Regional disparities in 1-month survival following traffic accident-related out-of-hospital cardiac arrest in Japan: A nationwide observational study

Regional disparities in 1-month survival following traffic accident-related out-of-hospital cardiac arrest in Japan: A nationwide observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Regional disparities in 1-month survival following traffic accident-related out-of-hospital cardiac arrest in Japan: A nationwide observational study Yutaka Takei, Tetsuhiro Adachi, Gen Toyama, Eiji Hori, Kentaro Omatsu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6900943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim : To clarify regional disparities in 1-month survival after traffic accident-related out-of-hospital cardiac arrest (OHCA) in Japan and examine associations with emergency medical services (EMS) and healthcare indicators. Methods : We conducted a retrospective study of 9,525 traffic accident-related OHCAs using national EMS data from 2018–2022. Prefectures were grouped by 1-month survival rates. Multivariable logistic regression and partial correlation analyses assessed factors related to patient characteristics, EMS, and medical resources. Results : In low-survival regions, rates of advanced airway management (37.7%) and epinephrine administration (29.8%) were significantly higher (p < 0.001). Conversely, the proportion of patients transported to level-3 hospitals was significantly higher in high-survival regions (p < 0.001). Logistic regression revealed that advanced airway management (OR: 1.37; 95% CI: 1.22–1.54; p < 0.001), epinephrine administration (OR: 1.43; 95% CI: 1.26–1.62; p < 0.001), and traffic accidents as the direct cause of cardiac arrest (OR: 1.17; 95% CI: 1.04–1.30; p = 0.006) were significantly associated with lower-survival regions. In contrast, witnessed arrests (OR: 0.82; 95% CI: 0.73–0.92; p = 0.001), BCPR (OR: 0.85; 95% CI: 0.75–0.96; p = 0.012), and transport to level-3 hospitals (OR: 0.71; 95% CI: 0.64–0.80; p < 0.001) were negatively associated with classification into low-survival regions. Partial correlation analysis showed positive associations between survival and the number of level-3 hospitals (r = 0.45) and physicians (r = 0.36, p = 0.08) per 100,000 population. Conclusion : Survival outcomes following traffic accident-related cardiac arrest varied across regions, and distribution of medical resources appeared to influence these disparities. Critical Care & Emergency Medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Out-of-hospital cardiac arrest (OHCA) remains a major global health challenge, characterized by high mortality and substantial variation in outcomes depending on geographic and systemic contexts [ 1 , 2 ]. While evidence-based guidelines for cardiopulmonary resuscitation (CPR) and emergency medical services (EMS) have improved survival rates over recent decades, substantial regional disparities persist in many countries, including Japan [ 3 , 4 ]. Previous studies have consistently shown that patient-level factors such as witnessed status, bystander CPR (BCPR), and initial cardiac rhythm significantly influence OHCA outcomes [ 5 ]. These variables, often included in standardized Utstein-style reporting, form the basis for evaluating EMS effectiveness and public engagement in emergency response. However, emerging research indicates that such individual factors alone do not fully account for the observed differences in survival and neurological recovery rates across regions [ 1 , 6 ]. In contrast, traumatic cardiac arrest appears to follow different survival mechanisms. A recent study using the Japan Trauma Data Bank showed that signs of life (e.g., gasping, pupil reactivity) at hospital arrival were strongly associated with survival and neurological outcomes in traumatic cardiac arrest patients, highlighting the need for trauma-specific predictors [ 7 ]. In Japan, the All-Japan Utstein Registry has enabled comprehensive analyses of nationwide OHCA data. Using this registry, Okubo et al. (2018) reported significant disparities in 1-month survival and favorable neurological outcome rates across the country’s 47 prefectures [ 6 ]. Notably, these differences were not explained by the availability of basic life support providers or automated external defibrillators (AEDs), suggesting the involvement of deeper system-level factors. Building on this, Tsugawa et al. (2015) investigated the association between regional healthcare spending and OHCA outcomes. Their findings showed that regions with lower per-capita health expenditure showed worse survival rates, reinforcing the notion that economic investment at the prefectural level can influence emergency care quality and patient prognosis [ 8 ]. More recent cohort studies, such as those by Okubo et al. (2017), have shown that while OHCA outcomes in Japan have improved over time, the pace of improvement varies between regions [ 9 ]. Similarly, Hasegawa et al. (2013) demonstrated a two-fold difference in neurologically favorable survival rates between the best- and worst-performing regions, even after adjusting for patient demographics and clinical presentation, highlighting systemic inequities in EMS infrastructure, training, and hospital integration [ 10 ]. Another complex dimension arises in OHCA cases resulting from traffic accidents. Miyashita et al. (2024) found that OHCA following traffic collisions often involves both traumatic and medical components, with prognoses differing significantly depending on the underlying cause [ 11 ]. These findings underscore the need to distinguish between medical and non-medical etiologies when analyzing OHCA data and interpreting regional performances. Despite a growing body of epidemiological and health systems research, limited studies have specifically examined how regional healthcare infrastructure and EMS capabilities influence OHCA outcomes in Japan [ 12 ]. Furthermore, it remains unclear to what extent such system-level characteristics affect the distribution and effectiveness of bystander interventions and EMS protocols, particularly in cases related to trauma or mixed etiologies [ 1 , 4 , 6 ]. Given these gaps, the present study aimed to assess regional variability in OHCA outcomes in Japan, focusing on prefecture-level factors. By leveraging national registry data, we seek to clarify the contributions of structural healthcare differences to patient survival and neurological recovery, thereby informing future efforts to reduce disparities and optimize prehospital emergency care across regions. 2. METHODS 2.1. Study Design and Data Sources This was a retrospective observational study of OHCAs related to traffic accidents in Japan. We used data from the Emergency Transport Registry and the Utstein Registry, both managed by the Fire and Disaster Management Agency (FDMA), covering 5-years from January 2018 to December 2022. These databases include information such as the date and time of occurrence, prefecture, patient sex, patient age, EMS on-scene arrival time, and hospital arrival time. The datasets were merged using these matching variables. Cases present in only one registry or with missing values in key variables were excluded from the analysis. 2.2. Case Inclusion and Classification Criteria Eligible cases were defined as those in which OHCA occurred on a roadway and was documented as traffic accident-related. The cause of arrest was determined based on diagnoses by physicians or information obtained by EMS personnel from receiving hospitals. We excluded cases that occurred off-road, those involving physician-staffed EMS units, and cases attributed to non-traffic causes. Physician-staffed EMS cases were excluded because they often involve advanced prehospital interventions that differ from standard EMS protocols, potentially introducing bias. One-month survival rates were calculated for each of Japan’s 47 prefectures. Based on the quartiles of survival rates, prefectures were categorized into three groups: the top 25% were defined as the “high-survival” group, the middle 50% as the “moderate-survival” group, and the bottom 25% as the “low-survival” group. 2.3. Emergency Medical Services System in Japan In Japan, the EMS is provided by municipal fire departments. Each ambulance is typically staffed by three personnel, including at least one emergency life-saving technician (ELST). ELSTs are qualified to perform advanced procedures such as intravenous line placement, supraglottic airway management, and administration of adrenaline in cardiac arrest cases. Over 700 fire departments operate nationwide, adhering to standardized EMS protocols under FDMA supervision. 2.4. Variables and Outcomes The following variables were included in the analysis: time of day OHCA occurred (daytime [07:00–22:59] or nighttime [23:00–06:59]), patient sex, patient age, cause of cardiac arrest (traffic-related or other), witness status, provision of BCPR, initial cardiac rhythm (shockable or non-shockable), type of airway management (advanced or basic), administration of adrenaline (yes or no), receiving hospital level (level 3 or below), and EMS activity times (response time, on-scene time, and transport time). The primary outcome was 1-month survival. We did not use neurological outcomes such as the Cerebral Performance Category (CPC) due to high levels of missing or inconsistent data across regions, which could compromise the validity of a nationwide comparison. 2.5. Structural Regional Indicators To explore structural regional factors potentially influencing survival, we obtained additional prefecture-level data from publicly available sources published by the Ministry of Health, Labor and Welfare and the Statistics Bureau of Japan [ 13 ]. These included: number of physicians per 100,000 population, number of level-3 hospitals per 100,000 population, population density (people per km²), and the aging rate (proportion of the population aged ≥ 65 years). Partial correlation analysis was conducted to evaluate the association between these variables and 1-month survival rates while controlling for confounding factors. 2.6. Statistical Analyses For group comparisons, chi-squared tests were used for categorical variables, and the Mann–Whitney U or Kruskal–Wallis tests were used for continuous variables. To identify factors independently associated with low-survival regions, we conducted multivariate logistic regression analysis and calculated adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Propensity score matching was not applied, as the objective of this study was not to compare individual cases but to identify structural contributors to regional variations in survival outcomes. All statistical analyses were conducted using JMP Pro version 17 (SAS Institute Inc., Cary, NC, USA), with a significance level set at p < 0.05. 2.7. Ethical Considerations This study was approved by the Ethics Committee of [ Blinded ] (Approval No. 19068–230602). All data were fully anonymized, and the requirement for informed consent was waived. 3. RESULTS 3.1. Study Population and Regional Stratification Between January 2018 and December 2022, 9,525 traffic accident-related OHCA cases were extracted from two nationwide EMS databases and included in the analysis (Fig. 1 ). Based on 1-month survival rates across Japan’s 47 prefectures, the regions were divided into quartiles. This stratification yielded 2,065 cases (21.7%) in the low-survival group, 4,868 cases (51.1%) in the moderate-survival group, and 2,592 cases (27.2%) in the high-survival group. 3.2. Comparison of Patient Characteristics and EMS Interventions Across Regions Significant differences were observed between the three regional survival groups regarding patient characteristics and prehospital interventions (Table 1 ). Daytime OHCA incidence (07:00–22:59) was more common in the low-survival group (75.6%, 1,562/2,065) than in the moderate (71.3%, 3,472/4,868) and high (70.7%, 1,832/2,592) survival groups ( p < 0.001). Witnessed arrests occurred in 35.6% of the low-survival group (735/2,065), 32.3% of the moderate-survival group (1,571/4,868), and 26.6% of the high-survival group (689/2,592) ( p < 0.001). Table 1 Characteristics of OHCAs related to traffic accidents across three regions Factors Low (n = 2,065) Moderate (n = 4,868) High (n = 2,592) p– value Time of the day < 0.001 Daytime (7:00–22:59) 75.6% (1,562) 71.3% (3,472) 70.7% (1,832) Nighttime (23:00–6:59) 24.4% (503) 28.7% (1,396) 29.3% ( 760) Patient's sex 0.047 Male 68.7% (1,418) 70.6% (3,438) 72.0% (1,866) Female 31.3% ( 647) 29.4% (1,430) 28.0% ( 726) Patient's age, median (25–75%) 67 y (48–79) 65 y (46–77) 63 y (44–76) < 0.001 Cause of arrest < 0.001 Traumatic cause 59.5% (1,229) 60.7% (2,955) 52.1% (1,350) Medical cause 40.5% ( 836) 39.3% (1,913) 47.9% (1,242) Witness status < 0.001 Witnessed 35.6% ( 735) 32.3% (1,571) 26.6% ( 689) Unwitnessed 64.4% (1,330) 67.7% (3,297) 73.4% (1,903) Bystander CPR < 0.001 Performed 77.6% (1,602) 76.8% (3,736) 71.9% (1,863) Not performed 22.4% ( 463) 23.3% (1,132) 28.1% ( 729) Initial cardiac rhythms 0.300 Shockable 2.6% ( 54) 3.0% ( 146) 3.4% ( 137) Unshockable 97.4% (2,011) 97.0% (4,722) 96.6% (2,504) Airway management < 0.001 Advanced airway 37.7% ( 778) 28.5% (1,387) 31.3% ( 812) Basic airway 62.3% (1,287) 71.5% (3,481) 68.7% (1,780) Adrenaline administration < 0.001 Implemented 29.8% ( 616) 22.4% (1,089) 19.9% ( 515) Not implemented 70.2% (1,449) 77.6% (3,779) 80.1% (2,077) The destination hospital < 0.001 Medical level 3 48.0% ( 991) 56.5% (2,751) 62.0% (1,607) Less than level 3 52.0% (1,074) 43.5% (2,117) 38.0% ( 985) Time factors, median (25–75%) EMS response time 10 min (8–14) 10 min (7–13) 9 min (7–12) < 0.001 On-scene time 11 min (7–16) 10 min (7–15) 9 min (6–13) < 0.001 Transport time 13 min (8–21) 13 min (8–19) 11 min (7–16) < 0.001 OHCA, out-of-hospital cardiac arrest: CPR, cardiopulmonary resuscitation: EMS, emergency medical service Footnote: All included cases were associated with traffic accidents. "Traumatic cause" refers to cardiac arrest due to direct physical trauma sustained in the accident. "Medical cause" refers to cases where a medical condition (e.g., myocardial infarction) may have precipitated the accident and subsequent cardiac arrest. BCPR was administered in 77.6% of cases in the low-survival group (1,602/2,065), 76.8% in the moderate-survival group (3,736/4,868), and 71.9% in the high-survival group (1,863/2,592) ( p < 0.001). Advanced airway management was more frequent in low-survival regions (37.7%, 778/2,065) compared to moderate (28.5%, 1,387/4,868) and high-survival regions (31.3%, 812/2,592) ( p < 0.001). Similarly, adrenaline was administered in 29.8% of the low-survival group (616/2,065), 22.4% of the moderate-survival group (1,089/4,868), and 19.9% of the high-survival group (515/2,592) ( p < 0.001). The proportion of patients transported to level-3 hospitals increased with regional survival: 48.0% in the low-survival group (991/2,065), 56.5% in the moderate-survival group (2,751/4,868), and 62.0% in the high-survival group (1,607/2,592) ( p < 0.001). EMS time intervals, including response, on-scene, and transport times, were also significantly shorter in the high-survival group (all p < 0.001). 3.3. Factors Associated with Low-Survival Regions Multivariate logistic regression analysis (Table 2 ) demonstrated that advanced airway management (OR: 1.37, 95% CI: 1.22–1.54, p < 0.001), adrenaline administration (OR: 1.43, 95% CI: 1.26–1.62, p < 0.001), and traffic accident etiology (OR: 1.17, 95% CI: 1.04–1.30, p = 0.006) were significantly associated with the low-survival region. In contrast, witnessed arrest (OR: 0.82, 95% CI: 0.73–0.92, p = 0.001), BCPR (OR: 0.85, 95% CI: 0.75–0.96, p = 0.012), and transport to a level-3 hospital (OR: 0.71, 95% CI: 0.64–0.80, p < 0.001) were associated with a significantly reduced likelihood of being in the low-survival region. Time-related EMS variables were not independently associated with regional classification in the adjusted model. A forest plot summarizing these results is presented in Fig. 4 . Table 2 Adjusted odds ratios related to the Low survival region by logistic regression analysis Factors ORs (CIs) for the Low survival region p - value Time of the day 0.003 Daytime (7:00–22:59) 1.22 (1.07–1.39) Nighttime (23:00–6:59) Reference Patient's sex 0.420 Male 0.95 (0.85–1.07) Female Reference Patient's age, for every 1 y increase 1.00 (0.99–1.00) 0.416 Cause of arrest 0.006 Traumatic cause 1.17 (1.04–1.30) Medical cause Reference Witness status 0.001 Witnessed 0.82 (0.73–0.92) Unwitnessed Reference Bystander CPR 0.012 Provided 0.85 (0.75–0.96) Not-provided Reference Initial cardiac rhythms 0.141 Shockable 0.79 (0.57–1.09) Unshockable Reference Airway management < 0.001 Advanced airway 1.37 (1.22–1.54) Basic airway Reference Adrenaline administration < 0.001 Implemented 1.43 (1.26–1.62) Not-implemented Reference The destination hospital < 0.001 Medical level 3 0.71 (0.64–0.80) Less than level 3 Reference Time factors, for every 1 min increase EMS response time 0.99 (0.99–1.00) 0.179 On-scene time 0.99 (0.99–1.00) 0.106 Transport time 0.99 (0.99–1.00) 0.640 CPR, cardiopulmonary resuscitation: EMS, emergency medical service: OR, odds ratio: CI, confidence interval Footnote: All included cases were associated with traffic accidents. "Traumatic cause" refers to cardiac arrest due to direct physical trauma sustained in the accident. "Medical cause" refers to cases where a medical condition (e.g., myocardial infarction) may have precipitated the accident and subsequent cardiac arrest. 3.4. Geographic Distribution of Survival and Correlation with Structural Indicators As illustrated in Fig. 2 , the 1-month survival rate showed considerable variation across Japan's 47 prefectures, ranging from 0–10.9%. Partial correlation analysis between survival and structural healthcare indicators (Fig. 3 ) revealed a moderate positive association with the number of level-3 hospitals per 100,000 population (r = 0.45). Physician density was positively correlated with survival but less strongly (r = 0.36, p = 0.08), while the aging rate showed a negative trend (r = − 0.31, p = 0.11). Population density was not significantly correlated with survival outcomes. 4. DISCUSSION 4.1. Novel Findings and Academic Significance of This Study This study provides a nationwide analysis of regional disparities in 1-month survival after traffic accident-related OHCA in Japan. Unlike previous studies that have primarily focused on individual-level clinical factors such as age, initial cardiac rhythm, or BCPR [ 5 ], the present study uniquely demonstrates that differences in regional survival outcomes may be substantially influenced by structural and systemic factors, even after adjusting for patient characteristics [ 14 , 15 ]. We found that low-survival regions exhibited higher rates of advanced airway management and adrenaline administration—interventions often associated with severe conditions—whereas high-survival regions demonstrated higher frequencies of witnessed arrests, BCPR, and transport to level-3 hospitals [ 16 ]. Additionally, prefectures with more physicians and level-3 hospitals per capita showed higher survival rates, suggesting that the availability and accessibility of healthcare resources at the regional level can independently affect survival after OHCA due to traffic accidents [ 17 ]. These results underscore the reality that even for unpredictable and external causes such as trauma-related cardiac arrests, a patient’s chance of survival may be significantly influenced by where the event occurs, revealing inequities in prehospital emergency care systems and the distribution of medical resources across regions [ 1 , 2 ]. 4.2. Comparison with Previous Studies and the Positioning of This Research Our previous study showed that among traffic accident-related OHCA cases, those caused by medical events (e.g., myocardial infarction or stroke) had significantly better neurological outcomes than those due to trauma [ 12 ]. That study highlighted the prognostic importance of shockable rhythms, shorter transport times, and the more frequent implementation of advanced life support interventions among patients with medical-origin OHCA [ 18 , 19 ]. In contrast, the present study focused exclusively on trauma-related OHCA and adopted a regional-level analytic approach. Even after adjusting for age, sex, witnessed status, and interventions, significant disparities in survival rates persisted between prefectures. This suggests that the local healthcare infrastructure, rather than individual patient characteristics alone, plays a substantial role in determining outcomes [ 15 , 20 ]. Although the univariable analysis showed lower rates of witnessed arrest and bystander CPR in high-survival regions, our multivariable analysis found that both factors were negatively associated with low-survival regions. Conversely, low-survival regions exhibited higher rates of advanced airway management and adrenaline administration, which may reflect prolonged on-scene time or greater trauma severity, rather than the efficacy of ALS interventions. In traumatic OHCA, patient outcomes may depend more on rapid transport and in-hospital care than on prehospital ALS measures. While our previous research emphasized "what type of patient" has better outcomes [ 12 ], the present study highlights "where the patient is" as an equally critical determinant. Together, these studies offer a complementary perspective, linking individual clinical severity with systemic, region-based factors in shaping survival [ 1 , 6 ]. 4.3. Interpretation of Structural Regional Factors Among the structural indicators examined, the number of level-3 hospitals and physicians per 100,000 population showed positive correlations with 1-month survival [ 11 ], implying that proximity to advanced care facilities and the availability of trained personnel are key contributors to improved outcomes. Although the associations with population density and aging rate were not significant, low-survival regions tended to have higher aging rates and lower population densities [ 21 ], which may reflect delayed EMS activation, limited community responders, or reduced access to timely care. These findings suggest that structural differences between regions are not merely background variables, but may directly define the limitations and opportunities within a region's emergency medical care system [ 22 ]. For example, the proportion of patients transported to level-3 hospitals exceeded 60% in high-survival regions but fell below 50% in low-survival regions [ 23 – 25 ], indicating a substantial gap in facility access. This disparity may lead to delays in definitive care, suboptimal on-site decision-making, and ultimately worse patient outcomes [ 15 ]. 4.4. Practical Implications and Future Interventions The study's findings offer multiple implications for improving the equity and effectiveness of trauma-related OHCA care. First, re-evaluating transport protocols and triage systems to ensure timely access to high-level facilities is essential, especially in low-survival areas [ 16 , 19 ]. This could include expanding wide-area coordination systems and enhancing real-time data sharing on hospital availability [ 23 ]. Second, while community-level CPR training and AED availability remain important elements of public health preparedness [ 7 ], their impact may be more limited in traumatic OHCA due to the nature of injuries [ 22 ]. Therefore, additional emphasis should be placed on rapid trauma assessment, early hemorrhage control, and efficient transport to trauma-capable facilities.. Third, strengthening EMS training and operational protocols to ensure standardized care across diverse regions will help bridge gaps in clinical response quality [ 22 ]. In particular, trauma-focused training—including prehospital triage skills, basic hemorrhage control techniques, and coordination with receiving trauma centers—may improve field-level decision-making and patient outcomes in traumatic OHCA [ 26 , 27 ]. These findings highlight the need for region-specific strategies that consider the unique characteristics of trauma-related cardiac arrest and the limitations of current EMS resources in some areas. 4.5. Limitations Several limitations must be considered. First, the classification of cardiac arrest causes relied on the EMS or hospital records and may have been subject to misclassification. Second, neurological outcomes such as the CPC were not analyzed due to data incompleteness and inconsistency. Third, while we included publicly available structural indicators, other influential variables such as socioeconomic status, transportation networks, and EMS staffing levels were not assessed. Future studies should incorporate both quantitative and qualitative approaches to examine operational characteristics of regional EMS systems, hospital referral pathways, and the impact of policy-level differences on patient outcomes. 5. CONCLUSION This study revealed significant regional variations in 1-month survival following traffic accident-related OHCA in Japan. The disparities could not be explained by individual-level clinical factors alone and were instead strongly associated with differences in regional healthcare infrastructure, transport systems, and community response capacity. These findings call for targeted interventions to reduce interregional gaps and promote more equitable, responsive emergency care systems tailored to the unique needs of each community. Declarations FUNDING SOURCES: This research was supported in part by a grant from the National Mutual Insurance Federation of Agricultural Cooperatives (ZENKYOREN). The funding sources had no role in the study design, data collection, analysis, interpretation, or manuscript preparation. AUTHOR CONTRIBUTIONS: [ Blinded ] and [ Blinded ] are equal contributors to this work and have been designated as co-first authors. [ Blinded ] conducted the statistical analysis and assisted with data interpretation. [ Blinded ] and [ Blinded ] contributed to data cleaning, management, and methodology development. [ Blinded ]. jointly revised the manuscript critically for important intellectual content. All authors reviewed and approved the final version of the manuscript. ACKNOWLEDGMENTS: The authors thank the Fire and Disaster Management Agency of Japan for providing access to the Emergency Transport and Utstein Registry data. We also acknowledge the assistance of the municipal fire departments and EMS personnel who contributed to data collection across Japan. References Farcas AM, Joiner AP, Rudman JS, Ramesh K, Torres G, Crowe RP et al (2023) Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review. 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JAMA Surg 153(6):e180674. 10.1001/jamasurg.2018.0674 Epub 2018 Jun 20 Kurosaki H, Takada K, Okajima M (2022) Time point for transport initiation in out-of-hospital cardiac arrest cases with ongoing cardiopulmonary resuscitation: a nationwide cohort study in Japan. Acute Med Surg 9(1):e802. 10.1002/ams2.802 Yasunaga H, Miyata H, Horiguchi H, Tanabe S, Akahane M, Ogawa T et al (2011) Population density, call-response interval, and survival of out-of-hospital cardiac arrest. Int J Health Geogr 10:26. 10.1186/1476-072X-10-26 Nordberg P, Jonsson M, Forsberg S, Ringh M, Fredman D, Riva G et al (2015) The survival benefit of dual dispatch of EMS and fire-fighters in out-of-hospital cardiac arrest may differ depending on population density–a prospective cohort study. Resuscitation 90:143–149. 10.1016/j.resuscitation.2015.02.036 Bosson N, Tolles J, Shavelle D, Niemann JT, Thomas JL, French WJ et al (2022 Nov-Dec) Variation in Post-Cardiac Arrest Care Within a Regional EMS System. Prehosp Emerg Care. 26(6):772–781. 10.1080/10903127.2021.1965681 Matsui S, Kitamura T, Kiyohara K, Sado J, Ayusawa M, Nitta M et al (2019) Sex Disparities in Receipt of Bystander Interventions for Students Who Experienced Cardiac Arrest in Japan. JAMA Netw Open 2(5):e195111. 10.1001/jamanetworkopen.2019.5111 Hosomi S, Zha L, Kiyohara K, Kitamura T, Irisawa T, Ogura H et al (2023) Sex disparities in prehospital advanced cardiac life support in out-of-hospital cardiac arrests in Japan. Am J Emerg Med 64:67–73. 10.1016/j.ajem.2022.11.025 Yasunaga H, Miyata H, Horiguchi H, Tanabe S, Akahane M, Ogawa T et al (2011) Population density, call-response interval, and survival of out-of-hospital cardiac arrest. Int J Health Geogr 10:26. 10.1186/1476-072X-10-26 Pusateri AE, Moore EE, Moore HB, Le TD, Guyette FX, Chapman MP et al (2020) Association of Prehospital Plasma Transfusion With Survival in Trauma Patients With Hemorrhagic Shock When Transport Times Are Longer Than 20 Minutes: A Post Hoc Analysis of the PAMPer and COMBAT Clinical Trials. JAMA Surg 155(2):e195085. 10.1001/jamasurg.2019.5085 Moore HB, Moore EE, Chapman MP, McVaney K, Bryskiewicz G, Blechar R et al (2018) Plasma-first resuscitation to treat haemorrhagic shock during emergency ground transportation in an urban area: a randomised trial. Lancet 392(10144):283–291. 10.1016/S0140-6736(18)31553-8 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6900943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471651315,"identity":"bd844a80-5fec-4465-b8a4-23c8f247ab83","order_by":0,"name":"Yutaka Takei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYJACxgYQycPAcOADA0MCRIwHnwZmsBYJkKKDM0jWwswD14IHGNzIP/hxRg1DHX/P4YOHbXcczjM4wPzwA4PMHTxakpklNxxjkJA425ZwOPfM4WKDA2zGQDuf4dPCIPmA7b8Ew3keg8O5bYcTtx1gMAP65TBeW34++McgIX+e/8NhS7AW9m+EtLBJbmxjkDA428NwmBGshQe/LZJnHptZzuxjkNx45pjBwd629MT9h3mKJRLw+IXveOLjmz3fGPjlziQ//vCzzTpxZnv7xg8fe3CHmMIBDCFmIE7swRSHAfkG7OI/cGsZBaNgFIyCEQcAG4ldk3Vdvt0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7992-0077","institution":"Niigata University of Health and Welfare","correspondingAuthor":true,"prefix":"","firstName":"Yutaka","middleName":"","lastName":"Takei","suffix":""},{"id":471654153,"identity":"e739035c-02b9-4b0c-ad13-e4ece3286fd0","order_by":1,"name":"Tetsuhiro Adachi","email":"","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Tetsuhiro","middleName":"","lastName":"Adachi","suffix":""},{"id":471654154,"identity":"4e1a5cbc-e0ce-4051-861c-587f2a0a952b","order_by":2,"name":"Gen Toyama","email":"","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Gen","middleName":"","lastName":"Toyama","suffix":""},{"id":471654155,"identity":"534f00df-86b8-4f06-a29a-9bbc94e3ae27","order_by":3,"name":"Eiji Hori","email":"","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Eiji","middleName":"","lastName":"Hori","suffix":""},{"id":471654156,"identity":"8c92e8d8-0dc6-4f8d-8dae-b511632e3552","order_by":4,"name":"Kentaro Omatsu","email":"","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Kentaro","middleName":"","lastName":"Omatsu","suffix":""}],"badges":[],"createdAt":"2025-06-16 02:39:56","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6900943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6900943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84920559,"identity":"8c9bbee1-0814-4343-a99a-c990809301d3","added_by":"auto","created_at":"2025-06-18 19:39:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39873,"visible":true,"origin":"","legend":"\u003cp\u003eFlow\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFoot note\u003c/strong\u003e\u003c/em\u003e: The Utstein Registry records only cases of cardiac arrest among all emergency transports. In contrast, the Emergency Transport Registry includes all emergency transport data, including cases of cardiac arrest. However, the Utstein Registry uses a specific format focused on cardiac arrest-related data, whereas the Emergency Transport Registry does not collect cardiac arrest-specific data but includes information not found in the Utstein Registry.\u003c/p\u003e\n\u003cp\u003eThe data points matched when merging the two registries included the prefecture of occurrence, date and time of occurrence, EMS on-scene arrival time, EMS hospital arrival time, as well as the patient's age and gender. Factors preventing successful merging included missing records for these variables in one of the registries.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6900943/v1/2b32e8dbe3337209e28bc7d5.png"},{"id":84920562,"identity":"3eeac84c-f732-4f9f-accd-34f1d1e10c8e","added_by":"auto","created_at":"2025-06-18 19:39:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":393322,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival rate distribution across Japan's 47 prefectures\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6900943/v1/3fecebde1e50bcd8f18565b7.png"},{"id":84921650,"identity":"3477447c-5a27-47f6-b086-e17a0ab57f71","added_by":"auto","created_at":"2025-06-18 19:55:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":313244,"visible":true,"origin":"","legend":"\u003cp\u003ePartial correlation analysis of medical disparities across Japan's 47 prefectures.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFoot note\u003c/strong\u003e\u003c/em\u003e: The partial correlation coefficient is a statistical measure that assesses the relationship between two variables while controlling for the influence of one or more additional variables, allowing the \"pure\" association between the two variables to be evaluated by removing the effects of other variables, with its value ranging from -1 to +1, where +1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation after controlling for the other variables, and it is calculated using a formula that adjusts the standard correlation coefficient between the two variables by accounting for their correlations with the control variables.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6900943/v1/fce7022f8e48043509fcc0e2.png"},{"id":84921232,"identity":"380e0abe-1439-4339-8e89-04b8d78a18c4","added_by":"auto","created_at":"2025-06-18 19:47:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":162636,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Logistic Regression for Association with Low-Survival Regions in Traffic Accident-Related OHCA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFoot note\u003c/strong\u003e\u003c/em\u003e: This forest plot illustrates the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for factors associated with classification into low-survival regions among patients with traffic accident-related out-of-hospital cardiac arrest (OHCA) in Japan. Factors included patient demographics (sex, age), timing of arrest (daytime vs. nighttime), etiology (traffic-related), bystander response (witnessed arrest, BCPR), initial cardiac rhythm, prehospital interventions (airway management, adrenaline administration), hospital level (transport to a level-3 hospital), and EMS time intervals (response time, on-scene time, transport time). An OR \u0026gt;1 indicates a positive association with low-survival regions, while an OR \u0026lt;1 suggests a protective factor. The vertical dashed line at OR = 1.0 represents the null value.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6900943/v1/3595c06c6c6b9b5a089d3753.png"},{"id":84922008,"identity":"c7ac37c5-4bcb-42e1-a798-ca3f8ce7febe","added_by":"auto","created_at":"2025-06-18 20:03:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1869497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6900943/v1/d2ebcee7-f441-4631-a654-b4020b963909.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRegional disparities in 1-month survival following traffic accident-related out-of-hospital cardiac arrest in Japan: A nationwide observational study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eOut-of-hospital cardiac arrest (OHCA) remains a major global health challenge, characterized by high mortality and substantial variation in outcomes depending on geographic and systemic contexts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While evidence-based guidelines for cardiopulmonary resuscitation (CPR) and emergency medical services (EMS) have improved survival rates over recent decades, substantial regional disparities persist in many countries, including Japan [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have consistently shown that patient-level factors such as witnessed status, bystander CPR (BCPR), and initial cardiac rhythm significantly influence OHCA outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These variables, often included in standardized Utstein-style reporting, form the basis for evaluating EMS effectiveness and public engagement in emergency response. However, emerging research indicates that such individual factors alone do not fully account for the observed differences in survival and neurological recovery rates across regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, traumatic cardiac arrest appears to follow different survival mechanisms. A recent study using the Japan Trauma Data Bank showed that signs of life (e.g., gasping, pupil reactivity) at hospital arrival were strongly associated with survival and neurological outcomes in traumatic cardiac arrest patients, highlighting the need for trauma-specific predictors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Japan, the All-Japan Utstein Registry has enabled comprehensive analyses of nationwide OHCA data. Using this registry, Okubo et al. (2018) reported significant disparities in 1-month survival and favorable neurological outcome rates across the country\u0026rsquo;s 47 prefectures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, these differences were not explained by the availability of basic life support providers or automated external defibrillators (AEDs), suggesting the involvement of deeper system-level factors. Building on this, Tsugawa et al. (2015) investigated the association between regional healthcare spending and OHCA outcomes. Their findings showed that regions with lower per-capita health expenditure showed worse survival rates, reinforcing the notion that economic investment at the prefectural level can influence emergency care quality and patient prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. More recent cohort studies, such as those by Okubo et al. (2017), have shown that while OHCA outcomes in Japan have improved over time, the pace of improvement varies between regions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, Hasegawa et al. (2013) demonstrated a two-fold difference in neurologically favorable survival rates between the best- and worst-performing regions, even after adjusting for patient demographics and clinical presentation, highlighting systemic inequities in EMS infrastructure, training, and hospital integration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another complex dimension arises in OHCA cases resulting from traffic accidents. Miyashita et al. (2024) found that OHCA following traffic collisions often involves both traumatic and medical components, with prognoses differing significantly depending on the underlying cause [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings underscore the need to distinguish between medical and non-medical etiologies when analyzing OHCA data and interpreting regional performances.\u003c/p\u003e \u003cp\u003eDespite a growing body of epidemiological and health systems research, limited studies have specifically examined how regional healthcare infrastructure and EMS capabilities influence OHCA outcomes in Japan [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, it remains unclear to what extent such system-level characteristics affect the distribution and effectiveness of bystander interventions and EMS protocols, particularly in cases related to trauma or mixed etiologies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these gaps, the present study aimed to assess regional variability in OHCA outcomes in Japan, focusing on prefecture-level factors. By leveraging national registry data, we seek to clarify the contributions of structural healthcare differences to patient survival and neurological recovery, thereby informing future efforts to reduce disparities and optimize prehospital emergency care across regions.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Data Sources\u003c/h2\u003e \u003cp\u003eThis was a retrospective observational study of OHCAs related to traffic accidents in Japan. We used data from the Emergency Transport Registry and the Utstein Registry, both managed by the Fire and Disaster Management Agency (FDMA), covering 5-years from January 2018 to December 2022. These databases include information such as the date and time of occurrence, prefecture, patient sex, patient age, EMS on-scene arrival time, and hospital arrival time. The datasets were merged using these matching variables. Cases present in only one registry or with missing values in key variables were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Case Inclusion and Classification Criteria\u003c/h2\u003e \u003cp\u003eEligible cases were defined as those in which OHCA occurred on a roadway and was documented as traffic accident-related. The cause of arrest was determined based on diagnoses by physicians or information obtained by EMS personnel from receiving hospitals. We excluded cases that occurred off-road, those involving physician-staffed EMS units, and cases attributed to non-traffic causes. Physician-staffed EMS cases were excluded because they often involve advanced prehospital interventions that differ from standard EMS protocols, potentially introducing bias. One-month survival rates were calculated for each of Japan\u0026rsquo;s 47 prefectures. Based on the quartiles of survival rates, prefectures were categorized into three groups: the top 25% were defined as the \u0026ldquo;high-survival\u0026rdquo; group, the middle 50% as the \u0026ldquo;moderate-survival\u0026rdquo; group, and the bottom 25% as the \u0026ldquo;low-survival\u0026rdquo; group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Emergency Medical Services System in Japan\u003c/h2\u003e \u003cp\u003eIn Japan, the EMS is provided by municipal fire departments. Each ambulance is typically staffed by three personnel, including at least one emergency life-saving technician (ELST). ELSTs are qualified to perform advanced procedures such as intravenous line placement, supraglottic airway management, and administration of adrenaline in cardiac arrest cases. Over 700 fire departments operate nationwide, adhering to standardized EMS protocols under FDMA supervision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables and Outcomes\u003c/h2\u003e \u003cp\u003eThe following variables were included in the analysis: time of day OHCA occurred (daytime [07:00\u0026ndash;22:59] or nighttime [23:00\u0026ndash;06:59]), patient sex, patient age, cause of cardiac arrest (traffic-related or other), witness status, provision of BCPR, initial cardiac rhythm (shockable or non-shockable), type of airway management (advanced or basic), administration of adrenaline (yes or no), receiving hospital level (level 3 or below), and EMS activity times (response time, on-scene time, and transport time). The primary outcome was 1-month survival. We did not use neurological outcomes such as the Cerebral Performance Category (CPC) due to high levels of missing or inconsistent data across regions, which could compromise the validity of a nationwide comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Structural Regional Indicators\u003c/h2\u003e \u003cp\u003eTo explore structural regional factors potentially influencing survival, we obtained additional prefecture-level data from publicly available sources published by the Ministry of Health, Labor and Welfare and the Statistics Bureau of Japan [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These included: number of physicians per 100,000 population, number of level-3 hospitals per 100,000 population, population density (people per km\u0026sup2;), and the aging rate (proportion of the population aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years). Partial correlation analysis was conducted to evaluate the association between these variables and 1-month survival rates while controlling for confounding factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analyses\u003c/h2\u003e \u003cp\u003eFor group comparisons, chi-squared tests were used for categorical variables, and the Mann\u0026ndash;Whitney U or Kruskal\u0026ndash;Wallis tests were used for continuous variables. To identify factors independently associated with low-survival regions, we conducted multivariate logistic regression analysis and calculated adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Propensity score matching was not applied, as the objective of this study was not to compare individual cases but to identify structural contributors to regional variations in survival outcomes. All statistical analyses were conducted using JMP Pro version 17 (SAS Institute Inc., Cary, NC, USA), with a significance level set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study was approved by the Ethics Committee of [\u003cem\u003eBlinded\u003c/em\u003e] (Approval No. 19068\u0026ndash;230602). All data were fully anonymized, and the requirement for informed consent was waived.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Population and Regional Stratification\u003c/h2\u003e \u003cp\u003eBetween January 2018 and December 2022, 9,525 traffic accident-related OHCA cases were extracted from two nationwide EMS databases and included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on 1-month survival rates across Japan\u0026rsquo;s 47 prefectures, the regions were divided into quartiles. This stratification yielded 2,065 cases (21.7%) in the low-survival group, 4,868 cases (51.1%) in the moderate-survival group, and 2,592 cases (27.2%) in the high-survival group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparison of Patient Characteristics and EMS Interventions Across Regions\u003c/h2\u003e \u003cp\u003eSignificant differences were observed between the three regional survival groups regarding patient characteristics and prehospital interventions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Daytime OHCA incidence (07:00\u0026ndash;22:59) was more common in the low-survival group (75.6%, 1,562/2,065) than in the moderate (71.3%, 3,472/4,868) and high (70.7%, 1,832/2,592) survival groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Witnessed arrests occurred in 35.6% of the low-survival group (735/2,065), 32.3% of the moderate-survival group (1,571/4,868), and 26.6% of the high-survival group (689/2,592) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of OHCAs related to traffic accidents across three regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,065)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,868)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,592)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u0026ndash;\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime (7:00\u0026ndash;22:59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.6% (1,562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.3% (3,472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.7% (1,832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime (23:00\u0026ndash;6:59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4% (503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7% (1,396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.3% ( 760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient's sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.7% (1,418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.6% (3,438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.0% (1,866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.3% ( 647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4% (1,430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.0% ( 726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient's age, median (25\u0026ndash;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 y (48\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 y (46\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 y (44\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraumatic cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.5% (1,229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7% (2,955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1% (1,350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5% ( 836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.3% (1,913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.9% (1,242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitness status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitnessed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6% ( 735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3% (1,571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.6% ( 689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnwitnessed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4% (1,330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.7% (3,297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.4% (1,903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBystander CPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.6% (1,602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8% (3,736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.9% (1,863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4% ( 463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3% (1,132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.1% ( 729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial cardiac rhythms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShockable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6% ( 54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0% ( 146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4% ( 137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnshockable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.4% (2,011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.0% (4,722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.6% (2,504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced airway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.7% ( 778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5% (1,387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3% ( 812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic airway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.3% (1,287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.5% (3,481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.7% (1,780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenaline administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplemented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8% ( 616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4% (1,089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.9% ( 515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot implemented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.2% (1,449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.6% (3,779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.1% (2,077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe destination hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical level 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0% ( 991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5% (2,751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.0% (1,607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than level 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.0% (1,074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5% (2,117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.0% ( 985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime factors, median (25\u0026ndash;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMS response time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 min (8\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 min (7\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 min (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-scene time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 min (7\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 min (7\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 min (6\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 min (8\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 min (8\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 min (7\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOHCA, out-of-hospital cardiac arrest: CPR, cardiopulmonary resuscitation: EMS, emergency medical service\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnote:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll included cases were associated with traffic accidents. \"Traumatic cause\" refers to cardiac arrest due to direct physical trauma sustained in the accident. \"Medical cause\" refers to cases where a medical condition (e.g., myocardial infarction) may have precipitated the accident and subsequent cardiac arrest.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBCPR was administered in 77.6% of cases in the low-survival group (1,602/2,065), 76.8% in the moderate-survival group (3,736/4,868), and 71.9% in the high-survival group (1,863/2,592) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Advanced airway management was more frequent in low-survival regions (37.7%, 778/2,065) compared to moderate (28.5%, 1,387/4,868) and high-survival regions (31.3%, 812/2,592) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, adrenaline was administered in 29.8% of the low-survival group (616/2,065), 22.4% of the moderate-survival group (1,089/4,868), and 19.9% of the high-survival group (515/2,592) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of patients transported to level-3 hospitals increased with regional survival: 48.0% in the low-survival group (991/2,065), 56.5% in the moderate-survival group (2,751/4,868), and 62.0% in the high-survival group (1,607/2,592) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). EMS time intervals, including response, on-scene, and transport times, were also significantly shorter in the high-survival group (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Factors Associated with Low-Survival Regions\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated that advanced airway management (OR: 1.37, 95% CI: 1.22\u0026ndash;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), adrenaline administration (OR: 1.43, 95% CI: 1.26\u0026ndash;1.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and traffic accident etiology (OR: 1.17, 95% CI: 1.04\u0026ndash;1.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) were significantly associated with the low-survival region. In contrast, witnessed arrest (OR: 0.82, 95% CI: 0.73\u0026ndash;0.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), BCPR (OR: 0.85, 95% CI: 0.75\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), and transport to a level-3 hospital (OR: 0.71, 95% CI: 0.64\u0026ndash;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with a significantly reduced likelihood of being in the low-survival region. Time-related EMS variables were not independently associated with regional classification in the adjusted model. A forest plot summarizing these results is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted odds ratios related to the Low survival region by logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORs (CIs) for the Low survival region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e - value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime (7:00\u0026ndash;22:59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.07\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime (23:00\u0026ndash;6:59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient's sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.85\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient's age, for every 1 y increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraumatic cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.04\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitness status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitnessed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.73\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnwitnessed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBystander CPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.75\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial cardiac rhythms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShockable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.57\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnshockable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced airway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.22\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic airway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenaline administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplemented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.26\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-implemented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe destination hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical level 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.64\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than level 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime factors, for every 1 min increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMS response time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-scene time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCPR, cardiopulmonary resuscitation: EMS, emergency medical service: OR, odds ratio: CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFootnote:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAll included cases were associated with traffic accidents. \"Traumatic cause\" refers to cardiac arrest due to direct physical trauma sustained in the accident. \"Medical cause\" refers to cases where a medical condition (e.g., myocardial infarction) may have precipitated the accident and subsequent cardiac arrest.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Geographic Distribution of Survival and Correlation with Structural Indicators\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the 1-month survival rate showed considerable variation across Japan's 47 prefectures, ranging from 0\u0026ndash;10.9%. Partial correlation analysis between survival and structural healthcare indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a moderate positive association with the number of level-3 hospitals per 100,000 population (r\u0026thinsp;=\u0026thinsp;0.45). Physician density was positively correlated with survival but less strongly (r\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08), while the aging rate showed a negative trend (r = \u0026minus;\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11). Population density was not significantly correlated with survival outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Novel Findings and Academic Significance of This Study\u003c/h2\u003e \u003cp\u003eThis study provides a nationwide analysis of regional disparities in 1-month survival after traffic accident-related OHCA in Japan. Unlike previous studies that have primarily focused on individual-level clinical factors such as age, initial cardiac rhythm, or BCPR [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the present study uniquely demonstrates that differences in regional survival outcomes may be substantially influenced by structural and systemic factors, even after adjusting for patient characteristics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We found that low-survival regions exhibited higher rates of advanced airway management and adrenaline administration\u0026mdash;interventions often associated with severe conditions\u0026mdash;whereas high-survival regions demonstrated higher frequencies of witnessed arrests, BCPR, and transport to level-3 hospitals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, prefectures with more physicians and level-3 hospitals per capita showed higher survival rates, suggesting that the availability and accessibility of healthcare resources at the regional level can independently affect survival after OHCA due to traffic accidents [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These results underscore the reality that even for unpredictable and external causes such as trauma-related cardiac arrests, a patient\u0026rsquo;s chance of survival may be significantly influenced by where the event occurs, revealing inequities in prehospital emergency care systems and the distribution of medical resources across regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Comparison with Previous Studies and the Positioning of This Research\u003c/h2\u003e \u003cp\u003eOur previous study showed that among traffic accident-related OHCA cases, those caused by medical events (e.g., myocardial infarction or stroke) had significantly better neurological outcomes than those due to trauma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. That study highlighted the prognostic importance of shockable rhythms, shorter transport times, and the more frequent implementation of advanced life support interventions among patients with medical-origin OHCA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, the present study focused exclusively on trauma-related OHCA and adopted a regional-level analytic approach. Even after adjusting for age, sex, witnessed status, and interventions, significant disparities in survival rates persisted between prefectures. This suggests that the local healthcare infrastructure, rather than individual patient characteristics alone, plays a substantial role in determining outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the univariable analysis showed lower rates of witnessed arrest and bystander CPR in high-survival regions, our multivariable analysis found that both factors were negatively associated with low-survival regions. Conversely, low-survival regions exhibited higher rates of advanced airway management and adrenaline administration, which may reflect prolonged on-scene time or greater trauma severity, rather than the efficacy of ALS interventions. In traumatic OHCA, patient outcomes may depend more on rapid transport and in-hospital care than on prehospital ALS measures. While our previous research emphasized \"what type of patient\" has better outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the present study highlights \"where the patient is\" as an equally critical determinant. Together, these studies offer a complementary perspective, linking individual clinical severity with systemic, region-based factors in shaping survival [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Interpretation of Structural Regional Factors\u003c/h2\u003e \u003cp\u003eAmong the structural indicators examined, the number of level-3 hospitals and physicians per 100,000 population showed positive correlations with 1-month survival [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], implying that proximity to advanced care facilities and the availability of trained personnel are key contributors to improved outcomes. Although the associations with population density and aging rate were not significant, low-survival regions tended to have higher aging rates and lower population densities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which may reflect delayed EMS activation, limited community responders, or reduced access to timely care. These findings suggest that structural differences between regions are not merely background variables, but may directly define the limitations and opportunities within a region's emergency medical care system [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, the proportion of patients transported to level-3 hospitals exceeded 60% in high-survival regions but fell below 50% in low-survival regions [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], indicating a substantial gap in facility access. This disparity may lead to delays in definitive care, suboptimal on-site decision-making, and ultimately worse patient outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Practical Implications and Future Interventions\u003c/h2\u003e \u003cp\u003eThe study's findings offer multiple implications for improving the equity and effectiveness of trauma-related OHCA care. First, re-evaluating transport protocols and triage systems to ensure timely access to high-level facilities is essential, especially in low-survival areas [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This could include expanding wide-area coordination systems and enhancing real-time data sharing on hospital availability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Second, while community-level CPR training and AED availability remain important elements of public health preparedness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], their impact may be more limited in traumatic OHCA due to the nature of injuries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, additional emphasis should be placed on rapid trauma assessment, early hemorrhage control, and efficient transport to trauma-capable facilities.. Third, strengthening EMS training and operational protocols to ensure standardized care across diverse regions will help bridge gaps in clinical response quality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In particular, trauma-focused training\u0026mdash;including prehospital triage skills, basic hemorrhage control techniques, and coordination with receiving trauma centers\u0026mdash;may improve field-level decision-making and patient outcomes in traumatic OHCA [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These findings highlight the need for region-specific strategies that consider the unique characteristics of trauma-related cardiac arrest and the limitations of current EMS resources in some areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations must be considered. First, the classification of cardiac arrest causes relied on the EMS or hospital records and may have been subject to misclassification. Second, neurological outcomes such as the CPC were not analyzed due to data incompleteness and inconsistency. Third, while we included publicly available structural indicators, other influential variables such as socioeconomic status, transportation networks, and EMS staffing levels were not assessed. Future studies should incorporate both quantitative and qualitative approaches to examine operational characteristics of regional EMS systems, hospital referral pathways, and the impact of policy-level differences on patient outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study revealed significant regional variations in 1-month survival following traffic accident-related OHCA in Japan. The disparities could not be explained by individual-level clinical factors alone and were instead strongly associated with differences in regional healthcare infrastructure, transport systems, and community response capacity. These findings call for targeted interventions to reduce interregional gaps and promote more equitable, responsive emergency care systems tailored to the unique needs of each community.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING SOURCES:\u003c/h2\u003e \u003cp\u003eThis research was supported in part by a grant from the National Mutual Insurance Federation of Agricultural Cooperatives (ZENKYOREN). The funding sources had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS:\u003c/h2\u003e \u003cp\u003e[\u003cem\u003eBlinded\u003c/em\u003e] and [\u003cem\u003eBlinded\u003c/em\u003e] are equal contributors to this work and have been designated as co-first authors. [\u003cem\u003eBlinded\u003c/em\u003e] conducted the statistical analysis and assisted with data interpretation. [\u003cem\u003eBlinded\u003c/em\u003e] and [\u003cem\u003eBlinded\u003c/em\u003e] contributed to data cleaning, management, and methodology development. [\u003cem\u003eBlinded\u003c/em\u003e]. jointly revised the manuscript critically for important intellectual content. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS:\u003c/h2\u003e \u003cp\u003eThe authors thank the Fire and Disaster Management Agency of Japan for providing access to the Emergency Transport and Utstein Registry data. We also acknowledge the assistance of the municipal fire departments and EMS personnel who contributed to data collection across Japan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFarcas AM, Joiner AP, Rudman JS, Ramesh K, Torres G, Crowe RP et al (2023) Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review. 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Lancet 392(10144):283\u0026ndash;291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(18)31553-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)31553-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Niigata University of Health and Welfare","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6900943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6900943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e: To clarify regional disparities in 1-month survival after traffic accident-related out-of-hospital cardiac arrest (OHCA) in Japan and examine associations with emergency medical services (EMS) and healthcare indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a retrospective study of 9,525 traffic accident-related OHCAs using national EMS data from 2018–2022. Prefectures were grouped by 1-month survival rates. Multivariable logistic regression and partial correlation analyses assessed factors related to patient characteristics, EMS, and medical resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: In low-survival regions, rates of advanced airway management (37.7%) and epinephrine administration (29.8%) were significantly higher (p \u0026lt; 0.001). Conversely, the proportion of patients transported to level-3 hospitals was significantly higher in high-survival regions (p \u0026lt; 0.001). Logistic regression revealed that advanced airway management (OR: 1.37; 95% CI: 1.22–1.54; p \u0026lt; 0.001), epinephrine administration (OR: 1.43; 95% CI: 1.26–1.62; p \u0026lt; 0.001), and traffic accidents as the direct cause of cardiac arrest (OR: 1.17; 95% CI: 1.04–1.30; p = 0.006) were significantly associated with lower-survival regions. In contrast, witnessed arrests (OR: 0.82; 95% CI: 0.73–0.92; p = 0.001), BCPR (OR: 0.85; 95% CI: 0.75–0.96; p = 0.012), and transport to level-3 hospitals (OR: 0.71; 95% CI: 0.64–0.80; p \u0026lt; 0.001) were negatively associated with classification into low-survival regions. Partial correlation analysis showed positive associations between survival and the number of level-3 hospitals (r = 0.45) and physicians (r = 0.36, p = 0.08) per 100,000 population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Survival outcomes following traffic accident-related cardiac arrest varied across regions, and distribution of medical resources appeared to influence these disparities.\u003c/p\u003e","manuscriptTitle":"Regional disparities in 1-month survival following traffic accident-related out-of-hospital cardiac arrest in Japan: A nationwide observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 19:39:07","doi":"10.21203/rs.3.rs-6900943/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6026e0a6-a561-4c8d-a185-2f381e26281d","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50082025,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2025-06-18T19:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 19:39:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6900943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6900943","identity":"rs-6900943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}