Impact of system shocks on pediatric prehospital time performance: a nationwide EMS registry study from South Korea

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We assessed how the COVID-19 pandemic and the 2024 workforce crisis were associated with pediatric prehospital time performance and whether crisis-related changes differed between pediatric and adult transports. Methods We conducted a retrospective observational study using nationwide ambulance transport records from South Korea’s National Fire Agency (2019–2024). Pediatric patients (< 18 years) transported between March and December of each year were included; adult transports (≥ 18 years) served as a comparator. Prespecified exposure periods were the COVID-19 pandemic (March 2020–April 2023) and the workforce crisis (March–December 2024). Outcomes were response, scene, transport, and total prehospital time intervals. We performed descriptive comparisons, interrupted time-series regression of monthly mean scene time to estimate level and trend changes at crisis onset, and difference-in-differences models to compare pediatric versus adult changes with covariate adjustment. Results A total of 636,495 pediatric transports were analyzed. After COVID-19 onset, all prehospital time intervals increased, peaked in 2021–2022, and improved in 2023. During the 2024 workforce crisis, total prehospital time increased again, driven predominantly by a subsequent increase in scene time, while other components showed less consistent worsening. This scene-time–dominant pattern was consistent across major pediatric chief complaint categories. In interrupted time-series analysis, COVID-19 onset was associated with an immediate increase in monthly mean scene time (1.88 minutes; 95% confidence interval 0.57–3.18), with no significant slope change thereafter. At the onset of the workforce crisis, no significant level or slope change in scene time was detected. In difference-in-differences analyses, scene time prolongation during both crises was similar in pediatric and adult transports. Conclusions Both crises were associated with prolonged pediatric prehospital times, largely driven by increased scene time. Similar pediatric–adult changes suggest system-wide strain affecting the interface between ambulance services and receiving hospitals, supporting crisis-responsive planning and routine monitoring of pediatric prehospital time performance. Trial registration: Not applicable. emergency medical services pediatric prehospital care response time scene time interrupted time series difference-in-differences COVID-19 health workforce South Korea Figures Figure 1 Figure 2 Background Emergency medical services (EMS) time performance is a core indicator of prehospital care quality and a pragmatic marker of system resilience. Large-scale health system shocks can compromise EMS capacity and time performance by altering care-seeking behavior, field workflow, and destination availability, producing measurable changes in response, scene, transport, and total prehospital time intervals. During the COVID-19 pandemic, multiple EMS systems reported changes in transport volume, prolongation of prehospital time intervals, and increased non-transport or difficulty in hospital acceptance, collectively indicating operational strain under crisis conditions [ 1 – 4 ]. Pediatric prehospital emergency care warrants specific attention because children have distinct physiology and a narrower clinical reserve than adults, making timely recognition, stabilization, and access to definitive care particularly important [ 5 – 7 ]. Evidence from pediatric time-sensitive emergencies, including out-of-hospital cardiac arrest, suggests that prehospital time intervals—especially scene time—are associated with survival and neurologic outcomes, implying that crisis-related deterioration in time performance may have clinically meaningful consequences for pediatric patients [ 8 – 10 ]. In Korea and other Asian settings, nationwide studies have described pediatric EMS utilization patterns, and standardized reporting frameworks such as the Utstein templates underscore the importance of consistent definitions for prehospital time intervals in resuscitation research [ 11 – 13 ]. However, it remains unclear how distinct crisis mechanisms—such as an infectious disease emergency versus a workforce-related disruption—translate into changes in pediatric prehospital time performance within the same national EMS system. Moreover, few studies have directly tested whether crisis-associated changes in key time intervals differ between pediatric and adult transports, limiting inference about pediatric-specific vulnerability versus system-wide strain. South Korea experienced two temporally distinct disruptions that plausibly threatened EMS time performance: the COVID-19 pandemic and a workforce-related crisis in 2024. Workforce disruptions have been associated with impaired hospital performance and may constrain emergency department capacity and acceptance, potentially affecting EMS destination decision-making and prehospital time intervals [ 14 – 15 ]. Therefore, we evaluated the association of these two crises with pediatric prehospital time intervals using nationwide ambulance transport data and assessed whether crisis-related changes differed between pediatric and adult transports using a Difference-in-Differences framework. Methods Aim and study design We conducted a retrospective observational study to evaluate how two distinct health system crises (the COVID-19 pandemic and a workforce-related crisis in 2024) were associated with pediatric prehospital time performance in a national emergency medical services (EMS) system and whether crisis-related changes differed between pediatric and adult transports. Data source, setting, and ethics This study used the nationwide 119 EMS registry operated by the National Fire Agency (NFA) in South Korea. South Korea’s 119 EMS is a single, publicly operated national EMS system. The registry contains patient demographics, EMS-recorded field triage category and chief complaint, and electronically captured timestamps for key phases of prehospital care[ 3 ]. The protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (CHAMC 2025-05-018). Informed consent was waived because de-identified secondary data were used. The NFA authorized use of the registry for research. Participants and study period We included pediatric patients (< 18 years) transported to hospital by ambulance between March 1 and December 31 of each year from 2019 through 2024 to ensure seasonal comparability. Non-transport encounters were excluded. Records with missing timestamps required to calculate prehospital time intervals were excluded. For comparative analyses, adult transported encounters (≥ 18 years) from the same calendar windows were included as a comparator group. Exposure periods and comparisons We prespecified two crisis exposure periods: the COVID-19 period (March 1, 2020–April 30, 2023) and the workforce crisis period (March 1–December 31, 2024). For Difference-in-Differences (DID) analyses, pre–post comparisons were defined as March–December 2019 versus March 2020–April 2023 for the pandemic, and May–December 2023 versus March–December 2024 for the workforce crisis. Variables Patient-level variables included age and sex. Operational timing variables included weekday/weekend and daytime (09:00–18:00) versus nighttime (18:00–09:00). Clinical and field variables included EMS-recorded reason for transport (disease vs non-disease), field triage category, and chief complaint. Metropolitan region was coded as a binary variable. Outcomes (prehospital time intervals) Primary outcomes were prehospital time intervals derived from EMS timestamps: response time interval (RTI; call receipt to scene arrival), scene time interval (STI; scene arrival to scene departure), transport time interval (TTI; scene departure to hospital arrival), and total prehospital time interval (PTI; RTI + STI + TTI) [ 3 ]. Statistical analysis Categorical variables were summarized as counts and percentages and compared using the chi-square test. Continuous variables were summarized as means with standard deviations and compared using the t-test (two groups) or analysis of variance (multiple groups), as appropriate. To evaluate temporal changes associated with each crisis, we performed interrupted time-series regression of monthly mean STI, estimating level and slope changes at each crisis onset and accounting for autocorrelation using an AR(1) error structure. For DID analyses, STI at the individual transport level was modeled with terms for period (pre vs post), age group (pediatric vs adult), and their interaction, with adjustment for prespecified covariates (age, sex, weekday/weekend, daytime/nighttime, and metropolitan region). Robust (sandwich) standard errors were used for inference. Two-sided P values < 0.05 were considered statistically significant. Because all eligible encounters within the study windows were included, no a priori sample size or power calculation was performed. Analyses were conducted using SAS Studio (SAS Institute Inc., Cary, NC, USA). Reporting followed the STROBE guidelines. Role of funding No external funding was received. No sponsor had any role in study design, analysis, interpretation, or reporting. Results From March 1 to December 31 of each year (2019–2024), 14,103,606 EMS activations were recorded nationwide, of which 9,457,764 (67.1%) resulted in patient transport. Among transported encounters, 636,495 (6.7%) involved pediatric patients (<18 years). Pediatric transport volume decreased in 2020, recovered through 2023, and decreased again in 2024. Cohort derivation is shown in Figure 1 . Baseline characteristics across study periods are summarized in Table 1 . Compared with the pre–COVID-19 period, the pediatric age distribution shifted toward older age groups during the early pandemic and the workforce crisis, and field triage patterns differed across periods, with fewer encounters categorized as “emergency” during the workforce crisis ( Table 1 ). Prehospital time intervals by period are presented in Table 2 , and temporal trends are illustrated in Figure 2 . Following the onset of COVID-19, RTI, STI, TTI, and PTI increased, peaking during 2021–2022 and improving in 2023. During the workforce crisis, STI increased again, with a corresponding increase in PTI ( Table 2 ; Figure 2 ). Time intervals stratified by major pediatric chief complaints are provided in Supplementary Table S1 (Additional file 1); the temporal pattern of STI was consistent across complaint categories ( Supplementary Table S1 (Additional file 1)). Interrupted time-series analysis demonstrated changes in STI associated with crisis onset ( Table 3 ). At the start of the COVID-19 pandemic, there was a significant immediate increase in monthly mean STI (1.88 minutes; 95% CI 0.57–3.18; p = 0.007), with no significant slope change during the pandemic period (p = 0.788). At the onset of the workforce crisis, the immediate STI change was not significant (0.52 minutes; 95% CI −0.84 to 1.88; p = 0.460), and the subsequent slope change was also not significant (p = 0.201). Difference-in-differences estimates for STI are summarized in Table 4 . The pediatric–adult difference in STI change was not significant during either crisis (COVID-19: DID 0.01 minutes, p = 0.85; workforce crisis: DID 0.00 minutes, p = 0.96). Models were adjusted for prespecified covariates available in the registry (age, sex, day of week, time of day, and metropolitan region; Table 4). A parallel visual comparison is shown in Supplementary Figure S1 (Additional file 2). Discussion Prehospital EMS time performance is a sensitive indicator of system resilience because it integrates demand, field workflow, and access to receiving facilities under time pressure. During the COVID-19 pandemic, multiple EMS systems reported changes in transport volume, prolonged prehospital time intervals, and increased non-transport or difficulty in hospital acceptance, collectively suggesting operational strain during infectious disease emergencies [ 1 – 4 , 17 ]. Similarly, workforce-related disruptions—including physician strikes—have been associated with adverse effects on hospital performance and emergency care delivery, supporting the premise that workforce shocks can destabilize acute care pathways [ 15 ]. Building on this literature, our nationwide analysis suggests that distinct crisis mechanisms can be associated with measurable changes in pediatric prehospital time performance within a single national EMS system. A key finding was that prehospital time intervals worsened after COVID-19 onset, peaked in 2021–2022, and improved in 2023, followed by a subsequent increase in total prehospital time during the 2024 workforce crisis that was driven predominantly by scene time. ITS models were consistent with an abrupt disruption at the onset of the COVID-19 pandemic, whereas we did not detect a statistically significant step or slope change at the prespecified onset of the 2024 workforce crisis. This discrepancy should be interpreted in light of how “onset” was operationalized. Unlike the pandemic, which had a clearer system-wide inflection early in 2020, workforce-related disruptions may evolve over weeks to months, vary by region and facility, and manifest as cumulative operational friction rather than an immediate, discrete shift. Accordingly, a period-level increase in scene time can coexist with an ITS model that does not identify a sharp discontinuity at a single start date, underscoring the importance of considering gradual or staggered disruption patterns when interpreting time-series signals. Our Difference-in-Differences analyses provide additional context: crisis-associated scene time prolongation was similar in pediatric and adult transports in both crises, suggesting system-wide strain rather than a pediatric-specific operational effect. This finding supports the interpretation that delays during health system disruptions may concentrate at the EMS–hospital interface—where destination selection, acceptance, and handover constraints can prolong the scene phase and propagate into total prehospital time—rather than arising primarily from pediatric care processes alone[ 18 ]. Consistent patterns across major pediatric chief complaint categories further reinforce a broadly shared operational mechanism rather than diagnosis-specific delays. Although pediatric-specific operational effects were not evident in the DID comparisons, pediatric patients may be particularly vulnerable to the clinical consequences of delay. Prior pediatric out-of-hospital cardiac arrest studies have linked scene time and other prehospital intervals with survival and neurologic outcomes, suggesting that both very short and prolonged on-scene times may be harmful and that the therapeutic window for scene management may be narrower in children [ 8 – 10 ]. In addition, pediatric trauma studies have reported systematic differences in prehospital scene and transport times between pediatric and adult patients [ 16 ]. Therefore, even when absolute delays are comparable across age groups, system-wide deterioration in time performance may carry disproportionate risk for pediatric patients depending on case mix and acuity distribution. Several practical implications follow. First, preparedness planning should prioritize scene-phase processes that tend to expand under system strain, including operationally feasible pediatric assessment and stabilization pathways and clear guidance for destination decision-making when hospital acceptance is constrained[ 18 ]. Second, routine performance surveillance that includes pediatric scene time and total prehospital time may enable earlier detection of emerging system stress, irrespective of whether the trigger is an infectious disease emergency or a workforce disruption. Third, strengthening coordination mechanisms between EMS and receiving hospitals—particularly around acceptance, diversion, and handover—may reduce the likelihood that downstream constraints translate into prolonged scene time and increased total prehospital time during future crises[ 18 ]. Limitations Several limitations warrant consideration. First, we analyzed transported encounters only; non-transport activations and repeat contacts were not captured, which may underestimate crisis-related changes in overall pediatric EMS demand and utilization patterns [ 17 ]. Second, time intervals were derived from routine EMS timestamps and the registry lacked granular measures of EMS–hospital interface processes (e.g., receiving-hospital acceptance delay, diversion/refusal events, and handover duration), limiting mechanistic attribution of scene-time prolongation [ 18 ]. Third, causal inference is limited by the observational design and unverifiable ITS/DID assumptions (including parallel trends), and gradual or heterogeneous workforce disruption may not be well represented by a single onset date. Finally, findings from a single national EMS system may not generalize to settings with different dispatch, staffing, or hospital access pathways. Conclusion In this national EMS system, both the COVID-19 pandemic and the 2024 workforce-related crisis were associated with worsening pediatric prehospital time performance, driven primarily by increased scene time and reflected in longer total prehospital time. These findings suggest system-wide strain at the EMS–hospital interface and support crisis-responsive strategies that routinely monitor pediatric time metrics, minimize avoidable on-scene delays when hospital capacity is constrained, and link EMS-to-hospital data to quantify clinical impact and identify modifiable targets for resilience. Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (CHAMC 2025-05-018), and the requirement for informed consent was waived due to the retrospective use of de-identified data. All methods were carried out in accordance with the Declaration of Helsinki and relevant guidelines and regulations. Data access and use were authorized by the National Fire Agency (NFA) of South Korea. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study were obtained from the National Fire Agency (NFA) of South Korea under permission for research use. The data are not publicly available due to data governance restrictions. Access may be considered by the NFA upon reasonable request and with appropriate approvals. Competing interests The authors declare that they have no competing interests. Funding This research did not receive external funding. Authors’ contributions MJK and HSM contributed equally to this work and share first authorship. MJK, HSM, and SHP contributed to the study conception and design. MJK and HSM contributed to data acquisition and interpretation. MJK performed the statistical analyses and drafted the initial manuscript. HSM and SHP critically reviewed and revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Şan İ, Usul E, Bekgöz B, Korkut S. Effects of the COVID-19 pandemic on emergency medical services. Int J Clin Pract. 2021;75(5):e13885. doi:10.1111/ijcp.13885. Handberry M, Bull-Otterson L, Dai M, et al. Changes in emergency medical services before and during the COVID-19 pandemic in the United States, January 2018–December 2020. Clin Infect Dis. 2021;73(Suppl 1):S84–S91. doi:10.1093/cid/ciab373. Park YJ, Song KJ, Hong KJ, et al. The impact of the COVID-19 outbreak on emergency medical service: an analysis of patient transportations and time intervals. J Korean Med Sci. 2023;38(42):e317. doi:10.3346/jkms.2023.38.e317. Lim YJ, Park SY. Increased prehospital emergency medical service time interval and nontransport rate of patients with fever using emergency medical services before and after COVID-19 in Busan, Korea. J Korean Med Sci. 2023;38(9):e69. doi:10.3346/jkms.2023.38.e69. Topjian AA, Raymond TT, Atkins DL, et al. Part 4: Pediatric basic and advanced life support: 2020 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2020;142(16 Suppl 2):S469–S523. Jewkes F, Woollard M. Assessment and management of paediatric primary survey positive patients. Emerg Med J. 2004;21(5):595–605. doi:10.1136/emj.2004.017780. Solazzo E, McCans K, Owusu-Ansah S, Williams KA. When Should EMS Call a Child a Small Adult: Inconsistency in Protocol Definitions. Int J Paramedicine. 2024;(6):171–184. doi:10.56068/KCYD7018. Kiyohara K, Okubo M, Komukai S, Izawa J, Gibo K, Matsuyama T, et al. Association between resuscitative time on the scene and survival after pediatric out-of-hospital cardiac arrest. Circ Rep. 2021;3(4):211–216. doi:10.1253/circrep.CR-21-0021. Tijssen JA, Prince DK, Morrison LJ, Atkins DL, Austin MA, Berg RA, et al. Time on the scene and interventions are associated with improved survival in pediatric out-of-hospital cardiac arrest. Resuscitation. 2015;94:1–7. doi:10.1016/j.resuscitation.2015.06.012. Goto Y, Funada A, Goto Y. Duration of prehospital cardiopulmonary resuscitation and favorable neurological outcomes for pediatric out-of-hospital cardiac arrests: a nationwide, population-based cohort study. Circulation. 2016;134(25):2046–2059. doi:10.1161/CIRCULATIONAHA.116.023821. Shin SD, Kim J, Song KJ, et al. Epidemiology of pediatric emergency patients in Korea. J Korean Med Sci. 2010;25(7):991–997. Perkins GD, Jacobs IG, Nadkarni VM, Berg RA, Bhanji F, Biarent D, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry templates for out-of-hospital cardiac arrest. Resuscitation. 2015;96:328–340. doi:10.1016/j.resuscitation.2014.11.002. Tham LP, Wah W, Phillips R, Shahidah N, Ng YY, Shin SD, et al. Epidemiology and outcome of paediatric out-of-hospital cardiac arrests: a paediatric sub-study of the Pan-Asian resuscitation outcomes study (PAROS). Resuscitation. 2018;125:111–117. doi:10.1016/j.resuscitation.2018.01.040. Choi A, Kim BJ, Lee J, Kim S, Bae W. Impact of the South Korean government’s medical school expansion announcement on pediatric emergency department visits. BMC Emerg Med. 2025;25:39. doi:10.1186/s12873-025-01189-w. Essex R, et al. The impact of strike action on healthcare delivery: a scoping review. Int J Health Plann Manage. 2023;38(3):599–627. doi:10.1002/hpm.3610. Ashburn NP, Hendley NW, Angi RM, et al. Prehospital trauma scene and transport times for pediatric and adult patients. West J Emerg Med. 2020;21(2):455–462. doi:10.5811/westjem.2019.11.44597. Pandya A, et al. Pediatric outcomes of emergency medical services non-transport before and during the COVID-19 pandemic. West J Emerg Med. 2024;25(2):246–253. doi:10.5811/westjem.18408. Katayama Y, Kitamura T, Kiyohara K, et al. Factors associated with difficulty in hospital acceptance at the scene by emergency medical service personnel: a population-based study in Osaka City, Japan. BMJ Open. 2016;6(10):e013849. doi:10.1136/bmjopen-2016-013849. Tables Table 1. Baseline characteristics across study periods (Panel A: demographics; Panel B: EMS and clinical) Pre COVID-19 COVID-19 pandemic Before resignation After resignation P-value 2019 Mar-Dec 2020 Mar-Dec 2021 Mar-Dec 2022 Mar-Dec 2023 Mar–Apr 2023 May–Dec 2024 Mar-Dec 112,527 72,637 93,087 125,180 27,134 107,328 98,602 Age,years ,median (IQR) 4(2-5) 4(3-5) 4(2-5) 4(2-5) 3(2-5) 2(2-3) 4(2-5) <0.01 Age group <0.01 Infant 6,665(5.9) 5,204(7.2) 6,469(7.0) 11,592(9.3) 2,210(8.1) 8,727(8.1) 6,871(7.0) Toddler 28,052(24.9) 12,706(17.5) 17,943(19.3) 27,953(22.3) 7,559(27.9) 22,383(20.9) 18,597(18.9) Preschool 18,548(16.5) 10,210(14.1) 13,034(14.0) 19,293(15.4) 4,806(17.7) 19,215(18.0) 12,222(12.4) School-age 26,897(23.9) 18,266(25.2) 24,045(25.8) 30,052(24.0) 5,509(20.3) 25,507(23.8) 25,588(26.0) Adolescent 32,365(28.8) 26,251(36.1) 31,596(33.9) 36,290(29.0) 7,050(26.0) 31,496(29.4) 35,324(35.8) Female 45,516(40.5) 29,559(40.7) 38,621(41.5) 51,113(40.8) 11,393(42.0) 44,258(41.2) 38,826(39.4) <0.01 Daytime 61,876(55.0) 41,827(57.6) 53,733(57.7) 66,704(53.3) 14,002(51.6) 57,374(53.5) 54,607(55.4) <0.01 Weekdays 79,584(70.7) 52,416(72.2) 66,390(71.3) 88,089(70.4) 18,723(69.0) 75,747(70.6) 68,919(70.0) <0.01 Values are presented as n (%) unless otherwise specified. Age is presented as median (IQR). Pandemic period: March 2020 –April 2023; Resignation period: Ma rch 202 4 –December 2024. Table 1. Baseline characteristics across study periods (Panel A: demographics; Panel B: EMS and clinical) Pre COVID-19 COVID-19 pandemic Before resignation After resignation P-value 2019 Mar-Dec 2020 Mar-Dec 2021 Mar-Dec 2022 Mar-Dec 2023 Mar–Apr 2023 May–Dec 2024 Mar-Dec 112,527 72,637 93,087 125,180 27,134 107,328 98,602 Disease 59,311(52.7) 34,497(47.5) 51,227(55.0) 77,854(62.2) 17,777(65.5) 68,178(63.5) 52,767(53.5) <0.01 Fi e ld Triage <0.01 Emergency 27,071(24.1) 14,866(20.5) 17,851(19.2) 26,717(21.3) 6,385(23.5) 25,718(24.0) 10,111(10.3) Sub-emergency 35,353(31.4) 22,337(30.8) 27,821(29.9) 40,653(32.5) 9,091(33.5) 36,308(33.8) 23,122(23.5) Non-emergency (Latency-emergency) 48,831(43.4) 33,729(46.4) 42,789(46.0) 56,617(45.2) 11,493(42.4) 44,562(41.5) 64,893(65.8) Death 47(0.0) 40(0.1) 33(0.0) 40(0.0) 5(0.0) 42(0.0) 31(0.0) Others 1,225(1.1) 1,665(2.3) 4,593(4.9) 1,153(0.9) 160(0.6) 698(0.7) 445(0.5) Values are presented as n (%) Pandemic period: March 2020 –April 2023; Resignation period: Ma rch 202 4 –December 2024. Table 2. Prehospital time intervals among transported pediatric patients, 2019–2024 Time intervals, min Pre COVID-19 COVID-19 pandemic Before resignation After resignation 2019 Mar-Dec 2020 Mar-Dec 2021 Mar-Dec 2022 Mar-Dec 2023 Mar–Apr 2023 May-Dec 2024 Mar-Dec RTI 8.23±6.77 10.38±9.78 14.35±19.27 10.49±7.17 9.31±6.11 9.45±6.08 9.45±6.40 STI 5.78±6.12 7.56±7.67 8.22±8.24 9.80±9.62 8.40±7.06 8.07±7.01 10.30±9.26 TTI 12.58±10.18 14.48±13.41 18.72±19.03 16.64±14.61 15.40±12.45 15.18±12.50 15.52±13.97 PTI 26.55±14.58 32.73±20.00 41.17±33.18 36.89±20.75 33.09±16.63 32.66±16.67 35.23±19.26 Values are presented as mean ± standard deviation (minutes). RTI = response time interval; STI = scene time interval; TTI = transport time interval; PTI = total prehospital time interval. Table 3. Interrupted time-series regression of monthly mean scene time interval (STI), 2019–2024 Variable Estimate 95% CI (Lower-Upper) p-value Intercept 5.61 4.45 - 6.78 <0.0001 Time (months) 0.04 -0.10 - 0.18 0.574 COVID-19 step 1.88 0.57 - 3.18 0.007 COVID-19 slope -0.02 -0.16 - 0.12 0.788 Resignation step 0.52 -0.84 - 1.88 0.460 Resignation slope 0.15 -0.08 - 0.38 0.201 Model accounted for autocorrelation using an AR(1) error structure. Dependent variable was monthly mean STI (minutes). CI = confidence interval. Table 4. Summary of Difference-in-Differences (DID) analysis for scene time interval (STI) (pediatric vs adult) Comparison Group Mean Before (min) Mean After (min) Change (min) DID Effect (Pediatric vs Adult) Pre–COVID-19 vs COVID-19 period , Adult 11.34 13.10 +1.76 - Pre–COVID-19 vs COVID-19 period , Pediatric 11.85 13.63 +1.78 +0.01(P = 0.850) Before resignation vs resignation , Adult 10.13 12.23 +2.10 - Before resignation vs resignation , Pediatric 9.99 12.09 +2.10 0.00(P = 0.960) Values are presented as mean STI (minutes). Pre and post periods were defined as follows: COVID-19 (pre: Mar–Dec 2019; post: Mar 2020–Apr 2023) and resignation (pre: May–Dec 2023; post: Mar–Dec 2024). DID models were adjusted for age, sex, weekday/weekend, daytime/nighttime, and metropolitan region (binary). DID = Difference-in-Differences; STI = scene time interval. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TableS1.docx Additionalfile2FigureS1.tif Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 04 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 26 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8705037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603911741,"identity":"f3f83199-5ee4-4fbb-a8ac-004f5af98f40","order_by":0,"name":"Min-Jung Kim","email":"","orcid":"","institution":"CHA Bundang Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Min-Jung","middleName":"","lastName":"Kim","suffix":""},{"id":603911742,"identity":"6e7381e3-6a05-48a5-bc9a-3c8f5f83ed77","order_by":1,"name":"Hwan Sun Moon","email":"","orcid":"","institution":"National Fire Agency","correspondingAuthor":false,"prefix":"","firstName":"Hwan","middleName":"Sun","lastName":"Moon","suffix":""},{"id":603911743,"identity":"99d3aa26-ed79-4834-baa3-246a7f97ffe7","order_by":2,"name":"So-Hyun Paek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACAxDxwYCNh429gQQtjDMK+OT4eQ6QoIWZ54OcseSMBCK1mEvkmD3gMTBL3HDz7cHPPBX3GPjbu/FrtpyRY24gYZCWuOF2XrI0z5liBokzZzfgd9iNHDMJA4NjQC05BpIz2xIYDCRyidCSYPAf6LAzxj9n/iNWywEDNqD3ecwkPjYQo+XMszLJBgM2YCDnmFl8OJbAQ9gvx5O3Sf/5A4rKM8Y3EmoS5Pjbe/FrYRBIQOXz4FcOAvwHCKsZBaNgFIyCEQ4Ai8pGvl7IVQMAAAAASUVORK5CYII=","orcid":"","institution":"CHA Bundang Medical Center","correspondingAuthor":true,"prefix":"","firstName":"So-Hyun","middleName":"","lastName":"Paek","suffix":""}],"badges":[],"createdAt":"2026-01-27 02:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8705037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8705037/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104546793,"identity":"bc5f21f3-664a-4c68-b329-86d410cc3cb1","added_by":"auto","created_at":"2026-03-13 07:29:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the study cohort of pediatric EMS transports between 2019 and 2024.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure shows all 119 EMS incidents recorded in the national registry, the subset resulting in transport, exclusion of non-transport encounters and records with missing timestamps required to compute prehospital intervals, and the final pediatric cohort (\u0026lt;18 years).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8705037/v1/f2d212ee5f9ea9ceb0b8759c.png"},{"id":104546796,"identity":"5d597b68-ae5e-4a31-8e72-1966a223b8e1","added_by":"auto","created_at":"2026-03-13 07:29:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual trends in pediatric prehospital time intervals from 2019 to 2024.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLines show yearly mean values for response time interval (RTI), scene time interval (STI), transport time interval (TTI), and total prehospital time interval (PTI) among pediatric transports.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8705037/v1/10777e7ab732375ef5997d4b.png"},{"id":104781182,"identity":"fac31ae7-547a-41ce-a5aa-c5e1d9d36ef7","added_by":"auto","created_at":"2026-03-17 07:55:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1770305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8705037/v1/8ea63b3e-f4a5-4a4f-aeca-ae7b5b89cdf2.pdf"},{"id":104546794,"identity":"46401ce4-971c-4436-b2bd-0da9252eb56e","added_by":"auto","created_at":"2026-03-13 07:29:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24141,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8705037/v1/ef8ad8306a8a7dbdde5d9c1b.docx"},{"id":104546795,"identity":"1abae7f3-a885-4039-90e8-ae74404b06ff","added_by":"auto","created_at":"2026-03-13 07:29:27","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":864700,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8705037/v1/87a8ced6b904a6892ebcb7ac.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of system shocks on pediatric prehospital time performance: a nationwide EMS registry study from South Korea","fulltext":[{"header":"Background","content":"\u003cp\u003eEmergency medical services (EMS) time performance is a core indicator of prehospital care quality and a pragmatic marker of system resilience. Large-scale health system shocks can compromise EMS capacity and time performance by altering care-seeking behavior, field workflow, and destination availability, producing measurable changes in response, scene, transport, and total prehospital time intervals. During the COVID-19 pandemic, multiple EMS systems reported changes in transport volume, prolongation of prehospital time intervals, and increased non-transport or difficulty in hospital acceptance, collectively indicating operational strain under crisis conditions [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePediatric prehospital emergency care warrants specific attention because children have distinct physiology and a narrower clinical reserve than adults, making timely recognition, stabilization, and access to definitive care particularly important [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Evidence from pediatric time-sensitive emergencies, including out-of-hospital cardiac arrest, suggests that prehospital time intervals\u0026mdash;especially scene time\u0026mdash;are associated with survival and neurologic outcomes, implying that crisis-related deterioration in time performance may have clinically meaningful consequences for pediatric patients [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Korea and other Asian settings, nationwide studies have described pediatric EMS utilization patterns, and standardized reporting frameworks such as the Utstein templates underscore the importance of consistent definitions for prehospital time intervals in resuscitation research [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, it remains unclear how distinct crisis mechanisms\u0026mdash;such as an infectious disease emergency versus a workforce-related disruption\u0026mdash;translate into changes in pediatric prehospital time performance within the same national EMS system. Moreover, few studies have directly tested whether crisis-associated changes in key time intervals differ between pediatric and adult transports, limiting inference about pediatric-specific vulnerability versus system-wide strain.\u003c/p\u003e \u003cp\u003eSouth Korea experienced two temporally distinct disruptions that plausibly threatened EMS time performance: the COVID-19 pandemic and a workforce-related crisis in 2024. Workforce disruptions have been associated with impaired hospital performance and may constrain emergency department capacity and acceptance, potentially affecting EMS destination decision-making and prehospital time intervals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, we evaluated the association of these two crises with pediatric prehospital time intervals using nationwide ambulance transport data and assessed whether crisis-related changes differed between pediatric and adult transports using a Difference-in-Differences framework.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim and study design\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective observational study to evaluate how two distinct health system crises (the COVID-19 pandemic and a workforce-related crisis in 2024) were associated with pediatric prehospital time performance in a national emergency medical services (EMS) system and whether crisis-related changes differed between pediatric and adult transports.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData source, setting, and ethics\u003c/h3\u003e\n\u003cp\u003eThis study used the nationwide 119 EMS registry operated by the National Fire Agency (NFA) in South Korea. South Korea\u0026rsquo;s 119 EMS is a single, publicly operated national EMS system. The registry contains patient demographics, EMS-recorded field triage category and chief complaint, and electronically captured timestamps for key phases of prehospital care[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (CHAMC 2025-05-018). Informed consent was waived because de-identified secondary data were used. The NFA authorized use of the registry for research.\u003c/p\u003e\n\u003ch3\u003eParticipants and study period\u003c/h3\u003e\n\u003cp\u003eWe included pediatric patients (\u0026lt;\u0026thinsp;18 years) transported to hospital by ambulance between March 1 and December 31 of each year from 2019 through 2024 to ensure seasonal comparability. Non-transport encounters were excluded. Records with missing timestamps required to calculate prehospital time intervals were excluded. For comparative analyses, adult transported encounters (\u0026ge;\u0026thinsp;18 years) from the same calendar windows were included as a comparator group.\u003c/p\u003e\n\u003ch3\u003eExposure periods and comparisons\u003c/h3\u003e\n\u003cp\u003eWe prespecified two crisis exposure periods: the COVID-19 period (March 1, 2020\u0026ndash;April 30, 2023) and the workforce crisis period (March 1\u0026ndash;December 31, 2024). For Difference-in-Differences (DID) analyses, pre\u0026ndash;post comparisons were defined as March\u0026ndash;December 2019 versus March 2020\u0026ndash;April 2023 for the pandemic, and May\u0026ndash;December 2023 versus March\u0026ndash;December 2024 for the workforce crisis.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003ePatient-level variables included age and sex. Operational timing variables included weekday/weekend and daytime (09:00\u0026ndash;18:00) versus nighttime (18:00\u0026ndash;09:00). Clinical and field variables included EMS-recorded reason for transport (disease vs non-disease), field triage category, and chief complaint. Metropolitan region was coded as a binary variable.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes (prehospital time intervals)\u003c/h2\u003e \u003cp\u003ePrimary outcomes were prehospital time intervals derived from EMS timestamps: response time interval (RTI; call receipt to scene arrival), scene time interval (STI; scene arrival to scene departure), transport time interval (TTI; scene departure to hospital arrival), and total prehospital time interval (PTI; RTI\u0026thinsp;+\u0026thinsp;STI\u0026thinsp;+\u0026thinsp;TTI) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were summarized as counts and percentages and compared using the chi-square test. Continuous variables were summarized as means with standard deviations and compared using the t-test (two groups) or analysis of variance (multiple groups), as appropriate.\u003c/p\u003e \u003cp\u003eTo evaluate temporal changes associated with each crisis, we performed interrupted time-series regression of monthly mean STI, estimating level and slope changes at each crisis onset and accounting for autocorrelation using an AR(1) error structure.\u003c/p\u003e \u003cp\u003eFor DID analyses, STI at the individual transport level was modeled with terms for period (pre vs post), age group (pediatric vs adult), and their interaction, with adjustment for prespecified covariates (age, sex, weekday/weekend, daytime/nighttime, and metropolitan region). Robust (sandwich) standard errors were used for inference. Two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003cp\u003eBecause all eligible encounters within the study windows were included, no a priori sample size or power calculation was performed. Analyses were conducted using SAS Studio (SAS Institute Inc., Cary, NC, USA). Reporting followed the STROBE guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRole of funding\u003c/h3\u003e\n\u003cp\u003eNo external funding was received. No sponsor had any role in study design, analysis, interpretation, or reporting.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrom March 1 to December 31 of each year (2019–2024), 14,103,606 EMS activations were recorded nationwide, of which 9,457,764 (67.1%) resulted in patient transport. Among transported encounters, 636,495 (6.7%) involved pediatric patients (\u0026lt;18 years). Pediatric transport volume decreased in 2020, recovered through 2023, and decreased again in 2024. Cohort derivation is shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics across study periods are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. Compared with the pre–COVID-19 period, the pediatric age distribution shifted toward older age groups during the early pandemic and the workforce crisis, and field triage patterns differed across periods, with fewer encounters categorized as “emergency” during the workforce crisis (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003ePrehospital time intervals by period are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e, and temporal trends are illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e. Following the onset of COVID-19, RTI, STI, TTI, and PTI increased, peaking during 2021–2022 and improving in 2023. During the workforce crisis, STI increased again, with a corresponding increase in PTI (\u003cstrong\u003eTable 2\u003c/strong\u003e; \u003cstrong\u003eFigure 2\u003c/strong\u003e). Time intervals stratified by major pediatric chief complaints are provided in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e(Additional file 1); the temporal pattern of STI was consistent across complaint categories (\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e(Additional file 1)).\u003c/p\u003e\n\u003cp\u003eInterrupted time-series analysis demonstrated changes in STI associated with crisis onset (\u003cstrong\u003eTable 3\u003c/strong\u003e). At the start of the COVID-19 pandemic, there was a significant immediate increase in monthly mean STI (1.88 minutes; 95% CI 0.57–3.18; p = 0.007), with no significant slope change during the pandemic period (p = 0.788). At the onset of the workforce crisis, the immediate STI change was not significant (0.52 minutes; 95% CI −0.84 to 1.88; p = 0.460), and the subsequent slope change was also not significant (p = 0.201).\u003c/p\u003e\n\u003cp\u003eDifference-in-differences estimates for STI are summarized in \u003cstrong\u003eTable 4\u003c/strong\u003e. The pediatric–adult difference in STI change was not significant during either crisis (COVID-19: DID 0.01 minutes, p = 0.85; workforce crisis: DID 0.00 minutes, p = 0.96). Models were adjusted for prespecified covariates available in the registry (age, sex, day of week, time of day, and metropolitan region; Table 4). A parallel visual comparison is shown in \u003cstrong\u003eSupplementary Figure S1\u003c/strong\u003e(Additional file 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrehospital EMS time performance is a sensitive indicator of system resilience because it integrates demand, field workflow, and access to receiving facilities under time pressure. During the COVID-19 pandemic, multiple EMS systems reported changes in transport volume, prolonged prehospital time intervals, and increased non-transport or difficulty in hospital acceptance, collectively suggesting operational strain during infectious disease emergencies [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, workforce-related disruptions\u0026mdash;including physician strikes\u0026mdash;have been associated with adverse effects on hospital performance and emergency care delivery, supporting the premise that workforce shocks can destabilize acute care pathways [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Building on this literature, our nationwide analysis suggests that distinct crisis mechanisms can be associated with measurable changes in pediatric prehospital time performance within a single national EMS system.\u003c/p\u003e \u003cp\u003eA key finding was that prehospital time intervals worsened after COVID-19 onset, peaked in 2021\u0026ndash;2022, and improved in 2023, followed by a subsequent increase in total prehospital time during the 2024 workforce crisis that was driven predominantly by scene time. ITS models were consistent with an abrupt disruption at the onset of the COVID-19 pandemic, whereas we did not detect a statistically significant step or slope change at the prespecified onset of the 2024 workforce crisis. This discrepancy should be interpreted in light of how \u0026ldquo;onset\u0026rdquo; was operationalized. Unlike the pandemic, which had a clearer system-wide inflection early in 2020, workforce-related disruptions may evolve over weeks to months, vary by region and facility, and manifest as cumulative operational friction rather than an immediate, discrete shift. Accordingly, a period-level increase in scene time can coexist with an ITS model that does not identify a sharp discontinuity at a single start date, underscoring the importance of considering gradual or staggered disruption patterns when interpreting time-series signals.\u003c/p\u003e \u003cp\u003eOur Difference-in-Differences analyses provide additional context: crisis-associated scene time prolongation was similar in pediatric and adult transports in both crises, suggesting system-wide strain rather than a pediatric-specific operational effect. This finding supports the interpretation that delays during health system disruptions may concentrate at the EMS\u0026ndash;hospital interface\u0026mdash;where destination selection, acceptance, and handover constraints can prolong the scene phase and propagate into total prehospital time\u0026mdash;rather than arising primarily from pediatric care processes alone[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consistent patterns across major pediatric chief complaint categories further reinforce a broadly shared operational mechanism rather than diagnosis-specific delays.\u003c/p\u003e \u003cp\u003eAlthough pediatric-specific operational effects were not evident in the DID comparisons, pediatric patients may be particularly vulnerable to the clinical consequences of delay. Prior pediatric out-of-hospital cardiac arrest studies have linked scene time and other prehospital intervals with survival and neurologic outcomes, suggesting that both very short and prolonged on-scene times may be harmful and that the therapeutic window for scene management may be narrower in children [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, pediatric trauma studies have reported systematic differences in prehospital scene and transport times between pediatric and adult patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, even when absolute delays are comparable across age groups, system-wide deterioration in time performance may carry disproportionate risk for pediatric patients depending on case mix and acuity distribution.\u003c/p\u003e \u003cp\u003eSeveral practical implications follow. First, preparedness planning should prioritize scene-phase processes that tend to expand under system strain, including operationally feasible pediatric assessment and stabilization pathways and clear guidance for destination decision-making when hospital acceptance is constrained[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Second, routine performance surveillance that includes pediatric scene time and total prehospital time may enable earlier detection of emerging system stress, irrespective of whether the trigger is an infectious disease emergency or a workforce disruption. Third, strengthening coordination mechanisms between EMS and receiving hospitals\u0026mdash;particularly around acceptance, diversion, and handover\u0026mdash;may reduce the likelihood that downstream constraints translate into prolonged scene time and increased total prehospital time during future crises[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations warrant consideration. First, we analyzed transported encounters only; non-transport activations and repeat contacts were not captured, which may underestimate crisis-related changes in overall pediatric EMS demand and utilization patterns [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Second, time intervals were derived from routine EMS timestamps and the registry lacked granular measures of EMS\u0026ndash;hospital interface processes (e.g., receiving-hospital acceptance delay, diversion/refusal events, and handover duration), limiting mechanistic attribution of scene-time prolongation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Third, causal inference is limited by the observational design and unverifiable ITS/DID assumptions (including parallel trends), and gradual or heterogeneous workforce disruption may not be well represented by a single onset date. Finally, findings from a single national EMS system may not generalize to settings with different dispatch, staffing, or hospital access pathways.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this national EMS system, both the COVID-19 pandemic and the 2024 workforce-related crisis were associated with worsening pediatric prehospital time performance, driven primarily by increased scene time and reflected in longer total prehospital time. These findings suggest system-wide strain at the EMS\u0026ndash;hospital interface and support crisis-responsive strategies that routinely monitor pediatric time metrics, minimize avoidable on-scene delays when hospital capacity is constrained, and link EMS-to-hospital data to quantify clinical impact and identify modifiable targets for resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (CHAMC 2025-05-018), and the requirement for informed consent was waived due to the retrospective use of de-identified data. All methods were carried out in accordance with the Declaration of Helsinki and relevant guidelines and regulations. Data access and use were authorized by the National Fire Agency (NFA) of South Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study were obtained from the National Fire Agency (NFA) of South Korea under permission for research use. The data are not publicly available due to data governance restrictions. Access may be considered by the NFA upon reasonable request and with appropriate approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJK and HSM contributed equally to this work and share first authorship. MJK, HSM, and SHP contributed to the study conception and design. MJK and HSM contributed to data acquisition and interpretation. MJK performed the statistical analyses and drafted the initial manuscript. HSM and SHP critically reviewed and revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eŞan İ, Usul E, Bekg\u0026ouml;z B, Korkut S. Effects of the COVID-19 pandemic on emergency medical services. Int J Clin Pract. 2021;75(5):e13885. doi:10.1111/ijcp.13885.\u003c/li\u003e\n\u003cli\u003eHandberry M, Bull-Otterson L, Dai M, et al. Changes in emergency medical services before and during the COVID-19 pandemic in the United States, January 2018\u0026ndash;December 2020. Clin Infect Dis. 2021;73(Suppl 1):S84\u0026ndash;S91. doi:10.1093/cid/ciab373.\u003c/li\u003e\n\u003cli\u003ePark YJ, Song KJ, Hong KJ, et al. The impact of the COVID-19 outbreak on emergency medical service: an analysis of patient transportations and time intervals. J Korean Med Sci. 2023;38(42):e317. doi:10.3346/jkms.2023.38.e317.\u003c/li\u003e\n\u003cli\u003eLim YJ, Park SY. Increased prehospital emergency medical service time interval and nontransport rate of patients with fever using emergency medical services before and after COVID-19 in Busan, Korea. J Korean Med Sci. 2023;38(9):e69. doi:10.3346/jkms.2023.38.e69.\u003c/li\u003e\n\u003cli\u003eTopjian AA, Raymond TT, Atkins DL, et al. Part 4: Pediatric basic and advanced life support: 2020 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2020;142(16 Suppl 2):S469\u0026ndash;S523.\u003c/li\u003e\n\u003cli\u003eJewkes F, Woollard M. Assessment and management of paediatric primary survey positive patients. Emerg Med J. 2004;21(5):595\u0026ndash;605. doi:10.1136/emj.2004.017780.\u003c/li\u003e\n\u003cli\u003eSolazzo E, McCans K, Owusu-Ansah S, Williams KA. When Should EMS Call a Child a Small Adult: Inconsistency in Protocol Definitions. Int J Paramedicine. 2024;(6):171\u0026ndash;184. doi:10.56068/KCYD7018.\u003c/li\u003e\n\u003cli\u003eKiyohara K, Okubo M, Komukai S, Izawa J, Gibo K, Matsuyama T, et al. Association between resuscitative time on the scene and survival after pediatric out-of-hospital cardiac arrest. Circ Rep. 2021;3(4):211\u0026ndash;216. doi:10.1253/circrep.CR-21-0021.\u003c/li\u003e\n\u003cli\u003eTijssen JA, Prince DK, Morrison LJ, Atkins DL, Austin MA, Berg RA, et al. Time on the scene and interventions are associated with improved survival in pediatric out-of-hospital cardiac arrest. Resuscitation. 2015;94:1\u0026ndash;7. doi:10.1016/j.resuscitation.2015.06.012.\u003c/li\u003e\n\u003cli\u003eGoto Y, Funada A, Goto Y. Duration of prehospital cardiopulmonary resuscitation and favorable neurological outcomes for pediatric out-of-hospital cardiac arrests: a nationwide, population-based cohort study. Circulation. 2016;134(25):2046\u0026ndash;2059. doi:10.1161/CIRCULATIONAHA.116.023821.\u003c/li\u003e\n\u003cli\u003eShin SD, Kim J, Song KJ, et al. Epidemiology of pediatric emergency patients in Korea. J Korean Med Sci. 2010;25(7):991\u0026ndash;997.\u003c/li\u003e\n\u003cli\u003ePerkins GD, Jacobs IG, Nadkarni VM, Berg RA, Bhanji F, Biarent D, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry templates for out-of-hospital cardiac arrest. Resuscitation. 2015;96:328\u0026ndash;340. doi:10.1016/j.resuscitation.2014.11.002.\u003c/li\u003e\n\u003cli\u003eTham LP, Wah W, Phillips R, Shahidah N, Ng YY, Shin SD, et al. Epidemiology and outcome of paediatric out-of-hospital cardiac arrests: a paediatric sub-study of the Pan-Asian resuscitation outcomes study (PAROS). Resuscitation. 2018;125:111\u0026ndash;117. doi:10.1016/j.resuscitation.2018.01.040.\u003c/li\u003e\n\u003cli\u003eChoi A, Kim BJ, Lee J, Kim S, Bae W. Impact of the South Korean government\u0026rsquo;s medical school expansion announcement on pediatric emergency department visits. BMC Emerg Med. 2025;25:39. doi:10.1186/s12873-025-01189-w.\u003c/li\u003e\n\u003cli\u003eEssex R, et al. The impact of strike action on healthcare delivery: a scoping review. Int J Health Plann Manage. 2023;38(3):599\u0026ndash;627. doi:10.1002/hpm.3610.\u003c/li\u003e\n\u003cli\u003eAshburn NP, Hendley NW, Angi RM, et al. Prehospital trauma scene and transport times for pediatric and adult patients. West J Emerg Med. 2020;21(2):455\u0026ndash;462. doi:10.5811/westjem.2019.11.44597.\u003c/li\u003e\n\u003cli\u003ePandya A, et al. Pediatric outcomes of emergency medical services non-transport before and during the COVID-19 pandemic. West J Emerg Med. 2024;25(2):246\u0026ndash;253. doi:10.5811/westjem.18408.\u003c/li\u003e\n\u003cli\u003eKatayama Y, Kitamura T, Kiyohara K, et al. Factors associated with difficulty in hospital acceptance at the scene by emergency medical service personnel: a population-based study in Osaka City, Japan. BMJ Open. 2016;6(10):e013849. doi:10.1136/bmjopen-2016-013849.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics across study periods (Panel A: demographics; Panel B: EMS and clinical)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore\u0026nbsp;\u003cbr\u003e\u0026nbsp;resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter\u003cbr\u003e\u0026nbsp;resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar\u0026ndash;Apr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMay\u0026ndash;Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e112,527\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72,637\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93,087\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e125,180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27,134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e107,328\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98,602\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge,years\u003c/strong\u003e,median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(3-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3(2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2(2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eInfant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,665(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5,204(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,469(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11,592(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,210(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8,727(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,871(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eToddler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28,052(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12,706(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17,943(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27,953(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,559(27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22,383(20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18,597(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePreschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18,548(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10,210(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13,034(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19,293(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,806(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19,215(18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12,222(12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSchool-age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e26,897(23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18,266(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e24,045(25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e30,052(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5,509(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25,507(23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25,588(26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAdolescent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e32,365(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e26,251(36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31,596(33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36,290(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,050(26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31,496(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35,324(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e45,516(40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e29,559(40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38,621(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51,113(40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11,393(42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e44,258(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38,826(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaytime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61,876(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e41,827(57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e53,733(57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e66,704(53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14,002(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e57,374(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e54,607(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeekdays\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e79,584(70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e52,416(72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e66,390(71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e88,089(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18,723(69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e75,747(70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68,919(70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as n (%) unless otherwise specified. Age is presented as median (IQR).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePandemic period:\u0026nbsp;\u003c/em\u003e\u003cem\u003eMarch 2020\u003c/em\u003e\u003cem\u003e\u0026ndash;April 2023; Resignation period: Ma\u003c/em\u003e\u003cem\u003erch\u003c/em\u003e\u003cem\u003e\u0026nbsp;202\u003c/em\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e\u0026ndash;December 2024.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBaseline characteristics across study periods (Panel A: demographics; Panel B: EMS and clinical)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore\u0026nbsp;\u003cbr\u003e\u0026nbsp;resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter\u003cbr\u003e\u0026nbsp;resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 47px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar\u0026ndash;Apr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMay\u0026ndash;Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e112,527\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72,637\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93,087\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e125,180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27,134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e107,328\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e98,602\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e59,311(52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e34,497(47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51,227(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e77,854(62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17,777(65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68,178(63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e52,767(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFi\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003eld Triage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27,071(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14,866(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17,851(19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e26,717(21.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,385(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25,718(24.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10,111(10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSub-emergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35,353(31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22,337(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27,821(29.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40,653(32.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9,091(33.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36,308(33.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e23,122(23.5) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNon-emergency\u003c/p\u003e\n \u003cp\u003e(Latency-emergency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e48,831(43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e33,729(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42,789(46.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e56,617(45.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11,493(42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e44,562(41.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e64,893(65.8) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e47(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e33(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,225(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,665(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,593(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,153(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e160(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e698(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e445(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as n (%)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePandemic period:\u0026nbsp;\u003c/em\u003e\u003cem\u003eMarch 2020\u003c/em\u003e\u003cem\u003e\u0026ndash;April 2023; Resignation period: Ma\u003c/em\u003e\u003cem\u003erch\u003c/em\u003e\u003cem\u003e\u0026nbsp;202\u003c/em\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e\u0026ndash;December 2024.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Prehospital time intervals among transported pediatric patients, 2019\u0026ndash;2024\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"787\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime intervals, min\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter resignation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019 Mar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020 Mar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021 Mar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022 Mar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023 Mar\u0026ndash;Apr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023 May-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024 Mar-Dec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.23\u0026plusmn;6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10.38\u0026plusmn;9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.35\u0026plusmn;19.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10.49\u0026plusmn;7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.31\u0026plusmn;6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.45\u0026plusmn;6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e9.45\u0026plusmn;6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5.78\u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.56\u0026plusmn;7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.22\u0026plusmn;8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e9.80\u0026plusmn;9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8.40\u0026plusmn;7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8.07\u0026plusmn;7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10.30\u0026plusmn;9.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.58\u0026plusmn;10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.48\u0026plusmn;13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18.72\u0026plusmn;19.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16.64\u0026plusmn;14.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e15.40\u0026plusmn;12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e15.18\u0026plusmn;12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.52\u0026plusmn;13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e26.55\u0026plusmn;14.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e32.73\u0026plusmn;20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e41.17\u0026plusmn;33.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e36.89\u0026plusmn;20.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e33.09\u0026plusmn;16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e32.66\u0026plusmn;16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e35.23\u0026plusmn;19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as mean \u0026plusmn; standard deviation (minutes).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRTI = response time interval; STI = scene time interval; TTI = transport time interval; PTI = total prehospital time interval.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Interrupted time-series regression of monthly mean scene time interval (STI), 2019\u0026ndash;2024\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"429\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI (Lower-Upper)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e4.45 - 6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.10 - 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 step\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.57 - 3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.16 - 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResignation step\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.84 - 1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResignation slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.08 - 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eModel accounted for autocorrelation using an AR(1) error structure. Dependent variable was monthly mean STI (minutes). CI = confidence interval.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Summary of Difference-in-Differences (DID) analysis for scene time interval (STI)\u003c/strong\u003e \u003cstrong\u003e(pediatric vs adult)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Before\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean After\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDID Effect\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Pediatric vs Adult)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u0026ndash;COVID-19 vs COVID-19 period , Adult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e13.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e+1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u0026ndash;COVID-19 vs COVID-19 period , Pediatric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e+1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e+0.01(P = 0.850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore resignation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;vs\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eresignation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAdult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e12.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e+2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore resignation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evs\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eresignation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePediatric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e+2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.00(P = 0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 703px;\"\u003e\n \u003cp\u003e\u003cem\u003eValues are presented as mean STI (minutes). Pre and post periods were defined as follows: COVID-19 (pre: Mar\u0026ndash;Dec 2019; post: Mar 2020\u0026ndash;Apr 2023) and resignation (pre: May\u0026ndash;Dec 2023; post: Mar\u0026ndash;Dec 2024). DID models were adjusted for age, sex, weekday/weekend, daytime/nighttime, and metropolitan region (binary). DID = Difference-in-Differences; STI = scene time interval.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"emergency medical services, pediatric, prehospital care, response time, scene time, interrupted time series, difference-in-differences, COVID-19, health workforce, South Korea","lastPublishedDoi":"10.21203/rs.3.rs-8705037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8705037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth system crises, such as pandemics and workforce disruptions, can compromise emergency medical services capacity and time performance, and pediatric patients may be particularly vulnerable to these system-level disruptions. We assessed how the COVID-19 pandemic and the 2024 workforce crisis were associated with pediatric prehospital time performance and whether crisis-related changes differed between pediatric and adult transports.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective observational study using nationwide ambulance transport records from South Korea\u0026rsquo;s National Fire Agency (2019\u0026ndash;2024). Pediatric patients (\u0026lt;\u0026thinsp;18 years) transported between March and December of each year were included; adult transports (\u0026ge;\u0026thinsp;18 years) served as a comparator. Prespecified exposure periods were the COVID-19 pandemic (March 2020\u0026ndash;April 2023) and the workforce crisis (March\u0026ndash;December 2024). Outcomes were response, scene, transport, and total prehospital time intervals. We performed descriptive comparisons, interrupted time-series regression of monthly mean scene time to estimate level and trend changes at crisis onset, and difference-in-differences models to compare pediatric versus adult changes with covariate adjustment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 636,495 pediatric transports were analyzed. After COVID-19 onset, all prehospital time intervals increased, peaked in 2021\u0026ndash;2022, and improved in 2023. During the 2024 workforce crisis, total prehospital time increased again, driven predominantly by a subsequent increase in scene time, while other components showed less consistent worsening. This scene-time\u0026ndash;dominant pattern was consistent across major pediatric chief complaint categories. In interrupted time-series analysis, COVID-19 onset was associated with an immediate increase in monthly mean scene time (1.88 minutes; 95% confidence interval 0.57\u0026ndash;3.18), with no significant slope change thereafter. At the onset of the workforce crisis, no significant level or slope change in scene time was detected. In difference-in-differences analyses, scene time prolongation during both crises was similar in pediatric and adult transports.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBoth crises were associated with prolonged pediatric prehospital times, largely driven by increased scene time. Similar pediatric\u0026ndash;adult changes suggest system-wide strain affecting the interface between ambulance services and receiving hospitals, supporting crisis-responsive planning and routine monitoring of pediatric prehospital time performance.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Impact of system shocks on pediatric prehospital time performance: a nationwide EMS registry study from South Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 07:29:16","doi":"10.21203/rs.3.rs-8705037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-24T17:23:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25611061316782213921149859022005888365","date":"2026-03-16T14:48:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T09:29:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T06:14:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T06:24:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T06:24:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-01-27T02:35:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"595a70f6-c8f0-4d69-8416-a5711f341034","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T07:29:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 07:29:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8705037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8705037","identity":"rs-8705037","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}