Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models

Sustainability

Jing Xie¹  TingOn Chan²  Ying Huang²  Yeran Sun²  Xuan Sun³ 

[1] Faculty of Architecture, The University of Hong Kong, Hong Kong, China;School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;Zhou Enlai School of Government, Nankai University, Tianjin 300350, China;

关键词: air pollution;    suicide rate;    spatial regression models;    matrix exponential spatial specification models;    eigenvector spatial filtering model;   

DOI  :  10.3390/su12187444

来源: DOAJ

【 摘 要 】

Air pollution can have adverse impacts on both the physical health and mental health of people. Increasing air pollution levels are likely to increase suicide rates, although the causal mechanisms underlying the relationship between pollution exposure and suicidal behaviour are not well understood. In this study, we aimed to further examine the spatial association of air pollution and suicidal behaviour. Specifically, we investigated whether or how PM_2.5 levels are spatially associated with the adult suicide rates at the district level across London. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of PM_2.5 levels and suicide. The empirical results show that PM_2.5 levels are spatially associated with the suicide rates across London. The two models show that PM_2.5 levels have a positive association with adult suicide rates over space. An area with a high percentage of White people or a low median household income is likely to suffer from a high suicide rate.

【 授权许可】

Unknown