Objectives
There is increasing recognition of the importance of neighborhood socioeconomic status (SES) for establishing an age-friendly society. Despite the benefits of improved neighborhood SES, little is known about the link of relative education between individuals and neighborhoods with healthy aging. This study aims to construct a healthy aging index (HAI) accounting for indicators’ interlinkages and to test the association of the HAI with relative education between neighborhoods and individuals.
Methods
The study used data from the China Health and Retirement Longitudinal Study from 2011 to 2018, including middle-aged and older adults (≥ 45 years). The final sample comprised 11633 participants residing in 443 neighborhoods with 34123 observations. Based on 13 health indicators, a hybrid method integrating network analysis with TOPSIS was applied to construct a HAI accounting for health interlinkages. Weighted multilevel linear and ordered logistic models were used to estimate the effects of neighborhood education.
Results
Among the 11633 participants (mean [SD] age, 58.20 [8.91] years; 6415 women [52.82%]), the mean (SD) HAI was 48.94 (7.55) at baseline, showing a downward trend with age. Approximately 10% of participants had a HAI trajectory characterized by a low starting point and fast decline. A one-year increase in neighborhood education was independently associated with a 0.37-point increase (95% CI, 0.23–0.52) in HAI. Regardless of individual education, each participant tended to gain benefits from a neighborhood with higher education. However, the effects of increased neighborhood education were weaker for individuals whose education was lower than the neighborhood average.
Conclusions
The HAI is an interaction system. Improving neighborhood education was beneficial to healthy aging, but individuals with lower education relative to the neighborhood average may experience poor person–environment fit and obtain fewer benefits from improved neighborhood education. Thus, in the process of improving neighborhood SES, individual-based interventions should be conducted for individuals whose education level is lower than the neighborhood average to achieve person–environment fit.
The frequency and duration of extreme temperature events continues to increase worldwide. However, the scale of population exposure and its quantitative relationship with health risks remains unknown on a global scale, limiting our ability to identify policy priorities in response to climate change. Based on data from 171 countries between 2010 and 2019, this study estimated the exposure of vulnerable populations to extreme temperatures, and their contemporary and lag associations with disease burden attributed to non-optimal temperatures. Fixed-effects models and dynamic panel models were applied. Increased vulnerable population exposure to extreme temperatures had adverse contemporary effects on the burden of disease attributed to non-optimal temperature. Health risks stemming from extreme cold could accumulate to a greater extent, exhibiting a larger lag effect. Population exposure to extreme cold was mainly distributed in high-income countries, while extreme heat occurred more in low-income and middle-income countries. However, the association between population exposure to extreme cold and burden of disease was much stronger in low-income and middle-income countries than in high-income countries, whereas the effect size of population exposure to extreme heat was similar. Our study highlighted that differential strategies should be determined and implemented according to the characteristics in different countries.
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