Short-term Trends Research Articles

Time trends of cancer incidence in young adults (20-49 years) in Italy. A population - based study, 2008-2017.

To evaluate short-term (2008-2017) cancer incidence trends in Italy for individuals aged 20-49 years by sex and cancer type. Observational study from population-based data collected by 20 Italian Cancer Registries, covering 33% of the Italian population. The age-standardized incidence rates (ASRs), overall and stratified by area, sex, cancer site or type, and major age groups (i.e., 20-39, 40-49), were computed. In 2008-2017, cancer incidence rates were almost two times higher in Italian women aged 20-49 than in age-corresponding men (202.2 vs 112.4 per 100,000) on account of elevated rates of breast and thyroid cancers. Contrasting trends emerged according to cancer sites/types. ASRs for female breast cancer increased steadily from 2008 (82.4) to 2014 (86.2) and remained unchanged thereafter (i.e., 86.5 in 2017). During the study period, there was an increase for testicular cancer, skin melanoma in both sexes, and thyroid cancer until 2013 (followed by a slight decrease from 2014 to 2017). Conversely, ASRs consistently declined for colorectal cancer and were substantially stable or slightly decreasing for cervix uteri (from 8.1 to 7.7), ovary (from 7.5 to 6.9) and non-Hodgkin lymphoma (from 8.3 to 7.6 in men and from 5.9 to 5.5 in women). Study findings do not support a unique temporal pattern for the incidence of early-onset cancer in Italy until 2017, as reported in other countries. Increases in incidence documented in both sexes for some tumor sites was counterbalanced by a decrease in other sites. The importance of supporting prevention strategies from the youngest of ages must be emphasized, and the role of anticipated screening should be carefully addressed.

Influence of Blood Sampling Service Process Reengineering on Medical Services Supply: Quasi-Experimental Study.

Tertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research. This study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR. Data were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends. BSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients' waiting time by an average of 26.1 (SD 3.8; 95% CI -33.64 to -18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36). BSSPR implementation for outpatients not only reduced waiting time and improved patients' experience but also augmented the hospital's capacity to provide medical services. This study's findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.

Advanced Short-Term Load Forecasting with XGBoost-RF Feature Selection and CNN-GRU

Accurate and efficient short-term load forecasting (STLF) is essential for optimizing power system operations. This study proposes a novel hybrid forecasting model that integrates XGBoost-RF feature selection with a CNN-GRU neural network to enhance prediction performance while reducing model complexity. The XGBoost-RF approach is first applied to select the most predictive features from historical load data, weather conditions, and time-based variables. A convolutional neural network (CNN) is then employed to extract spatial features, while a gated recurrent unit (GRU) captures temporal dependencies for load forecasting. By leveraging a dual-channel structure that combines long- and short-term historical load trends, the proposed model significantly mitigates cumulative errors from recursive predictions. Experimental results demonstrate that the model achieves superior performance with an average root mean square error (RMSE) of 53.29 and mean absolute percentage error (MAPE) of 3.56% on the test set. Compared to traditional models, the prediction accuracy improves by 28.140% to 110.146%. Additionally, the model exhibits strong robustness across different climatic conditions. This research validates the efficacy of integrating XGBoost-RF feature selection with CNN-GRU for STLF, offering reliable decision support for power system management.

Palynofacies as sea-level-sensitive proxy in Early Cretaceous marine mudstones – A critical evaluation

Stratigraphic distribution patterns of particulate organic matter (POM) have been widely used for facies recognition and paleoenvironmental interpretation as well as to decipher proximal to distal trends within fine-grained sediments. The Lower Cretaceous mudstone-dominated succession in the eastern Lower Saxony Basin (LSB) offers an excellent opportunity to critically evaluate such palynofacies parameters, commonly used to identify transgressive-regressive (T-R) cycles in marine sediments. For the seemingly monotonous succession, a robust sequence stratigraphic framework has been previously established by integrating high-resolution elemental intensity data from X-Ray Fluorescence (XRF) core scanning and biostratigraphy from four drill cores. In this study, the composition and distribution of the POM has been assessed by analysis of 220 strew mounts using transmitted-light microscopy. Overall, the POM composition indicates deposition in a mud-dominated proximal to distal shelf setting. The ratio of opaque versus translucent phytoclasts (OP/TR ratio) shows a distinct long-term increase from the Berriasian onwards with maximum values during the early Hauterivian, followed by a subsequent decrease in OP/TR ratio. This trend broadly reflects the overall T-R evolution of the succession interpreted from Si/Al changes. This also applies to the size and sorting of opaque phytoclasts, with the greatest amplitude changes in opaque particle size parameters being observed in the more proximal deposits of the studied succession. On the other hand, the ratio of terrestrial versus marine palynomorphs (T/M ratio), often applied as indicator of proximal to distal trends and distances from the coastline, shows no correlation with the T-R cycles. Systematic long- and short-term trends visible in T/M ratio correspond to variations in the XRF-derived Ca/Ti stratigraphic trend, which is interpreted to reflect variations in carbonate content. This may indicate that the T/M ratio in the LSB is largely controlled by variations in marine palynomorph flux, probably related to productivity changes of the organic-walled microplankton.

Population trend and spatio-temporal distribution of Greater White-fronted (Anser albifrons) and Bean Geese (Anser fabalis) in Korea

Geese have undergone significant changes in their distribution and population size due to human-induced impacts. To improve our understanding of the two main geese populations in East Asia — Greater White-fronted (Anser albifrons; GWFG) and Bean Geese (Anser fabalis sensu lato; BEAG), which includes the Tundra A. f. serrirostris (TDBG) and Taiga Bean Geese A. f. middendroffi (TGBG) — this study estimated their long- and short-term trends and the migration strategies based on their distribution during the migratory and wintering seasons, using nationwide census data collected over 24 years in Korea. The TRIM (TRends and Indices for Monitoring data) analysis supported a robust long-term population increase since 1999, recently stabilizing around 150,000 individuals in GWFG and 100,000 in TDBG, respectively. But TGBG stabilized at around 7000 with no significant change of population over time. The shorter-term trends within a season suggested that wintering populations of GWFG and TDBG in China might stop over in Korea in their autumn migration before their sea-crossing, evidenced by the large congregation near the Yellow Sea coast followed by gradual decrease. In contrast to autumn, there was no similar peak in spring migration, suggesting the potential for elliptical migration of some populations around the Yellow Sea. Our findings indicate a growing East Asian population and implies one of the migration strategies of the Greater White-fronted and Tundra Bean Geese. By collaborating with recent tracking data, long-term monitoring efforts could offer more detailed insights into population trends and migration strategies, thereby contributing to the effective management and conservation of goose species in East Asia.

Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit

This paper study the problem of real-time prediction of inbound passenger flow and the detection and alerting of abnormal passenger flows in urban rail transit (URT) networks. We propose a fused framework that combines a hybrid deep learning model and an evaluation strategy. Specifically, the learning model incorporates Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and attention mechanisms to effectively capture spatial and temporal correlations in passenger flow data. The evaluation strategy utilizes a depth-first search algorithm to determine the optimal travel paths for each individual passenger. And based on the paths, we develop a real-time method for estimating the origin–destination (OD) matrix that utilizes both long-term and short-term historical destination trend vectors to reduce dimensions while improving predictive accuracy. Through extensive testing using data from the Shanghai rail transit system, we demonstrate that this fused framework achieves high prediction accuracy for inbound passenger flow at various stations while efficiently identifying and warning sudden large-scale events involving significant increases in passenger flow volume. This research contributes towards improving overall passenger experience as well as operational resilience within urban rail systems when dealing with large-scale influxes of passengers.

Decoding the stock market dynamics in the banking sector: Short versus long-term insights

The severity of extreme fluctuations and crises within the global banking sector is escalating. Conventional models, operating on a single time scale, may misinterpret any shift as a change in the long-term trend, distorting market insights. To address this issue, the present paper introduces a hierarchical structure into the standard hidden Markov model, enabling the differentiation of short and long-term trends within the U.S. banking industry. Using NASDAQ Bank stock market index data from January 1, 2007, to July 31, 2023 at two different frequencies, we construct and evaluate different calibrations of the hierarchical hidden Markov model. Results reveal two long-term regimes: turbulent periods with high volatility, instability, and negative returns, and prevalent stable markets. Within each of them, two distinct states representing short-term trends are identified, exhibiting significant differences in persistence, likelihood, expected returns, and risk profiles. The results show that an investor should carefully differentiate between regimes on both hierarchies to make informed investment decisions.

“Built on the Money”: The Financialization of Capitalism and the Sense of History in Josie Rourke’s Stage Production of Shaw’s Saint Joan

ABSTRACT In a bold move, Josie Rourke reimagined Bernard Shaw’s Saint Joan for the 2016 audience at the Donmar Warehouse, setting it against the backdrop of today’s financial world. She transported Joan from the medieval battlefield to a modern corporate boardroom, turning historical drama into a commentary on financialization. Through this lens, Rourke’s production responds with Shaw’s paradoxical sense of history, characterized by a skepticism toward short-term trends, yet with an underlying optimism for long-term human progress driven by imagination. By examining egg futures and stock markets, this article adopts an interdisciplinary approach to position theatrical work within the realm of economics, an endeavor that may offer a literary framework of humanity when contemplating our current economic landscape.

Short-Term Trends and Site Differences of Methamphetamine and Ketamine Consumption in Two Cities by Wastewater Analysis

Short-Term Trends and Site Differences of Methamphetamine and Ketamine Consumption in Two Cities by Wastewater Analysis

Monthly Maximum Magnitude Prediction in the North–South Seismic Belt of China Based on Deep Learning

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes.

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

Comparative analysis of neural network architectures for short-term FOREX forecasting

This document outlines the analysis, design, implementation, and benchmarking of various neural network architectures in a short-term frequency prediction system for the FOREX market. Our objective is to emulate the decision-making process of a human expert (technical analyst) through a system that swiftly adapts to market condition changes, thereby optimizing short-term trading strategies. We have designed and implemented a series of LSTM neural network architectures that take exchange rate values as input to generate short-term market trend forecasts. Additionally, we developed a custom ANN architecture based on simulators for technical analysis indicators. We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

UKRAINE AND REPUBLIC OF MOLDOVA IN COOPERATION WITH THE EU AFTER 2014 YEAR: MODELS AND IMPACTS

Abstract. The paper is devoted to solving the problem of co-integration between Ukraine and the Republic of Moldova in the context of foreign trade. The study of co-integration makes it possible to determine common trends in the development of foreign trade and, based on the analysis of the dynamics of the development of one country, to forecast the development of foreign trade in another country. The study of the main indicators of foreign trade was carried out: volumes of exports, imports, trade balance. The share of export-import operations with the EU in the total volume of foreign trade of Ukraine and Moldova was chosen as the studied indicator. The article proposes to consider cointegrations both between long-term trends, which characterize the trend of foreign trade development, and between short-term trends, which reflect the cyclical component. A co-integration research mechanism has been developed, which includes three blocks: a block of analysis of literary sources, a block of mathematical tools, and a block of applied use. The use of decomposition models, trend models, spectral analysis, orthogonality indicators and pairwise correlations is proposed as a mathematical toolkit. Using the Foster-Stewart method, an analysis of the presence of a trend in the variance and average was carried out. The above calculations showed the presence of a trend and cyclical component, which allowed the use of additive decomposition models. For the study of long-term trends, a linear trend model was chosen for both Moldova and Ukraine. A spectral analysis of the cyclic component was carried out, which made it possible to identify five main harmonics with a unique frequency and amplitude. The combination of these harmonics caused 95.8% and 90.4% of the cyclical component for Moldova and Ukraine, respectively. An analysis of cointegration between short-term and long-term trend functions was conducted, which revealed medium cointegration in the long-term trend and high cointegration in the short-term trend. The cointegration analysis of the indicator values proved a high degree of cointegration for both long-term and short-term trends.

A study on the efficacy of an attention mechanism-based strategy planning method for stock trading portfolios

In order to take full advantage of the fact that there is a large amount of information and data available in the field of stock trading, this paper develops a new type of stock trading strategy, which reduces investment risk. A stock trading portfolio strategy planning method based on an attention mechanism, as disclosed in the present invention, relates to the field of stock trading technology. The introduction of an attention mechanism allows the strategy to focus on those stocks with higher evaluation coefficients, thereby improving the selectivity and accuracy of the strategy and thus the effectiveness of the portfolio strategy. Furthermore, based on the prediction results of the deep spatio-temporal neural network model, it is possible to determine the stock's short-term trends and identify the optimal trading points. This information can be utilized as a reference for investors, assisting them in making more informed decisions regarding the timing of buying and selling, improving the success rate and yield of trading, enabling them to swiftly and accurately assess the performance of each stock, facilitating timely adjustments to their portfolios, and enabling them to devise the optimal trading strategy and timing for each stock, enhancing the accuracy and efficiency of their trading decisions.

Utilising integrated bio-behavioural surveillance (IBBS) to investigate declining hepatitis C antibody prevalence among people who inject drugs in the Australian Needle and Syringe Program Survey

BackgroundPrevalence of hepatitis C virus (HCV) antibody (Ab) on dried blood spot (DBS) samples in the Australian Needle and Syringe Program Survey (ANSPS) decreased nationally from 57 % in 2015 to 32 % in 2022. We aimed to investigate potential explanations for this decline. MethodsChanges in DBS HCV Ab prevalence were investigated by redefining positive cases as those with those with either a positive HCV Ab test result or a self-reported history of ever having HCV treatment (modified prevalence), examining HCV Ab prevalence by birth and age cohorts, and assessing trends in key risk behaviours. ResultsOverall prevalence of DBS HCV Ab declined rapidly and significantly from 57 % in 2015 to 32 % in 2022 (p < 0.001) however modified HCV Ab prevalence remained stable over time (85 % and 88 % in 2015 and 2022, respectively, p = 0.357). The proportion of participants with negative HCV Ab and self-reported HCV infection increased from 20 % in 1995 to 40 % in 2022 (p < 0.001) and the proportion with negative HCV Ab and lifetime HCV treatment increased from 3 % in 1999 to 67 % in 2022 (p < 0.001). We also observed a decreasing trend in DBS HCV Ab prevalence in all birth and age cohorts with a noticeable acceleration in the decline commensurate with the advent of HCV DAA treatment. A long-term decreasing trend was also observed for key risk behaviours (p < 0.001) however the short-term trend was not significant for recent receptive syringe sharing. ConclusionThe temporal decline in HCV Ab prevalence appears related to reduced sensitivity of DBS HCV Ab detection with viral clearance following treatment. Since 2016, HCV treatment uptake has increased markedly including among people who inject drugs. In this context, continuing to monitor HCV Ab prevalence by DBS testing is problematic, with a shift to surveillance of active infection the most relevant to guide policy and practice in this setting.

Droughts and windstorms due to climate change increase variability in species and trait composition of a subtropical monsoon evergreen broadleaf forest in China

BackgroundClimate change is accelerating alterations in forest species and community composition worldwide, especially following extreme events like severe droughts and windstorms. Understanding these effects on subtropical forests is crucial for conservation and forest management, but it remains unclear whether the impacts are stochastic or deterministic. MethodsWe analyzed a unique dataset from a 1-ha permanent plot in a subtropical monsoon broadleaf evergreen forest in China, monitored over 26 years with six surveys from 1994 to 2020. The forest has been free from anthropogenic disturbances for over 400 years. In each survey, we measured all trees with a diameter at breast height (DBH) ​≥ ​1 ​cm, and recorded 11 plant functional traits relating to photosynthesis, wood properties, water use, and nutrient dynamics. Using this data, we calculated species and trait dispersion, assessing short-term (∼5 years) and long-term (26 years) trends in species and trait composition following severe droughts and windstorm events. ResultsSevere droughts, and subsequent droughts, increased both species and trait dispersion, while species composition converged, and trait dispersion remained relatively stable throughout the recovery period. Windstorm events led to increased species dispersion but decreased trait dispersion. We observed a clear directional shift in both species and trait composition under these climatic stressors, with a more pronounced increase in trait dispersion compared to species dispersion. ConclusionIn the short term (∼5 years), severe droughts and windstorms increased species composition divergence, while trait composition responses varied. Over 26 years, deterministic processes mainly drove community composition changes, especially for trait composition, although stochastic processes also played a role. These findings suggest enhancing forest resilience to climatic stressors by protecting adaptive species or increasing species diversity in management practices.

Publisher's Note

BackgroundPrevalence of hepatitis C virus (HCV) antibody (Ab) on dried blood spot (DBS) samples in the Australian Needle and Syringe Program Survey (ANSPS) decreased nationally from 57% in 2015 to 32% in 2022. We aimed to investigate potential explanations for this decline. MethodsChanges in DBS HCV Ab prevalence were investigated by redefining positive cases as those with those with either a positive HCV Ab test result or a self-reported history of ever having HCV treatment (modified prevalence), examining HCV Ab prevalence by birth and age cohorts, and assessing trends in key risk behaviours. ResultsOverall prevalence of DBS HCV Ab declined rapidly and significantly from 57% in 2015 to 32% in 2022 (p<0.001) however modified HCV Ab prevalence remained stable over time (85% and 88% in 2015 and 2022, respectively, p=0.357). The proportion of participants with negative HCV Ab and self-reported HCV infection increased from 20% in 1995 to 40% in 2022 (p<0.001) and the proportion with negative HCV Ab and lifetime HCV treatment increased from 3% in 1999 to 67% in 2022 (p<0.001). We also observed a decreasing trend in DBS HCV Ab prevalence in all birth and age cohorts with a noticeable acceleration in the decline commensurate with the advent of HCV DAA treatment. A long-term decreasing trend was also observed for key risk behaviours (p<0.001) however the short-term trend was not significant for recent receptive syringe sharing. ConclusionThe temporal decline in HCV Ab prevalence appears related to reduced sensitivity of DBS HCV Ab detection with viral clearance following treatment. Since 2016, HCV treatment uptake has increased markedly including among people who inject drugs. In this context, continuing to monitor HCV Ab prevalence by DBS testing is problematic, with a shift to surveillance of active infection the most relevant to guide policy and practice in this setting.

Classifying animal migration patterns using small-shuffle surrogate and PCA evaluating short-term trends

Classifying animal migration patterns using small-shuffle surrogate and PCA evaluating short-term trends

Modeling bitcoin price by using Euler-Maruyama method

&lt;p&gt;In this study, we use Euler-Maruyama method to simulate Bitcoin prices dynamics. We investigate a year-long movement of Bitcoin prices. Daily closing prices were collected over a period starting from May 27th, 2023 and ending on May 27th, 2024. This data provides a comprehensive picture of how Bitcoin behaved on a daily basis throughout that specific year. The Euler-Maruyama method is used as numerical method for solving stochastic differential equations (SDEs). The method involves discretizing the SDE into an iterative process to obtain a simulated price trajectory. The drift term was estimated by the average daily return of Bitcoin prices over the study period. The volatility term was estimated by the standard deviation of daily Bitcoin returns. A Monte Carlo simulation was performed to generate a range of possible price trajectories using the Euler-Maruyama method. The result shows that the Euler-Maruyama method was able to capture the short-term trend of Bitcoin prices effectively. However, the method can also be used to capture the long-term trend of Bitcoin prices.&lt;/p&gt;

National and Regional Trends in the Prevalence of Hypertension in South Korea Amid the Pandemic, 2009-2022: Nationwide Study of Over 3 Million Individuals.

Understanding the association between hypertension prevalence and socioeconomic and behavioral variables during a pandemic is essential, and this analysis should extend beyond short-term trends. This study aims to examine long-term trends in the prevalence of participants diagnosed with and receiving treatment for hypertension, using data collected by a nationally representative survey from 2009 to 2022, which includes the COVID-19 pandemic era. A nationwide, population-based, cross-sectional study used data collected from the South Korea Community Health Survey between 2009 and 2022. The study sample comprised 3,208,710 Korean adults over a period of 14 years. We aimed to assess trends in the prevalence of participants diagnosed with and receiving treatment for hypertension in the national population from 2009 to 2022, with a specific focus on the COVID-19 pandemic, using weighted linear regression models. Among the included 3,072,546 Korean adults, 794,239 (25.85%) were aged 19-39 years, 1,179,388 (38.38%) were aged 40-59 years; 948,097 (30.86%) were aged 60-79 years, and 150,822 (4.91%) were aged 80 years or older. A total of 1,426,379 (46.42%) were men; 761,896 (24.80%) and 712,264 (23.18%) were diagnosed with and received treatment for hypertension, respectively. Although the overall prevalence over the 14-year period increased, the upward trends of patients diagnosed with and receiving treatment for hypertension decreased during the COVID-19 pandemic era compared with the prepandemic era (β difference for trend during vs before the pandemic -.101, 95% CI -0.107 to -0.094 vs -.133, 95% CI -0.140 to -0.127). Notably, the trends in prevalence during the pandemic were less pronounced in subgroups of older adults (≥60 years old) and individuals with higher alcohol consumption (≥5 days/month). This nationwide representative study found that the national prevalence of participants diagnosed with and receiving treatment for hypertension increased during the prepandemic era. However, there was a marked decrease in these trends during the prepandemic era, compared with the pandemic era, particularly among specific subgroups at increased risk of negative outcomes. Future studies are needed to evaluate the factors associated with changes in the prevalence of hypertension during the COVID-19 pandemic.