The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
Association of heatwave exposure and multimorbidity with depression trajectories among older adults: evidence from the China Health and Retirement Longitudinal Study.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Fang Boye, Wang Youwei, Li Xubao, Hong Yanbi
Аннотация
BACKGROUND: Depression among older adults is a growing concern globally, influenced by both environmental stressors and individual health conditions. This study examines the impact of heatwave exposure and multimorbidity on depressive symptom trajectories among older Chinese adults.
METHODS: Data from 3819 adults aged 60 and older across 5 waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Latent growth curve modeling identified depressive trajectories, and machine learning algorithms (Random Forest, Decision Tree, XGBoost, and Support Vector Machine) were applied to predict trajectory categories. Multinomial logistic regression further explored the moderating effects of multimorbidity on the heatwave-depression relationship.
RESULTS: Five distinct depressive symptom trajectories were identified: consistently high, high but decreasing, consistently low, high and increasing, and low but increasing. Heatwave exposure was associated with a higher likelihood of persistent or worsening depressive symptoms, particularly among individuals with multimorbidity. Machine learning analysis highlighted maximum temperature as one of the most influential predictors, and further demonstrated that multimorbidity amplified the effect of heatwave exposure on depression trajectories. Multinomial logistic regression confirmed that individuals with multimorbidity were significantly more likely to exhibit worsening depressive symptoms when exposed to elevated temperatures.
CONCLUSIONS: This study highlights the vulnerability of older adults with multimorbidity to worsened depression under heatwave exposure, emphasizing the need for tailored mental health interventions. Integrating climate adaptation and multimorbidity care is crucial for mitigating mental health impacts in this population. Policymakers should prioritize targeted interventions, incorporating climate adaptation and heatwave preparedness into mental health protocols to reduce adverse outcomes.
METHODS: Data from 3819 adults aged 60 and older across 5 waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Latent growth curve modeling identified depressive trajectories, and machine learning algorithms (Random Forest, Decision Tree, XGBoost, and Support Vector Machine) were applied to predict trajectory categories. Multinomial logistic regression further explored the moderating effects of multimorbidity on the heatwave-depression relationship.
RESULTS: Five distinct depressive symptom trajectories were identified: consistently high, high but decreasing, consistently low, high and increasing, and low but increasing. Heatwave exposure was associated with a higher likelihood of persistent or worsening depressive symptoms, particularly among individuals with multimorbidity. Machine learning analysis highlighted maximum temperature as one of the most influential predictors, and further demonstrated that multimorbidity amplified the effect of heatwave exposure on depression trajectories. Multinomial logistic regression confirmed that individuals with multimorbidity were significantly more likely to exhibit worsening depressive symptoms when exposed to elevated temperatures.
CONCLUSIONS: This study highlights the vulnerability of older adults with multimorbidity to worsened depression under heatwave exposure, emphasizing the need for tailored mental health interventions. Integrating climate adaptation and multimorbidity care is crucial for mitigating mental health impacts in this population. Policymakers should prioritize targeted interventions, incorporating climate adaptation and heatwave preparedness into mental health protocols to reduce adverse outcomes.