Term Trends Research Articles

Mortality related to different circulatory diseases: a multiple causes of death analysis, 2008-2022

Abstract Background Mortality from circulatory diseases significantly increased in 2020 compared to pre-pandemic years in many countries. However, data were mostly limited to the underlying cause of death and to the first phases of the pandemic. Analyses of any mention of a disease in death certificates (multiple causes of death approach, MCOD) are more robust to changes in coding rules/practices and to the role of COVID-19 as a competing cause of death. Methods Mortality records were extracted from 2008 to 2022 among residents of the Veneto Region (Northeastern Italy, 4.9 million population). Based on MCOD, age-standardized mortality rates (2013 European standard) were computed for ischemic heart diseases (IHD), cerebrovascular diseases (CVD), atrial fibrillation (AF), and hypertensive diseases (HD). The annual percent change (APC) was estimated through the pre-pandemic period (2008-2019), and change in rates during pandemic years were computed compared to 2019. Results Before the pandemic, rates were steeply declining for IHD (APC -5.1%; 95%CI -5.5, -4.6) and CVD (-4.0%; -4.4, -3.5); mortality related to HD reduced at a slower pace (-1.9%; -2.3, -1.4), whereas AF-related mortality was increasing (+1.0%; 0.1, 1.8). During the first year of the pandemic, the growth in mortality compared to 2019 was +26% for HD, +18% for AF, +13% for CVD and +12% for IHD. In 2021-2022, rates for CVD and IHD returned to pre-pandemic levels; rates for HD reduced with respect to 2020 but remained above the baseline; AF-related mortality was still increasing. Conclusions The pandemic differentially impacted mortality associated to different circulatory diseases, depending on pre-existing long-term trends (increasing for AF) and on susceptibility to severe COVID-19 disease (higher for HD). Continuous surveillance based on MCOD is warranted to properly assess changes in mortality associated to specific circulatory diseases after the end of the pandemic. Key messages • Multiple causes of death analyses are warranted to assess how the pandemic affected pre-existing long term trends in cause-specific mortality. • Mortality related to different circulatory diseases increased in 2020, but pre-pandemic trends, the extent of the increase in 2020, and further changes observed in 2021-2022 largely diverged.

Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic

The U.S. South Atlantic coastal region is used by many marine birds for foraging, reproduction, and migration. We developed standardized indices of relative abundance from long–term (1980–2016), semi-structured monitoring data (eBird) for eight species: Brown Pelican (Pelecanus occidentalis), Double-Crested Cormorant (Nannopterum auritum), White Ibis (Eudocimus albus), Wood Stork (Mycteria americana), Piping Plover (Charadrius melodus), American Oystercatcher (Haematopus palliatus), Clapper Rail (Rallus crepitans), and Northern Gannet (Morus bassanus). Following a period of stable or declining abundance from the 1980s through the 1990s, most species have shown stable or slightly upward trends through the late 2000s; Brown Pelican and Piping Plover have shown some evidence of recent declines. Species–specific correlations between abundance indices developed from presence/absence data and those developed from count data were positive for all species and ranged from 0.53 to 0.86. Dynamic factor analysis identified common trends in abundance among several species, in particular, Brown Pelican, Double–Crested Cormorant, and White Ibis. Model performance was improved with inclusion of an indicator of sea level rise, but not forage fish abundance or temperature, indicating habitat availability mediated by changing water levels may explain some of the underlying abundance trends. Our results provide baseline information on long–term trends for several important coastal birds that can help inform research, monitoring and conservation efforts in the U.S. South Atlantic region.

Variability Effects of Meteorological Factors on Reported Malaria Cases in Kano and Lagos States of Nigeria

Abstract: Malaria is one of life’s threatening disease that contributed significantly to morbidity and mortality in Nigeria. The variability effects of meteorological factors between the Northern and Southern region of Nigeria on malaria transmission remain unclear and not well researched. This research was aimed at comparing the non-linear effects of seasonal, trend and meteorological variables on monthly malaria cases in Kano and Lagos State of Nigeria. The monthly malaria cases for age group above 5-year for Kano and Lagos State were obtained from DHIS for the period of January, 2016 to December, 2022. Also, the predictors/meteorological variables (Rainfall, Relative Humidity, Minimum Temperature and Maximum Temperature) were obtained from Nigeria Meteorological Agent. Generalized Additive Model was fitted using Negative Binomial to apply smooth function to estimate and compare the effects of the selected predictors on the malaria transmission in Kano and Lagos States. Rainfall and Maximum temperature were found not significant in explaining the effect of malaria transmission. Malaria transmission was found to be seasonal and varying in Kano State with highest transmission between September and October while the seasonal effect was reduced to linear in Lagos State. The long term trend had a significant increasing effect on malaria transmission in Kano Sate compared to non-significant effect in Lagos State. The effect of Relative Humidity was reduced to linear and increasing in the both State. Paying attention to the variability in the influence of season, trend and meteorological variables on malaria transmission will enhance respective State Government to develop an early warning and awareness that will help in controlling the malaria outbreak in their State.

The employment, retention and exit of public school teachers in New Brunswick, Canada: an analysis using linked administrative data

ObjectiveAs in many jurisdictions, New Brunswick (Canada) is facing increasing shortages of K-12 school teachers as retirements loom at the same time that the school age population of NB continues to exceed long term trends. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of teachers in the NB public education system in order to support ongoing planning around teacher staffing. ApproachThe analysis uses a unique linked administrative data combining province-wide individual-level teacher employment data, immigration records, university graduation data and public health insurance registration on NB teachers and individuals who obtained a B.Ed. degree in NB. Data are accessed through the secure facilities of the NB Institute for Research, Data and Training. The analysis includes both descriptive statistics and econometric methods appropriate to the particular outcomes of interest. ResultsThe analysis will present results on three types of labour market transitions: 1) from NB university education to employment as a NB teacher. 2) exits from employment as a teacher, including both retirement and pre-retirement exits, and 3) decisions of ex-teachers about remaining in NB. The potential effects of a range of demographic, geographic and system-level factors on these outcomes are considered. Conclusions and ImplicationResults from the analysis of entry to and exit from K-12 teaching in NB will be vital to human resource planning for a province dealing with looming retirements, growing labour shortages and an increasing population. Future work will consider subject-specific teacher shortages in critical fields like STEM.

China’s grand strategy within 21st-century megatrends

The main aim of this study is to identify possible components of China’s grand strategy and check how they can influence global megatrends. Based on a literature review, four grand strategy components are identified: 1) building up military power, 2) consolidating power at the peripheries and pushing for more control over disputed territories, 3) expanding economic influence through the Belt and Road Initiative, and 4) establishing new standards in digital technologies and promoting cyber sovereignty. These strategic tools can directly affect the following megatrends that are commonly mentioned in the literature: shift of economic power, emerging markets, new economic order; digitalisation; globalisation; deglobalization, trade reduction; diversity of governance, trust in the political system; and scarce resources. Using data taken from Google Trends it is shown that in the 2004-2023 period the trends of Chinese grand strategy terms correlate most with terms that represent the following three megatrends: digitalisation, globalisation, and scarce resources.

Long term relationships of electron density with solar activity

Greenhouse gases such as carbon dioxide and methane that are causing climate change may cause long term trends in the thermosphere and ionosphere. The paper aims to contribute to explore long term effects in the ionosphere focusing on the impact of solar activity changes. Peak electron density data derived from vertical sounding measurements covering 65 years at the ionosonde stations Juliusruh (JR055), Boulder (BC840) and Kokubunji (TO536), have been utilized to estimate the long-term behavior of daytime ionospheric F2 layer ionization in relation to the solar 10.7 cm radio flux index F10.7. In parallel, Global Navigation Satellite System (GNSS) based vertical total electron content (TEC) data over the ionosonde stations in combination with the peak electron density data have been used to derive the equivalent slab thickness τ for estimating long-term behavior in the time period 1996-2022. A new approach has been developed for deriving production and loss term proxies for studying long-term ionization effects from F2 layer peak electron density and TEC data. The derived coefficients allow estimating the long-term variation of atomic oxygen and molecular nitrogen concentrations including their ratio during winter months. The noon-time slab thickness values over Juliusruh correlate well with the decrease of F10.7 and the F2 layer peak height and enable estimating the neutral gas temperature. The equivalent slab thickness decreases by about 20 km per decade in the period 1996-2022, indicating a thermospheric cooling by about 100 K per decade for Juliusruh. Whereas the oxygen concentration decreases, the loss term, considered as a proxy for molecular components of the neutral gas, in particular N2, increases with the long-term solar activity variation. Considering 11 years averages of the production and loss terms under wintertime conditions, the long-term study reveals for the O/N2 ratio a percentage decrease of 5% per decade and for F10.7 about 3.1% per decade in a linear approach referred to the year 1970. Linear models of 11 years averaged NmF2 and foF2 from corresponding F10.7 show a very close correlation with the temporal variation of F10.7 until about 1990. The root mean square errors are in the order of 1.0 -1.3 ‧1010m-3 for NmF2 and 0.03-0.05 MHz for foF2. After 1990 the linear models clearly deviate from F10.7 at all selected ionosonde stations indicating a non-local effect.

Long Term Trends in Security Threats and an Approach for Integrating Them into System Architecture and Design

AbstractSecurity is an important consideration in today's systems, and it promises to be both relevant and challenging in the future. Our design and development processes are evolving to incorporate modern methods such as agile development and model‐based engineering with the intent to provide flexible and resilient capabilities to our customers quickly and with high quality. As we continue to enhance our practices to achieve these aspirations, security capabilities both at initial deployment and throughout the system lifespan are an important consideration. A literature review of security concerns in general and challenges across a range of domains including civil aviation, smart cities, eLearning, finance and defense reveals a set of recurring long‐term trends. These trends suggest challenges to security capabilities in the future. In this paper, we summarize and analyze these trends and propose an approach for addressing them in architecture and design activities.

Do Competition, Size, and Macro Variables Impact Bank Risk?

The paper investigates the correlation between bank competition, performance, and risk-taking behavior in MENA economies. Furthermore, it explores the risk level associated with dual banking as well as the bidirectional connectedness of competition and jeopardy in terms of size, GDP, and inflation rate. This work employs Generalised Method Moments (GMM) for regression findings and Two-Stage Least Squares (2SLS) for robustness tests. It uses an unbalanced panel dataset from 256 banks and 14 MENA countries from the year 2011 to 2022. According to the study’s findings, financial stability improves, and bank risk-taking decreases with less bank competition. The findings of this investigation corroborate the competition fragility theory. The competition square concept suggests that in a market with competition, risk will eventually rise. In terms of economic progression, risk-taking behavior is exactly the reverse of bank competition. Again, the finding shows that good performance reduces the bank’s risk, finally turning to greater financial stability. Furthermore, there is a substantial association between inflation rates and the extent of banks’ risk-taking activity. In a competitive market scenario, MENA countries inflation rate risk-taking behavior initially increases and then declines over time, while bank size exhibits a similar trend in the near term. When the inflation rate rises, banks’ risk-taking behavior initially decreases and then rises over time. This research will provide policymakers in MENA nations with a better grasp of how to establish strategies that benefit the banking environment.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are likely to be minimal when fertility is low. With the objective of forecasting monthly deliveries, this study concentrates on the analysis of registered births in Scotland. Through this approach, we examine the significance of precisely forecasting fertility trends, which can subsequently aid in the anticipation of demand in diverse sectors by allowing policymakers to anticipate changes in population dynamics and customize policies to tackle emerging demographic challenges. Consequently, this has implications for fiscal stability, national economic accounts and the environment. In conducting our analysis, we incorporated non-linear machine learning methods alongside traditional statistical approaches to forecast monthly births in an out-of-sample exercise that occurs one step in advance. The outcomes underscore the efficacy of machine learning in generating precise predictions within this particular domain. In sum, this research will comprehensively demonstrate a cutting-edge model of machine learning that utilizes several attributes to assist in clinical decision-making, predict potential complications during pregnancy and choose the appropriate delivery method, as well as help in medical diagnosis and treatment.

Recalibration of missing low-frequency variability and trends in the North Atlantic Oscillation

Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple “reddening” approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output.

Epidemiology, survival and new treatment modalities for intrahepatic cholangiocarcinoma (IHCC).

e16217 Background: Intrahepatic cholangiocarcinoma (IHCC), a rare biliary tract cancer, has been rising in incidence and is associated with poor survival. With the advent of new treatment modalities and various ongoing research, population-based perspective can be helpful to reflect the progress in managing this deadly disease and provide updated outcome information as a reference for comparison during upcoming clinical studies. We would like to provide a 20-year update on incidence and survival with reference to landmark trials, with a focus on survival outcomes of the recent 5 years for future insights. Recent and ongoing clinical trials will be discussed. Methods: This study extracted population data from the Surveillance, Epidemiology, and End Results (SEER) database. Age-adjusted incidence rate (AAIRs) and survival rate (SR) trends were calculated from January 2000 to December 2020 using 22 registries for the US population. Incidence and survival rates were collected from the SEER database, reported per 100,000 population and age adjusted to the 2020 US standard population. The annual percent change (APC) was used to report long term trends. Results: In this analysis of 19,948 IHCC patients, the AAIR increased from 0.49 per 100,000 in 2000 to 1.38 in 2020 (APC 6.94, 95% CI 6.32 to 7.56), with a notable decline from 2019 to 2020. Incidence rates overall displayed an uptrend course across subgroups divided by sex, race, age, and disease stage. The age-adjusted mOS climbed from 5.28 months in 2000 to 9.3 months in 2013, maintaining between 8.0-9.0 months after 2013. Using 2010 as a cutoff, when the ABC-02 trial was published, the decade-based mOS increased from 6.55 in 2000-2010 to 9.06 in 2010-2020, which remained statistically significant at 1-year, 3-year, and 5-year OS. During 2015-2020, the overall mOS was 8.8 months, with mOS of 24.3 months, 12.1 months, and 5.4 months for local, regional, and distant stages, respectively. Conclusions: These results indicate a steady rise in IHCC incidence since 2000, consistent across all subgroups. Survival rates have improved since 2000 but stabilized after 2013, following the publication of the ABC-02 trial published in 2010, which used systemic gemcitabine and cisplatin for unresectable iHCC. Updated survival data from 2015 onwards will serve as a reference of comparison for forthcoming clinical trials.

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO2 emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions.

Missing the target? Government commitment to education sector funding in Sub-Saharan Africa 2000–2023

It is assumed (at least implicitly) that the SDG4 Education for All targets are unlikely to be achieved unless governments commit at least 20% of total government expenditure to the education sector. This article focuses, therefore, on analysing medium-long term trends in the education funding share (EFS) for 40 countries in Sub-Saharan Africa since 2000. The two main conclusions are that the EFS is currently above 20% in less than 10% of these countries and that the EFS has been steadily declining in around half of all countries in SSA.

Long term trend of particulate matter in Philadelphia, Pennsylvania and its association with introduction of environmental policies

Since the 1970s, air quality has improved at the national level in the United States, coincident with the introduction of the Clean Air Act and other air pollution regulations at a greater frequency. We present a case study from Philadelphia, Pennsylvania—the sixth most populous city in the United States. The main objectives of this study are to analyze long-term trends of particulate matter (PM) from 1986 to 2021 in Philadelphia and to examine their association with the introduction of environmental policies relevant to air pollution at the federal, state, and local levels. We find that annual PM10 concentration decreased by 47% from 1986 to 2021 and annual PM2.5 concentration decreased by 31% from 2000 to 2021 in Philadelphia. We find that carbonaceous content (both elemental and organic carbon) has declined over the same period of 2000 to 2021, demonstrating its contribution to overall PM2.5 reduction in Philadelphia. In Philadelphia, high PM concentrations occur in the summer months; however, seasonal patterns have changed for PM10 in the last decade (2011–2020). Overall, PM reductions occurred over all seasons, with the greatest reductions occurring for PM2.5 during summer months and for PM10 during winter months. The Clean Air Act contributed to the creation of many regulatory policies that address unhealthy levels of PM. The introduction of various environmental policies that target the transportation sector has contributed to the reduction of PM levels in Philadelphia. Air quality would continue being improved by implementing such environmental policies specific to the emissions sectors.

Teaching in a time of pandemic: The case of the EMET Program at Pennsylvania State University - Fayette

The author shares his experiences of university teaching during the time of restrictions imposed on teaching and learning modes due to a pandemic. The relative scarcity of literature analyzing the response in the fields of science and engineering to the pandemic served as motivation. After providing general background information about the pandemic’s impact on educational systems worldwide, the article provides qualitative research with a narrative model for a case study of an engineering program at an American university. The analyzed case concerns the teaching and learning methodologies implemented in an electro-mechanical engineering technology bachelor’s degree program at Pennsylvania State University – the Fayette campus in the academic years 2020-22. Pennsylvania State University’s chosen teaching modes in the time of the pandemic to be used by instructors provide an example of adaptability of a higher education institution to the changing teaching and learning circumstances. The pedagogical approach to preparing, delivering, and assessment of learning effectiveness in engineering courses with a laboratory component is described. The article also shows how to use the learning management system, Canvas, with its analytical utility tool, to improve effectiveness and responsiveness of the teaching and learning process. The shortcomings and unexpected benefits of learning online pedagogy are shared and discussed. To assess the students’ perception and the study mode and their preferences in this regard, an anonymous, closed-ended, nominal-polytomous questionnaire was conducted, and its findings are analyzed. Further, to compare the students’ preferences as regards study modes depending on the academic discipline, engineering students were contrasted with students studying business as their major. The surveys also provide answers to trends in the longer term in students’ expectations for delivery of programs by higher education institutions.

Роль цифровых продуктов в развитии предпринимательства отечественного ретейла

The relevance of the research topic is due to the decline in the retail market, the development of digitalization of the economy and the steadily growing e-commerce market in Russia, which confirms the need to introduce digital products in retail. The purpose of the article is to determine the role of digital products in the development of Russian retail trade. Achieving the goal is determined by solving the following tasks: develop a classification system for digital products, prepare an analysis of the categories of digital products in retail, present the author’s definition of a digital product. The study analyzes industry development trends in the medium term. An important aspect of the study is the analysis of digital products necessary for integration into retail, based on the developed classification taking into account industry trends. The scientific novelty of the work lies in the development of: the author’s definition of a digital product, in the classification system of digital products, which contributes to the understanding of the role of digital products in modern retail trade and represents the theoretical value of the study. Consequently, determine the need for conducting further research aimed at enhancing the effectiveness of product management in real market conditions.

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Long term trends in global air pollution potential and its application to ventilation corridors

Long term trends in global air pollution potential and its application to ventilation corridors

Analysis of Spatiotemporal Dynamics of Land Desertification in Qilian Mountain National Park Based on Google Earth Engine

Notwithstanding the overall improvement in the ecological condition of the Qilian Mountains, there are localized occurrences of grassland degradation, desertification, and salinization. Moreover, timely and accurate acquisition of desertification information is a fundamental prerequisite for effective monitoring and prevention of desertification. Leveraging the Google Earth Engine (GEE) platform in conjunction with machine learning techniques, this study aims to identify and extract the spatiotemporal dynamics of desertification in the Qilian Mountain National Park (QMNP) and its surroundings (QMNPs) spanning from 1988 to 2023. Results show that based on the random forest algorithm, the multi-index inversion methodology achieves a commendable overall accuracy of 91.9% in desertification extraction. From 1988 to 2023, the gravity center of light desertification shifts southeastward, while centers characterized by moderate, severe, and extremely severe desertification display a westward retreat with fluctuations. The area of sandy land shows an expansion trend in the medium term, but after 2018, desertification in QMNPs reversed. As of 2023, the sandy land area measured 16,897.35 km2, accounting for 18.29% of the total area of QMNPs. The insights garnered from this study provide a valuable reference for regional desertification prevention and control in the future.