Pediatric prehospital emergency care in Espírito Santo, Brazil (SAMU 192), 2020–2021: a cross-sectional study

Pediatric prehospital emergency care in Espírito Santo, Brazil (SAMU 192), 2020–2021: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pediatric prehospital emergency care in Espírito Santo, Brazil (SAMU 192), 2020–2021: a cross-sectional study LUCAS MOTA SCHERRER, FELIPE COUTINHO VIEIRA, GUSTAVO CHECON SCAQUETI, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7993172/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Brazil’s SAMU 192 is a physician-regulated prehospital system that delivers remote guidance and/or dispatches mobile units according to risk. In pediatrics, improving triage and destination is pivotal to shorten response times and match resources to clinical urgency. We aimed to describe the spatial distribution and the dispatch criticality profile of pediatric attendances regulated by SAMU 192 in Espírito Santo, Brazil, and to test associations with sociodemographic, dispatch, and destination variables. Methods We conducted a cross-sectional observational study using 2020–2021 records from the state Medical Dispatch Center (SAMU 192). We included pediatric attendances aged 1–19 years. Variables comprised age, sex, region, time of day and day of week of the call, call origin (home vs. out-of-home), type of incident, type of dispatched resource (basic vs. advanced life support), and destination. We performed descriptive analyses and chi-square tests (α = 0.05). Spatial distribution was mapped in GIS using the official cartographic base. Results Among 4,860 attendances, 24.4% were triaged as critical at dispatch. The relative proportion of critical cases increased in 2021 versus 2020. Higher criticality proportions were observed during daytime shifts. Spatial patterns were heterogeneous, with concentrations in urban areas. Clinical incidents and responses involving Advanced Life Support units exhibited higher urgency levels. An age gradient was noted, with greater relative severity among children aged 1–4 years. No differences were observed by sex, and call origin (home vs. out-of-home) was similar. Conclusions Findings support tailoring resource allocation by shift and territory, reinforcing pediatric clinical/dispatch protocols, and routinely applying geotechnologies for space–time surveillance. Future multivariable and spatiotemporal analyses could refine prioritization and guide operational planning within pediatric prehospital care. emergency medical services prehospital care child adolescent triage medical dispatch geographic information systems Brazil Figures Figure 1 Figure 2 Background Brazil’s Mobile Emergency Medical Service (SAMU 192) is the prehospital component of the national urgent and emergency care network, operating under physician-led medical dispatch to match response modality to clinical risk, either through remote guidance or the deployment of mobile units [ 1 ]. Within this framework, the distinction between Basic Life Support and Advanced Life Support units, together with coverage and staffing criteria, underpins resource allocation according to case severity [ 1 , 2 ]. In Espírito Santo state, SAMU 192 has undergone successive expansions in bases and territorial coverage over time, culminating in consolidated statewide coverage and increased fleet and teams, which enhanced the capillarity of the mobile component [ 2 – 5 ]. In parallel, the use of geotechnologies has been recommended to support planning and decision-making by identifying space–time patterns that guide the prioritization of areas and resources [ 6 ]. In pediatrics, two complementary challenges stand out: (i) improving dispatch triage and hospital destination for children and adolescents who may require higher-level pediatric resources, and (ii) reducing non-urgent and repeat attendances through educational strategies and integration with primary care [ 7 , 8 ]. For this study, the operational pediatric population is defined as individuals aged 1–19 years; details and justification are provided in the Methods, based on Brazilian legal/programmatic frameworks and the WHO reference for adolescence [ 9 – 11 ]. Aim. To describe the spatial distribution and the dispatch criticality profile of pediatric attendances regulated by SAMU 192 in Espírito Santo, Brazil, and to examine associations with sociodemographic, dispatch, and destination variables. Methods Study design and setting We conducted a cross-sectional observational study using administrative records from the Medical Dispatch Center of SAMU 192 in Espírito Santo, Brazil, covering calendar years 2020–2021. The center, located in Serra (ES), coordinates physician-regulated prehospital responses across the state [1,2]. Data source and governance Data were extracted from the state medical dispatch information system used by SAMU 192–ES for operational regulation. The dataset comprises call metadata, dispatch decisions, and prehospital dispositions recorded by trained staff at the time of the event. Access was granted under institutional agreements for research and subject to confidentiality safeguards. Population, inclusion and exclusion criteria We included primary scene responses (non-interfacility transfers) for pediatric patients aged 1–19 years, of both sexes, in which a mobile resource was dispatched (field team deployment). We excluded records with missing values in essential study variables (age, sex, type of incident, urgency level, dispatched resource, and destination), duplicated entries detected in audit checks, and any entries flagged as inadequately completed after data quality review. The final analytic N is reported in Results. Variables and operational definitions We pre-specified and extracted the following variables for analysis (names reflect the operational fields in the dispatch system): Year of call (2020; 2021). Age (in years), categorized into life-cycle bands used in the service: 1–4, 5–9, 10–14, 15–19 years. Sex (female; male). Region (operational clusters). For analyses, municipalities were aggregated into SAMU 192–ES operational clusters anchored by base municipalities, consistent with service coverage during 2018–2021. Specifically: R. Cariacica (Cariacica, Viana); R. Domingos Martins (Domingos Martins, Marechal Floriano); R. Guarapari (Guarapari, Anchieta, Piúma); R. Santa Teresa (Santa Teresa, Santa Maria de Jetibá, Itarana, Itaguaçu); R. Venda Nova do Imigrante (Venda Nova do Imigrante, Afonso Cláudio, Brejetuba); Serra (Serra, Fundão); Vila Velha (Vila Velha); Vitória (Vitória). When municipalities incorporated during late-2020 expansion appeared in 2021 records, they were grouped under the corresponding clusters: Conceição do Castelo, Ibatiba → R. Venda Nova do Imigrante; Santa Leopoldina, Laranja da Terra → R. Santa Teresa. This aggregation follows official coverage lists released by the State Health Department (SESA) for April and December 2020. Type of incident (categorical): External causes, Clinical, Gyneco-obstetric, Psychiatric. Call timing: Time of day (operational shift): Overnight (Madrugada), Morning (Matutino), Afternoon (Vespertino), Evening (Noturno), according to SAMU 192–ES operational scheduling. Day of week: Weekday (Mon–Fri) vs Weekend (Sat–Sun). Call origin: Home vs Out-of-home (any location other than the patient’s residence). Urgency level at dispatch (four-level ordinal triage recorded by the medical regulator): Level 1 = absolute priority, Level 2 = moderate priority, Level 3 = low priority, Level 4 = minimal priority. For primary analyses we defined Critical = Level 1, and Non-critical = Levels 2–4. This reflects operational dispatch urgency rather than clinical outcomes. Dispatched resource type (based on staffing and scope): ALS (USA)—physician, nurse, and driver; BLS (USB)—nursing technician and driver; Intermediate support (USI)—nurse or nursing technician and driver. Destination / prehospital disposition (mutually exclusive categories as recorded by the service): Hospital; Emergency care / UPA 24h (24-hour walk-in urgent care units and other urgent care centers); Released at scene (evaluated/treated and released without transport); Refusal of care (patient/guardian declined assessment or care on scene); Refusal of transport (care accepted, transport declined); Transported by third parties (e.g., private vehicle or non-SAMU transport); Other removal services (transport executed by another removal/transport service formally recorded in the system); Not located (team dispatched but unable to locate the patient/event); Other (miscellaneous dispositions not fitting the above); Deaths (prehospital) (death certified/recorded by prehospital providers prior to arrival at a facility). For transparency, prehospital death was captured both as a destination subcategory and as a binary variable (Deaths: yes/no) in the database. In tables, we present the destination breakdown and, for completeness, a separate cross-tabulation of Deaths (yes/no). Rationale for the above categories and staffing follows the Brazilian SAMU regulatory framework and local operational standards [1,2]. Data handling and quality checks We performed structured checks for range and consistency (e.g., age within 1–19 years, sex coding, presence of mandatory fields), removed exact duplicates, and documented missingness at the variable level. Records missing any essential study variable were excluded from inferential analyses and the final N is reported. Where percentages are presented, the denominator is shown and may vary by analysis according to valid data. Statistical analysis We summarized categorical variables as counts and percentages and quantitative variables (age) as means and percentiles. We evaluated associations between Urgency (Critical vs Non-critical) and covariates using Pearson’s chi-square tests (two-sided, α=0.05). When the overall test was significant, we inspected standardized residuals (|residual| > 1.96) to identify cells contributing to the association. Given the exploratory nature of bivariate analyses, no adjustment for multiple comparisons was applied. Where relevant, 95% confidence intervals for proportions were calculated using standard binomial methods. Data were tabulated in Microsoft Excel and analyzed in IBM SPSS Statistics, version 29 [12]. Results are displayed in tables and figures (bar/column charts, histograms, box plots) as appropriate. Spatial analysis Municipal-level counts and proportions were joined to official municipal boundary files from the Instituto Jones dos Santos Neves (IJSN) via each municipality’s geocode [14]. Maps were produced in QGIS (open-source GIS) [13]. We used quantile-based choropleth classification (pre-specified number of classes for interpretability) and proportional-symbol maps to delineate spatial concentration of events and highlight critical areas. Figures include legend/color key, scale bar, and north arrow; map captions cite data sources and software. Ethics This project is part of the umbrella study “Urgent and Emergency Care Network: SAMU 192 in Espírito Santo” and was approved by the Research Ethics Committee of EMESCAM (approval No. 4.308.858, 29 September 2020). The study followed Brazilian National Health Council Resolution 466/2012 for human subjects research [15]. Results During the biennium analyzed, a total of 4,860 pediatric attendances were recorded, of which 1,185 (24.4%) were triaged as critical. The proportion of critical cases was higher in 2021 than in 2020 (27.9% vs 20.8%, p<0.001). By time of day, attendances were Overnight 973 (20.0%), Morning 915 (18.8%), Afternoon 1,764 (36.3%), and Evening 1,208 (24.9%), with critical proportions of 21.0%, 26.7%, 26.6%, and 22.6%, respectively (p=0.001). By day of week, Sat–Sun accounted for 1,448 (29.8%) attendances with 23.1% critical, and Mon–Fri 3,412 (70.2%) with 24.9% critical (p=0.187). By region, counts were: R. Cariacica 1,203 (24.8%); R. Domingos Martins 99 (2.0%); R. Guarapari 480 (9.9%); R. Santa Teresa 164 (3.4%); R. Venda Nova do Imigrante 236 (4.9%); Serra 1,075 (22.1%); Vila Velha 1,023 (21.0%); Vitória 580 (11.9%) (p=0.003). Call origin was Home 3,425 (70.5%) with 24.2% critical versus Out-of-home 1,435 (29.5%) with 24.4% critical (p=0.708). See Table 1 for full distributions; municipal-level counts are visualized in Figure 1. Table 1. Sociodemographic and call-timing characteristics of pediatric SAMU 192 attendances (Espírito Santo, 2020–2021) Variable Population n (%) Critical n (%) Non-critical n (%) p Population/overall distribution Total 4860 1185 3675 Year <0.001 2020 2390 (49.2) 497 (20.8) 1893 (79.2)** 2021 2470 (50.8) 688 (27.9)** 1782 (72.1) Time of day (operational shift) 0.001 Overnight 973 (20.0) 204 (21.0) 769 (79.0)** Morning 915 (18.8) 244 (26.7)** 671 (73.3) Afternoon 1764 (36.3) 469 (26.6)** 1295 (73.4) Evening 1208 (24.9) 268 (22.6) 940 (77.8)** Day of week 0.187 Sat–Sun 1448 (29.8) 335 (23.1) 1113 (76.9) Mon–Fri 3412 (70.2) 850 (24.9) 2562 (75.1) Region 0.003 R. Cariacica 1203 (24.8) 322 (26.8)** 881 (73.2) R. Domingos Martins 99 (2.0) 17 (17.2) 82 (82.8) R. Guarapari 480 (9.9) 108 (22.5) 372 (77.5) R. Santa Teresa 164 (3.4) 28 (17.1) 136 (82.9)** R. Venda Nova do Imigrante 236 (4.9) 40 (16.9) 196 (83.1)** Serra 1075 (22.1) 256 (23.8) 819 (76.2) Vila Velha 1023 (21.0) 265 (25.9) 758 (74.1) Vitória 580 (11.9) 149 (25.7) 431 (74.3) Call origin 0.708 Home 3425 (70.5) 830 (24.2) 2595 (75.8) Out-of-home 1435 (29.5) 355 (24.4) 1080 (75.3) Notes: p-values from Pearson’s chi-square tests (two-sided). Cells with |standardized residual| > 1.96 are indicated by a double asterisk (**). Operational shifts: Overnight (Madrugada), Morning (Matutino), Afternoon (Vespertino), Evening (Noturno). Table 2. Incident type, dispatched resource, sex, age group, destination and death, by dispatch urgency (Espírito Santo, 2020–2021) Variable Population n (%) Critical n (%) Non-critical n (%) p Population/overall distribution Total 4860 1185 3675 Type of incident <0.001 External causes 1784 (36.7) 433 (24.3) 1351 (75.7) Clinical 1832 (37.7) 594 (32.4)** 1238 (67.6) Gyneco-obstetric 232 (4.8) 55 (23.7) 177 (76.3) Psychiatric 1012 (20.8) 103 (10.2) 909 (89.9)** Dispatched resource <0.001 ALS (USA) 536 (11.0) 323 (60.3)** 213 (39.7) BLS (USB) 4242 (87.3) 833 (19.6) 3409 (80.4)** Intermediate support (USI) 82 (1.7) 29 (35.4)** 53 (64.6) Sex 0.876 Female 2175 (44.8) 528 (24.3) 1647 (75.7) Male 2685 (55.2) 657 (24.5) 2028 (75.5) Age group <0.001 1–4 years 533 (11.0) 215 (40.3)** 318 (59.7) 5–9 years 499 (10.3) 156 (31.3)** 343 (68.7) 10–14 years 942 (19.4) 217 (23.0) 725 (77.0) 15–19 years 2886 (59.4) 597 (20.7) 2289 (79.3)** Destination <0.001 Other removal services 37 (0.8) 7 (18.9) 30 (81.1) Hospital 2314 (47.6) 541 (23.4) 1773 (76.6) Released at scene 121 (2.5) 47 (38.8)** 74 (61.2) Not located 151 (3.1) 30 (19.9) 121 (80.1) Deaths (prehospital) 40 (0.8) 40 (100.0)** 0 (0.0) Other 174 (3.6) 35 (20.1) 139 (79.9) Emergency care / UPA 24h 1105 (22.7) 257 (23.3) 848 (76.7) Refusal of care 159 (3.3) 25 (15.7) 134 (84.3)** Refusal of transport 236 (4.9) 53 (22.5) 183 (77.5) Transported by third parties 523 (10.8) 150 (28.7)** 373 (71.3) Deaths 1.96 are indicated by a double asterisk (**). Abbreviations: ALS (Advanced Life Support), BLS (Basic Life Support), USI (Intermediate Support), UPA (24-hour Emergency Unit). By type of incident, attendances were External causes 1,784 (36.7%), Clinical 1,832 (37.7%), Gyneco-obstetric 232 (4.8%), and Psychiatric 1,012 (20.8%), with critical proportions of 24.3%, 32.4%, 23.7%, and 10.2%, respectively (p<0.001). By dispatched resource, ALS (USA) responded to 536 (11.0%) attendances with 60.3% critical; BLS (USB) to 4,242 (87.3%) with 19.6% critical; and Intermediate support (USI) to 82 (1.7%) with 35.4% critical (p<0.001). By sex, female comprised 2,175 (44.8%) attendances with 24.3% critical, and male 2,685 (55.2%) with 24.5% critical (p=0.876). By age group, counts and critical proportions were 1–4 years 533 (11.0%), 40.3% critical; 5–9 years 499 (10.3%), 31.3% critical; 10–14 years 942 (19.4%), 23.0% critical; and 15–19 years 2,886 (59.4%), 20.7% critical (p<0.001). By destination, distributions were Hospital 2,314 (47.6%), 23.4% critical; Emergency care / UPA 24h 1,105 (22.7%), 23.3% critical; Released at scene 121 (2.5%), 38.8% critical; Refusal of care 159 (3.3%), 15.7% critical; Refusal of transport 236 (4.9%), 22.5% critical; Transported by third parties 523 (10.8%), 28.7% critical; Other removal services 37 (0.8%), 18.9% critical; Not located 151 (3.1%), 19.9% critical; Other 174 (3.6%), 20.1% critical; and Deaths (prehospital) 40 (0.8%), 100.0% critical (p<0.001). For completeness, the separate Deaths variable showed 0.8% overall mortality (40/4,860), all of which were triaged as critical (p<0.001). See Table 2 for full distributions and abbreviations; Figure 2a–2d depicts municipal-level patterns by dispatch urgency level. Notes (as per tables): p-values from Pearson’s chi-square tests (two-sided). Cells contributing most to significant associations are indicated in the tables by |standardized residual| > 1.96. Table 1 covers sociodemographic, temporal, regional, and origin variables; Table 2 covers incident type, dispatched resource, sex, age, destination, and deaths. Discussion This analysis showed a relative increase in criticality in the second year of the biennium, a higher proportion of critical cases during daytime shifts, territorial heterogeneity with greater burden in urban areas, predominance of severity in clinical incidents, a higher concentration of critical cases in occurrences attended by Advanced Life Support units (USA), and an age gradient with greater relative severity among younger children. Taken together, these findings configure a care profile that is coherent with the logic of physician-led medical regulation and the organization of the mobile component of Brazil’s urgent and emergency care network. In Brazil, medical regulation directs the allocation of the resource appropriate to risk, distinguishing USB (Basic Life Support, for patients without an expected need for medical intervention en route) from USA (Advanced Life Support for high-risk care/transport, staffed by a physician and a nurse) and recommending population-coverage parameters by vehicle type—guidelines that help explain the higher proportion of criticality among occurrences attended by USA observed in this study [1]. The relative increase in criticality observed between years may reflect both contextual changes (e.g., system pressures and seasonal variation) and the period of SAMU–ES expansion. In 2017, service coverage reached about 56.8% of the population, with 22 USB and 9 USA in operation and gradual municipal expansion [2]. In April 2020, new bases expanded the service to five additional municipalities and the estimated population coverage reached 56% [3]. By the end of 2022, the state announced 100% population coverage, with expansion of bases and fleet across the territory [5]. In summary, the period analyzed is embedded in a trajectory of expansion, which can affect the case-mix and the availability of resources across regions and shifts. Regarding spatial distribution, the territorial pattern with higher density of occurrences in urban areas and heterogeneity among regions aligns with literature recommending the use of geotechnologies (GIS) for planning, prioritization, and decision-making in SAMU, integrating spatiotemporal analyses to guide the allocation of bases and resources [6]. From this perspective, the findings reinforce the utility of regular mapping to identify operational hotspots and support management decisions. Considering clinical profile and urgency level, the greater relative severity in clinical incidents and the concentration of critical cases in occurrences attended by USA are consistent with the operational design of the mobile component. National guidelines define referral through regulation and differential staffing/dimensioning between USB and USA, with specific roles and crews for high risk [1]. These organizational frameworks help interpret why advanced support aggregates, proportionally, more severe presentations in routine operations. With respect to the higher participation of critical cases during daytime shifts, this may reflect greater population exposure and service flow during those periods. Across the life course, the greater relative severity among 1–4-year-olds is compatible with the need for pediatric-specific criteria in dispatch triage and destination, given that small physiological variations can have greater impact on pediatric outcomes. In this sense, international initiatives propose consensus standards to identify children who require higher-level pediatric resources and to guide appropriate hospital destination—an axis that directly aligns with regulation and prehospital practice [7]. In addition, systematic reviews suggest that interventions aimed at reducing non-urgent and repeat pediatric demand can decompress peaks and optimize response to true risk, which is pertinent to the observed time-of-day asymmetry [8]. In light of the above, the results support: (i) adjustments in allocation by shift and territory (bases/vehicles, especially USA where criticality is higher); (ii) systematic use of geotechnologies for spatiotemporal surveillance and prioritization of critical areas; (iii) reinforcement of clinical and regulatory protocols with pediatric and medical-clinical focus; and (iv) integration with primary care and educational strategies to reduce non-urgent demand, particularly at times of greater pressure [1,6,8]. Interpretation is conditioned by the observational design and by the use of dispatch urgency level—that is, criticality as operational classification by the medical regulator—while the analysis was bivariate and may not capture confounding factors. The series reflect a period of service expansion in the state—from partial coverage (2017–2020) toward full coverage (2022)—which may influence the distribution of cases by region and shift [2,3,5]. Conclusions This study characterized the profile and distribution of dispatch criticality during the analyzed period, meeting the stated objectives. We observed a relative increase in severity in the second year; a higher proportion of critical cases during daytime shifts; territorial heterogeneity with greater burden in urban areas; a predominance of clinical incidents among the most severe presentations; a higher concentration of critical cases in occurrences attended by Advanced Life Support (ALS/USA); and an age gradient with greater vulnerability in younger age groups. Taken together, these findings delineate the service’s care panorama and reinforce the need for planning aligned with real demand, without introducing data beyond those presented. The results provide an objective basis for resource sizing and allocation, refinement of clinical and regulatory protocols, and ongoing monitoring. They also motivate further analyses with adjustment for potential confounders. We recommend advancing with multivariable models and spatiotemporal analyses to estimate independent effects and to inform public policy and operational decision-making. Abbreviations ALS Advanced Life Support BLS Basic Life Support USA Unidade de Suporte Avançado (ALS unit) USB Unidade de Suporte Básico (BLS unit) USI Unidade de Suporte Intermediário UPA 24h 24-hour emergency unit GIS geographic information system SAMU Serviço de Atendimento Móvel de Urgência. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee (Comitê de Ética em Pesquisa) of Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Brazil (CAAE: 36389420.0.0000.5065; approval number: 4.308.858). Given the use of de-identified administrative dispatch records and the impracticability of obtaining individual consent, the Committee waived the requirement for informed consent. The study was conducted in accordance with relevant guidelines and regulations. Consent for publication Not applicable. The manuscript does not contain any individual person’s data (images, videos, or other identifying details). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Undergraduate Research Program (PIVIC/EMESCAM). The funder had no role in study design; data collection, analysis, or interpretation; writing of the report; or the decision to submit the article for publication. Author Contribution LMS: conceptualization, methodology, data curation, formal analysis, visualization, writing—original draft.FCV: data curation, validation, visualization, writing—review & editing.GCS: data curation, validation, visualization, writing—review & editing.RFLS: data curation, validation, visualization, writing—review & editing.JVLO: supervision, methodology, project administration, writing—review & editing.All authors meet ICMJE criteria for authorship, read and approved the final manuscript, and agree to be accountable for all aspects of the work. Acknowledgements We thank the SAMU 192 – Espírito Santo operations and medical dispatch teams for facilitating access to de-identified administrative data. We acknowledge EMESCAM for fostering research through its Scientific Initiation Program (PIVIC). We are grateful to Prof. Caio Duarte Neto, MSc, for coordinating, supporting, and sustaining this project; to the Multidisciplinary Research Group on the Urgent and Emergency Care Network for their support; to Prof. Lucia Helena Sagrillo Pimassoni for conducting the statistical analyses; and to Igor Cardozo Boim, EMESCAM medical student, for assistance with map development. All acknowledged parties provided permission to be named. Data Availability De-identified administrative dispatch records from SAMU 192 – Espírito Santo (SAMU-ES) were used under institutional agreements and are not publicly available due to legal and privacy restrictions (Brazilian General Data Protection Law—LGPD) and data-owner policies. The dataset is stored on secure internal servers at EMESCAM / Santa Casa de Misericórdia de Vitória and may be shared upon reasonable request and subject to: (i) prior permission from SAMU-ES and the Espírito Santo State Health Secretariat; (ii) Research Ethics Committee approval (CAAE 36389420.0.0000.5065; approval No. 4.308.858); and (iii) execution of a data use agreement. Requests should be directed to the corresponding author at [email protected] . Aggregate data supporting the findings are provided within the manuscript (tables/figures). References Brasil. Ministério da Saúde. Política nacional de atenção às urgências. 3ª ed. Brasília: Ministério da Saúde; 2006. [Portuguese]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_atencao_urgencias_3ed.pdf . Accessed 15 Sep 2025. Oliveira JVL. Política nacional de atenção às urgências: regulação no cuidado do paciente idoso atendido pelo SAMU 192 no Espírito Santo [dissertation]. 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Vitória: SESA. 2023 Sep 14 [cited 2025 Sep 15]. Available from: https://saude.es.gov.br/Not%C3%ADcia/nova-frota-de-ambulancias-do-samu-192-chega-para-fortalecer-atendimento-no-estado Sogame LMC et al. Geotecnologias no Serviço de Atendimento Móvel de Urgência no ES. Vitória (ES): Editora EMESCAM; 2020. ISBN: 978-65-88041-04-8. [Portuguese]. Available from: https://www.editoraemescam.com.br/wp-content/uploads/2021/07/GEOTECNOLOGIAS-NO-SERVICO-DE-ATENDIMENTO-MOVEL-DE-URGENCIA-NO-ES-%E2%80%93-ISBN-on-line-978-65-88041-04-8.pdf . Accessed 15 Sep 2025. Studnek JR, Lerner EB, Shah MI, Browne LR, Brousseau DC, Cushman JT, et al. Consensus-based criterion standard for the identification of pediatric patients who need EMS transport to a hospital with higher-level pediatric resources. Acad Emerg Med. 2018;25(12):1409–14. 10.1111/acem.13625 . Poku BA, Hemingway P. Reducing repeat paediatric emergency department attendance for non-urgent care: a systematic review of the effectiveness of interventions. Emerg Med J. 2019;36(7):435–42. 10.1136/emermed-2018-207536 . Brasil. Lei nº 8.069, de 13 de julho de 1990 (Estatuto da Criança e do Adolescente) [Internet]. Brasília: Governo Federal; 1990 [cited 2025 Sep 15]. Available from: https://www.gov.br/mj/pt-br/assuntos/seus-direitos/classificacao-1/legislacao/eca.pdf Brasil. Ministério da Saúde. Portaria nº 1.130, de 5 de agosto de 2015: institui a Política Nacional de Atenção Integral à Saúde da Criança (PNAISC) [Internet]. Brasília: Ministério da Saúde; 2015 [cited 2025 Sep 15]. Available from: https://bvsms.saude.gov.br/bvs/saudelegis/gm/2015/prt1130_05_08_2015.html World Health Organization. Health for the world’s adolescents: a second chance in the second decade [Internet]. Geneva: WHO. 2014 [cited 2025 Sep 15]. Available from: https://www.who.int/publications/i/item/WHO-FWC-MCA-14.05 IBM Corp. IBM SPSS Statistics for Windows, Version 29.0. Armonk, NY: IBM Corp; 2022. QGIS Development Team. QGIS Geographic Information System. Version 3.x. Open Source Geospatial Foundation Project. 2023. Available from: https://qgis.org . Accessed 15 Sep 2025. Instituto Jones dos Santos Neves (IJSN). Limites municipais do Espírito Santo: base cartográfica oficial [Internet]. Vitória: IJSN; [year unknown] [cited 2025 Sep 15]. Available from: https://www.ijsn.es.gov.br/ (acessar seção de dados geográficos/cartografia oficial para o shapefile). Brasil. Conselho Nacional de Saúde. Resolução nº 466, de 12 de dezembro de 2012: Diretrizes e normas regulamentadoras de pesquisas envolvendo seres humanos [Internet]. Brasília: CNS. 2012 [cited 2025 Sep 15]. Available from: https://conselho.saude.gov.br/resolucoes/2012/Reso466.pdf Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":666529,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1spatialdistributionES2020202111.png","url":"https://assets-eu.researchsquare.com/files/rs-7993172/v1/b56180ebcb8498c6355a97ea.png"},{"id":97266226,"identity":"a72f0818-0d97-4b72-8473-59d0bde3ce7e","added_by":"auto","created_at":"2025-12-02 14:33:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":837392,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1spatialdistributionES2020202112.png","url":"https://assets-eu.researchsquare.com/files/rs-7993172/v1/10f92c34b58453acd3f2e111.png"},{"id":99316275,"identity":"37054539-a63b-4080-b868-7d295526522c","added_by":"auto","created_at":"2025-12-31 16:28:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2444188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7993172/v1/5cbee56e-72ed-4ef6-8bcd-ac55a296c824.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pediatric prehospital emergency care in Espírito Santo, Brazil (SAMU 192), 2020–2021: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eBrazil\u0026rsquo;s Mobile Emergency Medical Service (SAMU 192) is the prehospital component of the national urgent and emergency care network, operating under physician-led medical dispatch to match response modality to clinical risk, either through remote guidance or the deployment of mobile units [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Within this framework, the distinction between Basic Life Support and Advanced Life Support units, together with coverage and staffing criteria, underpins resource allocation according to case severity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Esp\u0026iacute;rito Santo state, SAMU 192 has undergone successive expansions in bases and territorial coverage over time, culminating in consolidated statewide coverage and increased fleet and teams, which enhanced the capillarity of the mobile component [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In parallel, the use of geotechnologies has been recommended to support planning and decision-making by identifying space\u0026ndash;time patterns that guide the prioritization of areas and resources [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn pediatrics, two complementary challenges stand out: (i) improving dispatch triage and hospital destination for children and adolescents who may require higher-level pediatric resources, and (ii) reducing non-urgent and repeat attendances through educational strategies and integration with primary care [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For this study, the operational pediatric population is defined as individuals aged 1\u0026ndash;19 years; details and justification are provided in the Methods, based on Brazilian legal/programmatic frameworks and the WHO reference for adolescence [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAim. To describe the spatial distribution and the dispatch criticality profile of pediatric attendances regulated by SAMU 192 in Esp\u0026iacute;rito Santo, Brazil, and to examine associations with sociodemographic, dispatch, and destination variables.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional observational study using administrative records from the Medical Dispatch Center of SAMU 192 in Esp\u0026iacute;rito Santo, Brazil, covering calendar years 2020\u0026ndash;2021. The center, located in Serra (ES), coordinates physician-regulated prehospital responses across the state [1,2].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData source and governance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were extracted from the state medical dispatch information system used by SAMU 192\u0026ndash;ES for operational regulation. The dataset comprises call metadata, dispatch decisions, and prehospital dispositions recorded by trained staff at the time of the event. Access was granted under institutional agreements for research and subject to confidentiality safeguards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation, inclusion and exclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included primary scene responses (non-interfacility transfers) for pediatric patients aged 1\u0026ndash;19 years, of both sexes, in which a mobile resource was dispatched (field team deployment). We excluded records with missing values in essential study variables (age, sex, type of incident, urgency level, dispatched resource, and destination), duplicated entries detected in audit checks, and any entries flagged as inadequately completed after data quality review. The final analytic N is reported in Results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and operational definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe pre-specified and extracted the following variables for analysis (names reflect the operational fields in the dispatch system):\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eYear of call (2020; 2021).\u003c/li\u003e\n \u003cli\u003eAge (in years), categorized into life-cycle bands used in the service: 1\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14, 15\u0026ndash;19 years.\u003c/li\u003e\n \u003cli\u003eSex (female; male).\u003c/li\u003e\n \u003cli\u003eRegion (operational clusters). For analyses, municipalities were aggregated into SAMU 192\u0026ndash;ES operational clusters anchored by base municipalities, consistent with service coverage during 2018\u0026ndash;2021. Specifically: R. Cariacica (Cariacica, Viana); R. Domingos Martins (Domingos Martins, Marechal Floriano); R. Guarapari (Guarapari, Anchieta, Pi\u0026uacute;ma); R. Santa Teresa (Santa Teresa, Santa Maria de Jetib\u0026aacute;, Itarana, Itagua\u0026ccedil;u); R. Venda Nova do Imigrante (Venda Nova do Imigrante, Afonso Cl\u0026aacute;udio, Brejetuba); Serra (Serra, Fund\u0026atilde;o); Vila Velha (Vila Velha); Vit\u0026oacute;ria (Vit\u0026oacute;ria). When municipalities incorporated during late-2020 expansion appeared in 2021 records, they were grouped under the corresponding clusters: Concei\u0026ccedil;\u0026atilde;o do Castelo, Ibatiba \u0026rarr; R. Venda Nova do Imigrante; Santa Leopoldina, Laranja da Terra \u0026rarr; R. Santa Teresa. This aggregation follows official coverage lists released by the State Health Department (SESA) for April and December 2020.\u003c/li\u003e\n \u003cli\u003eType of incident (categorical): External causes, Clinical, Gyneco-obstetric, Psychiatric.\u003c/li\u003e\n \u003cli\u003eCall timing:\u003cul type=\"circle\"\u003e\n \u003cli\u003eTime of day (operational shift): Overnight (Madrugada), Morning (Matutino), Afternoon (Vespertino), Evening (Noturno), according to SAMU 192\u0026ndash;ES operational scheduling.\u003c/li\u003e\n \u003cli\u003eDay of week: Weekday (Mon\u0026ndash;Fri) vs Weekend (Sat\u0026ndash;Sun).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eCall origin: Home vs Out-of-home (any location other than the patient\u0026rsquo;s residence).\u003c/li\u003e\n \u003cli\u003eUrgency level at dispatch (four-level ordinal triage recorded by the medical regulator): Level 1 = absolute priority, Level 2 = moderate priority, Level 3 = low priority, Level 4 = minimal priority. For primary analyses we defined Critical = Level 1, and Non-critical = Levels 2\u0026ndash;4. This reflects operational dispatch urgency rather than clinical outcomes.\u003c/li\u003e\n \u003cli\u003eDispatched resource type (based on staffing and scope):\u003cul type=\"circle\"\u003e\n \u003cli\u003eALS (USA)\u0026mdash;physician, nurse, and driver;\u003c/li\u003e\n \u003cli\u003eBLS (USB)\u0026mdash;nursing technician and driver;\u003c/li\u003e\n \u003cli\u003eIntermediate support (USI)\u0026mdash;nurse or nursing technician and driver.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eDestination / prehospital disposition (mutually exclusive categories as recorded by the service):\u003cul type=\"circle\"\u003e\n \u003cli\u003eHospital;\u003c/li\u003e\n \u003cli\u003eEmergency care / UPA 24h (24-hour walk-in urgent care units and other urgent care centers);\u003c/li\u003e\n \u003cli\u003eReleased at scene (evaluated/treated and released without transport);\u003c/li\u003e\n \u003cli\u003eRefusal of care (patient/guardian declined assessment or care on scene);\u003c/li\u003e\n \u003cli\u003eRefusal of transport (care accepted, transport declined);\u003c/li\u003e\n \u003cli\u003eTransported by third parties (e.g., private vehicle or non-SAMU transport);\u003c/li\u003e\n \u003cli\u003eOther removal services (transport executed by another removal/transport service formally recorded in the system);\u003c/li\u003e\n \u003cli\u003eNot located (team dispatched but unable to locate the patient/event);\u003c/li\u003e\n \u003cli\u003eOther (miscellaneous dispositions not fitting the above);\u003c/li\u003e\n \u003cli\u003eDeaths (prehospital) (death certified/recorded by prehospital providers prior to arrival at a facility).\u003cbr\u003e\u0026nbsp;For transparency, prehospital death was captured both as a destination subcategory and as a binary variable (Deaths: yes/no) in the database. In tables, we present the destination breakdown and, for completeness, a separate cross-tabulation of Deaths (yes/no).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRationale for the above categories and staffing follows the Brazilian SAMU regulatory framework and local operational standards [1,2].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData handling and quality checks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed structured checks for range and consistency (e.g., age within 1\u0026ndash;19 years, sex coding, presence of mandatory fields), removed exact duplicates, and documented missingness at the variable level. Records missing any essential study variable were excluded from inferential analyses and the final N is reported. Where percentages are presented, the denominator is shown and may vary by analysis according to valid data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe summarized categorical variables as counts and percentages and quantitative variables (age) as means and percentiles. We evaluated associations between Urgency (Critical vs Non-critical) and covariates using Pearson\u0026rsquo;s chi-square tests (two-sided, \u0026alpha;=0.05). When the overall test was significant, we inspected standardized residuals (|residual| \u0026gt; 1.96) to identify cells contributing to the association. Given the exploratory nature of bivariate analyses, no adjustment for multiple comparisons was applied. Where relevant, 95% confidence intervals for proportions were calculated using standard binomial methods. Data were tabulated in Microsoft Excel and analyzed in IBM SPSS Statistics, version 29 [12]. Results are displayed in tables and figures (bar/column charts, histograms, box plots) as appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMunicipal-level counts and proportions were joined to official municipal boundary files from the Instituto Jones dos Santos Neves (IJSN) via each municipality\u0026rsquo;s geocode [14]. Maps were produced in QGIS (open-source GIS) [13]. We used quantile-based choropleth classification (pre-specified number of classes for interpretability) and proportional-symbol maps to delineate spatial concentration of events and highlight critical areas. Figures include legend/color key, scale bar, and north arrow; map captions cite data sources and software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is part of the umbrella study \u0026ldquo;Urgent and Emergency Care Network: SAMU 192 in Esp\u0026iacute;rito Santo\u0026rdquo; and was approved by the Research Ethics Committee of EMESCAM (approval No. 4.308.858, 29 September 2020). The study followed Brazilian National Health Council Resolution 466/2012 for human subjects research [15].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the biennium analyzed, a total of 4,860 pediatric attendances were recorded, of which 1,185 (24.4%) were triaged as critical. The proportion of critical cases was higher in 2021 than in 2020 (27.9% vs 20.8%, p\u0026lt;0.001). By time of day, attendances were Overnight 973 (20.0%), Morning 915 (18.8%), Afternoon 1,764 (36.3%), and Evening 1,208 (24.9%), with critical proportions of 21.0%, 26.7%, 26.6%, and 22.6%, respectively (p=0.001). By day of week, Sat\u0026ndash;Sun accounted for 1,448 (29.8%) attendances with 23.1% critical, and Mon\u0026ndash;Fri 3,412 (70.2%) with 24.9% critical (p=0.187). By region, counts were: R. Cariacica 1,203 (24.8%); R. Domingos Martins 99 (2.0%); R. Guarapari 480 (9.9%); R. Santa Teresa 164 (3.4%); R. Venda Nova do Imigrante 236 (4.9%); Serra 1,075 (22.1%); Vila Velha 1,023 (21.0%); Vit\u0026oacute;ria 580 (11.9%) (p=0.003). Call origin was Home 3,425 (70.5%) with 24.2% critical versus Out-of-home 1,435 (29.5%) with 24.4% critical (p=0.708). See Table 1 for full distributions; municipal-level counts are visualized in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Sociodemographic and call-timing characteristics of pediatric SAMU 192 attendances (Esp\u0026iacute;rito Santo, 2020\u0026ndash;2021)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-critical n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation/overall distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2390 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e497 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1893 (79.2)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2470 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e688 (27.9)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1782 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime of day (operational shift)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOvernight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e973 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e204 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e769 (79.0)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e915 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e244 (26.7)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e671 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAfternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1764 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e469 (26.6)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1295 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEvening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1208 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e268 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e940 (77.8)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay of week\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.187\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSat\u0026ndash;Sun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1448 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e335 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1113 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMon\u0026ndash;Fri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3412 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e850 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2562 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR. Cariacica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1203 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e322 (26.8)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e881 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR. Domingos Martins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e99 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e17 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e82 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR. Guarapari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e480 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e108 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e372 (77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR. Santa Teresa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e164 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e28 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e136 (82.9)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR. Venda Nova do Imigrante\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e236 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e196 (83.1)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSerra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1075 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e256 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e819 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eVila Velha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1023 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e265 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e758 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eVit\u0026oacute;ria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e580 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e149 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e431 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.708\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3425 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e830 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2595 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOut-of-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1435 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e355 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1080 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: p-values from Pearson\u0026rsquo;s chi-square tests (two-sided). Cells with |standardized residual| \u0026gt; 1.96 are indicated by a double asterisk (**). Operational shifts: Overnight (Madrugada), Morning (Matutino), Afternoon (Vespertino), Evening (Noturno).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Incident type, dispatched resource, sex, age group, destination and death, by dispatch urgency (Esp\u0026iacute;rito Santo, 2020\u0026ndash;2021)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-critical n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation/overall distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of incident\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eExternal causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1784 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e433 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1351 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1832 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e594 (32.4)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1238 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGyneco-obstetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e232 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e55 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e177 (76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ePsychiatric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1012 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e103 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e909 (89.9)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDispatched resource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eALS (USA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e536 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e323 (60.3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e213 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eBLS (USB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4242 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e833 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3409 (80.4)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIntermediate support (USI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e82 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e29 (35.4)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e53 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.876\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2175 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e528 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1647 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2685 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e657 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2028 (75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1\u0026ndash;4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e533 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e215 (40.3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e318 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e499 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e156 (31.3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e343 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10\u0026ndash;14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e942 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e217 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e725 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2886 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e597 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2289 (79.3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDestination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOther removal services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e37 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e7 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e30 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2314 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e541 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1773 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eReleased at scene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e121 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e47 (38.8)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e74 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNot located\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e151 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e30 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e121 (80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eDeaths (prehospital)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (100.0)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e174 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e35 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e139 (79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEmergency care / UPA 24h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1105 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e257 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e848 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eRefusal of care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e159 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e25 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e134 (84.3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eRefusal of transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e236 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e53 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e183 (77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTransported by third parties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e523 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e150 (28.7)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e373 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 461px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4820 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1145 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3675 (76.2)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (100.0)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: p-values from Pearson\u0026rsquo;s chi-square tests (two-sided). Cells with |standardized residual| \u0026gt; 1.96 are indicated by a double asterisk (**). Abbreviations: ALS (Advanced Life Support), BLS (Basic Life Support), USI (Intermediate Support), UPA (24-hour Emergency Unit).\u003c/p\u003e\n\u003cp\u003eBy type of incident, attendances were External causes 1,784 (36.7%), Clinical 1,832 (37.7%), Gyneco-obstetric 232 (4.8%), and Psychiatric 1,012 (20.8%), with critical proportions of 24.3%, 32.4%, 23.7%, and 10.2%, respectively (p\u0026lt;0.001). By dispatched resource, ALS (USA) responded to 536 (11.0%) attendances with 60.3% critical; BLS (USB) to 4,242 (87.3%) with 19.6% critical; and Intermediate support (USI) to 82 (1.7%) with 35.4% critical (p\u0026lt;0.001). By sex, female comprised 2,175 (44.8%) attendances with 24.3% critical, and male 2,685 (55.2%) with 24.5% critical (p=0.876). By age group, counts and critical proportions were 1\u0026ndash;4 years 533 (11.0%), 40.3% critical; 5\u0026ndash;9 years 499 (10.3%), 31.3% critical; 10\u0026ndash;14 years 942 (19.4%), 23.0% critical; and 15\u0026ndash;19 years 2,886 (59.4%), 20.7% critical (p\u0026lt;0.001). By destination, distributions were Hospital 2,314 (47.6%), 23.4% critical; Emergency care / UPA 24h 1,105 (22.7%), 23.3% critical; Released at scene 121 (2.5%), 38.8% critical; Refusal of care 159 (3.3%), 15.7% critical; Refusal of transport 236 (4.9%), 22.5% critical; Transported by third parties 523 (10.8%), 28.7% critical; Other removal services 37 (0.8%), 18.9% critical; Not located 151 (3.1%), 19.9% critical; Other 174 (3.6%), 20.1% critical; and Deaths (prehospital) 40 (0.8%), 100.0% critical (p\u0026lt;0.001). For completeness, the separate Deaths variable showed 0.8% overall mortality (40/4,860), all of which were triaged as critical (p\u0026lt;0.001). See Table 2 for full distributions and abbreviations; Figure 2a\u0026ndash;2d depicts municipal-level patterns by dispatch urgency level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes (as per tables):\u003c/em\u003e p-values from Pearson\u0026rsquo;s chi-square tests (two-sided). Cells contributing most to significant associations are indicated in the tables by |standardized residual| \u0026gt; 1.96. Table 1 covers sociodemographic, temporal, regional, and origin variables; Table 2 covers incident type, dispatched resource, sex, age, destination, and deaths.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis analysis showed a relative increase in criticality in the second year of the biennium, a higher proportion of critical cases during daytime shifts, territorial heterogeneity with greater burden in urban areas, predominance of severity in clinical incidents, a higher concentration of critical cases in occurrences attended by Advanced Life Support units (USA), and an age gradient with greater relative severity among younger children. Taken together, these findings configure a care profile that is coherent with the logic of physician-led medical regulation and the organization of the mobile component of Brazil\u0026rsquo;s urgent and emergency care network. In Brazil, medical regulation directs the allocation of the resource appropriate to risk, distinguishing USB (Basic Life Support, for patients without an expected need for medical intervention en route) from USA (Advanced Life Support for high-risk care/transport, staffed by a physician and a nurse) and recommending population-coverage parameters by vehicle type\u0026mdash;guidelines that help explain the higher proportion of criticality among occurrences attended by USA observed in this study [1].\u003c/p\u003e\n\u003cp\u003eThe relative increase in criticality observed between years may reflect both contextual changes (e.g., system pressures and seasonal variation) and the period of SAMU\u0026ndash;ES expansion. In 2017, service coverage reached about 56.8% of the population, with 22 USB and 9 USA in operation and gradual municipal expansion [2]. In April 2020, new bases expanded the service to five additional municipalities and the estimated population coverage reached 56% [3]. By the end of 2022, the state announced 100% population coverage, with expansion of bases and fleet across the territory [5]. In summary, the period analyzed is embedded in a trajectory of expansion, which can affect the case-mix and the availability of resources across regions and shifts.\u003c/p\u003e\n\u003cp\u003eRegarding spatial distribution, the territorial pattern with higher density of occurrences in urban areas and heterogeneity among regions aligns with literature recommending the use of geotechnologies (GIS) for planning, prioritization, and decision-making in SAMU, integrating spatiotemporal analyses to guide the allocation of bases and resources [6]. From this perspective, the findings reinforce the utility of regular mapping to identify operational hotspots and support management decisions.\u003c/p\u003e\n\u003cp\u003eConsidering clinical profile and urgency level, the greater relative severity in clinical incidents and the concentration of critical cases in occurrences attended by USA are consistent with the operational design of the mobile component. National guidelines define referral through regulation and differential staffing/dimensioning between USB and USA, with specific roles and crews for high risk [1]. These organizational frameworks help interpret why advanced support aggregates, proportionally, more severe presentations in routine operations.\u003c/p\u003e\n\u003cp\u003eWith respect to the higher participation of critical cases during daytime shifts, this may reflect greater population exposure and service flow during those periods. Across the life course, the greater relative severity among 1\u0026ndash;4-year-olds is compatible with the need for pediatric-specific criteria in dispatch triage and destination, given that small physiological variations can have greater impact on pediatric outcomes. In this sense, international initiatives propose consensus standards to identify children who require higher-level pediatric resources and to guide appropriate hospital destination\u0026mdash;an axis that directly aligns with regulation and prehospital practice [7]. In addition, systematic reviews suggest that interventions aimed at reducing non-urgent and repeat pediatric demand can decompress peaks and optimize response to true risk, which is pertinent to the observed time-of-day asymmetry [8].\u003c/p\u003e\n\u003cp\u003eIn light of the above, the results support: (i) adjustments in allocation by shift and territory (bases/vehicles, especially USA where criticality is higher); (ii) systematic use of geotechnologies for spatiotemporal surveillance and prioritization of critical areas; (iii) reinforcement of clinical and regulatory protocols with pediatric and medical-clinical focus; and (iv) integration with primary care and educational strategies to reduce non-urgent demand, particularly at times of greater pressure [1,6,8].\u003c/p\u003e\n\u003cp\u003eInterpretation is conditioned by the observational design and by the use of dispatch urgency level\u0026mdash;that is, criticality as operational classification by the medical regulator\u0026mdash;while the analysis was bivariate and may not capture confounding factors. The series reflect a period of service expansion in the state\u0026mdash;from partial coverage (2017\u0026ndash;2020) toward full coverage (2022)\u0026mdash;which may influence the distribution of cases by region and shift [2,3,5].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study characterized the profile and distribution of dispatch criticality during the analyzed period, meeting the stated objectives. We observed a relative increase in severity in the second year; a higher proportion of critical cases during daytime shifts; territorial heterogeneity with greater burden in urban areas; a predominance of clinical incidents among the most severe presentations; a higher concentration of critical cases in occurrences attended by Advanced Life Support (ALS/USA); and an age gradient with greater vulnerability in younger age groups. Taken together, these findings delineate the service’s care panorama and reinforce the need for planning aligned with real demand, without introducing data beyond those presented.\u003c/p\u003e\u003cp\u003eThe results provide an objective basis for resource sizing and allocation, refinement of clinical and regulatory protocols, and ongoing monitoring. They also motivate further analyses with adjustment for potential confounders. We recommend advancing with multivariable models and spatiotemporal analyses to estimate independent effects and to inform public policy and operational decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdvanced Life Support\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBasic Life Support\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnidade de Suporte Avan\u0026ccedil;ado (ALS unit)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUSB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnidade de Suporte B\u0026aacute;sico (BLS unit)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnidade de Suporte Intermedi\u0026aacute;rio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUPA 24h\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e24-hour emergency unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egeographic information system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAMU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eServi\u0026ccedil;o de Atendimento M\u0026oacute;vel de Urg\u0026ecirc;ncia.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e This study was approved by the Research Ethics Committee (Comitê de Ética em Pesquisa) of Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Brazil (CAAE: 36389420.0.0000.5065; approval number: 4.308.858). Given the use of de-identified administrative dispatch records and the impracticability of obtaining individual consent, the Committee waived the requirement for informed consent. The study was conducted in accordance with relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable. The manuscript does not contain any individual person’s data (images, videos, or other identifying details).\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Undergraduate Research Program (PIVIC/EMESCAM). The funder had no role in study design; data collection, analysis, or interpretation; writing of the report; or the decision to submit the article for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLMS: conceptualization, methodology, data curation, formal analysis, visualization, writing—original draft.FCV: data curation, validation, visualization, writing—review \u0026amp; editing.GCS: data curation, validation, visualization, writing—review \u0026amp; editing.RFLS: data curation, validation, visualization, writing—review \u0026amp; editing.JVLO: supervision, methodology, project administration, writing—review \u0026amp; editing.All authors meet ICMJE criteria for authorship, read and approved the final manuscript, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe thank the SAMU 192 – Espírito Santo operations and medical dispatch teams for facilitating access to de-identified administrative data. We acknowledge EMESCAM for fostering research through its Scientific Initiation Program (PIVIC). We are grateful to Prof. Caio Duarte Neto, MSc, for coordinating, supporting, and sustaining this project; to the Multidisciplinary Research Group on the Urgent and Emergency Care Network for their support; to Prof. Lucia Helena Sagrillo Pimassoni for conducting the statistical analyses; and to Igor Cardozo Boim, EMESCAM medical student, for assistance with map development. All acknowledged parties provided permission to be named.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDe-identified administrative dispatch records from SAMU 192 – Espírito Santo (SAMU-ES) were used under institutional agreements and are not publicly available due to legal and privacy restrictions (Brazilian General Data Protection Law—LGPD) and data-owner policies. The dataset is stored on secure internal servers at EMESCAM / Santa Casa de Misericórdia de Vitória and may be shared upon reasonable request and subject to: (i) prior permission from SAMU-ES and the Espírito Santo State Health Secretariat; (ii) Research Ethics Committee approval (CAAE 36389420.0.0000.5065; approval No. 4.308.858); and (iii) execution of a data use agreement. Requests should be directed to the corresponding author at [email protected]. Aggregate data supporting the findings are provided within the manuscript (tables/figures).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Pol\u0026iacute;tica nacional de aten\u0026ccedil;\u0026atilde;o \u0026agrave;s urg\u0026ecirc;ncias. 3\u0026ordf; ed. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2006. [Portuguese]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_atencao_urgencias_3ed.pdf\u003c/span\u003e\u003cspan address=\"https://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_atencao_urgencias_3ed.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 Sep 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira JVL. Pol\u0026iacute;tica nacional de aten\u0026ccedil;\u0026atilde;o \u0026agrave;s urg\u0026ecirc;ncias: regula\u0026ccedil;\u0026atilde;o no cuidado do paciente idoso atendido pelo SAMU 192 no Esp\u0026iacute;rito Santo [dissertation]. Vit\u0026oacute;ria: Escola Superior de Ci\u0026ecirc;ncias da Santa Casa de Miseric\u0026oacute;rdia de Vit\u0026oacute;ria (EMESCAM); 2018. [Portuguese].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSecretaria de Estado da Sa\u0026uacute;de do Esp\u0026iacute;rito Santo (SESA). Samu 192 vai atender mais cinco munic\u0026iacute;pios das regi\u0026otilde;es Serrana e do Capara\u0026oacute; [Internet]. Vit\u0026oacute;ria: SESA. 2020 Apr 23 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saude.es.gov.br/Not%C3%ADcia/samu-192-vai-atender-mais-cinco-municipios-das-regioes-serrana-e-do-caparao\u003c/span\u003e\u003cspan address=\"https://saude.es.gov.br/Not%C3%ADcia/samu-192-vai-atender-mais-cinco-municipios-das-regioes-serrana-e-do-caparao\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSecretaria de Estado da Sa\u0026uacute;de do Esp\u0026iacute;rito Santo (SESA). Governo do Estado investe mais de R\u003cspan\u003e$\u003c/span\u003e 38 milh\u0026otilde;es em amplia\u0026ccedil;\u0026atilde;o do Samu 192 e garante 100% de cobertura populacional do servi\u0026ccedil;o [Internet]. Vit\u0026oacute;ria: SESA. 2022 Dec 20 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saude.es.gov.br/Not%C3%ADcia/governo-do-estado-investe-mais-de-r-38-milhoes-em-ampliacao-do-samu-192-e-garante-100-de-cobertura-populacional-do-servico\u003c/span\u003e\u003cspan address=\"https://saude.es.gov.br/Not%C3%ADcia/governo-do-estado-investe-mais-de-r-38-milhoes-em-ampliacao-do-samu-192-e-garante-100-de-cobertura-populacional-do-servico\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSecretaria de Estado da Sa\u0026uacute;de do Esp\u0026iacute;rito Santo (SESA). Nova frota de ambul\u0026acirc;ncias do SAMU 192 chega para fortalecer atendimento no Estado [Internet]. Vit\u0026oacute;ria: SESA. 2023 Sep 14 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saude.es.gov.br/Not%C3%ADcia/nova-frota-de-ambulancias-do-samu-192-chega-para-fortalecer-atendimento-no-estado\u003c/span\u003e\u003cspan address=\"https://saude.es.gov.br/Not%C3%ADcia/nova-frota-de-ambulancias-do-samu-192-chega-para-fortalecer-atendimento-no-estado\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSogame LMC et al. Geotecnologias no Servi\u0026ccedil;o de Atendimento M\u0026oacute;vel de Urg\u0026ecirc;ncia no ES. Vit\u0026oacute;ria (ES): Editora EMESCAM; 2020. ISBN: 978-65-88041-04-8. [Portuguese]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.editoraemescam.com.br/wp-content/uploads/2021/07/GEOTECNOLOGIAS-NO-SERVICO-DE-ATENDIMENTO-MOVEL-DE-URGENCIA-NO-ES-%E2%80%93-ISBN-on-line-978-65-88041-04-8.pdf\u003c/span\u003e\u003cspan address=\"https://www.editoraemescam.com.br/wp-content/uploads/2021/07/GEOTECNOLOGIAS-NO-SERVICO-DE-ATENDIMENTO-MOVEL-DE-URGENCIA-NO-ES-%E2%80%93-ISBN-on-line-978-65-88041-04-8.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 Sep 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStudnek JR, Lerner EB, Shah MI, Browne LR, Brousseau DC, Cushman JT, et al. Consensus-based criterion standard for the identification of pediatric patients who need EMS transport to a hospital with higher-level pediatric resources. Acad Emerg Med. 2018;25(12):1409\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/acem.13625\u003c/span\u003e\u003cspan address=\"10.1111/acem.13625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoku BA, Hemingway P. Reducing repeat paediatric emergency department attendance for non-urgent care: a systematic review of the effectiveness of interventions. Emerg Med J. 2019;36(7):435\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/emermed-2018-207536\u003c/span\u003e\u003cspan address=\"10.1136/emermed-2018-207536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Lei n\u0026ordm; 8.069, de 13 de julho de 1990 (Estatuto da Crian\u0026ccedil;a e do Adolescente) [Internet]. Bras\u0026iacute;lia: Governo Federal; 1990 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.br/mj/pt-br/assuntos/seus-direitos/classificacao-1/legislacao/eca.pdf\u003c/span\u003e\u003cspan address=\"https://www.gov.br/mj/pt-br/assuntos/seus-direitos/classificacao-1/legislacao/eca.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Portaria n\u0026ordm; 1.130, de 5 de agosto de 2015: institui a Pol\u0026iacute;tica Nacional de Aten\u0026ccedil;\u0026atilde;o Integral \u0026agrave; Sa\u0026uacute;de da Crian\u0026ccedil;a (PNAISC) [Internet]. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2015 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bvsms.saude.gov.br/bvs/saudelegis/gm/2015/prt1130_05_08_2015.html\u003c/span\u003e\u003cspan address=\"https://bvsms.saude.gov.br/bvs/saudelegis/gm/2015/prt1130_05_08_2015.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Health for the world\u0026rsquo;s adolescents: a second chance in the second decade [Internet]. Geneva: WHO. 2014 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/WHO-FWC-MCA-14.05\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/WHO-FWC-MCA-14.05\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIBM Corp. IBM SPSS Statistics for Windows, Version 29.0. Armonk, NY: IBM Corp; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQGIS Development Team. QGIS Geographic Information System. Version 3.x. Open Source Geospatial Foundation Project. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://qgis.org\u003c/span\u003e\u003cspan address=\"https://qgis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 Sep 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInstituto Jones dos Santos Neves (IJSN). Limites municipais do Esp\u0026iacute;rito Santo: base cartogr\u0026aacute;fica oficial [Internet]. Vit\u0026oacute;ria: IJSN; [year unknown] [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ijsn.es.gov.br/\u003c/span\u003e\u003cspan address=\"https://www.ijsn.es.gov.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (acessar se\u0026ccedil;\u0026atilde;o de dados geogr\u0026aacute;ficos/cartografia oficial para o shapefile).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Conselho Nacional de Sa\u0026uacute;de. Resolu\u0026ccedil;\u0026atilde;o n\u0026ordm; 466, de 12 de dezembro de 2012: Diretrizes e normas regulamentadoras de pesquisas envolvendo seres humanos [Internet]. Bras\u0026iacute;lia: CNS. 2012 [cited 2025 Sep 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://conselho.saude.gov.br/resolucoes/2012/Reso466.pdf\u003c/span\u003e\u003cspan address=\"https://conselho.saude.gov.br/resolucoes/2012/Reso466.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"emergency medical services, prehospital care, child, adolescent, triage, medical dispatch, geographic information systems, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-7993172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7993172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBrazil\u0026rsquo;s SAMU 192 is a physician-regulated prehospital system that delivers remote guidance and/or dispatches mobile units according to risk. In pediatrics, improving triage and destination is pivotal to shorten response times and match resources to clinical urgency. We aimed to describe the spatial distribution and the dispatch criticality profile of pediatric attendances regulated by SAMU 192 in Esp\u0026iacute;rito Santo, Brazil, and to test associations with sociodemographic, dispatch, and destination variables.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional observational study using 2020\u0026ndash;2021 records from the state Medical Dispatch Center (SAMU 192). We included pediatric attendances aged 1\u0026ndash;19 years. Variables comprised age, sex, region, time of day and day of week of the call, call origin (home vs. out-of-home), type of incident, type of dispatched resource (basic vs. advanced life support), and destination. We performed descriptive analyses and chi-square tests (α\u0026thinsp;=\u0026thinsp;0.05). Spatial distribution was mapped in GIS using the official cartographic base.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 4,860 attendances, 24.4% were triaged as critical at dispatch. The relative proportion of critical cases increased in 2021 versus 2020. Higher criticality proportions were observed during daytime shifts. Spatial patterns were heterogeneous, with concentrations in urban areas. Clinical incidents and responses involving Advanced Life Support units exhibited higher urgency levels. An age gradient was noted, with greater relative severity among children aged 1\u0026ndash;4 years. No differences were observed by sex, and call origin (home vs. out-of-home) was similar.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eFindings support tailoring resource allocation by shift and territory, reinforcing pediatric clinical/dispatch protocols, and routinely applying geotechnologies for space\u0026ndash;time surveillance. Future multivariable and spatiotemporal analyses could refine prioritization and guide operational planning within pediatric prehospital care.\u003c/p\u003e","manuscriptTitle":"Pediatric prehospital emergency care in Espírito Santo, Brazil (SAMU 192), 2020–2021: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:33:09","doi":"10.21203/rs.3.rs-7993172/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcbec5be-ac68-457b-9226-7b9a1dfcc9f9","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T05:39:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:33:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7993172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7993172","identity":"rs-7993172","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}