Shaobin Wang1, Kunli Luo2, Yonglin Liu3, Shixi Zhang4, Xiaoxu Lin5, Runxiang Ni1, Xinglei Tian1, Xing Gao6. 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China. 2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. Electronic address: luokl@igsnrr.ac.cn. 3. College of Geography and Tourism, Chongqing Normal University, Chongqing 400047, China. 4. Department of Chemistry, Tsinghua University, Beijing 100084, China. 5. College of Earth and Mineral Sciences, Pennsylvania State University, University Park, PA 16802, USA. 6. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Abstract
OBJECTIVE: We show the variation of longevity indicators in China during the past 60 years and its correlation patterns with per capita GDP (GDPpc) both at provincial and inner-provincial level. METHODS: Population data from six national population censuses in China (1953-2010) at provincial level and in several typical provinces in 2010 at county-level were selected. Four main longevity indicators were calculated. Pearson's r and distributed lags time series analysis between longevity indicators and GDPpc were conducted. RESULTS: The results show that Guangxi and Hainan Provinces maintain relatively high long-lived population (population over the age of 90) across various population censuses. The distributions of the population over the age of 80 and life expectancy are significantly affected by both contemporaneous and historical GDPpc at provincial level. However, areas of high long-lived population (over the age of 90) exhibit continuously stable features that lack any significant correlation with GDPpc both at provincial and inner-provincial level. CONCLUSION: Our results indicate a mixed distribution pattern of several longevity indexes and different relation to GDPpc. It shows consistent trend with Preston curve, that is, economic conditions may have limited influence on human longevity, especially for those who live longer than 90 years old. This study suggests that the economic development may favor the local residents to have access to live as old as 80 years old, but it is still difficult for most residents to reach the level of centenarians.
OBJECTIVE: We show the variation of longevity indicators in China during the past 60 years and its correlation patterns with per capita GDP (GDPpc) both at provincial and inner-provincial level. METHODS: Population data from six national population censuses in China (1953-2010) at provincial level and in several typical provinces in 2010 at county-level were selected. Four main longevity indicators were calculated. Pearson's r and distributed lags time series analysis between longevity indicators and GDPpc were conducted. RESULTS: The results show that Guangxi and Hainan Provinces maintain relatively high long-lived population (population over the age of 90) across various population censuses. The distributions of the population over the age of 80 and life expectancy are significantly affected by both contemporaneous and historical GDPpc at provincial level. However, areas of high long-lived population (over the age of 90) exhibit continuously stable features that lack any significant correlation with GDPpc both at provincial and inner-provincial level. CONCLUSION: Our results indicate a mixed distribution pattern of several longevity indexes and different relation to GDPpc. It shows consistent trend with Preston curve, that is, economic conditions may have limited influence on human longevity, especially for those who live longer than 90 years old. This study suggests that the economic development may favor the local residents to have access to live as old as 80 years old, but it is still difficult for most residents to reach the level of centenarians.
Authors: Qucheng Deng; Yongping Wei; Lijuan Chen; Wei Liang; Jijun Du; Yuling Tan; Yinjun Zhao Journal: Int J Environ Res Public Health Date: 2019-10-03 Impact factor: 3.390