Abstract
INTRODUCTION: The global increase in the aging population presents critical challenges for healthcare systems, social security, and economic stability worldwide. Although the studies of the global rate of aging have increased more than four times in the past two decades, few studies have integrated the potential combined effects of socio-economic, climatic, and environmental factors. METHODS: We calculated the geographic heterogeneity of aging population growth rates from 218 countries between 1960 and 2022. Public databases were then integrated to assess the impacts of seven global stressors: socio-economic vulnerability, temperature, drought, seasonality, climate extremes, air pollution, and greening vulnerability on growth rates of aging population (a totally 156 countries). Linear regression models were primarily used to test the statistically significant effects of these stressors on the rate of aging, and multiple model inference was then used to test whether the number of stressors exceeding specific thresholds (e.g., > 25, 50, and 75%) was consistently significant in the best models. The importance of stressors and the number of stressors exceeding thresholds was verified using random forest models for countries experiencing different population aging rates. RESULTS: Our analysis identified significant heterogeneity in growth rates of aging population globally, with many African countries exhibiting significantly lower aging rates compared with Europe. High socio-economic vulnerability, increased climate risks (such as high temperature and intensive extreme climate), and decreased environmental quality were found to significantly increase growth rates of the aging population (P < 0.05). The positive combined impacts of these stressors were diminished at medium-high levels of stressors (i.e., relative to their maximum levels observed in nature). The number of global stressors exceeding the 25% threshold emerged as an important predictor of global aging rates. Demographic changes in regions with relatively rapid aging (e.g., Africa and Asia) are more sensitive to climate change (e.g., extreme climate and drought) and the number of global stressors, and regions with low to medium rates of aging (e.g., Europe and the Americas) are more sensitive to socio-economic vulnerability and environmental stability (e.g., drought, green fragility and air pollution). CONCLUSIONS: Our findings underscore that policy tools or methods must be developed that consider the holistic dimension of the global factor. Further investigations are essential to understand the complex interactions between multiple stressors and their combined effects on global aging.