Presumed Regional Incidence Rate of Out-of-Hospital Cardiac Arrest in Korea

Young Sun Ro; Seung-sik Hwang; Sang Do Shin; Daikwon Han; Sungchan Kang; Kyoung Jun Song; Sung-il Cho

doi:10.3346/jkms.2015.30.10.1396

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Ro, Hwang, Shin, Han, Kang, Song, and Cho: Presumed Regional Incidence Rate of Out-of-Hospital Cardiac Arrest in Korea

Original Article

Occupation & Environmental Medicine

Journal of Korean Medical Science 2015; 30(10): 1396-1404.

Published online: 12 September 2015

DOI: https://doi.org/10.3346/jkms.2015.30.10.1396

Presumed Regional Incidence Rate of Out-of-Hospital Cardiac Arrest in Korea

Young Sun Ro¹

, Seung-sik Hwang²

, Sang Do Shin³

, Daikwon Han⁴

, Sungchan Kang²

, Kyoung Jun Song³

, Sung-il Cho⁵

¹Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea.

²Department of Social & Preventive Medicine, Inha University School of Medicine, Incheon, Korea.

³Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea.

⁴Department of Epidemiology & Biostatistics, Texas A&M University, College Station, TX, USA.

⁵Department of Preventive Medicine, Graduate School of Public Health, Seoul National University, Seoul, Korea.

Address for Correspondence: Seung-sik Hwang, MD. Department of Social & Preventive Medicine, Inha University School of Medicine, 366 Seohae-daero, Jung-gu, Incheon 22332 Korea. Tel: +82.32-890-0972, Fax: +82.2-766-1185, cyberdoc@inha.ac.kr

Received 3 March 2015 Accepted 23 June 2015

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The regional incidence rates of out-of-hospital cardiac arrest (OHCA) were traditionally calculated with the residential population as the denominator. The aim of this study was to estimate the true incidence rate of OHCA and to investigate characteristics of regions with overestimated and underestimated OHCA incidence rates. We used the national OHCA database from 2006 to 2010. The nighttime residential and daytime transient populations were investigated from the 2010 Census. The daytime population was calculated by adding the daytime influx of population to, and subtracting the daytime outflow from, the nighttime residential population. Conventional age-standardized incidence rates (CASRs) and daytime corrected age-standardized incidence rates (DASRs) for OHCA per 100,000 person-years were calculated in each county. A total of 97,291 OHCAs were eligible. The age-standardized incidence rates of OHCAs per 100,000 person-years were 34.6 (95% CI: 34.3-35.0) in the daytime and 24.8 (95% CI: 24.5-25.1) in the nighttime among males, and 14.9 (95% CI: 14.7-15.1) in the daytime, and 10.4 (95% CI: 10.2-10.6) in the nighttime among females. The difference between the CASR and DASR ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. Through the Bland-Altman plot analysis, we found the difference between the CASR and DASR increased as the average CASR and DASR increased as well as with the larger daytime transient population. The conventional incidence rate was overestimated in counties with many OHCA cases and in metropolitan cities with large daytime population influx and nighttime outflow, while it was underestimated in residential counties around metropolitan cities.

Keywords: Out-of-Hospital Cardiac Arrest, Incidence, Epidemiology

INTRODUCTION

Out-of-hospital cardiac arrest (OHCA) is a global health problem and incidences of OHCA vary greatly across different regions not only within countries but also between countries (1 2 3 4). Resource allocation to improve OHCA survivals targets communities with either high incidences or low survival rates. To implement effective community interventions, the true incidence rates and survival outcomes of OHCA must be evaluated.

Previous OHCA studies have extensively reported on the definite temporal variability and time-geographic distribution patterns (5 6 7 8). Epidemiological reports support the circadian variability with a daytime excess and overnight nadir in the occurrence of OHCAs (5 6 7 9). Furthermore, incidence and mortality are not uniformly distributed geographically; distinct hot spots exist in which these variables are greater than expected (1 8 10 11 12 13). Understanding temporal variability and geographic distribution observations provides a framework that may lead to the maximization of the benefits of interventions to increase the probability of a favorable outcome (5 9 12 13 14).

The regional OHCA incidence rates were traditionally calculated with the number of incidence cases in the region as the nominator and the residential population as the denominator (population at risk during a specific period of time) (1 2 3 8 9 10 11 12 13). Based on the regional OHCA incidence rates, interventions to improve OHCA outcomes, including ambulances and autonomic external defibrillators, were strategized and provided in high-risk regions. However, it is possible that certain areas may have more transient persons at risk during the daytime hours, resulting in a higher absolute number of arrests (12). For example, town centers concentrated with a large number of offices, railway stations and international airports were reported as hot spots for OHCA with an unexpected cluster attributing to the influx of daytime transient population and nonresident arrests (8 15).

The daytime transient population who migrate inter-regions for work or school during the daytime hours should be considered to calculate the incidence rate of acute medical events such as OHCA. The accurate measurement of event incidence may not be informative in determining the prevalence of chronic diseases, but it is a crucial component in effective resource allocation when developing community interventions for acute medical illness. Furthermore, because OHCAs have circadian variability and geographic distribution patterns, the daytime transient population should be considered to calculate the regional incidence rate in order to determine the true high risk population (2 5 6 7 8 15). To date, there have been few studies that incorporated the daytime transient population in calculations to investigate the OHCA incidence rate.

The goal of this study was to estimate the true regional incidence rate of OHCA in Korea and to investigate the characteristics of the regions with large differences between the previously reported rate and the newly calculated incidence rate that considers nighttime residential population and daytime transient population as population at risk.

MATERIALS AND METHODS

Data source and setting

The cardiovascular disease surveillance (CAVAS) database is a large-scale, nationwide, retrospective observational database of patients in Korea who have experienced confirmed OHCA (16 17 18 19). The database is population-based and contains emergency medical service (EMS)-assessed OHCAs among the entire population of the country. The cases were abstracted from the ambulance run sheets in which an OHCA was coded. For this study, data from January 2006 to December 2010 were used.

Ambulance run sheets are electronically stored in each provincial EMS headquarter, which is operated by the respective fire department. Trained medical record reviewers visited the study hospitals and reviewed the medical records to collect information related to risks and outcomes using the Utstein guidelines for reporting cardiac arrest and resuscitation data (20). All reviewers were formally trained and were provided with an operation manual to accurately abstract the data from the medical records and transcribe the data onto case report forms.

The Korean EMS system is single tiered and sponsored by the government. Basic-to-intermediate service level of ambulances are operated by 16 provincial headquarters of the national fire department. There are approximately 1,350 ambulance stations across the nation, which serve 48 million people over an area of approximately 100,000 square kilometers. The annual run volume of the prehospital service was slightly greater than 1.4 million. Two or three crew members usually ride in each ambulance.

Study population

Korea is comprised of 248 counties, the boundaries of which are defined by statute for geographical administrative purposes. The counties are classified as metropolitan (Gu), urban (Si), and rural (Gun) according to their population size. The metropolitan counties (Gu) are segmented administrative districts of a metropolitan city (for example, Seoul) with populations of more than 500,000; the urban counties (Si) are located in urban areas with populations of more than 100,000; the rural counties (Gun) are located in rural areas with populations of less than 100,000. There were 95 metropolitan, 67 urban, and 86 rural counties in 2010, and the median population sizes and population densities of each level were 298,748 residents (range: 47,260 to 640,732) and 8,873.0 people per square kilometer (range: 358.5 to 28,731.2) in metropolitan (Gu) counties, 183,673 residents (range: 41,395 to 565,201) and 531.7 people per square kilometer (range: 60.9 to 9,226.6) in urban (Si) counties, and 41,481 residents (range: 7,737 to 185,811) and 77.0 people per square kilometer (range: 19.4 to 495.7) in rural (Gun) counties (16). Each county has its own health care authorities and administrative authorities that serve the population.

Eligible patients were hospital record-confirmed OHCA patients during the five years of the study period. Each OHCA was assigned to a county according to the location of collapse because neighborhood factors including bystanders' early response, nearest autonomic external defibrillators, and prompt EMS resuscitative efforts are critical to survival outcomes. If a case was missing data on the location of collapse, the address of fire department that had the response ambulance was used. The time and date of the call received at dispatch was used as a surrogate measure for the time of OHCA occurrence (5). The time of day was divided into two 12-hr time intervals (0601-1800, 1801-0600), which were defined as daytime (0601-1800) and nighttime (1801-0600) to maintain comparability with previous reports (5).

Outcome measure

Information about the Korean population was obtained from the Korea Census data (Supplementary Table). The nighttime residential and daytime transient populations were investigated in the 2010 Census. The daytime transient population was calculated based on internal migration statistics as provided by the Korean National Statistical Office (KNSO). The KNSO reports such statistics based on survey results from 10% of the sampled population.

The nighttime population was the same as the registered residential population. The daytime population was defined by adding the daytime influx of population to the nighttime residential population and subtracting the daytime outflow. The D index (Daytime population index) was defined as the ratio of the daytime population to the nighttime population and was calculated by dividing the daytime population by the nighttime population and multiplying the result by 100.

The conventional age-standardized incidence rates (CASRs) for EMS-assessed OHCA per 100,000 person-years by gender were calculated for each county using the total nighttime population in 2010 as the standard population. Daytime corrected age-standardized incidence rates (DASR) of EMS-assessed OHCA per 100,000 person-years were calculated with standardized daytime incidence rates and standardized nighttime incidences. To reflect the actual risk population, the daytime incidences were calculated from the OHCAs that occurred during the daytime and from the daytime population, and the nighttime incidences were calculated from the OHCAs that occurred during the nighttime and from the nighttime population.

Statistical analysis

The distributions of categorical variables are reported as percentages. The continuous variables that were not distributed normally are presented as medians with interquartile ranges. The CASRs and DASRs are presented in quintiles. Bland-Altman plots were used to compare the CASRs and DASRs by gender. The CASR, DASR, D index, the daytime and nighttime population, and the number of OHCAs during the 5 yr were used to evaluate the characteristics of the regions with the greatest and smallest differences between the CASR and the DASR by gender. Geographical maps of the CASRs and DASRs by gender were evaluated in order to identify the differences in the geographical distribution using administrative boundaries.

Ethics statement

The study was approved by the institutional review board (IRB) of the Seoul National University Hospital (IRB No. 1206-063-414). Informed consent was waived by the board.

RESULTS

There were 97,291 eligible EMS-assessed OHCA cases during the study period. The characteristics of the patients, community, EMS factors, and survival outcomes for OHCAs are presented by gender in Table 1.

Of the OHCAs, 34.5% and 29.4% occurred during the daytime hours from 6 AM to noon and from noon to 6 PM, respectively, whereas 20.3% and 15.8% occurred during the nighttime hours from 6 PM to midnight and from midnight to 6 AM, respectively (Fig. 1). In Korea, the age-standardized incidence rates of OHCAs per 100,000 person-years by gender and occurrence time were 34.6 (95% CI: 34.3-35.0) during the daytime and 24.8 (95% CI: 24.5-25.1) during the nighttime among males, and 14.9 (95% CI: 14.7-15.1) during the daytime and 10.4 (95% CI: 10.2-10.6) during the nighttime among females.

The mean CASR and DASR per 100,000 person-years of each county by gender were 69.2 (range 14.0-180.7) and 66.8 (range 14.7-154.6) in males and 30.8 (range 7.8-65.2) and 30.6 (range 7.8-64.6) in females, respectively.

Bland-Altman plots of the CASRs and DASRs by gender are shown in Fig. 2. In both genders, there were positive linear trends between the average CASR and DASR and the difference between the CASR and DASR. The difference between the CASR and DASR increased as the average CASR and DASR increased.

Scatter plots of the D index and the difference between the CASR and DASR by gender are presented in Fig. 3. The difference between the CASR and DASR ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. There were positive linear trends between the D index and the difference between the CASR and DASR in both genders. The difference between the CASR and the DASR increased as the D index increased.

The characteristics of the 5 regions with the greatest and the 5 regions with the smallest differences between the CASR and the DASR are evaluated in Table 2. The 5 regions with the greatest differences between the CASR and the DASR had larger D index, indicating larger daytime population and smaller nighttime population, while the regions with the smallest differences had small D index for both genders. Nine of the 10 regions with the greatest differences and 7 of the 10 regions with the smallest difference between the CASR and the DASR were in metropolitan area (-gu in county name).

Fig. 4 shows the geographical distribution of the annual CASRs and DASRs of EMS-assessed OHCAs per 100,000 person-years by gender. Several metropolitan counties had large differences between the CASR and DASR.

DISCUSSION

Defining the at-risk population was essential to measure the disease burden based on the incidence rate. The incidence rates varied depending on the denominator of the at-risk population. In this study, the difference between the CASR and DASR per 100,000 person-year ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. Although many studies have explored the incidence of OHCA, only a few equivalent quantitative studies have elucidated the differences between the nighttime population and the daytime transient population as the denominators when measuring OHCA incidence.

There were significant temporal and geographical variations in the OHCA incidence. A daytime excess and an overnight nadir of OHCAs was observed, which is generally consistent with previous studies regardless of the characteristics of the patients and arrest circumstances (5 6 7 9). In this study, 63.9% of OHCAs occurred during the daytime, comparable to the value of 59.6% reported in the ROC study (5). The greater occurrence of OHCAs during the day may be associated with endogenous rhythms and environmental factors, including temperature (21 22). Numerous epidemiological reports support the finding that there is circadian variability with a daytime excess and an overnight nadir in the occurrence of OHCA, as well as a seasonal variation with lower incidence rates in the summer and higher rates in the winter months in both hemispheres (5 6 7 9 21). Furthermore, this circadian and seasonal variability affected the survival outcomes with the lowest survival to discharge rate when the OHCA occurred during the 0001-0600 time interval and the highest during the 1201-1800 time interval, while the odds of survival to discharge were statistically lower during the winter and spring (5 9 23). These results may be due to an association between the survival outcomes of OHCA and temporal variability in the capabilities of EMS and hospital resources (21 22).

The nationwide OHCA registry includes geographic and temporal information for all OHCAs. The two maps in this study show the regional OHCA incidences by gender with the different population denominators. The differences between the CASR and the DASR of OHCA were greater in counties with a larger daytime transient population. The regions with the greatest difference between the CASR and the DASR had large D index values, indicating larger daytime population and smaller nighttime population, while the regions with the smallest differences between the CASR and the DASR had small D index values in both genders, indicating smaller daytime population and larger nighttime population. Most (16 out of 20) of the regions with the greatest and smallest differences between the CASR and the DASR in both genders were in a metropolitan area (Gu) with a large migrating population. In other words, the CASR in a commercial and industrial area of a metropolitan city with a large daytime population influx and a nighttime outflow was overestimated, whereas the CASR in a residential area of a metropolitan city with a nighttime population influx was underestimated. In addition, the Bland-Altman plots indicated that the differences between the CASRs and the DASRs tended to increase as the incidence of OHCA increased. Many studies have identified high-risk hot spots of OHCA (11 12). A previous study reported an unexpected cluster at town centers, railway stations and international airports (8 15). Although the incidences may have been affected by the daytime transient population and nonresident arrests, the daytime transient population was not considered due to the difficulty in measuring this population. In order to accurately estimate the burden of OHCA that integrates the characteristics of daytime excess and hot spots with daytime transient population, the denominator for calculating the OHCA regional incidences should be the true population at risk (2 5 6 7 8 15).

These findings have many public health implications such as resource allocation for the chain of survival including cost-effective public access automatic external defibrillator (AED) strategy, planning ambulance deployment, organizing targeted cardiopulmonary resuscitation (CPR) training, raising community awareness, and mobilizing community action (5 8). Recognition and consideration of temporal and geographical characteristics of OHCA is crucial in developing such public health measures and would greatly increase their effectiveness. Recent studies have demonstrated that the best outcomes are achieved when devices are placed in areas with a high incidence of cardiac arrests and when there is ongoing supervision of emergency plans. However, operational decisions based on conventional incidence rate analysis can be limited. For example, AEDs had been disseminated in public places such as airports, train stations, and public buildings based on incidence rate with conventional methods (8 24 25 26). However, the publicly placed AED could not cover the majority of OHCA, because the true incidence rates in residential areas were underestimated and 65% to 80% of OHCAs occur in residential areas (27 28). In this study, the conventional incidence rate was found to be overestimated in a commercial and industrial area of a metropolitan city with a large daytime population influx and a nighttime outflow, while the conventional incidence rate was underestimated in a residential area of a metropolitan city with a nighttime population influx. Areas with a greater incidence rate of OHCA based on the true at-risk population should be allocated more resources, such as bystander CPR education programs to reduce CPR initiation time, implementation of public access AED programs to shorten the time to defibrillation, and ambulances for the fastest ambulance response times (1 12 29). Thus, community interventions such as neighborhood access defibrillators and community first responder programs for cardiac arrest should be considered based on the true regional incidence rate.

Our study has limitations. Temporal variability studies of OHCA are limited by their ability to accurately determine the precise time of OHCA occurrence. The daytime transient population was not confirmed of its validity, as the data was based on the internal migration statistics, which were the reported statistics of 10% sampled population provided by the Korean National Statistical Office. We accounted the daytime transient population as circadian variability into two 12-hr time intervals. The present studies are a prelude to further analysis to understand the causes of these variations as well as the implementation of targeted interventions to reduce the variability (3 4).

In conclusion, this study was conducted in a nationwide setting to demonstrate that the regional incidence rate considering the daytime transient population is different from the conventional incidence rate of OHCA. Conventional incidence rate was found to be overestimated in counties that had many cases of cardiac arrest and in metropolitan cities with a large daytime population influx and a nighttime outflow, while the rate was underestimated in a residential area of a metropolitan city with a nighttime population influx. An accurate estimation of the burden of OHCA is essential for effective resource allocation strategies and appropriate resource planning for community support, which would enhance survival outcomes of cardiac arrest.

Figures and Tables

Fig. 1

Time distribution of out-of-hospital cardiac arrest. OHCA, out-of-hospital cardiac arrest.

Fig. 2

Bland-Altman plots of conventional age-standardized rates and daytime corrected age-standardized rates by gender. CASR, conventional age-standardized rate per 100,000 person-years; DASR, daytime corrected age-standardized rate per 100,000 person-years.

Fig. 3

Scatter plots of the D index and the difference between the conventional age-standardized rates and daytime corrected age-standardized rates. D index = daytime population/nighttime residential population ^*100.

Fig. 4

Geographical maps of conventional age-standardized rates and daytime corrected age-standardized rates by gender. CASR, conventional age-standardized rate per 100,000 person-years; DASR, daytime corrected age-standardized rate per 100,000 person-years.

Table 1

Epidemiologic characteristics of out-of-hospital cardiac arrest patients

Parameters	Total		Male		Female		P value
Parameters	N	%	N	%	N	%	P value
	97,291		63,162		34,129
Age							<0.01
Years, median (IQR)	65 (49-76)		61 (48-73)		72 (55-81)		<0.01
Primary ECG							< 0.01
Shockable^*	3,389	3.5	2,504	4.0	885	2.6
Non-shockable^†	93,902	96.5	60,658	96.0	33,244	97.4
Etiology							< 0.01
Presumed cardiac	72,273	74.3	45,982	72.8	26,291	77.0
Non-cardiac	25,018	25.7	17,180	27.2	7,838	23.0
Year							0.02
2006	15,745	16.2	10,248	16.2	5,497	16.1
2007	17,391	17.9	11,427	18.1	5,964	17.5
2008	19,333	19.9	12,470	19.7	6,863	20.1
2009	21,530	22.1	14,054	22.3	7,476	21.9
2010	23,292	23.9	14,963	23.7	8,329	24.4
Season							< 0.01
Spring (Mar to May)	24,226	24.9	15,746	24.9	8,480	24.8
Summer (Jun to Aug)	23,027	23.7	15,299	24.2	7,728	22.6
Fall (Sep to Nov)	24,677	25.4	15,933	25.2	8,744	25.6
Winter (Dec to Feb)	25,361	26.1	16,184	25.6	9,177	26.9
Hour							< 0.01
0001-0600	15,406	15.8	10,548	16.7	4,858	14.2
0601-1200	33,529	34.5	21,375	33.8	12,154	35.6
1201-1800	28,558	29.4	18,453	29.2	10,105	29.6
1801-2400	19,798	20.3	12,786	20.2	7,012	20.5
Arrest witnessed							< 0.01
Witnessed	37,154	38.2	24,603	39.0	12,551	36.8	< 0.01
Bystander CPR							< 0.01
CPR	2,078	2.1	1,424	2.3	654	1.9	< 0.01
Interval from call to EMS arrival							< 0.01
Minute, Median (IQR)	6 (5-9)		7 (5-10)		6 (5-9)		< 0.01
Interval from call to hospital arrival							< 0.01
Minute, Median (IQR)	22 (16-30)		22 (16-31)		21 (16-29)		< 0.01
Outcome
ROSC	15,365	15.8	9,867	15.6	5,498	16.1	0.05
Survival to admission	9,113	9.4	5,796	9.2	3,317	9.7	< 0.01
Survival to discharge	2,937	3.0	2,061	3.3	876	2.6	< 0.01
Good CPC	831	0.9	608	1.0	223	0.7	< 0.01

^*Shockable ECG, ventricular fibrillation, ventricular tachycardia; ^†Non-shockable ECG, asystole, pulseless electrical activity. CPR, cardiopulmonary resuscitation; ECG, electrocardiogram; ROSC, return of spontaneous circulation; CPC, cerebral performance category scale; IQR, interquartile range.

Table 2

Characteristics of the highest and lowest 5 regions of differences between conventional age-standardized rates and daytime corrected age-standardized rates per 100,000 person-years

		County name^*	CASR	DASR	Difference	D index^†	Daytime population	Residential population	OHCA at daytime^‡	OHCA at nighttime^§
Male
Highest 5
	1	Jung-gu, Seoul	104.5	69.1	35.4	396	226,128	57,113	186	137
	2	Gangseo-gu, Busan	96.5	65.4	31.1	287	74,180	25,887	86	49
	3	Jinan-gun, Jeonbuk	180.7	154.6	26.1	119	11,333	9,489	80	19
	4	Jongno-gu, Seoul	87.0	62.6	24.4	265	192,662	72,607	210	133
	5	Jung-gu, Busan	107.9	84.5	23.4	197	44,513	22,547	89	67
Lowest 5
	1	Jung-gu, Ulsan	56.1	67.7	-11.6	70	76,796	109,287	174	98
	2	Suji-gu, Yongin	29.3	35.4	-6.1	70	100,074	143,750	102	87
	3	Dobong-gu, Seoul	47.2	53.0	-5.8	74	122,103	165,782	212	187
	4	Yangcheon-gu, Seoul	48.7	54.5	-5.8	77	172,640	224,554	272	202
	5	Wonmi-gu, Bucheon	52.6	58.3	-5.7	78	163,030	210,326	247	198
Female
Highes 5
	1	Jung-gu, Seoul	51.0	44.9	6.1	302	179,850	59,518	81	73
	2	Jung-gu, Busan	49.8	44.8	5.0	165	40,076	24,338	47	28
	3	Jongno-gu, Seoul	35.3	30.7	4.6	214	164,942	77,228	84	57
	4	Jung-gu, Daegu	40.8	37.5	3.3	171	63,899	37,410	57	42
	5	Dong-gu, Gwangju	41.7	38.7	3.0	130	68,594	52,621	84	41
Lowest 5
	1	Gwangmyoung-si, Gyonggi	31.5	32.5	-1.0	84	139,488	166,161	129	76
	2	Euiwang-si, Gyonggi	27.7	28.7	-1.0	90	64,490	72,047	56	28
	3	Hanam-si, Gyonggi	32.7	33.6	-0.9	91	62,450	68,435	54	46
	4	Jungrang-gu, Seoul	29.6	30.5	-0.9	84	171,525	203,021	150	107
	5	Dobong-gu, Seoul	28.0	28.8	-0.8	86	151,752	177,448	133	85

^*County name was classified as metropolitan (-gu), urban (-si), and rural (-gun) area; ^†D index=daytime population/nighttime residential population ^*100; ^‡OHCA at daytime is the number of OHCAs occurred at daytime in each gender during 5 yr; ^§OHCA at nighttime is the number of OHCAs occurred at nighttime in each gender during 5 yr. CASR, conventional age-standardized rate per 100,000 person-years; DASR, daytime corrected age-standardized rate per 100,000 person-years, OHCA; out-of-hospital cardiac arrest.

Notes

^Funding This study was financially supported by the Seoul Metropolitan City Government (2008), the Korean Centers for Disease Control and Prevention (CDC) (2009-2013) and the National Emergency Management Agency (NEMA) (2008-2013) (Grant No. 0720090210 and No. 0720100440).

^DISCLOSURE All of the authors have no conflicts of interest to disclose.

^{AUTHOR CONTRIBUTION} Conception and design: Ro YS, Hwang SS. Performed the experiments: Ro YS, Hwang SS, Shin SD, Kang S. Analysis of data: Ro YS, Hwang SS, Kang S. Contributed reagents/materials/analysis tools: Ro YS, Hwang SS, Kang S. Writing: Ro YS, Hwang SS. Shin SD, Han D, Song KJ, Cho SI. Agree with manuscript results and conclusions: all authors

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