Rapid Access to Emergency Medical Services Within Historically Redlined Areas

Cherisse Berry; Joseph Obiajulu; N Clay Mann; Dustin T Duncan; Charles DiMaggio; Ashley Pfaff; Spiros Frangos; Jakka Sairamesh; Natalie Escobar; Gbenga Ogedegbe; Ran Wei

doi:10.1001/jamanetworkopen.2025.25681

. 2025 Aug 5;8(8):e2525681. doi: 10.1001/jamanetworkopen.2025.25681

Rapid Access to Emergency Medical Services Within Historically Redlined Areas

Cherisse Berry ^1,^✉, Joseph Obiajulu ², N Clay Mann ³, Dustin T Duncan ⁴, Charles DiMaggio ², Ashley Pfaff ², Spiros Frangos ², Jakka Sairamesh ⁵, Natalie Escobar ⁶, Gbenga Ogedegbe ⁷, Ran Wei ⁸

¹Department of Surgery, Rutgers Health, New Jersey Medical School, Newark

²Department of Surgery, New York University Grossman School of Medicine, New York

³National Emergency Medical Services Information System (NEMSIS) Technical Assistance Center (TAC) University of Utah School of Medicine, Salt Lake City

⁴Columbia University Mailman School of Public Health, New York, New York

⁵CapsicoHealth, Inc, Palo Alto, California

⁶Department of Surgery, University of California, San Francisco School of Medicine, San Francisco

⁷Department of Population Health, Department of Medicine, Institute for Excellence in Health Equity, New York University Grossman School of Medicine, New York

⁸University of California Riverside School of Public Policy, Riverside

Accepted for Publication: May 28, 2025.

Published: August 5, 2025. doi:10.1001/jamanetworkopen.2025.25681

Correction: This article was corrected on September 8, 2025, to fix errors in Table 2.

^✉

Corresponding Author: Cherisse Berry, MD, Department of Surgery, Rutgers Health, New Jersey Medical School, Department of Surgery, 185 S Orange Ave, Medical Science Building, Room G-508, Newark, NJ 07103 (cherisse.berry@rutgers.edu).

Author Contributions: Dr Berry had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Berry, Obiajulu, Duncan, Escobar, Ogedegbe, Wei.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Berry, Obiajulu, Pfaff, Frangos, Escobar, Wei.

Critical review of the manuscript for important intellectual content: Berry, Obiajulu, Mann, Duncan, DiMaggio, Frangos, Sairamesh, Escobar, Ogedegbe, Wei.

Statistical analysis: Berry, Obiajulu, Mann, Duncan, Sairamesh, Wei.

Obtained funding: Berry, Duncan, Ogedegbe, Wei.

Administrative, technical, or material support: Berry, Mann, Frangos, Sairamesh, Escobar.

Supervision: Berry, DiMaggio, Wei.

Conflict of Interest Disclosures: None reported.

Funding/Support: The work was supported by grant 5R01MD018177 from the National Institute of Minority Health and Health Disparities of the National Institutes of Health.

Role of the Funder/Sponsor: This funding supported the time of the principal investigator and some of the coauthors (ie, co-investigators on this grant) in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. However, the funding agency had no role in these activities.

Disclaimer: Dr Ogedegbe is an associate editor of JAMA Network Open but was not involved in any of the decisions regarding review of the manuscript or its acceptance.

Meeting Presentation: This study was presented as a podium presentation at the 34th Annual Scientific Meeting of the Society of Black Academic Surgeons; September 20, 2024; Sacramento, California.

Data Sharing Statement: See the Supplement.

^✉

Corresponding author.

PMCID: PMC12326277 PMID: 40762912

Key Points

Question

Are historically redlined areas less likely to have rapid access to emergency medical services (EMS) care?

Findings

This cross-sectional study analyzed the geographic distribution of EMS centers across 236 US cities, identifying more than 41 million people residing in areas graded A to D by 1930s Home Owners’ Loan Corporation security maps. Among them, more than 2.2 million lacked rapid EMS access, with grade D (“hazardous”) areas having a significantly higher proportion of residents without rapid EMS access compared with grade A (“most desirable”) areas.

Meaning

In this study, the historical policy of redlining was associated with inequities in modern-day access to expeditious EMS care.

This cross-sectional study evaluates whether individuals living in historically redlined neighborhoods have equitable access to emergency medical services.

Abstract

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.

Introduction

Timely access to emergency medical services (EMS) care for patients who have suffered major trauma,^1,2,3 stroke,^4,5 cardiac arrest,^6,7,8,9 or septic shock^10,11,12 impacts patient outcomes. In fact, national guidelines emphasize time-centered definitive treatment after critically ill patients are transported by EMS to hospitals for definitive care: emergency department (ED) door-to–balloon inflation time for ST segment elevation myocardial infarction (<90 minutes),^13,14 ED door-to-needle time for tissue plasminogen activator for ischemic strokes (≤60 minutes),¹⁵ and time to intravenous antibiotics for septic shock (<60 minutes).^16,17,18,19 For critically injured trauma patients, receiving definitive care within the “golden hour”^20,21,22—from the time of injury to arrival at a verified trauma center—is associated with significantly reduced injury-related morbidity and mortality. To minimize total prehospital time, the National Fire Protection Association (NFPA) has established a benchmark goal of a 9-minute EMS response time for the arrival of an EMS-equipped unit to the scene of injury.^23,24,25 For high-priority patients with life-threatening conditions, the benchmark is reduced to 5 minutes, underscoring rapid EMS access as a critical structural determinant of health.^23,24,25

Equitable access to health care is a fundamental social determinant of health.^26,27,28 Historically discriminatory policies, such as redlining—anchored in structural racism—have produced enduring health inequities among racially and ethnically marginalized populations.^{29,30,31,32,33,34,35} Redlining, originally created when the federal government commissioned the Home Owner’s Loan Association (HOLC) in 1935 to create color-coded maps with residential regions graded A (“best,” green), B (“still desirable,” blue), C (“definitely declining,” yellow) or D (“hazardous,” red)^36,37 that were used to approve or deny a mortgage loan. Populations residing in redlined zones or grade D areas were often denied mortgage loans by banks and lenders, which has contributed to the large racial wealth gap seen today among African American individuals and other historically excluded and marginalized communities.³⁸ This large racial wealth gap impacts public health and patient outcomes, particularly among individuals with cancer. Patients in historically redlined areas were found to have increased rates of estrogen receptor–negative breast cancer diagnoses and increased hazard of mortality among non-Hispanic Black women.³⁹ Among patients with colon cancer, people living in HOLC grade D or redlined areas were more likely to be diagnosed with late-stage colon cancer when compared with people living in HOLC grade A areas.⁴⁰ Yet, the impact of redlining on access to prehospital care is unknown. While data has shown that the rates of bystander cardiopulmonary resuscitation decline across HOLC grades (41.8% in grade A to 35.8% in grade D),⁴¹ no study has evaluated the modern-day impact of redlined areas and access to expeditious EMS care. Thus, we sought to examine the association between historic HOLC areas and rapid access to EMS care in the United States.

Methods

Study Sample

We used longitude and latitude coordinates from the Homeland Infrastructure Foundation Level Data open-source database from the National Geospatial Intelligence Agency, spatial distribution of EMS agencies across the United States, and Census block group centroid or population-weighted center of a block group from the 2020 US Census to evaluate the location of ground EMS stations inclusive of ambulance and fire service combined, ambulance services, fire and rescue services, rescue service, and forest firefighting per capita across the United States. This study was determined not to involve human participant research, as it utilized deidentified data from a large national database. Accordingly, the institutional review board at Rutgers Health, New Jersey Medical School, deemed this study exempt from the requirement for informed consent. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

Study Variables

Exposure Measure

We obtained historical traffic information through ArcGIS StreetMap Premium data (ArcGIS Streetmap) and used the ArcGIS Network Analyst to perform network analysis and to identify optimal routes and EMS response travel times (ArcGIS Network Analysis). We calculated response travel time (in minutes) from the block group centroids to the EMS stations within 5 minutes based on average automobile speed from historical traffic data. This study analyzes geographic data from 236 cities with historical data from the University of Richmond’s Mapping Inequality database⁴² describing historical 1935 HOLC residential security maps grades A to D. Redlining data were paired with US Census Block groups in 2020 with their 5-minute travel distance radius centered by population-weight and the geolocation of EMS centers across the United States. This sample of cities is inclusive of all cities impacted by redlining policies enacted via HOLC grading.

Outcome Measures

The primary outcomes were the proportion of the population with rapid access to EMS (≤5 minutes) and the odds ratio (OR) comparing the likelihood of lacking rapid EMS access between residents of HOLC grade D areas and those in grade A areas. Secondary outcomes included sociodemographic characteristics (eg, population density, median income, high school completion or greater percentage, Area Deprivation Index [ADI] as a measure of socioeconomic conditions, and race and ethnicity) of the population without rapid access to EMS.

Statistical Analysis

The total population with rapid access to EMS and without rapid access to EMS were calculated for each HOLC grade and then stratified for each of the 5 National Association of State EMS Officials regions: East, South, Great Lakes, Western Plains, and West. Proportions of the population with rapid access to EMS were compared between HOLC grades A and D, with statistical significance assessed using a χ² test. For each region, we calculated the OR comparing the likelihood of residents in HOLC grade D areas vs those in grade A areas in lacking timely access to EMS services. Population density and socioeconomic descriptive statistics, such as median income; high school completion or greater percentage; and 4 racial and ethnic groups (Hispanic, non-Hispanic Asian, non-Hispanic Black, and non-Hispanic White), obtained from the US Census data; and the ADI, obtained from the Neighborhood Atlas⁴³ at the census block group level, of each HOLC area (grades A-D) without access to rapid EMS were calculated. Statistical significance set at a P value less than .05 was derived from analysis of variance. SciPy version 1.12.0 was used to conduct analyses.

Results

Among the US population (N = 333 036 755), 41 367 025 individuals (12.42%) lived in the 236 cities with historical HOLC area, grades A to D. Of these, 2 208 269 (5.34%) were found to be without rapid access to EMS (Table 1). When stratified by HOLC grades, 119 660 of 2 746 228 residents of grade A areas (4.36%) lacked rapid access to EMS and 764 120 of 10 820 752 (7.06%) lacked rapid access in grade D areas (P < .001) (Table 1). The odds of lacking rapid access to EMS were 1.67 (95% CI, 1.66-1.68) times higher for residents of HOLC grade D areas compared with those in grade A areas. When disaggregated by region, the East region had the highest percentage of the population in grade D vs grade A (524 956 of 7 618 715 [10.91%] vs 51 173 of 956 757 [5.35%]; P < .001) areas without rapid access to EMS when compared with the West region (11 948 of 3 661 472 [0.61%] vs 0 of 323 684; P < .001) (Table 2). The odds of lacking rapid access to EMS were 2.99 (95% CI, 2.90-3.07) times higher for residents in HOLC grade D vs grade A areas of the Great Lakes region, compared with 1.09 (95% CI, 1.08-1.10) times higher in the South region.

Table 1. US Population With and Without Rapid Access to EMS Care by Historical HOLC Areas, Grades A to D.

HOLC grade	Population with rapid access to EMS, No.	Population without rapid access to EMS, No.	Proportion of population without rapid access to EMS, %
A	2 626 568	119 660	4.36
B	8 954 068	494 065	5.23
C	17 521 488	830 424	4.52
D	10 056 632	764 120	7.06
Total	39 158 756	2 208 269	5.34

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Abbreviations: EMS, emergency medical services; HOLC, Home Owners’ Loan Corporation.

Table 2. Proportion of Population Without Rapid Access to EMS Care by Historical HOLC Areas and Region.

Region	Total population in HOLC areas, No.	Population without rapid access to EMS (≤5 min), No. (%)				P value^a	Odds ratio 95% CI^b
Region	Total population in HOLC areas, No.	Grade A	Grade B	Grade C	Grade D	P value^a	Odds ratio 95% CI^b
East	18 066 892	51 173 (5.35)	396 981 (8.92)	629 780 (8.02)	524 956 (10.91)	<.001	2.17 (2.15-2.19)
South	4 767 396	48 677 (7.89)	57 100 (5.44)	77 121 (4.85)	129 127 (8.54)	<.001	1.09 (1.08-1.10)
Great Lakes	8 753 761	5345 (1.01)	32 402 (1.83)	92 022 (2.05)	58 145 (2.96)	<.001	2.99 (2.90-3.07)
Western Plains	2 414 592	14 465 (4.51)	7582 (1.01)	25 584 (3.40)	39 944 (6.80)	<.001	1.54 (1.51-1.58)
West	7 364 384	0	0	5917 (0.16)	11 948 (0.61)	<.001	NA^c

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Abbreviations: EMS, emergency medical services; HOLC, Home Owners’ Loan Corporation; NA, not applicable.

^{^a}

Population proportions with rapid access to EMS were compared between HOLC grades A and D, with statistical significance derived using a χ² test.

^{^b}

Odds ratio is the ratio of the odds of people residing in grade D vs grade A without rapid access to EMS.

^{^c}

Valid odds ratio cannot be derived due to zero element in frequency table.

Analysis of sociodemographic characteristics within HOLC-designated areas lacking rapid EMS access (Table 3) revealed that, compared with grade A areas, grade D areas had significantly higher 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); 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 (10.38% [95% CI, 7.14%-13.62%] vs 27.85% [95% CI, 25.4%-30.3%]; P < .001), Hispanic (17.43% [95% CI, 13.67%-21.19%] vs 21.58% [95% CI, 19.72%-23.44%]; P < .001), and non-Hispanic Asian (3.66% [95% CI, 2.68%-4.64%] vs 6.87% [95% CI, 5.87%-7.87%]; P < .001) populations; and a lower median income ($31 616 [95% CI, $26 329.13-$36 902.51] vs $26 407 [95% CI, $24 358.31-$28 456.19]; P < .001). Compared with grade D areas, grade A areas had a higher ADI national percentile (38.82 [95% CI, 35.88-41.76] vs 43.20 [95% CI, 36.72-49.68]; P < .001) and a lower percentage of residents completing high school or greater (66.51% [95% CI, 56.17%-76.85%] vs 56.7% [95% CI, 53.62%59.78%]; P < .001).

Table 3. Socioeconomic and Demographic Characteristics of Census Block Groups Without Rapid Access to Emergency Medical Services Across Historical Home Owners’ Loan Corporation Areas, Grades A to D.

Characteristic	Mean (95%)				P value^a
Characteristic	Grade A	Grade B	Grade C	Grade D	P value^a
Population density, people/km²	7500.72 (4341.26-10 660.18)	13 805.96 (11 143.7-16 468.22)	11 283.85 ± 1491.81 (9792.04-12 775.66)	15 277.87 (13 281.7-17 274.04)	<.001
Median income, $	31 615.82 (26 329.13-36 902.51)	23 914.08 (21 929.43-25 898.73)	23 688.93 (21 999.03-25 378.83)	26 407.25 (24 358.31-28 456.19)	<.001
Area Deprivation Index, percentile	43.20 (36.72-49.68)	38.11 (34.85-41.37)	38.34 (35.7-40.98)	38.82 (35.88-41.76)	<.001
High school or greater, %	56.7 (53.62-59.78)	58.17 (55.96-60.38)	58.05 (56.57-59.53)	66.51 (56.17-76.85)	<.001
Hispanic, %	17.43 (13.67-21.19)	28.95 (26.11-31.79)	24.45 (22.56-26.34)	21.58 (19.72-23.44)	<.001
Non-Hispanic Asian, %	3.66 (2.68-4.64)	6.97 (5.76-8.18)	10.11 (21.77-26.03)	6.87 (5.87-7.87)	<.001
Non-Hispanic Black, %	10.38 (7.14-13.62)	19.06 (16.34-21.78)	23.9 (21.77-26.03)	27.85 (25.4-30.3)	<.001
Non-Hispanic White, %	65.21 (59.43-70.99)	41.68 (38.65-44.71)	37.2 (34.99-39.41)	39.36 (36.99-41.73)	<.001

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^{^a}

P value was calculated by running 1-way analysis of variance to compare the mean across Home Owners’ Loan Corporation areas grades A to D.

Discussion

In this study of 236 cities, we found that redlined areas, particularly within the Great Lakes region, were significantly less likely to have rapid access to EMS. In fact, the odds of lacking rapid access to EMS were 1.67 times higher for residents of HOLC grade D areas compared with those in grade A areas. Compared with grade A areas, historically redlined grade D areas had a lower percentage of non-Hispanic White residents, a higher percentage of non-Hispanic Black residents, and greater population density. However, grade A areas were more socioeconomically disadvantaged, with a lower percentage of residents completing high school, when compared with historically redlined grade D areas. The intersection of historical practices like redlining, rooted in structural racism, has significantly shaped inequitable structural determinants of health and perpetuated spatial injustice. Spatial justice combines social justice and geography, which aims to achieve equity in the allocation of resources within specific geographic areas.⁴⁴ Inequity in the availability of rapid access to EMS care is an example of spatial injustice.

We previously evaluated the association between socioeconomic deprivation and timely access to EMS care and found that 8 690 353 people or 2.6% of the US population living in urban regions and 7 986 779 people (8.9%) living within rural regions reside within an ambulance desert,⁴⁵ defined as a populated Census Block with its geographic center located outside of the nearest 25-minute ambulance service area.⁴⁶ In this previous study⁴⁶ evaluating geographic socioeconomic disadvantage, we found the number of EMS stations available per capita was negatively correlated with the area deprivation index (ADI) (r_s = −0.25, P < .001), indicating that people living in more socioeconomically disadvantaged neighborhoods were likely to have fewer EMS stations available.

While disparities in the availability in EMS care exist, the current study expands on this spatial injustice by revealing inequities in rapid access to EMS care. For time-sensitive emergencies, such as stroke, cardiac arrest, and major traumatic injury, rapid access to EMS in the prehospital phase of care directly impacts patient outcomes. Gauss et al¹ found that prolonged total prehospital time resulted in an increase in in-hospital all-cause mortality, where for each 10-minute increase in prehospital time, the odds of death increased by 8%. Thus, for high-priority patients with life-threatening conditions, the national benchmark per the NFPA is an EMS response time of 5 minutes.²³ However, this study found that residents in historically redlined areas were significantly less likely to have rapid access to EMS despite being less socioeconomically disadvantaged. While our previous work showed people living in more socioeconomically disadvantaged neighborhoods are likely to have fewer EMS stations available, this study shows that a unique disparity exists in the strategic location of EMS stations among more densely populated areas mostly represented by non-Hispanic African American, non-Hispanic Asian, and Hispanic residents, which reflects spatial injustice. Thus, disparities in rapid EMS response within historically redlined areas represent a structural determinant of health, rooted in systemic racism, that may adversely affect outcomes in time-sensitive medical emergencies.

These findings support the need for strategic and targeted multilevel policy interventions—national, regional, and local—to eliminate disparities in access to time-sensitive emergency prehospital care among the most critically ill or critically injured patients to prevent increased risks of preventable mortality and severe morbidity, particularly among the nation’s most vulnerable and marginalized patients. Several strategies to eliminate these disparities include: (1) create equity dashboards to track EMS equity metrics for performance improvement; (2) implement a certificate-of-need oversight through an equity lens to ensure spatial equity and historically redlined areas are considered when new EMS stations and providers are authorized by individual states; (3) use GIS and geostatistical modeling to redistribute EMS stations and EMS providers and deploy units closer to historically redlined areas^44,47; (4) redesign response unit protocols based on patient acuity to ensure rapid response; (5) mandate public EMS reporting to ensure transparency of EMS access disparities to drive accountability; (6) incorporate community input (eg, perceptions of EMS reliability or responsiveness) into quality improvement frameworks; (7) appropriate state and federal funds to test spatial justice–based EMS redesign in urban and rural settings and invest the necessary resources to ensure equity in rapid access to EMS care; (8) implement trauma system equity assessments to conduct spatial equity audits to identify EMS access gaps and redesign care pathways⁴⁸; and (9) financially invest in longitudinal outcomes research to study the impact of delayed EMS response on mortality and morbidity, particularly within redlined and socioeconomically disadvantaged areas to inform investment priorities.⁴⁸

Limitations and Strengths

There are limitations to our study. First, our retrospective study includes US population statistics derived only from the 2020 US Census. Second, the relative sparsity of historically redlined residential districts compared with contemporary residential areas may limit statistical power of the analysis. Of the total 333 036 755 individuals captured in the 2020 US Census, only 41 367 025 (12.42%) resided in regions with historical redlining data available. Third, a 5-minute travel distance estimated via geospatial analysis does not necessarily reflect actual EMS response time, which may be prolonged due to call volumes, triage protocols, traffic conditions, road closures, construction-related detours, transport to hospital times, and return to availability at the EMS station. Moreover, EMS response time constitutes only one component of total prehospital time. This study could not account for delays in 911 call processing and dispatch, bystander intervention, EMS on-scene time, or transport duration. Furthermore, as a cross-sectional analysis, this study models projected response times without linking the 5- and 9-minute national EMS benchmarks to morbidity or mortality outcomes.

Despite these limitations, our study is the first we know of to analyze the association of redlining with rapid access to prehospital EMS using nationwide data. Furthermore, our work uncovers a novel yet significant disparity related to the historical policy of redlining that adds to the growing body of literature demonstrating disparities in health outcomes associated with redlining^{30,34,35,49,50,51,52} to inform strategies that effectively address disparities in EMS response times to improve rapid access in historically redlined areas.

Conclusions

In this cross-sectional study, US residents living in grade D areas were less likely than those living in grade A areas to have rapid access to EMS. To our knowledge, this is the first study to find inequities in rapid access to EMS across historically redlined areas, highlighting the enduring impact of redlining practices on contemporary health care. These findings serve as a crucial call to action, urging investment in the prehospital system, strategic resource allocation and system redesign to address these inequities in prehospital emergency care, and the advancement of health policies designed to ensure equitable access to time-sensitive emergency care.

Supplement.

Data Sharing Statement

jamanetwopen-e2525681-s001.pdf^{(16.2KB, pdf)}

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15.Fonarow GC, Zhao X, Smith EE, et al. Door-to-needle times for tissue plasminogen activator administration and clinical outcomes in acute ischemic stroke before and after a quality improvement initiative. JAMA. 2014;311(16):1632-1640. doi: 10.1001/jama.2014.3203 [DOI] [PubMed] [Google Scholar]
16.Rhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. 2017;43(3):304-377. doi: 10.1007/s00134-017-4683-6 [DOI] [PubMed] [Google Scholar]
17.Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign bundle: 2018 update. Intensive Care Med. 2018;44(6):925-928. doi: 10.1007/s00134-018-5085-0 [DOI] [PubMed] [Google Scholar]
18.Ferrer R, Martin-Loeches I, Phillips G, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med. 2014;42(8):1749-1755. doi: 10.1097/CCM.0000000000000330 [DOI] [PubMed] [Google Scholar]
19.Liu VX, Fielding-Singh V, Greene JD, et al. The timing of early antibiotics and hospital mortality in sepsis. Am J Respir Crit Care Med. 2017;196(7):856-863. doi: 10.1164/rccm.201609-1848OC [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Rogers FB, Rittenhouse KJ, Gross BW. The golden hour in trauma: dogma or medical folklore? Injury. 2015;46(4):525-527. doi: 10.1016/j.injury.2014.08.043 [DOI] [PubMed] [Google Scholar]
21.Sampalis JS, Lavoie A, Williams JI, Mulder DS, Kalina M. Impact of on-site care, prehospital time, and level of in-hospital care on survival in severely injured patients. J Trauma. 1993;34(2):252-261. doi: 10.1097/00005373-199302000-00014 [DOI] [PubMed] [Google Scholar]
22.Sampalis JS, Denis R, Lavoie A, et al. Trauma care regionalization: a process-outcome evaluation. J Trauma. 1999;46(4):565-579. doi: 10.1097/00005373-199904000-00004 [DOI] [PubMed] [Google Scholar]
23.DC.gov. Fire and EMS Department: EMS response time. Accessed May 6, 2025. https://fems.dc.gov/page/ems-reponse-time
24.Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002;9(4):288-295. doi: 10.1197/aemj.9.4.288 [DOI] [PubMed] [Google Scholar]
25.Pons PT, Haukoos JS, Bludworth W, Cribley T, Pons KA, Markovchick VJ. Paramedic response time: does it affect patient survival? Acad Emerg Med. 2005;12(7):594-600. doi: 10.1197/j.aem.2005.02.013 [DOI] [PubMed] [Google Scholar]
26.World Health Organization. WHO housing and health guidelines. Accessed May 7, 2025. https://iris. who.int/handle/10665/276001
27.McCartney G, Hearty W, Arnot J, Popham F, Cumbers A, McMaster R. Impact of Political Economy on Population Health: A Systematic Review of Reviews. Am J Public Health. 2019;109(6):e1-e12. doi: 10.2105/AJPH.2019.305001 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Institute of Health Equity . The final report of the Commission of the Pan American Health Organization on Equity and Health Inequalities in the Americas. Accessed July 1, 2025. https://www.instituteofhealthequity.org/resources-reports/the-final-report-of-the-commission-of-the-pan-american-health-organisation-on-equity-and-health-inequalities-in-the-americas
29.Yearby R. Structural racism and health disparities: reconfiguring the social determinants of health framework to include the root cause. J Law Med Ethics. 2020;48(3):518-526. doi: 10.1177/1073110520958876 [DOI] [PubMed] [Google Scholar]
30.Linde S, Walker RJ, Campbell JA, Egede LE. Historic residential redlining and present-day diabetes mortality and years of life lost: the persistence of structural racism. Diabetes Care. 2022;45(8):1772-1778. doi: 10.2337/dc21-2563 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Nardone AL, Casey JA, Rudolph KE, Karasek D, Mujahid M, Morello-Frosch R. Associations between historical redlining and birth outcomes from 2006 through 2015 in California. PLoS One. 2020;15(8):e0237241. doi: 10.1371/journal.pone.0237241 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Motairek I, Lee EK, Janus S, et al. Historical neighborhood redlining and contemporary cardiometabolic risk. J Am Coll Cardiol. 2022;80(2):171-175. doi: 10.1016/j.jacc.2022.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Diaz A, O’Reggio R, Norman M, Thumma JR, Dimick JB, Ibrahim AM. Association of historic housing policy, modern day neighborhood deprivation and outcomes after inpatient hospitalization. Ann Surg. 2021;274(6):985-991. doi: 10.1097/SLA.0000000000005195 [DOI] [PubMed] [Google Scholar]
34.Kowalski K, Auerbach J, Martenies SE, et al. Neighborhood walkability, historical redlining, and childhood obesity in Denver, Colorado. J Urban Health. 2023;100(1):103-117. doi: 10.1007/s11524-022-00703-w [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Jadow BM, Hu L, Zou J, et al. Historical redlining, social determinants of health, and stroke prevalence in communities in New York City. JAMA Netw Open. 2023;6(4):e235875. doi: 10.1001/jamanetworkopen.2023.5875 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Hillier AE. Redlining and the Home Owners’ Loan Corporation. J Urban Hist. 2003;29(4):394-420. doi: 10.1177/0096144203029004002 [DOI] [Google Scholar]
37.Hillier AE. Who received loans? Home Owners’ Loan Corporation lending and discrimination in Philadelphia in the 1930s. J Plann Hist. 2003;2(1):3-24. doi: 10.1177/1538513202239694 [DOI] [Google Scholar]
38.Rothstein R. The Color of Law: A Forgotten History of How Our Government Segregated America. Liveright Publishing Corporation; 2017. [Google Scholar]
39.Miller-Kleinhenz JM, Barber LE, Maliniak ML, et al. Historical redlining, persistent mortgage discrimination, and race in breast cancer outcomes. JAMA Netw Open. 2024;7(2):e2356879. doi: 10.1001/jamanetworkopen.2023.56879 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Hussaini SMQ, Fan Q, Barrow LCJ, Yabroff KR, Pollack CE, Nogueira LM. Association of historical housing discrimination and colon cancer treatment and outcomes in the United States. JCO Oncol Pract. 2024;20(5):678-687. doi: 10.1200/OP.23.00426 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Motairek I, Rvo Salerno P, Chen Z, et al. ; CARES Surveillance Group . Historical neighborhood redlining and bystander CPR disparities in out-of-hospital cardiac arrest. Resuscitation. 2024;201:110264. doi: 10.1016/j.resuscitation.2024.110264 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Nelson R, Winling L, Marciano R, Connolly N. Mapping inequality: redlining in New Deal America. Accessed May 7, 2025. https://dsl.richmond.edu/panorama/redlining
43.Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible—The Neighborhood Atlas. N Engl J Med. 2018;378(26):2456-2458. doi: 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Okundi AO, Varol C. Spatial justice in healthcare: Advancing equitable geographic access to primary healthcare in Migori County, Kenya. Soc Sci Humanit Open. 2024;9:100784. doi: 10.1016/j.ssaho.2023.100784 [DOI] [Google Scholar]
45.Berry C, Escobar N, Mann NC, et al. Ambulance deserts and inequities in access to EMS Care: are injured patients at risk for delayed care in the prehospital system? J Trauma Acute Care Surg. Published online May 23, 2025. doi: 10.1097/TA.0000000000004579 [DOI] [PubMed] [Google Scholar]
46.Jonk Y, Milkowski C, Croll Z, Pearson K. Ambulance deserts: geographic disparities in the provision of ambulance services. University of Southern Maine. Accessed May 7, 2025. https://digitalcommons.usm.maine.edu/ems/16/
47.Li M, Wang F, Kwan MP, Chen J, Wang J. Equalizing the spatial accessibility of emergency medical services in Shanghai: a trade-off perspective. Comput Environ Urban Syst. 2022;9:101745. doi: 10.1016/j.compenvurbsys.2021.101745 [DOI] [Google Scholar]
48.Owusu-Ansah S, Tripp R, N Weisberg S, P Mercer M, Whitten-Chung K, NAEMSP Diversity, Equity, & Inclusion Committee . Essential principles to create an equitable, inclusive, and diverse EMS workforce and work environment: a position statement and resource document. Prehosp Emerg Care. 2023;27(5):552-556. doi: 10.1080/10903127.2023.2187103 [DOI] [PubMed] [Google Scholar]
49.Nardone AL, Casey JA, Rudolph KE, Karasek D, Mujahid M, Morello-Frosch R. Associations between historical redlining and birth outcomes from 2006 through 2015 in California. PLoS One. 2020;15(8):e0237241. doi: 10.1371/journal.pone.0237241 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Bikomeye JC, Zhou Y, McGinley EL, et al. Historical redlining and breast cancer treatment and survival among older women in the United States. J Natl Cancer Inst. 2023;115(6):652-661. doi: 10.1093/jnci/djad034 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Bradford JM, Eldin MM, Golestani S, et al. Structural racism, residential segregation, and exposure to trauma: the persistent impact of redlining. J Trauma Acute Care Surg. 2024;97(6):891-895. doi: 10.1097/TA.0000000000004290 [DOI] [PubMed] [Google Scholar]
52.Li M, Yuan F. Historical redlining and resident exposure to COVID-19: a study of New York City. Race Soc Probl. 2022;14(2):85-100. doi: 10.1007/s12552-021-09338-z [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Supplement.

Data Sharing Statement

jamanetwopen-e2525681-s001.pdf^{(16.2KB, pdf)}

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[zoi250725r14] 14.O’Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. J Am Coll Cardiol. 2013;61(4):e78-e140. doi: 10.1016/j.jacc.2012.11.019 [DOI] [PubMed] [Google Scholar]

[zoi250725r15] 15.Fonarow GC, Zhao X, Smith EE, et al. Door-to-needle times for tissue plasminogen activator administration and clinical outcomes in acute ischemic stroke before and after a quality improvement initiative. JAMA. 2014;311(16):1632-1640. doi: 10.1001/jama.2014.3203 [DOI] [PubMed] [Google Scholar]

[zoi250725r16] 16.Rhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. 2017;43(3):304-377. doi: 10.1007/s00134-017-4683-6 [DOI] [PubMed] [Google Scholar]

[zoi250725r17] 17.Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign bundle: 2018 update. Intensive Care Med. 2018;44(6):925-928. doi: 10.1007/s00134-018-5085-0 [DOI] [PubMed] [Google Scholar]

[zoi250725r18] 18.Ferrer R, Martin-Loeches I, Phillips G, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med. 2014;42(8):1749-1755. doi: 10.1097/CCM.0000000000000330 [DOI] [PubMed] [Google Scholar]

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[zoi250725r20] 20.Rogers FB, Rittenhouse KJ, Gross BW. The golden hour in trauma: dogma or medical folklore? Injury. 2015;46(4):525-527. doi: 10.1016/j.injury.2014.08.043 [DOI] [PubMed] [Google Scholar]

[zoi250725r21] 21.Sampalis JS, Lavoie A, Williams JI, Mulder DS, Kalina M. Impact of on-site care, prehospital time, and level of in-hospital care on survival in severely injured patients. J Trauma. 1993;34(2):252-261. doi: 10.1097/00005373-199302000-00014 [DOI] [PubMed] [Google Scholar]

[zoi250725r22] 22.Sampalis JS, Denis R, Lavoie A, et al. Trauma care regionalization: a process-outcome evaluation. J Trauma. 1999;46(4):565-579. doi: 10.1097/00005373-199904000-00004 [DOI] [PubMed] [Google Scholar]

[zoi250725r23] 23.DC.gov. Fire and EMS Department: EMS response time. Accessed May 6, 2025. https://fems.dc.gov/page/ems-reponse-time

[zoi250725r24] 24.Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002;9(4):288-295. doi: 10.1197/aemj.9.4.288 [DOI] [PubMed] [Google Scholar]

[zoi250725r25] 25.Pons PT, Haukoos JS, Bludworth W, Cribley T, Pons KA, Markovchick VJ. Paramedic response time: does it affect patient survival? Acad Emerg Med. 2005;12(7):594-600. doi: 10.1197/j.aem.2005.02.013 [DOI] [PubMed] [Google Scholar]

[zoi250725r26] 26.World Health Organization. WHO housing and health guidelines. Accessed May 7, 2025. https://iris. who.int/handle/10665/276001

[zoi250725r27] 27.McCartney G, Hearty W, Arnot J, Popham F, Cumbers A, McMaster R. Impact of Political Economy on Population Health: A Systematic Review of Reviews. Am J Public Health. 2019;109(6):e1-e12. doi: 10.2105/AJPH.2019.305001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r28] 28.Institute of Health Equity . The final report of the Commission of the Pan American Health Organization on Equity and Health Inequalities in the Americas. Accessed July 1, 2025. https://www.instituteofhealthequity.org/resources-reports/the-final-report-of-the-commission-of-the-pan-american-health-organisation-on-equity-and-health-inequalities-in-the-americas

[zoi250725r29] 29.Yearby R. Structural racism and health disparities: reconfiguring the social determinants of health framework to include the root cause. J Law Med Ethics. 2020;48(3):518-526. doi: 10.1177/1073110520958876 [DOI] [PubMed] [Google Scholar]

[zoi250725r30] 30.Linde S, Walker RJ, Campbell JA, Egede LE. Historic residential redlining and present-day diabetes mortality and years of life lost: the persistence of structural racism. Diabetes Care. 2022;45(8):1772-1778. doi: 10.2337/dc21-2563 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r31] 31.Nardone AL, Casey JA, Rudolph KE, Karasek D, Mujahid M, Morello-Frosch R. Associations between historical redlining and birth outcomes from 2006 through 2015 in California. PLoS One. 2020;15(8):e0237241. doi: 10.1371/journal.pone.0237241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r32] 32.Motairek I, Lee EK, Janus S, et al. Historical neighborhood redlining and contemporary cardiometabolic risk. J Am Coll Cardiol. 2022;80(2):171-175. doi: 10.1016/j.jacc.2022.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r33] 33.Diaz A, O’Reggio R, Norman M, Thumma JR, Dimick JB, Ibrahim AM. Association of historic housing policy, modern day neighborhood deprivation and outcomes after inpatient hospitalization. Ann Surg. 2021;274(6):985-991. doi: 10.1097/SLA.0000000000005195 [DOI] [PubMed] [Google Scholar]

[zoi250725r34] 34.Kowalski K, Auerbach J, Martenies SE, et al. Neighborhood walkability, historical redlining, and childhood obesity in Denver, Colorado. J Urban Health. 2023;100(1):103-117. doi: 10.1007/s11524-022-00703-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r35] 35.Jadow BM, Hu L, Zou J, et al. Historical redlining, social determinants of health, and stroke prevalence in communities in New York City. JAMA Netw Open. 2023;6(4):e235875. doi: 10.1001/jamanetworkopen.2023.5875 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r36] 36.Hillier AE. Redlining and the Home Owners’ Loan Corporation. J Urban Hist. 2003;29(4):394-420. doi: 10.1177/0096144203029004002 [DOI] [Google Scholar]

[zoi250725r37] 37.Hillier AE. Who received loans? Home Owners’ Loan Corporation lending and discrimination in Philadelphia in the 1930s. J Plann Hist. 2003;2(1):3-24. doi: 10.1177/1538513202239694 [DOI] [Google Scholar]

[zoi250725r38] 38.Rothstein R. The Color of Law: A Forgotten History of How Our Government Segregated America. Liveright Publishing Corporation; 2017. [Google Scholar]

[zoi250725r39] 39.Miller-Kleinhenz JM, Barber LE, Maliniak ML, et al. Historical redlining, persistent mortgage discrimination, and race in breast cancer outcomes. JAMA Netw Open. 2024;7(2):e2356879. doi: 10.1001/jamanetworkopen.2023.56879 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r40] 40.Hussaini SMQ, Fan Q, Barrow LCJ, Yabroff KR, Pollack CE, Nogueira LM. Association of historical housing discrimination and colon cancer treatment and outcomes in the United States. JCO Oncol Pract. 2024;20(5):678-687. doi: 10.1200/OP.23.00426 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r41] 41.Motairek I, Rvo Salerno P, Chen Z, et al. ; CARES Surveillance Group . Historical neighborhood redlining and bystander CPR disparities in out-of-hospital cardiac arrest. Resuscitation. 2024;201:110264. doi: 10.1016/j.resuscitation.2024.110264 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r42] 42.Nelson R, Winling L, Marciano R, Connolly N. Mapping inequality: redlining in New Deal America. Accessed May 7, 2025. https://dsl.richmond.edu/panorama/redlining

[zoi250725r43] 43.Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible—The Neighborhood Atlas. N Engl J Med. 2018;378(26):2456-2458. doi: 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r44] 44.Okundi AO, Varol C. Spatial justice in healthcare: Advancing equitable geographic access to primary healthcare in Migori County, Kenya. Soc Sci Humanit Open. 2024;9:100784. doi: 10.1016/j.ssaho.2023.100784 [DOI] [Google Scholar]

[zoi250725r45] 45.Berry C, Escobar N, Mann NC, et al. Ambulance deserts and inequities in access to EMS Care: are injured patients at risk for delayed care in the prehospital system? J Trauma Acute Care Surg. Published online May 23, 2025. doi: 10.1097/TA.0000000000004579 [DOI] [PubMed] [Google Scholar]

[zoi250725r46] 46.Jonk Y, Milkowski C, Croll Z, Pearson K. Ambulance deserts: geographic disparities in the provision of ambulance services. University of Southern Maine. Accessed May 7, 2025. https://digitalcommons.usm.maine.edu/ems/16/

[zoi250725r47] 47.Li M, Wang F, Kwan MP, Chen J, Wang J. Equalizing the spatial accessibility of emergency medical services in Shanghai: a trade-off perspective. Comput Environ Urban Syst. 2022;9:101745. doi: 10.1016/j.compenvurbsys.2021.101745 [DOI] [Google Scholar]

[zoi250725r48] 48.Owusu-Ansah S, Tripp R, N Weisberg S, P Mercer M, Whitten-Chung K, NAEMSP Diversity, Equity, & Inclusion Committee . Essential principles to create an equitable, inclusive, and diverse EMS workforce and work environment: a position statement and resource document. Prehosp Emerg Care. 2023;27(5):552-556. doi: 10.1080/10903127.2023.2187103 [DOI] [PubMed] [Google Scholar]

[zoi250725r49] 49.Nardone AL, Casey JA, Rudolph KE, Karasek D, Mujahid M, Morello-Frosch R. Associations between historical redlining and birth outcomes from 2006 through 2015 in California. PLoS One. 2020;15(8):e0237241. doi: 10.1371/journal.pone.0237241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r50] 50.Bikomeye JC, Zhou Y, McGinley EL, et al. Historical redlining and breast cancer treatment and survival among older women in the United States. J Natl Cancer Inst. 2023;115(6):652-661. doi: 10.1093/jnci/djad034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi250725r51] 51.Bradford JM, Eldin MM, Golestani S, et al. Structural racism, residential segregation, and exposure to trauma: the persistent impact of redlining. J Trauma Acute Care Surg. 2024;97(6):891-895. doi: 10.1097/TA.0000000000004290 [DOI] [PubMed] [Google Scholar]

[zoi250725r52] 52.Li M, Yuan F. Historical redlining and resident exposure to COVID-19: a study of New York City. Race Soc Probl. 2022;14(2):85-100. doi: 10.1007/s12552-021-09338-z [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Rapid Access to Emergency Medical Services Within Historically Redlined Areas

Cherisse Berry, MD

Joseph Obiajulu, MD

N Clay Mann, PhD, MS, MBA

Dustin T Duncan, ScD

Charles DiMaggio, PhD

Ashley Pfaff, MD

Spiros Frangos, MD, MPH

Jakka Sairamesh, PhD

Natalie Escobar, MD

Gbenga Ogedegbe, MD, MPH

Ran Wei, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Sample

Study Variables

Exposure Measure

Outcome Measures

Statistical Analysis

Results

Table 1. US Population With and Without Rapid Access to EMS Care by Historical HOLC Areas, Grades A to D.

Table 2. Proportion of Population Without Rapid Access to EMS Care by Historical HOLC Areas and Region.

Table 3. Socioeconomic and Demographic Characteristics of Census Block Groups Without Rapid Access to Emergency Medical Services Across Historical Home Owners’ Loan Corporation Areas, Grades A to D.

Discussion

Limitations and Strengths

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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