Global burden of mental disorders in 204 countries and territories, 1990 - 2021: results from the global burden of disease study 2021

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Background Mental disorders, one of the leading causes global health-related burden, which has been exacerbated by emergence COVID-19 pandemic. In this study, we aim to provide global, regional, and national estimates mental disorders burden from 1990 2021, including during Methods We collected data on incidence, DALYs, ASIR, ASR for 12 at levels 204 countries regions across 21 geographical areas spanning 2021. We utilized joinpoint regression analysis estimate Average Annual Percentage Change (AAPC). also determined trends in ASIR pandemic (2019-2021). Results Globally, between there was an upward trend both [15.23% (12.97% 17.60%)] [17.28% (15.06% 19.44%)]. Regionally, were increases incidences DALYs all GBD regions. highest observed Central Sub-Saharan Africa (8706.11), while lowest East Asia (3340.99). Australia (2787.87) had ASR. Nationally, Greenland, Greece, United States, ASRs. During pandemic, showed five SDI regions, except Asia, where they remained stable. females higher than that males. Among subtypes, major depressive disorder (557.87) anxiety (524.33) Major ranked first 13 worldwide. Despite overall [AAPC: 5.96; 95%CI: (4.99, 6.92)], exhibited varying among different with experiencing most significant increase. Conclusions GBD 2021 still rise gradually worldwide significantly High-middle should be paid more attention. To reduce future providing comprehensive health support, establishing effective knowledge dissemination tailored interventions are great need.

Language: Английский

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 1, 2025

Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living standards, posing significant public health challenges. The MDs are influenced a complex interplay of genetic factors, lifestyle choices, socioeconomic conditions. Additionally, environmental pollutants, particularly air pollutants (APs), attracted increasing attention for their potential role in exacerbating these MDs. However, the impact APs on remains unclear. This study introduces novel machine learning (ML) pipeline, an Algorithm Spatial Relationships Analysis between Exposome Diseases (ASEMD), to analyze spatial associations at prefecture-level city scale China. ASEMD pipeline comprises three main steps: (i) autocorrelation is evaluated using Moran's I statistic Local Indicators Association (LISA) maps. (ii) dimensionality reduction similarities identification clusters Principal Component (PCA), k-means clustering, Jaccard index calculations, further validated through (iii) AP exposure adjusted demographic confounders predict models (e.g., eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LightGBM, Multi-Layer Perceptron (MLP)). SHAP values employed identify key that linked Model performance 10-fold cross-validation five different metrics. data utilized include CHARLS (2015) meteorological (2013-2015). Significant correlations were found prevalence higher rates observed alignment elevated concentrations. By adjusting confounders, effectively predicted risk developing (AUROC=0.890, 0.877, 0.710 respectively). results showed $$\mathrm CO$$ , PM_{2.5}$$ AQI$$ strongly correlated whereas NO_{2}$$ PM_{10}$$ significantly associated dyslipidemia. For O_{3}$$ mostly correlated. Sensitivity analyses across regions types underscored robustness our conclusions. successfully integrates ML models, epidemiological methods, analysis techniques, providing robust framework understanding interactions We also identified specific APs, including $$PM_{10}$$ SO_{2}$$ as being hypertension central northern cities. Future region-specific strategies or interventions, especially those areas high pollutant levels, needed mitigate pollution's metabolic health.

Language: Английский

Global, regional and national disparities and temporal trends of common autoimmune disease burdens among children and adolescents from 1990 to 2019

BMJ Global Health, Journal Year: 2025, Volume and Issue: 10(4), P. e017187 - e017187

Published: April 1, 2025

Introduction Previous evidence lacked a thorough review of the disparities autoimmune diseases (AD) burdens among countries and regions, which led to an insufficient basis for developing country-specific developmental level relevant preventive measures. This study aimed analyse trends global, regional national burden common ADs in children adolescents from 1990 2019 investigate associations between specific varied country indexes. Methods All data four major were obtained Global Burden Diseases Study 2019. Age period-cohort modelling was conducted disentangle age, period birth cohort effects on AD incidence Local regression smoothing models used fit correlation sociodemographic index (SDI). Pearson’s country-level risk factors disease burden. Results A global increase observed 1.57 million 1.63 0–24 age group. The age-standardised rate overall showed substantial variation with highest high SDI regions. distributions significantly, especially countries. Relative expected associated SDI, distribution by regions depending ADs. Countries higher levels socioeconomic development, better quality life easier access healthcare system lower Conclusions patterns considerably according time generational cohort, across world Incidences significantly correlated indexes involving risks environment, human rights health safety life.

Language: Английский