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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Jing Lin

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Open access Aug 2026

Sex inequality in disease burden attributable to smoking, secondhand smoke and chewing tobacco: A systematic analysis for the Global Burden of Disease Study 2023

Summary Background Despite extraordinary progress in global tobacco control and prevention, smoking, secondhand smoke (SHS) and chewing tobacco continue to profoundly impact public health, affecting males and females differently. Existing studies have not systematically characterized sex disparities in disease burden attributable to these three tobacco risks within a unified framework, nor have they quantified the contributions of demographic and exposure-related drivers to these disparities or projected their future trajectories. We aimed to assess patterns of sex disparity in smoking-, SHS-, and chewing tobacco-attributable disease burden from 1990 to 2023, decompose the drivers of these disparities, and project sex-specific burden to 2050 under alternative scenarios. Methods We used data from the Global Burden of Disease Study (GBD) 2023, including sex-specific estimates of disability-adjusted life years (DALYs) and the age-standardized DALY rates (ASDR) attributable to smoking, SHS, and chewing tobacco. Sex disparities were examined across geographic, temporal, and age dimensions. Decomposition analysis was applied to quantify the contributions of population growth, population aging, change in risk exposure, and change in risk-deleted burden to sex-specific DALY changes between 1990 and 2023. Sex ratios were derived from ASDR and their 95% confidence intervals (CI) were estimated using the delta method. Future sex disparities were projected to 2050 under four counterfactual tobacco exposure scenarios. Findings Over the past three decades, the tobacco-attributable disease burden among males has consistently exceeded that among females. In 2023, the overall smoking-attributable ASDR remained substantially higher in males than in females (3146.9 [95% uncertainty interval 2591.8–3703.8] vs. 507.8 [361.0–677.1] per 100,000). The all-cause ASDR attributable to SHS was slightly higher in males than in females (540.7 [434.3–672.9] vs. 506.6 [402.8–612.5] per 100,000). For chewing tobacco, the attributable burden was also markedly greater in males (95.3 [61.3–144.1] vs. 47.8 [25.4–78.5] per 100,000). Decomposition analysis showed that population growth and aging were the predominant drivers of increasing absolute DALYs in both sexes, with consistently larger effects among males, while change in risk exposure contributed differently to sex-specific burden trajectories across the three tobacco risks. Among GBD regions, the sex ratio of smoking-attributable ASDR of all causes was notably lowest in Australasia and high-income North America. The sex ratio of SHS-attributable all-cause ASDR increased from below 1.0 in 1990 to above 1.0 in 2023 across several GBD regions like high-income Asia Pacific and Andean Latin America. Globally, the sex ratio of overall ASDR attributable to chewing tobacco has remained stable, from 1990 (ratio: 1.9 95% CI 1.0–4.0) to 2023 (ratio: 2.0 95% CI 1.0–4.0). Among age groups, smoking-attributable DALY rates increased with age, peaking in the 75+ group in 2023, and the burden among males always exceeded that among females in all age brackets. Compared to the sex ratios of DALY rates attributable to smoking risk in the same regions, the fluctuations in disease burden attributable to SHS and chewing tobacco between sexes across age groups were less pronounced in 2023. Projections suggested that sex disparities would persist to 2050, with alternative tobacco control scenarios yielding divergent patterns of future burden. Interpretation While smoking- and SHS-attributable ASDRs have declined and chewing tobacco-attributable ASDR has remained stable, sex disparities have varied by time, region, and life stage. These findings may inform the integration of sex-specific perspectives into tobacco control strategies, which could strengthen the implementation of the Framework Convention on Tobacco Control and help mitigate the future burden of tobacco use. Funding National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project.

Sheng Li, Zhe Xu, Brooks Morgan et al. · 0 citations
Conference Jul 2026

Machine Learning-Based Cardiovascular Disease Prediction Model

Cardiovascular disease, as a highly prevalent chronic condition, has shown a continuously rising incidence in China and now ranks as the leading cause of death among both urban and rural residents. Mainstream Cardiovascular disease risk prediction models have mostly been developed based on European and American populations, which do not align well with the physical characteristics and disease patterns of the Chinese population. Moreover, traditional statistical methods have inherent limitations, further restricting the clinical applicability of these models. To address this, the present study constructed a Cardiovascular disease risk prediction model tailored to the Chinese population using machine learning algorithms based on the China Health and Retirement Longitudinal Study database. The dataset was split into a training set and a test set at a ratio of 7:3. Seven algorithms were employed for parallel modeling, and multi-dimensional performance comparisons were conducted. The study found that, in addition to traditional risk factors such as blood pressure and blood glucose, sleep indicators—including nap duration and nighttime sleep duration—were also important influencing factors for Cardiovascular disease. The comparative results demonstrated that the LightGBM model achieved the best predictive performance, with an AUC of 0.828, a recall of 0.717, and an F1 score of 0.568. The integration of SHapley Additive exPlanations further validated the internal logic and rationality of the model. This model can assist clinicians in risk assessment, thereby effectively improving the accuracy and efficiency of cardiovascular disease prediction.

Longfa Chu, Jing Lin, Zekai Li et al. · 0 citations

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