Quantitatively evaluate the environmental impact factors of the life expectancy in Tibet, China

Zha, Xinjie; Tian, Yuan; Xing, Guangfu; Wei, Wang; Yu, Chengqun

doi:10.1007/s10653-018-0211-z

Cited by 11 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to China's sixth census data, Tibet's population that is over 65 years old accounts for only 5.09%, and the average life expectancy is 68.2 years 31 . This was consistent with studies that have shown that when the altitude was higher, life expectancy was shorter 32 . This led to difficulty in investigating individuals over 61 years old, especially in Shigatse, which is located 4200 metres above sea level.…”

Section: Discussionsupporting

confidence: 93%

Diagnostic reference values for sarcopenia in Tibetans in China

Wen²,

Chen³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Sarcopenia is an age-associated disease characterized by loss of muscle mass and function, but the diagnostic cutoff values remain controversial. To investigate the diagnostic cutoff values and incidence of sarcopenia in a plateau population, the limb skeletal muscle mass, gait speed and grip strength of 2318 Tibetan adults were measured according to the criteria of the Asian Working Group for Sarcopenia. We found that the diagnostic reference values for sarcopenia in the high-altitude population were significantly lower than those in the plain population, and the incidences of sarcopenia in the highaltitude population over 60 years old were 17.2% in men and 36.0% in women, which were significantly higher than those in the plain population. Our study proposes reference values for the diagnosis of sarcopenia in Tibet. We suggest that the cutoff value for sarcopenia in the plateau population should be established based on altitude. Hypoxia may be an important risk factor for sarcopenia.Sarcopenia is a progressive and generalized skeletal muscle disorder involving the accelerated loss of muscle mass and function that is associated with increased adverse outcomes, including falls, functional decline, frailty, and mortality 1 . With the ageing of the population, sarcopenia has become one of the major public health problems in the world 2,3 . Since 2010, the study of sarcopenia in the field of international geriatrics has become increasingly intense, focusing on the diagnostic criteria and pathogenesis of sarcopenia. Both the European and Asian sarcopenia working groups have recommended diagnostic criteria of sarcopenia based on muscle mass, muscle strength and physical function presented by skeletal muscle mass index (SMI), handgrip strength (HS) and gait speed (GS), respectively 4,5 . Currently, the incidence of sarcopenia and the cutoff values for SMI and HS in different populations are very different. The incidence of sarcopenia is related not only to age but also to racial differences. Ethnic factors may be one of the main reasons for the differences in the incidence of sarcopenia among different populations 6-8 . The European Working Group on Sarcopenia in Older People (EWGSOP) has suggested that primary sarcopenia is mainly related to ageing and is a phenotype in the process of biological senescence. Ageing is a sequential process of genetic programming, which inevitably results in a decrease in the amount of ageing muscle, but this process is affected by the environment, lifestyle and ageing-related diseases 9 and shows significant individual differences. Therefore, ethnic and individual differences should be taken into account in determining diagnostic reference values for sarcopenia, and the best reference values should include a wide range of races and ages. Asia is the most dense and fastest ageing region in the world, and sarcopenia will have a very large impact on the Asian population 5 . The Asian Working Group for Sarcopenia (AWGS) has recommended that a large number of investigations should be carried ...

show abstract

Section: Discussionsupporting

confidence: 93%

Diagnostic reference values for sarcopenia in Tibetans in China

Wen²,

Chen³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Many previous studies have shown that LE was mainly affected by economic factors [36,38,39]. For example, one study found that the main reason for the spatial distribution pattern of LE in China was the economy [40].…”

Section: Discussionmentioning

confidence: 99%

“…As could be seen in Table 6, the results show that the highest ranges of CLOUR, GPC, NOCS, MCEOUR and UR related to the highest level of LE, while the lowest ranges of BR, TDR, NORPH and WRPC corresponded to the highest level of LE. The optimal range of factors corresponds to the maximum value of the LE [36]. Therefore, we displayed the optimal range of factors in Table 6.…”

Section: The Optimal Range Of Factors For the Maximum Le Based On Rismentioning

confidence: 99%

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

et al. 2020

IJERPH

View full text Add to dashboard Cite

Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high-high and low-low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS. Int. J. Environ. Res. Public Health 2020, 17, 906 2 of 15 2010 [3]. Therefore, it is an urgent task to explore the spatial characteristics and influencing factors of LE in order to deal with the regional inequality of LE in China.There have been many studies concerning the various influencing factors of LE. These factors can mainly be divided into two categories: biological and social and environmental factors [4]. Biological factors involve the individual's genetic factors, living habits, etc. Differences in LE between men and women are mainly due to differences in gene expression intensity. In addition, a study also found that people who smoked, drank and exercised less were vulnerable to cancer, cardiovascular and cerebrovascular diseases, which caused a reduction of LE [5]. However, social and environmental factors were the most frequently considered and important influencing factors in LE research [6]. Many studies found that economy and demographic composition were key influencing factors of LE [7-10]. Studies have found that health care and services had a positive effect on disease prevention and treatment, so the LE level was often higher in areas with good medical conditions [11]. People with a higher educat...

show abstract

“…Topography and other natural conditions have a profound impact on human economic activities. Some empirical studies have shown that natural factors, such as topography and climate, have a significant impact on life expectancy [35][36][37]. The altitude of western China is higher than that of eastern China, with Tibet and Qinghai over 3000 m and other provinces and regions in western China around 1000 m, while most of eastern China is below 500 m. The high altitude, low temperature, and arid areas and sand in western China are not conducive to human survival [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The altitude of western China is higher than that of eastern China, with Tibet and Qinghai over 3000 m and other provinces and regions in western China around 1000 m, while most of eastern China is below 500 m. The high altitude, low temperature, and arid areas and sand in western China are not conducive to human survival [13]. However, a suitable climate, including moderate temperature and rainfall, has a positive impact on life expectancy [37,38]. Meanwhile, some studies have shown that air quality has a negative impact on life expectancy by increasing the lung cancer mortality rate [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions

Huang

Yang

Liu

2020

IJERPH

View full text Add to dashboard Cite

Improving life expectancy, as well as people’s health and wellbeing, is an important goal both for the Chinese government and the United Nations. Therefore, to analyze the main factors influencing life expectancy in prefecture-level cities in China, this study uses classical ordinary least-squares regression and geographical weighted regression on the data of the latest census. Moreover, regional differences induced by each influencing factor are also depicted in this study. The results demonstrate that there is significant heterogeneity and spatial positive correlation among the distribution of life expectancy in prefecture-level cities, with a generally higher life expectancy in the provincial capitals and eastern China, and lower in western China. The geographically weighted regression analysis shows that the economic development level, medical conditions, demographic structure, natural environment, and city attributes all affect the distribution of life expectancy, but that their effects have significant spatial heterogeneity. Life expectancy of the less developed areas in Western China is affected dominantly by economic development level, whereas medical services and education are of great importance in determining the life expectancy in Northern and Southern China, respectively. Thus, it is crucial to solve health problems based on local conditions, especially focusing on the improvement of health and health care in underdeveloped areas. Meanwhile, for the eastern developed areas, special attention should be paid to environmental protection in the economic process, while striving to achieve high-quality development.

show abstract

Quantitatively evaluate the environmental impact factors of the life expectancy in Tibet, China

Cited by 11 publications

References 30 publications

Diagnostic reference values for sarcopenia in Tibetans in China

Diagnostic reference values for sarcopenia in Tibetans in China

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions

Contact Info

Product

Resources

About