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Published on: August 11, 2015
Urban green spaces, air pollution, temperature, and severity of major depressive disorder: results from the FREEDOM
Elisa Borroni1, Vincenzo Giannico2, Paola Monti1
1EPIGET Lab, Department of Clinical Sciences and Community Health, Dipartimento di Eccellenza 2023-2027, University of Milan, Via San Barnaba 8, 20122, Milan, Italy.
Background:
Major Depressive Disorder (MDD) is a psychiatric condition whose onset and progression may be influenced by environmental factors. Our study investigates the association between greenness exposure and MDD severity, while also exploring the role of air pollutants and temperature.
Methods:
Three hundred ninety-six MDD patients were included. MDD severity was assessed using five rating scales. Two- and three-dimensional indicators of urban space were estimated around participants' houses: Normalized Difference Vegetation Index (NDVI), Green Volume, Grey Volume, and Normalized Difference Green-Grey Volume Index (NDGG). Annual exposure estimates of air pollutants and temperature were assigned to the participants' residential addresses. Multivariate linear and ordinal regression models were applied to assess associations between exposure variables and MDD severity, adjusting for and stratifying by air pollutants and temperature.
Results:
Increasing levels of all urban space indicators (within a 100 m radius), except for Grey Volume, were associated with decreased MDD severity. The strongest effect was observed for Green Volume (e.g., Hamilton Depression Rating Scale [HAMD]: β = -2.67, 95 % confidence interval [CI]: -4.57; -0.76, for an interquartile range [IQR] increase in the exposure variable). The inverse associations between greenness and MDD severity were confirmed only when values of particulate matter ≤2.5 μm (PM2.5) were below the median (e.g.
, Hamd:
β = -5.32, 95 %CI: -8.28; -2.36 for an IQR increase in Green Volume). The same occurred with temperature levels below the median.
Conclusions:
Green spaces are associated with lower severity of depression, especially when PM2.5 and temperature levels are low.
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