PDF(Pubmed)
Abstract：
BACKGROUND: The literature has discussed the relationship between environmental factors and depressive disorders; however, the results are inconsistent in different studies and regions, as are the interaction effects between environmental factors. We hypothesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.
OBJECTIVE: To investigate the effects of meteorological factors and air pollution on depressive disorders, including their lagged effects and interactions.
METHODS: The samples were obtained from a class 3 hospital in Harbin, China. Daily hospital admission data for depressive disorders from January 1, 2015 to December 31, 2022 were obtained. Meteorological and air pollution data were also collected during the same period. Generalized additive models with quasi-Poisson regression were used for time-series modeling to measure the non-linear and delayed effects of environmental factors. We further incorporated each pair of environmental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.
RESULTS: Data for 2922 d were included in the study, with no missing values. The total number of depressive admissions was 83905. Medium to high correlations existed between environmental factors. Air temperature (AT) and wind speed (WS) significantly affected the number of admissions for depression. An extremely low temperature (-29.0 ℃) at lag 0 caused a 53% [relative risk (RR)= 1.53, 95% confidence interval (CI): 1.23-1.89] increase in daily hospital admissions relative to the median temperature. Extremely low WSs (0.4 m/s) at lag 7 increased the number of admissions by 58% (RR = 1.58, 95%CI: 1.07-2.31). In contrast, atmospheric pressure and relative humidity had smaller effects. Among the six air pollutants considered in the time-series model, nitrogen dioxide (NO2) was the only pollutant that showed significant effects over non-cumulative, cumulative, immediate, and lagged conditions. The cumulative effect of NO2 at lag 7 was 0.47% (RR = 1.0047, 95%CI: 1.0024-1.0071). Interaction effects were found between AT and the five air pollutants, atmospheric temperature and the four air pollutants, WS and sulfur dioxide.
CONCLUSIONS: Meteorological factors and the air pollutant NO2 affect daily hospital admissions for depressive disorders, and interactions exist between meteorological factors and ambient air pollution.
OBJECTIVE: To investigate the effects of meteorological factors and air pollution on depressive disorders, including their lagged effects and interactions.
METHODS: The samples were obtained from a class 3 hospital in Harbin, China. Daily hospital admission data for depressive disorders from January 1, 2015 to December 31, 2022 were obtained. Meteorological and air pollution data were also collected during the same period. Generalized additive models with quasi-Poisson regression were used for time-series modeling to measure the non-linear and delayed effects of environmental factors. We further incorporated each pair of environmental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.
RESULTS: Data for 2922 d were included in the study, with no missing values. The total number of depressive admissions was 83905. Medium to high correlations existed between environmental factors. Air temperature (AT) and wind speed (WS) significantly affected the number of admissions for depression. An extremely low temperature (-29.0 ℃) at lag 0 caused a 53% [relative risk (RR)= 1.53, 95% confidence interval (CI): 1.23-1.89] increase in daily hospital admissions relative to the median temperature. Extremely low WSs (0.4 m/s) at lag 7 increased the number of admissions by 58% (RR = 1.58, 95%CI: 1.07-2.31). In contrast, atmospheric pressure and relative humidity had smaller effects. Among the six air pollutants considered in the time-series model, nitrogen dioxide (NO2) was the only pollutant that showed significant effects over non-cumulative, cumulative, immediate, and lagged conditions. The cumulative effect of NO2 at lag 7 was 0.47% (RR = 1.0047, 95%CI: 1.0024-1.0071). Interaction effects were found between AT and the five air pollutants, atmospheric temperature and the four air pollutants, WS and sulfur dioxide.
CONCLUSIONS: Meteorological factors and the air pollutant NO2 affect daily hospital admissions for depressive disorders, and interactions exist between meteorological factors and ambient air pollution.
摘要：
背景：文献讨论了环境因素与抑郁症之间的关系；然而，不同研究和地区的结果不一致，环境因素之间的相互作用效应也是如此。我们假设气象因素和环境空气污染单独影响并相互作用以影响抑郁症的发病率。
目的：研究气象因素和空气污染对抑郁症的影响，包括它们的滞后效应和相互作用。
方法：样本来自哈尔滨某三级医院，中国。获得2015年1月1日至2022年12月31日的抑郁症患者每日入院数据。同期还收集了气象和空气污染数据。具有拟Poisson回归的广义累加模型用于时间序列建模，以测量环境因素的非线性和延迟效应。我们进一步将每对环境因素纳入双变量响应面模型，以检查对抑郁症住院的相互作用影响。
结果：2922天的数据包括在研究中，没有缺失的值。抑郁症患者的总人数为83905。环境因素之间存在中等到高度的相关性。气温（AT）和风速（WS）显着影响抑郁症的入院人数。滞后0时极低的温度（-29.0℃）导致每日住院率相对于中位温度增加53％[相对风险（RR）=1.53，95％置信区间（CI）：1.23-1.89]。滞后7时极低的WS（0.4m/s）使入院人数增加了58％（RR=1.58，95CI：1.07-2.31）。相比之下,大气压力和相对湿度的影响较小。在时间序列模型中考虑的六种空气污染物中，二氧化氮（NO2）是唯一显示出非累积效应的污染物，累积,立即,和落后的条件。NO2在滞后7时的累积效应为0.47%(RR=1.0047,95CI:1.0024-1.0071)。在AT和五种空气污染物之间发现了相互作用效应，大气温度和四种空气污染物，WS和二氧化硫。
结论：气象因素和空气污染物NO2会影响抑郁症患者的每日住院人数，气象因素与环境空气污染之间存在相互作用。
目的：研究气象因素和空气污染对抑郁症的影响，包括它们的滞后效应和相互作用。
方法：样本来自哈尔滨某三级医院，中国。获得2015年1月1日至2022年12月31日的抑郁症患者每日入院数据。同期还收集了气象和空气污染数据。具有拟Poisson回归的广义累加模型用于时间序列建模，以测量环境因素的非线性和延迟效应。我们进一步将每对环境因素纳入双变量响应面模型，以检查对抑郁症住院的相互作用影响。
结果：2922天的数据包括在研究中，没有缺失的值。抑郁症患者的总人数为83905。环境因素之间存在中等到高度的相关性。气温（AT）和风速（WS）显着影响抑郁症的入院人数。滞后0时极低的温度（-29.0℃）导致每日住院率相对于中位温度增加53％[相对风险（RR）=1.53，95％置信区间（CI）：1.23-1.89]。滞后7时极低的WS（0.4m/s）使入院人数增加了58％（RR=1.58，95CI：1.07-2.31）。相比之下,大气压力和相对湿度的影响较小。在时间序列模型中考虑的六种空气污染物中，二氧化氮（NO2）是唯一显示出非累积效应的污染物，累积,立即,和落后的条件。NO2在滞后7时的累积效应为0.47%(RR=1.0047,95CI:1.0024-1.0071)。在AT和五种空气污染物之间发现了相互作用效应，大气温度和四种空气污染物，WS和二氧化硫。
结论：气象因素和空气污染物NO2会影响抑郁症患者的每日住院人数，气象因素与环境空气污染之间存在相互作用。
