心理疾病為導致失能的主要原因之一，其中思覺失調症和躁鬱症因其致病原因還未完全釐清和難以醫治復原，因此佔醫療負擔之首。近年來已有許多研究揭示了綠地環境對心理健康廣泛的益處，然而，過去研究大多以植生指數、綠地覆蓋面積，作為整體綠地的暴露指標，以探討整體綠化程度和心理健康的關聯，但實際上仍有許多與綠地有關的因子可能與心理健康有關，例如：綠地的大小、分布特徵、形狀、近鄰性等 (可用「綠地結構指標」來定量)，然而這方面的研究有限，且還未有研究探討綠地結構指標與心理疾病發生率的關聯性，因此，釐清綠地結構與思覺失調症和躁鬱症的關聯性孰為重要，進一步地，從經濟效益評估的角度，僅了解關聯性 (風險值)，仍不足以提供充分的訊息給予政府單位來做為擬定政策的參考依據，須將風險值予以可量化（貨幣化），才能進一步與成本進行比較分析。因此，本研究的核心目的為了解綠地結構與心理健康之關聯，並透過兩個工作目標來回應核心目的，依序為估算綠地結構指標與思覺失調症和躁鬱症的關聯性，以及進一步估算暴露不同結構指標的綠地後，可能節省的兩個心理疾病之終身醫療費用。
首先，本研究使用能夠代表台灣人就醫情況的健保資料庫200萬隨機抽樣歸人檔來挑選研究對象。並透過國際疾病分類標準碼（The International Statistical Classification of Diseases and Related Health Problems, ICD）來篩選思覺失調症 (ICD-9: 295；ICD-10：F20 ) 和躁鬱症病人 (ICD-9: 296.0、296.4至296.8；ICD-10：F30 和F31)。於研究期間 (2005年至2016年)，共有1,099位新發生思覺失調症和20,548位躁鬱症病人。接著，本研究透過地理資訊系統（ArcGIS）和量化地景景觀的軟體 (FRAGSRATS 4)　將第二次國土利用調查資料中的森林綠地，量化成綠地結構指標，和整理NASA MODIS衛星影像資料，獲得常態化差異植生指標(Normalized difference vegetation index, NDVI) 和增揚植被指數 (Enhanced vegetation index, EVI)。除此之外，本研究也收集與兩個疾病有關的因子來做校正，包含：空氣污染物－二氧化氮(Nitrogen dioxide, NO2)、二氧化硫 (Sulfur dioxide, SO2)、臭氧 (Ozone, O3) 和懸浮微粒其直徑小於或等於2.5微米 (µm) (particulate matter 2.5 micrometers or less in aerodynamic diameter, PM2.5)、溫度、降雨、社會經濟狀態和代謝症候群。當資料整理齊全後，首先透過Spearman’s相關係數來挑選變項，初步避免風險因子之間的共線性過高。接著，透過有考量空間自相關的Cox比例風險模型 (Frailty model) 來進行存活分析，估計綠地結構指標與兩個心理疾病發生率之間的相關性 (風險值)。進一步地透過iSQoL2來估算躁鬱症的終身醫療費用。最後，將估算出來的終身醫療花費與算好的風險值，一起透過可歸因風險的公式來估算暴露不同結構指標的綠地後，於現在情境和未來情境下，可能節省的兩個心理疾病之終身醫療費用。
研究結果發現暴露的綠地面積和邊緣愈大、形狀愈複雜、綠地彼此間的距離愈近，似乎可能降低罹患思覺失調症的風險；暴露的綠地面積和邊緣愈大、形狀愈單純、綠地彼此間的距離愈近，似乎可能降低罹患躁鬱症的風險。緊接著，本研究從iSQoL2軟體估算出的結果中，觀察到整體新發生躁鬱症的病人、疾病嚴重程度輕微的病人、嚴重的病人、女性病人、男性病人、30歲之前被診斷的病人、30歲後被診斷的病人，其平均的預期壽命損失依序為10.54、9.57、15.59、9.95、10.89、16.95、8.36年。接著，研究結果顯示平均每一個躁鬱症病人、輕微狀態、嚴重狀態的病人、女性、男性、30歲之前、30歲之後被診斷的病人，其終身醫療花費依序為49,370、47,613、72,872、51,125、49,263、41,409和49,997元美金，結果均顯示罹患躁鬱症後，會減少壽命且需要不小的醫療支出。
此外，研究透過可歸因風險的假設與公式，可估算暴露不同結構指標的綠地後，可能節省的兩個心理疾病之終身醫療費用。從思覺失調症相關的結果中，觀察到依照整體綠地指標 (包含NDVI和EVI)、面積指標、形狀指標、近鄰性指標的順序，研究期間 (現在情境)，全台平均每10萬人從綠地可能節省健保醫療支出約0.29、0.52、0.18、0.14和0.10 (百萬美金)。接著當綠地轉變為理想的型態時 (未來情境)，從綠地可能節省健保醫療支出約為0.28、0.33、0.24、0.36和0.08 (百萬美金)。緊接著，從躁鬱症相關的結果中，觀察到研究期間 (現在情境)，從綠地可能節省健保醫療支出約為7.16、8.53、0.98、3.27和3.98 (百萬美金)。接著在理想的型態時 (未來情境)，額外從綠地可能節省健保醫療支出約為7.62、8.02、2.97、1.49和5.27(百萬美金)，表示暴露不同形狀的綠地，可能可以減少兩個心理疾病的醫療負擔。
本研究是第一個較完整探討綠地結構與心理疾病之關聯性，以及估算綠地暴露對兩個心理疾病之可能健康經濟效益的研究。本研究的發現，可以提供政府在規劃綠地時，依照特定指標和其對應風險最小的閾值範圍來設計綠地。並進一步地將現在和未來兩種情境下，綠地暴露所可能帶來的健康效益用健保花費作貨幣化數值，期望能在改善全國心理健康的公共衛生政策上，作為參考依據，尤其是都市地區的林地規劃。最後，對於學術界來說，本研究在綠地結構與心理健康的研究上，扮演一個先驅的角色，因此研究中所使用的研究設計和統計方法，可以提供給未來相關研究做為參考。同時也盼望未來能有更多研究，驗証此綠地、及其結構對預防精神病之假說。

Mental disorders (mental illnesses) are one of the leading causes of disability worldwide. Of all mental disorders, schizophrenia and bipolar disorder are among the most burdensome diseases due to their unclear pathophysiology and irreversibility. A growing body of research has indicated that overall greenness exposure can positively offer potential psychological benefits over these mental disorders. In order to comprehensively explore the relationship between overall green space and mental health, most of the these studies utilized vegetation index and green space coverage as indices of overall green space exposure. Nevertheless, there are still many other crucial green space indices such as the size, distribution characteristics, shape, and proximity of green space are not studied yet, especially their potential associations with mental disorders. Therefore, evaluation of the relationship between these green space structures and schizophrenia and bipolar disorder could be beneficial for our society and offers a potential therapeutic approach for mental illness treatments. To effectively translate the outcome of these association studies (risk value) into a reference basis of the government for health policy and management, these values of risks and associations must be quantified (monetized) for cost-benefit analysis (especially to estimate the saving lifetime healthcare costs under the exposure effects of these green space structures). Thus, the core purpose of this research is to understand the relationship between green space structure and mental health. Furthermore, two objectives are set, which are to statistically estimate the relationship between green space structure indices and schizophrenia and bipolar disorder, and to further estimate the saving lifetime healthcare costs of two mental disorders attributed to green space exposure.
In the present study, we used the 2 million random sampled sub-data from the health insurance database representing the overall Taiwanese population. To screen patients with schizophrenia and bipolar disorder, the International Statistical Classification of Diseases and Related Health Problems (ICD) was utilized [ICD-9: 295; ICD-10: F20 (schizophrenia) and ICD-9: 296.0, 296.4 to 296.8; ICD-10: F30 and F31 (bipolar disorder)]. During the study period (2005 to 2016), a total of 1,099 newly developed schizophrenia and 20,548 bipolar disorder patients were positively diagnosed. Subsequently, we utilized the forest green space extracted from the Taiwan’s second land use survey data, and further quantified them into green space structural indices through the geographic information system (ArcGIS) and FRAGSRATS 4. In the case of greenness, NASA MODIS satellite image data was used to obtain normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). For association study, risk factors related to these two disorders such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (ozone, O3) and suspended particles whose aerodynamic diameter is less than or equal to 2.5 micrometers (µm) (PM2.5), temperature, rainfall, socioeconomic status, and metabolic syndrome were included for adjustment. Moreover, Spearman's correlation coefficient was used to select variables and to avoid excessive collinearity among these risk factors. Then, the relationship between the indices of green space structure and the incidence of these two mental disorders was estimated using the frailty model. iSQoL2 software was specifically utilized to estimate the lifetime healthcare costs of bipolar disorder. Together, the estimated lifetime healthcare costs and the calculated risk value were used to estimate the potential saving lifetime healthcare costs for these two mental disorders attributed to green spaces exposure (in both current and future scenario).
The results of the study revealed that higher exposure to green space structures with larger area and edges, more complex shape, and closer distance between each green space, might reduce the risk of schizophrenia; meanwhile, higher exposure to green space structure with larger area and edges, simpler shape and green spaces, closer distance between each green space, potentially lower the risk of bipolar disorder. Overall, this implies a positive relationship between green space structures and reducing risk of mental disorders, indicating a potential therapeutic and preventive treatment from green space structures on mental health.
Through the iSQoL2 software, the average expected years of life loss of overall patients with new bipolar disorder, patients with mild disease severity, severe patients, female patients, male patients, patients diagnosed before and after the age of 30 are 10.54, 9.57, 15.59, 9.95, 10.89, 16.95, 8.36 years, respectively, meanwhile, the lifetime healthcare costs of patients with bipolar disorder are USD$ 49,370, 47,613, 72,872, 51,125, 49,263, 41,409 and 49,997, respectively. This highlights that suffering from bipolar disorder reduces the life expectancy and requires considerable medical expenses, suggesting the need for the effective treatments or preventive measures. Given that green space structures have been revealed to potentially possess the preventive treatment on mental health, here, we thoroughly estimated the potential saving lifetime healthcare costs of these two mental disorders (schizophrenia and bipolar disorder) attributed to green space exposure. In the case of patients with schizophrenia in the current scenario, in the order of indices of greenness (including NDVI and EVI), area index, shape index, and proximity index, it was observed that the average potential saving lifetime healthcare costs per 100,000 people in Taiwan (in millions USD$) were 0.29, 0.52, 0.18, 0.14 and 0.10, respectively. Moreover, when the green space was changed to an ideal form (future scenario), the additionally average potential saving lifetime healthcare costs (in millions USD$) were revealed as 0.28, 0.33, 0.24, 0.36 and 0.08, respectively. Similarly, in the case of bipolar disorder in current scenario, the average potential saving lifetime healthcare costs (in millions USD$) were 7.16, 8.53, 0.98, 3.27 and 3.98. Meanwhile, in the ideal form (future scenario), the additionally average potential saving lifetime healthcare costs (in millions USD$) were revealed as 7.62, 8.02, 2.97, 1.49, and 5.27, respectively. Overall, the study indicates that exposure to green space structures may reduce the medical burden of these two mental disorders.
This is the first study to comprehensively explore the relationship between green space structures and mental disorders, and revealed the potential health economic benefits of its exposure on these two mental disorders. The findings provide government officials regarding the design green spaces in accordance with specific indices and their corresponding minimum risk thresholds. Also, the information regarding the monetary health care costs (in both present and future scenario) attributed to green space exposure provided in this work could be a reference basis for the formulation of public health policies, especially regarding to urban forest management plan with respect to improving mental health throughout the country. For academia and literature, the present work is a pioneering study, whose research design and statistical approaches can be used as a reference for future research directions in this field. Simultaneously, more studies will be needed in the future to corroborate the hypothesis of this green space and its structure for the prevention of mental disorder.

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