This cross-sectional study estimates county-level drive time to the nearest verified burn center and characterizes regional, rural-urban, and income-related disparities in access to these burn centers across the contiguous US.
Key Points
Question
How accessible are verified burn centers across US counties, and does this access vary by geography and community income?
Findings
In this cross-sectional study of 3109 US counties, the population-weighted mean drive time to the nearest verified burn center was under 1 hour, but nearly 1 in 3 counties, concentrated in rural, small town, and Western regions, had drive times exceeding 2 hours. Lower county median household income was associated with longer drive times.
Meaning
These findings suggest that geographic and socioeconomic disparities in access to verified burn centers persist across the US, highlighting opportunities for targeted trauma system planning and resource allocation.
Abstract
Importance
Timely access to verified burn centers is associated with improved survival and functional outcomes after severe burn injury, yet no contemporary study has jointly characterized county-level burn center access, rural-urban gradients, and socioeconomic disparities using updated burn center data.
Objective
To estimate county-level drive time to the nearest verified burn center across the contiguous US and to characterize regional, rural-urban, and income-related disparities in access.
Design, Setting, and Participants
This was a cross-sectional, population-based geospatial analysis of counties in the contiguous US, including Washington, DC. Data were from 2020 population-weighted county centroids, geocoded verified burn center locations from the American Burn Association and BurnSurvivor.com directories, 2020 Rural-Urban Commuting Area classifications, and 2022 American Community Survey median household income. Data were analyzed December 2025.
Exposures
County rural-urban classification (metropolitan, micropolitan, small town, and rural) and US census region (Northeast, Midwest, South, and West).
Main Outcomes and Measures
Road-network drive time from each county’s population-weighted centroid to the nearest verified burn center, estimated using the Google Maps Distance Matrix API; unweighted and population-weighted mean and median drive times nationally and by region; proportion of counties and population with drive times exceeding 2 and 4 hours; and the association between drive time and median household income.
Results
Among 3109 counties (population, 329 260 619), the unweighted mean (SD) drive time was 1.96 (1.47) hours (median [IQR], 1.61 [1.02-2.39] hours) vs a population-weighted mean of 0.92 hours (median, 0.59 hours). Overall, 1107 counties (35.6%; 10.8% of the population) had drive times exceeding 2 hours, and 266 counties (8.6%; 1.7% of the population) exceeded 4 hours. The West had the greatest drive time burden (mean [SD], 3.08 [2.10] hours; 262 of 414 counties [63.3%] >2 hours) and the Northeast the least burden (mean [SD], 1.24 [0.74] hours; 35 of 218 counties [16.1%] >2 hours). Rural and small town counties had longer median drive times (1.76 [IQR, 1.26-2.53] hours and 2.18 [IQR, 1.54-3.27] hours, respectively) than metropolitan counties (0.86 [IQR, 0.47-1.58] hours). Lower income was associated with longer drive time nationally (R2 = 0.100) and regionally (R2 range, 0.070-0.273), most strongly in the West.
Conclusions and Relevance
In this cross-sectional study of 3109 US counties, substantial disparities in drive time access to verified burn centers existed, concentrated among rural, small town, and Western counties, and were attenuated, although not eliminated, when weighted by population. These findings may inform burn system regionalization, aeromedical transport planning, and telemedicine expansion for underserved areas.
Introduction
Severe burn injuries require rapid access to specialized, resource-intensive care to optimize survival, minimize complications, and improve long-term functional outcomes.1,2 Verified burn centers provide multidisciplinary expertise in resuscitation, critical care, surgical care with excision and grafting, and rehabilitation, yet their distribution across the US remains uneven.3,4 National reports have long emphasized that large geographic areas, particularly rural and low-population regions, lack timely access to designated burn facilities, creating structural disparities in emergency care delivery.3,4,5,6 Prior studies have shown that prolonged transport time to definitive burn care is associated with delayed resuscitation, increased complication rates, and increased mortality, underscoring the importance of geographic proximity within trauma system design.3,4,7
Despite the recognized importance of timely burn center access, national-level evaluations of spatial distribution remain limited. Much existing work relies on coarse distance measures, outdated center lists, or regional rather than county-level analyses.4,5,8,9 Furthermore, rural-urban differences in access are well documented across other high-acuity specialties but have not been comprehensively characterized for burn care using modern geospatial approaches.3,4,5,6,10 Understanding where travel burden is greatest is essential for guiding burn care resource allocation, aeromedical transport planning, and telemedicine for burns infrastructure development.11,12
To address these gaps, this study provides an updated, county-level analysis of drive time accessibility to verified burn centers nationwide. Unlike prior population- or regional-level work, it integrates contemporary burn center data, population-weighted centroids, and Rural-Urban Commuting Area (RUCA) classifications to quantify regional, state, and rural-urban disparities in travel burden and their association with median household income. By combining these geographic, rural-urban, and socioeconomic dimensions in a single national framework, this work aims to inform trauma system planning and highlight opportunities to reduce inequities in specialized burn care.
Methods
Data Sources and Identification of Burn Centers
This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. This study used only publicly available, aggregate, county-level data and did not involve human participants, individual patient records, or protected health information. Therefore, the University of Texas Medical Branch Institutional Review Board deemed that this study did not constitute human participants research and waived the requirement for informed consent.
A national inventory of burn centers was compiled using publicly available information from BurnSurvivor.com and the American Burn Association (ABA), online resources that maintain continuously updated directories of burn treatment facilities across the US. Only facilities designated as verified burn centers by the ABA verification program, jointly administered with the American College of Surgeons, were included in the final analysis; nonverified burn treatment facilities identified in either source were excluded. Verification status was cross-referenced against the current list of verified burn centers published by the ABA as of January 2026. The database included center names, verification status, and links to burn center websites. These entries were merged with a dataset containing Federal Information Processing Standards (FIPS) codes to ensure complete geographic information. Each verified burn center address was geocoded and linked to its corresponding FIPS code when available. The final set of unique verified burn center coordinates served as the reference point dataset for all distance and drive time calculations.
County-Level Geographic Data
County boundaries were obtained from the 2022 US Census Bureau TIGER/Line cartographic county shapefiles, the most recent county-level dataset available. All spatial data were processed using GeoPandas and standardized to the WGS84 geographic coordinate system (EPSG:4326). The analysis was limited to the contiguous US, excluding Alaska and Hawaii and US territories (FIPS prefixes 02, 15, 60, 66, 69, 72, and 78). County population centers were obtained from the US Census Bureau’s 2020 Centers of Population dataset, which identifies each county’s population-weighted centroid, the balance point if every resident carried equal weight. These coordinates, rather than geometric centroids, were used for all distance and drive time calculations to better reflect where residents actually live.
Drive Time Estimation
Drive time from each county’s population-weighted centroid to the nearest burn center was obtained using the Google Maps Platform Distance Matrix API, which returns road network–based driving times reflecting standard (not peak-adjusted, not live traffic) travel conditions. This approach incorporates actual road network geometry, speed limits, and routing rather than relying on geodesic distance approximations. For each county, the minimum travel time across all burn centers was retained. Although geodesic calculations do not incorporate road network geometry, they provide a reproducible and widely accepted approximation of national-level travel burden. Additional mapping analyses capped drive time values at 11 hours and capped distance values at 1000 km for improved visualization of extreme outlier regions.
Regional, State-, and RUCA-Level Summaries
Counties were assigned to 1 of the 4 US Census Regions (Northeast, Midwest, South, and West) using state FIPS codes. A validation step confirmed that every state FIPS code mapped to exactly 1 region before region-level summaries were generated, by calculating the median, mean, and standard deviation of drive time values for each region, as well as the number of counties included. State-level metrics included the number of counties per state, mean and median drive times to the nearest burn center, and the number of counties with drive times above or below the state-specific mean. RUCA urban core type classifications from the 2020 RUCA dataset were merged with county FIPS codes at the tract level. Because RUCA classifications are defined at the census tract level, each county was assigned to a single dominant urban core type corresponding to the classification held by the majority of its tract-level population, ensuring each county was counted once. Counties outside the contiguous US were excluded to maintain consistency with the primary dataset (n = 3109 counties). Mean and median drive times were then calculated for metropolitan (urban area ≥50 000 population), micropolitan (urban area 10 000-49 999 population), small town (urban area up to 9999 population), and rural (census tract not part of an urban area) county types.
Median Household Income Data
County-level median household income estimates from 2022 were obtained from the US Census Bureau’s American Community Survey. Individual state-level files were batch-imported and standardized using GeoPandas. FIPS codes were converted to zero-padded 5-digit strings to ensure accurate merging with county geospatial identifiers. Income data were merged with drive time estimates to examine socioeconomic differences in access to burn centers.
Income vs Drive Time Statistical Modeling
Associations between county median household income and drive time burden were evaluated using multiple regression models. Four candidate models (linear, log-linear [log-x], exponential, and power-law) were fitted for each region and for the national dataset using the NumPy polyfit function. The model with the highest coefficient of determination (R2) was selected as the optimal fit. Scatterplots with best-fit trendlines, equation annotations, and R2 values were generated for each region and exported as interactive HTML files.
RUCA-Level Drive Time Analysis
Drive time estimates were merged with county-level RUCA urban core type classifications, with each county assigned the urban core type held by the majority of its population, to characterize rural-urban gradients while avoiding duplicate county counts across categories. Summary tables reported mean and median drive times and the number of counties in each RUCA category, enabling comparisons across urban, suburban, micropolitan, small town, and rural settings.
Mapping Procedures
Choropleth and point-overlay maps were created using Plotly Graph Objects. Custom color gradients were applied to highlight clinically relevant travel thresholds. Burn centers were overlaid as red point markers. All maps used a carto-positron basemap and consistent national-scale viewing parameters. Interactive HTML outputs were generated to support reproducibility and dissemination.
Statistical Analysis
All descriptive and other analyses were performed December 2025 using Python, version 3.9.6 (Python Software Foundation). Key packages included Pandas for data management, GeoPandas for spatial operations, geopy for distance calculations, NumPy for regression modeling, and Plotly for visualization. All derived datasets and figures were exported in open formats (CSV, Excel, and HTML) to ensure reproducibility.
Results
A total of 3109 counties in the contiguous US, including the District of Columbia, were included in analyses (2022 population, 329 260 619). Road network drive time from each population-weighted centroid of each county to the nearest verified burn center was estimated using Google Maps Distance Matrix API. The unweighted mean (SD) drive time was 1.96 (1.47) hours (median, 1.61 [IQR, 1.02-2.39] hours) vs a population-weighted mean of 0.92 hours (median, 0.59 hours), reflecting the concentration of burn centers near dense population areas. Overall, 1107 counties (35.6%; 10.8% of the population) exceeded 2-hour drive times, and 266 counties (8.6%; 1.7% of the population) exceeded 4 hours. Accessibility was further evaluated across census regions, RUCA classifications, and individual states.
Regional Drive Time Variation
Substantial regional disparities in unweighted accessibility were observed (Table 1 and Figure 1). The West had the greatest burden and variability in drive times (mean [SD], 3.08 [2.10] hours; median, 2.69 [IQR, 1.49-4.33] hours; n = 414), reflecting large service areas and low burn center density; 262 of 414 counties (63.3%) exceeded 2 hours and 125 of 414 counties (30.2%) exceeded 4 hours. The Midwest (mean [SD], 2.20 [1.69] hours; median, 1.69 [IQR, 1.07-2.76] hours; n = 1055 counties) and South (mean [SD], 1.57 [0.77] hours; median, 1.51 [IQR, 1.01-2.04] hours; n = 1422 counties) drive times fell in between. The Northeast had the shortest times nationwide (mean [SD], 1.24 [0.74] hours; median, 1.16 [IQR, 0.63-1.71] hours; n = 218 counties), with only 35 of 218 counties (16.1%) exceeding 2 hours, consistent with dense population and greater burn center availability. Population weighting narrowed these differences: weighted mean drive time was 0.92 hours in both the West and Midwest, 1.01 hours in the South (the highest of any region on this basis), and 0.69 hours in the Northeast.
Table 1. Drive Time Accessibility to the Nearest Burn Center by US Census Region and by RUCA Urban Core Type.
| US region or dominant urban core type | No. of counties | Drive time, h | |
|---|---|---|---|
| Mean (SD) | Median (IQR) | ||
| Midwest | 1055 | 2.20 (1.69) | 1.69 (1.07-2.76) |
| Northeast | 218 | 1.24 (0.74) | 1.16 (0.63-1.71) |
| South | 1422 | 1.57 (0.77) | 1.51 (1.01-2.04) |
| West | 414 | 3.08 (2.10) | 2.69 (1.49-4.33) |
| Metropolitan | 685 | 1.15 (1.02) | 0.86 (0.47-1.58) |
| Micropolitan | 304 | 2.08 (1.31) | 1.71 (1.21-2.60) |
| Rural | 1979 | 2.18 (1.51) | 1.76 (1.26-2.53) |
| Small town | 141 | 2.63 (1.63) | 2.18 (1.54-3.27) |
Abbreviation: RUCA, Rural-Urban Commuting Area.
Figure 1. Maps Illustrating the Geographic Accessibility and Socioeconomic Context of Burn Center Care Across the Contiguous United States.
Maps were created using OpenStreetMaps. Red dots in B and C denote verified burn center locations.
RUCA-Based Rurality Differences
Drive time accessibility showed a clear rural-urban gradient (Table 1). Metropolitan core counties (n = 685) had the shortest times (mean [SD], 1.15 [1.02] hours; median, 0.86 [IQR, 0.47-1.58] hours). Burden increased across micropolitan (n = 304 counties; mean [SD], 2.08 [1.31] hours; median, 1.71 [IQR, 1.21-2.60] hours) and rural (n = 1979 counties; mean [SD], 2.18 [1.51] hours; median, 1.76 [IQR, 1.26-2.53] hours) counties and was greatest in small town core counties (n = 141 counties; mean, 2.63 [1.63] hours; median, 2.18 [IQR, 1.54-3.27] hours). Rurality and geographic isolation were thus associated with longer estimated travel times to specialized burn care.
State-Level Drive Time Patterns
Marked heterogeneity was evident at the state level (Table 2). The District of Columbia (mean [SD], 0.14 [0.00] hours; median 0.14 [IQR, 0.14-0.14] hours), Rhode Island (mean [SD], 0.42 [0.23] hours; median, 0.38 [IQR, 0.25-0.62] hours), New Jersey (mean [SD], 0.71 [0.27] hours; median, 0.72 [IQR, 0.48-0.84] hours), and Connecticut (mean [SD], 0.81 [0.29] hours; median, 0.82 [IQR, 0.63-1.06] hours) had the shortest drive times.
Table 2. State-Level Drive Time Accessibility to the Nearest Burn Center.
| State name | No. of counties | Drive time, h | No. of counties | Counties below national mean, % | ||
|---|---|---|---|---|---|---|
| State mean (SD) | State median (IQR) | Below national mean | Over national mean | |||
| Alabama | 67 | 1.57 (0.63) | 1.51 (1.21-1.89) | 52 | 15 | 77.6 |
| Arizona | 15 | 2.32 (0.90) | 2.61 (1.85-2.89) | 4 | 11 | 26.7 |
| Arkansas | 75 | 1.47 (0.55) | 1.48 (1.11-1.91) | 57 | 18 | 76 |
| California | 58 | 1.55 (1.36) | 1.05 (0.64-2.06) | 41 | 17 | 70.7 |
| Colorado | 64 | 2.57 (1.38) | 2.55 (1.49-3.58) | 24 | 40 | 37.5 |
| Connecticut | 9 | 0.81 (0.29) | 0.82 (0.63-1.06) | 9 | 0 | 100 |
| Delaware | 3 | 1.47 (0.61) | 1.51 (1.18-1.79) | 2 | 1 | 66.7 |
| District of Columbia | 1 | 0.14 (0.00) | 0.14 (0.14-0.14) | 1 | 0 | 100 |
| Florida | 67 | 1.46 (0.84) | 1.28 (0.84-1.88) | 52 | 15 | 77.6 |
| Georgia | 159 | 1.66 (0.75) | 1.61 (1.03-2.27) | 101 | 58 | 63.5 |
| Idaho | 44 | 3.27 (1.95) | 2.66 (1.74-5.14) | 15 | 29 | 34.1 |
| Illinois | 102 | 1.40 (0.61) | 1.39 (0.91-1.91) | 81 | 21 | 79.4 |
| Indiana | 92 | 1.17 (0.45) | 1.15 (0.90-1.49) | 90 | 2 | 97.8 |
| Iowa | 99 | 1.97 (0.74) | 1.99 (1.46-2.42) | 48 | 51 | 48.5 |
| Kansas | 105 | 2.10 (1.05) | 1.85 (1.35-3.01) | 56 | 49 | 53.3 |
| Kentucky | 120 | 1.53 (0.66) | 1.49 (1.03-1.98) | 88 | 32 | 73.3 |
| Louisiana | 64 | 1.09 (0.61) | 1.01 (0.61-1.57) | 58 | 6 | 90.6 |
| Maine | 16 | 1.76 (1.14) | 1.59 (0.99-2.27) | 11 | 5 | 68.8 |
| Maryland | 24 | 1.21 (0.70) | 1.10 (0.70-1.54) | 19 | 5 | 79.2 |
| Massachusetts | 14 | 1.31 (0.95) | 1.08 (0.56-1.82) | 11 | 3 | 78.6 |
| Michigan | 83 | 1.86 (1.42) | 1.41 (0.81-2.58) | 51 | 32 | 61.4 |
| Minnesota | 87 | 2.32 (1.32) | 2.24 (1.24-3.10) | 39 | 48 | 44.8 |
| Mississippi | 82 | 1.37 (0.47) | 1.41 (1.09-1.68) | 72 | 10 | 87.8 |
| Missouri | 115 | 1.46 (0.61) | 1.47 (1.04-1.95) | 89 | 26 | 77.4 |
| Montana | 56 | 6.35 (2.02) | 6.36 (4.64-8.01) | 0 | 56 | 0 |
| Nebraska | 93 | 2.54 (1.17) | 2.56 (1.62-3.32) | 32 | 61 | 34.4 |
| Nevada | 17 | 3.09 (1.19) | 3.17 (2.50-3.73) | 2 | 15 | 11.8 |
| New Hampshire | 10 | 1.62 (0.51) | 1.53 (1.22-1.89) | 8 | 2 | 80.0 |
| New Jersey | 21 | 0.71 (0.27) | 0.72 (0.48-0.84) | 21 | 0 | 100 |
| New Mexico | 33 | 2.27 (1.05) | 2.35 (1.66-3.08) | 14 | 19 | 42.4 |
| New York | 62 | 1.15 (0.69) | 1.16 (0.58-1.66) | 51 | 11 | 82.3 |
| North Carolina | 100 | 1.54 (0.71) | 1.48 (0.99-2.08) | 72 | 28 | 72.0 |
| North Dakota | 53 | 6.52 (1.58) | 6.38 (5.50-7.65) | 0 | 53 | 0 |
| Ohio | 88 | 0.96 (0.44) | 0.96 (0.60-1.29) | 88 | 0 | 100 |
| Oklahoma | 77 | 1.56 (0.76) | 1.46 (1.07-2.03) | 55 | 22 | 71.4 |
| Oregon | 36 | 2.87 (1.74) | 2.68 (1.46-4.21) | 15 | 21 | 41.7 |
| Pennsylvania | 67 | 1.39 (0.64) | 1.49 (0.91-1.92) | 54 | 13 | 80.6 |
| Rhode Island | 5 | 0.42 (0.23) | 0.38 (0.25-0.62) | 5 | 0 | 100 |
| South Carolina | 46 | 1.57 (0.56) | 1.62 (1.21-2.04) | 30 | 16 | 65.2 |
| South Dakota | 66 | 5.15 (1.38) | 5.14 (4.09-6.11) | 0 | 66 | 0 |
| Tennessee | 95 | 1.85 (0.89) | 1.76 (1.24-2.45) | 58 | 37 | 61.1 |
| Texas | 254 | 1.76 (0.94) | 1.67 (1.12-2.25) | 165 | 89 | 65.0 |
| Utah | 29 | 2.21 (1.30) | 2.24 (0.88-2.90) | 13 | 16 | 44.8 |
| Vermont | 14 | 1.37 (0.79) | 1.35 (0.72-1.71) | 11 | 3 | 78.6 |
| Virginia | 133 | 1.37 (0.73) | 1.23 (0.86-1.79) | 102 | 31 | 76.7 |
| Washington | 39 | 2.61 (1.54) | 2.50 (1.46-3.95) | 18 | 21 | 46.2 |
| West Virginia | 55 | 1.91 (0.70) | 1.90 (1.54-2.41) | 28 | 27 | 50.9 |
| Wisconsin | 72 | 1.74 (0.93) | 1.67 (1.02-2.28) | 42 | 30 | 58.3 |
| Wyoming | 23 | 3.95 (1.56) | 3.94 (2.70-5.35) | 3 | 20 | 13.0 |
In contrast, several large, sparsely populated states had the greatest burdens: North Dakota (mean [SD], 6.52 [1.58] hours; median, 6.38 [IQR, 5.50-7.65] hours), Montana (mean [SD], 6.35 [2.02] hours; median, 6.36 [IQR, 4.64-8.01] hours), South Dakota (mean [SD], 5.15 [1.38] hours; median, 5.14 [IQR, 4.09-6.11] hours), Wyoming (mean [SD], 3.95 [1.56] hours; median, 3.94 [IQR, 2.70-5.35] hours), and Idaho (mean [SD], 3.27 [1.95] hours; median, 2.66 [IQR, 1.74-5.14] hours). Intrastate variability was also substantial: in Texas (254 counties; mean [SD], 1.76 [0.94] hours), 108 counties (42.5%) exceeded the state mean, while all 9 Connecticut counties fell below the national mean, showing that state-level means can mask considerable within-state heterogeneity.
Overall National Accessibility Pattern
Metropolitan counties had among the shortest drive times nationally (mean [SD], 1.15 [1.02] hours; median [IQR], 0.86 [0.47-1.58] hours) (Figure 1). In the Mountain West, drive times were markedly longer (West census region mean [SD], 3.08 [2.10] hours). In the northern Great Plains, drive times were similarly prolonged (Midwest census region mean [SD], 2.20 [1.69] hours), with several counties more than 5 to 6 hours from the nearest verified burn center. Because these counties are sparsely populated, however, population-weighted estimates show only 10.8% of the US population lived more than 2 hours away, underscoring that the geographic footprint of underserved counties was considerably larger than the share of people actually affected.
Association Between Drive Time and Median Household Income
County income showed a weak, inverse, nonlinear correlation with drive time (fitted logarithmic equation: y = −6868.553 ln(x) + 66028.5) (Figure 2). Nationally, a logarithmic model fit best among 4 candidates (linear, log-linear, exponential, and power-law) (R2 = 0.100; n = 3108 counties), meaning income explained only a small share of drive time variability. This varied by region (eFigures 1-4 in Supplement 1): strongest in the West (log-linear, R2 = 0.273; n = 414 counties) and South (log-linear, R2 = 0.158; n = 1421 counties), exponential in the Northeast (R2 = 0.194; n = 218 counties), and weakest in the Midwest (log-linear, R2 = 0.070; n = 1055 counties). Since the best-fit model form varied by region, coefficients were not directly comparable across regions; even in the West, income accounted for only about a quarter of the variance in drive time (West, y = −10968.013 ln(x) + 78340.4; South, y = −10866.420 ln(x) + 61581.8; Midwest, y = −3927.678 ln(x) + 66949.4; and Northeast, y = 86302.85e−0.142x).
Figure 2. Line and Scatter Plot Showing the Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties.
Discussion
In this national, cross-sectional geospatial analysis of 3109 counties, we found substantial county-level variation in road network drive time to the nearest verified burn center, with the greatest unweighted burden concentrated in the West, in small town and rural counties, and in several sparsely populated states in the Northern Plains and Mountain West. However, when drive times were weighted by county population, disparities narrowed considerably: 10.8% of the US population lived more than 2 hours from a verified burn center, even though more than a third of counties exceeded this threshold. This distinction matters for interpretation. Because a county-level analysis gives equal weight to a county of a few thousand residents and one of several million, unweighted summaries best describe the geographic footprint of underserved areas, while population-weighted summaries better describe the number of people actually affected; both are relevant to trauma system planning, but neither alone should be read as the complete picture.
Timely access to burn expertise is critical because initial management strongly influences outcomes.13,14 Delays in fluid resuscitation, airway control, escharotomy, and early excision can increase morbidity and mortality, particularly in large or inhalation-associated burns.15,16 Yet our results demonstrate that many counties, especially in rural and low-population regions, were located several hours from the nearest burn center. In these areas, initial care is often provided by facilities without dedicated burn centers, relying heavily on general emergency physicians, plastic surgeons, or general surgeons who may treat only a handful of major burns per year.4,5,17 As a result, frontline clinicians in isolated regions must navigate complex resuscitation protocols, rapidly evolving physiology, and wound care decisions without immediate access to specialized support.13,18
The clear rural-urban gradient observed in this study reinforces longstanding concerns about the maldistribution of specialty trauma services.12,19 Counties classified as micropolitan, small town, or rural showed median travel times roughly double to more than 2.5 times those of metropolitan core counties (median, 0.86 hours). This pattern mirrors disparities documented in stroke, cardiac, and high-level trauma care, suggesting that geographic isolation remains a structural determinant of emergency care access.20,21,22 Importantly, these disparities are not confined to the most remote states; even populous states with multiple burn centers exhibited substantial intrastate variability, underscoring that proximity to specialized care can differ markedly within the same health system.3,4,5
Our findings carry several clinically actionable implications. First, enhancing regional burn trauma networks remains essential. Many patients with burn injury in high-burden counties are initially evaluated at hospitals without a burn center, making early consultation with burn specialists crucial.23,24 Expanding around the clock telehealth services may improve triage accuracy, guide resuscitation, and reduce unnecessary transfers while facilitating timely transfers for patients who require specialized burn care and operative management.25,26 Second, long transport distances emphasize the importance of optimizing aeromedical transport protocols. Clear criteria for helicopter activation, preplanned routing, and regional coordination may significantly reduce delays in areas where ground transport times exceed several hours.27,28,29 Third, states with large geographic coverage areas may benefit from hub-and-spoke referral models, enabling hospitals without a burn center to serve as stabilization hubs equipped with burn-specific pathways, while burn centers function as definitive care sites.3,24,30
Beyond immediate clinical care, our results have broader public health and policy implications. Geographic disparities may contribute to differences in burn morbidity, hospital length of stay, and postdischarge recovery, especially for patients with limited socioeconomic resources or transportation barriers.5,31,32 Counties with prolonged drive times also tended to have somewhat lower median household incomes, although this correlation was weak nationally (R2 = 0.100) and varied considerably by region, ranging from R2 = 0.070 in the Midwest to R2 = 0.273 in the West. Because our data are cross-sectional and county-level, they cannot establish whether lower income causes longer travel times, reflects historical siting decisions for burn centers, or is confounded by rurality itself. The modest R2 values argue against interpreting income as a primary driver of access disparities; geography, and specifically the physical distribution of verified burn centers relative to population centers, appears to be the dominant factor, with community income acting at most as a secondary, regionally variable correlate. Policymakers should consider these findings when allocating resources for trauma system development, telehealth expansion, and emergency preparedness planning. Incorporating geospatial accessibility metrics into state trauma system evaluations may support more equitable distribution of specialty services; however, sustainable burn center operation ultimately depends on maintaining sufficient patient volume to justify their presence.
Limitations
This study has several limitations. Drive times were estimated using the Google Maps Distance Matrix API under standard, not peak and not live, traffic conditions; actual times may vary with weather, construction, and time of day and may differ from routing of emergency vehicles displaying lights and sounding a siren. Only ground transport was modeled; air medical transport, often used for rural or time-critical patients with burn injury, was not modeled and may reduce true access barriers in some remote areas.33 Analysis was limited to ABA-verified centers, which may underestimate access via high-performing but nonverified units, though ABA verification remains the national standard. Population-weighted centroids approximate, but may not fully capture, where residents live within large, unevenly populated counties. Finally, the association between income and drive time should be interpreted cautiously: R2 values were modest across regions, the models used aggregate rather than individual-level income data, and the cross-sectional design could not establish causality.
Despite these limitations, this analysis offers an updated, county-level assessment of drive time access to verified burn centers, jointly incorporating road network estimates, rural-urban classification, and socioeconomic data, extending prior population- and regional-level work. The identified disparities are actionable targets for clinical practice, burn-system planning, and policy reform. Improving access will require coordinated efforts across emergency medicine, surgery, critical care, health systems, and public agencies. Expanding telehealth, refining transfer pathways, and strengthening regional trauma networks could meaningfully reduce delays and support more equitable outcomes for patients with burn injury nationwide.
Conclusions
In this cross-sectional study of 3109 US counties, road network drive time to verified burn centers varied substantially, with the greatest burden in rural, small town, and Western counties. Disparities narrowed but were still present when weighted by population, showing that both the geographic footprint of underserved counties and the number of people affected matter for trauma system planning. County income was only weakly and inconsistently correlated with drive time and should not be viewed as a primary driver of these disparities. These findings can help guide regional burn system planning, telehealth expansion, and aeromedical transport protocols. Future work incorporating transport routing (including air transport), time-of-day variation, and patient-level outcomes would help refine these estimates and support policy aimed at more equitable burn care access nationwide.
eFigure 1. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the West Census Region
eFigure 2. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the South Census Region
eFigure 3. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the Midwest Census Region
eFigure 4. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the Northeast Census Region
Data Sharing Statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eFigure 1. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the West Census Region
eFigure 2. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the South Census Region
eFigure 3. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the Midwest Census Region
eFigure 4. Association Between Drive Time to the Nearest Burn Center and Median Household Income Among US Counties in the Northeast Census Region
Data Sharing Statement
