A Spatial Epidemiological Study Of Greenness And Mental Disorders

ObjectiveTo explore the spatial correlation between greenness and hospitalization rates of mental disorders（schizophrenia,bipolar disorder,depression）in each township of Pingyi;to analyze the association effect between greenness and mental disorder co-morbidity,with the aim of providing environmental health recommendations for public health policy makers and mental health practitioners.MethodsIn this study,we collected demographic data of 3409 patients hospitalized for psychiatric disorders at the psychological hospital in Pingyi from 2017 to 2020,and used satellite remote sensing to invert the measures of greenness（NDVI,SAVI,EVI,LAI）and variables of built environment(population density,spatial GDP,walking index,road density,PM_2.5);univariate spatial autocorrelation was used to analyze the spatial aggregation of greenness and mental disorders（schizophrenia,bipolar disorder,and depression）in each township in Pingyi;bivariate spatial correlation was used to analyze the correlation between greenness and hospitalization rates of major types of mental disorders（schizophrenia,bipolar disorder,and depression）in each township in Pingyi;A case-case study design was used to explore the association effect of greenness and mental disorder co-morbidity.ResultsThe results of univariate spatial autocorrelation analysis showed that there was positive spatial aggregation of greenness in 2020;the townships in the south of Pingyi（Liuyu Town,Baiyan Town,Zheng Town）showed high-high aggregation of greenness,and the townships in the north of Pingyi（Wutai Town,Zhongcun Town,Baotai Town,Berlin Town）showed low-low aggregation of greenness;other years and regions showed random distribution.The results of bivariate spatial correlation analysis showed that in 2020,Berlin township showed low-high clustering,Berlin township had low greenness and high depression hospitalization rate;Weizhuang township showed high-low clustering,Weizhuang township had high greenness and low depression hospitalization rate;greenness and depression in other townships were not spatially significant.In the case-case study,model 1（unadjusted model）found:no statistically significant difference between NDVI and psychiatric disorder co-morbidity at the Q1-Q3 group（OR（odds ratio）includes 1）,a 66%reduction in risk of psychiatric disorder co-morbidity at the Q4 group（highest）relative to the Q1 group（lowest）（OR:0.34,95%CI（confidence interval）（0.25,0.47））,and EVI,SAVI,and LAI were consistent with NDVI trends;Model 2（adjusting for sex,age,length of hospitalization,and diagnosed illness）found:no statistically significant difference between NDVI and psychiatric disorder co-morbidity in the Q1-Q3 group（OR included 1）,a 66%reduction in risk of psychiatric disorder co-morbidity in the Q4 group relative to the Q1 group（OR:0.34,95%CI（0.17,0.34））,and EVI,SAVI,and LAI were consistent with NDVI trends;Model 3（adding walking index,population density,road density,and PM2.5 to Model2）found that the difference between NDVI in the Q1-Q3 group,greenness and mental disorder co-morbidity was not statistically significant（OR contained 1）,and the risk of mental disorder co-morbidity was reduced by 34%in the Q4 group relative to the Q1group（OR:0.66,95%CI（0.30,0.66））,with trends in EVI,SAVI,and LAI consistent with NDVI.ConclusionGreenness was positively spatially aggregated in some years;areas with high greenness had low depression hospitalization rates;and greenness was protective against mental disorder co-morbidity when it was located in the highest group.