Spatiotemporal trajectory of life expectancy and its disparity in China 2000 - 2030：Modelling and prediction

Spatiotemporal trajectory of life expectancy and its disparity in China 2000 - 2030：Modelling and prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal trajectory of life expectancy and its disparity in China 2000 - 2030：Modelling and prediction Yuqing Feng, Jinfeng Wang, Naliang Guo, Yue Cai, Qian Yin, Shiyong Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4760315/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Life expectancy (LE) is one of crucial metrics of human evolution. However, the evolutionary trajectories of LE in different regions of China and the regional inequalities expected in 2030 are still unclear yet. Method This study collected provincial LE data and relevant explanatory variables for the years of 2000, 2010, 2020 in China. The Geotree method was employed to reconstruct the evolution trajectories of LE, while a multilevel model was used to predict LEs at the provincial levels in the country for the year 2030. Finding : The LE in China exhibits significant geographical pattern, decreasing from the east to the west of the country. LE increases with the socio-economic development but is constrained by the natural environment. The physical limitation to LE is significant in western China but are being alleviated with the development of socio-economic conditions. LE will increase in all provinces by 2030, with the overall LE in China reaching 80.05 years (95% confidence interval: 78.93 ~ 81.28), and regional inequalities will diminish. Conclusion LE is increasing with the improvement of socioeconomic condition over time; the constraints imposed by the natural environment on LE are being overridden with the improvement of socio-economic conditions. Life expectancy Tree like evolution trajectory Physical and socioeconomic determinants Projection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Life expectancy (LE) serves not only as a pivotal indicator reflecting the health status and quality of life in a country or region but also as a crucial reference in the establishment of public health policies and social security schemes by governments(Central Committee of the Chinese Communist Party, 2016 ). Since the year 2000, China has joined the ranks of countries with high longevity. The "Healthy China 2030" Plan, released in 2016 by the State Council, identifying LE as a significant metric and delineate specific developmental targets(Central Committee of the Chinese Communist Party, 2016 ; General Office of the State Council of the People’s Republic of China, 2022 ). By 2020, the average LE in China had escalated to 77.9 years, marking a substantial increase since 2000. The projection of LE at the provincial level for future periods is instrumental in enabling government authorities and decision-makers to foresee impending health hazards and resource demands with greater accuracy. This insight is crucial in the formulation of pertinent strategies and action plans(Bai et al., 2023 ). Majority of existing studies on LE are focusing on the LE estimated age-specific mortality rates (Bai et al., 2023 ; Foreman et al., 2018 ; Kontis et al., 2017 ). Spatiotemporal trend and its determinants remain nebulous. In this study, the Geotree method is employed to elucidate the underlying mechanisms correlating LE with natural environment and socio-economic variables. Subsequently, a multilevel modeling is adopted to capitalize the environmental and socio-economic determinants of LE, and to forecast LE across the 31 provincial administrative divisions in China and analyzed regional inequalities in 2030. Data and materials Socioeconomic determinants of LE We collected the explanatory variables of LE from four aspects: socioeconomic development, population characteristic, healthcare resource and environmental exposures. Socioeconomic development exerts a direct influence on the populace's quality of life. Numerous studies have indicated that socio-economic progress is a significant factor affecting LE (Huang et al., 2020a ; Song et al., 2016 ; S. Wang et al., 2022 ). In this research, socioeconomic development across regions is represented through the adoption of various metrics: per capita GDP, average years of schooling, and urbanization rate. The healthcare services and protection in various regions are gauged by the number of physicians per 1,000 people and the proportion of out-of-pocket health expenditure (OOP). LE disparities between genders, influenced by an amalgamation of biological, social, and environmental factors, signify that females live longer than males(Zarulli et al., 2018 ). This phenomenon suggests an association between the gender composition of a population and its overall LE. Gross dependency ratio reflects to the basic relation between population and economic development from the demographic perspectives(W. Wang et al., 2022 ). Environmental determinants exert a significant influence on LE (Burtscher, 2013 ; Finch and Tanzi, 1997 ). Research indicates that, even after adjusting for socio-economic and healthcare variables, areas endowed with extensive green spaces and temperate climates are correlated with extended LE(Poudyal et al., 2009 ; Zha et al., 2019 ). Elevation is also an important environmental factor affecting LE(Burtscher, 2013 ). Air pollution, one of the major environmental factors affecting public health, results in the deaths of one million people in China each year(Pope et al., 2009 ; Song et al., 2016 ; Yue et al., 2020 ). Conclusively, the environmental variables utilized in this study encompass population-weighted elevation, temperature, precipitation, normalized difference vegetation index (NDVI), and PM2.5 concentration. Table 1 Selection basis of factors affecting life expectancy Type Variable Reference Socioeconomic development GDP per capita S. Wang et al., 2022 ; Song et al., 2016 ; Zha et al., 2019 , Huang et al., 2020b Urbanization rate Average years of schooling Healthcare resource Number of practicing (assistant) physicians per 1,000 population Kaplan and Milstein, 2019 ; W. Wang et al., 2022 Proportion of out-of-pocket (OOP) health expenditure Population characteristics Gross dependency ratio W. Wang et al., 2022 , Zarulli et al., 2018 Sex ratio Environmental exposures Population-weighted elevation Burtscher, 2013 , Poudyal et al., 2009 , Pope et al., 2009 ; S. Wang et al., 2022 ; Yue et al., 2020 , Poudyal et al., 2009 , Zha et al., 2019 Population-weighted NDVl Populationweighted PM2.5 Population-weighted temperature Population-weighted precipitation LE and Socioeconomic data The provincial LE data for the years 2000, 2010 and 2020 in China are sourced from the China Health and Health Statistics Yearbook. Indicators such as urbanization rate, per capita GDP, average years of schooling, out-of-pocket health expenditure (OOP), gross dependency ratio, and gender ratio are obtained from national or provincial statistical yearbooks. The elevation data with a spatial resolution of 1 kilometer is obtained from the Resource and Environment Science and Data Center. The population density data with a spatial resolution of 1 kilometer is sourced from WorldPop. The calculation formula for population-weighted elevation ( \(\:{wDEM}_{p}\) ) is as follows: $$\:{wDEM}_{p}={\sum\:}_{i}^{n}{w}_{i,p}\times\:{DEM}_{i,p}$$ $$\:{w}_{i,p}=\frac{{pop}_{i,p}}{{\sum\:}_{i}^{n}{pop}_{i,p}}$$ Herein, i represents the raster pixel, p represents a province, n is the total count of pixels within the province, \(\:{pop}_{i,p}\) denotes the population within pixel i of province p . \(\:{w}_{i,p}\) signifies the weight of pixel i within province p , \(\:{DEM}_{i,p}\:\) indicates the elevation of pixel i within province p . Future data Future provincial population by age and sex, education attainment data, urbanization rates were obtained from figshare(Chen et al., 2020 ). Future socio-economic data was obtained from Science Data Bank(Jiang et al., n.d.). We collected data from the middle road (SSP2) scenario, which indicates the world will maintain the development of recent decades. The 2030 OOP data are from the "Healthy China 2030" Plan. Method LE in provinces in the years of 2000, 2010 and 2020 were categorized by natural environment and development stages. These categorizations were then modelled by Geotree to reconstruct the geographical evolutionary trajectories of LE across 31 provincial administrations. After validation of the modelling, we estimated the LEs in 2030 using a multilevel model combined with the hierarchical structure of the Geotree (Fig. 1 ). Geodetector The stratified heterogeneity of LE was detected using Geodetector’s q-statistic value. The GeoDetector is a linearity free model to measure the association between the spatial distributions of LE and influencing factors(Wang et al., 2010 , 2016 ; Wang and Xu, 2017 ). The definition of q as follows: $$\:q=(1-\:\frac{{\sum\:}_{h=1}^{L}{N}_{h}{\sigma\:}_{h}^{2}}{N{\sigma\:}^{2}})\times\:100\%$$ where \(\:h\:(h=1,\:2,\:\dots\:,\:L)\) is the spatial stratification of the influencing factors, \(\:{N}_{h}\) and \(\:N\) are the numbers of units in the \(\:{h}^{th}\) stratum and the whole area, respectively. \(\:{\sigma\:}_{h}^{2}\) and \(\:{\sigma\:}^{2}\) are variances in LEs in the \(\:{h}^{th}\) stratum and the whole area, respectively. The \(\:q\) value varies between 0% and 100%, which can be interpreted as deterministic power of the explanatory variable, i.e., the percent of variance of LE explained by an explanatory variable. Geotree method The Geotree model, inspired by the principles of biological evolution, reconstructs the trajectory of the evolutionary object using observed cross-section data. Geotree is applicable for the phenomena evolved in strata(Jing et al., 2022 ; Lei et al., 2023 ; Wang et al., 2012 ; Wang and Wang, 2020 ). The Geotree comprises branches, twigs on the branches, and leaves on the twigs. The branches and twigs represent natural environmental types and socioeconomic developmental stages of the leaves or provinces, respectively. In this study, the branches utilize population-weighted elevation to represent the natural environment of the regions. Based on this measure, all provinces are classified into three categories. The twigs categorize the development stages of each province based on urbanization rates. According to the Northam curve(Northam, 1975 ), these provinces are delineated into initial, intermediate, and advanced stages. Each province is represented by a leaf on the tree, situated on the branches corresponding to its specific type and developmental stage. The details of the province types and development stages are listed in Table 2 . Table 2 Indicators of types and development stages of provinces. Natural environment types Development stages Population-weighted elevation (m) Urbanization rate (%) Ⅰ (1) < 300 950 > 70 Multilevel method The multilevel model (MLM) is suitable for modeling data with a hierarchical structure(Hox, 1998 ; Rice and Jones, 1997 ). Geotree provides a framework for MLM, while MLM models the Geotree. We employ the Geotree constructed as described above to create a MLM with cross random effects. The model can be expressed as follows: $$\:{y}_{i(t,s)}={\beta\:}_{0}+{\beta\:}_{1}{x}_{i(t,s)}+{u}_{t}+{u}_{s}{+e}_{i(t,s)}$$ $$\:{u}_{t}\:\sim\:N\left(0,{\sigma\:}_{u\left(t\right)}^{2}\right),\:{u}_{s}\:\sim\:N\left(0,{\sigma\:}_{u\left(s\right)}^{2}\right),{e}_{i(t,s)}\:\sim\:N\left(0,{\sigma\:}_{e}^{2}\right)$$ where t and s represent the type and the developmental stage of a province, respectively, \(\:{y}_{i(t,s)}\) represents the LE of province i in the branch t and development stage s . \(\:{\beta\:}_{0}\) is intercept, \(\:{x}_{i(t,s)}\) indicates the factors influencing LE incorporated into the model \(\:{\beta\:}_{1}\) is the coefficient of the explanatory variables, \(\:{u}_{s}\:\) and \(\:{u}_{t}\:\) represent the random effects of type t and development stage s , respectively, \(\:{e}_{i(t,s)}\) is the residual error term. We employed a stepwise regression approach to select variables for inclusion in the model. Ultimately, the explanatory variables in the MLM model encompass five indicators: per capita GDP, average years of education, out-of-pocket expenditure (OOP), gross dependency ratio, and sex ratio. This model is used to forecast the LE of various provinces in China in the future. Result Natural environment types and socioeconomic development stages The distribution of natural environment type is illustrated in Fig. 2 a. Provinces with a Type I are primarily located in the eastern part of China, featuring plains with elevations below 500 meters. Approximately 75.8% of China's population resides in this region. Provinces with a Type II are mainly situated in the central and northwest regions of China, with elevations ranging from 1000 to 2000 meters. Approximately 23.6% of the population lives in this area. Provinces with a Type III include Tibet and Qinghai, situated in the Qinghai-Tibet Plateau, with an average elevation exceeding 4000 meters. These regions have a sparse population, with only 0.6% of people residing there. Different types of natural environment exhibit significant variations in LE across different developmental stages (Fig. 2 b). When considering the same natural environment type, higher developmental stage are associated with higher LE. Meanwhile, when comparing provinces at the same developmental stage, those with a Type I natural environment demonstrate higher LE, followed by Types II and III. Both natural environment type and development stage have strong explanatory power to LE (Fig. 2 c). From 2000 to 2020, the q-value of natural factors decreased, while the q-value of urbanization increased. This suggests that over time, the impact of the natural environment on LE has diminished, and the influence of social development has grown, indicating an improvement in human adaptive capabilities to the environment. LE in space and tree-like structure There is an increasing trend in LE over the years. By 2020, all provinces are situated in the intermediate and advanced stages, and provinces at advanced stages exist only in regions with type I natural environments. Spatially, LE demonstrates a gradual decrease from east to west, and over time, this pattern continues to persist (Fig. 3 ). The natural environment type, developmental stage, and LE exhibit a similar spatial pattern, demonstrating significant stratified heterogeneity and coupling relationships. We further mapped the three variables into a hierarchical tree-like structure and built a Geotree, which provides an “attribute-space coordinate system”. The variation in leaf color within branches indicates different LE under various natural environment types (Fig. 4 ). On twigs, with the socio-economic development, the color of leaves transitions from yellow to green, accompanied by a gradual increase in LE. These trends are substantiated by the boxplots in Fig. 2 . Prediction of LE in 2030 Based on the structure of the Geotree, we developed a multilevel model and compared it with generalized linear regression. Cross-validation results (Table S2) show that multilevel model better better captures the evolution of LE under different types of natural environments and developmental stages, and it was used to estimate LE for each province in 2030. LE in China will reach 80.05 (95% confidence interval: 78.82 ~ 81.28) years by 2030, an increase of 2.12 years compared to 77.93 years in 2020. In the eastern regions, the provincial LE remains at the forefront. The spatial pattern of higher in the east and lower in the west persists. All provinces have left the first stage of development (Fig. 5 a). During the period from 2000 to 2030, an increase in LE was observed across all provinces, albeit with an overall declining trend in growth rates. Type I provinces have the lowest average growth rate in LE, with an average increase of 2.62% (3.31 years) from 2020 to 2030 (Fig. 5 b). Next are the provinces in type III, with an increase of 3.26 years (4.43%), while type II provinces have the highest growth rate (4.63 years, 3.54%). The inequality in LE among regions is gradually decreasing by 2030 (Fig. 5 c). Discussion This study delineated the spatial distribution pattern of LE, unveiled its developmental trajectory and forecasted LE at subnational level in China for 2030. We observed a steady increase in LE from 2000 to 2020, closely correlated with improvements in socio-economic and healthcare conditions. Although LE is increasing in all provinces, the persistent inter-provincial disparities pose an enduring and significant challenge(Luo and Xie, 2020 ). The evolution of LE is an outcome shaped by the joint influence of the natural environment and social development, and China's LE projected to grow to 80.05 by 2030. The natural environment, which includes many aspects such as climate, terrain, and vegetation, is one of the most important influences on the health of the population. Extensive green space is significantly associated with extended LE (Poudyal et al., 2009 ; Zha et al., 2019 ). Elevation affects mortality rates or potential lifespan by altering variables such as temperature, atmospheric pressure, oxygen concentration in the air, and intensity of ultraviolet radiation(Burtscher, 2013 ). In this study, provinces were categorized into three types based on population-weighted elevation, which is used to represent the types of natural environment, and the spatial distribution of these types aligns closely with the three steps of China’s terrain. By calculating the q-values of population-weighted elevation and visualizing the developmental trajectory of LE as a 'tree,' we identified natural environment as one of the primary influencing factors explaining spatial heterogeneity in LE(Burtscher, 2013 ; Finch and Tanzi, 1997 ). Each type of natural environment exhibits distinct environmental characteristics Tibet and Qinghai, characterized by natural environment type III, are primarily located in the Qinghai-Tibet Plateau, with an elevation exceeding 4000 meters. The primary environmental factors affecting the health of the region include low levels of oxygen in the air and cold temperatures. A series of studies suggests a close association between hypoxic and the onset of tumors(Wong et al., 2017 ; Yeo, 2019 ), cardiovascular diseases(Wong et al., 2017 ), diabetes, and neurodegenerative diseases(Luo et al., 2022 ). Prenatal hypoxia in pregnant women can also impact the development of the infant's brain, heart, and nervous system. Prenatal hypoxia in pregnant women can lead to the underdevelopment of the fetal brain(Yeo, 2019 ), heart(Aljunaidy et al., 2017 ), nervous system, and other organs(Nalivaeva et al., 2018 ; Wang et al., 2021 ). Environments with higher oxygen concentrations have been linked to extended anticipated lifespans(Zou et al., 2023 ). In the past 50 years, the annual average temperature on the Qinghai-Tibet Plateau has been 5.85 degrees Celsius(C. Wang et al., 2022 ). Numerous studies indicate an association between low temperatures and increased risks of various cardiovascular, respiratory, and other diseases(Analitis et al., 2008 ; Curriero et al., 2002 ; Group, 1997 ; Yang et al., 2019 ). The majority of the mortality burden related to non-optimal temperatures is attributed to the contribution of cold(Gasparrini et al., 2015 ). Type I of natural environment is primarily located in the eastern regions of China, characterized by a monsoon climate. This area exhibits the highest population density among all regions. The remaining areas are classified as type Ⅱ, characterized primarily by arid conditions in the northwest and mountainous terrain in the south. In addition, elevation also influences the distribution of population density within the study area, thereby impacting local socio-economic development. People tend to prefer residing in areas with flat terrain and favorable climates, with approximately 75% of China's population living in regions characterized by type Ⅰ. Socioeconomic development acts as the driving force for the improvement of LE. Significant disparities in LE among different income groups, with noticeably higher mortality rates in socioeconomically disadvantaged populations compared to those with higher socioeconomic status(Chetty et al., 2016 ; Mackenbach et al., 2008 ). LE demonstrates a significant positive correlation with indicators reflecting socioeconomic development, such as income, healthcare levels, and educational attainment(Huang et al., 2020a ; Sasson, 2016 ; Song et al., 2016 ; “Spatial variations and social determinants of LE in China, 2005–2020,” 2022; S. Wang et al., 2022 ; Wang and Ren, 2019 ). Socioeconomic development signifies the improvement in people's economic, educational, and healthcare levels, enhancing their adaptability to the natural environment and thereby promoting a reduction in mortality rates. The dominant influences differ at different stages of the evolution of LE. Currently, the driving force behind the decline in mortality levels in China has shifted from being primarily propelled by investments in medical facilities to being primarily driven by social development(Li and Yan, 2023 ). Using the urbanization rate to represent the stage of regional development. In the early stages of urbanization, the economy is primarily agrarian, with healthcare and education lagging behind. At this point, LE is mainly determined by the local natural environment. In the intermediate, the urbanization process accelerates, and human adaptability to the environment gradually improves, diminishing the impact of the natural environment on LE. In the advanced, socioeconomic factors become the primary influencers of LE. LE at different stages of development exhibits significant variations (Fig. 2 b), with provinces at higher development stages having correspondingly higher LE. It is crucial to note that human life is consistently exposed to the natural environment. At any stage of development, the influence of the natural environment on LE cannot be eliminated; in other words, the impact of the natural environment on LE persists. LE undergoes categorized development evolution under the influence of natural environmental and socio-economic conditions. The development of the natural environment constrains the evolution of LE, and the progress in socio-economic conditions can weaken this limitation. The stratification of LE across natural environment types and developmental stages could facilitate the identification of populations at higher health risk, enables the formulation of targeted policy improvements and resource support. Over the past three decades, there has been a steady increase in LE in China, and it is projected to reach the Healthy China 2030' goal of 79.0 by 2030, which is consistent with findings from previous studies(Bai et al., 2023 ). Since 1990, mainland China has experienced a transition in epidemiological cause-of-death patterns from communicable to non-communicable diseases, and LE at birth has increased significantly in all provinces(Zhou et al., 2016 ). By the year 2030, LE in China is projected to increase to 80.05. Yet a spatial pattern persists with a gradual decrease from east to west. The persisting inequality in LE among provinces remains a challenge. Strengthening medical facility investments in provinces other than Type Ⅰ, particularly in provinces of Type Ⅲ can effectively reduce health inequality among regions. Based on the structure of Geotree, the sources of inequality in LE can be broadly categorized into natural (non-intervenable) and social development (intervenable), with social development encompassing healthcare, education, and economic aspects. When social development reaches a certain level, apart from individual behavior, regional differences in LE are primarily due to local environmental characteristics. Therefore, by enhancing healthcare, education, and other levels, regional inequalities can be reduced. Due to the catch-up effect, the inequality in LE between regions is gradually decreasing. In the future, in addition to active economic development, increased investment in medical facilities in provinces other than Type I, especially Type III provinces, will help to reduce the inequality of LE among China's provinces. Geotree clearly identified the provinces that are relatively lagging in national health, allowing for tailored strategies. Strategies for promoting health can be proposed from both natural environment and social development perspectives. Provinces of the same type can draw on the experiences of those at a higher developmental stage in the same category, exploring strategies for health improvement that are suitable for their own context. This holds significant implications for the future development of the health sector in China. However, the evolutionary trajectory of LE is specific to China and requires further investigation to determine its applicability to other countries and regions. Conclusion This study reconstructed the provincial LE evolution trajectory in China from 2000 to 2020 and predicts it for 2030. The results indicate a spatial pattern of declining LE from east to west and significant stratified heterogeneity. LE evolves with the development stages of society, constrained by natural environmental factors. By 2030, China's LE is projected to reach 80.05 years, achieving the 'Healthy China 2030' goal, with regional inequalities also gradually decreasing. The western regions of China need to enhance socio-economic development and healthcare infrastructure to overcome the constraints imposed by natural environments. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments and Funding Disclosure This research was financially supported by the National Key Research and Development Plan of China (2022YFC3600802; 2023YFF1305403), National Natural Science Foundation of China (42071375), and National Social Science Foundation of China (21&ZD186). Professor Jinfeng Wang State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11, Datun Road, Chaoyang District, Beijing, China E-mail: [email protected] Professor Shiyong Wu Center for Health Statistics and Information, National Health Commission, No.1 Xizhimenwai South Road, Xicheng District, Beijing, China E-mail: [email protected] Author Contribution JW initially conceived the research idea. JW and YF designed the study. YF performed data management and analysis, and drafted the manuscript. NG performed data management and analysis. YC, QY, SW and JW directed the study. YF, JW, NG, YC, QY, and SW critically revised the manuscript. All authors read and approved the final manuscript. Data availability Data will be made available on request. References Aljunaidy MM, Morton JS, Cooke C-LM, Davidge ST. Prenatal hypoxia and placental oxidative stress: linkages to developmental origins of cardiovascular disease. Am J Physiology-Regulatory Integr Comp Physiol. 2017;313:R395–9. https://doi.org/10.1152/ajpregu.00245.2017 . Analitis A, Katsouyanni K, Biggeri A, Baccini M, Forsberg B, Bisanti L, Kirchmayer U, Ballester F, Cadum E, Goodman P. others, 2008. Effects of cold weather on mortality: results from 15 European cities within the PHEWE project. American journal of epidemiology 168, 1397–1408. https://doi.org/10.1093/aje/kwn266 . Bai R, Liu Y, Zhang L, Dong W, Bai Z, Zhou M. Projections of future life expectancy in China up to 2035: a modelling study. Lancet Public Health. 2023;0. https://doi.org/10.1016/S2468-2667(22)00338-3 . Burtscher M. Effects of Living at Higher Altitudes on Mortality: A Narrative Review. Aging Dis. 2013;5:274–80. https://doi.org/10.14336/AD.2014.0500274 . Central Committee of the Chinese Communist Party. 2016. Central Committee of the Chinese Communist Party. State Council: The plan for Healthy China 2030 [WWW Document]. URL https://www.gov.cn/zhengce/2016-10/25/content_5124174.htm (accessed 10.21.23). Chen Y, Guo F, Wang J, Cai W, Wang C, Wang K. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Sci Data. 2020;7:83. https://doi.org/10.1038/s41597-020-0421-y . Chetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, Bergeron A, Cutler D. The Association Between Income and Life Expectancy in the United States, 2001–2014. JAMA. 2016;315:1750–66. https://doi.org/10.1001/jama.2016.4226 . Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA. Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol. 2002;155:80–7. https://doi.org/10.1093/aje/155.1.80 . Finch CE, Tanzi RE. Genetics of aging. Science. 1997;278:407–11. https://doi.org/10.1126/science.278.5337.407 . Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan C-W, Brown JC, Friedman J, He J, Heuton KR, Holmberg M, Patel DJ, Reidy P, Carter A, Cercy K, Chapin A, Douwes-Schultz D, Frank T, Goettsch F, Liu PY, Nandakumar V, Reitsma MB, Reuter V, Sadat N, Sorensen RJD, Srinivasan V, Updike RL, York H, Lopez AD, Lozano R, Lim SS, Mokdad AH, Vollset SE, Murray CJL. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392:2052–90. https://doi.org/10.1016/S0140-6736(18)31694-5 . Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, Tobias A, Tong S, Rocklöv J, Forsberg B, Leone M, De Sario M, Bell ML, Guo Y-LL, Wu C, Kan H, Yi S-M, de Coelho SZS, Saldiva M, Honda PHN, Kim Y, Armstrong H, B. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet. 2015;386:369–75. https://doi.org/10.1016/S0140-6736(14)62114-0 . General Office of the State Council of the People’s Republic of China. 2022. 14th Five-year National Health Plan [WWW Document]. URL https://www.gov.cn/zhengce/zhengceku/2022-05/20/content_5691424.htm (accessed 10.21.23). Group TE. Cold exposure and winter mortality from ischaemic heart disease, cerebrovascular disease, respiratory disease, and all causes in warm and cold regions of Europe. Lancet. 1997;349:1341–6. https://doi.org/10.1016/S0140-6736(96)12338-2 . Hox J. Multilevel Modeling: When and Why. In: Balderjahn I, Mathar R, Schader M, editors. Classification, Data Analysis, and Data Highways. Berlin, Heidelberg: Springer Berlin Heidelberg; 1998. pp. 147–54. Huang D, Yang S, Liu T. Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions. Int J Environ Res Public Health. 2020a;17:6597. https://doi.org/10.3390/ijerph17186597 . Huang D, Yang S, Liu T. Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions. Int J Environ Res Public Health. 2020b;17:6597. https://doi.org/10.3390/ijerph17186597 . Jiang T, Su B, Wang Y, Huang J, Jing C, Gao M, Wang G, Lin Q, Jiang S, Liu S, Luo. Y., n.d. Gridded datasets for population and economy under Shared Socioeconomic Pathways. https://doi.org/10.57760/sciencedb.01683 . Jing S, Wang J, Xu C, Yang J. Tree-like evolution pathways of global urban land expansion. J Clean Prod. 2022;378:134562. https://doi.org/10.1016/j.jclepro.2022.134562 . Kaplan RM, Milstein A. Contributions of Health Care to Longevity: A Review of 4 Estimation Methods. Annals Family Med. 2019;17:267–72. https://doi.org/10.1370/afm.2362 . Kontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389:1323–35. https://doi.org/10.1016/S0140-6736(16)32381-9 . Lei Y, Wang J, Wang Y, Xu C. Geographical evolutionary pathway of global tuberculosis incidence trends. BMC Public Health. 2023;23:755. https://doi.org/10.1186/s12889-023-15553-7 . Li T, Yan Y. Differences in Mortality by Regions in China since the 1980s and Their Evolution: The Staged Synergy between Medical Investment and Socioeconomic Development. Population research. (Peking China). 2023;47:35–50. Luo W, Xie Y. Economic growth, income inequality and life expectancy in China. Soc Sci Med. 2020;256:113046. https://doi.org/10.1016/j.socscimed.2020.113046 . Luo Z, Tian M, Yang G, Tan Q, Chen Y, Li G, Zhang Q, Li Y, Wan P, Wu J. Hypoxia signaling in human health and diseases: implications and prospects for therapeutics. Signal Transduct Target Therapy. 2022;7:218. https://doi.org/10.1038/s41392-022-01080-1 . Mackenbach JP, Stirbu I, Roskam A-JR, Schaap MM, Menvielle G, Leinsalu M, Kunst AE. Socioeconomic Inequalities in Health in 22 European Countries. N Engl J Med. 2008;358:2468–81. https://doi.org/10.1056/NEJMsa0707519 . Nalivaeva NN, Turner AJ, Zhuravin IA. Role of Prenatal Hypoxia in Brain Development, Cognitive Functions, and Neurodegeneration. Front NeuroSci. 2018;12. https://doi.org/10.3389/fnins.2018.00825 . Northam RM. 1975. Urban Geography. Wiley. Pope CA, Ezzati M, Dockery DW. Fine-Particulate Air Pollution and Life Expectancy in the United States. N Engl J Med. 2009;360:376–86. https://doi.org/10.1056/NEJMsa0805646 . Poudyal NC, Hodges DG, Bowker JM, Cordell HK. Evaluating natural resource amenities in a human life expectancy production function. For Policy Econ. 2009;11:253–9. https://doi.org/10.1016/j.forpol.2009.04.007 . Rice N, Jones A. Multilevel models and health economics. Health Econ. 1997;6:561–75. https://doi.org/10.1002/(sici)1099-1050(199711)6:63.0.co;2-x . Sasson I. Trends in Life Expectancy and Lifespan Variation by Educational Attainment: United States, 1990–2010. Demography. 2016;53:269–93. https://doi.org/10.1007/s13524-015-0453-7 . Song W, Li Y, Hao Z, Li H, Wang W. Public health in China: An environmental and socio-economic perspective. Atmos Environ. 2016;129:9–17. https://doi.org/10.1016/j.atmosenv.2015.12.021 . Spatial variations and social determinants of life expectancy in China. 2005–2020: A population-based spatial panel modelling study, 2022. Lancet Reg Health - Western Pac 23, 100451. https://doi.org/10.1016/j.lanwpc.2022.100451 . Wang B, Zeng H, Liu J, Sun M. Effects of Prenatal Hypoxia on Nervous System Development and Related Diseases. Front NeuroSci. 2021;15. https://doi.org/10.3389/fnins.2021.755554 . Wang C, Dai C, Song C. 2022. Analysis of the Temporal and Spatial Distribution Characteristics of Climate Change in the Qinghai-Tibetan Plateau. Yellow River 004. Wang J, Li X, Christakos G, Liao Y, Zhang T, Gu X, Zheng X. Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. Int J Geogr Inf Sci. 2010;24:107–27. https://doi.org/10.1080/13658810802443457 . Wang J, Liu X, Peng L, Chen H, Driskell L, Zheng X. Cities evolution tree and applications to predicting urban growth. Popul Environ. 2012;33:186–201. https://doi.org/10.1007/s11111-011-0142-4 . Wang J, Xu C. Geodetector: Principle prospective. 2017;72:116–34. https://doi.org/10.11821/dlxb201701010 . Wang J-F, Zhang T-L, Fu B-J. A measure of spatial stratified heterogeneity. Ecol Ind. 2016;67:250–6. https://doi.org/10.1016/j.ecolind.2016.02.052 . Wang S, Ren Z. Spatial variations and macroeconomic determinants of life expectancy and mortality rate in China: a county-level study based on spatial analysis models. Int J Public Health. 2019;64:773–83. https://doi.org/10.1007/s00038-019-01251-y . Wang S, Ren Z, Liu X, Yin Q. Spatiotemporal trends in life expectancy and impacts of economic growth and air pollution in 134 countries: A Bayesian modeling study. Soc Sci Med. 2022;293:114660. https://doi.org/10.1016/j.socscimed.2021.114660 . Wang W, Liu Y, Ye P, Xu C, Qiu Y, Yin P, Liu J, Qi J, You J, Lin L, Wang L, Li J, Shi W, Zhou M. Spatial variations and social determinants of life expectancy in China, 2005–2020: A population-based spatial panel modelling study. Lancet Reg Health - Western Pac. 2022;23:100451. https://doi.org/10.1016/j.lanwpc.2022.100451 . Wang Y, Wang J. Modelling and prediction of global non-communicable diseases. BMC Public Health. 2020;20:822. https://doi.org/10.1186/s12889-020-08890-4 . Wong BW, Marsch E, Treps L, Baes M, Carmeliet P. Endothelial cell metabolism in health and disease: impact of hypoxia. EMBO J. 2017;36:2187–203. https://doi.org/10.15252/embj.201696150 . Yang Z, Wang Q, Liu P. Extreme temperature and mortality: evidence from China. Int J Biometeorol. 2019;63:29–50. https://doi.org/10.1007/s00484-018-1635-y . Yeo E-J. Hypoxia and aging. Exp Mol Med. 2019;51:1–15. https://doi.org/10.1038/s12276-019-0233-3 . Yue H, He C, Huang Q, Yin D, Bryan BA. Stronger policy required to substantially reduce deaths from PM2.5 pollution in China. Nat Commun. 2020;11:1462. https://doi.org/10.1038/s41467-020-15319-4 . Zarulli V, Jones JAB, Oksuzyan A, Lindahl-Jacobsen R, Christensen K, Vaupel JW. 2018. Women live longer than men even during severe famines and epidemics. Proceedings of the National Academy of Sciences 115, E832–E840. https://doi.org/10.1073/pnas.1701535115 . Zha X, Tian Y, Gao X, Wang W, Yu C. Quantitatively evaluate the environmental impact factors of the life expectancy in Tibet, China. Environ Geochem Health. 2019;41:1507–20. https://doi.org/10.1007/s10653-018-0211-z . Zhou M, Wang H, Zhu J, Chen W, Wang, Linhong, Liu S, Li Y, Wang, Lijun, Liu Y, Yin P, Liu J, Yu S, Tan F, Barber RM, Coates MM, Dicker D, Fraser M, González-Medina D, Hamavid H, Hao Y, Hu G, Jiang G, Kan H, Lopez AD, Phillips MR, She J, Vos T, Wan X, Xu G, Yan LL, Yu C, Zhao Y, Zheng Y, Zou X, Naghavi M, Wang Y, Murray CJL, Yang G, Liang X. Cause-specific mortality for 240 causes in China during 1990–2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet. 2016;387:251–72. https://doi.org/10.1016/S0140-6736(15)00551-6 . Zou Q, Lai Y, Lun Z-R. Exploring the Association between Oxygen Concentration and Life Expectancy in China: A Quantitative Analysis. Int J Environ Res Public Health. 2023;20:1125. https://doi.org/10.3390/ijerph20021125 . Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviews received at journal 22 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers agreed at journal 12 Sep, 2024 Reviewers invited by journal 09 Sep, 2024 Editor invited by journal 21 Jul, 2024 Editor assigned by journal 19 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 18 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4760315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335785245,"identity":"a4ef3009-8647-46a3-a56e-3191a1616831","order_by":0,"name":"Yuqing Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3Ov4rCMBzA8V8pJMtP65geiq8QKPSQE3yVTk49bhCcHDLVyd3Bh/DeIBJwKnV1cLhDcDmHFI6jg4OtdW31NsF8IX+G34cEwGR6wKgoT3TAlvIugtextitI8D/S5xL5fR9DOlM/OAbkmzhd/Z52H6/C/tYw2VUTTIZvmAC62/el6kSH3kISj8H6UEkGLPS9RgTY3DaWyhWKMwAfLKGqX+keSwKb+EuxU0HoXz1h6O0L0pIhrDQpCN54BUPfWiQM3fmQKyvKiY0jFqxrCI09fRz3B46j9mlWfIxOP7WeVJM88oKEXW42XvZ8BXUgH0kzUt6srH7SZDKZnrQzQVdRRszCBusAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Geographic Sciences and Natural Resources Research","correspondingAuthor":true,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Feng","suffix":""},{"id":335785246,"identity":"17da6dcc-dca4-4acd-9ad7-2cde9027672a","order_by":1,"name":"Jinfeng Wang","email":"","orcid":"","institution":"Institute of Geographic Sciences and Natural Resources Research","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Wang","suffix":""},{"id":335785247,"identity":"96c1cd48-dcaa-4419-8fb3-c3144326d4b3","order_by":2,"name":"Naliang Guo","email":"","orcid":"","institution":"Institute of Geographic Sciences and Natural Resources Research","correspondingAuthor":false,"prefix":"","firstName":"Naliang","middleName":"","lastName":"Guo","suffix":""},{"id":335785248,"identity":"178a4c64-2183-44e5-bf3e-f6874e27594e","order_by":3,"name":"Yue Cai","email":"","orcid":"","institution":"National Health Commission","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Cai","suffix":""},{"id":335785249,"identity":"b2f2ffd2-382b-40b5-b586-bba0fd015c03","order_by":4,"name":"Qian Yin","email":"","orcid":"","institution":"Institute of Geographic Sciences and Natural Resources Research","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Yin","suffix":""},{"id":335785250,"identity":"0b02140d-069a-4bbd-b208-adf9362e0850","order_by":5,"name":"Shiyong Wu","email":"","orcid":"","institution":"National Health Commission","correspondingAuthor":false,"prefix":"","firstName":"Shiyong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-07-18 06:16:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4760315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4760315/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25201-x","type":"published","date":"2025-11-12T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62401459,"identity":"ec089b71-1812-4488-b69d-f2fc5f9d6d41","added_by":"auto","created_at":"2024-08-13 19:05:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404772,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the analysis based on the Geotree framework, which comprises three components: branches, twigs, and leaves.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/3e4ba6aa4fc46af342f70913.jpg"},{"id":62401461,"identity":"2c8d0eb9-0466-4fd5-85a4-75371a81050e","added_by":"auto","created_at":"2024-08-13 19:05:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4989116,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Geographical distribution of the types of provinces; (b) LE in different natural environment types and development stages, in which natural environment types are denoted by Ⅰ, Ⅱ, Ⅲ, and development stages are denoted by 1, 2, 3, respectively; (c) q-values of LE explained by population-weighted elevation and urbanization rate.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/67f45002b517a1d1aab37e98.jpg"},{"id":62401464,"identity":"9a3759bd-8708-4644-8337-80565215f351","added_by":"auto","created_at":"2024-08-13 19:05:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5012785,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of LE and development stages by province in (a) 2000,\u003c/p\u003e\n\u003cp\u003e(b) 2010 and (c) 2020.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/98790952e0b8c9a8d8b537aa.jpg"},{"id":62401818,"identity":"1315d78a-6ad0-4c22-a1a5-7e0adce0b9ad","added_by":"auto","created_at":"2024-08-13 19:13:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3936558,"visible":true,"origin":"","legend":"\u003cp\u003eGeotree of LE in 2000, 2010, 2020. (BJ: Beijing, TJ: Tianjin, HEB: Hebei, SX: Shanxi, Inner Mongolia: MO, LN: Liaoning, JL: Jilin, HL: Heilongjiang, SH: Shanghai, JS: Jiangsu, ZJ: Zhejiang, AH: Anhui, FJ: Fujian, JX: Jiangxi, SD: Shandong, HEN: Henan, HUB: Hubei, HUN: Hunan, GD: Guangdong, GX: Guangxi, HAN: Hainan, CQ: Chongqing, SC: Sichuan, GZ: Guizhou, YN: Yunnan, XZ: Tibet, SHX: Shaanxi, GS: Gansu, QH: Qinghai, NX: Ningxia, XJ: Xinjiang.)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/1eb45a248137dacffaf0db86.jpg"},{"id":62401460,"identity":"49a1473f-b8b1-4df8-bb31-c404d82fc0b9","added_by":"auto","created_at":"2024-08-13 19:05:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2319127,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Spatial distribution of LE and development stages by province in 2030; (b) Increase in life expectancy per decade, 2000-2030; (c) Life expectancy growth rate per decade, 2000-2030.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/b68427a07fae111fd6f94d2f.jpg"},{"id":96105825,"identity":"27f28ae2-729c-4bce-8066-7f84ceca72c2","added_by":"auto","created_at":"2025-11-17 16:11:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17366418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/47da1dba-ab25-4e4a-9474-497d38558256.pdf"},{"id":62402235,"identity":"6b70fffc-87d8-416f-a36c-13e0cf9824bb","added_by":"auto","created_at":"2024-08-13 19:21:49","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":22303,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4760315/v1/0ddd9fa6caf399dba2c8850f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal trajectory of life expectancy and its disparity in China 2000 - 2030：Modelling and prediction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLife expectancy (LE) serves not only as a pivotal indicator reflecting the health status and quality of life in a country or region but also as a crucial reference in the establishment of public health policies and social security schemes by governments(Central Committee of the Chinese Communist Party, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since the year 2000, China has joined the ranks of countries with high longevity. The \"Healthy China 2030\" Plan, released in 2016 by the State Council, identifying LE as a significant metric and delineate specific developmental targets(Central Committee of the Chinese Communist Party, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; General Office of the State Council of the People\u0026rsquo;s Republic of China, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By 2020, the average LE in China had escalated to 77.9 years, marking a substantial increase since 2000. The projection of LE at the provincial level for future periods is instrumental in enabling government authorities and decision-makers to foresee impending health hazards and resource demands with greater accuracy. This insight is crucial in the formulation of pertinent strategies and action plans(Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMajority of existing studies on LE are focusing on the LE estimated age-specific mortality rates (Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Foreman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kontis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Spatiotemporal trend and its determinants remain nebulous. In this study, the Geotree method is employed to elucidate the underlying mechanisms correlating LE with natural environment and socio-economic variables. Subsequently, a multilevel modeling is adopted to capitalize the environmental and socio-economic determinants of LE, and to forecast LE across the 31 provincial administrative divisions in China and analyzed regional inequalities in 2030.\u003c/p\u003e\n"},{"header":"Data and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic determinants of LE\u003c/h2\u003e \u003cp\u003eWe collected the explanatory variables of LE from four aspects: socioeconomic development, population characteristic, healthcare resource and environmental exposures. Socioeconomic development exerts a direct influence on the populace's quality of life. Numerous studies have indicated that socio-economic progress is a significant factor affecting LE (Huang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; S. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this research, socioeconomic development across regions is represented through the adoption of various metrics: per capita GDP, average years of schooling, and urbanization rate. The healthcare services and protection in various regions are gauged by the number of physicians per 1,000 people and the proportion of out-of-pocket health expenditure (OOP).\u003c/p\u003e \u003cp\u003eLE disparities between genders, influenced by an amalgamation of biological, social, and environmental factors, signify that females live longer than males(Zarulli et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This phenomenon suggests an association between the gender composition of a population and its overall LE. Gross dependency ratio reflects to the basic relation between population and economic development from the demographic perspectives(W. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental determinants exert a significant influence on LE (Burtscher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Finch and Tanzi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Research indicates that, even after adjusting for socio-economic and healthcare variables, areas endowed with extensive green spaces and temperate climates are correlated with extended LE(Poudyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zha et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Elevation is also an important environmental factor affecting LE(Burtscher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Air pollution, one of the major environmental factors affecting public health, results in the deaths of one million people in China each year(Pope et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conclusively, the environmental variables utilized in this study encompass population-weighted elevation, temperature, precipitation, normalized difference vegetation index (NDVI), and PM2.5 concentration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection basis of factors affecting life expectancy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocioeconomic development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zha et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Huang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanization rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage years of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealthcare resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of practicing (assistant) physicians per 1,000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKaplan and Milstein, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; W. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of out-of-pocket (OOP) health expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross dependency ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zarulli et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEnvironmental\u003c/p\u003e \u003cp\u003eexposures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-weighted elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBurtscher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Poudyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Pope et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; S. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Poudyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Zha et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-weighted NDVl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulationweighted PM2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-weighted temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-weighted precipitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLE and Socioeconomic data\u003c/h2\u003e \u003cp\u003eThe provincial LE data for the years 2000, 2010 and 2020 in China are sourced from the China Health and Health Statistics Yearbook. Indicators such as urbanization rate, per capita GDP, average years of schooling, out-of-pocket health expenditure (OOP), gross dependency ratio, and gender ratio are obtained from national or provincial statistical yearbooks. The elevation data with a spatial resolution of 1 kilometer is obtained from the Resource and Environment Science and Data Center. The population density data with a spatial resolution of 1 kilometer is sourced from WorldPop. The calculation formula for population-weighted elevation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{wDEM}_{p}\\)\u003c/span\u003e\u003c/span\u003e) is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{wDEM}_{p}={\\sum\\:}_{i}^{n}{w}_{i,p}\\times\\:{DEM}_{i,p}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{w}_{i,p}=\\frac{{pop}_{i,p}}{{\\sum\\:}_{i}^{n}{pop}_{i,p}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHerein, \u003cem\u003ei\u003c/em\u003e represents the raster pixel, \u003cem\u003ep\u003c/em\u003e represents a province, \u003cem\u003en\u003c/em\u003e is the total count of pixels within the province, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{pop}_{i,p}\\)\u003c/span\u003e\u003c/span\u003e denotes the population within pixel \u003cem\u003ei\u003c/em\u003e of province \u003cem\u003ep\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i,p}\\)\u003c/span\u003e\u003c/span\u003e signifies the weight of pixel \u003cem\u003ei\u003c/em\u003e within province \u003cem\u003ep\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DEM}_{i,p}\\:\\)\u003c/span\u003e\u003c/span\u003eindicates the elevation of pixel \u003cem\u003ei\u003c/em\u003e within province \u003cem\u003ep\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFuture data\u003c/h2\u003e \u003cp\u003eFuture provincial population by age and sex, education attainment data, urbanization rates were obtained from figshare(Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future socio-economic data was obtained from Science Data Bank(Jiang et al., n.d.). We collected data from the middle road (SSP2) scenario, which indicates the world will maintain the development of recent decades. The 2030 OOP data are from the \"Healthy China 2030\" Plan.\u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cp\u003eLE in provinces in the years of 2000, 2010 and 2020 were categorized by natural environment and development stages. These categorizations were then modelled by Geotree to reconstruct the geographical evolutionary trajectories of LE across 31 provincial administrations. After validation of the modelling, we estimated the LEs in 2030 using a multilevel model combined with the hierarchical structure of the Geotree (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeodetector\u003c/h2\u003e \u003cp\u003eThe stratified heterogeneity of LE was detected using Geodetector\u0026rsquo;s q-statistic value. The GeoDetector is a linearity free model to measure the association between the spatial distributions of LE and influencing factors(Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang and Xu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The definition of q as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:q=(1-\\:\\frac{{\\sum\\:}_{h=1}^{L}{N}_{h}{\\sigma\\:}_{h}^{2}}{N{\\sigma\\:}^{2}})\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\:(h=1,\\:2,\\:\\dots\\:,\\:L)\\)\u003c/span\u003e\u003c/span\u003e is the spatial stratification of the influencing factors, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{h}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e are the numbers of units in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{th}\\)\u003c/span\u003e\u003c/span\u003e stratum and the whole area, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e are variances in LEs in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{th}\\)\u003c/span\u003e\u003c/span\u003e stratum and the whole area, respectively. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:q\\)\u003c/span\u003e\u003c/span\u003e value varies between 0% and 100%, which can be interpreted as deterministic power of the explanatory variable, i.e., the percent of variance of LE explained by an explanatory variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeotree method\u003c/h2\u003e \u003cp\u003eThe Geotree model, inspired by the principles of biological evolution, reconstructs the trajectory of the evolutionary object using observed cross-section data. Geotree is applicable for the phenomena evolved in strata(Jing et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang and Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Geotree comprises branches, twigs on the branches, and leaves on the twigs. The branches and twigs represent natural environmental types and socioeconomic developmental stages of the leaves or provinces, respectively. In this study, the branches utilize population-weighted elevation to represent the natural environment of the regions. Based on this measure, all provinces are classified into three categories. The twigs categorize the development stages of each province based on urbanization rates. According to the Northam curve(Northam, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), these provinces are delineated into initial, intermediate, and advanced stages. Each province is represented by a leaf on the tree, situated on the branches corresponding to its specific type and developmental stage. The details of the province types and development stages are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicators of types and development stages of provinces.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural environment types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevelopment stages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-weighted elevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrbanization rate (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMultilevel method\u003c/h2\u003e \u003cp\u003eThe multilevel model (MLM) is suitable for modeling data with a hierarchical structure(Hox, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rice and Jones, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Geotree provides a framework for MLM, while MLM models the Geotree. We employ the Geotree constructed as described above to create a MLM with cross random effects. The model can be expressed as follows:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i(t,s)}={\\beta\\:}_{0}+{\\beta\\:}_{1}{x}_{i(t,s)}+{u}_{t}+{u}_{s}{+e}_{i(t,s)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{u}_{t}\\:\\sim\\:N\\left(0,{\\sigma\\:}_{u\\left(t\\right)}^{2}\\right),\\:{u}_{s}\\:\\sim\\:N\\left(0,{\\sigma\\:}_{u\\left(s\\right)}^{2}\\right),{e}_{i(t,s)}\\:\\sim\\:N\\left(0,{\\sigma\\:}_{e}^{2}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e and \u003cem\u003es\u003c/em\u003e represent the type and the developmental stage of a province, respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i(t,s)}\\)\u003c/span\u003e\u003c/span\u003e represents the LE of province \u003cem\u003ei\u003c/em\u003e in the branch \u003cem\u003et\u003c/em\u003e and development stage \u003cem\u003es\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i(t,s)}\\)\u003c/span\u003e\u003c/span\u003e indicates the factors influencing LE incorporated into the model \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the coefficient of the explanatory variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{s}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003erepresent the random effects of type \u003cem\u003et\u003c/em\u003e and development stage \u003cem\u003es\u003c/em\u003e, respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{i(t,s)}\\)\u003c/span\u003e\u003c/span\u003e is the residual error term. We employed a stepwise regression approach to select variables for inclusion in the model. Ultimately, the explanatory variables in the MLM model encompass five indicators: per capita GDP, average years of education, out-of-pocket expenditure (OOP), gross dependency ratio, and sex ratio. This model is used to forecast the LE of various provinces in China in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eNatural environment types and socioeconomic development stages\u003c/h2\u003e\n \u003cp\u003eThe distribution of natural environment type is illustrated in Fig. \u003cspan\u003e2\u003c/span\u003ea. Provinces with a Type I are primarily located in the eastern part of China, featuring plains with elevations below 500 meters. Approximately 75.8% of China\u0026apos;s population resides in this region. Provinces with a Type II are mainly situated in the central and northwest regions of China, with elevations ranging from 1000 to 2000 meters. Approximately 23.6% of the population lives in this area. Provinces with a Type III include Tibet and Qinghai, situated in the Qinghai-Tibet Plateau, with an average elevation exceeding 4000 meters. These regions have a sparse population, with only 0.6% of people residing there.\u003c/p\u003e\n \u003cp\u003eDifferent types of natural environment exhibit significant variations in LE across different developmental stages (Fig. \u003cspan\u003e2\u003c/span\u003eb). When considering the same natural environment type, higher developmental stage are associated with higher LE. Meanwhile, when comparing provinces at the same developmental stage, those with a Type I natural environment demonstrate higher LE, followed by Types II and III.\u003c/p\u003e\n \u003cp\u003eBoth natural environment type and development stage have strong explanatory power to LE (Fig. \u003cspan\u003e2\u003c/span\u003ec). From 2000 to 2020, the q-value of natural factors decreased, while the q-value of urbanization increased. This suggests that over time, the impact of the natural environment on LE has diminished, and the influence of social development has grown, indicating an improvement in human adaptive capabilities to the environment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eLE in space and tree-like structure\u003c/h2\u003e\n \u003cp\u003eThere is an increasing trend in LE over the years. By 2020, all provinces are situated in the intermediate and advanced stages, and provinces at advanced stages exist only in regions with type I natural environments. Spatially, LE demonstrates a gradual decrease from east to west, and over time, this pattern continues to persist (Fig. \u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe natural environment type, developmental stage, and LE exhibit a similar spatial pattern, demonstrating significant stratified heterogeneity and coupling relationships. We further mapped the three variables into a hierarchical tree-like structure and built a Geotree, which provides an \u0026ldquo;attribute-space coordinate system\u0026rdquo;. The variation in leaf color within branches indicates different LE under various natural environment types (Fig. \u003cspan\u003e4\u003c/span\u003e). On twigs, with the socio-economic development, the color of leaves transitions from yellow to green, accompanied by a gradual increase in LE. These trends are substantiated by the boxplots in Fig. \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003ePrediction of LE in 2030\u003c/h2\u003e\n \u003cp\u003eBased on the structure of the Geotree, we developed a multilevel model and compared it with generalized linear regression. Cross-validation results (Table S2) show that multilevel model better better captures the evolution of LE under different types of natural environments and developmental stages, and it was used to estimate LE for each province in 2030. LE in China will reach 80.05 (95% confidence interval: 78.82\u0026thinsp;~\u0026thinsp;81.28) years by 2030, an increase of 2.12 years compared to 77.93 years in 2020. In the eastern regions, the provincial LE remains at the forefront. The spatial pattern of higher in the east and lower in the west persists. All provinces have left the first stage of development (Fig. \u003cspan\u003e5\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eDuring the period from 2000 to 2030, an increase in LE was observed across all provinces, albeit with an overall declining trend in growth rates. Type I provinces have the lowest average growth rate in LE, with an average increase of 2.62% (3.31 years) from 2020 to 2030 (Fig. \u003cspan\u003e5\u003c/span\u003eb). Next are the provinces in type III, with an increase of 3.26 years (4.43%), while type II provinces have the highest growth rate (4.63 years, 3.54%). The inequality in LE among regions is gradually decreasing by 2030 (Fig. \u003cspan\u003e5\u003c/span\u003ec).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study delineated the spatial distribution pattern of LE, unveiled its developmental trajectory and forecasted LE at subnational level in China for 2030. We observed a steady increase in LE from 2000 to 2020, closely correlated with improvements in socio-economic and healthcare conditions. Although LE is increasing in all provinces, the persistent inter-provincial disparities pose an enduring and significant challenge(Luo and Xie, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The evolution of LE is an outcome shaped by the joint influence of the natural environment and social development, and China's LE projected to grow to 80.05 by 2030.\u003c/p\u003e \u003cp\u003eThe natural environment, which includes many aspects such as climate, terrain, and vegetation, is one of the most important influences on the health of the population.\u003c/p\u003e \u003cp\u003eExtensive green space is significantly associated with extended LE (Poudyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zha et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Elevation affects mortality rates or potential lifespan by altering variables such as temperature, atmospheric pressure, oxygen concentration in the air, and intensity of ultraviolet radiation(Burtscher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, provinces were categorized into three types based on population-weighted elevation, which is used to represent the types of natural environment, and the spatial distribution of these types aligns closely with the three steps of China\u0026rsquo;s terrain. By calculating the q-values of population-weighted elevation and visualizing the developmental trajectory of LE as a 'tree,' we identified natural environment as one of the primary influencing factors explaining spatial heterogeneity in LE(Burtscher, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Finch and Tanzi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach type of natural environment exhibits distinct environmental characteristics Tibet and Qinghai, characterized by natural environment type III, are primarily located in the Qinghai-Tibet Plateau, with an elevation exceeding 4000 meters. The primary environmental factors affecting the health of the region include low levels of oxygen in the air and cold temperatures. A series of studies suggests a close association between hypoxic and the onset of tumors(Wong et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yeo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), cardiovascular diseases(Wong et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), diabetes, and neurodegenerative diseases(Luo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Prenatal hypoxia in pregnant women can also impact the development of the infant's brain, heart, and nervous system. Prenatal hypoxia in pregnant women can lead to the underdevelopment of the fetal brain(Yeo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), heart(Aljunaidy et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), nervous system, and other organs(Nalivaeva et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Environments with higher oxygen concentrations have been linked to extended anticipated lifespans(Zou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the past 50 years, the annual average temperature on the Qinghai-Tibet Plateau has been 5.85 degrees Celsius(C. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Numerous studies indicate an association between low temperatures and increased risks of various cardiovascular, respiratory, and other diseases(Analitis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Curriero et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Group, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The majority of the mortality burden related to non-optimal temperatures is attributed to the contribution of cold(Gasparrini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Type I of natural environment is primarily located in the eastern regions of China, characterized by a monsoon climate. This area exhibits the highest population density among all regions. The remaining areas are classified as type Ⅱ, characterized primarily by arid conditions in the northwest and mountainous terrain in the south. In addition, elevation also influences the distribution of population density within the study area, thereby impacting local socio-economic development. People tend to prefer residing in areas with flat terrain and favorable climates, with approximately 75% of China's population living in regions characterized by type Ⅰ.\u003c/p\u003e \u003cp\u003eSocioeconomic development acts as the driving force for the improvement of LE. Significant disparities in LE among different income groups, with noticeably higher mortality rates in socioeconomically disadvantaged populations compared to those with higher socioeconomic status(Chetty et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mackenbach et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). LE demonstrates a significant positive correlation with indicators reflecting socioeconomic development, such as income, healthcare levels, and educational attainment(Huang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Sasson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; \u0026ldquo;Spatial variations and social determinants of LE in China, 2005\u0026ndash;2020,\u0026rdquo; 2022; S. Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang and Ren, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Socioeconomic development signifies the improvement in people's economic, educational, and healthcare levels, enhancing their adaptability to the natural environment and thereby promoting a reduction in mortality rates.\u003c/p\u003e \u003cp\u003eThe dominant influences differ at different stages of the evolution of LE. Currently, the driving force behind the decline in mortality levels in China has shifted from being primarily propelled by investments in medical facilities to being primarily driven by social development(Li and Yan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Using the urbanization rate to represent the stage of regional development. In the early stages of urbanization, the economy is primarily agrarian, with healthcare and education lagging behind. At this point, LE is mainly determined by the local natural environment. In the intermediate, the urbanization process accelerates, and human adaptability to the environment gradually improves, diminishing the impact of the natural environment on LE. In the advanced, socioeconomic factors become the primary influencers of LE. LE at different stages of development exhibits significant variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), with provinces at higher development stages having correspondingly higher LE.\u003c/p\u003e \u003cp\u003eIt is crucial to note that human life is consistently exposed to the natural environment. At any stage of development, the influence of the natural environment on LE cannot be eliminated; in other words, the impact of the natural environment on LE persists. LE undergoes categorized development evolution under the influence of natural environmental and socio-economic conditions. The development of the natural environment constrains the evolution of LE, and the progress in socio-economic conditions can weaken this limitation. The stratification of LE across natural environment types and developmental stages could facilitate the identification of populations at higher health risk, enables the formulation of targeted policy improvements and resource support.\u003c/p\u003e \u003cp\u003eOver the past three decades, there has been a steady increase in LE in China, and it is projected to reach the Healthy China 2030' goal of 79.0 by 2030, which is consistent with findings from previous studies(Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since 1990, mainland China has experienced a transition in epidemiological cause-of-death patterns from communicable to non-communicable diseases, and LE at birth has increased significantly in all provinces(Zhou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). By the year 2030, LE in China is projected to increase to 80.05. Yet a spatial pattern persists with a gradual decrease from east to west. The persisting inequality in LE among provinces remains a challenge.\u003c/p\u003e \u003cp\u003eStrengthening medical facility investments in provinces other than Type Ⅰ, particularly in provinces of Type Ⅲ can effectively reduce health inequality among regions. Based on the structure of Geotree, the sources of inequality in LE can be broadly categorized into natural (non-intervenable) and social development (intervenable), with social development encompassing healthcare, education, and economic aspects. When social development reaches a certain level, apart from individual behavior, regional differences in LE are primarily due to local environmental characteristics. Therefore, by enhancing healthcare, education, and other levels, regional inequalities can be reduced. Due to the catch-up effect, the inequality in LE between regions is gradually decreasing. In the future, in addition to active economic development, increased investment in medical facilities in provinces other than Type I, especially Type III provinces, will help to reduce the inequality of LE among China's provinces.\u003c/p\u003e \u003cp\u003eGeotree clearly identified the provinces that are relatively lagging in national health, allowing for tailored strategies. Strategies for promoting health can be proposed from both natural environment and social development perspectives. Provinces of the same type can draw on the experiences of those at a higher developmental stage in the same category, exploring strategies for health improvement that are suitable for their own context. This holds significant implications for the future development of the health sector in China. However, the evolutionary trajectory of LE is specific to China and requires further investigation to determine its applicability to other countries and regions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reconstructed the provincial LE evolution trajectory in China from 2000 to 2020 and predicts it for 2030. The results indicate a spatial pattern of declining LE from east to west and significant stratified heterogeneity. LE evolves with the development stages of society, constrained by natural environmental factors. By 2030, China's LE is projected to reach 80.05 years, achieving the 'Healthy China 2030' goal, with regional inequalities also gradually decreasing. The western regions of China need to enhance socio-economic development and healthcare infrastructure to overcome the constraints imposed by natural environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAcknowledgments and Funding Disclosure\u003c/h2\u003e \u003cp\u003eThis research was financially supported by the National Key Research and Development Plan of China (2022YFC3600802; 2023YFF1305403), National Natural Science Foundation of China (42071375), and National Social Science Foundation of China (21\u0026amp;ZD186).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eProfessor Jinfeng Wang\u003c/h2\u003e \u003cp\u003eState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11, Datun Road, Chaoyang District, Beijing, China\u003c/p\u003e \u003cp\u003eE-mail: [email protected]\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProfessor Shiyong Wu\u003c/strong\u003e \u003cp\u003eCenter for Health Statistics and Information, National Health Commission,\u003c/p\u003e \u003cp\u003eNo.1 Xizhimenwai South Road, Xicheng District, Beijing, China\u003c/p\u003e \u003cp\u003eE-mail: [email protected]\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJW initially conceived the research idea. JW and YF designed the study. YF performed data management and analysis, and drafted the manuscript. NG performed data management and analysis. YC, QY, SW and JW directed the study. YF, JW, NG, YC, QY, and SW critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAljunaidy MM, Morton JS, Cooke C-LM, Davidge ST. Prenatal hypoxia and placental oxidative stress: linkages to developmental origins of cardiovascular disease. Am J Physiology-Regulatory Integr Comp Physiol. 2017;313:R395\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpregu.00245.2017\u003c/span\u003e\u003cspan address=\"10.1152/ajpregu.00245.2017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnalitis A, Katsouyanni K, Biggeri A, Baccini M, Forsberg B, Bisanti L, Kirchmayer U, Ballester F, Cadum E, Goodman P. others, 2008. Effects of cold weather on mortality: results from 15 European cities within the PHEWE project. American journal of epidemiology 168, 1397\u0026ndash;1408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwn266\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwn266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai R, Liu Y, Zhang L, Dong W, Bai Z, Zhou M. Projections of future life expectancy in China up to 2035: a modelling study. Lancet Public Health. 2023;0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(22)00338-3\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(22)00338-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurtscher M. Effects of Living at Higher Altitudes on Mortality: A Narrative Review. Aging Dis. 2013;5:274\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14336/AD.2014.0500274\u003c/span\u003e\u003cspan address=\"10.14336/AD.2014.0500274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCentral Committee of the Chinese Communist Party. 2016. Central Committee of the Chinese Communist Party. State Council: The plan for Healthy China 2030 [WWW Document]. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/zhengce/2016-10/25/content_5124174.htm\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/zhengce/2016-10/25/content_5124174.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 10.21.23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Guo F, Wang J, Cai W, Wang C, Wang K. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Sci Data. 2020;7:83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-020-0421-y\u003c/span\u003e\u003cspan address=\"10.1038/s41597-020-0421-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, Bergeron A, Cutler D. The Association Between Income and Life Expectancy in the United States, 2001\u0026ndash;2014. JAMA. 2016;315:1750\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2016.4226\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016.4226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA. Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol. 2002;155:80\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/155.1.80\u003c/span\u003e\u003cspan address=\"10.1093/aje/155.1.80\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinch CE, Tanzi RE. Genetics of aging. Science. 1997;278:407\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.278.5337.407\u003c/span\u003e\u003cspan address=\"10.1126/science.278.5337.407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForeman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan C-W, Brown JC, Friedman J, He J, Heuton KR, Holmberg M, Patel DJ, Reidy P, Carter A, Cercy K, Chapin A, Douwes-Schultz D, Frank T, Goettsch F, Liu PY, Nandakumar V, Reitsma MB, Reuter V, Sadat N, Sorensen RJD, Srinivasan V, Updike RL, York H, Lopez AD, Lozano R, Lim SS, Mokdad AH, Vollset SE, Murray CJL. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016\u0026ndash;40 for 195 countries and territories. Lancet. 2018;392:2052\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(18)31694-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)31694-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, Tobias A, Tong S, Rockl\u0026ouml;v J, Forsberg B, Leone M, De Sario M, Bell ML, Guo Y-LL, Wu C, Kan H, Yi S-M, de Coelho SZS, Saldiva M, Honda PHN, Kim Y, Armstrong H, B. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet. 2015;386:369\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(14)62114-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(14)62114-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneral Office of the State Council of the People\u0026rsquo;s Republic of China. 2022. 14th Five-year National Health Plan [WWW Document]. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/zhengce/zhengceku/2022-05/20/content_5691424.htm\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/zhengce/zhengceku/2022-05/20/content_5691424.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 10.21.23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroup TE. Cold exposure and winter mortality from ischaemic heart disease, cerebrovascular disease, respiratory disease, and all causes in warm and cold regions of Europe. Lancet. 1997;349:1341\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(96)12338-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(96)12338-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHox J. Multilevel Modeling: When and Why. In: Balderjahn I, Mathar R, Schader M, editors. Classification, Data Analysis, and Data Highways. Berlin, Heidelberg: Springer Berlin Heidelberg; 1998. pp. 147\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Yang S, Liu T. Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions. Int J Environ Res Public Health. 2020a;17:6597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17186597\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17186597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Yang S, Liu T. Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions. Int J Environ Res Public Health. 2020b;17:6597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17186597\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17186597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang T, Su B, Wang Y, Huang J, Jing C, Gao M, Wang G, Lin Q, Jiang S, Liu S, Luo. Y., n.d. Gridded datasets for population and economy under Shared Socioeconomic Pathways. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.57760/sciencedb.01683\u003c/span\u003e\u003cspan address=\"10.57760/sciencedb.01683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJing S, Wang J, Xu C, Yang J. Tree-like evolution pathways of global urban land expansion. J Clean Prod. 2022;378:134562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2022.134562\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2022.134562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan RM, Milstein A. Contributions of Health Care to Longevity: A Review of 4 Estimation Methods. Annals Family Med. 2019;17:267\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1370/afm.2362\u003c/span\u003e\u003cspan address=\"10.1370/afm.2362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389:1323\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(16)32381-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)32381-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei Y, Wang J, Wang Y, Xu C. Geographical evolutionary pathway of global tuberculosis incidence trends. BMC Public Health. 2023;23:755. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-023-15553-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-15553-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Yan Y. Differences in Mortality by Regions in China since the 1980s and Their Evolution: The Staged Synergy between Medical Investment and Socioeconomic Development. Population research. (Peking China). 2023;47:35\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo W, Xie Y. Economic growth, income inequality and life expectancy in China. Soc Sci Med. 2020;256:113046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.socscimed.2020.113046\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2020.113046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Z, Tian M, Yang G, Tan Q, Chen Y, Li G, Zhang Q, Li Y, Wan P, Wu J. Hypoxia signaling in human health and diseases: implications and prospects for therapeutics. Signal Transduct Target Therapy. 2022;7:218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-022-01080-1\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-01080-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackenbach JP, Stirbu I, Roskam A-JR, Schaap MM, Menvielle G, Leinsalu M, Kunst AE. Socioeconomic Inequalities in Health in 22 European Countries. N Engl J Med. 2008;358:2468\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMsa0707519\u003c/span\u003e\u003cspan address=\"10.1056/NEJMsa0707519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNalivaeva NN, Turner AJ, Zhuravin IA. Role of Prenatal Hypoxia in Brain Development, Cognitive Functions, and Neurodegeneration. Front NeuroSci. 2018;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2018.00825\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2018.00825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNortham RM. 1975. Urban Geography. Wiley.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope CA, Ezzati M, Dockery DW. Fine-Particulate Air Pollution and Life Expectancy in the United States. N Engl J Med. 2009;360:376\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMsa0805646\u003c/span\u003e\u003cspan address=\"10.1056/NEJMsa0805646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoudyal NC, Hodges DG, Bowker JM, Cordell HK. Evaluating natural resource amenities in a human life expectancy production function. For Policy Econ. 2009;11:253\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.forpol.2009.04.007\u003c/span\u003e\u003cspan address=\"10.1016/j.forpol.2009.04.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRice N, Jones A. Multilevel models and health economics. Health Econ. 1997;6:561\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(sici)1099-1050(199711)6:6\u0026lt;561::aid-hec288\u0026gt;3.0.co;2-x\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1099-1050(199711)6:6%3C561::aid-hec288%3E3.0.co;2-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasson I. Trends in Life Expectancy and Lifespan Variation by Educational Attainment: United States, 1990\u0026ndash;2010. Demography. 2016;53:269\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13524-015-0453-7\u003c/span\u003e\u003cspan address=\"10.1007/s13524-015-0453-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong W, Li Y, Hao Z, Li H, Wang W. Public health in China: An environmental and socio-economic perspective. Atmos Environ. 2016;129:9\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2015.12.021\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2015.12.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpatial variations and social determinants of life expectancy in China. 2005\u0026ndash;2020: A population-based spatial panel modelling study, 2022. Lancet Reg Health - Western Pac 23, 100451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lanwpc.2022.100451\u003c/span\u003e\u003cspan address=\"10.1016/j.lanwpc.2022.100451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Zeng H, Liu J, Sun M. Effects of Prenatal Hypoxia on Nervous System Development and Related Diseases. Front NeuroSci. 2021;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2021.755554\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2021.755554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Dai C, Song C. 2022. Analysis of the Temporal and Spatial Distribution Characteristics of Climate Change in the Qinghai-Tibetan Plateau. Yellow River 004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Li X, Christakos G, Liao Y, Zhang T, Gu X, Zheng X. Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. Int J Geogr Inf Sci. 2010;24:107\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13658810802443457\u003c/span\u003e\u003cspan address=\"10.1080/13658810802443457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Liu X, Peng L, Chen H, Driskell L, Zheng X. Cities evolution tree and applications to predicting urban growth. Popul Environ. 2012;33:186\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11111-011-0142-4\u003c/span\u003e\u003cspan address=\"10.1007/s11111-011-0142-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Xu C. Geodetector: Principle prospective. 2017;72:116\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11821/dlxb201701010\u003c/span\u003e\u003cspan address=\"10.11821/dlxb201701010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J-F, Zhang T-L, Fu B-J. A measure of spatial stratified heterogeneity. Ecol Ind. 2016;67:250\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2016.02.052\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2016.02.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Ren Z. Spatial variations and macroeconomic determinants of life expectancy and mortality rate in China: a county-level study based on spatial analysis models. Int J Public Health. 2019;64:773\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00038-019-01251-y\u003c/span\u003e\u003cspan address=\"10.1007/s00038-019-01251-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Ren Z, Liu X, Yin Q. Spatiotemporal trends in life expectancy and impacts of economic growth and air pollution in 134 countries: A Bayesian modeling study. Soc Sci Med. 2022;293:114660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.socscimed.2021.114660\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2021.114660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Liu Y, Ye P, Xu C, Qiu Y, Yin P, Liu J, Qi J, You J, Lin L, Wang L, Li J, Shi W, Zhou M. Spatial variations and social determinants of life expectancy in China, 2005\u0026ndash;2020: A population-based spatial panel modelling study. Lancet Reg Health - Western Pac. 2022;23:100451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lanwpc.2022.100451\u003c/span\u003e\u003cspan address=\"10.1016/j.lanwpc.2022.100451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wang J. Modelling and prediction of global non-communicable diseases. BMC Public Health. 2020;20:822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-020-08890-4\u003c/span\u003e\u003cspan address=\"10.1186/s12889-020-08890-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong BW, Marsch E, Treps L, Baes M, Carmeliet P. Endothelial cell metabolism in health and disease: impact of hypoxia. EMBO J. 2017;36:2187\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15252/embj.201696150\u003c/span\u003e\u003cspan address=\"10.15252/embj.201696150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Wang Q, Liu P. Extreme temperature and mortality: evidence from China. Int J Biometeorol. 2019;63:29\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00484-018-1635-y\u003c/span\u003e\u003cspan address=\"10.1007/s00484-018-1635-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo E-J. Hypoxia and aging. Exp Mol Med. 2019;51:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s12276-019-0233-3\u003c/span\u003e\u003cspan address=\"10.1038/s12276-019-0233-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue H, He C, Huang Q, Yin D, Bryan BA. Stronger policy required to substantially reduce deaths from PM2.5 pollution in China. Nat Commun. 2020;11:1462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-15319-4\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-15319-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarulli V, Jones JAB, Oksuzyan A, Lindahl-Jacobsen R, Christensen K, Vaupel JW. 2018. Women live longer than men even during severe famines and epidemics. Proceedings of the National Academy of Sciences 115, E832\u0026ndash;E840. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1701535115\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1701535115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZha X, Tian Y, Gao X, Wang W, Yu C. Quantitatively evaluate the environmental impact factors of the life expectancy in Tibet, China. Environ Geochem Health. 2019;41:1507\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10653-018-0211-z\u003c/span\u003e\u003cspan address=\"10.1007/s10653-018-0211-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M, Wang H, Zhu J, Chen W, Wang, Linhong, Liu S, Li Y, Wang, Lijun, Liu Y, Yin P, Liu J, Yu S, Tan F, Barber RM, Coates MM, Dicker D, Fraser M, Gonz\u0026aacute;lez-Medina D, Hamavid H, Hao Y, Hu G, Jiang G, Kan H, Lopez AD, Phillips MR, She J, Vos T, Wan X, Xu G, Yan LL, Yu C, Zhao Y, Zheng Y, Zou X, Naghavi M, Wang Y, Murray CJL, Yang G, Liang X. Cause-specific mortality for 240 causes in China during 1990\u0026ndash;2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet. 2016;387:251\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(15)00551-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)00551-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou Q, Lai Y, Lun Z-R. Exploring the Association between Oxygen Concentration and Life Expectancy in China: A Quantitative Analysis. Int J Environ Res Public Health. 2023;20:1125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph20021125\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20021125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Life expectancy, Tree like evolution trajectory, Physical and socioeconomic determinants, Projection","lastPublishedDoi":"10.21203/rs.3.rs-4760315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4760315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLife expectancy (LE) is one of crucial metrics of human evolution. However, the evolutionary trajectories of LE in different regions of China and the regional inequalities expected in 2030 are still unclear yet.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study collected provincial LE data and relevant explanatory variables for the years of 2000, 2010, 2020 in China. The Geotree method was employed to reconstruct the evolution trajectories of LE, while a multilevel model was used to predict LEs at the provincial levels in the country for the year 2030.\u003c/p\u003e\u003ch2\u003eFinding\u003c/h2\u003e \u003cp\u003e: The LE in China exhibits significant geographical pattern, decreasing from the east to the west of the country. LE increases with the socio-economic development but is constrained by the natural environment. The physical limitation to LE is significant in western China but are being alleviated with the development of socio-economic conditions. LE will increase in all provinces by 2030, with the overall LE in China reaching 80.05 years (95% confidence interval: 78.93\u0026thinsp;~\u0026thinsp;81.28), and regional inequalities will diminish.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLE is increasing with the improvement of socioeconomic condition over time; the constraints imposed by the natural environment on LE are being overridden with the improvement of socio-economic conditions.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal trajectory of life expectancy and its disparity in China 2000 - 2030：Modelling and prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 19:05:45","doi":"10.21203/rs.3.rs-4760315/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T09:07:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T05:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-04-16T08:15:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-23T01:04:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235466064910717672743166821838147556368","date":"2024-09-16T21:03:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209407126209802644353756801009607061648","date":"2024-09-12T17:50:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-09T10:09:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-21T09:18:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-19T09:05:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-19T03:32:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-07-18T06:15:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1531018-82c7-4fe6-94bd-be1e5dc142a3","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:09:09+00:00","versionOfRecord":{"articleIdentity":"rs-4760315","link":"https://doi.org/10.1186/s12889-025-25201-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-12 15:58:39","publishedOnDateReadable":"November 12th, 2025"},"versionCreatedAt":"2024-08-13 19:05:45","video":"","vorDoi":"10.1186/s12889-025-25201-x","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25201-x","workflowStages":[]},"version":"v1","identity":"rs-4760315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4760315","identity":"rs-4760315","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}