Trend Analysis Of Data Research Articles

The Impact of the COVID-19 Pandemic on Household Expenditures in the EU Countries (The Case of Central and Eastern Europe)

The COVID-19 pandemic has been one of the critical significant impact factors at the micro and microeconomic levels and has affected financial resources at both the state and household levels. The article aims to study the pandemic’s impact on the dynamics and structure of household expenditures in Central and Eastern Europe. The research is based on statistical data analysis and trend analysis. Household performance indicators were studied in the period 2017-2021, with a focus on the period 2019-2021, when there was an increase in the incidence of COVID-19. The obtained results allow us to state that households in the countries of Central and Eastern Europe received less income due to the COVID-19 pandemic effect, significantly reduced their expenditures, and preferred savings over investment. Households reoriented their expenditures by reducing spending on recreation and culture, restaurants and hotels, clothing and footwear, transport, electricity, water, gas, housing, food, and non-alcoholic beverages. The share of education costs remained unchanged. In the near term, excluding additional risk factors and uncertainty in current prices, the costs of households in Central and Eastern European countries will decrease. Still, their specific weight in the structure of GDP will decline.

Prediction of climate change impacts on availability of surface water resources in the semi-arid Tugwi Mukosi catchment of Zimbabwe

Climate change impact drivers includes increased rainfall variability, increased extreme events, and changes in the mean behaviour of hydro-meteorological variables such as air temperature, rainfall, and runoff. As such, this study assessed the potential impacts of climate change on surface water resources in a semi-arid Tugwi Mukosi catchment of Zimbabwe. The Mann-Kendall trend analysis, climate change downscaling and streamflow modelling were used as the primary methodology. The analysis of historical hydro-climatic data trends were performed using the Mann-Kendall trend test and Sen's slope method. Equally, observed daily rainfall was extended using bias-corrected CHIRPS satellite rainfall. Thus, a total of 9 Global Climate Models including (CCCma-CanESM2, Miroc Miroc 5, MOHCHadGEM2, MPI-M-MPI-ESM-LR, and NCCNorESM1-M) on the Sveriges Meteorologiska och Hydrologiska Institut (SMHI) Rosby Centre Atmosphere model version 4 (RCA4) RCM under the Representative Concentrated Pathways 4.5 and 8.5 were extracted and analysed for the 2030s (2021–2050) and 2060s (2051–2080) period. The results show significant (p<0.05) decreasing rainfall trends at the analysed stations, as well as significant decrease in rainfall for the 2030s and the 2060s under GCMs CCCma-CanESM 2 model. The Hydrologic Engineering Centre-Hydrological modeling System (HECHMS) (1997–1999) was calibrated and validated for the period 1981–2016. Calibration results on gauged catchment runoff and peak flows are satisfactory (Ngezi observed peak discharge and simulated peak discharge at 42.4 m3/s and 34.8 m3/s respectively, NSE 62.8%, Upper Muzhwi 30 m3/s and 19.5 m3/s, NSE 53.55%, Musogwezi 18.7 m3/,s and 16.5 m3/s, NSE 58.4%). The GCM-CanESM RCP 4.5 model showed a decrease in the runoff predicted of above 17% in both the 2030s and a significant decrease in the 2060s of 28.41% and 5% respectively. The combination of climate downscaling and water resources modelling is paramount to the management of water resources in a semi-arid area. Hydrological modelling and climate downscaling techniques gives important insights for water resources planning and development to support the water sector adaptation to climate change. The methodology applies to other data-scarce and semi-arid areas whose climate change impacts require further investigation.

Electrical conduction investigation of pre-stressed carbon/epoxy fabric laminates heated by ampere-level currents

A study on electrical conduction of carbon/epoxy laminates has so far been conducted in an ad hoc nature without a standardised method, involving many extrinsic factors. How these factors affect electrical conduction of carbon/epoxy laminates has not been well established. The objectives of this work are to ascertain the effects of electrical currents, temperatures, and clamping torques on the anisotropic electrical conduction of carbon/epoxy laminates. Two-probe method with solid electrodes was developed with machined carbon/epoxy laminate specimens of various dimensions. The contributions of elevated temperatures and clamping pressures to electrical conduction were investigated. Various contact conditions with or without conductive paint were examined. The relationship of electrical resistance correlating with temperature and clamping pressure was developed to aid an analysis of data trends. From the average test results of 18 groups, aided with qualitative predictions, the milliampere-to-ampere increases of current led to significant reductions in electrical conductivities in both in-plane and through-the-thickness directions. The rises of temperatures resulted in the similar reductions in electrical conductivity due to the increased resistance. The increase in clamping torque increased the electrical conductivity values in both directions. Applying conductive paint to the contact faces did not appear to affect the contact resistance. Thus, the enhanced values of electrical conductivity from the painted specimens were attributed to their lower body temperatures, as the conductive paint at the contact faces soaked up the substantial amount of the electrical energies.

Change analysis of All India and regional rainfall data series at annual and monsoon scales

AbstractRainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871–2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957–65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze.

Rainfall and Streamflow Trends in the Upper &lt;em&gt;Maduru Oya&lt;/em&gt; Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

Analysis of historical data on rainfall and streamflow provides useful information for water resources management in a river basin. The objective of this study was to assess the variations and trend analysis of rainfall and streamflow data in the upper Maduru Oya sub watershed in Sri Lanka during a 30 year period (1989 October to 2019 September) using daily rainfall data from two gauging locations (Padiyathalawa and Bibile) and daily average streamflow data from one gauging location (Padiyathalawa). Descriptive statistics were used to assess the variations. The Mann Kendal Trend Test (MK test) and Innovative Trend Analysis method (ITA) were used to analyze the trend, and Sen’s Slope Estimator was used to assess the magnitude of the trend. Based on the MK test, most of the rainfall trends obtained are in an increasing direction, and streamflow trends are in a decreasing direction. Rainfall trends found in Padiyathalawa are not statistically significant. Annual, First Inter Monsoon, March, and May time scales of rainfall showed significant upward trends at Bibile (p&lt;0.1) while streamflow in January, April, and September showed significant negative trends (p&lt;0.1) at Padiyathalawa. The results displayed by all three tests were in very good agreement for rainfall. ITA results showed similar trend types of 77%, 95% and 41% at Padiyathalawa and Bibile rainfall and streamflow, respectively. A significant positive (ρ&lt;0.01) strong and lower correlation between the trend value of the ITA and the Z value of the MK test were found for rainfall and streamflow, respectively. Changes in rainfall are not sufficient to explain the observed trends in streamflow. Therefore, future research should be directed at the causal aspects of streamflow change in this watershed.

Linkages between men's wealth status and the ideal number of children: A trend and multilevel analysis of survey data in Nigeria.

Most African societies practice a patriarchal family system that endows a man with authority and dominance in the family and society with a defined role of being the breadwinner of the home. A man is expected to have a great influence in determining the ideal number of children in the family and take a domineering role in decision-making, especially those related to household resource allocation. Therefore, this study examines the relationship between men's wealth status and an ideal number of children. The study used secondary data from the National Demographic Health Survey (NDHS) from 2003 to 2018. The objectives were achieved using descriptive and inferential statistics, including frequency, mean, ANOVA, and multilevel analysis techniques. Wealth status significantly influenced the ideal number of children considering the crude and adjusted regression analysis. After adjusting for individual-level and contextual factors, the odd ratio of ideal number of children was significantly lower among men in the richest categories of the wealth index. Moreover, men with two wives and above, uneducated men, Northern residents, men living in high community family norms, low community family planning, high community poverty, and low community level of education desired a high number of children. The analyses suggest the need for a consideration of community structures to provide lucrative employment for men and would experience an appreciable fertility decline in line with the objectives and targets stated in Nigeria's population policies and programmes.

Drought Disasters in China from 1991 to 2018: Analysis of Spatiotemporal Trends and Characteristics

Droughts have emerged as a global problem in contemporary societies. China suffers from different degrees of drought almost every year, with increasing drought severity each year. Droughts in China are seasonal and can severely impact crops. This study used spatiotemporal trend and characteristics analysis of drought disaster data from 1991 to 2018 in Chinese provinces, in addition to the Mann–Kendall test and wavelet analysis. The drought disaster data included the crop damage area, drought-affected area of the crops, and crop failure area. The outputs of the crops decreased by 10%, 30%, and 80%, respectively. The population with reduced drinking water caused by drought, and the domestic animals with reduced drinking water caused by drought, were numbered in the tens of thousands. The results of the study show that the crop damage areas owing to drought disasters, drought-affected areas of crops, and crop failure areas in China were mainly distributed in the northern, eastern, northeaster, and southwestern regions. The number of people and domestic animals with reduced drinking water owing to drought in China were mainly concentrated in the northern and southwestern regions. These indicators showed a general increasing trend. Tibet, Fujian, Shandong, Jiangsu, Anhui, and Henan provinces and autonomous regions also showed a slightly increasing trend. In particular, the number of domestic animals with reduced drinking water caused by drought in the Inner Mongolia Autonomous Region showed a clear increasing trend with a significant Z-value of 2.2629. The results of this research can be used to provide scientific evidence for predicting future trends in drought and for practising the best management of drought prevention and resistance.

Analysis of Uncertainty in the Depth Profile of Soil Organic Carbon

The soil organic carbon (SOC) depth profile provides information for many applications, including monitoring climate change, carbon sequestration, reforestation, and land erosion. Models of the SOC profile support data interpolation, trend analysis, and carbon mapping, and can be used in larger pedometric models in support of carbon farming. Model errors may be due to statistical variability in discrete data and the limited sample size available for model calibration. Uncertainties in the model can arise from a process of iterative parameter adjustment and can be estimated by gradient-based methods or probabilistic methods. A comparison between Frequentist and Bayesian approaches to the construction of regression-based models revealed that the results were very similar when used for calibrating a model for the SOC profile. The model was applied to four representative regional sites in Victoria.

Scenario of renewable energy transition from fossil energy resources towards net zero emission in Indonesia

In 2011, Indonesia set a 26% reduction goal for greenhouse gas emissions by 2030 to mitigate the climate change. Based on data from BPS, Indonesia's renewable energy mix in 2021 is 12.16% with a target of 23% in 2025. This indicates that there are challenges faced by Indonesia in many sectors, especially the upstream oil and gas industry as one of the largest emitters of greenhouse gases, in achieving the energy transition target. In this study, trend analysis and data forecasting were carried out using trend analysis of time series data on oil and gas energy supply and consumption data as baseline to propose scenarios for both consumption and utilization energy to achieve net zero emission (NZE) in 2060. This study found that NZE may be achieved by applying energy consumption scenarios including the use of electric vehicles by 10% in 2030, and 90% in 2060 and the use of electric stoves by 25% in 2030, and 90% in 2060. Renewable energy utilization scenarios include geothermal (50%), hydro (50%), mini hydro (50%), solar (80%), and wind (15%) of the existing potential. In addition, early retirement for coal-fired power plants is needed.

Failure forecast method applied on burst test data to valuate its practicability for acoustic emission testing and monitoring purposes

This paper describes the performance of a laboratory burst test on a test object with a severe real-world crack, the interpretation and evaluation of the acoustic emission (AE) data in general and in particular the results of further AE data trend analysis utilising the failure forecast method at different sections in testing time. The objective of this exercise is to gain experience in application of this method and to check whether or not it could be employed for testing and/or monitoring purposes. The burst pressure prediction as well as the ratio between the actual test pressure and the burst pressure prediction, the failure convergence, are consistent with the usual AE data evaluation throughout the test. Test sequences with a strong progressive increase of activity trend in AE data are highlighted by a gradually increasing failure convergence. This method has the potential to complement state-of-the-art evaluation techniques for testing applications, in particular with activity trend analysis. Due to the good results obtained already with a rather simple approach, remaining useful life prediction for on-line monitoring applications seems to be within reach.

A Retrospective Analysis of Malaria Trends in Leka Dulecha Health Center over the Last Ten Years (2013-2022), Western Oromia, East Wollega Zone.

Malaria is a serious public health concern in the world, and it causes a major socioeconomic problem in Ethiopia. Malaria data trend analysis of health facilities is useful to understand the prevalence and incidence of malaria cases and implementing evidence-based malaria control strategies. Hence, the main objective of this study was to investigate the malaria trends over the last ten years (2013-2022) at Leka Dulecha Health Center, East Wollega Zone, Western Oromia. Methodology. A retrospective study was conducted at Leka Dulecha Health Center to determine the trends of malaria prevalence by considering the malaria registration laboratory logbook for the last ten years from 2013 to 2022. Hence, to do this, sociodemographic data, years, months, and malaria prevalence were collected using a predesigned data collection sheet recorded from perspective between years. In the last ten years, a total of 30,576.00 suspected malaria cases were examined at Leka Dulecha Health Center, and out of these, 7,413.00 (24.24%) confirmed malaria cases were reported. In this health center, malaria cases were reported among both sexes and all age categories, but male (3,951.00, 54%) and age groups ≥ 15 years (3,994, 54%) were the most affected. The highest peak of malaria cases was reported during the autumn season (September, October, and November) followed by the spring season (March, April, and May) in the years of 2013 and 2007. In this study, the prevalence of malaria species was identified as Plasmodium falciparum, Plasmodium vivax, and mixed cases, with 5,014 (68%), 1,123 (15%), and 1,848 (25%), while Plasmodium falciparum was reported as the highest recorded cases. Males and above 15 years old were more affected than the others. The highest peak malaria prevalence appeared from September to December of 2017 and 2013 years. Therefore, proper planning, implementation, and monitor of malaria prevention and control activities should be strengthened at all levels.

Trends and Variabilities in Rainfall and Streamflow: A Case Study of the Nilwala River Basin in Sri Lanka

Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.

Mean sea level modelling using the neural network along the Chennai coast

Abstract This study focuses on the trend analysis of sea level data along the Chennai coast and thereby checks the structural change in the dataset using the Chow method. This study also proposed a methodology for predicting the mean sea level with the feed-forward neural network (FFNN) and wavelet transform neural network (WTNN) models. The data analysis shows that a breakpoint is observed in the year 1994 and found an overall increasing trend during the selected time period at the Chennai coast. For model development, a better understanding of the influencing parameters of the sea level is essential. Hence, correlation analyses have been performed and found that wind speed, sea surface salinity, and surface pressure are influencing variables for modelling sea level data. Apparently, these influencing variables have been considered as potential inputs for model development. To compare the performance of all the developed models, the Root Mean Square Error, Correlation Coefficient, and Nash–Sutcliffe Efficiency (NSE) were utilized. The results of performance indices and the graphical indicators also show that WTNN Model 4 outperformed all the other developed models. It was noticed that the percentage increase in the efficiency of NSE was 29.52% for WTNN Model 4 as compared to other developed models.

Historical and projected spatial and temporal rainfall status of Dar es Salaam, Tanzania, from 1982 to 2050

Dar es Salaam, like other cities in Africa, experiences flash floods during the rainfall season that destroy infrastructure due to the overflow of rivers and blocked sewage. This study investigates the historical and future variability and changes in spatial and temporal rainfall over Dar es Salaam. Station data and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) gridded data crossing 38 years (1982–2019) were used as a baseline and the Coordinated Regional Climate Downscaling Experiment (CORDEX) dataset from 2021 to 2050 was used for projection under Representative Concentration Pathway (RCP 4.5) forcing scenarios. A trend analysis of historical data was conducted at monthly, seasonal, and annual timescales. Mann–Kendall statistical tests and Sen’s slope estimator were applied to identify the current trend direction and magnitude of changes in rainfall patterns over time. A standardized anomaly index (SAI) was also employed to detect the region’s trends in wetness and dryness. The spatial distribution of rainfall in the city was investigated using an inverse distance weighted (IDW) interpolation technique. The statistical results reveal that a non-significant trend in rainfall was observed on monthly, seasonal, and annual timescales. Generally, in the future (2021–2050), the annual cycle of rainfall shows a slight decrease in monthly rainfall, especially from January to August, and an increase from September to December compared to historical (1982–2019) rainfall, for most of studied locations. Spatially, the distribution of projected rainfall shows that the southern part of the city will experience higher rainfall than other parts. The most significant findings were a decrease in annual projected rainfall by 20%, the MAM projected rainfall season increased by 42%, and an increase of 38% of the OND-projected rainfall season. The findings of this study will be useful for the improved management and planning of the city.

Trend analysis of annual and seasonal precipitation data in Arcadia region (Greece)

Trend analysis of annual and seasonal precipitation data in Arcadia region (Greece)

First trend analysis of antifungals consumption in Lebanon using the World Health Organization collaborating center for drug statistics methodology

BackgroundAntimicrobial resistance has reached an alarming rate globally, especially in middle-income countries such as Lebanon. The development of antifungal resistance is associated with the increased population’s injudicious consumption. This study aims to measure antifungals consumption in Lebanon as a trend analysis of national data from 2004 to 2018.MethodsThis is a trend analysis of the consumption of antifungal agents in the Lebanese community. Data were obtained from the Intercontinental Marketing Statistics Database between 2004 and 2018. It measures the total consumptions per year, per drug, and the percentage of its correspondents for three routes of administration (oral, parenteral, and topical). Results were reported by Defined Daily Dose (DDD) per 1000 inhabitants per day and the total number of DDDs.ResultsCommunity consumption of antifungals in Lebanon has increased by approximately 18.64% between 2004 and 2018, as measured by the number of DDDs per 1000 inhabitants per day; and amplified by approximately 87.76% as measured by the number of DDDs. The highest consumption level was noted in 2017, with 1.52 DDDs/1000 inhabitants/day and 3,386,930 DDDs. Fluconazole was the most consumed antifungal while micafungin was the least with 6,723,869.2 (20.99%) and 48.5 (0.0002%) DDDs respectively. Topical antifungals ranked the first type consumed followed by oral and parenteral antifungals representing 51.72%, 48.24%, and 0.033% of the total consumption respectively.ConclusionThe findings from this study indicate a marked increase in antifungal consumption in the Lebanese community. This accelerates the need of implementing disease management guidelines and national antifungal stewardship. Moreover, these findings may be used in further benchmark utilization and antimicrobial resistance studies in Lebanon.

Key Concepts of Designing Business Processes in Hospitality Enterprises: Trend Analysis

The article is devoted to studying design business processes concepts with the use of trend analysis. The study aims to develop a methodology for designing business processes based on the study of the business design theoretical foundations and data trend analysis. The field of research is the business processes of the hospitality industry, which includes the operation of hotels and restaurants. The article proposes a methodology as well as a set of methods for business process design. These are statistical and economic analysis methods, trend analysis, correlation analysis, and horizontal and vertical analysis. The result of the study is the methodology for designing business processes for new and existing hospitality enterprises. The flowchart of business process formation, based on the outgoing data, is shown. Outgoing data for new companies are collected by market research, demand or expert methods. The trend analysis method is used for existing companies, taking into account the seasonality level. Practical value of the research in improving the business analysis methodology in the hospitality sector.

Trend Analysis and Forecasting of Long-Term Temperature Data to Anticipate Local Warming: A Case Study of Jawa Timur, Indonesia

Numerous studies have confirmed the significant rise in the mean land surface air temperature in recent decades at the global scale. However, studies on the land surface air temperature at local scales in Indonesia in recent decades are rare. Such studies are necessary to comprehend the imminence of the threat of climate change in regions with intensive agricultural industry, such as Jawa Timur, Indonesia. The first objective of this study is to analyse the trend of air temperature in six locations in Jawa Timur based on at least 30-year air temperature data retrieved from NOAA Global Summary of the Day data. After the significance or insignificance of the temperature trend is verified, the second objective is to compare the accuracies of two forecasting methods, namely Exponential Smoothing and ARIMA, on the long-term air temperature data in each location. The Mann-Kendall test along with sieve-bootstrapping is used in the air temperature trend analysis. Mean Absolute Error (MAE) metric is used to measure the accuracy of the methods. The trend analysis shows that statistically significant monotonic rises in air temperature are found in four locations, namely Bawean, Kalianget Madura, Surabaya Perak, and Banyuwangi. It is also found that ARIMA (3,1,3) is able to forecast the temperature data for 2021 with MAE < 1.30 degrees Celsius and it outperforms the Exponential Smoothing method.

Combination of Wilcoxon test and scatter diagram for trend analysis of hydrological data

• An alternative trend analysis method is proposed in this study. • The approach includes combination of scatter diagram and Wilcoxon test. • Suggested methodology provides visual representation and numerical results. • The method can be compared with both Şen’s ITA and Mann-Kendall results. In this study, time series data is divided into two equal parts, as in the basic principle of the Innovative Trend Analysis (ITA) method. However, unlike the ITA, the data are not ordered when comparing the halves to avoid any change in the time series. Wilcoxon Signed Rank Test is used to consider the differences between the two equal halves and to determine if there is any trend in time series. A graphical presentation is provided like ITA approach and is expressed as the Mann-Kendall (MK) test for trend detection at a given confidence interval (significance level). In the application phase, minimum, maximum and average discharge time series measured at Yakabaşı, Söğütlühan, Arıcılar, and Topluca stations along the northern line from west to east of Turkey are used. The trend conditions of the data are determined by the proposed method and compared with ITA and MK tests results. No trend is observed in 8 out of 12 cases for stations at the 95% confidence interval. While decreasing trends are detected in the maximum discharge of Yakabaşı station, the minimum discharge of Arıcılar station, and the average discharge of Yakabaşı station, an increasing trend is detected in the average discharge records of Topluca station. Here, an easy-to-use model for decision-makers is proposed that presents numerical as well as graphical analyses of trend identification studies.

Patient-Level and County-Level Trends in Nonfatal Opioid-Involved Overdose Emergency Medical Services Encounters - 491 Counties, United States, January 2018-March 2022.

The number of nonfatal opioid-involved overdoses treated by health care providers has risen in the United States; the median number of emergency department (ED) visits for these overdoses was significantly higher during 2020 than during 2019 (1). ED visit data can underestimate nonfatal opioid-involved overdose incidence because, increasingly, persons experiencing a nonfatal opioid overdose are refusing transport to EDs by emergency medical services (EMS) (2). A study in Kentucky found that during a 6-month period, 19.8% of persons treated by EMS for an opioid overdose refused transport to an ED (2). Thus, EMS encounter data involving suspected nonfatal opioid-involved overdoses complement ED data and also allow for near real-time analysis (3). This report describes trends in rates of EMS encounters for nonfatal opioid-involved overdoses per 10,000 total EMS encounters (rates) by selected patient- and county-level characteristics during January 2018–March 2022 in 491 counties from 21 states using data from biospatial, Inc.* During this period, the nonfatal opioid-involved overdose rate increased, on average, 4.0% quarterly. Rates increased for both sexes and for most age groups. Rates were highest among non-Hispanic White (White) and non-Hispanic Native Hawaiian or other Pacific Islander (NH/OPI) persons, and increases were largest among non-Hispanic Black (Black), followed by Hispanic or Latino (Hispanic) persons. Rates increased in both urban and rural counties and for all quartiles of county-level characteristics (i.e., unemployment, education, and uninsured), except in counties with the lowest percentage of uninsured persons. Rates were highest and rate increases were largest in urban counties and counties with higher unemployment rates. This analysis of nonfatal opioid-involved overdose trends in EMS data highlights the utility of these data and the importance of addressing inequities that contribute to disproportionate overdose risk, such as through focused outreach to racial and ethnic minority groups, who disproportionately experience these inequities, and communities with higher levels of unemployment. EMS providers are in a unique position to engage in postoverdose response protocols and promote evidence-based overdose education and facilitate linkage to care and harm reduction services.†,§