Feng Wang1, Hui Liu2, Hui Li3, Jiajia Liu4, Xiaojie Guo5, Jie Yuan6, Yonghua Hu7, Jing Wang8, Lin Lu9. 1. Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China; National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, 100191 Beijing, China. Electronic address: pkuwangfeng@bjmu.edu.cn. 2. Peking University Medical Informatics Center, Peking University, 100191 Beijing, China. Electronic address: ymauil@bjmu.edu.cn. 3. Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China; National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, 100191 Beijing, China. Electronic address: lhappa@hsc.pku.edu.cn. 4. Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China; National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, 100191 Beijing, China. Electronic address: liujiajia_sdu@bjmu.edu.cn. 5. Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China; National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, 100191 Beijing, China. Electronic address: guoxiaojie@bjmu.edu.cn. 6. North China University of Science and Technology, 063000, Hebei Province, China. Electronic address: yuanjie@ncst.edu.cn. 7. Peking University Medical Informatics Center, Peking University, 100191 Beijing, China. Electronic address: yhhu@bjmu.edu.cn. 8. Peking University Medical Informatics Center, Peking University, 100191 Beijing, China. Electronic address: bjmu-wangjing@163.com. 9. Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China; National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, 100191 Beijing, China. Electronic address: linlu@bjmu.edu.cn.
Abstract
OBJECTIVE: Air pollution with high ambient concentrations of particulate matter (PM) has been frequently reported in China. However, no Chinese study has looked into the short-term effect of PM on hospitalization for depression. We used a time-stratified case-crossover design to identify possible links between ambient PM levels and hospital admissions for depression in 26 Chinese cities. METHODS: Electronic hospitalization summary reports (January 1, 2014-December 31, 2015) were used to identify hospital admissions related to depression. Conditional logistic regression was applied to determine the association between PM levels and hospitalizations for depression, with stratification by sex, age, and comorbidities. RESULTS: Both PM2.5 and PM10 levels were positively associated with the number of hospital admissions for depression. The strongest effect was observed on the day of exposure (lag day 0) for PM10, with an interquartile range increase in PM10 associated with a 3.55% (95% confidence interval: 1.69-5.45) increase in admissions for depression. For PM2.5, the risks of hospitalization peaked on lag day 0 (2.92; 1.37-4.50) and lag day 5 (3.65; 2.09-5.24). The elderly (>65) were more sensitive to PM2.5 exposure (9.23; 5.09-13.53) and PM10 exposure (6.35; 3.31-9.49) on lag day 0, and patients with cardiovascular disease were likely to be hospitalized for depression following exposure to high levels of PM10 (4.47; 2.13-6.85). CONCLUSIONS: Short-term elevations in PM may increase the risk of hospitalization for depression, particularly in the elderly and in patients with cardiovascular disease.
OBJECTIVE: Air pollution with high ambient concentrations of particulate matter (PM) has been frequently reported in China. However, no Chinese study has looked into the short-term effect of PM on hospitalization for depression. We used a time-stratified case-crossover design to identify possible links between ambient PM levels and hospital admissions for depression in 26 Chinese cities. METHODS: Electronic hospitalization summary reports (January 1, 2014-December 31, 2015) were used to identify hospital admissions related to depression. Conditional logistic regression was applied to determine the association between PM levels and hospitalizations for depression, with stratification by sex, age, and comorbidities. RESULTS: Both PM2.5 and PM10 levels were positively associated with the number of hospital admissions for depression. The strongest effect was observed on the day of exposure (lag day 0) for PM10, with an interquartile range increase in PM10 associated with a 3.55% (95% confidence interval: 1.69-5.45) increase in admissions for depression. For PM2.5, the risks of hospitalization peaked on lag day 0 (2.92; 1.37-4.50) and lag day 5 (3.65; 2.09-5.24). The elderly (>65) were more sensitive to PM2.5 exposure (9.23; 5.09-13.53) and PM10 exposure (6.35; 3.31-9.49) on lag day 0, and patients with cardiovascular disease were likely to be hospitalized for depression following exposure to high levels of PM10 (4.47; 2.13-6.85). CONCLUSIONS: Short-term elevations in PM may increase the risk of hospitalization for depression, particularly in the elderly and in patients with cardiovascular disease.
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