Trends In Dynamics Research Articles

Research on the coupling measurement of medical education and health resource allocation under the background of healthy China

Objective To analyze the coupling and coordination level of medical education and health resource allocation in China, and to provide scientific basis for promoting the high-quality development of medical education and the efficient allocation of health resources.MethodsBased on the panel data from 2011 to 2021, the coupling coordination degree model was used to measure the coupling coordination index of medical education and health resources in China. The spatial auto-correlation model was used to analyze the development status and distribution characteristics of the coupling coordination degree of the two systems. The kernel density estimation method was used to analyze the dynamic evolution trend of the coupling coordination of the two systems. The QR quantile regression model was used to explore the key factors affecting the coupling coordination degree of the two systems.ResultsDuring the observation period, the coupling coordination degree of the two systems increased from 0.393 to 0.465, with a growth rate of 18.3%. The coupling coordination degree between regions gradually decreased in the eastern-central and eastern-western regions, and there were still large differences between the central and western regions. The coupling coordination degree of the two systems in the region was significantly different in the eastern and western regions, and the central region was relatively similar. There is a positive spatial correlation between the provinces, and 25.81% of the provinces have transitions. Finally, the number of points in the first and third quadrants is higher than that in the second and fourth quadrants. In the process of dynamic distribution, the degree of polarization of the coupling coordination degree curve of the two systems is gradually weakened. Per capita GDP, residents ' income difference and population size are the positive and significant factors driving the coupling and coordinated development of the two systems.ConclusionThe coupling and coordination degree of the two systems of medical education and health resource allocation showed a stable upward trend during the observation period, and the global spatial positive correlation also gradually increased, showing the spatial agglomeration characteristics of ' high-high agglomeration ' and ' low-low agglomeration '. The spatial difference of coupling coordination degree shows a shrinking trend and develops towards equalization. The coupling coordination degree of the two systems is affected by social, economic and demographic factors to varying degrees. Therefore, it is necessary to innovate the coordinated development mechanism of the two systems, promote the two-way flow of medical education and health resource allocation in talents, technology and other elements, and then promote the coupling and coordinated development of the two systems.

Analyzing the online word of mouth dynamics: A novel approach

In today's digital economy, virtually everything from products and services to political debates and cultural phenomena can spark WOM on social media. Analyzing online WOM poses at least three challenges. First, online WOM typically consists of unstructured data that can transform into myriad variables, necessitating effective dimension reduction. Second, online WOM is often continuous and dynamic, with the potential for rapid, time-varying changes. Third, significant events may trigger symmetric or asymmetric responses across various entities, resulting in “bursty” and intense WOM from multiple sources. To address these challenges, we introduce a new computationally efficient method—multi-view sequential canonical covariance analysis. This method is designed to solve the myriad online WOM conversational dimensions, detect online WOM dynamic trends, and examine the shared online WOM across different entities. This approach not only enhances the capability to swiftly interpret and respond to online WOM data but also shows potential to significantly improve decision-making processes across various contexts. We illustrate the method's benefits through two empirical examples, demonstrating its potential to provide profound insights into online WOM dynamics and its extensive applicability in both academic research and practical scenarios.

Vegetation’s Dynamic Changes, Spatial Trends, and Responses to Drought in the Yellow River Basin, China

Drought is a complex and recurrent natural disaster that can have devastating impacts on economies, societies, and ecosystems around the world. In light of climate change, the frequency, duration, and severity of drought events worldwide have increased, and extreme drought events have caused more severe and irreversible damage to terrestrial ecosystems. Therefore, estimating the resilience of different vegetation to drought events and vegetation’s response to damage is crucial to ensuring ecological security and guiding ecological restoration. Based on meteorological and remote-sensing datasets from 1982 to 2022, the spatial distribution characteristics and temporal variability of vegetation were identified in the Yellow River Basin (YRB), the dynamic changes and recurrence periods of typical drought events were clarified, and the driving effects of different drought types on vegetation were revealed. The results indicated that (1) during the research period, the standardized vegetation water-deficit index (SVWI) showed a downward trend in the YRB, with a 99.52% probability of abrupt seasonal changes in the SVWI occurring in January 2003; (2) the characteristic values of the grid trend Zs were −1.46 and 0.20 in winter and summer, respectively, indicating a significant downward trend in the winter SVWI; (3) the drought with the highest severity (6.48) occurred from September 1998 to February 1999, with a recurrence period of 8.54 years; and (4) the growth of vegetation was closely related to drought, and as the duration of drought increased, the sensitivity of vegetation to drought events gradually weakened. The research results provide a new perspective for identifying vegetation’s dynamic changes and responses to drought, which is of great significance in revealing the adaptability and potential influencing factors of vegetation in relation to climate.

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.

In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (β=-0.016) and average annual income of employees (β=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (β=1.416) and kindergarten (β=0.412) was a risk factor. HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

Early Prognostics of Remaining Useful Life in Lithium Ion Batteries Using Hybrid LSTM-Att-MLP Model with Fusing Aging Information

Predicting the remaining useful lifetime (RUL) stands as a crucial aspect of lithium-ion battery management systems, acting as a core component of their functioning. Accurately predicting the RUL is essential for ensuring safety, preventing failures, and averting catastrophic incidents, but it is challenging, due to capacity degradation and aging effects. To overcome this, a hybrid model termed LAM: LSTM with an attention mechanism and MLP, for early RUL prediction, leveraging fused aging information is proposed. LSTM adeptly captures the significance embedded within feature sequences, preserving essential long-term features while effectively filtering out less pertinent information. Embedding attention mechanism with LSTM, the model dynamically focuses on different parts of the input sequence by assigning varying importance levels to different aging information, enhancing the prediction performance. For capturing the dynamic and nonlinear degradation trend of batteries and predicting RUL by effectively learning intricate degradation patterns MLP is utilized. The proposed model’s efficacy is evaluated using a NASA dataset through leave-one-out evaluation, utilizing 50% of the training data from three batteries to predict the others, and with varying starting points. The results indicate that under conditions of limited historical samples, the LAM attains higher accuracy and achieves minimum Mean Squared Error of 3.9962 × 10−5.

Analysis of land cover change in Magelang Regency And Magelang City over 15 years

Background: Land cover change is an ongoing phenomenon with significant impacts on the environment and human quality of life. An analysis of land cover change was conducted in the Magelang area, which includes both Magelang Regency and Magelang City, over a period of fifteen years from 2007 to 2023. This study aims to analyze these changes using spatial methods based on land cover maps from 2007 and 2023. Method: In this analysis, land cover was classified into five categories: settlements, dry fields, paddy fields, forests, and plantations, and then compared between the two years. Result: The analysis results demonstrate a significant increase in the area of settlements and dry fields, reflecting urban and rural development. On the other hand, there has been a decrease in the area of paddy fields and forests, which may be related to land conversion for industrial or infrastructure purposes. The change in the area of plantations was not significant, indicating continuity in the management of these lands. Conclusion: The findings of this study provide valuable insights into the dynamics of land cover change in the region and can serve as a basis for further actions to maintain a balance between development and environmental conservation. Novelty/Originality of this study: This study presents the first longitudinal spatial analysis of land cover changes over 15 years (2007–2023), revealing significant trends in urban and rural land use dynamics. This study provides a scientific basis for sustainable spatial planning by highlighting the urgency to balance development infrastructure with preserving farmland and forests.

Change point detection in length-biased lognormal distribution

In this paper, we develop two procedures for identifying the dynamic trends in the parameters of length-biased lognormal distribution based on likelihood ratio and modified information criterion. These methods mainly consider the test of the existence of the change point and provide the maximum likelihood estimation of the change point when the change point exists. In addition, the asymptotic distribution of test statistic based on likelihood ratio is derived as an extreme value distribution, while the asymptotic distribution of test statistic based on modified information criterion is derived as a chi-square distribution. And the consistency of parameter estimation is proved. Simulations are conducted to study the performance of the proposed method in terms of power, coverage probabilities and average sizes of confidence sets. The proposed methods are applied to a real data to illustrate the detecting procedures.

Clinical characteristics of COVID-19 clusters in three schools in Beijing, China: A retrospective study

BackgroundThis retrospective analysis aims to investigate the clinical characteristics of students infected with the SARS-CoV-2 Omicron variant in three Beijing schools. Additionally, we explore the dynamic trends of nucleic acid cycle threshold values (Ct values) and serum antibody titers throughout the disease course. MethodsDemographic, clinical, nucleic acid Ct values, and antibody titer data were collected from cases in a COVID-19 cluster in Beijing Ditan Hospital, Capital Medical University, spanning from September 6 to October 1, 2022. ResultsA total of 107 students infected with Omicron (BA.5.2 and BA.2.76) were identified across three schools. Primary clinical manifestations included fever and upper respiratory symptoms (85/107, 79.4 %), with the majority being classified as mild cases (96/107, 89.7 %). Notably, middle school students in the second school exhibited a higher peak body temperature compared to college students in the first and third schools (39.5 °C vs. 38.4 °C, adjusted P = 0.005; 39.5 °C vs. 38.6 °C, adjusted P = 0.002). Analysis of dynamic changes in Ct values revealed the lowest median Ct value in nasopharyngeal swabs on the third day of illness, reaching 35 after 9–11 days. Oropharyngeal swab nucleic acid median Ct value reached 35 approximately 3–5 days post-onset. Serum antibody detection showed continuous negativity of IgM antibody titers from days 1–10, while IgG antibody titers were positive on the first day and increased rapidly after one week. ConclusionsThe three COVID-19 cluster school outbreaks primarily resulted from Omicron infections, with no severe or fatal cases observed. Clinically, the selection of different types of SARS-CoV-2 nucleic acid swabs for virus detection can be tailored based on the infection's course.

Simulation of China’s Carbon Peak Path Based on Random Forest and Sparrow Search Algorithm—Long Short-Term Memory

How to decouple economic growth from carbon dioxide emissions and achieve low-carbon transformation of the Chinese economy has become an urgent problem that needs to be solved. Firstly, the Tapio index is used to identify China’s carbon peak status, and then the Technology Choice Index (TCI) and economic complexity are introduced into the comprehensive factor analysis framework for carbon dioxide emissions. Key influencing factors are identified using random forest and ridge regression. On this basis, a novel sparrow search algorithm–long short-term memory (SSA-LSTM) model which has more prediction accuracy compared with past studies is constructed to predict the dynamic evolution trend of carbon dioxide emissions, and in combination with scenario analysis, the path towards the carbon peak is simulated. The following conclusions are obtained: The benchmark scenario peaks in 2031, with a peak of 12.346 billion tons, and the low-carbon scenario peaks in 2030, with a peak of 11.962 billion tons. The extensive scenario peaks in 2037, with a peak of 13.291 billion tons. Under six scenarios, it can be concluded that energy intensity is the key factor in reducing the peak. These research results provide theoretical support for decision-makers to formulate emission reduction policies and adjust the carbon peak path.

Postoperative body weight change and its influencing factors in patients with gastric cancer.

The high incidence and mortality of gastric cancer (GC) pose a significant threat to human life and health, and it has become an important public health challenge in China. Body weight loss is a common complication after surgical treatment in patients with GC and is associated with poor prognosis and GC recurrence. However, current attention to postoperative weight change in GC patients remains insufficient, and the descriptions of postoperative weight change and its influencing factors are also different. To investigate body weight changes in patients with GC within 6 mo after gastrectomy and identify factors that influence dynamic body weight changes. We conducted a prospective longitudinal study of 121 patients with GC and collected data before (T0) and 1 (T1), 3 (T2), and 6 (T3) mo after gastrectomy using a general data questionnaire, psychological distress thermometer, and body weight measurements. The general estimation equation (GEE) was used to analyze the dynamic trends of body weight changes and factors that influence body weight changes in patients with GC within 6 mo of gastrectomy. The median weight loss at T1, T2, and T3 was 7.29% (2.84%, 9.40%), 11.11% (7.64%, 14.91%), and 14.75% (8.80%, 19.84%), respectively. The GEE results showed that preoperative body mass index (BMI), significant psychological distress, religious beliefs, and sex were risk factors for weight loss in patients with GC within 6 mo after gastrectomy (P < 0.05). Compared with preoperative low-weight patients, preoperative obese patients were more likely to have weight loss (β = 14.685, P < 0.001). Furthermore, patients with significant psychological distress were more likely to lose weight than those without (β = 2.490, P < 0.001), and religious patients were less likely to lose weight 6 mo after gastrectomy than those without religious beliefs (β = -6.844, P = 0.001). Compared to female patients, male patients were more likely to experience weight loss 6 mo after gastrectomy (β = 4.262, P = 0.038). Male patients with GC with high preoperative BMI, significant psychological distress, and no religious beliefs are more likely to lose weight after gastrectomy.

Revealing alcoholization-related volatile compounds and determining alcoholization indices in tobacco using GC-IMS coupled with chemometrics

Alcoholization is an integral part of tobacco processing and volatile compounds are key to assessing tobacco alcoholization. In this study, a total of 154 volatiles from nine categories were determined by gas chromatography-ion mobility spectrometry (GC-IMS) from four grades of tobacco, of which 114 were better identified. And then, the dynamic trends of volatile compounds with significant changes in tobacco alcoholization were analyzed. The relevant volatiles with the alcoholization indices (AIs) (R > 0.8) were screened as indicators of tobacco alcoholization. Cinnamyl isobutyrate, linolenic acid alcohol, propanoic acid-M and propanoic acid-D in all tobacco samples were highly correlated with the AIs and tended to increase during the alcoholization process. In addition, linear discriminant analysis (LDA), back-propagation neural network (BPNN) and random forest (RF) classifiers were constructed for discrimination of tobacco AIs. Three classifiers trained with a combination of 20 volatiles achieved satisfactory results with area under the curve (AUC) of 0.95 (LDA), 0.94 (BPNN) and 0.97 (RF), respectively. The RF classifier gained optimal accuracy of 100 % and 96.1 % for the training and test sets, respectively. The study confirmed that GC-IMS can be used to characterize the changes of volatile compounds in tobacco during alcoholization and combined with machine learning to achieve the determination of AIs. The results of the study may provide a new means for the tobacco industry to monitor the alcoholization process and determine the degree of alcoholization.

Dynamic Evolution and Trend Prediction of the Development Level of China's Agricultural and Rural Modernization

Dynamic Evolution and Trend Prediction of the Development Level of China's Agricultural and Rural Modernization

Exploring the ancient roots and modern global brews of tea and herbal beverages: A comprehensive review of origins, types, health benefits, market dynamics, and future trends.

Tea, a culturally significant beverage, originated around 2700 B.C. in ancient Chinese civilization, with a profound understanding of its therapeutic properties. Herbal medicines from diverse plant sources have been esteemed for their phytochemical content. Today, tea's appeal spans the globe, with various processing techniques creating distinct tea varieties. This review article comprehensively explores tea and herbal teas, encompassing their origins, types, trade history, health benefits, chemical composition, and market and future dynamics. This review examines tea's evolution from ancient China to its global significance and analyzes the impact of tea trade routes on cultural exchanges and trade dynamics. The review covers conventional teas (black, green, and oolong), blended teas, and herbal teas. It primarily focuses on herbal beverages' chemical composition and active components derived from diverse plants and botanicals, highlighting their traditional uses and health-promoting applications. The review provides valuable insights into the dynamic herbal tea market, growth, consumer preferences, industry trends, and future aspects of the herbal beverage. Additionally, it explores the proper classification and preparation of herbal drinks for maximum benefits, shedding light on tea manufacturing and preparation processes. This review is a valuable resource for tea enthusiasts, health-conscious individuals, and industry stakeholders, offering profound insights into teas and their multifaceted allure.

Unveiling the fate of metal leaching in bimetal-catalyzed Fenton-like systems: pivotal role of aqueous matrices and machine learning prediction

Metal-based catalytic materials exhibit exceptional properties in degrading emerging pollutants within Fenton-like systems. However, the potential risk of metal leaching has become pressing environmental concern. This study addressed scientific issues pertaining to the leaching behavior and control strategies for metal-based catalytic materials. Innovative cobalt-aluminum hydrotalcite (CoAl-LDH) triggered peroxymonosulfate (PMS) activation system was constructed and achieved near-complete removal of Ciprofloxacin (CIP) across diverse water quality environments. Notably, it was found that the tunable ion exchange and Al3+ stabilization of CoAl-LDH occurred due to the particularity of neutral water quality, resulting in significantly lower Co2+ leaching levels (0.321 mg/L) compared to acidic conditions (5.103 mg/L). In light of this, machine learning technology was then employed for the first time to simulate the dynamic trend of Co2+ leaching and elucidated the critical regulatory roles and mechanisms of Al3+, aqueous matrix, and reaction rate. Furthermore, degradation systems based on different water quality and metal leaching levels regulated the generation levels of SO4.- and O2∙-, and the unique advantages of free radical attack paths were clarified through CIP degradation products and ecotoxicity analysis. These findings introduced novel insights and approaches for engineering application and pollution control in metal-based Fenton-like water treatment.

Long‐term observation of waterfowl migration in the south‐east of the forest zone of Western Siberia, Russia

The aim of this research is an analysis of the long‐term dynamics of the composition and number of migratory waterfowl in the southeast of the forest zone of Western Siberia.Visual observations of waterfowl migration were carried out in the format of daily 2‐hour morning counts in April‐May through a period 18 years (1998–2023) near the Tomsk on the Tom river. Additionally, in 2002–2003, simultaneous migration was also observed on the Ob river in the southern, central and northern parts of the Tomsk region. The method of E.I. Gavrilova and E.V. Kumari was used with some modifications. Birds were counted which were transit flying via the territory, as well as birds sitting on the lake water in the area of the observation point.Twenty‐one species of waterfowl have been registered. Analysis of long‐term dynamics has revealed a trend towards a decrease in the total number of migrating waterfowl. In the most abundant species, Anas acuta, this trend is statistically significant. In some species – Anas strepera and Cygnus Cygnus – the trend of dynamics is positive.Most likely reason for the decline in waterfowl numbers is excessive spring hunting. A reduced hunting duration or the introduction of a moratorium every few years may be among the responses required. We suggest using the period of mass migration to monitor the waterfowl resource. This period is shorter and when the greatest number of birds migrate.

Just-in-time learning method based on two kinds of local samples combined with two-stage training parallel learner for nonlinear chemical process soft sensing

Just-in-time Learning (JITL) is a soft sensor method to develop the corresponding local model for each query sample, which has strong adaptability. Aiming at the lack of feature information of dynamic law of chemical processes in similar samples, a JITL method based on two kinds of local samples combined with two-stage training parallel learner (JITLTKLS-TSTPL) was proposed to address this issue. Firstly, similarity measurement and moving window are used to select local samples respectively. And the length of two kinds of samples is optimized by genetic algorithm. Then, the proposed two-stage training parallel learner is proposed to learn local nonlinear features of similar samples and dynamic trend information of dynamic samples by cross-freezing network weights. Finally, the JITL model was verified by three chemical processes. The results show that the total error and maximum error of JITLTKLS-TSTPL are the smallest compared with the JITL method using a single sample for modeling. In the public data set, compared with other research methods, the R2 of JITLTKLS-TSTPL was the highest, reaching 0.983.

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

From efficiency to resilience: unraveling the dynamic coupling of land use economic efficiency and urban ecological resilience in Yellow River Basin

This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales.

Dynamic water migration and flavor analysis of sea cucumber in the process of Sichuan pepper seasoning soak

Soaking in seasoning solution is the main process of sea cucumber seasoning. This study analyzed the dynamic changes in water migration and flavor substances in sea cucumbers during soaking in a Sichuan pepper solution. It was found that the sea cucumber experienced a process of water absorption followed by water loss during the 0–48 h soaking process. During this period, the flavor compounds in sea cucumbers showed different dynamic trends. A total of 46 volatiles were identified, of which 29 were key flavor compounds. Its flavor profiles tended to stabilize as soaking time increased. m-Xylene, d-Limonene, Eucalyptol, p-Xylene, Sabinene, Beta-Myrcene, and Beta-Phellandrene were the main characteristic substances contributing to the differences in sea cucumber flavor. Correlation analysis predicted the relationship between water migration and the dynamic shifts in flavor compounds. This study provides a crucial reference for future studies on the processing and flavor modulation of sea cucumber products.

The global burden of disease attributable to preterm birth and low birth weight in 204 countries and territories from 1990 to 2019: An analysis of the Global Burden of Disease Study.

Preterm birth and low birth weight (PBLBW), recognised globally as primary contributors to infant mortality in children under five, have not been sufficiently investigated in terms of their worldwide impact. In this study we aimed to thoroughly evaluate the contemporary trends in disease burden attributable to PBLBW. We analysed data from 204 countries and territories between 1990-2019, as sourced from the 2019 Global Burden of Disease Study. We analysed the global incidence of mortality and disability-adjusted life years (DALYs) associated with PBLBW, stratified by age, gender, year, and geographic location, alongside the socio-demographic index (SDI). We calculated the annual percentage changes to evaluate the dynamic trends over time. We employed a generalised linear model and scrutinised the relationship between the SDI and the disease burden attributed to PBLBW. In 2019, the global age-standardised rate of deaths and DALYs related to PBLBW showed significant declines. Over the period 1990-2019, both death and DALY rates displayed substantial downward trends, with similar change trends observed for both females and males. Age-specific ratios revealed a decrease in PBLBW-related deaths and DALYs with increasing age, primarily during the neonatal stages (zero to 27 days). The leading three causes of PBLBW-related DALYs in 2019 were neonatal disorders, lower respiratory infections, and sudden infant death syndrome. Furthermore, the association between SDI and PBLBW-related DALYs indicated that the age-standardised DALY rates in 204 countries and territories worldwide were negatively correlated with SDI in 2019. From 1990 to 2019, the age-standardised DALY rates decreased linearly in most regions, except sub-Saharan Africa. The persistent global burden of disease associated with PBLBW is particularly pronounced in neonates aged less than 28 days and in regions with low SDI. In this study, we highlighted the critical need for tailored interventions aimed at mitigating the detrimental effects of PBLBW to attain specific sustainable development goals, particularly those centred on enhancing child survival and overall well-being.