Cardiac arrest rates upon arrival at the hospital among patients transported by ambulances from the seacoast

Cardiac arrest rates upon arrival at the hospital among patients transported by ambulances from the seacoast | 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 Cardiac arrest rates upon arrival at the hospital among patients transported by ambulances from the seacoast Chung-Han Hsieh, Kenko Fukui, Hiroshi Yoshimoto, Kazuhiro Sekine, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6561109/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 Seacoasts are generally considered dangerous, and the situation of patients being transported from the coast to hospitals for emergency care has not been fully examined. To clarify the acute health risk associated with the seacoast, we investigated the survival status of patients who arrived at the hospital following transportation from the seacoast by ambulance compared with patients from other locations, and analyzed related factors. Methods All patients who were transported by ambulance to hospitals between 2020 and 2021 in Japan were enrolled in this study. Patients transported from the seacoast were compared with the patients from other locations with respect to the cardiac arrest rate as a survival state. The variables of sex, age, response time, and cause of injury or illness were also compared between the two groups. To adjust for the influence of these variables, we conducted propensity score matching between the two groups and compared the cardiac arrest rates again. Results Of the enrolled patients, 7,003 were from coastal areas, and 9,780,140 were from other locations. Among seacoast patients, 9.1% experienced cardiac arrest upon hospital arrival, whereas 1.6% of patients from other locations experienced cardiac arrest (P < 0.001). In addition, patients from the seacoastal region were more likely to be male, younger, have a longer response time, and have a higher percentage of drowning. Even after adjusting for these factors via using propensity score matching, the cardiac arrest rate still remained higher in the patients from the seacoast (7.3% vs 4.8%, P < 0.001). Conclusions Even if the characteristic factors of patients from the seacoast, including longer response time and a high percentage of drowning were adjusted by propensity matching, the rate of cardiac arrest remained higher in these patients. Seacoast Ambulance Emergency medical Services Out-of-hospital cardiac arrest Drowning Introduction Seacoasts are generally considered to be dangerous locations with drownings being a typical geographical threat [1,2]. Some reports have focused on the epidemiology of drowning in seacoasts [3,4]. However, these reports covered a limited area, whereas other reports are limited to certain age groups that focus on children [5,6]. Considering these situations, a summative verification based on a population-based registry, not just of a limited region, age, and cause of injury or illness, is needed. Notably, drowning, other types of injuries, or medical emergencies are health threats that occur in coastal settings [7]. Therefore, we conducted an inclusive epidemiological survey of patients transported from seacoasts to clarify the epidemiological features of acute health risks to seacoasts. Using nationwide inclusive data concerning patients transported by ambulances in the emergency medical system in Japan, we described the characteristics of patients transported from seacoasts compared with those transported from other locations, with a particular focus on the rate of cardiac arrest upon arrival at hospitals. If the ratio of cardiac arrest among patients transported from the seacoast is greater than that of other patients, exploring this cause may broaden the understanding of the background features of the acute health risk of the seacoast and be utilized to promote safety. Therefore, we investigated the influence of related factors that characterize coastal patients on the rate of cardiac arrest. Methods Study Design and Data Source This nationwide observational study was based on data provided by the Fire and Disaster Management Agency (FDMA) of Japan. The data were obtained from a population-based registry of patients transported to the hospital by ambulance. In this study, we enrolled all patients who were transported by ambulance between January 1, 2020, and December 31, 2021. This study was approved by the Research Ethics Committee of Kyoto Tachibana University (24 − 14) and exempted it from informed consent. All research was performed in accordance with the Declaration of Helsinki. Study Setting The study area covered Japan, spanning 377,976 km 2 as of 2020 [8]. Its population was 126,146,099 (61,349,581 men and 64,796,518 women) as registered in the national census in 2020 [9]. Data collection Data including sex (male or female), age, location of occurrence, response time, survival status upon arrival at the hospital, and cause of injury or illness were collected as variables for analyses. The response time was defined as the time period from the emergency call to the contact of the ambulance crew with the patient. Survival status and the types of injuries or illnesses were diagnosed by the physicians in charge of initial care upon arrival at the hospital. The types of injuries or illnesses were categorized according to the ICD-10 [10]. With respect to survival status, we collected data on whether the patients were in cardiac arrest upon arrival at the hospitals. Cardiac arrest upon arrival at the hospital was defined as the absence of signs of circulation at the time of ambulance arrival at the hospital. These cases are nothing more than out-of-hospital cardiac arrest, except patients who were successfully resuscitated until arrival at the hospital. Analyses To elucidate the health risks associated with seacoasts, we compared the rates of cardiac arrest upon arrival in patients transported from seacoasts to other locations. We also described other factors, such as sex, age, cause of injury or illness, and response time between the two groups to clarify the characteristics of coastal patients, with a focus on drowning for cause of injury or illness. We then conducted a logistic regression analysis to examine the associations between incident location (seacoast vs non-seacoast) and factors such as sex, age, cause of injury or illness (drowning vs not drowning), and response time. These factors were set as explanatory variables, and the location of the incident (seacoast vs non-seacoast) was set as the outcome variable. Using the logistic regression analysis, propensity scores were calculated for each patient based on the basis of these explanatory variables, to estimate the probability that the patient was transported from the seacoast. Patients from the seacoast were matched with those from other locations on the basis of propensity scores. One-to-one greedy nearest neighbor matching without replacement was performed [11] with a caliper width set to 0.2 standard deviations of the propensity score [12]. The balance between the two groups was evaluated via the Wilcoxon rank-sum test for continuous variables and Pearson's chi-square test for categorical variables following the matching. Propensity score matching was conducted via the MatchIt package [13]. Sensitivity analyses were performed to assess the degree of unmeasured confounders on the basis of Rosenbaum’s sensitivity analysis [14]. For this analysis, we used the rbounds package [15]. Propensity score matching and sensitivity analyses were conducted in R version 4.2.0 (R Foundation for Statistical Computing). All other statistical analyses were performed via IBM SPSS version 29. Statistical significance was set at P < 0.05. Results Patients During the 2-year study period, 9,896,971 patients were transported by ambulance to a hospital in Japan. Among the patients, 7003 cases were from the seacoast and 9,780,140 were from other locations. In the remaining 109,828 cases, the locations where incidents occurred were unknown. Missing values for patients in the study were shown in additional table [see Additional file 1]. Patients with missing values, representing 0.9% of patients from seacoast and 2.6% from other locations, were excluded from logistic regression analysis and propensity score matching. Characteristics of patients from seacoast Patients from the seacoast were clearly differed from the other patients in terms of sex and age. The number of male patients was greater (78.3%) than the number of female patients, whereas it was approximately the same among the other patients. The median age of patients from the coast was 49.0 years (interquartile range [IQR] of 29–67 years. This number was markedly lower than that of patients form non-seacoast in which the median age was 72.0 years (IQR, 49–84 years). The cardiac arrest rate upon hospital arrival also differed between the two groups. Among patients from the seacoast 9.1% were in cardiac arrest upon arrival at the hospital, whereas only 1.6% of patients transported from other locations were in cardiac arrest. Among the factors related to this different survival state, a longer response time was observed in patients from the seacoast than in those from other locations. The median response time was 13.0 min with an IQR of 10–19 min, in patients from the seacoast. Those duration were 10.0 and 8–12 min in the patients from the other locations (Table 1 ). Table 1 Characteristics of patients by location of incidents occurrence Location of occurrence Patients, No Sex (Male), No (%) Age, Median (IQR)* Cardiac arrests No (%) Response time, Median (IQR)* Seacoast 7,003 5,453 (78.3%) 49 (29 – 67) 637 (9.1%) 13 (10 – 19) Non - seacoast 9,780,140 4,928,064 (50.5%) 72 (49 – 84) 155,149 (1.6%) 10 (8 – 12) Residence 6,463,517 3,151,587 (49.2%) 74 (54–84) 110,458 (1.7%) 10 (8–12) Public access facilities Cinemas/Theaters 5,787 2,640 (45.9%) 65 (30–84) 133 (2.3%) 9 (7–11) Stores 305,253 156,444 (52.2%) 58 (34–75) 1,271 (0.4%) 9 (7–11) Medical facilities 34,209 17,115 (50.8%) 67 (44–81) 400 (1.2%) 9 (7–12) Schools 104,061 60,820 (58.6%) 15 (10–18) 132 (0.1%) 9 (7–11) Traffic facilities 63,045 86,948 (54.5%) 51 (28–70) 942 (0.6%) 9 (7–11) Entertainment facilities 74,814 53,218 (71.9%) 41 (19–68) 462 (0.6%) 10 (8–12) Religious sites 12,507 6,744 (54.4%) 73 (60–82) 135 (1.1%) 10 (8–13) Bathhouses 20,438 12,982 (64.6%) 73 (60–80) 603 (3.0%) 9 (7–12) Nursing homes 867,990 313,155 (36.1%) 88 (82–92) 27,885 (3.2%) 9 (8–12) Hotels 58,231 35,858 (62.4%) 53 (32–69) 748 (1.3%) 11 (8–15) Public facilities and others 134,642 79,882 (60.0%) 56 (33–73) 886 (0.7%) 9 (6–11) Workplaces Factory 128,169 102,438 (80.1%) 49 (36–61) 1,239 (1.0%) 10 (8–12) Offices and others 125,187 79,412 (63.7%) 49 (35–91) 909 (0.7%) 9 (7–12) Road 1,187,170 703,245 (60.4%) 57 (33–75) 6,040 (0.5%) 9 (7–12) Other location Bare land 40,954 28,219 (70.5%) 45 (11–73) 543 (1.4%) 10 (8–13) Wilderness areas 41,240 29,052 (70.8%) 72 (59–81) 1,256 (3.1%) 13 (10–18) River/Pond 10,305 6,856 (67.8%) 66 (39–78) 917 (9.1%) 12 (9–18) Railway territory 2,621 1,449 (58.3%) 53 (29–74) 190 (7.7%) 11 (9–14) Others (Unknown) 109,828 47,769 (52.5%) 71 (46–83) 972 (1.1%) 8 (6–10) Total 9,896,971 4,981,286 (51.0%) 72 (49–84) 156,758 (1.6%) 10 (8–12) *IQR: interquartile range. The causes of incidents also differed between seacoast and non-seacoast patients. Drowning accounted for 17% of cases among seacoast patients, whereas it accounted for only 0.1% of the cases among patients from other locations (Table 2 ). Table 2 Cause of injury or illness of patients from the seacoast vs non-seacoast including rate of cardiac arrest upon hospital arrival Cause Seacoast Non-seacoast Patients, No (column %) Cardiac arrest No (%) Patients, No (column %) Cardiac arrest No (%) Drowning 1,196 (17.1%) 384 (32.1%) 12,830 (0.1%) 3,938 (30.7%) Other than drowning 5,807 (82.9%) 253 (4.4%) 9,767,310 (99.9%) 151,211 (1.5%) External cause Trauma 1,599 (22.8%) 2 (0.1%) 1,111,975 (11.4%) 1,918 (0.2%) Poisoning 24 (0.3%) 0 (0.0%) 17,687 (0.2%) 119 (0.7%) Burns 14 (0.2%) 0 (0.0%) 11,034 (0.1%) 83 (0.8%) Other external cause 2,751 (39.3%) 141 (5.1%) 1,845,285 (18.9%) 23,994 (1.3%) Internal cause Cerebrovascular diseases 249 (3.6%) 1 (0.4%) 540,342 (5.5%) 3,168 (0.6%) Cardiovascular diseases 212 (3.0%) 67 (31.6%) 612,638 (6.3%) 56,448 (9.2%) Respiratory diseases 67 (1.0%) 1 (1.5%) 609,027 (6.2%) 4,991 (0.8%) Digestive diseases 95 (1.4%) 0 (0.0%) 913,241 (9.3%) 2,015 (0.2%) Genitourinary diseases 39 (0.6%) 0 (0.0%) 297,469 (3.0%) 576 (0.2%) Skin diseases 14 (0.2%) 0 (0.0%) 49,682 (0.5%) 22 (0.0%) Musculoskeletal diseases 52 (0.7%) 0 (0.0%) 223,075 (2.3%) 62 (0.0%) Mental and behavioral disorders 36 (1.9%) 0 (0.0%) 432,271 (4.4%) 100 (0.0%) Eye or ear diseases 78 (1.1%) 0 (0.0%) 315,057 (3.2%) 176 (0.1%) Pregnancy or perinatal disorders 2 (0.0%) 0 (0.0%) 15,367 (0.2%) 41 (0.3%) Other internal cause 460 (6.6%) 33 (7.2%) 2,764,521 (28.3%) 56,507 (2.0%) Unidentified 15 (0.2%) 8 (53.3%) 8,639 (0.1%) 991 (11.5%) Total 7,003 (100.0%) 637 (9.1%) 9,780,140 (100.0%) 155,149 (1.6%) Associations of the factors with the locations of incidents The results of the logistic regression analysis revealed that sex, age, response time, and diagnosis (drowning vs not drowning) were clearly associated with the location of the incident occurred (seacoast vs non seacoast; Table 3 ). As both univariate and multivariate analyses demonstrated, males were more likely to experience incidents at seacoasts than females were. The adjusted odds ratio for males was 3.1 (vs females). Both the crude and adjusted odds ratios for age revealed that younger age was closely associated with the seacoast. A longer response time was also associated with seacoast in both the crude and adjusted odds ratios. Patients from seacoast are more likely to suffer from drowning. The adjusted odds ratio of drowning for patients from the seacoast was 164 in multivariate analysis (Table 3 ). Table 3 Results of logistic regression for location of occurrence of incidents (patients from seacoast vs from non-seacoast) Logistic regression Variable Odds ratio 95% Confidence interval Univariate analysis Sex (male) 3.468 3.277–3.672 Age (year) 0.979 0.978–0.980 Response Time (min) 1.013 1.012–1.014 Cause (Drowning) 156.793 146.985–167.257 Multivariate analysis Sex 3.104 2.930–3.288 Age (year) 0.979 0.978–0.980 Response Time (min) 1.012 1.011–1.013 Cause (Drowning) 164.072 153.369–175.522 The area under the receiver operating characteristic curve in this binary logistic regression analysis was 0.81, indicating the model's good discriminatory ability in predicting whether an incident occurred at sea or at other locations on the basis on sex, age, response time, and whether the patient suffered drowning, as demonstrated in an additional figure [see Additional file 3]. Cardiac arrest rate after propensity score matching Matching on the basis of the propensity scores calculated via logistic regression analysis revealed that 6,396 pairs were formed between patients from the seacoast and patients from other locations. The variables of sex, age, response time, and whether the patient suffered from drowning between the two groups were balanced via this procedure, and no significant differences were detected between patients from the seacoast location and those from other locations. The difference in cardiac arrest rates upon hospital arrival decreased after matching. However, even after matching, the cardiac arrest rates remained significantly different, occurring in 6.8% of seacoast patients, whereas in non-seacoast patients it was 4.6% (P < 0.001; Table 4 ). Table 4 Results of propensity score matching (patients from seacoast vs from non-seacoast) Variable Before matching After matching Seacoast Non-seacoast Significance Seacoast (No.= 6,498) Non-seacoast (No. = 6,498) Significance Sex, No (%) Male 5,453 (78.3%) 4,927,506 (50.9%) P < 0.001 5,032 (77.4%) 5,077 (78.1%) P = 0.293 Female 1,515 (21.7%) 4,748,343 (49.1%) 1,466 (22.6%) 1,421 (21.9%) Age, Median (IQR) 49 (29–67) 72 (49–84) P < 0.001 49 (29–67) 49 (30–67) P = 0.287 Response time, Median (IQR) 13 (10–19) 10 (8–12) P < 0.001 13 (10–18) 13 (10–18) P = 0.073 Cause, No (%) P = 0.979 Drowning 1,189 (17.0%) 12,575 (0.1%) P < 0.001 743 (11.4%) 744 (11.4%) P = 0.979 Others 5,801 (83.0%) 9,671,448 (99.9%) 5,755 (88.6%) 5,754 (88.6%) Cardiac arrest, No (%) Cardiac arrest 637 (9.1%) 155,149 (1.6%) P < 0.001 474 (7.3%) 315 (4.8%) P < 0.001 Not cardiac arrest 6,353 (90.9%) 9,528,874 (98.4%) 6,024 (92.7%) 6,183 (95.2%) Result of the sensitivity analysis Although the model showed good discriminatory ability between an incident occurring at sea and at other locations, the result of Rosenbaum’s sensitivity analysis revealed that the upper bound of the p-value exceeded 0.05 when the gamma value surpassed 1.5 (p = 0.0798 at Gamma = 1.5) indicating that the potential influence of hidden bias was not accounted for in the matching procedure [see Additional file 3]. Discussion To date, the number of prior studies in the nationwide epidemiological survey of emergency cases from seacoasts has been limited. We believe that this study provides data for promoting coastal safety and adds to the basic knowledge. A major finding of our survey was the high percentage of cardiac arrest in patients from the seacoast upon hospital arrival. The rate of cardiac arrest upon hospital arrival was approximately 10% among patients from the coast, which contrasts with the < 2% rate reported in the patients from other locations. Therefore, clarifying the background characteristics of patients from the seacoast is essential for understanding the health risk of the seacoast. The demographic features, such as sex and age, of patients from the coast demonstrated unique characteristics; they had a greater proportion of males, approximately half of the patients from other locations, and they were clearly younger. Although these characteristics have been reported in other regions [3], describing and confirming these findings via large-scale epidemiological data may be worthwhile. However, these demographic characteristics do not explain the high percentage of cardiac arrests in patients transported from seacoasts. Time is an important factor in emergencies. In general, densely populated areas where emergency dispatch centers are located are distant from the coast. Therefore, response time should be highlighted, and findings that the response time extended in patients from the coast must be noted. In addition, the close relationship between drowning and patients from seacoast accentuates the characteristics of these patients. We speculated that these two factors play a major role in explaining the high percentage of cardiac arrests upon hospital arrival. Considering the good discrimination between patients from the seacoast and those from other locations in the logistic regression analysis, which was demonstrated by a sufficiently high area under the receiver operating characteristic curve, we expected the survival status of these patients to be equal after propensity matching. As expected, the difference in the outcomes between the two groups narrowed after propensity score matching. However, significant differences were still observed in the outcomes. The differences remaining after matching should be discussed in more detail. The most plausible answer is the existence of hidden variables. The result of the Rosenberg sensitivity analysis also suggests the existence of hidden bias in the matching procedure, while the result demonstrates some level of robustness. The cause of injury or illness factor should be reconsidered in particular, as the only factor included was whether the injury or illness was drowning. Since the percentage of cardiac arrests due to drowning was similar between seacoast and non-seacoast patients, we adjusted for this variable by focusing on drowning cases. Two possible points might be considered to explain remaining the difference in the percentage of cardiac arrest. Traumatic risk among seacoasts was indicated in a previous report [16]. However in our result, although we had more traumatic cases from seacoast, the number of cases of cardiac arrests was limited. Instead, we noted the high percentage of case occurrences and cardiac arrests in the category of “other external cause.” Although we could not clarify the detailed patient information in this category, consideration of the influence of environmental factors, such as heat stroke, might be an issue for further investigation. This is because cardiac arrest cases due to other external causes tended to occur during the summer [see Additional file 2]. Another possible explanation is the high percentage of cardiac arrests due to cardiovascular diseases among the patients from seacoast. Although patients with cardiovascular diseases transported by ambulances tend to be seriously ill in general, patients from the coast were particularly critical, with one-third experiencing cardiac arrest. Thus, speculation regarding causes other than drowning may be needed through a more detailed epidemiological survey. Since the epidemiological features of drowning patients were reported at a younger age and in males [17], an epidemiological survey of patients with other causes of illness, including cardiovascular disease, may be expected in future studies. The most important limitation of this study is the accuracy and quality of the recorded data. In the Japanese ambulance system, some variables, such as the time of the emergency call, are recorded automatically. However, many variables, including other time factors, must be recorded manually by ambulance crews. Data missing are inevitable in this large-scale comprehensive survey. Our missing table shows the tendency that if one of the categories is missing in the case, this case contains two or more missing data. For example, if the location of injury occurrence or illness was missing, the percentage of missing sex data reached 17% [see Additional file 1]. The number of cases without missing data from the seacoast and other locations reached 99.1% and 97.4%, respectively. Therefore, to obtain unbiased results in our dataset, we excluded cases containing missing data from logistic regression analysis and propensity score matching. Another important limitation of this study is that the physicians’ decisions were recorded by the ambulance crew only at initial hospital care for transported patients. The cause of injury or illness might be difficult to determine in some of cases particularly with respect to internal causes. However, we believe that drowning as the cause injury was accurately decided. Survival status (cardiac arrest vs noncardiac arrest) was also accurately determined. Therefore, we conclude that the percentage of patients with cardiac arrest from seacoasts on hospital arrival was clearly greater than that of patients from other locations. Among the characteristics of patients from the seacoast, longer response times and a higher percentage of drowning cases contributed to a high percentage of cardiac arrest on hospital arrival. However, even after adjusting for these factors through propensity score matching, the rate of cardiac arrest remained high among the patients from seacoast. Thus, other injuries or illnesses may also contribute to a higher percentage of cardiac arrests in these patients. Declarations Ethics Statement Approval of the research protocol: This study was approved by the Research Ethics Committee of Kyoto Tachibana University (24-14). Informed Consent: The ethics committees waived the need for informed consent for this study. Registry and the Registration No. of the study/Trial: N/A Animal studies: N/A Conflict of i nterest s tatement The authors report that there are no competing interests to declare. Funding No funding was received for conducting this study. Acknowledgments We thank the Ambulance Service Planning Office of the Fire and Disaster Management Agency for providing the database. We would like to thank Editage (www.editage.com) for English language editing. Data availability The data that support the findings of this study are available from the corresponding author, Atushi Hiraide, upon reasonable request. Authors' contributions CH and AH designed and conceived this study. KS collected data. CH and AH analyzed and interpreted the results and drafted the manuscript. KF and HY supported statistical analyses. All authors reviewed and approved the final manuscript. References Koon W, Peden A, Lawes JC, et al. Coastal drowning: A scoping review of burden, risk factors, and prevention strategies. PLoS One. 2021; 1: e0246034. doi: 10.1371/journal.pone.0246034. Abelairas-Gómez C, Tipton MJ, González-Salvado V, et al. Drowning: epidemiology, prevention, pathophysiology, resuscitation, and hospital treatment. Emergencias. 2019; 31: 270–280. Bessereau J, Fournier N, Mokhtari T, Brun PM, Desplantes A, Grassineau D, et al. Epidemiology of unintentional drowning in a metropolis of the French Mediterranean coast: a retrospective analysis (2000–2011). Int J Inj Contr Saf Promot. 2016; 23: 317–322. doi: 10.1080/17457300.2015.1047862. Klein AHF, Santana GG, Diehl FL, Menezes JT. 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The Report of Statistical reports on the land area by prefectures and municipalities in Japan in 2020 Japan; 2022. Ministry of Land, Infrastructure and Transport of Japan. https://www.gsi.go.jp/KOKUJYOHO/MENCHO/backnumber/GSI-menseki20201001.pdf. Accessed 29th April 2025. Statistics Bureau of Japan. 2020 Population Census; 2022. Ministry of Internal Affairs and Communications of Japan. https://www.e-stat.go.jp/en/stat-search/files?page=1&layout=datalist&toukei=00200521&tstat=000001136464&cycle=0&year=20200&month=24101210&tclass1=000001136466 Accessed 29th April 2025. WHO. ICD-10: International statistical classification of diseases and related health problems: Tenth revision. 2nd ed. Geneva: WHO; 2004. Rosenbaum PR, Rubin DB. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. Am Stat 1985; 39: 33–38. doi:10.2307/2683903. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10: 150–161. doi:10.1002/pst.433. Ho D, Imai K, King G, Stuart, E A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw 2011; 42: 1–28. doi:10.18637/jss.v042.i08 Rosenbaum, P. R. (1987). Sensitivity Analysis for Certain Permutation Inferences in Matched Observational Studies. Biometrika, 74, 13–26. Doi.org/10.1093/biomet/74.1.13. Keele LJ. Perform Rosenbaum Bounds Sensitivity Tests for Matched and Unmatched Data. https://www.vps.fmvz.usp.br/CRAN/web/packages/rbounds/rbounds.pdf Accessed 29th April 2025. Thom O, Roberts K, Leggat PA, Devine S, Peden AE, Franklin RC. Cervical spine injuries occurring at the beach: epidemiology, mechanism of injury and risk factors. BMC Public Health 2022; 22: 1404. doi: 10.1186/s12889-022-13810-9. Erasmus E, Robertson C, van Hoving DJ. 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Supplementary Files AdditionalInformationTable1.docx Additional File1 (Additional Table1.docx) Missing data table (Seacoast vs Non-seacoast) AdditionalinformationTable2.docx Additional File2 (Additional Table2.docx) Causes of injury or illness in patients by season (seacoast vs non-seacoast) and cardiac arrest rate upon arrival at hospitals AdditionalInformationTable3.docx Additional File3 (Additional Table3.docx) Result of the Rosenberg sensitivity test AdditionalInfornmationFigure.pptx ROC curve for the location of occurrence of incidents (seacoast vs non-seacoast) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6561109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454994342,"identity":"b74878e7-67c2-4a3d-a9a5-19f7e095ecd1","order_by":0,"name":"Chung-Han Hsieh","email":"","orcid":"","institution":"Meiji University of Integrative Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chung-Han","middleName":"","lastName":"Hsieh","suffix":""},{"id":454994343,"identity":"fc1d49a0-5da7-4c49-8cbb-686241f92153","order_by":1,"name":"Kenko Fukui","email":"","orcid":"","institution":"Meiji University of Integrative Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kenko","middleName":"","lastName":"Fukui","suffix":""},{"id":454994344,"identity":"26617494-cfa7-4572-a813-baa39e38d6e3","order_by":2,"name":"Hiroshi Yoshimoto","email":"","orcid":"","institution":"Kyoto Tachibana University","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Yoshimoto","suffix":""},{"id":454994345,"identity":"e6c80330-6994-4951-803e-4ea448e5c701","order_by":3,"name":"Kazuhiro Sekine","email":"","orcid":"","institution":"Kyoto Tachibana University","correspondingAuthor":false,"prefix":"","firstName":"Kazuhiro","middleName":"","lastName":"Sekine","suffix":""},{"id":454994346,"identity":"d65b3d24-dcce-4acd-a40a-9c36dc12b944","order_by":4,"name":"Atsushi Hiraide","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACAxCRYCAhx8bewMCMIopXy4cKC2N+ngMkaGGccaYiUXJGAkILXmAu3X51M2+bRILBzTeGnwsqbBj42xsYigvwaLGcc6bsNlBLnsHtHGPpGWfSGCTOHGAwnoHPYTdy0kBaioFaDKR52w4zGEgkMBjzEKElccPNM8a/idSSfuzmjDMSiTNn8JgRacudM2w3PlRIAAM5rcya50waj8SZgw34/XK7/dmNBIM6YFQe3nybp8JGjr+9+ZgxvhBjkOCBxRsHmAF0EmObMT4dDBLsD6AsOIOB+TFeLaNgFIyCUTDSAADKo04rNo2ppwAAAABJRU5ErkJggg==","orcid":"","institution":"Meiji University of Integrative Medicine","correspondingAuthor":true,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Hiraide","suffix":""}],"badges":[],"createdAt":"2025-04-30 04:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6561109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6561109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82858897,"identity":"28791d9c-07ec-464f-914d-91ac196ddfe6","added_by":"auto","created_at":"2025-05-16 06:09:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":994276,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6561109/v1/694e0d4c-e2ef-4225-8f24-1f525b4f0a3b.pdf"},{"id":82513741,"identity":"13997b48-5bc6-4eed-bda2-bf83a344564f","added_by":"auto","created_at":"2025-05-12 11:14:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21652,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File1 (Additional Table1.docx)\u003c/p\u003e\n\u003cp\u003eMissing data table (Seacoast vs Non-seacoast)\u003c/p\u003e","description":"","filename":"AdditionalInformationTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6561109/v1/5909f876e98d9eb219a9a982.docx"},{"id":82513739,"identity":"0a5acd8a-7497-4a23-bb59-7fc7340e7fb6","added_by":"auto","created_at":"2025-05-12 11:14:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29852,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File2 (Additional Table2.docx)\u003c/p\u003e\n\u003cp\u003eCauses of injury or illness in patients by season (seacoast vs non-seacoast) and cardiac arrest rate upon arrival at hospitals\u003c/p\u003e","description":"","filename":"AdditionalinformationTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6561109/v1/001c4f50c8c5b63ada85d553.docx"},{"id":82513744,"identity":"48407cff-0348-4ca0-8171-384b5a9acb98","added_by":"auto","created_at":"2025-05-12 11:14:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19681,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File3 (Additional Table3.docx)\u003c/p\u003e\n\u003cp\u003eResult of the Rosenberg sensitivity test\u003c/p\u003e","description":"","filename":"AdditionalInformationTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6561109/v1/286eacfe4da61a68b4501137.docx"},{"id":82513750,"identity":"32f477fb-7316-461c-b9ad-4375e4f21b9f","added_by":"auto","created_at":"2025-05-12 11:14:49","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10147229,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the location of occurrence of incidents (seacoast vs non-seacoast)\u003c/p\u003e","description":"","filename":"AdditionalInfornmationFigure.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6561109/v1/628aafd565d2b30ac94e568e.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cardiac arrest rates upon arrival at the hospital among patients transported by ambulances from the seacoast","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSeacoasts are generally considered to be dangerous locations with drownings being a typical geographical threat [1,2]. Some reports have focused on the epidemiology of drowning in seacoasts [3,4]. However, these reports covered a limited area, whereas other reports are limited to certain age groups that focus on children [5,6]. Considering these situations, a summative verification based on a population-based registry, not just of a limited region, age, and cause of injury or illness, is needed. Notably, drowning, other types of injuries, or medical emergencies are health threats that occur in coastal settings [7]. Therefore, we conducted an inclusive epidemiological survey of patients transported from seacoasts to clarify the epidemiological features of acute health risks to seacoasts. Using nationwide inclusive data concerning patients transported by ambulances in the emergency medical system in Japan, we described the characteristics of patients transported from seacoasts compared with those transported from other locations, with a particular focus on the rate of cardiac arrest upon arrival at hospitals.\u003c/p\u003e \u003cp\u003eIf the ratio of cardiac arrest among patients transported from the seacoast is greater than that of other patients, exploring this cause may broaden the understanding of the background features of the acute health risk of the seacoast and be utilized to promote safety. Therefore, we investigated the influence of related factors that characterize coastal patients on the rate of cardiac arrest.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eThis nationwide observational study was based on data provided by the Fire and Disaster Management Agency (FDMA) of Japan. The data were obtained from a population-based registry of patients transported to the hospital by ambulance. In this study, we enrolled all patients who were transported by ambulance between January 1, 2020, and December 31, 2021.\u003c/p\u003e \u003cp\u003e This study was approved by the Research Ethics Committee of Kyoto Tachibana University (24\u0026thinsp;\u0026minus;\u0026thinsp;14) and exempted it from informed consent. All research was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Setting\u003c/h3\u003e\n\u003cp\u003eThe study area covered Japan, spanning 377,976 km\u003csup\u003e2\u003c/sup\u003e as of 2020 [8]. Its population was 126,146,099 (61,349,581 men and 64,796,518 women) as registered in the national census in 2020 [9].\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData including sex (male or female), age, location of occurrence, response time, survival status upon arrival at the hospital, and cause of injury or illness were collected as variables for analyses. The response time was defined as the time period from the emergency call to the contact of the ambulance crew with the patient. Survival status and the types of injuries or illnesses were diagnosed by the physicians in charge of initial care upon arrival at the hospital. The types of injuries or illnesses were categorized according to the ICD-10 [10]. With respect to survival status, we collected data on whether the patients were in cardiac arrest upon arrival at the hospitals. Cardiac arrest upon arrival at the hospital was defined as the absence of signs of circulation at the time of ambulance arrival at the hospital. These cases are nothing more than out-of-hospital cardiac arrest, except patients who were successfully resuscitated until arrival at the hospital.\u003c/p\u003e\n\u003ch3\u003eAnalyses\u003c/h3\u003e\n\u003cp\u003eTo elucidate the health risks associated with seacoasts, we compared the rates of cardiac arrest upon arrival in patients transported from seacoasts to other locations. We also described other factors, such as sex, age, cause of injury or illness, and response time between the two groups to clarify the characteristics of coastal patients, with a focus on drowning for cause of injury or illness. We then conducted a logistic regression analysis to examine the associations between incident location (seacoast vs non-seacoast) and factors such as sex, age, cause of injury or illness (drowning vs not drowning), and response time. These factors were set as explanatory variables, and the location of the incident (seacoast vs non-seacoast) was set as the outcome variable. Using the logistic regression analysis, propensity scores were calculated for each patient based on the basis of these explanatory variables, to estimate the probability that the patient was transported from the seacoast. Patients from the seacoast were matched with those from other locations on the basis of propensity scores. One-to-one greedy nearest neighbor matching without replacement was performed [11] with a caliper width set to 0.2 standard deviations of the propensity score [12]. The balance between the two groups was evaluated via the Wilcoxon rank-sum test for continuous variables and Pearson's chi-square test for categorical variables following the matching. Propensity score matching was conducted via the MatchIt package [13].\u003c/p\u003e \u003cp\u003eSensitivity analyses were performed to assess the degree of unmeasured confounders on the basis of Rosenbaum\u0026rsquo;s sensitivity analysis [14]. For this analysis, we used the rbounds package [15].\u003c/p\u003e \u003cp\u003ePropensity score matching and sensitivity analyses were conducted in R version 4.2.0 (R Foundation for Statistical Computing). All other statistical analyses were performed via IBM SPSS version 29. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eDuring the 2-year study period, 9,896,971 patients were transported by ambulance to a hospital in Japan. Among the patients, 7003 cases were from the seacoast and 9,780,140 were from other locations. In the remaining 109,828 cases, the locations where incidents occurred were unknown. Missing values for patients in the study were shown in additional table [see Additional file 1]. Patients with missing values, representing 0.9% of patients from seacoast and 2.6% from other locations, were excluded from logistic regression analysis and propensity score matching.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacteristics of patients from seacoast\u003c/h3\u003e\n\u003cp\u003ePatients from the seacoast were clearly differed from the other patients in terms of sex and age. The number of male patients was greater (78.3%) than the number of female patients, whereas it was approximately the same among the other patients. The median age of patients from the coast was 49.0 years (interquartile range [IQR] of 29\u0026ndash;67 years. This number was markedly lower than that of patients form non-seacoast in which the median age was 72.0 years (IQR, 49\u0026ndash;84 years). The cardiac arrest rate upon hospital arrival also differed between the two groups. Among patients from the seacoast 9.1% were in cardiac arrest upon arrival at the hospital, whereas only 1.6% of patients transported from other locations were in cardiac arrest. Among the factors related to this different survival state, a longer response time was observed in patients from the seacoast than in those from other locations. The median response time was 13.0 min with an IQR of 10\u0026ndash;19 min, in patients from the seacoast. Those duration were 10.0 and 8\u0026ndash;12 min in the patients from the other locations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients by location of incidents occurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of occurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients, No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex (Male),\u003c/p\u003e \u003cp\u003eNo (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge, Median (IQR)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCardiac arrests No (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResponse time, Median (IQR)*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeacoast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7,003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5,453 (78.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49 (29\u003c/b\u003e\u0026ndash;\u003cb\u003e67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e637 (9.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13 (10\u003c/b\u003e\u0026ndash;\u003cb\u003e19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon\u003c/b\u003e-\u003cb\u003eseacoast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,780,140\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4,928,064 (50.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e72 (49\u003c/b\u003e\u0026ndash;\u003cb\u003e84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e155,149 (1.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e10 (8\u003c/b\u003e\u0026ndash;\u003cb\u003e12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,463,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,151,587 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (54\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110,458 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic access facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCinemas/Theaters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,640 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (30\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305,253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156,444 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (34\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,271 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,115 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (44\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60,820 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (10\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraffic facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86,948 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (28\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e942 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntertainment facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53,218 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (19\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e462 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,744 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (60\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBathhouses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,982 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (60\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e603 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing homes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e867,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313,155 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (82\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,885 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHotels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,858 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (32\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e748 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (8\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic facilities and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79,882 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (33\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e886 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (6\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkplaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102,438 (80.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (36\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,239 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffices and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79,412 (63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (35\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e909 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,187,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e703,245 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (33\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,040 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,219 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (11\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e543 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilderness areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29,052 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (59\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,256 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (10\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiver/Pond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,856 (67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (39\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e917 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (9\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRailway territory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,449 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (29\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (Unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,769 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (46\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e972 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (6\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,896,971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,981,286 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (49\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156,758 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*IQR: interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe causes of incidents also differed between seacoast and non-seacoast patients. Drowning accounted for 17% of cases among seacoast patients, whereas it accounted for only 0.1% of the cases among patients from other locations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCause of injury or illness of patients from the seacoast vs non-seacoast including rate of cardiac arrest upon hospital arrival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSeacoast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-seacoast\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients, No (column %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardiac arrest No (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients, No (column %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCardiac arrest No (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrowning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,196 (17.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e384 (32.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12,830 (0.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3,938 (30.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther than drowning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5,807 (82.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e253 (4.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9,767,310 (99.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e151,211 (1.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,599 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,111,975 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,918 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoisoning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,687 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,034 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther external cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,751 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,845,285 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,994 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e540,342 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,168 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e612,638 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56,448 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e609,027 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,991 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e913,241 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,015 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e297,469 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e576 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49,682 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223,075 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental and behavioral disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e432,271 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye or ear diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e315,057 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e176 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy or perinatal disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,367 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther internal cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,764,521 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56,507 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnidentified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,639 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e991 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,003 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e637 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,780,140 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155,149 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAssociations of the factors with the locations of incidents\u003c/h3\u003e\n\u003cp\u003eThe results of the logistic regression analysis revealed that sex, age, response time, and diagnosis (drowning vs not drowning) were clearly associated with the location of the incident occurred (seacoast vs non seacoast; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As both univariate and multivariate analyses demonstrated, males were more likely to experience incidents at seacoasts than females were. The adjusted odds ratio for males was 3.1 (vs females). Both the crude and adjusted odds ratios for age revealed that younger age was closely associated with the seacoast. A longer response time was also associated with seacoast in both the crude and adjusted odds ratios. Patients from seacoast are more likely to suffer from drowning. The adjusted odds ratio of drowning for patients from the seacoast was 164 in multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of logistic regression for location of occurrence of incidents (patients from seacoast vs from non-seacoast)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.277\u0026ndash;3.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u0026ndash;0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse Time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.012\u0026ndash;1.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCause (Drowning)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146.985\u0026ndash;167.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.930\u0026ndash;3.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u0026ndash;0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse Time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.011\u0026ndash;1.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCause (Drowning)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.369\u0026ndash;175.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe area under the receiver operating characteristic curve in this binary logistic regression analysis was 0.81, indicating the model's good discriminatory ability in predicting whether an incident occurred at sea or at other locations on the basis on sex, age, response time, and whether the patient suffered drowning, as demonstrated in an additional figure [see Additional file 3].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCardiac arrest rate after propensity score matching\u003c/h2\u003e \u003cp\u003eMatching on the basis of the propensity scores calculated via logistic regression analysis revealed that 6,396 pairs were formed between patients from the seacoast and patients from other locations. The variables of sex, age, response time, and whether the patient suffered from drowning between the two groups were balanced via this procedure, and no significant differences were detected between patients from the seacoast location and those from other locations.\u003c/p\u003e \u003cp\u003eThe difference in cardiac arrest rates upon hospital arrival decreased after matching. However, even after matching, the cardiac arrest rates remained significantly different, occurring in 6.8% of seacoast patients, whereas in non-seacoast patients it was 4.6% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of propensity score matching (patients from seacoast vs from non-seacoast)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeacoast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-seacoast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeacoast \u003c/p\u003e \u003cp\u003e(No.= 6,498)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-seacoast \u003c/p\u003e \u003cp\u003e(No. = 6,498)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, No (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,453 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,927,506 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,032 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,077 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,515 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,748,343 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,466 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,421 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (29\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (49\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (29\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (30\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse time, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (10\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (10\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause, No (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrowning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,189 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,575 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e743 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e744 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,801 (83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,671,448 (99.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,755 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,754 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest, No (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e637 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155,149 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e474 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e315 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot cardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,353 (90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,528,874 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,024 (92.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,183 (95.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResult of the sensitivity analysis\u003c/h2\u003e \u003cp\u003eAlthough the model showed good discriminatory ability between an incident occurring at sea and at other locations, the result of Rosenbaum\u0026rsquo;s sensitivity analysis revealed that the upper bound of the p-value exceeded 0.05 when the gamma value surpassed 1.5 (p\u0026thinsp;=\u0026thinsp;0.0798 at Gamma\u0026thinsp;=\u0026thinsp;1.5) indicating that the potential influence of hidden bias was not accounted for in the matching procedure [see Additional file 3].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo date, the number of prior studies in the nationwide epidemiological survey of emergency cases from seacoasts has been limited. We believe that this study provides data for promoting coastal safety and adds to the basic knowledge.\u003c/p\u003e \u003cp\u003eA major finding of our survey was the high percentage of cardiac arrest in patients from the seacoast upon hospital arrival. The rate of cardiac arrest upon hospital arrival was approximately 10% among patients from the coast, which contrasts with the \u0026lt;\u0026thinsp;2% rate reported in the patients from other locations. Therefore, clarifying the background characteristics of patients from the seacoast is essential for understanding the health risk of the seacoast.\u003c/p\u003e \u003cp\u003eThe demographic features, such as sex and age, of patients from the coast demonstrated unique characteristics; they had a greater proportion of males, approximately half of the patients from other locations, and they were clearly younger. Although these characteristics have been reported in other regions [3], describing and confirming these findings via large-scale epidemiological data may be worthwhile. However, these demographic characteristics do not explain the high percentage of cardiac arrests in patients transported from seacoasts.\u003c/p\u003e \u003cp\u003eTime is an important factor in emergencies. In general, densely populated areas where emergency dispatch centers are located are distant from the coast. Therefore, response time should be highlighted, and findings that the response time extended in patients from the coast must be noted. In addition, the close relationship between drowning and patients from seacoast accentuates the characteristics of these patients. We speculated that these two factors play a major role in explaining the high percentage of cardiac arrests upon hospital arrival. Considering the good discrimination between patients from the seacoast and those from other locations in the logistic regression analysis, which was demonstrated by a sufficiently high area under the receiver operating characteristic curve, we expected the survival status of these patients to be equal after propensity matching. As expected, the difference in the outcomes between the two groups narrowed after propensity score matching. However, significant differences were still observed in the outcomes. The differences remaining after matching should be discussed in more detail.\u003c/p\u003e \u003cp\u003eThe most plausible answer is the existence of hidden variables. The result of the Rosenberg sensitivity analysis also suggests the existence of hidden bias in the matching procedure, while the result demonstrates some level of robustness. The cause of injury or illness factor should be reconsidered in particular, as the only factor included was whether the injury or illness was drowning. Since the percentage of cardiac arrests due to drowning was similar between seacoast and non-seacoast patients, we adjusted for this variable by focusing on drowning cases.\u003c/p\u003e \u003cp\u003eTwo possible points might be considered to explain remaining the difference in the percentage of cardiac arrest. Traumatic risk among seacoasts was indicated in a previous report [16]. However in our result, although we had more traumatic cases from seacoast, the number of cases of cardiac arrests was limited. Instead, we noted the high percentage of case occurrences and cardiac arrests in the category of \u0026ldquo;other external cause.\u0026rdquo; Although we could not clarify the detailed patient information in this category, consideration of the influence of environmental factors, such as heat stroke, might be an issue for further investigation. This is because cardiac arrest cases due to other external causes tended to occur during the summer [see Additional file 2]. Another possible explanation is the high percentage of cardiac arrests due to cardiovascular diseases among the patients from seacoast. Although patients with cardiovascular diseases transported by ambulances tend to be seriously ill in general, patients from the coast were particularly critical, with one-third experiencing cardiac arrest. Thus, speculation regarding causes other than drowning may be needed through a more detailed epidemiological survey. Since the epidemiological features of drowning patients were reported at a younger age and in males [17], an epidemiological survey of patients with other causes of illness, including cardiovascular disease, may be expected in future studies.\u003c/p\u003e \u003cp\u003eThe most important limitation of this study is the accuracy and quality of the recorded data. In the Japanese ambulance system, some variables, such as the time of the emergency call, are recorded automatically. However, many variables, including other time factors, must be recorded manually by ambulance crews. Data missing are inevitable in this large-scale comprehensive survey. Our missing table shows the tendency that if one of the categories is missing in the case, this case contains two or more missing data. For example, if the location of injury occurrence or illness was missing, the percentage of missing sex data reached 17% [see Additional file 1]. The number of cases without missing data from the seacoast and other locations reached 99.1% and 97.4%, respectively. Therefore, to obtain unbiased results in our dataset, we excluded cases containing missing data from logistic regression analysis and propensity score matching.\u003c/p\u003e \u003cp\u003eAnother important limitation of this study is that the physicians\u0026rsquo; decisions were recorded by the ambulance crew only at initial hospital care for transported patients. The cause of injury or illness might be difficult to determine in some of cases particularly with respect to internal causes. However, we believe that drowning as the cause injury was accurately decided. Survival status (cardiac arrest vs noncardiac arrest) was also accurately determined.\u003c/p\u003e \u003cp\u003eTherefore, we conclude that the percentage of patients with cardiac arrest from seacoasts on hospital arrival was clearly greater than that of patients from other locations. Among the characteristics of patients from the seacoast, longer response times and a higher percentage of drowning cases contributed to a high percentage of cardiac arrest on hospital arrival. However, even after adjusting for these factors through propensity score matching, the rate of cardiac arrest remained high among the patients from seacoast. Thus, other injuries or illnesses may also contribute to a higher percentage of cardiac arrests in these patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval of the research protocol: This study was approved by the Research Ethics Committee of Kyoto Tachibana University (24-14).\u003c/p\u003e\n\u003cp\u003eInformed Consent:\u0026nbsp;The ethics committees waived the need for informed consent for this study.\u003c/p\u003e\n\u003cp\u003eRegistry and the Registration No. of the study/Trial: N/A\u003c/p\u003e\n\u003cp\u003eAnimal\u0026nbsp;studies: N/A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003enterest\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report\u0026nbsp;that\u0026nbsp;there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Ambulance Service Planning Office\u0026nbsp;of\u0026nbsp;the\u0026nbsp;Fire and Disaster Management Agency for\u0026nbsp;providing\u0026nbsp;the database. We would like to thank Editage (www.editage.com) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, Atushi Hiraide, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCH and AH designed and conceived this study. KS collected data. CH and AH analyzed and interpreted the results and drafted the manuscript. KF and HY supported statistical analyses. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKoon W, Peden A, Lawes JC, et al. Coastal drowning: A scoping review of burden, risk factors, and prevention strategies. PLoS One. 2021; 1: e0246034. doi: 10.1371/journal.pone.0246034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbelairas-G\u0026oacute;mez C, Tipton MJ, Gonz\u0026aacute;lez-Salvado V, et al. Drowning: epidemiology, prevention, pathophysiology, resuscitation, and hospital treatment. Emergencias. 2019; 31: 270\u0026ndash;280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessereau J, Fournier N, Mokhtari T, Brun PM, Desplantes A, Grassineau D, et al. Epidemiology of unintentional drowning in a metropolis of the French Mediterranean coast: a retrospective analysis (2000\u0026ndash;2011). Int J Inj Contr Saf Promot. 2016; 23: 317\u0026ndash;322. doi: 10.1080/17457300.2015.1047862.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein AHF, Santana GG, Diehl FL, Menezes JT. Analysis of Hazards Associated with Sea Bathing: Results of Five Years Work in Oceanic Beaches of Santa Catarina State, Southern Brazil. J Coast Res. 2003; S1(35) : 107\u0026ndash;116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb AC, Wheeler A, Ricci A, Foxworthy B, Hinten B, Shah N et al. Descriptive Epidemiology of Pediatric Drowning Patients Presenting to a Large Southern US Children's Hospital. South Med J 2021; 114: 266\u0026ndash;270. doi: 10.14423/smj.0000000000001250..\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen N, Scolnik D, Rimon A, Balla U, Glatstein M. Childhood Drowning: Review of Patients Presenting to the Emergency Departments of 2 Large Tertiary Care Pediatric Hospitals Near and Distant From the Seacoast. Pediatr Emerg Care 2020; 36: e258\u0026ndash;e262. doi: 10.1097/pec.0000000000001394.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly S, Daw S, Lawes JC. Beyond drowning: Characteristics, trends, the impact of exposure on unintentional non-drowning coastal fatalities between 2012 and 22. Aust N Z J Public Health 2024; 4: 100113.doi: 10.1016/j.anzjph.2023.100113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeospatial Information Authority of Japan. The Report of Statistical reports on the land area by prefectures and municipalities in Japan in 2020 Japan; 2022. Ministry of Land, Infrastructure and Transport of Japan. https://www.gsi.go.jp/KOKUJYOHO/MENCHO/backnumber/GSI-menseki20201001.pdf. Accessed 29th April 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistics Bureau of Japan. 2020 Population Census; 2022. Ministry of Internal Affairs and Communications of Japan. https://www.e-stat.go.jp/en/stat-search/files?page=1\u0026amp;layout=datalist\u0026amp;toukei=00200521\u0026amp;tstat=000001136464\u0026amp;cycle=0\u0026amp;year=20200\u0026amp;month=24101210\u0026amp;tclass1=000001136466 Accessed 29th April 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. ICD-10: International statistical classification of diseases and related health problems: Tenth revision. 2nd ed. Geneva: WHO; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenbaum PR, Rubin DB. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. Am Stat 1985; 39: 33\u0026ndash;38. doi:10.2307/2683903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10: 150\u0026ndash;161. doi:10.1002/pst.433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo D, Imai K, King G, Stuart, E A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw 2011; 42: 1\u0026ndash;28. doi:10.18637/jss.v042.i08\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenbaum, P. R. (1987). Sensitivity Analysis for Certain Permutation Inferences in Matched Observational Studies. Biometrika, 74, 13\u0026ndash;26. Doi.org/10.1093/biomet/74.1.13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeele LJ. Perform Rosenbaum Bounds Sensitivity Tests for Matched and Unmatched Data. https://www.vps.fmvz.usp.br/CRAN/web/packages/rbounds/rbounds.pdf Accessed 29th April 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThom O, Roberts K, Leggat PA, Devine S, Peden AE, Franklin RC. Cervical spine injuries occurring at the beach: epidemiology, mechanism of injury and risk factors. BMC Public Health 2022; 22: 1404. doi: 10.1186/s12889-022-13810-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErasmus E, Robertson C, van Hoving DJ. The epidemiology of operations performed by the National Sea Rescue Institute of South Africa over a 5-year period. Int Marit Health 2018; 69: 1\u0026ndash;7. doi: 10.5603/imh.2018.0001.\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":"Seacoast, Ambulance, Emergency medical Services, Out-of-hospital cardiac arrest, Drowning","lastPublishedDoi":"10.21203/rs.3.rs-6561109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6561109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeacoasts are generally considered dangerous, and the situation of patients being transported from the coast to hospitals for emergency care has not been fully examined. To clarify the acute health risk associated with the seacoast, we investigated the survival status of patients who arrived at the hospital following transportation from the seacoast by ambulance compared with patients from other locations, and analyzed related factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAll patients who were transported by ambulance to hospitals between 2020 and 2021 in Japan were enrolled in this study. Patients transported from the seacoast were compared with the patients from other locations with respect to the cardiac arrest rate as a survival state. The variables of sex, age, response time, and cause of injury or illness were also compared between the two groups. To adjust for the influence of these variables, we conducted propensity score matching between the two groups and compared the cardiac arrest rates again.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the enrolled patients, 7,003 were from coastal areas, and 9,780,140 were from other locations. Among seacoast patients, 9.1% experienced cardiac arrest upon hospital arrival, whereas 1.6% of patients from other locations experienced cardiac arrest (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, patients from the seacoastal region were more likely to be male, younger, have a longer response time, and have a higher percentage of drowning. Even after adjusting for these factors via using propensity score matching, the cardiac arrest rate still remained higher in the patients from the seacoast (7.3% vs 4.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEven if the characteristic factors of patients from the seacoast, including longer response time and a high percentage of drowning were adjusted by propensity matching, the rate of cardiac arrest remained higher in these patients.\u003c/p\u003e","manuscriptTitle":"Cardiac arrest rates upon arrival at the hospital among patients transported by ambulances from the seacoast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 11:14:43","doi":"10.21203/rs.3.rs-6561109/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":"048d6720-0e18-49d3-93c1-30fb967c7328","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-16T05:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 11:14:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6561109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6561109","identity":"rs-6561109","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}