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Objective: To analyze the spatiotemporal distribution of life expectancy (LE) and health-adjusted life expectancy (HALE) in Guangzhou from 2010 to 2019, and quantize the comprehensive impact of different causes and sequelae on health. Methods: The LE, HALE, and cause-excluded health adjusted life expectancy (CEHALE) were estimated using cause-of-death surveillance datasets from Guangzhou Municipal Center for Disease Control and Prevention from 2010 to 2019 and open data from the Global Burden of Disease Study. Joinpoint log-linear regression model was used to analyze the temporal trend and described spatial distribution. Results: In 2019, the LE in residents in Guangzhou was 82.9 years (80.1 years in men and 85.9 years in women), and the HALE was 75.6 years (74.0 years in men and 77.3 years in women). Compared with the urban fringe, the central urban area had higher LE and HALE, and the differences between LE and HALE were small. The LE and HALE in Guangzhou showed an increasing trend from 2010 to 2019. The LE increased by 2.8 years (AAPC=0.4, 95% CI : 0.3-0.4), with the increase of 2.8 years in men and 2.9 years in women. The HALE increased by 2.4 years (AAPC=0.3, 95% CI : 0.3-0.4), with the increase of 2.5 years in men and 2.2 years in women. The median healthy life lost due to communicable, maternal, neonatal, and nutritional diseases was 6.2 years (AAPC=-4.2, 95% CI : -5.3--3.1), while the median healthy life lost due to non-communicable diseases was 14.7 years (AAPC=1.6, 95% CI : 0.9-2.3), the median healthy life expectancy reduced by injury was 6.3 years (AAPC=-3.5, 95% CI : -4.5--2.6). Musculoskeletal disorders, skin and subcutaneous diseases, cardiovascular diseases, nutritional deficiencies, diabetes and kidney diseases were the top five diseases causing healthy life expectancy loss. Conclusion: The LE and HALE in residents in Guangzhou increased steadily from 2010 to 2019, but the quality of life in the urban fringe was lower than that of the central urban area. Non-communicable diseases were the leading causes of healthy life expectancy loss. Health policies and prevention measures should be developed according to area specific characteristics, and social medical resources should be rationally allocated to key diseases to reduce their disease burden.

Objective: To establish a risk early warning model of human infection with avian influenza A (H7N9) virus and predict the area with high risk of the outbreak of H7N9 virus infection., Methods: The incidence data of human infection with H7N9 virus at prefecture level in China from February 2013 to June 2014 were collected, and the geographic and meteorological data during the same period in these areas were collected too. Spatial auto regression (SAR) model and generalized additive model (GAM) were used to estimate different risk factors. Afterwards, the risk area map was created based on the predicted value of both models., Results: All the human infections with H7N9 virus occurred in the predicted areas by the early warning model in February 2014. The early warning model successfully predicted the spatial moving trend of the disease, and this trend was verified by two outbreaks in northern China in April and May 2014., Conclusion: The established early warning model showed accuracy and precision in short-term prediction, which might be applied in the active surveillance, early warning and prevention/control of the outbreak of human infection with H7N9 virus.