Decrease in phase slip rates and phase cone structures during seizure evolution and epileptogenic activities derived from microgrid ECoG data.

Sudden phase changes are related to cortical phase transitions, which likely change in frequency and spatial distribution as epileptogenic activity evolves. A 100 s long section of micro-ECoG data obtained before and during a seizure was selected and analyzed. In addition, nine other short-duration epileptic events were also examined. The data was collected at 420 Hz, imported into MATLAB, downsampled to 200 Hz, and filtered in the 1-50 Hz band. The Hilbert transform was applied to compute the analytic phase, which was then unwrapped, and detrended to look for sudden phase changes. The phase slip rate (counts/s) and its acceleration (counts/s2) were computed with a stepping window of 1-s duration and with a step size of 5 ms. The analysis was performed for theta (3-7 Hz), alpha (7-12 Hz), and beta (12-30 Hz) bands. The phase slip rate on all electrodes in the theta band decreased while it increased for the alpha and beta bands during the seizure period. Similar patterns were observed for isolated epileptogenic events. Spatiotemporal contour plots of the phase slip rates were also constructed using a montage layout of 8 × 8 electrode positions. These plots exhibited dynamic and oscillatory formation of phase cone-like structures which were higher in the theta band and lower in the alpha and beta bands during the seizure period and epileptogenic events. These results indicate that the formation of phase cones might be an excellent biomarker to study the evolution of a seizure and also the cortical dynamics of isolated epileptogenic events.

Telehealth Utilization for Opioid Use Disorder: A Nationwide Analysis Before and After the COVID-19 Public Health Emergency Declaration.

Introduction: The COVID-19 pandemic has led to the rapid and widespread adoption of telehealth services. Telehealth may aid in bridging gaps in access to care. The specific impact of telehealth on opioid use disorder (OUD) and its treatment remains uncertain. Methods: A retrospective review of commercial insurance claim records within the United States was conducted to investigate the association between the COVID-19 pandemic and changes in the rates of(a) OUD treatments with and without telehealth support and (b) prescriptions for medications for opioid use disorder (MOUD) with and without telehealth support among individuals diagnosed with OUD. Results: In a study population of 1,340,506 individuals, OUD diagnosis rates were 5 per 1,000 in-person and 1 per 1,000 via telehealth. COVID-19 decreased in-person OUD diagnoses by 0.89 per 1,000, while telehealth diagnoses increased by 0.83 per 1,000. In-person MOUD treatment rates increased by 0.07 per 1,000 during COVID-19, while telehealth rates remained low. The onset of COVID-19 saw a 1.13 per 1,000 higher increase in telehealth-supported MOUD treatment compared to solely in-person treatment. Conclusions: A retrospective review of commercial insurance claim records within the United States was conducted to investigate the association between the COVID-19 pandemic and changes in the rates of (a) OUD treatments with and without telehealth support and (b) prescriptions for MOUD with and without telehealth support among individuals diagnosed with OUD.

Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks.

The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time-series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesised that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with time-history terms, namely leaky integrator ESNs (LI-ESN) and chaotic echo state networks (ChESN). The results suggest that compared with ESNs without time-history terms, the reservoir dynamics of LI-ESN and ChESN can maintain diversity and stability while possessing higher delay capacity, leading to their superior performance. Explaining ESN performance through dynamical metrics are crucial for evaluating the numerous ESN architectures recently proposed from a general perspective and for the development of more sophisticated architectures, and this study contributes to such efforts.

Classifying contaminated cell cultures using time series features.

We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.

Epidemic change-point detection in general integer-valued time series.

In this paper, we consider the structural change in a class of discrete valued time series, where the true conditional distribution of the observations is assumed to be unknown. The conditional mean of the process depends on a parameter θ∗ which may change over time. We provide sufficient conditions for the consistency and the asymptotic normality of the Poisson quasi-maximum likelihood estimator (QMLE) of the model. We consider an epidemic change-point detection and propose a test statistic based on the QMLE of the parameter. Under the null hypothesis of a constant parameter (no change), the test statistic converges to a distribution obtained from increments of a Browninan bridge. The test statistic diverges to infinity under the epidemic alternative, which establishes that the proposed procedure is consistent in power. The effectiveness of the proposed procedure is illustrated by simulated and real data examples.

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions.

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

The increasing health burden of Legionella Pneumophila in NSW.

BACKGROUND: Legionella pneumophila can cause severe respiratory disease and is notifiable in NSW. An analysis of notifications linked to hospitalisation and death data over the period 2010-2022 was conducted to determine the burden of disease and any association with the introduction of NSW regulatory changes in 2018. METHODS: Cases were retrospectively identified from the Notifiable Conditions Records for Epidemiology and Surveillance (NCRES). Data on related morbidity and mortality were obtained from linked data within the NSW Communicable Disease Register (CDR). The impact of the regulatory change was evaluated by analysing monthly count data using an interrupted time series analysis. RESULTS: A total of 928 cases were notified with 84% admitted to hospital. Annual adjusted notification and admission rates increased over the period from 4.40 to 7.92 cases and 3.72 to 7.20 admissions, per 1,000,000 population, respectively. The mean length of hospital stay (LOS) was 14 days with a median of 8 days (range 1-262 days). Time series analysis identified an underlying increasing time trend in cases notified per month with an IRR of 1.069 (95% ci 0.751-1.523) post 2018 regulatory implementation. CONCLUSION: L. pneumophila is posing an increasing burden of disease with an underlying upward trend in notification incidence despite the introduction of regulatory changes in 2018. IMPLICATION FOR PUBLIC HEALTH PRACTICE: This study demonstrates how linking notification, hospitalisation and death data can measure the health burden of a notifiable condition. Furthermore, time-series analysis using these data is able to identify underlying temporal trends and evaluate policy changes.

Impact of climate change on dengue incidence in Singapore: time-series seasonal analysis.

This study aimed to identify the meteorological factors that contribute to dengue epidemics. The monthly incidence of dengue was used as the outcome variable, while maximum temperature, humidity, precipitation, and sunshine hours were used as independent variables. The results showed a consistent increase in monthly dengue cases from 2013 to 2021, with seasonal patterns observed in stationary time-series data. The ARIMA (2, 1, 3) × seasonal (0, 1, 2)12 model was used based on its lowest Akaike Information Criterion (AIC) values. The analysis revealed that a 1-unit increase in rainfall was positively correlated with a small 0.062-unit increase in dengue cases, whereas a 1-unit increase in humidity was negatively associated, leading to a substantial reduction of approximately 16.34 cases. This study highlights the importance of incorporating weather data into national dengue prevention programs to enhance public awareness and to promote recommended safety measures.

[Spatial Distribution Characteristics of PM2.5 and O3 in Beijing-Tianjin-Hebei Region Based on Time Series Decomposition].

Notably, clear spatial differences occur in the distribution of air pollution among cities in the Beijing-Tianjin-Hebei (BTH) Region. Clarifying the concentration distribution of PM2.5 and O3 at different time scales is helpful to formulate scientific and effective pollution prevention and control measures. Here, the concentrations of PM2.5 and O3 were decomposed using a seasonal-trend decomposition procedure based on the loess (STL) method; their long-term, seasonal, and short-term components were obtained; and their temporal and spatial distribution characteristics were studied. The results showed that the decrease in PM2.5 concentration in the BTH Region from 2017 to 2021 was higher than that of O3. There was a positive correlation between PM2.5 and O3 concentrations in spring and summer and a negative correlation in autumn and winter. The short-term component and seasonal component had the greatest contribution to PM2.5 and O3 concentrations, respectively. There were two principal components in the seasonal and short-term components of PM2.5 and the long-term and short-term components of O3, corresponding to the central and southern part of Hebei Province and the northern part of the BTH Region. Sub-regional distribution of PM2.5 and O3 in the BTH Region at different time scales were found. Compared with that in the original series, the long-term component could better reflect the evolution trend of PM2.5 and O3 concentrations, and the standard deviation (SD) of the seasonal component and short-term component could be used to measure the fluctuation in PM2.5 and O3 concentrations in various cities. The SD of the seasonal and short-term components of the PM2.5 concentration in every city in front of Taihang Mountain was higher, and the SD of the short-term component of the O3 concentration in Tangshan was the highest.

The impact of incentive scheme on rural healthcare workforce availability: a case study of Kazakhstan.

BACKGROUND: During the 1990-2000, Kazakhstan experienced a decline in the number of healthcare professionals working in rural areas. Since 2009, the national government has been implementing financial incentives to encourage healthcare professionals to relocate to rural areas. This study aims to investigate the temporal and spatial patterns in the distribution of the rural healthcare workforce and evaluate the impact of this incentive scheme. METHODS: Interrupted Time Series Analysis using ARIMA models and Difference in Differences analyzes were conducted to examine the impact of the incentive scheme on the density of different categories of the healthcare workforce in rural Kazakhstan in the period from 2009 to 2020. RESULTS: There was a significant increase in the number of rural healthcare professionals from 2009 to 2020 in comparison to the period from 1998 to 2008. However, this increase was less pronounced in per capita terms. Moreover, a decline in the density of internists and pediatricians was observed. There is substantial variation in the density of rural nurses and physicians across different regions of Kazakhstan. The incentive scheme introduced in 2009 by the government of Kazakhstan included a one-time allowance and housing incentive. This scheme was found to have contributed insignificantly to the observed increase in the number of rural healthcare professionals. CONCLUSION: Future research should be undertaken to examine the impact made by the incentive scheme on other medical subspecialties, particularly primary practitioners. Addressing the shortage of healthcare workers in rural areas is a complex issue that requires a multifaceted approach. Aside from financial incentives, other policies could be considered to increase relocation and improve the retention of healthcare professionals in rural areas.

Effect of the COVID-19 pandemic on utilization of essential health services in Iran evidence from an interrupted time series analysis.

BACKGROUND: The COVID-19 disrupted the provision of essential health services in numerous countries, potentially leading to outbreaks of deadly diseases. This study aims to investigate the effect of the COVID-19 pandemic on the utilization of essential health services in Iran. METHODS: An analytical cross-sectional study was conducted using interrupted time series (ITS) analysis. Data about five indicators, including 'childhood vaccination, infant care, hypertension screening, diabetes screening, and breast cancer screening,' were obtained from the electronic health record System in two-time intervals: 15 months before (November 2018 to January 2020) and 15 months after (January 2020 to May 2021) the onset of the COVID-19 pandemic. The data were analyzed by utilizing ITS. In addition, a Poisson model was employed due to the usage of count data. The Durbin-Watson (DW) test was used to identify the presence of lag-1 autocorrelation in the time series data. All statistical analysis was performed using R 4.3.1 software, considering a 5% significance level. RESULTS: The ITS analysis showed that the COVID-19 pandemic significantly affected the utilization of all essential health services (P < 0.0001). The utilization of hypertension screening (RR = 0.51, p < 0.001), diabetes screening (RR = 0.884, p < 0.001), breast cancer screening (RR = 0.435, p < 0.001), childhood vaccination (IRR = 0.947, p < 0.001), and infant care (RR = 1.666, p < 0.001), exhibited a significant decrease in the short term following the pandemic (P < 0.0001). However, the long-term trend for all service utilization, except breast cancer screening (IRR = 0.952, p < 0.001), demonstrated a significant increase. CONCLUSIONS: The COVID-19 pandemic affected utilization of essential health care in Iran. It is imperative to utilize this evidence to develop policies that will be translated into targeted planning and implementation to sustain provision and utilization of essential health services during public health emergencies. It is also vital to raise awareness and public knowledge regarding the consequences of interruptions in essential health services. In addition, it is important to identify the supply- and demand-side factors contributing to these disruptions.

Anthropogenic particle concentrations and fluxes in an urban river are temporally variable and impacted by storm events.

Anthropogenic particles (AP), which include microplastics and other synthetic, semisynthetic, and anthropogenically modified materials, are pollutants of concern in aquatic ecosystems worldwide. Rivers are important conduits and retention sites for AP, and time series data on the movement of these particles in lotic ecosystems are needed to assess the role of rivers in the global AP cycle. Much research assessing AP pollution extrapolates stream loads based on single time point measurements, but lotic ecosystems are highly variable over time (e.g., seasonality and storm events). The accuracy of models describing AP dynamics in rivers is constrained by the limited studies that examine how frequent changes in discharge drive particle retention and transport. This study addressed this knowledge gap by using automated, high-resolution sampling to track AP concentrations and fluxes during multiple storm events in an urban river (Milwaukee River) and comparing these measurements to commonly monitored water quality metrics. AP concentrations and fluxes varied significantly across four storm events, highlighting the temporal variability of AP dynamics. When data from the sampling periods were pooled, there were increases in particle concentration and flux during the early phases of the storms, suggesting that floods may flush AP into the river and/or resuspend particles from the benthic zone. AP flux was closely linked to river discharge, suggesting large loads of AP are delivered downstream during storms. Unexpectedly, AP concentrations were not correlated with other simultaneously measured water quality metrics, including total suspended solids, fecal coliforms, chloride, nitrate, and sulfate, indicating that these metrics cannot be used to estimate AP. These data will contribute to more accurate models of particle dynamics in rivers and global plastic export to oceans. PRACTITIONER POINTS: Anthropogenic particle (AP) concentrations and fluxes in an urban river varied across four storm events. AP concentrations and fluxes were the highest during the early phases of the storms. Storms increased AP transport downstream compared with baseflow. AP concentrations did not correlate with other water quality metrics during storms.

Is the surge in cesarean section rates during the COVID-19 pandemic truly substantiated?

BACKGROUND: Cesarean section (C-section) rates, deemed a critical health indicator, have experienced a historical increase. The advent of the COVID-19 pandemic significantly impacted healthcare patterns including delays or lack of follow-up in treatment and an increased number of patients with acute problems in hospitals. This study aimed to explore whether the observed surge is a genuine consequence of pandemic-related factors. METHODS: This study employs an Interrupted Time Series (ITS) design to analyze monthly C-section rates from March 2018 to January 2023 in Kurdistan province, Iran. Segmented regression modeling is utilized for robust data analysis. RESULTS: The C-section rate did not show a significant change immediately after the onset of COVID-19. However, the monthly trend increased significantly during the post-pandemic period (p < 0.05). Among primigravid women, a significant monthly increase was observed before February 2020 (p < 0.05). No significant change was observed in the level or trend of C-section rates among primigravid women after the onset of COVID-19. CONCLUSION: This study underscores the significant and enduring impact of the COVID-19 pandemic in further increasing the C-section rates over the long term, the observed variations in C-section rates among primigravid women indicate that the COVID-19 pandemic had no statistically significant impact.

A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario.

Sensor-Based Indoor Fire Forecasting Using Transformer Encoder.

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios.

National taxation on sugar-sweetened beverages and its association with overweight, obesity, and diabetes.

BACKGROUND: Consumption of sugar-sweetened beverages (SSBs) has been linked to several adverse health outcomes, thus many countries introduced taxation to reduce it. OBJECTIVES: To summarize national SSB taxation laws and to assess their association with obesity, overweight and diabetes. METHODS: We conducted a systematic scoping review up to January 2022 on PubMed, Web of Science, Embase, and Google Search to identify taxes on SSBs. An interrupted time series analysis (ITSA) was conducted on 17 countries with taxation implemented in 2013 or before to evaluate the level and slope modifications in the rate of change of standardized prevalence rates of overweight, obesity, and diabetes. Random-effects meta-regression was used to assess whether year of entry into force of the law, national income, and tax design affected observed results. RESULTS: We included 76 tax laws issued between 1940 and 2020 by 43 countries, which were heterogeneous in terms of tax design, amount, and taxed products. Among children and adolescents, ITSA showed level or slope reduction for prevalence of overweight and obesity in 5 (Brazil, Samoa, Palau, Panama, Tonga) and 6 (El Salvador, Uruguay, Nauru, Norway, Palau, Tonga) countries out of 17, respectively. No clear pattern of modification of results according to investigated factors emerged from the meta-regression analysis. CONCLUSIONS: Taxation is highly heterogeneous across countries in terms of products and design, which might influence its effectiveness. Our findings provide some evidence regarding a deceleration of the increasing prevalence rates of overweight and obesity among children occurring in some countries following introduction of taxation. PROSPERO registration number: CRD42021233309.

Estimating plastic waste generation using supervised time-series learning techniques in Johannesburg, South Africa.

In recent times, many investigators have delved into plastic waste (PW) research, both locally and internationally. Many of these studies have focused on problems related to land-based and marine-based PW management with its attendant impact on public health and the ecosystem. Hitherto, there have been little or no studies on forecasting PW quantities in developing countries (DCs). The key objective of this study is to provide a forecast on PW generation in the city of Johannesburg (CoJ), South Africa over the next three decades. The data used for the forecasting were historical data obtained from Statistics South Africa (StatsSA). For effective prediction and comparison, three-time series models were employed in this study. They include exponential smoothing (ETS), Artificial Neural Network (ANN), and the Gaussian Process Regression (GPR). The exponential kernel GPR model performed best on the overall plastic prediction with a determination coefficient (R2) of 0.96, however, on individual PW estimation, ANN was better with an overall R2 of 0.93. From the result, it is predicted that between 2021 and 2050, the total PW generated in CoJ is forecasted to be around 6.7 megatonnes with an average of 0.22 megatonnes/year. In addition, the estimated plastic composition is 17,910 tonnes PS per year; 13,433 tonnes PP per year; 59,440 tonnes HDPE per year; 4478 tonnes PVC per year; 85,074 tonnes PET per year; 34,590 tonnes LDPE per year and 8955 tonnes other PWs per year.

Impact of COVID-19 pandemic on the number of otolaryngologic surgeries in Japan.

OBJECTIVE: Previous studies show that the COVID-19 pandemic affected the number of surgeries performed. However, data on the association between the COVID-19 pandemic and otolaryngologic surgeries according to subspecialties are lacking. This study was performed to evaluate the impact of the COVID-19 pandemic on various types of otolaryngologic surgeries. METHODS: We retrospectively identified patients who underwent otolaryngologic surgeries from April 2018 to February 2021 using a Japanese national inpatient database. We performed interrupted time-series analyses before and after April 2020 to evaluate the number of otolaryngologic surgeries performed. The Japanese government declared its first state of emergency during the COVID-19 pandemic in April 2020. RESULTS: We obtained data on 348,351 otolaryngologic surgeries. Interrupted time-series analysis showed a significant decrease in the number of overall otolaryngologic surgeries in April 2020 (-3619 surgeries per month; 95% confidence interval, -5555 to -1683; p < 0.001). Removal of foreign bodies and head and neck cancer surgery were not affected by the COVID-19 pandemic. In the post-COVID-19 period, the number of otolaryngologic surgeries, except for ear and upper airway surgeries, increased significantly. The number of tracheostomies and peritonsillar abscess incisions did not significantly decrease during the COVID-19 pandemic. CONCLUSION: The COVID-19 pandemic was associated with a decrease in the overall number of otolaryngologic surgeries, but the trend differed among subspecialties.

Changes in the number and outcome of takeaway food outlet planning applications in response to adoption of management zones around schools in England: A time series analysis.

Physical exposure to takeaway food outlets ("takeaways") is associated with poor diet and excess weight, which are leading causes of excess morbidity and mortality. At the end of 2017, 35 local authorities (LAs) in England had adopted takeaway management zones (or "exclusion zones"), which is an urban planning intervention designed to reduce physical exposure to takeaways around schools. In this nationwide, natural experimental study, we used interrupted time series analyses to estimate the impact of this intervention on changes in the total number of takeaway planning applications received by LAs and the percentage rejected, at both first decision and after any appeal, within management zones, per quarter of calendar year. Changes in these proximal process measures would precede downstream retail and health impacts. We observed an overall decrease in the number of applications received by intervention LAs at 12 months post-intervention (6.3 fewer, 95% CI -0.1, -12.5), and an increase in the percentage of applications that were rejected at first (additional 18.8%, 95% CI 3.7, 33.9) and final (additional 19.6%, 95% CI 4.7, 34.6) decision, the latter taking into account any appeal outcomes. This effect size for the number of planning applications was maintained at 24 months, although it was not statistically significant. We also identified three distinct sub-types of management zone regulations (full, town centre exempt, and time management zones). The changes observed in rejections were most prominent for full management zones (where the regulations are applied irrespective of overlap with town centres), where the percentage of applications rejected was increased by an additional 46.1% at 24 months. Our findings suggest that takeaway management zone policies may have the potential to curb the proliferation of new takeaways near schools and subsequently impact on population health.

Impact of the early phase of the COVID-19 pandemic on emergency department-to-intensive care unit admissions in Korea: an interrupted time-series analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant disruptions to critical care systems globally. However, research on the impact of the COVID-19 pandemic on intensive care unit (ICU) admissions via the emergency department (ED) is limited. Therefore, this study evaluated the changes in the number of ED-to-ICU admissions and clinical outcomes in the periods before and during the pandemic. METHODS: We identified all adult patients admitted to the ICU through level 1 or 2 EDs in Korea between February 2018 and January 2021. February 2020 was considered the onset point of the COVID-19 pandemic. The monthly changes in the number of ED-to-ICU admissions and the in-hospital mortality rates before and during the COVID-19 pandemic were evaluated using interrupted time-series analysis. RESULTS: Among the 555,793 adult ED-to-ICU admissions, the number of ED-to-ICU admissions during the pandemic decreased compared to that before the pandemic (step change, 0.916; 95% confidence interval [CI] 0.869-0.966], although the trend did not attain statistical significance (slope change, 0.997; 95% CI 0.991-1.003). The proportion of patients who arrived by emergency medical services, those transferred from other hospitals, and those with injuries declined significantly among the number of ED-to-ICU admissions during the pandemic. The proportion of in-hospital deaths significantly increased during the pandemic (step change, 1.054; 95% CI 1.003-1.108); however, the trend did not attain statistical significance (slope change, 1.001; 95% CI 0.996-1.007). Mortality rates in patients with an ED length of stay of ≥ 6 h until admission to the ICU rose abruptly following the onset of the pandemic (step change, 1.169; 95% CI 1.021-1.339). CONCLUSIONS: The COVID-19 pandemic significantly affected ED-to-ICU admission and in-hospital mortality rates in Korea. This study's findings have important implications for healthcare providers and policymakers planning the management of future outbreaks of infectious diseases. Strategies are needed to address the challenges posed by pandemics and improve the outcomes in critically ill patients.