Trend Analysis Research Articles

A Study on the Role of MGNREGA in Fostering Economic Development via Welfare Initiatives, Employment Opportunities, and Asset Formation in Rural Bangalore

Abstract: Over the years, the evolution of the Indian economy has witnessed substantial transformation and enhancement. The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) has emerged as a pivotal component in fostering the development of rural India, primarily through its dual impact of employment generation and asset formation. This research paper employs trend analysis to investigate the pivotal role of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in driving economic development in rural areas of Bangalore. MGNREGA, a flagship welfare initiative, is examined through its multifaceted impact on welfare enhancement, employment generation, and asset accumulation. The study delves into the effectiveness of MGNREGA in improving the socio-economic well-being of rural communities by providing critical welfare benefits, facilitating employment opportunities, and promoting asset formation. The findings of this study provide valuable insights into the nuanced relationship between MGNREGA initiatives and economic development outcomes in rural Bangalore. By uncovering significant trends and patterns, the research offers actionable recommendations for policymakers, practitioners, and stakeholders to optimize MGNREGA's impact on fostering inclusive and sustainable economic growth in rural areas

Tax Implications of Real Estate Investments: A Comprehensive Analysis

Real estate investments constitute a significant portion of many investors' portfolios, offering the potential for long-term wealth accumulation and diversification. However, beyond considerations of property location, market trends, and financing options, investors must also navigate the intricate landscape of tax implications associated with real estate investments. Taxes play a critical role in shaping investment decisions, impacting cash flow, profitability, and overall returns. Therefore, understanding and effectively managing tax obligations is paramount for maximizing investment success in the real estate market. This comprehensive analysis aims to delve into the multifaceted realm of tax implications related to real estate investments. By examining various tax strategies, structures, regulations, and their implications, this study seeks to provide investors with valuable insights and tools to optimize their tax position and enhance investment outcomes. From the tax treatment of rental income and capital gains to the utilization of tax-efficient investment structures like REITs and LLCs, every aspect of real estate taxation will be explored in detail. Moreover, with the ever-evolving nature of tax legislation and economic conditions, it is imperative for investors to stay abreast of changes that may impact their tax liabilities and investment strategies. Through a thorough analysis of historical trends and current tax laws, this study aims to equip investors with the knowledge and foresight necessary to adapt and thrive in a dynamic tax environment. In essence, this comprehensive analysis endeavors to empower real estate investors with the tools and understanding needed to navigate the complex terrain of tax implications effectively. By unraveling the intricacies of real estate taxation and providing actionable insights, this study aspires to assist investors in making informed decisions, optimizing their tax position, and ultimately realizing the full potential of their real estate investments.

Analysis of trends in radiofrequency ablation in a tertiary care center practice.

Radiofrequency ablation (RFA) for thyroid nodules has recently been introduced into the United States healthcare system landscape. Little is known about the process of incorporating this procedure into existing clinical practice. A retrospective chart review of a single institution was conducted to examine referral patterns and decision-making after the introduction of RFA into an endocrine surgery-focused practice. Patient demographics and thyroid-specific data were recorded. Two reviewers abstracted and coded reasons for the noncompletion of RFA. Two-sample t tests were used to compare groups; linear regression was used to assess trends and practice patterns. Chart review identified 451 patients referred for consideration of RFA from January 2020 to December 2022. Only 255 (56.5%) went on to receive the treatment. There was no significant difference in nodule volume between treated and nontreated groups (18.5 vs. 14.9 cm3, p = 0.07). Concern for malignancy on genetic testing, size (too large/too small), recommendation for Ethanol ablation, and multinodular disease without target nodules were the most common reasons for physician deferral. Of patients who declined to proceed, 46% opted to undergo surgical excision. Linear regression showed that referral numbers significantly increased with time; however, the proportion of patients receiving treatment decreased yearly, primarily because of higher rates of physician deferral. This study reflects the complex decision-making in offering minimally invasive thyroid nodule ablation. Despite a greater number of referrals over time, physician criteria became increasingly selective. Optimal candidacy in RFA is an evolving determination requiring patient and physician input to guide ideal practice patterns.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Gender Differences in Authorship and Quality of Anesthesia Clinical Practice Guidelines From 2016 to 2020 Using the Appraisal of Guidelines for Research and Evaluation II Instrument.

Women continue to be underrepresented in academic anesthesiology. This study assessed guidelines in anesthesia journals over the past 5 years, evaluating differences in woman-led versus man-led guidelines in terms of author gender, quality, and changes over time. We hypothesized that anesthesia guidelines would be predominately man-led, and that there would be differences in quality between woman-led versus man-led guidelines. All clinical practice guidelines published in the top 10 anesthesia journals were identified as per Clarivate Analytics Impact Factor between 2016 and 2020. Fifty-one guidelines were included for author, gender, and quality analysis using the Appraisal of Guidelines for Research & Evaluation (AGREE) II instrument. Each guideline was assessed across 6 domains and 23 items and given an overall score, overall quality score, and overall rating/recommendation. Stratified and trend analyses were performed for woman-led versus man-led guidelines. Fifty out of 51 guidelines were included: 1 was excluded due to unidentifiable first-author gender. In total, 255 of 1052 (24%) authors were women, and woman-led guidelines (woman-first author) represented 12 of 50 (24%) overall guidelines. Eighteen percent (9 of 50) of guidelines had all-male authors, and a majority (26 of 50, 52%) had less than one-third of female authors. The overall number and percentage of woman-led guidelines did not change over time. There was a significantly higher percentage of female authors in woman-led versus man-led guidelines, median 39% vs 20% (P = .012), as well as a significantly higher number of female coauthors in guidelines that were woman-led median 3.5 vs 1.0, P = .049. For quality, there was no significant difference in the overall rating or objective quality of woman- versus man-led guidelines. However, there was a significant increase in the overall rating of all the guidelines over time (P = .010), driven by the increase in overall rating among man-led guidelines, P = .002. The overall score of guidelines did not increase over time; however, they increased in man-led but not woman-led guidelines. There was no significant correlation between the percentage of female authors per guideline and either overall score or overall rating. There is a substantial disparity in the number of women leading and contributing to guidelines which has not improved over time. Woman-led guidelines included more women and a higher percentage of women. There was no difference in quality of guidelines by first-author gender or percentage of female authors. Further systematic and quota-driven sponsorship is needed to promote gender equity, diversity, and inclusion in anesthesia guidelines.

Analysis of Stock Market Trends for Market Capitalization and Securities Management: An approach to Nepalese Stock Market

Attraction towards share market is increasing day by day. Relevance for guiding people towards purchase and sale of share is also increasing everywhere in the world. Nepal is a poor country having well developed share market, but some sort of hurdles and technical disparities are still creating problems in operation of this market. Increasing economic crisis for last two years has made this market critical. Decrease in price of share is caused by increasing losses in business and industry at present. The main objective of the study is to examine the growth trend of the Nepalese securities market. It helps to analyze the trend of Nepalese stock market in terms of market capitalization, number of transaction, number of securities traded and number of securities listed and to examine the space of primary market growth in Nepal. this work is motivated by the works of (see[6-31]).

Perinatal mortality after Chornobyl in contaminated regions of Ukraine.

Belarus and Ukraine were the countries most affected by the consequences of the Chornobyl nuclear power plant in 1986. A study of perinatal mortality in Belarus found a highly statistically significant increase in the 1990s in the most contaminated oblast Gomel but no increase during this period in the rest of Belarus. As a possible mechanism to interpret this increase as a late Chornobyl effect, it has been suggested that strontium-90 contained in Chornobyl fallout, incorporated during menarche, impairs the immune system of pregnant women which in turn increases perinatal mortality. In the present study, this hypothesis is tested using data from Ukraine. Annual data on perinatal mortality, in the period 1981-2006 were provided by the Ministry of Public Health of Ukraine. Trends in perinatal mortality rates in the most contaminated regions of Ukraine (Kyiv and Zhytomyr oblasts and the city of Kyiv; study region) were compared with rates in the rest of Ukraine (control region). To identify any differences in perinatal mortality trends between the study and control regions, the ratios of perinatal mortality rates in the study region to the rates in the control region were analyzed using the calculated strontium concentration as a predictor. A trend analysis of perinatal mortality rates in Ukraine revealed two bell-shaped deviations from a long-term exponential trend with maxima at the beginning and end of the 1990s. The same pattern was found in the data from the study and control regions, but the deviations were almost three times higher in the study region than in the control region. An analysis of the ratios of perinatal mortality rates in the study region to the rates in the control region (odds ratios) showed an increase and decrease during the 1990s which was approximated by a lognormal density distribution. The calculated strontium concentration, when used as a predictor, also fitted the data well. Thus, the data from Ukraine confirms the results from Belarus. The analysis of the odds ratios revealed about 1000 excess perinatal deaths in the study region in the period 1990-2004. The corresponding figure for Ukraine as a whole was estimated at 3500 perinatal deaths. It is hypothesized that the observed increase in perinatal mortality in the 1990s may be a late effect of incorporated strontium-90 on the immune system of pregnant women. The analysis is based on a theoretical model, as no data on strontium concentrations were available; the results should therefore be interpreted with caution. An updated study for Belarus would be desirable to corroborate the results.

Public Attitudes and Sentiments toward Common Prosperity in China: A Text Mining Analysis Based on Social Media

Since 2021, China’s promotion of common prosperity has captured global attention and sparked considerable debate. Yet, scholarly examination of the Chinese public’s attitudes toward this policy, which is crucial for guiding China’s strategic directions, remains limited. To address this gap, this paper collects 256,233 Sina Weibo posts from 2021 to 2023 and utilizes text mining methods such as temporal and trend analysis, keyword analysis, topic analysis, and sentiment analysis to investigate the attitudes and emotions of the Chinese people towards common prosperity. The posts holding negative sentiments are also analyzed, so as to uncover the underlying reasons for the dissatisfaction among Chinese citizens regarding common prosperity. Our analysis reveals that China’s strategy for promoting common prosperity is primarily focused on economic development rather than wealth redistribution. Emphasis is placed on enhancing education, achieving regional balance, implementing market-oriented reforms, and improving livelihoods. Notably, there is increasing public dissatisfaction, particularly with issues such as irregularities in financial and real estate markets, growing wealth inequality, exploitation by capital, generation of illicit income, and regional development imbalances. These challenges necessitate urgent and effective policy interventions.

Bibliometric analysis of hotspots and trends of global myopia research.

To gain insights into the global research hotspots and trends of myopia. Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Temporal trends in clinical features of patients with primary aldosteronism over 20 years.

Primary aldosteronism (PA) accounts for approximately 5-10% of hypertension cases. Over the past 20 years, the reported incidence of PA has increased due to widespread screening for secondary hypertension and imaging studies. We aimed to evaluate the temporal trends in the clinical characteristics and subtypes of PA. A total of 1064 patients with PA in two tertiary hospitals between 2000 and 2021 were categorized into three groups according to the year of diagnosis: 2000-2009, 2010-2015, and 2016-2021. The clinical characteristics of the patients over the three time periods were compared using a trend analysis. The age at diagnosis and sex of patients with PA did not change over 20 years. The proportion of patients with bilateral hyperaldosteronism (BHA) increased (11%, 25%, and 40%, P for trend <0.001). The proportion of hypokalemia (87%, 61%, and 40%) and plasma aldosterone concentration (36.0, 30.8, and 26.6 ng/dL) decreased (all P for trend <0.001). There was a trend toward an increased proportion of incidentally detected patients compared to clinically symptomatic patients (36%, 55%, and 61%, P for trend <0.001). The concordance rate of imaging and adrenal venous sampling results decreased (91%, 70%, and 57% P for trend <0.001). However, the proportion of patients with resistant hypertension and comorbidities did not differ. In conclusion, among patients with PA, patients with BHA and incidental detection have increased over 20 years, and more patients are likely to present with milder clinical symptoms and biochemical profiles.

Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to the global efforts to address climate change.

Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature.

Topic modeling of literature. Our study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics". Our analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics. Our NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.

A Combined Seasonal Mann–Kendall and Innovative Approach for the Trend Analysis of Streamflow Rate in Two Croatian Rivers

Climate change profoundly impacts hydrological systems, particularly in regions such as Croatia, which is renowned for its diverse geography and climatic variability. This study examined the effect of climate change on streamflow rates in two Croatian rivers: Bednja and Gornja Dobra. Using seasonal Mann–Kendall (MK) tests, overall streamflow trends were evaluated. Additionally, innovative polygon trend analysis (IPTA), innovative visualization for innovative trend analysis (IV-ITA), and Bayesian changepoint detection and time series decomposition (BEAST) algorithms were used to assess the trends’ magnitudes and transitions. The seasonal MK analysis identified significant decreasing trends, primarily during summer. The results of IPTA and IV-ITA revealed consistent decreasing trends throughout most months, with a notable increase in September, especially at high flow values. The rivers’ behavior differed between the first and second halves of the month. BEAST analysis detected abrupt changes, including earlier shifts (1951–1968) in the Bednja and more recent ones (2013–2015) in both the Bednja and, to a lesser extent, the Gornja Dobra rivers. This comprehensive approach enhances our understanding of long-term streamflow trends and short-term fluctuations induced by climate change.

Evaluating the revenue and taxation implications of cannabis legalization in South Africa: Insights from Canada and the United States

Purpose: This study aims to evaluate the impact of cannabis legalization on revenue mobilization and taxation in South Africa by analyzing the experiences of jurisdictions that have legalized cannabis. Methodology: A qualitative approach was employed, including a literature review and trend analysis. Data from Canada, California, Colorado, and Washington State, where cannabis has been legalized, were analyzed for 2018-2021. Results: The findings demonstrate that cannabis legalization significantly impacts revenue collection and taxation. However, optimal pricing and taxation policies are crucial to capture the illicit market, minimize negative externalities, and ensure industry growth. Legacy growers and previously disadvantaged individuals should be integrated into the legal market. Theoretical Contribution: The study contributes to the theoretical framework of Pigouvian taxation by examining its applicability to the cannabis industry and the challenges posed by illicit markets and product substitutability with alcohol and tobacco. Practical Implications: The study provides recommendations for South African policymakers on taxation policies, market regulation, and inclusive strategies to ensure a successful and sustainable cannabis industry while maximizing revenue mobilization.

Artificial intelligence applications in the field of streamflow: A bibliometric analysis of recent trends

Artificial intelligence applications in the field of streamflow: A bibliometric analysis of recent trends

A predictive algorithm for the analysis of AMR trends and healthcare decision support

Background Translating available AMR surveillance data to observe evolving patterns of microbial resistance to antimicrobial agents while identifying regions at high risk of resistant cases and serving as a decision-support tool is an aspect of AMR surveillance that is rarely explored nationwide and uncommon globally. Therefore, we developed a two-tier dashboard algorithm (PATHFINDER) that can analyse antimicrobial surveillance datasets, observe evolving global spatiotemporal patterns of AMR, and integrate local AMR gene resources to identify functional AMR determinant genes and antibiotic classes from the query organism genome. Methods The Python-based plotly library was used to develop the interactive variables of the Antimicrobial Testing Leadership Surveillance (ATLAS) dataset in an adjustable spatiotemporal environment. A lightweight database containing multiple known resistant genes from the ResFinder database was used as a prototype to identify unique AMR determinant genes from query nucleotide sequences. In R, a function was created to accept query genome sequences and generate Kmers of length 250 using the blaster package. The GPT-4 API plug-in was embedded with adequate prompt parsing for it as an interpretation LLM function. Results AMR trendline plots were designed for invasive infections and customised based on the class of antibiotics and infection types on the surveillance dashboard. The decision-support tool correctly predicted resistant genes with a sensitivity of 75% on pre-confirmed organisms. The observed specificity score (51.5%) was due to the need for more filtering and optimisation and not to PATHFINDER performance. When run against the reference gene dataset containing pre-identified AMR genes, the support tool generated a BLAST table with identified AMR gene determinants in a nucleotide sequence. Conclusions The PATHFINDER algorithm has the potential to revolutionise healthcare decision-making. It can inform targeted interventions, guide antimicrobial stewardship efforts at a national level, promote appropriate antibiotic use, and significantly reduce the risk of resistance development.

Climate change effects on animal presence in the Massaciuccoli Lake basin

Big-data mining approaches based on Artificial Intelligence models can help forecast biodiversity changes before they happen. These approaches can predict macroscopic species distribution patterns and trends that can inform preventive measures to avoid the loss of ecosystem functions and services. They can, therefore, help study and mitigate climate change implications on biodiversity conservation in fragile ecosystems. Wetlands are particularly fragile ecosystems where climate change poses severe risks and has dramatically reduced their size over the past century, with profound consequences on biodiversity and ecosystem services. Through big-data mining approaches, we can predict future wetland biodiversity trends in the context of climate change. This paper proposes such predictive analysis for a specific wetland: The Massaciuccoli Lake basin in Tuscany, Italy. This basin is a critical tourist attraction due to its rich biodiversity, making it an area of interest for citizens, tourists, and scientists. However, the region's suitability for native and non-native species is at risk due to climate and land-use change. Using machine-learning models, we predict the potential effects of climate change on animal spatial distribution in the basin under different greenhouse gas emission scenarios. The results suggest that habitat suitability has generally improved from 1950 to today, presumably owing to the targeted conservation strategies adopted in the area, but climate change will severely reduce bird biodiversity by 2050 while favouring several insect species' proliferation and other species' habitat change, even under a medium-emission scenario. This will lead to significant changes in the basin's biodiversity. Our methodology is adaptable to other wetland basins, being fully based on open data and models. The spatially explicit modelling used in this research provides valuable information for policymakers and spatial planners, complementing traditional biodiversity trend analyses.

Ten-year trend analysis of malaria prevalence in Gindabarat district, West Shawa Zone, Oromia Regional State, Western Ethiopia

BackgroundMalaria is a major public health concern in Ethiopia, where more than half of the population lives in malaria risk areas. While several studies have been conducted in different eco-epidemiological settings in Ethiopia, there is a notable scarcity of data on the prevalence of malaria in the Gindabarat district. Therefore, this study aimed to analyse 10-year trend of malaria prevalence in Gindabarat district, West Shawa Zone of Oromia, Western Ethiopia.MethodsA retrospective laboratory record review was conducted at Gindabarat General Hospital and Gindabarat District Health Office from September 2011 to August 2020. The retrieved data included the date of examination, age, sex and laboratory results of the blood smears, including the Plasmodium species identified. Data were summarized and presented in the form of tables, figures, and frequencies to present the results. The data were analysed using SPSS (version 25.0) and Microsoft Excel.ResultsOver the course of 10 years, a total of 11,478 blood smears were examined in the public health facilities in the district. Of the total blood smears examined, 1372 (11.95%) were microscopically confirmed malaria. Plasmodium falciparum, Plasmodium vivax and mixed infections (P. falciparum and P. vivax) accounted for 70.77%, 20.55% and 8.67% of the cases, respectively. Malaria prevalence was significantly higher among individuals aged ≥ 15 years (12.60%, x2 = 13.6, df = 2, p = 0.001) and males (14.21%, x2 = 59.7, df = 1, p = 0.001). The highest number of malaria cases was recorded from September to November.ConclusionMalaria remains a public health problem in the district. P. falciparum was the most predominant parasite species in the area. Malaria prevalence was significantly higher among individuals aged ≥ 15 years and males. There was a remarkable fluctuation in the number of malaria cases in different months and years. In the study area malaria cases peaked in 2015 and 2017 then decreasing from 2017 to 2019, with sharp increase in 2020. Moreover, this study showed malaria cases were reported in all seasons and months, but the highest was observed from September to November. Strengthening malaria control activities is essential to further reduce the burden of malaria and pave the way for the anticipated elimination.

Examining climate trends and patterns and their implications for agricultural productivity in Bagamoyo District, Tanzania

Climate variability impacts various global challenges, including food security, biodiversity loss, water scarcity, and human well-being. However, climate patterns and trends and community perceptions at spatially-explicit levels have been minimally addressed. The spatial and temporal trends of climate conditions in Bagamoyo District in Tanzania were examined using historical (1983–2010) and projected (2022–2050) meteorological and climate model data, respectively. Community knowledge and experiences of past climate occurrences were included. The implications of projected climate change for regional agricultural production and food security were assessed. The study drew on empirical data obtained from household surveys conducted in seven villages in the district. Effectively, 309 households were randomly sampled across the villages to provide the perception of climate change and associated impacts on agriculture and livelihoods. Both qualitative and quantitative statistical techniques were employed to analyse the incidence, frequency, and intensity of regional extreme climate events. The meteorological and climate modelling data were subjected to trend analysis using the Mann–Kendall and Sen’s slope estimator tests, and the present and projected spatial and temporal trends of climatic variables in the region were analysed. The household-based questionnaire results were combined with climate modelling and the literature to determine the implications of climate change for regional agricultural production and food security. The results revealed that local knowledge and climate model data strongly concur on regional climate changes. Furthermore, the region is highly likely to experience increased warming and decreased precipitation at varying magnitudes. The shifts in climate trends and patterns are anticipated to greatly impact agricultural production, affecting livelihoods and hampering food security efforts. Recommendations include adopting context-specific measures and tailored strategies for enhancing resilience throughout the entire region.

Liver cancer in China: the analysis of mortality and burden of disease trends from 2008 to 2021

BackgroundLiver cancer is one of the most common cancers in China. To understand the basic death situation and disease burden change trend, we analyze the death information of liver cancer among Chinese residents from 2008 to 2021.MethodsData was collected from the Cause-of-Death Surveillance dataset of the National Cause-of-Death Surveillance System from 2008 to 2021. Excel 2016 was used for data entry and to calculate the Crude Mortality Rate (CMR), Age-Standardized Mortality Rate (ASMR), Potential Years of Life Lost (PYLL), and Potential Years of Life Lost Rate (PYLLR). SPSS 25.0 was used to statistically analyze CMR, ASMR, PYLL, and other indicators. Annual percent change (APC) and average APC(AAPC) was used for trend analysis and tested by t tests. Joinpoint 4.9.1.0 was used to calculate APC and AAPC. Age-Period-Cohort model was used to assess the effects of age, period, and birth cohort on liver cancer mortality.ResultsFrom 2008 to 2021, 491,701 liver cancer deaths were reported in the National Disease Surveillance Points System. The ASMR of liver cancer in Chinese residents decreased from 27.58/100,000 in 2008 to 17.95/100,000 in 2021 at an average annual rate of 3.40% (t = -5.10, P < 0.001). The mortality rate was higher in males than in females (all P < 0.001) and higher in rural areas than in urban areas (all P < 0.001). The mortality rate of liver cancer varied significantly among eastern, central, and western China (all P < 0.001). The PYLLR of liver cancer in Chinese residents decreased from 2.89‰ in 2008 to 2.06‰ in 2021 at an average annual rate of 2.40% (t = -5.10, P < 0.001). Males had a lower PYLLR than females, decreasing at average annual rates of 2.20% (t = -5.40, P < 0.001) and 2.90% (t = -8.40, P < 0.001), respectively. Urban areas had a lower PYLLR than rural areas, decreasing at average annual rate of 3.30% (t = -4.00, P < 0.001) and 2.50% (t = -11.60, P < 0.001), respectively. Eastern, central, and western China decreased at average annual rates of 3.40%, 2.30%, and 2.10%, respectively (t = -7.80, -3.60, -7.10, P < 0.001 for all). The risk of China liver cancer mortality increased with age, decreased with birth cohort.ConclusionsThe mortality and disease burdens of liver cancer in China decreased yearly and were higher in males and in people living in rural areas, with significant differences among those living in eastern, central, and western China.