[Comparative analysis of comprehensive health status among 31 provinces in China and 134 countries (regions) in 2015].

Objective: To evaluate comprehensive health status of 31 provinces in China and compare with other countries (regions). Methods: Social-demographic index, life expectancy and healthy life expectancy in 134 countries (regions) and 31 provinces in China were collected from the Global Burden of Disease Study 2015. K-means clustering method was used to classify comprehensive health status of various countries (regions) in the world. HemI 1.0.3 software was applied to draw distribution heat maps of social-demographic index, life expectancy and healthy life expectancy in different provinces of Mainland China. Discriminant analysis was used to evaluate comprehensive health status of different provinces in Mainland China. Results: Comprehensive health status of 134 countries (regions) was grouped into category 1-8 from good to poor, and Mainland China was in the category 4. The comprehensive health status of provinces in Mainland China is better in the east coast and poorer in the west inland, among which Shanghai and Beijing were grouped into the category 1, Zhejiang, Jiangsu, Guangdong and Tianjin into the category 2, Fujian, Liaoning and Shandong into the category 3, Yunnan, Guangxi, Xinjiang and Guizhou into the category 5, Qinghai and Tibet into the category 6, and the rest 16 provinces into the category 4. Conclusion: Comprehensive health status of Mainland China ranked middle to upper level in the world, and health status disparities were observed among different provinces in Mainland China.

[Influencing factors on healthy life expectancy in adults in Beijing].

OBJECTIVE: To understand the main influencing factors related to healthy life expectancy (HALE) among adults in Beijing. METHODS: The calculation on health-adjusted life expectancy was performed by Sullivan METHODS. Data from the self-reported health survey program on adults in Beijing 2012 was gathered. Hierarchical ordered probit model was used to estimate the severity-weighted prevalence of disability and then combined with the period life table to obtain the HALE. Factors associated with the severity-adjusted prevalence of the disabled were analyzed under the generalized additive models (GAM). RESULTS: The main influencing factors of HALE would include age (t=40.351, P<0.001), sex (t=9.689, P<0.001), levels of education (t=5.021, P< 0.001), exercise (t=5.487, P<0.001) and alcohol intake (t=-2.380, P=0.017) etc. The influence of per capita monthly income (χ(2) =3.949, P=0.044) showed as non-linear. CONCLUSIONS: Levels of income would directly influence the severity-weighted prevalence of the disability, which also affecting the HALE. Programs on improving healthy life style and health care in women should be promoted.