, 2022 using 9-1-1 EMS responses for children in 50 states from the National EMS Information Systems (NEMSIS). For 37 states with continuous data over the study period, we evaluated spatial changes over time. We included children aged 0 to 17 years with a 9-1-1 EMS response including transports, nontransports, and deaths at the scene. We stratified by child (0-10 years) and adolescent (11-17 years) age groups. The outcome was firearm injury, regardless of intent or severity. RESULTS:There were 10 521 575 9-1-1 EMS responses from 30 393 incident zip codes, including 26 101 (0.25%) for firearm injuries (3679 [14.1%] in children and 22 422 [85.9%] in adolescents). Among 3679 children with firearm injuries, 2975 (80.9%) occurred in their home zip code and 1490 (40.5%) occurred in a cluster. Among 22 422 adolescents with firearm injuries, 15 635 (69.7%) occurred in their home zip code and 11 551 (51.5%) occurred in a cluster. Among 37 states (n = 6 103 297 events, n = 11 433 zip codes), 213 of 446 (47.8%) clustered zip codes for children were new in 2022 and 148 of 461 (32.1%) clustered zip codes for adolescents were new. Results were similar when using home zip codes. CONCLUSIONS:There was spatial clustering of pediatric firearm injuries, commonly in their home zip code. The number of zip codes included in pediatric firearm hotspots is increasing.

Electrification of vehicle fleets has advanced significantly in recent years to achieve net-zero greenhouse gas (GHG) emissions. As a cost-effective strategy, shared charging facilities are increasingly used by public and private sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge other electric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizations considering transitions to electrified mobility. It is especially difficult when considering the variability in daily fleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimization model designed to strategically guide the replacement of vehicle fleets with EV. The model aligns the spatialtemporal dynamics of vehicle routes with the availability of shared charging facilities. It is particularly relevant for organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals of minimizing GHG emissions from fuel consumption and vehicle idling, and reducing operational delays (e.g. detour and charging time for the EV fleet). We applied this model to the University of Utah campus fleet, utilizing shared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies for replacing vehicles with varied operational characteristics, offering detailed plans and schedules that balance GHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assess the effects of different battery sizes, station disruptions, and traffic delays on the model's outcomes and a feasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation for fleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage shared charging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenance costs. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems.

Few studies have examined the connection between the ecological structure of neighborhoods and policing activities during pedestrian and traffic stops-the most frequent types of interactions between police and the public in the U.S. This study aims to fill that gap using data from multiple sources collected in San Diego, California. Using a Heckman-Probit model, we test how built and social environment attributes are associated with the probability of a stop resulting in a consent search-which may reflect an officer's reasonable suspicion toward the person being stopped-and with the likelihood of finding no contraband after a search, an indicator of potential overpolicing. The results suggest that individuals stopped in neighborhoods with higher percentages of Hispanic and African American residents, poverty rates, and higher crime levels are more likely to be searched but less likely to be found with contraband. In contrast, stops in areas with higher densities of transit stops, alcohol outlets, and vacant housing units are not necessarily associated with increased search rates. However, stops near transit-dense locations are associated with lower probabilities of discovering contraband. The paper concludes with research and policy implications aimed at reducing place-based biases in policing practices.

Total demand suitably served and facility workload balance are two important considerations in location coverage. Previous work has dealt with workload balancing issues using a number of approaches, including imposing facility capacities and the use of multiple objectives focused on workload variation. However, a facility is usually restricted to a single service unit, inconsistent with strategies that allow for increased staffing such as multiple service units in dealing with higher levels of demand. This article proposes a new bi-objective optimization model that maximizes total demand coverage and minimizes workload differences simultaneously while allowing more than one service unit to be co-located at a site. Since the proposed model is strongly NP hard, a heuristic algorithm is developed for efficient solution. The model is applied to support postal delivery service planning. Results show that the proposed model offers improved performance compared to approaches that do not permit co-location. The proposed algorithm is able to produce high-quality solutions that evenly distribute allocated service demand, and does so much faster with higher-quality solutions compared to exact solution approaches.

Census blocks are administrative units that serve as statistical areas for the decennial Census in the United States. Visible and nonvisible features bound blocks, including roads, railroads, streams, property lines, and city boundaries. The Census Bureau builds blocks using the Master Address File (MAF), which includes field-verified geographic information about the location of housing unit addresses. Unfortunately, there are substantial errors in the counts of housing units at the block level, even with the purported quality checks by the Census Bureau. This paper aims to detail a method of identifying problematic blocks (i.e., ghost blocks) that report the presence of housing units, but no such units exist. Further, we identify the implications of using ghost blocks in location models using the maximal covering location problem (MCLP) in a case study for sensor locations in Los Angeles, California. We discuss policy implications and strategies to address these errors for developing higher-fidelity location models.

As global concerns about climate change intensify, the transition towards zero-emission freight is becoming increasingly vital. Drayage is an important segment of the freight system, typically involving the transport of goods from seaports or intermodal terminals to nearby warehouses. This sector significantly contributes to not only greenhouse gas emissions, but also pollution in densely populated areas. This study presents a holistic optimization model designed for an efficient transition to zero-emission drayage, offering cost-effective strategies for the coordinated investment planning for power systems, charging infrastructure, and electric drayage trucks. The model is validated in the Greater Los Angeles area, where regulatory goals are among the most ambitious. Furthermore, the model's design allows for easy adaptation to other regions. By focusing on drayage trucks, this study also paves the way for future research into other freight categories, establishing a foundation for a more extensive exploration in this field.