Association Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States | Emergency Medicine | JAMA Surgery | JAMA Network
[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.  Flowchart of Emergency Medical Service (EMS) Activations and Counties Included in the Study
Flowchart of Emergency Medical Service (EMS) Activations and Counties Included in the Study

MVC indicates motor vehicle crash; NEMSIS, National Emergency Medical Services Information System.

Figure 2.  Crude Association Between County Median Emergency Medical Service (EMS) Response Time and County Rate of Motor Vehicle Crash (MVC) Mortality
Crude Association Between County Median Emergency Medical Service (EMS) Response Time and County Rate of Motor Vehicle Crash (MVC) Mortality

Blue circles represent the mean county MVC mortality rate associated with each incremental increase in county response time. Lines represent the best-fit polynomial to mean MVC mortality rates (solid line) and 95% CIs (dashed lines) associated with increasing county response times.

Table 1.  Comparison of County Characteristics Across Quartiles of EMS Response Timea
Comparison of County Characteristics Across Quartiles of EMS Response Timea
Table 2.  Hierarchical Negative Binomial Model for County MVC Mortality
Hierarchical Negative Binomial Model for County MVC Mortality
1.
Centers for Disease Control and Prevention. Injury prevention and control. 2015. https://www.cdc.gov/injury/wisqars/facts.html. Accessed February 20, 2018.
2.
National Highway Traffic Safety Administration. FARS annual crash statistics. 2017. https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars. Accessed February 20, 2018.
3.
Nathens  AB, Jurkovich  GJ, Rivara  FP, Maier  RV.  Effectiveness of state trauma systems in reducing injury-related mortality: a national evaluation.  J Trauma. 2000;48(1):25-30. doi:10.1097/00005373-200001000-00005PubMedGoogle ScholarCrossref
4.
Nathens  AB, Jurkovich  GJ, Cummings  P, Rivara  FP, Maier  RV.  The effect of organized systems of trauma care on motor vehicle crash mortality.  JAMA. 2000;283(15):1990-1994. doi:10.1001/jama.283.15.1990PubMedGoogle ScholarCrossref
5.
Insurance Institute for Highway Safety Highway Loss Data Institute. General statistics. 2015. https://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/state-by-state-overview. Accessed February 20, 2018
6.
National Academies of Sciences, Engineering, and Medicine.  Medicine. A National Trauma Care System: Integrating Military and Civilian Trauma Systems to Achieve Zero Preventable Deaths After Injury. Washington, DC: The National Academies Press; 2016.
7.
Brown  J, Sajankila  N, Claridge  JA.  Prehospital assessment of trauma.  Surg Clin North Am. 2017;97(5):961-983. doi:10.1016/j.suc.2017.06.007PubMedGoogle ScholarCrossref
8.
National EMS Information System website. 2017. https://nemsis.org. Accessed Feburary 20, 2018.
9.
Mann  NC, Kane  L, Dai  M, Jacobson  K.  Description of the 2012 NEMSIS public-release research dataset.  Prehosp Emerg Care. 2015;19(2):232-240. Published online October 7, 2014. doi:10.3109/10903127.2014.959219PubMedGoogle ScholarCrossref
10.
National Highway Traffic Safety Administration. Fatality Analysis Reporting System (FARS) encyclopedia. 2017. https://www-fars.nhtsa.dot.gov/Main/index.aspx . Accessed February 20, 2018.
11.
National Highway Traffic Safety Administration. FARS analytical user’s manual. 2017. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812602. Accessed February 20, 2018.
12.
US Census Bureau. Population and housing unit estimates. 2017. https://www.census.gov/programs-surveys/popest/data/data-sets.html. Accessed February 20, 2018.
13.
World Health Organization. Road traffic injuries. 2018http://www.who.int/en/news-room/fact-sheets/detail/road-traffic-injuries.Accessed February 20, 2018.
14.
Tavris  DR, Kuhn  EM, Layde  PM.  Age and gender patterns in motor vehicle crash injuries: importance of type of crash and occupant role.  Accid Anal Prev. 2001;33(2):167-172. doi:10.1016/S0001-4575(00)00027-0PubMedGoogle ScholarCrossref
15.
US Department of Agriculture. Rural-urban continuum codes. 2016. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/. Accessed February 20, 2018.
16.
Branas  CC, MacKenzie  EJ, Williams  JC,  et al.  Access to trauma centers in the United States.  JAMA. 2005;293(21):2626-2633. doi:10.1001/jama.293.21.2626PubMedGoogle ScholarCrossref
17.
Rutledge  R, Fakhry  SM, Meyer  A, Sheldon  GF, Baker  CC.  An analysis of the association of trauma centers with per capita hospitalizations and death rates from injury.  Ann Surg. 1993;218(4):512-521. doi:10.1097/00000658-199310000-00011PubMedGoogle ScholarCrossref
18.
Rhinehart  ZJ, Guyette  FX, Sperry  JL,  et al.  The association between air ambulance distribution and trauma mortality.  Ann Surg. 2013;257(6):1147-1153. doi:10.1097/SLA.0b013e31827ee6b0PubMedGoogle ScholarCrossref
19.
Galvagno  SM  Jr, Haut  ER, Zafar  SN,  et al.  Association between helicopter vs ground emergency medical services and survival for adults with major trauma.  JAMA. 2012;307(15):1602-1610. doi:10.1001/jama.2012.467PubMedGoogle ScholarCrossref
20.
American College of Surgeons. Searching for verified trauma centers. 2017. https://www.facs.org/search/trauma-centers. Accessed February 20, 2018.
21.
American Trauma Society. Find your local trauma center. 2017. https://www.amtrauma.org/?page=findtraumacenter. Accessed February 20, 2018.
22.
The Atlas and Database of Air Medical Services (ADAMS). Air medical civilian fleet by rotor wing make/model. 2014. http://www.adamsairmed.org/pubs/rw_make_model_in_ADAMS_multi_year.pdf. Accessed February 20, 2018.
23.
The Atlas and Database of Air Medical Services (ADAMS). National & state overview of air medical coverage in 2016. 2016. http://www.adamsairmed.org/pubs/ADAMS_Intro.pdf. Accessed February 20, 2018.
24.
ADAMS annual public atlas. 2018. http://www.adamsairmed.org/public_site.html. Accessed February 20, 2018.
25.
United States Census Bureau. TIGER/Line with selected demographic and economic data. 2017. https://www.census.gov/geo/maps-data/data/tiger-data.html. Accessed February 20, 2018.
26.
Gallaher  MM, Sewell  CM, Flint  S,  et al.  Effects of the 65-mph speed limit on rural interstate fatalities in New Mexico.  JAMA. 1989;262(16):2243-2245. doi:10.1001/jama.1989.03430160065031PubMedGoogle ScholarCrossref
27.
Wagenaar  AC, Streff  FM, Schultz  RH.  Effects of the 65 mph speed limit on injury morbidity and mortality.  Accid Anal Prev. 1990;22(6):571-585. doi:10.1016/0001-4575(90)90029-KPubMedGoogle ScholarCrossref
28.
Robertson  LS.  Estimates of motor vehicle seat belt effectiveness and use: implications for occupant crash protection.  Am J Public Health. 1976;66(9):859-864. doi:10.2105/AJPH.66.9.859PubMedGoogle ScholarCrossref
29.
Rivara  FP, Thompson  DC, Cummings  P.  Effectiveness of primary and secondary enforced seat belt laws.  Am J Prev Med. 1999;16(1)(suppl):30-39. doi:10.1016/S0749-3797(98)00113-5PubMedGoogle ScholarCrossref
30.
Zador  PL, Lund  AK, Fields  M, Weinberg  K.  Fatal crash involvement and laws against alcohol-impaired driving.  J Public Health Policy. 1989;10(4):467-485. doi:10.2307/3342519PubMedGoogle ScholarCrossref
31.
Yao  J, Johnson  MB, Tippetts  S.  Enforcement uniquely predicts reductions in alcohol-impaired crash fatalities.  Addiction. 2016;111(3):448-453. doi:10.1111/add.13198PubMedGoogle ScholarCrossref
32.
Klauer  SG, Guo  F, Simons-Morton  BG, Ouimet  MC, Lee  SE, Dingus  TA.  Distracted driving and risk of road crashes among novice and experienced drivers.  N Engl J Med. 2014;370(1):54-59. doi:10.1056/NEJMsa1204142PubMedGoogle ScholarCrossref
33.
Safety IIfH. Highway safety topics. 2017.http://www.iihs.org/iihs/topics#statelaws. Accessed Feburary 20, 2018.
34.
Governors Highway Safety Association. State laws by issue. 2017. https://www.ghsa.org/state-laws/issues. Accessed February 20, 2018.
35.
World Health Organization. Metrics: population attributable fraction (PAF). 2017. http://www.who.int/healthinfo/global_burden_disease/metrics_paf/en/. Accessed February 20, 2018.
36.
Mansournia  MA, Altman  DG.  Population attributable fraction.  BMJ. 2018;360:k757. doi:10.1136/bmj.k757PubMedGoogle ScholarCrossref
37.
Rockhill  B, Newman  B, Weinberg  C.  Use and misuse of population attributable fractions.  Am J Public Health. 1998;88(1):15-19. doi:10.2105/AJPH.88.1.15PubMedGoogle ScholarCrossref
38.
Miettinen  OS.  Proportion of disease caused or prevented by a given exposure, trait or intervention.  Am J Epidemiol. 1974;99(5):325-332. doi:10.1093/oxfordjournals.aje.a121617PubMedGoogle ScholarCrossref
39.
Harmsen  AM, Giannakopoulos  GF, Moerbeek  PR, Jansma  EP, Bonjer  HJ, Bloemers  FW.  The influence of prehospital time on trauma patients outcome: a systematic review.  Injury. 2015;46(4):602-609. doi:10.1016/j.injury.2015.01.008PubMedGoogle ScholarCrossref
40.
Gonzalez  RP, Cummings  G, Mulekar  M, Rodning  CB.  Increased mortality in rural vehicular trauma: identifying contributing factors through data linkage.  J Trauma. 2006;61(2):404-409. doi:10.1097/01.ta.0000229816.16305.94PubMedGoogle ScholarCrossref
41.
Feero  S, Hedges  JR, Simmons  E, Irwin  L.  Does out-of-hospital EMS time affect trauma survival?  Am J Emerg Med. 1995;13(2):133-135. doi:10.1016/0735-6757(95)90078-0PubMedGoogle ScholarCrossref
42.
Newgard  CD, Schmicker  RH, Hedges  JR,  et al; Resuscitation Outcomes Consortium Investigators.  Emergency medical services intervals and survival in trauma: assessment of the “golden hour” in a North American prospective cohort.  Ann Emerg Med. 2010;55(3):235-246.e4. doi:10.1016/j.annemergmed.2009.07.024PubMedGoogle ScholarCrossref
43.
Myers  JB, Slovis  CM, Eckstein  M,  et al; U.S. Metropolitan Municipalities’ EMS Medical Directors.  Evidence-based performance measures for emergency medical services systems: a model for expanded EMS benchmarking.  Prehosp Emerg Care. 2008;12(2):141-151. doi:10.1080/10903120801903793PubMedGoogle ScholarCrossref
44.
Lam  SS, Zhang  J, Zhang  ZC,  et al.  Dynamic ambulance reallocation for the reduction of ambulance response times using system status management.  Am J Emerg Med. 2015;33(2):159-166. doi:10.1016/j.ajem.2014.10.044PubMedGoogle ScholarCrossref
45.
Kononen  DW, Flannagan  CA, Wang  SC.  Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes.  Accid Anal Prev. 2011;43(1):112-122. doi:10.1016/j.aap.2010.07.018PubMedGoogle ScholarCrossref
46.
MacKenzie  EJ, Rivara  FP, Jurkovich  GJ,  et al.  A national evaluation of the effect of trauma-center care on mortality.  N Engl J Med. 2006;354(4):366-378. doi:10.1056/NEJMsa052049PubMedGoogle ScholarCrossref
47.
Centers for Disease Control and Prevention. Buckle up: restraint use state fact sheets. 2015. https://www.cdc.gov/motorvehiclesafety/seatbelts/states.html. Accessed February 20, 2018.
48.
Peura  C, Kilch  JA, Clark  DE.  Evaluating adverse rural crash outcomes using the NHTSA State Data System.  Accid Anal Prev. 2015;82:257-262. doi:10.1016/j.aap.2015.06.005PubMedGoogle ScholarCrossref
49.
Brown  JB, Rosengart  MR, Forsythe  RM,  et al.  Not all prehospital time is equal: influence of scene time on mortality.  J Trauma Acute Care Surg. 2016;81(1):93-100. doi:10.1097/TA.0000000000000999PubMedGoogle ScholarCrossref
50.
Federal Interagency Committee on Emergency Medical Services. FICEM annual report to Congress. 2013. https://www.ems.gov/pdf/2011-2012-FICEMS-RTC-Mikulski.pdf. Accessed February 20, 2018.
51.
Morgenstern  H.  Ecologic studies in epidemiology: concepts, principles, and methods.  Annu Rev Public Health. 1995;16:61-81. doi:10.1146/annurev.pu.16.050195.000425PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    February 6, 2019

    Association Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States

    Author Affiliations
    • 1Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
    • 2Clinical Epidemiology Program, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • 3Division of General Surgery, University of Toronto, Toronto, Ontario, Canada
    • 4National Emergency Medical Service Information System Technical Assistance Center, Salt Lake City, Utah
    • 5Department of Pediatrics, University of Utah School of Medicine, Salt Lake City
    • 6Department of Surgery, Sunnybrook Health Sciences Center, University of Toronto, Toronto, Ontario, Canada
    • 7Department of Surgery, St. Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
    • 8Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
    JAMA Surg. 2019;154(4):286-293. doi:10.1001/jamasurg.2018.5097
    Key Points

    Question  Are regional emergency medical service response time capabilities associated with motor vehicle crash mortality?

    Findings  In this population-based study of 2268 US counties, longer emergency medical service response times were associated with higher rates of motor vehicle crash mortality after adjusting for measures of rurality, emergency medical service on-scene and transport times, access to trauma resources, and traffic safety laws. With use of the population attributable fraction, a significant proportion of motor vehicle crash deaths were found to be associated with prolonged response times in rural/wilderness and urban/suburban settings.

    Meaning  The findings suggest that trauma system–level efforts to address regional disparities in motor vehicle crash mortality should evaluate emergency medical service response times as a potential contributor.

    Abstract

    Importance  Motor vehicle crashes (MVCs) are a leading public health concern. Emergency medical service (EMS) response time is a modifiable, system-level factor with the potential to influence trauma patient survival. The relationship between EMS response time and MVC mortality is unknown.

    Objectives  To measure the association between EMS response times and MVC mortality at the population level across US counties.

    Design, Setting, and Study Population  This population-based study included MVC-related deaths in 2268 US counties, representing an estimated population of 239 464 121 people, from January 1, 2013, through December 31, 2015. Data were analyzed from October 1, 2017, through April 30, 2018.

    Exposure  The median EMS response time to MVCs within each county (county response time), derived from data collected by the National Emergency Medical Service Information System.

    Main Outcomes and Measures  The county rate of MVC-related death, calculated using crash fatality data recorded in the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration.

    Results  During the study period, 2 214 480 ambulance responses to MVCs were identified (median, 229 responses per county [interquartile range (IQR), 73-697 responses per county]) in 2268 US counties. The median county response time was 9 minutes (IQR, 7-11) minutes. Longer response times were significantly associated with higher rates of MVC mortality (≥12 vs <7 minutes; mortality rate ratio, 1.46; 95% CI, 1.32-1.61) after adjusting for measures of rurality, on-scene and transport times, access to trauma resources, and traffic safety laws. This finding was consistent in both rural/wilderness and urban/suburban settings, where a significant proportion of MVC fatalities (population attributable fraction: rural/wilderness, 9.9%; urban/suburban, 14.1%) were associated with prolonged response times (defined by the median value, ≥10 minutes and ≥7 minutes, respectively).

    Conclusions and Relevance  Among 2268 US counties, longer EMS response times were associated with higher rates of MVC mortality. A significant proportion of MVC-related deaths were associated with prolonged response times in both rural/wilderness and urban/suburban settings. These findings suggest that trauma system–level efforts to address regional disparities in MVC mortality should evaluate EMS response times as a potential contributor.

    Introduction

    Motor vehicle crashes (MVCs) are a leading cause of death and injury in the United States.1 Improvements to road infrastructure, vehicle design, and traffic safety legislation have led to a decrease in crash mortality from 15.9 per 100 000 person-years in 1995 to 10.9 per 100 000 person-years in 2015.2 The implementation of organized trauma systems has further reduced deaths by ensuring that patients with severe injury receive timely access to trauma care.3,4 However, significant variation in crash mortality persists, with rates varying by an order of magnitude between states from 3 to 25 deaths per 100 000 person-years.5 The contributing role of modifiable trauma system–level factors in this disparity is unclear.

    Emergency medical service (EMS) response time, defined as time elapsed between EMS notification and arrival on scene, is a system-level factor with potential to influence survival. Emergency medical services provide the critical link between injury and definitive care.6 Early arrival of EMS at the crash scene allows for stabilization of occupants with life-threatening injuries, timely triage, and transport to hospital.6,7 Conversely, delays could lead to a greater risk of death.

    We postulated that a relationship exists between regional EMS response capabilities and crash mortality and that reducing the time to first medical contact in the field might decrease deaths due to road traffic injuries at the system level. Quiz Ref IDTherefore, we measured the association between EMS response times and MVC mortality at the population level across US counties and estimated the number of fatalities that might be prevented if response times were shortened.

    Methods
    Study Design

    This study was a population-based analysis of MVC-related deaths within US counties from January 1, 2013, through December 31, 2015. The specific study objectives were to (1) to measure the association between EMS response time and MVC mortality at the US county level and (2) estimate the proportion of MVC-related deaths associated with prolonged response times. Data were analyzed from October 1, 2017, through April 30, 2018. This project was approved by the Sunnybrook Health Sciences Center research ethics board (Toronto, Ontario, Canada). The data sources used are publicly available and deidentified; informed consent was waived.

    Data Sources
    Emergency Medical Service Response Time

    Data related to ground EMS activations during calendar years 2013 through 2015 were provided by the National Emergency Medical Services Information System (NEMSIS),8 a federally funded project designed to standardize EMS patient care reporting and facilitate collection of data for assessment of EMS systems of care.9 As of 2015, NEMSIS collected data related to EMS activations performed by EMS agencies in 2497 of 3144 US counties and county equivalents. We included all ground ambulance responses for possible injury due to MVCs (Figure 1). Emergency medical service response times were aggregated at the county level. The exposure was defined as the county median EMS response time (henceforth referred to as county response time), an ecologic measure of regional EMS responsiveness to traffic crashes. Emergency medical service activations with missing response time were excluded (<0.2%). Counties with fewer than 5 EMS responses to MVCs across the 3-year study period were also excluded (4%).

    Motor Vehicle Crash Mortality Rate

    Motor vehicle crash–related deaths were derived from the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration.10 The Fatality Analysis Reporting System is a population-based registry that collects data related to all MVCs on public roads in the United States that result in at least 1 fatality within 30 days.11Quiz Ref ID To reduce the potential for confounding, fatalities were limited to occupants of passenger vehicles. Crashes involving heavy trucks, motorcycles, off-road vehicles, cyclists, or pedestrians were excluded. The US Census Bureau provided intercensal population estimates for each county and year.12 The primary outcome was the county rate of MVC mortality reported as deaths per 100 000 person-years. To account for demographic differences known to be associated with risk of MVC death,13,14 county mortality rates were stratified by age (<15, 15-34, 35-64, and ≥65 years) and sex.

    Linkage of Emergency Medical Service and Motor Vehicle Crash Mortality Data

    The NEMSIS Technical Assistance Center performed linkage of crash mortality and EMS data at the county level using Federal Information Processing Standards (FIPS) codes. County FIPS codes were then replaced using a random identifier to preserve anonymity. Counties where identifiable combinations of data might pose a risk to anonymity were excluded (<3% of counties). The stepwise derivation and structure of the final analytic data set is described in the eMethods in the Supplement.

    Potential Confounders

    We considered several factors in addition to population age and sex that might confound the association between EMS response time and MVC mortality. County rurality was estimated using county population density and rural-urban continuum codes.15 Population densities were calculated using US census population estimates12 and grouped into quartiles (<16, 17-42, 43-108, and ≥109 persons/mile2). Rural-urban continuum codes were used to group counties into 4 categories of rurality (urban, suburban, rural, and wilderness) based on population and proximity to metropolitan areas (eTable 1 in the Supplement).

    Emergency medical services on-scene and transport times to hospital might also be associated with crash mortality. These time intervals were derived for ambulance responses for which the final destination was a hospital. Median on-scene and transport times were calculated for each county.

    Regional differences in EMS response time are also likely to be correlated with access to trauma resources.16 We accounted for the proximity of trauma centers17 and helicopter EMS18,19 to county populations. American College of Surgeons–verified and state-designated level I and II trauma centers were identified from the American College of Surgeons 20 and American Trauma Society21 databases and geocoded by address using geographic information system (GIS) software. Counties were then categorized by trauma center proximity (within county, adjacent county, or no proximate trauma center) (eFigure 1 in the Supplement).

    Access to helicopter EMS was estimated for each county as the proportion of the population within 25-mile flight circles of a helicopter EMS base (corresponding to areas within 15- to 20–minute helicopter EMS response times22,23) (eFigure 2 in the Supplement). Helicopter EMS bases were identified in the Atlas & Database of Air Medical Services24 and geocoded by zip code. The geographic distribution of populations within counties were geocoded using the centroids of census blocks provided by the US Census Bureau.25

    Finally, state traffic safety laws known to influence the risk of MVC fatality were considered. These laws included the maximum posted speed limits on urban and rural highways and interstates,26,27 primary enforcement of seat belt laws,28,29 administrative license revocation for alcohol-impaired driving,30,31 and legislation prohibiting texting while driving.32 Traffic safety laws were obtained from the Insurance Institute for Highway Safety33 and the Governors Highway Safety Administration.34

    Statistical Analysis

    To assess the generalizability of our results, characteristics of counties included in the study were compared with those of excluded counties. Univariable analyses compared county characteristics across quartiles of county response time. The Wilcoxon rank sum and Kruskal-Wallis tests were used in analysis of continuous variables, and frequencies were compared using the χ2 test.

    A hierarchical negative binomial regression model was used to estimate the risk-adjusted association between county response time and MVC mortality. This model was a generalized linear mixed model with random effects to account for clustering of counties within states.

    To estimate the proportion of MVC-related deaths associated with prolonged EMS response times, we used the population attributable fraction.35-37 The population attributable fraction is defined as the fraction of all cases (in this case, MVC fatalities) in a population that is attributable to a specific exposure (in this case, prolonged response times), assuming a causal relationship. This was estimated using adjusted mortality rate ratios (MRRs) from the hierarchical model using the formula described by Rockhill et al37 and Miettinen38: population attributable fraction = Pc (1 − 1/MRR) × 100%, where Pc is the proportion of MVC fatalities that occurred in counties with prolonged response times (prevalence of the exposure among cases). In this study, the population attributable fraction can be interpreted as the estimated fraction of all MVC fatalities that might have been prevented if all counties achieved response times below the defined threshold.

    Owing to geographic and resource constraints, it is unlikely that rural counties would be able to achieve EMS response times comparable to those of urban counties. Therefore, we performed a stratified analysis grouping counties into rural/wilderness and urban/suburban counties using rural-urban continuum codes. The median county response time was calculated separately for rural/wilderness and urban/suburban counties to define the prolonged response time threshold appropriate to each context. The population attributable fraction was then estimated for rural/wilderness and urban/suburban counties using these benchmarks.

    Geospatial analyses deriving variables for proximity of trauma centers and helicopter EMS to county populations were performed using Esri ArcMap GIS software, version 10.5 (Esri). Statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc).

    Results

    During 2013 through 2015, there were 77 941 796 EMS activations in 2497 US counties (Figure 1). We identified 2 214 480 ambulance responses to MVCs that met inclusion criteria in 2268 counties (72% of all US counties). The median number of ambulance responses to MVCs for each county was 229 responses (interquartile range [IQR], 73-697 responses).

    Counties included were from 49 US states (including the District of Columbia) and accounted for an estimated 239 464 121 persons (75% of the total US population). Densely populated urban areas with greater access to trauma center care were overrepresented among the counties analyzed (eTable 2 in the Supplement).

    Table 1 compares the characteristics of counties across quartiles of county response time. Quiz Ref IDThe median county response time was 9 minutes (IQR, 7-11 minutes). Counties with longer response times were more often rural, had longer on-scene and transport times, had less access to level I or II trauma centers, and had lower helicopter EMS availability. Conversely, counties with shorter response times were associated with greater presence of protective state traffic safety laws.

    The MVC mortality rate was significantly higher in counties with longer response times. In counties with response times of 12 minutes or greater, the mortality rate was 11.9 per 100 000 person-years; counties with response times of less than 7 minutes had a mortality rate of 4.9 per 100 000 person-years (unadjusted MRR, 1.95; 95% CI, 1.72-2.22). A near-linear association was observed between county response time and MVC mortality rate (Figure 2).

    Hierarchical Negative Binomial Model

    Table 2 shows the results of our hierarchical negative binomial model for county MVC mortality. Quiz Ref IDAfter adjusting for potential confounders, longer county response times were associated with higher rates of MVC-related death (county response time, ≥12 vs <7 minutes; MRR, 1.46; 95% CI, 1.32-1.61). Crash mortality was highest among males aged 15 to 34 years and in rural counties with low population density. The presence of a level I or II trauma center within a county was associated with lower mortality (MRR, 0.65; 95% CI, 0.59-0.72), whereas higher maximum speed limits on rural highways and interstates were associated with greater mortality. On-scene times, transport times, access to helicopter EMS, and other traffic safety laws were not significantly associated with MVC mortality.

    MVC Deaths Associated With Prolonged EMS Response Times

    We estimated the population attributable fraction of MVC deaths associated with prolonged response times in rural/wilderness and urban/suburban counties (eTable 3 in the Supplement). The median county response time among rural/wilderness counties was 10 minutes (IQR, 8-12 minutes) compared with 7 minutes (IQR, 6-9 minutes) among urban/suburban counties. In rural/wilderness counties, the proportion of crash fatalities associated with EMS response times of 10 minutes or longer was 9.9% (95% CI, 4.8%-14.1%), representing approximately 333 of 3363 passenger vehicle deaths. In urban or suburban counties, the proportion of crash fatalities associated with EMS response times of 7 minutes or longer was 14.1% (95% CI, 11.5%-16.4%), representing approximately 1796 of 12 735 passenger vehicle deaths. Quiz Ref IDTaken together, an estimated 2129 passenger vehicle deaths per year (13.2% of all crash fatalities within the 2268 counties evaluated) might have been prevented if county response times were shorter than the specified benchmarks in rural/wilderness (10 minutes) and urban/suburban (7 minutes) areas.

    Discussion

    In this population-based analysis of 2268 US counties, longer EMS response times were associated with higher rates of MVC mortality after accounting for measures of rurality, on-scene and transport times, access to trauma resources, and traffic safety laws. This finding was consistent in both rural/wilderness and urban/suburban settings, where a significant proportion of crash fatalities (9.9% and 14.1%, respectively) were associated with prolonged county response times.

    These data have important implications for trauma system design and health policy because they suggest that efforts to address regional disparities in MVC mortality should evaluate EMS response times as a potential contributor. That ensuring short EMS response times could save lives seems intuitive. Severe trauma is a time-dependent condition. Prompt arrival of first responders at the scene of a crash provides the greatest opportunity for early stabilization of occupants with life-threatening injuries, timely triage, and mobilization of the broader trauma system to achieve disposition to definitive trauma care.7 Conversely, delays may confer a greater risk of death among those in need of urgent medical attention. Thus, regional systems in which delays are more common would exhibit higher rates of crash mortality after accounting for other regional differences.

    It is important to place the results of this study in context with the current debate surrounding use of response time as an EMS performance measure. To date, there has been scarce evidence that response times influence trauma-related mortality.39 Studies that previously examined response time intervals were hindered by a lack of risk adjustment40,41 or were not designed to make system-level recommendations.42 Furthermore, a greater emphasis on speed has the potential to put the safety of first responders at risk.7 For these reasons, opinion has moved away from the use of response time standards as performance indicators.

    The present study adds to this discussion by showing, for the first time to our knowledge, that EMS response times may be associated with outcomes at the regional trauma-system level. These findings do not necessarily imply that EMS should simply drive faster, but rather that trauma systems should be organized to achieve a quick response to MVCs. Approaches to shortening response time intervals include ensuring adequate numbers43 and optimizing the distribution of first-responding units in a dynamic and predictive fashion.44 In regions where geographic and resource constraints are especially limiting, improvements to on-board telematics systems that predict the risk of severe injury may serve to better triage deployment.45 Although these approaches can be costly, our results suggest that such strategies may yield a public health benefit, particularly where delays in EMS response are common. Studies of cost-effectiveness are required in this area.

    On-scene and transport times were not significantly associated with crash mortality. This observation is notable because counties with longer EMS response times also had longer on-scene and transport times, reflecting differences in crash characteristics, EMS practices, and greater travel distances in rural areas. The finding that only EMS response times were significantly associated with mortality after accounting for other regional differences suggests that early medical contact with prehospital personnel is uniquely important to MVC occupant survival at the trauma-system level. Further research is needed to clarify what additional EMS structures and processes of care, such as the level of first-responding unit, provider training, or prehospital interventions, might contribute to the observed mortality benefit of early EMS arrival.

    Greater access to trauma center care was associated with lower MVC mortality. Counties with level I or II trauma centers had crash mortality rates 35% lower than those with no nearby trauma center, providing further evidence that trauma centers are associated with reduced risk of injury-related death.17,46 Conversely, state laws allowing higher speed limits were associated with greater mortality, reaffirming the dominant role of speed in the epidemiology of fatal road traffic crashes.26,27

    Contrary to previous reports,4,28-31 primary enforcement of seat belt and administrative license revocation laws were not significantly associated with MVC mortality. These findings likely reflect the updated era in which the data were derived. During 2013 through 2015, seat belt use in the United States surpassed 86%47 and all states had enforced the blood alcohol content limit of 0.08%.33 Therefore, although seat belt and alcohol-impaired driving laws prevent traffic deaths, the measurable effect is likely diminished compared with previous periods.

    Limitations

    This study has several important limitations. First, there is potential for residual confounding due to regional differences in road traffic crash characteristics. Crash characteristics might confound the relationship between EMS response time and mortality if vehicle occupants in counties with longer response times are more likely to experience forces leading to greater injury severity. As a consequence, a greater proportion of on-scene deaths (including those not modifiable by post-crash interventions) would be observed in regions with longer response times. However, we accounted for several variables that are correlated with crash characteristics, including measures of rurality,48 on-scene time (prolonged with extrication),49 and traffic safety laws (particularly those related to speed). Confounding attributable to other processes of care, such as EMS notification, the quality of medical care delivered, and patterns of interfacility transfer, was also mitigated through adjustment for rurality and regional differences in access to trauma resources (trauma centers and helicopter EMS). Second, the 2268 of 3144 US counties included in our analyses were overrepresentative of densely populated urban areas with greater access to trauma center care. However, that a substantial majority of US counties (and population) were included, representing the full spectrum of rurality and resources, is a compensating strength of the study. Third, it is difficult to confirm the completeness of the EMS activations collected by NEMSIS. However, NEMSIS captured an average of 26 million EMS activations annually during the study period (74% of an estimated 35 million nationally50). Therefore, it is reasonable to infer that the data from which our exposure variable was derived were comprehensive for the 72% of US counties included. Finally, the exposure variable itself (median EMS response time) was an ecologic measure of regional EMS response capabilities. Therefore, caution should be taken in making inferences at the individual level from our results (ecologic fallacy).51 However, individual-level effects were not a concern within the stated objectives of the study, which were to ascertain county-level effects relevant to trauma system design and broader health policy.

    Conclusions

    Among 2268 US counties, longer EMS response times were associated with higher rates of MVC mortality. A significant proportion of MVC-related deaths were associated with prolonged response times in both rural/wilderness and urban/suburban settings. These findings suggest that trauma system–level efforts to address regional disparities in MVC mortality should evaluate EMS response time as a potential contributor. Further work is needed to identify other prehospital structures and processes of care that might contribute to regional disparities in trauma outcomes.

    Back to top
    Article Information

    Accepted for Publication: September 29, 2018.

    Corresponding Author: James P. Byrne, PhD, MD, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, 2075 Bayview Ave, Room D-574, Toronto, ON M4N 3M5, Canada (jpbyrne@gmail.com).

    Published Online: February 6, 2019. doi:10.1001/jamasurg.2018.5097

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

    Concept and design: Byrne, Nathens.

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

    Drafting of the manuscript: Byrne, Nathens.

    Critical revision of the manuscript for important intellectual content: Mann, Dai, Mason, Karanicolas, Rizoli.

    Statistical analysis: Byrne, Mann, Dai, Mason.

    Administrative, technical, or material support: Mann, Mason, Nathens.

    Supervision: Nathens.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Dr Nathens is supported by funds from the De Souza Chair in Trauma Research and the Canadian Institutes of Health Research Canada Research Chair in Systems of Trauma Care.

    Role of the Funder/Sponsor: The funder had no role 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.

    References
    1.
    Centers for Disease Control and Prevention. Injury prevention and control. 2015. https://www.cdc.gov/injury/wisqars/facts.html. Accessed February 20, 2018.
    2.
    National Highway Traffic Safety Administration. FARS annual crash statistics. 2017. https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars. Accessed February 20, 2018.
    3.
    Nathens  AB, Jurkovich  GJ, Rivara  FP, Maier  RV.  Effectiveness of state trauma systems in reducing injury-related mortality: a national evaluation.  J Trauma. 2000;48(1):25-30. doi:10.1097/00005373-200001000-00005PubMedGoogle ScholarCrossref
    4.
    Nathens  AB, Jurkovich  GJ, Cummings  P, Rivara  FP, Maier  RV.  The effect of organized systems of trauma care on motor vehicle crash mortality.  JAMA. 2000;283(15):1990-1994. doi:10.1001/jama.283.15.1990PubMedGoogle ScholarCrossref
    5.
    Insurance Institute for Highway Safety Highway Loss Data Institute. General statistics. 2015. https://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/state-by-state-overview. Accessed February 20, 2018
    6.
    National Academies of Sciences, Engineering, and Medicine.  Medicine. A National Trauma Care System: Integrating Military and Civilian Trauma Systems to Achieve Zero Preventable Deaths After Injury. Washington, DC: The National Academies Press; 2016.
    7.
    Brown  J, Sajankila  N, Claridge  JA.  Prehospital assessment of trauma.  Surg Clin North Am. 2017;97(5):961-983. doi:10.1016/j.suc.2017.06.007PubMedGoogle ScholarCrossref
    8.
    National EMS Information System website. 2017. https://nemsis.org. Accessed Feburary 20, 2018.
    9.
    Mann  NC, Kane  L, Dai  M, Jacobson  K.  Description of the 2012 NEMSIS public-release research dataset.  Prehosp Emerg Care. 2015;19(2):232-240. Published online October 7, 2014. doi:10.3109/10903127.2014.959219PubMedGoogle ScholarCrossref
    10.
    National Highway Traffic Safety Administration. Fatality Analysis Reporting System (FARS) encyclopedia. 2017. https://www-fars.nhtsa.dot.gov/Main/index.aspx . Accessed February 20, 2018.
    11.
    National Highway Traffic Safety Administration. FARS analytical user’s manual. 2017. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812602. Accessed February 20, 2018.
    12.
    US Census Bureau. Population and housing unit estimates. 2017. https://www.census.gov/programs-surveys/popest/data/data-sets.html. Accessed February 20, 2018.
    13.
    World Health Organization. Road traffic injuries. 2018http://www.who.int/en/news-room/fact-sheets/detail/road-traffic-injuries.Accessed February 20, 2018.
    14.
    Tavris  DR, Kuhn  EM, Layde  PM.  Age and gender patterns in motor vehicle crash injuries: importance of type of crash and occupant role.  Accid Anal Prev. 2001;33(2):167-172. doi:10.1016/S0001-4575(00)00027-0PubMedGoogle ScholarCrossref
    15.
    US Department of Agriculture. Rural-urban continuum codes. 2016. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/. Accessed February 20, 2018.
    16.
    Branas  CC, MacKenzie  EJ, Williams  JC,  et al.  Access to trauma centers in the United States.  JAMA. 2005;293(21):2626-2633. doi:10.1001/jama.293.21.2626PubMedGoogle ScholarCrossref
    17.
    Rutledge  R, Fakhry  SM, Meyer  A, Sheldon  GF, Baker  CC.  An analysis of the association of trauma centers with per capita hospitalizations and death rates from injury.  Ann Surg. 1993;218(4):512-521. doi:10.1097/00000658-199310000-00011PubMedGoogle ScholarCrossref
    18.
    Rhinehart  ZJ, Guyette  FX, Sperry  JL,  et al.  The association between air ambulance distribution and trauma mortality.  Ann Surg. 2013;257(6):1147-1153. doi:10.1097/SLA.0b013e31827ee6b0PubMedGoogle ScholarCrossref
    19.
    Galvagno  SM  Jr, Haut  ER, Zafar  SN,  et al.  Association between helicopter vs ground emergency medical services and survival for adults with major trauma.  JAMA. 2012;307(15):1602-1610. doi:10.1001/jama.2012.467PubMedGoogle ScholarCrossref
    20.
    American College of Surgeons. Searching for verified trauma centers. 2017. https://www.facs.org/search/trauma-centers. Accessed February 20, 2018.
    21.
    American Trauma Society. Find your local trauma center. 2017. https://www.amtrauma.org/?page=findtraumacenter. Accessed February 20, 2018.
    22.
    The Atlas and Database of Air Medical Services (ADAMS). Air medical civilian fleet by rotor wing make/model. 2014. http://www.adamsairmed.org/pubs/rw_make_model_in_ADAMS_multi_year.pdf. Accessed February 20, 2018.
    23.
    The Atlas and Database of Air Medical Services (ADAMS). National & state overview of air medical coverage in 2016. 2016. http://www.adamsairmed.org/pubs/ADAMS_Intro.pdf. Accessed February 20, 2018.
    24.
    ADAMS annual public atlas. 2018. http://www.adamsairmed.org/public_site.html. Accessed February 20, 2018.
    25.
    United States Census Bureau. TIGER/Line with selected demographic and economic data. 2017. https://www.census.gov/geo/maps-data/data/tiger-data.html. Accessed February 20, 2018.
    26.
    Gallaher  MM, Sewell  CM, Flint  S,  et al.  Effects of the 65-mph speed limit on rural interstate fatalities in New Mexico.  JAMA. 1989;262(16):2243-2245. doi:10.1001/jama.1989.03430160065031PubMedGoogle ScholarCrossref
    27.
    Wagenaar  AC, Streff  FM, Schultz  RH.  Effects of the 65 mph speed limit on injury morbidity and mortality.  Accid Anal Prev. 1990;22(6):571-585. doi:10.1016/0001-4575(90)90029-KPubMedGoogle ScholarCrossref
    28.
    Robertson  LS.  Estimates of motor vehicle seat belt effectiveness and use: implications for occupant crash protection.  Am J Public Health. 1976;66(9):859-864. doi:10.2105/AJPH.66.9.859PubMedGoogle ScholarCrossref
    29.
    Rivara  FP, Thompson  DC, Cummings  P.  Effectiveness of primary and secondary enforced seat belt laws.  Am J Prev Med. 1999;16(1)(suppl):30-39. doi:10.1016/S0749-3797(98)00113-5PubMedGoogle ScholarCrossref
    30.
    Zador  PL, Lund  AK, Fields  M, Weinberg  K.  Fatal crash involvement and laws against alcohol-impaired driving.  J Public Health Policy. 1989;10(4):467-485. doi:10.2307/3342519PubMedGoogle ScholarCrossref
    31.
    Yao  J, Johnson  MB, Tippetts  S.  Enforcement uniquely predicts reductions in alcohol-impaired crash fatalities.  Addiction. 2016;111(3):448-453. doi:10.1111/add.13198PubMedGoogle ScholarCrossref
    32.
    Klauer  SG, Guo  F, Simons-Morton  BG, Ouimet  MC, Lee  SE, Dingus  TA.  Distracted driving and risk of road crashes among novice and experienced drivers.  N Engl J Med. 2014;370(1):54-59. doi:10.1056/NEJMsa1204142PubMedGoogle ScholarCrossref
    33.
    Safety IIfH. Highway safety topics. 2017.http://www.iihs.org/iihs/topics#statelaws. Accessed Feburary 20, 2018.
    34.
    Governors Highway Safety Association. State laws by issue. 2017. https://www.ghsa.org/state-laws/issues. Accessed February 20, 2018.
    35.
    World Health Organization. Metrics: population attributable fraction (PAF). 2017. http://www.who.int/healthinfo/global_burden_disease/metrics_paf/en/. Accessed February 20, 2018.
    36.
    Mansournia  MA, Altman  DG.  Population attributable fraction.  BMJ. 2018;360:k757. doi:10.1136/bmj.k757PubMedGoogle ScholarCrossref
    37.
    Rockhill  B, Newman  B, Weinberg  C.  Use and misuse of population attributable fractions.  Am J Public Health. 1998;88(1):15-19. doi:10.2105/AJPH.88.1.15PubMedGoogle ScholarCrossref
    38.
    Miettinen  OS.  Proportion of disease caused or prevented by a given exposure, trait or intervention.  Am J Epidemiol. 1974;99(5):325-332. doi:10.1093/oxfordjournals.aje.a121617PubMedGoogle ScholarCrossref
    39.
    Harmsen  AM, Giannakopoulos  GF, Moerbeek  PR, Jansma  EP, Bonjer  HJ, Bloemers  FW.  The influence of prehospital time on trauma patients outcome: a systematic review.  Injury. 2015;46(4):602-609. doi:10.1016/j.injury.2015.01.008PubMedGoogle ScholarCrossref
    40.
    Gonzalez  RP, Cummings  G, Mulekar  M, Rodning  CB.  Increased mortality in rural vehicular trauma: identifying contributing factors through data linkage.  J Trauma. 2006;61(2):404-409. doi:10.1097/01.ta.0000229816.16305.94PubMedGoogle ScholarCrossref
    41.
    Feero  S, Hedges  JR, Simmons  E, Irwin  L.  Does out-of-hospital EMS time affect trauma survival?  Am J Emerg Med. 1995;13(2):133-135. doi:10.1016/0735-6757(95)90078-0PubMedGoogle ScholarCrossref
    42.
    Newgard  CD, Schmicker  RH, Hedges  JR,  et al; Resuscitation Outcomes Consortium Investigators.  Emergency medical services intervals and survival in trauma: assessment of the “golden hour” in a North American prospective cohort.  Ann Emerg Med. 2010;55(3):235-246.e4. doi:10.1016/j.annemergmed.2009.07.024PubMedGoogle ScholarCrossref
    43.
    Myers  JB, Slovis  CM, Eckstein  M,  et al; U.S. Metropolitan Municipalities’ EMS Medical Directors.  Evidence-based performance measures for emergency medical services systems: a model for expanded EMS benchmarking.  Prehosp Emerg Care. 2008;12(2):141-151. doi:10.1080/10903120801903793PubMedGoogle ScholarCrossref
    44.
    Lam  SS, Zhang  J, Zhang  ZC,  et al.  Dynamic ambulance reallocation for the reduction of ambulance response times using system status management.  Am J Emerg Med. 2015;33(2):159-166. doi:10.1016/j.ajem.2014.10.044PubMedGoogle ScholarCrossref
    45.
    Kononen  DW, Flannagan  CA, Wang  SC.  Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes.  Accid Anal Prev. 2011;43(1):112-122. doi:10.1016/j.aap.2010.07.018PubMedGoogle ScholarCrossref
    46.
    MacKenzie  EJ, Rivara  FP, Jurkovich  GJ,  et al.  A national evaluation of the effect of trauma-center care on mortality.  N Engl J Med. 2006;354(4):366-378. doi:10.1056/NEJMsa052049PubMedGoogle ScholarCrossref
    47.
    Centers for Disease Control and Prevention. Buckle up: restraint use state fact sheets. 2015. https://www.cdc.gov/motorvehiclesafety/seatbelts/states.html. Accessed February 20, 2018.
    48.
    Peura  C, Kilch  JA, Clark  DE.  Evaluating adverse rural crash outcomes using the NHTSA State Data System.  Accid Anal Prev. 2015;82:257-262. doi:10.1016/j.aap.2015.06.005PubMedGoogle ScholarCrossref
    49.
    Brown  JB, Rosengart  MR, Forsythe  RM,  et al.  Not all prehospital time is equal: influence of scene time on mortality.  J Trauma Acute Care Surg. 2016;81(1):93-100. doi:10.1097/TA.0000000000000999PubMedGoogle ScholarCrossref
    50.
    Federal Interagency Committee on Emergency Medical Services. FICEM annual report to Congress. 2013. https://www.ems.gov/pdf/2011-2012-FICEMS-RTC-Mikulski.pdf. Accessed February 20, 2018.
    51.
    Morgenstern  H.  Ecologic studies in epidemiology: concepts, principles, and methods.  Annu Rev Public Health. 1995;16:61-81. doi:10.1146/annurev.pu.16.050195.000425PubMedGoogle ScholarCrossref
    ×