搜索结果：Software and systems modeling

Comprehensive, comparable, and timely estimates of demographic metrics-including life expectancy and age-specific mortality-are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study-part of the latest GBD release, GBD 2023-aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time. We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950-2023. For the first time, we used complete birth history data for ages 5-14 years, age-specific sibling history data for ages 15-49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution. In 2023, 60·1 million (95% UI 59·0-61·1) deaths occurred globally, of which 4·67 million (4·59-4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2-38·4) over the 1950-2023 study period, during which the global age-standardised all-cause mortality rate declined by 66·6% (65·8-67·3). Trends in age-specific mortality rates between 2011 and 2023 varied by age group and location, with the largest decline in under-5 mortality occurring in east Asia (67·7% decrease); the largest increases in mortality for those aged 5-14 years, 25-29 years, and 30-39 years occurring in high-income North America (11·5%, 31·7%, and 49·9%, respectively); and the largest increases in mortality for those aged 15-19 years and 20-24 years occurring in Eastern Europe (53·9% and 40·1%, respectively). We also identified higher than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 5-14 years (87·3% higher in GBD 2023 than GBD 2021 on average across countries and territories over the 1950-2021 period) and for females aged 15-29 years (61·2% higher), as well as lower than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 50 years and older (13·2% lower), reflecting advances in our modelling approach. Global life expectancy followed three distinct trends over the study period. First, between 1950 and 2019, there were considerable improvements, from 51·2 (50·6-51·7) years for females and 47·9 (47·4-48·4) years for males in 1950 to 76·3 (76·2-76·4) years for females and 71·4 (71·3-71·5) years for males in 2019. Second, this period was followed by a decrease in life expectancy during the COVID-19 pandemic, to 74·7 (74·6-74·8) years for females and 69·3 (69·2-69·4) years for males in 2021. Finally, the world experienced a period of post-pandemic recovery in 2022 and 2023, wherein life expectancy generally returned to pre-pandemic (2019) levels in 2023 (76·3 [76·0-76·6] years for females and 71·5 [71·2-71·8] years for males). 194 (95·1%) of 204 countries and territories experienced at least partial post-pandemic recovery in age-standardised mortality rates by 2023, with 61·8% (126 of 204) recovering to or falling below pre-pandemic levels. There were several mortality trajectories during and following the pandemic across countries and territories. Long-term mortality trends also varied considerably between age groups and locations, demonstrating the diverse landscape of health outcomes globally. This analysis identified several key differences in mortality trends from previous estimates, including higher rates of adolescent mortality, higher rates of young adult mortality in females, and lower rates of mortality in older age groups in much of sub-Saharan Africa. The findings also highlight stark differences across countries and territories in the timing and scale of changes in all-cause mortality trends during and following the COVID-19 pandemic (2020-23). Our estimates of evolving trends in mortality and life expectancy across locations, ages, sexes, and SDI levels in recent years as well as over the entire 1950-2023 study period provide crucial information for governments, policy makers, and the public to ensure that health-care systems, economies, and societies are prepared to address the world's health needs, particularly in populations with higher rates of mortality than previously known. The estimates from this study provide a robust framework for GBD and a valuable foundation for policy development, implementation, and evaluation around the world. Gates Foundation.

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.

Pharmacoepidemiology and population health studies using electronic health care records (EHRs) must define study variables through available electronic data. These variables are operationalized through phenotypes, which are a defined set of criteria used to identify specific traits or medical conditions. There is diversity across phenotype libraries (collections of code lists or algorithms) which intend to standardize these sets of criteria. This review aimed to characterize the landscape of phenotype libraries and how phenotypes are constructed, validated, managed, and reused across research settings. We conducted a systematic review of existing phenotype libraries to appraise their attributes. We systematically searched three databases (Scopus, PubMed, and Web of Science) up to November 2025 to identify studies on key characteristics of phenotype libraries. The search combined Medical Subject Headings (MeSH) terms related to "electronic health record," "phenotype algorithm," and "phenotype library". A structured hand search was performed to identify relevant web-based resources without accompanying publications to ensure comprehensive inclusion of libraries available to date. We extracted information on library size, vocabularies, phenotype construction methods, validation practices, management, and portability. Of 336 articles, 37 met eligibility criteria for full-text review, of which 25 were excluded because they were not EHR-based phenotype libraries (representing single algorithms, genomic resources, or study-specific phenotypes rather than reusable libraries), leaving 10 unique libraries described across 12 articles. A structured hand search identified seven more libraries. In total, 17 phenotype libraries met the inclusion criteria, including Education and Child Health Insights from Linked Data (ECHILD) Phenotype Code List Repository, Centralized Interactive Phenomics Resource (CIPHER), Chronic Condition Data Warehouse (CCW), ClinicalCodes Library, Clinical Classifications Software Refined (CCSR), ComPLy, CALIBER (Health Data Research UK (HDR UK) Phenotype Library or CALIBER), Jigsaw Algorithm Repository (JAR), Manitoba Centre for Health Policy (MCHP) Concept Dictionary, Open CodeLists, Observational Health Data Sciences and Informatics (OHDSI) ATLAS, PheCode, Phenotype KnowledgeBase (PheKB), Phenotype Execution and Modeling Architecture (PhEMA) Workbench, PheMap, Sharing and Reusing Computable Phenotype Definitions (SharePhe), Value Set Authority Center (VSAC). Libraries varied substantially in scope, size, and phenotype representation, including rule-based algorithms, probabilistic phenotypes, and standardized code groupings. Validation practices were heterogeneous and reported only for a subset of libraries. All the libraries utilized a web-based platform and met at least the minimum requirements for library management, including phenotype definitions, metadata, and version control. We observed large variations in library construction and validation across diverse libraries built in varied EHR research settings. The transparency of phenotypes and creating computable phenotypes enhance portability and streamline the effective reuse of phenotypes for different systems. Electronic health records (EHRs) contain real‐world information about patients' medical conditions, treatments, and test results. Researchers use this data to study diseases and improve patient outcomes. To this end, researchers must specify how to define specific conditions in EHR data. These definitions are called phenotypes. Phenotype libraries are platforms where such definitions are collected, documented, and shared, allowing researchers to reuse them and ensure consistency across studies. In this study, we reviewed existing phenotype libraries to understand how they are built and how they support health research. We found 17 libraries, each with unique features. Most use rule‐based methods to define conditions, and some use machine learning and natural language processing to construct phenotypes. All are accessible through web platforms and readable by both humans and computers, but not all include validation of their definitions. User interfaces vary across libraries. Our findings show that phenotype libraries play a key role in improving the reliability and reproducibility of research leveraging EHR data. We also suggest improvements to increase their accessibility, quality, and ability to work across different systems.

Violence against women and against children are human rights violations with lasting harms to survivors and societies at large. Intimate partner violence (IPV) and sexual violence against children (SVAC) are two major forms of such abuse. Despite their wide-reaching effects on individual and community health, these risk factors have not been adequately prioritised as key drivers of global health burden. Comprehensive x§and reliable estimates of the comparative health burden of IPV and SVAC are urgently needed to inform investments in prevention and support for survivors at both national and global levels. We estimated the prevalence and attributable burden of IPV among females and SVAC among males and females for 204 countries and territories, by age and sex, from 1990 to 2023, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023. We searched several global databases for data on self-reported exposure to IPV and SVAC and undertook a systematic review to identify the health outcomes associated with each of these risk factors. We modelled IPV and SVAC prevalence using spatiotemporal Gaussian process regression, applying data adjustments to account for measurement heterogeneity. We employed burden-of-proof methodology to estimate relative risks for outcomes associated with IPV and SVAC. These estimates informed the calculation of population attributable fractions, which were then used to quantify disability-adjusted life-years (DALYs) attributable to each risk factor. Globally, in 2023, we estimated that 608 million (95% uncertainty interval 518-724) females aged 15 years and older had ever been exposed to IPV, and 1·01 billion (0·764-1·48) individuals aged 15 years and older had experienced sexual violence during childhood. 18·5 million (8·74-30·0) DALYs were attributed to IPV among females and 32·2 million (16·4-52·5) DALYs were attributed to SVAC among males and females in 2023. IPV and SVAC were among the top contributors to the global disease burden in 2023, particularly among females aged 15-49 years, ranking as the fourth and fifth leading risk factors, respectively, for DALYs in this group. Among the eight health outcomes found to be associated with IPV, anxiety disorders and major depressive disorder were the leading causes of IPV-attributed DALYs, accounting for 5·43 million (-1·25 to 14·6) and 3·96 million (1·71 to 6·92) DALYs in 2023, respectively. SVAC was associated with 14 health outcomes, including mental health disorder, substance use disorder, and chronic and infectious disease outcomes. Self-harm and schizophrenia were the leading causes of SVAC-attributed burden, with SVAC accounting for 6·71 million (2·00 to 12·7) DALYs due to self-harm and 4·15 million (-1·92 to 13·1) DALYs due to schizophrenia in 2023. IPV and SVAC are substantial contributors to global health burden, and their health consequences span a variety of individual health outcomes. Importantly, mental health disorders account for the greatest share of disease burden among survivors. Investing in prevention of these avoidable risk factors has the potential to avert millions of DALYs and considerable premature mortality each year. Our findings represent strong evidence for global and national leaders to elevate IPV and SVAC among public health priorities. Sustained investments are needed to prevent IPV and SVAC and to implement interventions focused on supporting the complex social and health needs of survivors. Gates Foundation.

Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients' quality of life. Therefore, it is critical to track epileptic seizures, diagnose them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project's goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized ensuring that the proposed system is realistic and achievable. Perspectives on the feasibility of data-driven medical software that meets the project's goals and demonstrates practical feasibility regarding resource availability, time constraints, and technological capabilities are presented. A 4-round Delphi study using focus group discussions was conducted with 7 diverse panels of experts from Oulu University Hospital to address the research questions and evaluate the feasibility of data-driven medical software for monitoring individuals with epilepsy. This collaborative approach fostered a thorough understanding of the topic and considered the perspectives of various stakeholders. In addition, a qualitative study was carried out using semistructured interviews. Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study identified practical strategies, such as real-time EEG seizure monitoring, predictive modeling for seizure occurrence, and data-driven analytics integration to enhance decision-making. These strategies were aimed at reducing diagnostic delays and providing personalized care. We are actively working on integrating these features into clinical workflows. However, further case studies and pilot implementations are planned for future studies. The results of this study will guide system developers in the meticulous design and development of systems that meet user needs in the DCPE.

Cancer is a leading cause of death globally. Accurate cancer burden information is crucial for policy planning, but many countries do not have up-to-date cancer surveillance data. To inform global cancer-control efforts, we used the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework to generate and analyse estimates of cancer burden for 47 cancer types or groupings by age, sex, and 204 countries and territories from 1990 to 2023, cancer burden attributable to selected risk factors from 1990 to 2023, and forecasted cancer burden up to 2050. Cancer estimation in GBD 2023 used data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Cancer mortality was estimated using ensemble models, with incidence informed by mortality estimates and mortality-to-incidence ratios (MIRs). Prevalence estimates were generated from modelled survival estimates, then multiplied by disability weights to estimate years lived with disability (YLDs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the GBD standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. We used the GBD 2023 comparative risk assessment framework to estimate cancer burden attributable to 44 behavioural, environmental and occupational, and metabolic risk factors. To forecast cancer burden from 2024 to 2050, we used the GBD 2023 forecasting framework, which included forecasts of relevant risk factor exposures and used Socio-demographic Index as a covariate for forecasting the proportion of each cancer not affected by these risk factors. Progress towards the UN Sustainable Development Goal (SDG) target 3.4 aim to reduce non-communicable disease mortality by a third between 2015 and 2030 was estimated for cancer. In 2023, excluding non-melanoma skin cancers, there were 18·5 million (95% uncertainty interval 16·4 to 20·7) incident cases of cancer and 10·4 million (9·65 to 10·9) deaths, contributing to 271 million (255 to 285) DALYs globally. Of these, 57·9% (56·1 to 59·8) of incident cases and 65·8% (64·3 to 67·6) of cancer deaths occurred in low-income to upper-middle-income countries based on World Bank income group classifications. Cancer was the second leading cause of deaths globally in 2023 after cardiovascular diseases. There were 4·33 million (3·85 to 4·78) risk-attributable cancer deaths globally in 2023, comprising 41·7% (37·8 to 45·4) of all cancer deaths. Risk-attributable cancer deaths increased by 72·3% (57·1 to 86·8) from 1990 to 2023, whereas overall global cancer deaths increased by 74·3% (62·2 to 86·2) over the same period. The reference forecasts (the most likely future) estimate that in 2050 there will be 30·5 million (22·9 to 38·9) cases and 18·6 million (15·6 to 21·5) deaths from cancer globally, 60·7% (41·9 to 80·6) and 74·5% (50·1 to 104·2) increases from 2024, respectively. These forecasted increases in deaths are greater in low-income and middle-income countries (90·6% [61·0 to 127·0]) compared with high-income countries (42·8% [28·3 to 58·6]). Most of these increases are likely due to demographic changes, as age-standardised death rates are forecast to change by -5·6% (-12·8 to 4·6) between 2024 and 2050 globally. Between 2015 and 2030, the probability of dying due to cancer between the ages of 30 years and 70 years was forecasted to have a relative decrease of 6·5% (3·2 to 10·3). Cancer is a major contributor to global disease burden, with increasing numbers of cases and deaths forecasted up to 2050 and a disproportionate growth in burden in countries with scarce resources. The decline in age-standardised mortality rates from cancer is encouraging but insufficient to meet the SDG target set for 2030. Effectively and sustainably addressing cancer burden globally will require comprehensive national and international efforts that consider health systems and context in the development and implementation of cancer-control strategies across the continuum of prevention, diagnosis, and treatment. Gates Foundation, St Jude Children's Research Hospital, and St Baldrick's Foundation.

Automated closed-loop systems may improve the adaptation of mechanical ventilatory support to an individual's ventilatory needs. They may also facilitate systematic and early recognition of the patient's ability to breathe spontaneously and come off the ventilator. This is an update of a Cochrane review originally published in 2013 and last updated in 2014. To evaluate the benefits and harms of automated weaning systems compared with non-automated weaning methods in critically ill, mechanically ventilated adults and children. We searched MEDLINE ALL, Embase Classic+Embase, the Cochrane Library (Wiley), CINAHL (EBSCO), the Web of Science Core Collection, and trial registries on 2 February 2024. We checked the reference lists of included studies and relevant systematic reviews for other potentially eligible studies. We included randomized controlled trials (RCTs) evaluating automated closed-loop ventilator applications versus non-automated weaning methods (including non-protocolized usual care and protocolized weaning) in people aged over four weeks who were receiving invasive mechanical ventilation in an intensive care unit (ICU). Our critical outcomes were duration of mechanical ventilation (from randomization to successful unassisted breathing or death), mortality, ICU length of stay, and hospital length of stay. Our important outcomes included other ventilation durations, adverse events related to mechanical ventilation, and health-related quality of life. Two review authors independently assessed risk of bias using the Cochrane risk of bias tool RoB 1. Two review authors independently extracted study data. We synthesized results for each outcome using meta-analysis (random-effects modeling). Subgroup and sensitivity analyses were conducted according to pre-established criteria. We used GRADE to assess the certainty of evidence for each outcome. This update included 62 trials (59 in adults, 3 in children) with 5052 participants (4834 adults, 218 children). The trials evaluated 10 commercially available automated closed-loop systems and one non-commercial system. Forty trials were conducted in mixed or medical ICU populations, the remainder in surgical ICU populations. Automated closed-loop systems probably reduce the duration of mechanical ventilation compared with non-automated weaning methods (mean difference [MD] -0.28 log hours, 95% confidence interval [CI] -0.36 to -0.20; I2 = 87%; 51 RCTs, 3929 participants; moderate-certainty evidence). These data translate to a relative reduction of 24% (95% CI 18% to 30%). Automated closed-loop systems probably result in little to no difference in mortality compared with non-automated weaning methods (risk ratio [RR] 0.94, 95% CI 0.82 to 1.07; I2 = 0%; 38 RCTs, 3620 participants, 618 events; moderate-certainty evidence). Automated closed-loop systems probably reduce ICU length of stay compared with non-automated weaning methods (MD -0.15 log days, 95% CI -0.20 to -0.09; I2 = 71%; 40 RCTs, 3571 participants; moderate-certainty evidence). These data translate to a relative reduction of 14% (95% CI 9% to 18%). Automated closed-loop systems probably reduce hospital length of stay compared with non-automated weaning methods (MD -0.11 log days, 95% CI -0.16 to -0.05; I2 = 43%; 26 RCTs, 2094 participants; moderate-certainty evidence). These data translate to a relative reduction of 10% (95% CI 5% to 15%). In relation to adverse events related to mechanical ventilation, automated closed-loop systems compared with non-automated weaning methods probably reduce the need for reintubation (RR 0.73, 95% CI 0.59 to 0.89; I2 = 0%; 28 RCTs, 2670 participants; moderate-certainty evidence), non-invasive ventilation following extubation (RR 0.74, 95% CI 0.62 to 0.88, I2 = 0%; 23 RCTs, 2451 participants; moderate-certainty evidence), prolonged ventilation (RR 0.54, 95% CI 0.34 to 0.87; I2 = 0%; 11 RCTs, 1191 participants; moderate-certainty evidence), and tracheostomy (RR 0.75, 95% CI 0.62 to 0.91; I2 = 0%; 17 RCTs, 1857 participants; moderate-certainty evidence). No studies reported health-related quality of life. Evidence certainty was downgraded for heterogeneity or imprecision. Based on moderate-certainty evidence from 62 trials including over 5000 critically ill people (mainly adults), we found that automated closed-loop systems probably reduce the duration of mechanical ventilation and the length of ICU and hospital stay compared with non-automated weaning methods. Automated systems probably have little to no effect on mortality but probably reduce the need for reintubation, non-invasive ventilation, prolonged ventilation, and tracheostomy. Given the moderate-certainty evidence of benefit and no evidence of harm, the adoption of automated closed-loop ventilation systems into adult critical care clinical practice warrants consideration. There is a need for further adequately powered multi-center trials in adults and children. Future trials should include health-related quality of life among their outcomes. This review received no funding. The original review was registered with the Cochrane Database of Systematic Reviews, registration number CD009235. The original protocol, published in 2011, is available at DOI: 10.1002/14651858.CD009235. Previous versions of the review are available at DOI: 10.1002/14651858.CD009235.pub2 (2013) and DOI: 10.1002/14651858.CD009235.pub3 (2014).

Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (-11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by -29·9% (-33·6 to -25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Gates Foundation, St Jude Children's Research Hospital.

Mobile health portals (MHPs) are becoming increasingly important and prevalent among health care organizations for engaging and retaining patients. However, their success hinges on patient adoption and usage. Organizations face ongoing challenges with high adoption but low usage of MHPs. While affordance theory offers a valuable theoretical perspective for exploring patient perceptions and uses of MHPs, there is a gap in research that empirically investigates the distinct types of affordances and their determinants. This study develops and empirically evaluates an integrated sociotechnical research model and hypotheses to bridge the literature gap by connecting theoretical constructs with empirical evidence and practical guidelines, thereby enhancing the understanding of patient MHP use. We extend the existing literature by differentiating 3 MHP affordance types: general, contextual, and behavioral. We examined the effects of patient absorptive capacity and hospital MHP innovativeness on the 3 types of affordances and their relationships. Based on the integrated sociotechnical research model and empirical results, we provide theoretical insights and practical guidelines on how organizations may enhance patient MHP use by enhancing patient absorptive capacity and improving patient-centered designs. We evaluated our research model and hypotheses using survey data from 401 patients regarding a WeChat-based MHP platform in 4 large urban hospitals in China. We pretested the measures via 3 rounds of Q-sorting, supplemented by field observations, expert reviews, and patient pilot tests. We tested the research model and hypotheses with partial least squares-structural equation modeling using the SmartPLS software. General affordance was significantly affected by patient absorptive capacity (b=.191, P<.001) and MHP innovativeness (b=.567, P<.001). Contextual affordance was influenced significantly by patient absorptive capacity (b=.148, P<.001) and general affordance (b=.484, P<.001), but not by MHP innovativeness (b=.036, P=.564). Behavioral affordance was significantly influenced by contextual affordance (b=.430, P<.001) and general affordance (b=.140, P=.014). General affordance and contextual affordance mediate the effects of patient absorptive capacity and the innovativeness of hospital MHPs on behavioral affordance. Patient absorptive capacity and the innovativeness of MHPs in hospitals play distinct roles in predicting the 3 types of affordances. While the technology innovativeness of MHPs affects only general affordance but not contextual affordance, patient absorptive capacity affects both. Yet, contextual affordance is 3 times more effective in explaining behavioral affordance than general affordance. An unexpected finding related to the only unsupported hypothesis suggests that while the technology innovativeness of MHPs is necessary for general affordance, it does not directly influence MHP use; rather, it is indirectly and fully mediated by contextual affordance. Understanding and appropriately managing the contextual affordance of MHPs is critical in enhancing patient MHP use. To achieve the benefits of investing in MHPs, organizations must holistically understand and manage the 3 types of affordances, their determinants, and the relationships between them.

Stillbirth, the loss of a fetus after the 20th week of pregnancy, affects about 1 in 160 deliveries in the United States and nearly 1 in 70 globally. It profoundly affects parents, often resulting in grief, depression, anxiety, and posttraumatic stress disorder, exacerbated by societal stigma and a lack of public awareness. However, no comprehensive analysis has explored social media discussions of stillbirth. This study aimed to analyze stillbirth-related content on Instagram and X (formerly Twitter) by (1) identifying dominant themes using topic modeling, evaluated using latent Dirichlet allocation, non-negative matrix factorization (NMF), and BERTopic; (2) detecting influential hashtags via co-occurrence network analysis; (3) examining sentiments and emotions using transformer-based models; (4) categorizing visual representations of stillbirth on Instagram (Meta) through manual image analysis with a predefined codebook; and (5) screening for misinformation relating to stillbirth on X. Stillbirth-related posts were collected via RapidAPI (N=27,395), with Instagram posts (#stillbirth: n=7415; #stillbirthawareness: n=8312; 2023-2024) and X posts (#stillbirth: n=11,668; 2020-2024) analyzed using Python 3.12.7 (Python Software Foundation), with NetworkX for hashtag co-occurrence networks and the PageRank algorithm; comparative analyses were restricted to 2023-2024 due to Instagram application programming interface constraints. Topic modeling was evaluated using latent Dirichlet allocation, NMF, and BERTopic, with coherence scores guiding our model selection. Sentiment and emotion were analyzed using transformer-based RoBERTa and DistilRoBERTa. Misinformation screening was applied to X posts. On Instagram, 2 representative image samples (n=366) were manually categorized using a predefined codebook, with the interrater reliability being assessed using Cohen Kappa. Health-related hashtags (eg, #COVID19) appeared more frequently on X. Topic modeling showed that NMF achieved the highest coherence scores (#stillbirthawareness=0.624 and #stillbirth=0.846 on Instagram, #stillbirth=0.816 on X). Medical misinformation appeared in 27.8% (149/536) of tweets linking COVID-19 vaccines to stillbirth. In the image analysis, "Image of text" was most common, followed by remembrance visuals (eg, gravesites and stillborn infants). The interrater reliability was strong, &#x3ba;=0.837 (95% CI 0.773-0.891) and &#x3ba;=0.821 (95% CI 0.755-0.879), with high Pearson correlation (r=0.999; P<.001) and no significant difference (&#x3c7;&#xb2;7=12.4; P=.09). The sentiment analysis found that positive sentiments exceeded negative sentiments. The emotion analysis showed that fear and sadness were dominant, with fear being more prevalent on X. Instagram emphasizes emotional expression while X focuses on public health and informational content. Evidence-based communication is necessary to counter misinformation, especially on X, whose real-time affordances amplify fear-based narratives during crises, such as COVID-19. In addition, Instagram's visual and commemorative content offers an opportunity to legitimize parental grief and to validate and humanize loss by directly involving bereaved parents in awareness campaigns. Platform-specific strategies and stronger moderation could enhance health discourse credibility. Future research should examine targeted approaches to counter misinformation and assist affected populations.

Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived artificial intelligence for accurate preoperative assessment of the invasive risk. The PubMed, Embase, Cochrane Library and Web of Science databases were searched, and pertinent English-language papers were collected. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR, respectively) of all the papers were calculated using Stata software. The results were plotted on a summary receiver operating characteristic (SROC) curve, publication bias and threshold effects were evaluated, and meta-regression and subgroup analyses were conducted to explore the possible causes of intratumoral heterogeneity. MRI-based radiomics revealed pooled sensitivity (SEN) and specificity (SPE) values of 0.85 and 0.82 for the prediction of high-grade EC; 0.80 and 0.85 for deep myometrial invasion (DMI); 0.85 and 0.73 for lymphovascular space invasion (LVSI); 0.79 and 0.85 for microsatellite instability (MSI); and 0.90 and 0.72 for lymph node metastasis (LNM), respectively. For LVSI prediction and high-grade histological analysis, meta-regression revealed that the image segmentation and MRI-based radiomics modeling contributed to heterogeneity (p&#x2009;=&#x2009;0.003 and 0.04). Through a systematic review and meta-analysis of the reported literature, preoperative MRI-derived ML could help clinicians accurately evaluate EC risk factors, potentially guiding individual treatment thereafter.

With global aging and the increasing burden of noncommunicable diseases (NCDs), the primary health care (PHC) system plays a pivotal role in disease prevention and management of NCDs. Nurses, as key PHC members, significantly influence health equity and service quality. To examine tasks and role functions of Chinese PHC nurses. A mixed-methods convergent study was conducted and reported in accordance with the Good Reporting of a Mixed Methods Study (GRAMMS) framework. A cross-sectional survey and focus group interviews with nurses from 10 PHC institutions in Northeast China were conducted. The Chinese Nurses' Task Survey Questionnaire was used to collect data on the tasks of nurses. Face-to-face semistructured focus group interviews were conducted to explore the nurses' perceptions, understandings, and attitudes toward their tasks. Quantitative data were collated and analyzed using EpiData 3.1 and SPSS 27.0 software, and qualitative data were analyzed through natural language processing (NLP)-based BERTopic topic modeling and conventional content analysis. The quantitative and qualitative results were integrated, merged, and refined to construct the role functions of PHC nurses in China. A total of 123 PHC nurses (n&#x2009;=&#x2009;123) completed the survey, and 10 focus group interviews were conducted with 75 participants (n&#x2009;=&#x2009;75). The results indicated that the current tasks of PHC nurses in China were mainly focused on providing basic medical services, health management, and health education. Three core role functions of Chinese PHC nurses were summarized and refined: guardian of family health, early warner of major diseases, and facilitator of health promotion. Compared with previous studies, the scope of role functions of PHC nurses in China has expanded, with a greater emphasis focus on the management of common NCDs among residents. However, the roles of PHC nurses could not be fully fulfilled, and functional deficiencies still existed. Healthcare managers should fundamentally clarify the role positioning and job responsibilities of PHC nurses. Relevant policies and regulations should be formulated, standardized training systems established, financial compensation mechanisms provided, and professional title promotion pathways optimized to maximize the potential of PHC nurses, thereby enhancing the accessibility of medical services.

Computerized clinical decision support (CDS) has the potential to improve patient outcomes by offering evidence-based guidance at the point of care-enhancing guideline adherence and diagnostic accuracy-and supporting system-level outcomes by enabling predictive analytics for more efficient resource planning. Prior work has identified factors that affect adoption, such as clinicians' expectations of usefulness, ease of use, alignment with workflows, and resources to support utilization. However, CDS adoption is not static and changes according to dynamic systems of behaviors and workflows, requiring a deeper understanding of how evolving conditions affect implementation and outcomes. To explore the dynamic factors influencing CDS adoption, we examined the implementation of the "Unplanned readmission model version 1," developed by Epic Medical Records System, at Duke University Health System, using group model building and system dynamics modeling. We first conducted group model-building workshops with staff (case managers, physical and occupational therapists, hospitalist faculty physicians, and resident physicians) who participate in decisions about discharging patients. Study team members guided participants to identify and connect variables in causal loop diagrams. We coded workshop transcripts in software designed for system dynamics analysis to identify themes, aggregated them into a causal loop diagram, and reviewed them with participants to converge on a common model. A team member applied equations to the pathways and tested data to simulate conditions leading to full, limited, or no adoption of a tool. We identified key balancing loops driven by external pressure (eg, Centers for Medicare & Medicaid Services penalties) that motivated initial adoption and reinforcing loops based on perceived internal benefits to sustain use. While institutional incentives led to early training and tool use, efforts declined due to staff turnover, competing priorities (eg, COVID-19), and workflow changes. Reinforcing loops emerged when staff described clinical utility, such as improved discharge planning and team communication. However, staff also suggested that these loops were often weak due to difficulty linking the use of the tool to outcomes in real time. Simulation modeling showed that while strong external pressure and rapid training led to initial success, interest in using the tool waned as workflows improved and readmission rates approached Centers for Medicare & Medicaid Services goals. When conflicting priorities were introduced, adoption stalled earlier, and fewer staff were trained. In contrast, when internal motivation was strengthened by reducing the amount of evidence needed to perceive success, individual interest remained high even as institutional attention declined, sustaining tool use and further reducing readmissions. External pressure to improve can be a strong motivator for initial adoption, but in the face of conflicting demands for attention, it may fall short of sustained long-term tool use. Tools are more likely to have extensive and sustained use when those using the tools can perceive internal benefits.

Depression is one of the leading causes of disease burden worldwide, with profound effects on quality of life, productivity, and life expectancy. In the United States, its prevalence is particularly high, placing substantial strain on both public health systems and economic stability. Despite advances in treatment and growing awareness, depression remains underdiagnosed and undertreated, especially among low-income and vulnerable populations. As the burden of mental illness continues to rise, quantifying its long-term health and economic impacts is essential for guiding healthcare policy and resource allocation. This study projects the future burden of depression in the United States by estimating healthcare expenditures and mortality for 2023-2032, drawing on nationally representative datasets including the Behavioral Risk Factor Surveillance System (BRFSS), the National Survey on Drug Use and Health (NSDUH), and the Healthcare Cost and Utilization Project (HCUP). Using linear regression modeling, the analysis examines trends in prevalence, healthcare utilization, treatment costs, and mortality, highlighting both direct healthcare costs and indirect costs from lost productivity and premature death. While linear modeling offers a straightforward approach to trend estimation, it may not fully capture nonlinear dynamics in depression prevalence and outcomes, and results should be interpreted with this limitation in mind. By 2030, the annual economic burden of major depressive disorder is projected to exceed $540 billion, with nearly 3,000 depression-related deaths annually. These findings underscore the urgent need for early intervention, expanded access to care, and targeted policies to address treatment disparities, thereby reducing both the economic and human toll of depression.

Enoxaparin is an important drug for the treatment and prevention of venous thromboembolism (VTE) in pregnant women. However, enoxaparin has not been adequately studied and its efficacy, safety, and appropriate dosage have not been established in pregnant women with renal insufficiency. This study aims to combine the pharmacovigilance system with physiologically based pharmacokinetic (PBPK) technology to predict pharmacokinetic and adjust dosage of enoxaparin in pregnant women with renal insufficiency. In this study, based on the FDA Adverse Event Reporting System (FAERS) database to detect adverse drug event signals using the report odds ratio (ROR) method. We used PK-sim and Mobi software to develop and validate enoxaparin PBPK model, and extrapolated the model to pregnant patients with renal insufficiency. The pharmacovigilance studies results revealed that the bleeding risk and dosing problems with enoxaparin in pregnant patients required a high degree of vigilance. Moreover, the PBPK model simulation results showed that when given a clinically standardized dosing regimen, the target anti-Xa factor levels exceed in pregnant with renal insufficiency. The dosage of enoxaparin administered to women with mild, moderate, and severe renal insufficiency pregnancies for prevention of VTE was adjusted to 31-33 mg, 22-24 mg, and 11-12 mg, and that for the treatment of VTE, the dosage was adjusted to 0.75-0.85 mg/kg, 0.5-0.6 mg/kg, and 0.2-0.25 mg/kg, respectively. In addition, an appropriate dose increase is required in late pregnancy compared to early pregnancy. This conclusion provides a basis for dose selection in clinical practice, but the study has certain limitations, and the dosing recommendations may not be considered definitive guidelines.

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build consensus models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualization of biosynthesis pathways, growth assessment, and an agreement-based curation workflow. GEMsembler-curated consensus models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimizing gene-protein-reaction (GPR) combinations from consensus models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPR alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.IMPORTANCEGenome-scale metabolic models (GEMs) capture our knowledge of cellular metabolism as encoded in the genome, enabling us to describe and predict how cells function under different conditions. While several automated tools can generate these models directly from genome data, the resulting models often contain gaps and uncertainties, highlighting areas where our metabolic knowledge is incomplete. Here, we introduce a new tool called GEMsembler, which integrates GEMs constructed by different methods, evaluate model uncertainty, and build consensus models, harnessing the unique features of each approach. These consensus models more accurately reflect experimentally observed metabolic traits, such as nutrient requirements and condition-specific gene essentiality. GEMsembler facilitates comprehensive analysis of model structure and function, helping to pinpoint knowledge gaps and prioritize experiments to address them. By synthesizing information from diverse sources, GEMsembler accelerates the development of more reliable and biologically meaningful models, advancing research in metabolic engineering, pathogen biology, and microbial community studies.

Accessibility is a key indicator for assessing the spatial configuration of elderly care institutions (ECIs). Identification of its spatial disparities is crucial for improving resource allocation efficiency and promoting healthy aging. However, existing accessibility evaluations have insufficient modeling precision in terms of adapting to dynamic traffic conditions and the actual needs of elderly people. This study introduces a novel approach by integrating real-time navigation data, the elderly's preferences, and traditional accessibility evaluations to analyse the accessibility of ECIs at district and street-scale spatial units in Fuzhou, China. Using interpolation, trend, and cluster analyses in ArcGIS and SPSS software, we further identified the spatial disparity characteristics of elderly care institution accessibility. The results show that the current spatial distribution of ECIs does not adequately align with the demand distribution of elderly populations. This is manifested in several ways: (1) inefficient allocation of elderly care resources, as evidenced by the fact that while the central areas and their surroundings offer a greater number of accessible ECIs, these areas also have a higher elderly population densities and generally longer service access times; (2) significant spatial disparities in elderly care institution accessibility with polarized characteristics: central areas characterized by intensive development exhibit markedly higher accessibility compared to peripheral areas; (3) mismatches between supply and demand (i.e., accessibility of ECIs and elderly population) in certain urban local areas, specifically within and around central areas, as well as certain coastal areas. These findings highlight the urgent need for implementing differentiated planning strategies and policy interventions to ensure a more equitable distribution of ECIs. The proposed method in this study provides urban planners and policymakers with a more refined scientific tool for effectively assessing the accessibility of ECIs, identifying shortcomings in resource allocation, and creating more age-friendly living environments. This approach can be adapted to support healthy aging initiatives in diverse urban contexts.

In case report studies (adverse effects of drugs/vaccines) that are unstructured, a structured relational model is not applicable for designing a database, necessitating the use of an unstructured data model, specifically NoSQL. Therefore, the most important and optimal unstructured data model for eCRF, which has the nature of a form, is the document-oriented model. This paper develops and evaluates a reporting system for drug intervention studies with high variability in adverse events that utilizes a document-based NoSQL data model and the eCRF nature, allowing for the management of structured and unstructured data. The main objective of the research is to create a flexible, fast, and efficient system for collecting and analyzing data related to drug/vaccine adverse events, especially the COVID-19 vaccine, for stakeholders. This research is of an applied-descriptive type. In this research, first, after studying library resources, the requirements and requirements for the design of the proposed system were determined in the form of the Software requirements specification (SRS) standard. Then, the design and implementation included modeling and creating a prototype of the web-based system. To evaluate usability, the User Experience Questionnaire (UEQ) was used, system security was assessed using the Application Security Verification Standard (ASVS) questionnaire, and a comparative evaluation of the performance between MongoDB and SQLServer was performed. This research was conducted with the aim of designing and evaluating a reporting system for COVID-19 vaccine side effects, based on a document-oriented data model. It includes key components such as the information and side effects collection module, the document management module, and the reporting module. The results indicated that the user experience of the system, in terms of attractiveness, transparency, efficiency, reliability, motivation, and innovation, had an average score of 2.31, placing it within the top 10% of results. Additionally, the evaluations showed that the system employs effective security controls; however, improvements were needed in certain areas such as meeting management and authentication. A comparative assessment of the performance between the document-oriented data model and the relational data model demonstrated that the proposed system was able to provide better performance in response time and management of unstructured data. Evaluations have shown that utilizing case report forms, along with the advantages of a document-oriented data model, can be effective in collecting the minimum necessary data set for interventional studies, particularly those related to drug side effects such as the COVID-19 vaccine. Given the variable nature of the virus and the potential for unknown side effects, this requires flexible and precise approaches. Additionally, the use of the existing system, considering the results of the security and usability assessment, could be effective if access to external systems is improved.

This study aims to examine the mediating role of fear of future violence in the relationship between exposure to violent events at work and burnout among physicians and nurses. Workplace violence is an escalating concern within healthcare institutions in T&#xfc;rkiye. Increasing exposure to violent events among healthcare professionals contributes to heightened fear of future violence, which may in turn exacerbate burnout. A cross-sectional research design was utilized, collecting data via an online survey distributed to 387 physicians and nurses. The hypothesized model was analyzed using structural equation modeling techniques implemented in AMOS and SmartPLS software. The analysis revealed that exposure to workplace violence significantly elevated the depersonalization and emotional exhaustion dimensions of burnout. Conversely, it was associated with a paradoxical decrease in the personal accomplishment dimension. Notably, no statistically significant relationship was found between exposure to violence and fear of future violence. These findings underscore the complex interplay between various burnout dimensions and the impact of workplace violence on physicians and nurses. Workplace violence emerges as a significant contributor to burnout among physicians and nurses, affecting emotional exhaustion, depersonalization, and personal accomplishment in multifaceted ways. However, the anticipated relationship between exposure to violence and fear of future violence was not supported by the data, indicating a need for further research in this area. The findings of this study indicate that health policies in T&#xfc;rkiye should prioritize strategies aimed at preventing and managing violence against physicians and nurses. Measures such as violence management training, strengthening organizational support systems, and implementing clear protocols for reporting and responding to violent incidents can help mitigate burnout among physicians and nurses.