Exploring the impact of pandemic fear on visitation to park attractions in urban city: A case study in Seoul, South Korea.

This research explores changes in perceptions and utilization of parks during the COVID-19 pandemic in Seoul, South Korea. It investigates the relationship between fear of the pandemic and individuals' opinions about open spaces and their visiting decisions. The study surveyed 600 adults from February 22-23, 2022, and used structural equation modeling to analyze the data. The findings revealed that increased fear of the pandemic led to more positive park sentiments, resulting in higher park visits and fewer visits to other public spaces. The research highlights the significance of parks during the COVID-19 pandemic and how people's perceptions were influenced by their pandemic-related fear.

Temporal dynamics of public transportation ridership in Seoul before, during, and after COVID-19 from urban resilience perspective.

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning.

The Impact of Wireless Emergency Alerts on a Floating Population in Seoul, South Korea: Panel Data Analysis.

BACKGROUND: Wireless emergency alerts (WEAs), which deliver disaster information directly to individuals' mobile phones, have been widely used to provide information related to COVID-19 and to encourage compliance with social distancing guidelines during the COVID-19 pandemic. The floating population refers to the number of people temporarily staying in a specific area, and this demographic data can be a useful indicator to understand the level of social distancing people are complying with during the COVID-19 pandemic. OBJECTIVE: This study aimed to empirically analyze the impact of WEAs on the floating population where WEAs were transmitted in the early stages of the COVID-19 pandemic. As most WEA messages focus on compliance with the government's social distancing guidelines, one of the goals of transmitting WEAs during the COVID-19 pandemic is to control the floating population at an appropriate level. METHODS: We investigated the empirical impact of WEAs on the floating population across 25 districts in Seoul by estimating a panel regression model at the district-hour level with a series of fixed effects. The main independent variables were the number of instant WEAs, the daily cumulative number of WEAs, the total cumulative number of WEAs, and information extracted from WEAs by natural language processing at the district-hour level. The data set provided a highly informative empirical setting as WEAs were sent by different local governments with various identifiable district-hour-level data. RESULTS: The estimates of the impact of WEAs on the floating population were significantly negative (-0.013, P=.02 to -0.014, P=.01) across all specifications, implying that an additional WEA issuance reduced the floating population by 1.3% (=100(1-e-0.013)) to 1.4% (=100(1-e-0.014)). Although the coefficients of DCN (the daily cumulative number of WEAs) were also negative (-0.0034, P=.34 to -0.0052, P=.15) across all models, they were not significant. The impact of WEAs on the floating population doubled (-0.025, P=.02 to -0.033, P=.005) when the first 82 days of observations were used as subsamples to reduce the possibility of people blocking WEAs. CONCLUSIONS: Our results suggest that issuing WEAs and distributing information related to COVID-19 to a specific district was associated with a decrease in the floating population of that district. Furthermore, among the various types of information in the WEAs, location information was the only significant type of information that was related to a decrease in the floating population. This study makes important contributions. First, this study measured the impact of WEAs in a highly informative empirical setting. Second, this study adds to the existing literature on the mechanisms by which WEAs can affect public response. Lastly, this study has important implications for making optimal WEAs and suggests that location information should be included.

Impact of ambient temperature on respiratory disease: a case-crossover study in Seoul.

BACKGROUND: Respiratory diseases contribute to global morbidity and mortality, and temperature is a significant factor. We investigated the association between ambient temperature and emergency department (ED) visits for various respiratory diseases in Seoul, South Korea. METHODS: Using data from the National Emergency Department Information System (2008-2017), we analysed 1,616,644 ED visits for respiratory diseases, categorised according to the Korean Standard Classification of Diseases 7th revision codes (J00-J99). Using a time-stratified case-crossover design and a distributed lag nonlinear model, we investigated the effect of temperature exposure on ED visits for respiratory diseases, calculating the relative risk (RR) for the maximum risk temperature (MaxRT) of both cold and hot extremes compared to the minimum risk temperature (MinRT). RESULTS: Cold temperatures (MaxRT: -9.0 °C) resulted in a 2.68-fold increase (RR = 2.68, 95% CI = 2.26-3.14) in ED visits for total respiratory diseases, while hot temperatures (MaxRT: 29.4 °C) led to a 1.26-fold increase (RR = 1.26, 95% CI = 1.11-1.42) compared to the MinRT (24.8 °C). Cold temperatures increased the risk of most respiratory diseases, except interstitial lung disease, whereas hot temperatures increased ED visits for acute upper respiratory infections and influenza. Cold temperatures increased ED visits for all age groups, especially those aged 18-64 (RR = 3.54, 95% CI = 2.90-4.33), while hot temperatures significantly affected those < 18 (RR = 1.45, 95% CI = 1.27-1.66). The risk levels were similar in both males and females, regardless of hot and cold temperatures. CONCLUSION: Our findings underscore the significant impact of both cold and heat exposure on ED visits for respiratory diseases, with varying intensities and risk profiles across different population groups.

A comparison of S.T.O.N.E nephrolithometry scoring system, Guy's stone score, and Seoul National University Renal Stone Complexity (S-ReSC) in predicting mini-PCNL stone-free rate.

The main goal of the study is to evaluate the three stone scoring systems (S.T.O.N.E nephrolithometry scoring system (STONE), Guy's stone score (GSS) and Seoul National University Renal Stone Complexity (S-ReSC)) for the stone-free rate (SFR) of mini-PCNL. We retrospectively analyzed 72 patients who received mini-PCNL from February 2018 to October 2020. The SFR, complication rates, hospitalization days and recurrence rates were analyzed using three stone scoring systems. The result showed statistical significance in the association between scoring system and stone-free (STONE: OR 95%CI 0.409 (0.221-0.759), p = 0.0045; S-ReSC OR 95%CI 0.633 (0.401-0.999), p = 0.0497), but not GSS (OR 95%CI 0.776 (0.397-1.516), p = 0.4581). After adjusting the potential confounding factors, the area under curve (AUC) of STONE, GSS, and S-ReSC was 0.86, 0.78, and 0.81, respectively, and Akaike information criterion (AIC) of STONE, GSS, and S-ReSC was 64.65, 74.89, and 69.92, respectively. The accuracy rate of STONE, GSS, and S-ReSC was 0.81, 0.75, and 0.79, respectively. There was no statistically difference of predicting stone recurrence (p = 0.46, 0.53, 0.86), complications (p = 0.74, 0.51, 0.16) and hospitalization days (p = 0.77, 0.86, 0.87) in STONE, GSS, and S-ReSC, respectively. In conclusion, both the STONE and S-ReSC stone scoring systems are viable for predicting the SFR following mini-PCNL, especially after variable adjustment with the STONE system demonstrating superiority over S-ReSC.

Changes in total volatile organic compound concentration in Seoul subway stations before (2019) and after (2021) the COVID-19 outbreak.

Volatile organic compounds (VOCs) are major air pollutants often designated as specific hazardous or toxic. This study analyzed the trends in concentration changes and influencing factors of VOCs in underground subway stations in the Seoul Metro before (2019) and after (2021) the COVID-19 pandemic. A total of 506 samples were collected from 253 stations on lines 1-8 between May 2019 and September 2021. Total VOC concentrations in Seoul Metro increased after the COVID-19 pandemic 3.8 times over. The deeper the underground station platform, the greater the difference in the VOC concentrations between 2019 and 2021, which was positively related. Average VOC concentration was the highest (52.8 µg/m3) at a depth of 25-30 m and the lowest (23.9 µg/m3) at a depth of < 10 m in 2019. In conclusion, excessive disinfection during the COVID-19 pandemic resulted in increased VOC concentrations in the Seoul Metro, especially in the deeper underground stations. Less frequent quarantine disinfection is recommended to improve air quality.

Exploring the spatio-temporal clusters of closed restaurants after the COVID-19 outbreak in Seoul using relative risk surfaces.

This study explores the clusters of closed restaurants in Seoul in response to the COVID-19 pandemic using the relative risk surface (RRS). The RRS developed based on kernel density estimation provides alternative perspectives for finding the cluster by combining different control and case events. Specifically, the varying impacts on diverse types of restaurants are examined by comparing the densities of closed casual restaurants and cafes. The clusters of closed businesses following the COVID-19 outbreak are subsequently explored through a comparison of the densities of the closed businesses preceding the outbreak. Furthermore, this analysis estimates the clusters of declined commercial areas after the pandemic outbreak based on the comparison between the densities of opened and closed restaurants. Finally, the specific time and region of the clusters are explored using space-time RRS. The analysis results effectively demonstrate various aspects of the closed restaurant clusters. For example, in the central business areas, the densities of closed cafes have decreased after the pandemic outbreak, and the density of closed cafes is significantly higher than that of opened cafes. This study would contribute to the literature on spatial data analysis and urban policy support in response to future epidemics.

Do heat waves worsen air quality? A 21-year observational study in Seoul, South Korea.

Heat waves are generally known to deteriorate air quality. However, the impacts of heat waves on air quality can substantially vary depending on the characteristics of heat waves. In this study, we examine air quality changes in Seoul during heat waves and their associations with large-scale atmospheric patterns. For this, air quality data from 25 stations and meteorological data from 23 weather stations and reanalysis datasets during July and August of 2001-2021 are used. Under heat waves, the mean daily PM10, NO2, and CO concentrations decrease by 7.9 %, 6.1 %, and 4.6 %, respectively, whereas the mean daily PM2.5, O3, and SO2 concentrations increase by 4.1 %, 17.2 %, and 2.9 %, respectively. The atmospheric circulation under heat waves is less favorable for long-range transport of air pollutants to Seoul. The PM2.5/PM10 ratio increases under heat waves, indicating that the secondary formation of aerosols becomes more important under heat waves. 37 % of the heat wave days are accompanied by severe O3 pollution exceeding the O3 concentration standard in South Korea. There is a significant variability of air quality in Seoul within heat waves. The heat wave days with higher concentrations of PM2.5, PM10, O3, NO2, and CO than their non-heat wave means exhibit a prominent difference in large-scale atmospheric pattern from the heat wave days with lower concentrations. This difference is characterized by a zonal wave-like pattern of geopotential height, which is similar to the circumglobal teleconnection pattern known as one of the major patterns for heat waves in South Korea. This zonal wave-like pattern produces more stagnant conditions over Seoul.

Genotypic shift in rotavirus associated with neonatal outbreaks in Seoul, Korea.

BACKGROUND: Rotavirus group A (RVA) is a causative agent of acute gastroenteritis among young children worldwide, despite the global expansion of rotavirus vaccination. In Korea, although the prevalence of RVA has been reduced among young children owing to vaccination, nosocomial infections still occur among neonates. OBJECTIVES: The aim of this study was to investigate the molecular epidemiology of RVA strains associated with several neonatal outbreaks in Seoul from 2017 to 2020. STUDY DESIGN: Clinical and environmental samples were collected and screened for the presence of RVA using ELISA and PCR targeting VP6, respectively. RVA-positive strains were genotyped via RT-PCR and subsequent sequencing of VP4 and VP7 and were phylogenetically compared with RVA strains from other countries. RESULTS: During 2017-2020, a total of 15 RVA outbreaks occurred at neonatal facilities (six in hospital neonatal wards and nine in postpartum care centers) in Seoul, and only two RVA genotypes were detected: G4P[6] and G8P[6]. G8P[6] emerged in Seoul November 2018 and immediately became the predominant genotype among neonates, at least up to 2020. Phylogenetic analysis revealed that the G8P[6] genotype in this study was closely related to G8P[6] strains first identified in Korea in 2017, but differed from G8P[6] strains detected in Africa. CONCLUSIONS: A novel G8P[6] genotype of RVA strains has emerged and caused outbreaks among neonates in Seoul. Continued surveillance for circulating RVA genotypes is imperative to monitor genotype changes and their potential risks to public health.

LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data.

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

Seroprevalence of SARS-CoV-2 antibodies during the third wave of COVID-19 in the Seoul metropolitan area of Korea.

OBJECTIVES: After the third wave of coronavirus disease 2019 (COVID-19), by mid-February 2021, approximately 0.16% of the Korean population was confirmed positive, which appeared to be among the lowest rates worldwide at that time. However, asymptomatic transmission is challenging for COVID-19 surveillance. Therefore, a community-based serosurvey of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was conducted to understand the effectiveness of Korea's strong containment strategy. METHODS: We collected 5,002 residual sera samples from January 30 to March 3, 2021, from 265 medical facilities in Seoul, 346 in Gyeonggi Province, and 57 in Incheon. Sixty samples from tertiary institutions were excluded. We defined the sub-regions according to the addresses of the medical facilities where the specimens were collected. Elecsys Anti-SARS-CoV-2 was used for screening, and positivity was confirmed using the SARS-CoV-2 sVNT Kit. Prevalence was estimated using sampling weights and the Wilson score interval for a binomial proportion with a 95% confidence interval. RESULTS: Among the 4,942 specimens, 32 and 25 tested positive for COVID-19 in the screening and confirmatory tests, respectively. The overall crude prevalence of SARS-CoV-2 antibodies was 0.51%. The population-adjusted overall prevalence was 0.55% in women and 0.38% in men. The region-specific estimation was 0.67% and 0.30% in Gyeonggi Province and Seoul, respectively. No positive cases were detected in Incheon. CONCLUSIONS: The proportion of undetected cases in Korea remained low as of early 2021. Therefore, an infection control strategy with exhaustive tracing and widespread pre-emptive testing appears to be effective in containing community spread of COVID-19.

Social Efficiency of Public Transportation Policy in Response to COVID-19: Model Development and Application to Intercity Buses in Seoul Metropolitan Area.

Although more than two years have passed since the appearance of the coronavirus disease 2019 (COVID-19), few policies on public transportation have been implemented to reduce its spread. It is common knowledge that public transportation is vulnerable to COVID-19, but it has not been easy to formulate an appropriate public transportation policy based on a valid rationale. In this study, a modified SEIHR model was developed to evaluate the socioeconomic effects of public transportation policies. By applying the developed model to intercity buses in the Seoul metropolitan area, the socioeconomic efficiency of the policy of reducing the number of passengers was evaluated. The analysis showed that the optimal number of passengers decreased as the number of initially infected people increased; in addition, the basic reproduction number R0, illness cost per person, and probability of infection with a single virus were higher. However, depending on these variable conditions, the policy to reduce the number of passengers in a vehicle may not be required, so it is necessary to make an appropriate judgment according to the situation. In particular, the emergence of a new mutant COVID-19 will necessitate the development of appropriate countermeasures by comprehensively examining the change in the number of infected individuals and the fatality rate. This study can guide the development of such countermeasures.

The relative importance of macro versus micro geographical scale in explaining suicide variation in Seoul, South Korea 2014-2016.

BACKGROUND: As ecological factors are getting attention as important determinants of suicide, it is important to identify the unit at which the largest variation exists for more tailed strategy to prevent suicide. We examined the relative importance of two administrative levels for geographic variation in the suicide rate between 2014-2016 in Seoul, the capital city of Korea. METHODS: Two-level linear regression with Dongs (level 1) nested within Gus (level 2) was performed based on suicide death data aggregated at the Dong-level. We performed pooled analyses and then year-stratified analyses. Dong-level socioeconomic status and environmental characteristics were included as control variables. RESULTS: The overall age- and sex- standardized suicide rate across all Dongs decreased over time from 24.9 deaths per 100,000 in 2014 to 23.7 deaths in 2016. When Dong and Gu units were simultaneously considered in a multilevel analysis, most of the variation in suicide rate was attributed to within-Gu, between-Dong differences with a contribution of Gu-level being small and decreasing over time in year (Variance partitioning coefficient of Gu = 5.3% in 2014, <0.1% in 2015 and 2016). The number of divorce cases per 100,000 explained a large fraction of variation in suicide rate at the Dong-level. CONCLUSIONS: Findings from this study suggest that ecological micro-area unit is more important in reducing the geographic variation in the suicide rate. More diverse ecological-level data needs to be collected for targeted area-based suicide prevention policies in Korea.

Analysis of the Rate of Confirmed COVID-19 Cases in Seoul and Factors Affecting It Using Big Data Analysis.

Coronavirus disease (COVID-19) is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and presents with mild to severe symptoms. Vaccines have been developed, but COVID-19 persists. Therefore, it is necessary to analyze big data at an early stage to establish an effective infection prevention strategy. To reduce SARS-CoV-2 infection, this study aimed to analyze the infection factors by region within Seoul, Korea and identify the major factors affecting the infection rate. For ease of data aggregation, the study was conducted after a data refinement operation that organized data in the same group into categories, and classified them in detail by specific keywords. Based on the results of this study, if preventive measures are established after identifying the representative infectious factors, periods, and routes of COVID-19 infection, the infection rate could be effectively reduced in the future.

Clinical application of intraoperative somatic tissue oxygen saturation for detecting postoperative early kidney dysfunction patients undergoing living donor liver transplantation: A propensity score matching analysis.

BACKGROUND: Somatic tissue oxygen saturation (SstO2) is associated with systemic hypoperfusion. Kidney dysfunction may lead to increased mortality and morbidity in patients who undergo living donor liver transplantation (LDLT). We investigated the clinical utility of SstO2 during LDLT for identifying postoperative kidney dysfunction. PATIENTS AND METHODS: Data from 304 adults undergoing elective LDLT between January 2015 and February 2020 at Seoul St. Mary's Hospital were retrospectively collected. Thirty-six patients were excluded based on the exclusion criteria. In total, 268 adults were analyzed, and 200 patients were 1:1 propensity score (PS)-matched. RESULTS: Patients with early kidney dysfunction had significantly lower intraoperative SstO2 values than those with normal kidney function. Low SstO2 (< 66%) 1 h after graft reperfusion was more highly predictive of early kidney dysfunction than the values measured in other intraoperative phases. A decline in the SstO2 was also related to kidney dysfunction. CONCLUSIONS: Kidney dysfunction after LDLT is associated with patient morbidity and mortality. Our results may assist in the detection of early kidney dysfunction by providing a basis for analyzing SstO2 in patients undergoing LDLT. A low SstO2 (< 66%), particularly 1 h after graft reperfusion, was significantly associated with early kidney dysfunction after surgery. SstO2 monitoring may facilitate the identification of early kidney dysfunction and enable early management of patients.

Risk factors for hemorrhoidal disease among healthy young and middle-aged Korean adults.

Hemorrhoidal disease is a highly prevalent anorectal condition causing substantial discomfort, disability, and decreased quality of life. Evidence on preventable risk factors for hemorrhoidal disease is limited. We conducted a cross-sectional study of 194,620 healthy men and women who completed a health screening exam including colonoscopy in 2011-2017. We evaluated potential risk factors of hemorrhoidal disease, including lifestyle factors, medical history, birth history, gastrointestinal symptoms, and anthropometric measurements. The prevalence of hemorrhoidal disease was 16.6%, and it was higher in females than in males (17.2 vs. 16.3%; P < 0.001). Compared to men, the prevalence of hemorrhoidal disease was higher in parous women (adjusted odds ratio [OR] 1.06; 95% confidence interval [CI] 1.02-1.10), and lower in nulliparous women (adjusted OR 0.92; 95% CI 0.86-0.98). In the adjusted analyses, older age, female sex, smoking, overweight, and being hypertensive were independently associated with the presence of hemorrhoidal disease. The prevalence of hemorrhoidal disease was positively associated with body mass index and waist circumference in parous women. The prevalence of hemorrhoidal disease was higher in older age, females, ever-smokers, and hypertensive participants. The association of excess adiposity with the prevalence of hemorrhoidal disease differed by sex and parity.

Local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area, Korea: a view from a Bayesian perspective.

OBJECTIVES: The purpose of this study was to enhance the understanding of the local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area (GSA), Korea, after its initial outbreak in January 2020. METHODS: Using the weekly aggregates of coronavirus disease 2019 (COVID-19) cases of 77 municipalities in the GSA, we examined the relative risks of COVID-19 infection across local districts over 50 consecutive weeks in 2020. To this end, we employed a spatiotemporal generalized linear mixed model under the hierarchical Bayesian framework. This allowed us to empirically examine the random effects of spatial alignments, temporal autocorrelation, and spatiotemporal interaction, along with fixed effects. Specifically, we utilized the conditional autoregressive and the weakly informative penalized complexity priors for hyperparameters of the random effects. RESULTS: Spatiotemporal interaction dominated the overall variability of random influences, followed by spatial correlation, whereas the temporal correlation appeared to be small. Considering these findings, we present dynamic changes in the spread of COVID-19 across local municipalities in the GSA as well as regions at elevated risk for further policy intervention. CONCLUSIONS: The outcomes of this study can contribute to advancing our understanding of the local-level COVID-19 spread dynamics within densely populated regions in Korea throughout 2020 from a different perspective, and will contribute to the development of regional safety planning against infectious diseases.

Burden of dust storms on years of life lost in Seoul, South Korea: A distributed lag analysis.

Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002-2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0-178.5 years) and 34.4 years (4.0-64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5-83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest.

BACKGROUND: Rapid emergency medical service (EMS) response is an important prognostic factor in out-of-hospital cardiac arrest (OHCA). This study aims to evaluate the association between local hourly EMS demand and ambulance response in OHCA. METHODS: OHCA occurring in 24 districts of Seoul from 2013 to 2018 was analyzed. Hourly ambulance demand per ambulance in each local district of patient location at the hour of cardiac arrest was calculated as the crowding index. The crowding index was categorized according to quartiles (1Q: ≤0.43, 2Q: 0.44-0.67, 3Q: 0.68-0.99, 4Q: ≥1.0 calls/h\r/ambulance). The primary outcome was ambulance dispatched within 1 km of the OHCA scene. Multivariable logistic regression analysis was performed to test the association between the local hourly ambulance demand and outcomes. RESULTS: A total of 26,479 patients were analyzed. The rate of ambulance dispatched within 1 km decreased according to the crowding quartile (1Q: 31.3%, 2Q: 30.0%, 3Q: 28.8%, and 4Q: 26.6%). Compared to 1Q, adjusted odds ratios (95% CIs) of dispatch distance within 1 km in 2Q, 3Q, and 4Q were 0.92 (0.86-0.99), 0.86 (0.80-0.94), and 0.77 (0.71-0.84), respectively. CONCLUSION: Crowding in local ambulance demand was associated with less ambulance dispatched within 1 km and delayed response to the scene in OHCA. Strategies to mitigate and adjust to ambulance demand crowding may be considered for better EMS response performance.