Global burden of stomach cancer attributable to smoking from 1990 to 2019 and predictions to 2044.

OBJECTIVES: This study aimed to assess the global temporal trends of stomach cancer attributable to smoking from 1990 to 2019 and to predict the global burden by 2044. STUDY DESIGN: This was a comprehensive analysis based on data provided by the Global Burden of Disease Study 2019. METHODS: Based on the Global Burden of Disease Study 2019, mortality, disability-adjusted life years (DALYs), and corresponding age-standardised rates of stomach cancer attributable to smoking by sociodemographic index (SDI), region, country, sex, and age were used to assess temporal trends from 1990 to 2019 by calculating the average annual percentage change (AAPC). In addition, the global burden of stomach cancer attributable to smoking up to 2044 was predicted using age-period-cohort models. RESULTS: Globally, in 2019, 17.96% of stomach cancer deaths (1.72 million) and 17.15% of stomach cancer DALYs (38.13 million) were attributable to smoking, representing an increase compared to 1990; however, smoking-attributable age-standardised rates of mortality (ASMRs) and DALYs (ASDRs) significantly declined to 2.12/100,000 and 45.82/100,000 in 2019, respectively. While stomach cancer ASMR and ASDR attributable to smoking decreased in all regions and in most countries, they increased by >10% in some countries. A positive correlation was found between SDI and age-standardised rates (rASMR = 0.28, P < 0.01; rASDR = 0.29, P < 0.01). By 2044, although global age-standardised rates for smoking-attributable stomach cancer are predicted to decline, deaths and DALYs are estimated to increase to 2.22 million and 42.14 million, respectively. CONCLUSIONS: Stomach cancer deaths and DALYs attributable to smoking have increased over the past 30 years and will continue to increase. Consequently, targeted prevention efforts and tobacco-control strategies need to be further developed and improved.

Assessment of Anxiety, Depression, and Sleep Quality in Mothers of Children with Atopic Dermatitis: A Qualitative Questionnaire Study.

Purpose: To compare the anxiety, depression and sleep quality of mothers of healthy control children and mothers of children with atopic dermatitis (AD) of varying severity, both before and after treatment. Methods: A total of 120 parent-child dyads participated in the study. These dyads were divided into four subgroups of 30 patients each: mild AD, moderate AD, severe AD, and control groups. The children's symptoms, their mothers' psychological status, and their mothers' sleep quality were evaluated using the Scoring of Atopic Dermatitis (SCORAD), the Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI), respectively, before and after a one-month comprehensive treatment. Results: SCORAD, representing differences in severity of children's AD, decreased significantly after one month's treatment (p < 0.001). Anxiety in mothers significantly decreased in all AD severity groups after treatment (p < 0.05). However, for depression, only the mothers in the mild and moderate AD groups showed a decrease after treatment (p < 0.05). The PSQI total score also decreased in the mild AD group after treatment (p < 0.05). Conclusion: The most severe effect was seen in the psychology and sleep quality of mothers of children with severe AD. After one month of treatment, the psychological health and sleep quality of the mothers in the mild AD group significantly improved, while those of mothers in the moderate and severe AD groups showed partial improvement.

Assessment on the declining degree of farmland shelter forest in a desert oasis based on LiDAR and hyperspectrum imagery.

We examined the growth decline and health status of farmland protective forest belt (Populus alba var. pyramidalis and Populus simonii shelterbelts) in Ulanbuh Desert Oasis by using airborne hyperspectral and ground-based LiDAR to collect the hyperspectral images and point cloud data of the whole forest belt respectively. Through correlation analysis and stepwise regression analysis, we constructed the evaluation model of the decline degree of farmland protection forest with the spectral differential value, vegetation index, and forest structure parameters as independent variables and the tree canopy dead branch index of the field survey as dependent variables. We further tested the accuracy of the model. The results showed that the evaluation accuracy of the decline degree of P. alba var. pyramidalis and P. simonii by LiDAR method was better than that by hyperspectral method, and that the evaluation accuracy of the combined LiDAR and hyperspectral method was the highest. Using the LiDAR method, hyperspectral method, the combined method, the optimal model of P. alba var. pyramidalis was all light gradient boosting machine model, with the overall classification accuracy being 0.75, 0.68, 0.80, and Kappa coefficient being 0.58, 0.43, 0.66, respectively. The optimal model of P. simonii was random forest model, random forest model, and multilayer perceptron model, with the overall classification accuracy being 0.76, 0.62, 0.81, and Kappa coefficient being 0.60, 0.34, 0.71, respectively. This research method could accurately check and monitor the decline of plantations.