Menopause (New York, N.Y.) · 2026
The association between extreme weather events and depression risk in postmenopausal women: cross-sectional and longitudinal evidence from the China Health and Retirement Longitudinal Study (CHARLS).
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Ren Jiajie, Li Qiang, Gao Qianqian, Guo Luqin, Niu Dingren, Wang Shimeng, Chang Zhuo, Feng Xiaoling
Аннотация
OBJECTIVE: Investigating the association between different types of extreme weather events and the risk of depression in postmenopausal women.
METHODS: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) covering four waves from 2011 to 2018, with a focus on postmenopausal women aged 45 and older. Depression symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CESD-10). Based on baseline data from 2011, five types of extreme weather exposure indicators (extreme low temperature [LTD], extreme high temperature [HTD], extreme rainfall [ERD], extreme drought [EDD], and the climate physical risk index [CPRI]) were used to construct generalized linear models to analyze the associations between these five extreme weather exposures and the baseline (2011) prevalence of depression among postmenopausal women. Cox proportional hazards models were employed to assess the relationship between baseline extreme weather exposures and the incidence of new depression (2013-2018). Restrictive cubic splines (RCS) were used to test the nonlinear relationship between exposure and depression, and quantile g-computation was used to examine the joint effects of multiple extreme weather exposures.
RESULTS: In the cross-sectional analysis of 5,986 participants, the depression prevalence was 35.9% (2,147 cases). After adjusting for confounding factors, HTD (OR=1.007, 95% CI: 1.005-1.011, P <0.05), EDD (OR=1.016, 95% CI: 1.010-1.022, P <0.001), and CPRI (OR=1.019, 95% CI: 1.009-1.029, P <0.001) were positively associated with depression, whereas ERD was negatively associated (OR=0.994, 95% CI: 0.990-0.998, P <0.05). Restrictive cubic spline (RCS) analysis revealed nonlinear associations between ERD, CPRI, and LTD (P_Nonlinear < 0.05). In the longitudinal analysis, which included 3,839 participants, LTD (HR=1.014, 95% CI: 1.002-1.025, P =0.017) and CPRI (HR=1.012, 95% CI: 1.002-1.022, P =0.022) were significantly associated with the risk of incident depression, and further RCS analysis revealed that LTD and EDD showed nonlinear associations (P_Nonlinear < 0.05), while CPRI exhibited a linear association (P_Nonlinear > 0.05). Quantile g-computation identified EDD and HTD as the main positive risk contributors, with an overall trend of increased risk associated with climate exposure.
CONCLUSIONS: This study reveals significant associations between various extreme weather exposures and depression in postmenopausal women. Although these effects are small, they may accumulate over long-term exposure or in high-risk populations, potentially leading to greater public health impacts at the population level. This research provides important empirical support for a deeper understanding of the effects of climate change on mental health.
METHODS: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) covering four waves from 2011 to 2018, with a focus on postmenopausal women aged 45 and older. Depression symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CESD-10). Based on baseline data from 2011, five types of extreme weather exposure indicators (extreme low temperature [LTD], extreme high temperature [HTD], extreme rainfall [ERD], extreme drought [EDD], and the climate physical risk index [CPRI]) were used to construct generalized linear models to analyze the associations between these five extreme weather exposures and the baseline (2011) prevalence of depression among postmenopausal women. Cox proportional hazards models were employed to assess the relationship between baseline extreme weather exposures and the incidence of new depression (2013-2018). Restrictive cubic splines (RCS) were used to test the nonlinear relationship between exposure and depression, and quantile g-computation was used to examine the joint effects of multiple extreme weather exposures.
RESULTS: In the cross-sectional analysis of 5,986 participants, the depression prevalence was 35.9% (2,147 cases). After adjusting for confounding factors, HTD (OR=1.007, 95% CI: 1.005-1.011, P <0.05), EDD (OR=1.016, 95% CI: 1.010-1.022, P <0.001), and CPRI (OR=1.019, 95% CI: 1.009-1.029, P <0.001) were positively associated with depression, whereas ERD was negatively associated (OR=0.994, 95% CI: 0.990-0.998, P <0.05). Restrictive cubic spline (RCS) analysis revealed nonlinear associations between ERD, CPRI, and LTD (P_Nonlinear < 0.05). In the longitudinal analysis, which included 3,839 participants, LTD (HR=1.014, 95% CI: 1.002-1.025, P =0.017) and CPRI (HR=1.012, 95% CI: 1.002-1.022, P =0.022) were significantly associated with the risk of incident depression, and further RCS analysis revealed that LTD and EDD showed nonlinear associations (P_Nonlinear < 0.05), while CPRI exhibited a linear association (P_Nonlinear > 0.05). Quantile g-computation identified EDD and HTD as the main positive risk contributors, with an overall trend of increased risk associated with climate exposure.
CONCLUSIONS: This study reveals significant associations between various extreme weather exposures and depression in postmenopausal women. Although these effects are small, they may accumulate over long-term exposure or in high-risk populations, potentially leading to greater public health impacts at the population level. This research provides important empirical support for a deeper understanding of the effects of climate change on mental health.