Date of Award

1-1-2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Environmental Health Sciences

Content Description

1 online resource (xii, 122 pages) : illustrations (some color)

Dissertation/Thesis Chair

Shao Lin

Committee Members

Jerald Brotzge, Melissa Tracy, Xiaobo Romeiko, Howard Chang

Keywords

Climate change, Exposure assessment, Green space, High-resolution, Mental health, Temperature, Climatic changes, Human beings, Meteorology, Mental illness, Urban ecology (Biology), Urban ecology (Sociology)

Subject Categories

Environmental Health

Abstract

Global climate change is linked to the frequency and intensity of local climate extremes such as heatwaves, cold spells, and flooding. Scientific evidence indicates that heatwaves and higher ambient temperature could increase the risk of hospital admission and emergency department (ED) visits for mental disorders (MDs). On the other hand, urban green space is considered a way to reduce heat island effects and provide comfort for area residents. However, there still exist huge research gaps. Except for temperature, few studies have examined how other meteorological factors (relative humidity (RH), solar radiation (SR), heat index (HI), and rainfall) are associated with ED utilization (ED visits) for various subtypes of MDs. The joint effects or health thresholds of meteorological factors remain unknown. Exposure misclassification may impact previous results, even conclusions. Few studies assess how green space modifies the effects of extreme heat or multiple meteorological factors on MDs.To address the research gaps above, I proposed three Specific Aims. In Aim 1, I built a two-stage downscaling model with machine learning models and Bayesian spatial temporal models to generate high-resolution gridded meteorological datasets. In Aim 2, I assessed the association between different meteorological factors and MDs. In Aim 3, I assessed the effect modification of green space on the association between meteorological factors and MDs. To achieve Aim 1, two-stage downscaling models for temperature, SR, RH, and rainfall were built. The model performance on the testing set had R2 = 0.992, root-mean-square error (RMSE) = 0.996 °C, and mean absolute error (MAE) = 0.703 °C for temperature; R2 = 0.876, RMSE = 4.698 %, and MAE = 3.580 % for RH; R2 = 0.952, RMSE = 1.743 MJ/m2, and MAE = 1.261 MJ/m2 for SR; and R2 = 0.774, RMSE = 3.895 mm, and MAE = 1.603 mm for rainfall. The overall performance of the optimal two-stage downscaling model across training set and testing set had R2 = 0.999 for temperature, R2 = 0.978 for RH, R2 = 0.996 for SR, and R2 = 0.980 for rainfall. To achieve Aim 2, I assessed the association between meteorological factors and MDs. Both SR and RH showed the largest risk for MD-related ED visits at lag 0-9 days. While temperature presented a short-term risk, HI increased risk over 10 days, and rainfall showed a non-statistically significant association with MD-related ED utilization. Additionally, I observed a stronger association of SR, temperature, and HI in September and October. The combination of high SR, RH, temperature, and rainfall was associated with the largest increase in ED utilization for MDs. To achieve Aim 3, I assessed the effect modification of green space on the association between meteorological factors and ED visits for MDs. Generally, temperature, SR, and HI were inversely associated with green space (both tree canopy coverage (percentage of tree canopy) and normalized difference vegetation index (NDVI)), while RH was positively associated with green space. Shorter risk periods of high temperature and HI on MD-related ED visits were observed in areas with more green space and strong protective effects were observed in later lag days, especially for tree canopy coverage. The protective effects of tree canopy were slightly stronger than those of NDVI. The risks of ED utilization for MDs associated with SR in areas with less green space were generally higher than in areas with more green space. In conclusion, by building a two-stage downscaling model, this study found that temperature, SR, RH, and HI were associated with increased MD-related ED utilization. Hot and humid weather, especially the joint effect of high SR, temperature, RH, and rainfall showed the highest risk of MD-related ED visits. Green space could mitigate the risk of meteorological factors on ED utilization for MDs. Compared to NDVI, tree canopy coverage was more important to offset the risk of meteorological factors on ED utilization for MDs.

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