Epidemiological and demographic drivers of lung cancer mortality from 1990 to 2019: results from the global burden of disease study 2019.

Background: Understanding the effects of demographic drivers on lung cancer mortality trends is critical for lung cancer control. We have examined the drivers of lung cancer mortality at the global, regional, and national levels. Methods: Data on lung cancer death and mortality were extracted from the Global Burden of Disease (GBD) 2019. Estimated annual percentage change (EAPC) in the age-standardized mortality rate (ASMR) for lung cancer and all-cause mortality were calculated to measure temporal trends in lung cancer from 1990 to 2019. Decomposition analysis was used to analyze the contributions of epidemiological and demographic drivers to lung cancer mortality. Results: Despite a non-significant decrease in ASMR [EAPC = -0.31, 95% confidence interval (CI): -1.1 to 0.49], the number of deaths from lung cancer increased by 91.8% [95% uncertainty interval (UI): 74.5-109.0%] between 1990 and 2019. This increase was due to the changes in the number of deaths attributable to population aging (59.6%), population growth (56.7%), and non-GBD risks (3.49%) compared with 1990 data. Conversely, the number of lung cancer deaths due to GBD risks decreased by 19.8%, mainly due to tobacco (-12.66%), occupational risks (-3.52%), and air pollution (-3.47%). More lung cancer deaths (1.83%) were observed in most regions, which were due to high fasting plasma glucose levels. The temporal trend of lung cancer ASMR and the patterns of demographic drivers varied by region and gender. Significant associations were observed between the contributions of population growth, GBD risks and non-GBD risks (negative), population aging (positive), and ASMR in 1990, the sociodemographic index (SDI), and the human development index (HDI) in 2019. Conclusion: Population aging and population growth increased global lung cancer deaths from 1990 to 2019, despite a decrease in age-specific lung cancer death rates due to GBD risks in most regions. A tailored strategy is needed to reduce the increasing burden of lung cancer due to outpacing demographic drivers of epidemiological change globally and in most regions, taking into account region- or gender-specific risk patterns.

[Health risk assessment of typical metalloid and heavy metals in rural drinking water in Shaanxi Province in 2020].

OBJECTIVE: Evaluate the health risks of metalloid and heavy metals in rural drinking water in Shaanxi Province, and provide a scientific basis for ensuring the safety of drinking water for rural residents. METHODS: In 2020, the rural drinking water in Shaanxi Province were monitored, referring to the "Sanitary Standards for Drinking Water"(GB 5749-2006) for arsenic(As), cadmium(Cd), chromium(Cr~(6+)), lead(Pb), and mercury(Hg) in the water. To evaluate the concentration of five metalloid and heavy metals, the health risk assessment method recommended by United States Environmental Protection Agency was used to evaluate the health risks of adults in rural areas exposed to metalloid and heavy metals through drinking water. RESULTS: In 2020, a total of 6232 water samples were monitored. The concentrations of Cd, Pb and Hg in the water all met the standards, and the compliance rates of As and Cr~(6+) were 99.98% and 98.09%, respectively. The carcinogenic risk level of As was 3.53×10~(-5), and the non-carcinogenic health risk levels of the four heavy metals in descending order were Pb>Cr~(6+)>Cd>Hg, and the total risk level was 0.105. The concentration level of As and the distribution of health risk levels were all in northern Shaanxi>Guanzhong region>southern Shaanxi(P<0.05). The concentration level and health risk level distribution of As and Cr~(6+) were all groundwater>surface water(P<0.05), and the concentration level and health risk level distribution of Cd, Pb and Hg were all surface water>groundwater(P<0.05). CONCLUSION: There was a potential health risk of As exposure in rural drinking water in Shaanxi Province in 2020. The non-carcinogenic health risks of four heavy metals intake of Cd, Hg, Pb and Cr~(6+) through drinking water were still at an acceptable level. The control of arsenic pollution in drinking water should be further strengthened to ensure drinking water safety.

Predictive Value of a Thyroid-Absorbed Dose with a Shorter Effective Half-Life on Efficacy in Graves Disease Patients Receiving Iodine-131 Therapy.

BACKGROUND Although radioiodine therapy (RIT) efficacy is thoroughly validated for Graves disease (GD), there is a lack of research on the predictive factors of RIT, especially the optimal thyroid-absorbed dose (TD) with a shorter effective half-life (Teff ≤5 days). The goal of this study was to explore the predictive value of TD in GD patients receiving RIT with a shorter Teff. MATERIAL AND METHODS We studied 208 GD patients receiving RIT with a shorter Teff. Plotting the receiver-operating characteristic (ROC) curve verified the accuracy of TD for predicting RIT efficacy in GD patients. In addition, we conducted univariate and multivariate analyses to investigate the influence of 14 factors, including thyroid weight, TD, 24-h radioiodine uptake rate (RAIU), the highest RAIU, thyrotrophin receptor antibody level, thyroglobulin antibody level, thyroid peroxidase antibody level, and others, on curative effects of RIT. RESULTS Of the 208 study participants, complete remission and the total effectiveness rates were 68.3% and 92.3%, respectively. The threshold value of TD to predict RIT efficacy was 70.2 Gy, based on ROC analysis. Univariate analysis showed that 24-h RAIU, Teff, total iodine dose, iodine dose per gram of thyroid tissue, TD, and thyrotropin receptor antibody level were significantly associated with RIT efficacy. Multivariate analysis indicated that 24-h RAIU, total iodine dose, iodine dose per gram of thyroid tissue, and TD were significant independent predictors of RIT efficacy. CONCLUSIONS Predicting RIT efficacy from TD with a shorter Teff was feasible in GD patients, and TD above 70.2 Gy had an especially high predictive accuracy.