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| 研究生： |
葉家豪 Yeh, Chia-Hao |
|---|---|
| 論文名稱： |
以疾病負擔評估WHO情境下綠地對躁鬱症的健康效益–以臺灣為例 Using burden of disease to evaluate the health benefits of greenery on bipolar disorder under WHO recommendations - A case study of Taiwan |
| 指導教授： |
吳治達
Wu, Chih-Da |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工學院 - 測量及空間資訊學系 Department of Geomatics |
| 論文出版年： | 2022 |
| 畢業學年度： | 110 |
| 語文別： | 中文 |
| 論文頁數： | 99 |
| 中文關鍵詞： | 躁鬱症 、環境綠蔽度 、常態化差異植生指標 、DALY 、不平等 |
| 外文關鍵詞： | Bipolar Disorder, Surrounding Greenness, Normalized Difference Vegetation Index, Disability-Adjusted Life Year, Inequalities |
| 相關次數： | 點閱：145 下載：5 |
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精神疾病的重要性在全球疾病負擔(Global Burden of Disease, GBD)中被特別強調，其不僅會導致人們健康狀況長期不佳或失能失去工作，甚至需花費大量時間與金錢治療。 其中躁鬱症為近年來嚴重劇增的精神疾病之一，尤其是在高人口密度的亞太地區更為嚴重。而綠地對人體身心健康的益處也被大量證實，甚至世界衛生組織也提出都市居民居住在綠地(≧0.5公頃)直線距離300公尺內有益健康的建議，然而過去研究僅在歐洲地區進行符合此WHO建議綠地暴露情境之健康效益分析，有關亞太地區之相關研究目前仍然缺如。除此之外，由於綠地具健康效益，會潛移默化影響人們身心健康，故其分佈與健康效益在不同社會經濟地位下之表現等環境健康不平等議題也被學者們關注，但過去之研究不僅使用之社會經濟指標各異，且亦多在歐美與紐澳等地進行，且結果也各異。為回應上述問題，本論文中共包含兩部份主要研究。
研究一利用DALY(Disability-Adjusted Life Year)與收入兩種量化指標，基於現況綠地分布與WHO建議之綠地情境，分析環境綠蔽度對臺灣躁鬱症的疾病負擔效益。於研究一中首先使用資料更新率相較於綠地百分比更佳的常態化差異植生指標(Normalized Difference Vegetation Index, NDVI)做為綠地暴露指標因子，並使用廣義加乘混合模型(Generalized Additive Mixed Model, GAMM)以確認2013-2014年臺灣本島NDVI與躁鬱症發生率關係；其次由於過去文獻指出，當人口普查區做為行政區內至少有25.6%的綠地(≧0.5公頃)面積時，即已符合WHO提出的都市居民居住在綠地(≧0.5公頃)直線距離300公尺內的之綠地暴露建議，故本研究以機器學習建構綠地百分比與NDVI之轉換模型，以推估達到WHO綠地暴露建議下之NDVI值；也因此本研究設鄉鎮市區之綠地(≧0.5公頃)面積若達25.6%，即達到WHO綠地暴露建議，然而我們於GAMM中是以NDVI做為綠地暴露因子，故此階段我們以機器學習建構綠地百分比與NDVI之轉換模型，以推估若達到WHO綠地暴露建議下之NDVI值，以利後續搭配GAMM風險結果計算效益；最終使用基於DALY與收入兩種指標，結合躁鬱症發生人數以及基於GAMM風險結果計算出的人口預防分數(Prevented Fraction for the Population, PFP)，以分析計算出環境綠蔽度對躁鬱症在不同情境下之效益。研究一結果顯示，NDVI對躁鬱症的相對風險為0.14，其95%信賴區間為[0.04, 0.52]，即若NDVI上升1單位(由0到1)，躁鬱症的發生率就降低了86%，此結果於統計上具顯著性(p < 0.05)、也經過敏感性與次群組分析之檢驗。而綠地百分比與NDVI機器學習模型的Adjusted R2為0.86，此模型也透過外部與時空間等驗證體現其穩定性。最終在當前情境下，2013-2014年全臺減少0.052年/10萬人的DALY與增加212.73百萬美金的收入；在將五十個不符合WHO建議之鄉鎮提升為達標時，全臺可進一步減少0.006年/10萬人的DALY與增加23.48百萬美金的收入；最後將二者加總，在全臺鄉鎮均符合WHO建議之情境下，臺灣之綠地可減少0.057年/10萬人因躁鬱症導致的DALY、與增加236.21百萬美金的收入。
研究二則著眼於分析不同社會經濟地位(Socio-Economic Status, SES)之鄉鎮，臺灣綠地對躁鬱症疾病負擔效益之不平等現象，並比較不同社會經濟分級系統所得結果之異同。研究二之第一個步驟係發展臺灣在地化之綜合社會經濟地位(Integrated Socio-Economics Status Index)指標，本研究由人口、經濟、都市化與綠度四種不同社會經濟地位(Socio-Economics Status, SES)面相中，收集共計34個社會經濟相關變數，並將所有變數進行標準化(Standardization)後，遵循澳大利亞統計局(Australian Bureau of Statistics, ABS)所開發之方法，先進行相關矩陣(Correlation Matrix)計算，並刪除兩兩相關係數絕對值大於0.8且影響SES相同方向之變數，接續使用主成分分析(Principal Components Analysis, PCA)，於PCA中刪除第一主成分(PC1)中負荷量((loadings)絕對值小於0.3之變數，並選擇PC1搭配最終ABS所提供之公式計算出SES分數，以完成綜合社會經濟地位指標之建置；除了綜合指標外，本研究另外分別由人口、經濟、都市化與綠度不同社會經濟面相中，挑選出反老化指數(人口面相)、所得平均數(經濟面相)、都市化分級(都市化面相)與綠地(≧0.5公頃)百分比(綠度面相)等4個經標準化之變數，以做為四種單一面向社會經濟分級指標，之後依照各項指標之三分位距，將全臺鄉鎮市區分成三層級；最終將研究一計算出的當前以及WHO情境下之全臺鄉鎮市區效益，與前面五種不同的社會經濟分級系統(一種綜合指標與4種單一面向指標)對應在一起，並依照低、中、高之三層級進行綠蔽度對躁鬱症效益值之加總與平均，進而比較不同社會經濟分級系統下，臺灣綠地對躁鬱症的疾病負擔效益之差異。研究二結果顯示，在剔除高相關以及PC1中負荷量絕對值小於0.3之變數後，最終PCA建構出之綜合社會經濟地位指標係基於9個變數建置完成，並且具有70%的整體解釋變異佔比。而在比較不同社會經濟分級系統下，臺灣綠地對躁鬱症的疾病負擔效益的結果發現，在減少DALY方面，在當前情境下，綜合社會經濟地位指標之結果顯示，鄉鎮市區的整體社會經濟地位越高，其因為環境綠蔽度而降低躁鬱症發生率，進而帶來減少的DALY越低；而在經濟與都市化分級系統下也有同樣的情況發生；但在人口分級系統下則是出現不連續的情況；而綠度分級系統則是其層級越高，減少的DALY越多。但若是在WHO情境下，社會經濟地位、經濟與都市化分級系統，其中與高層級的DALY效益被提高，縮短了DALY效益的不平等。而在增加收入方面則發現，不論在何種情境下，均顯示鄉鎮市區的社會經濟地位越高，其因為環境綠蔽度而降低躁鬱症發生率，進而帶來的增加收入越高；而人口、經濟與都市化分級系統下也都有相同情況發生；而綠度分級系統則是分級越高、增加的收入越少。總結來說，發現使用綜合指標較單一指標之社會經濟分級系統更為全面，而比較各社會經濟分級系統的不同層級之結果亦顯示，綠地帶來之效益在臺灣確實有不平等之現象。
本論文研究一提供了具體的綠地對躁鬱症效益，並於研究二透過綠地不平等之視角來解讀此效益，所有結果將提供政府在未來都市規劃上針對綠地的配置有所參考。
The importance of mental illness is highlighted in the Global Burden of Disease (GBD), and bipolar disorder is one of the serious mental illnesses in recent years, especially in the Asia-Pacific region with high population density. The World Health Organization (WHO) proposes that residents living in cities should have at least 0.5 hectares of green space within a 300-meter buffer to increase residents’ accessibility to green space and obtain health benefits. However, this recommendation is only adopted by European countries. In addition, the environmental health inequality issues of the benefits from green space have also been paid attention to by scholars. The Asia-Pacific region still lacks the above related research. Therefore, two main studies are included in this paper. Study 1 used two quantitative indicators, DALY (Disability-Adjusted Life Year) and income, based on the current surrounding greenness distribution and the surrounding greenness scenario recommended by WHO, to analyze the disease burden benefit of surrounding greenness on bipolar disorder in Taiwan. Study 2 focused on the analysis of the inequalities between towns and towns with different Socio-Economic Status (SES) and Taiwan's green space for bipolar disorder disease burden benefit and compared the similarities and differences of the results obtained by different socio-economic level systems.
In Study 1, we used the Normalized Difference Vegetation Index (NDVI) as the green space exposure index factor and used Generalized Additive Mixed Model (GAMM) to confirm the relationship between NDVI and the incidence of bipolar disorder in Taiwan main island in 2013-2014. Secondly, based on past research, we assumed that if the area of green space (≧ 0.5 hectares) in townships reached 25.6%, it would meet the WHO's green space exposure recommendation. However, we used NDVI as the green space exposure factor in GAMM. Therefore, we used machine learning to construct a conversion model between green space percentage and NDVI to estimate the NDVI value under the WHO's green space exposure recommendation. Finally, based on the two indicators of DALY and income, combined with the number of people with bipolar disorder and the Prevented Fraction for the Population (PFP) calculated based on the GAMM risk results, to analyze and calculate the benefit of surrounding greenness on bipolar disorder in different scenarios.
In Study 2, we develop a comprehensive SES index of Taiwanese localization and four different single socio-economics level system. The 34 socio-economic-related variables were collected from four different SES aspects of population, economy, urbanization and greenness, and all variables were standardized. On comprehensive SES Index we followed the method developed by the Australian Bureau of Statistics (ABS), removed the variables with higher correlation coefficient and used Principal Components Analysis (PCA). After removing the variables with absolute value of loadings in PC1 was less than 0.3, we could calculate the SES score to complete the comprehensive SES indicator. In addition, we selected other four standardized variables that were the anti-aging index (population aspect), the average income tax (economic aspect), the urbanization classification (urbanization aspect) and the percentage of green space (≧0.5 hectares) (greenness aspect) to be four single socio-economic level indicators. For all five socio-economics level systems, we divided them into three levels according to the tertile. Finally, the benefits of townships in current and WHO scenario in Study 1 were correspond to the five different socioeconomic level systems. Moreover, according to the three levels of low, medium and high, the average of the benefits of surrounding greenness on bipolar disorder was carried out, and then the differences in the disease burden benefit of Taiwan surrounding greenness on bipolar disorder under different socioeconomic classification systems were compared.
The results of study 1 showed that the relative risk of NDVI for bipolar disorder was 0.14, and its 95% confidence interval was [0.04, 0.52], that is, if the NDVI increased by 1 unit (from 0 to 1), the incidence of bipolar disorder was reduced by 86% , this result was statistically significant (p < 0.05) and was also tested by sensitivity and subgroup analysis. The adjusted R2 of the green space percentage and the NDVI machine-learning model is 0.86. This model also reflects its stability through external and time-space verification. In the end, under the current scenario, Taiwan decreased 0.052 (years/100K people) DALY, and increased 212.73 (million USD) in 2013 to 2014. When 50 townships that did not meet the WHO recommendations were upgraded to meet the standards, the whole Taiwan could decreased 0.006 (years/100K people) DALY, and increased 23.48 (million USD) in 2013 to 2014. Finally adding up the two, if all towns in Taiwan meet the WHO recommendations, the total green space in Taiwan can decreased 0.057(years/100K people) DALY, and increased 236.21 (million USD) in 2013 to 2014.
The results of the study 2 showed that after excluding the variables with high correlation and the absolute value of the loading in PC1 less than 0.3, the final comprehensive SES index constructed by PCA was completed based on 9 variables, and had 70% of the overall proportion of variance explained (PVE). When comparing different socioeconomic level systems, the results of the disease burden benefit of Taiwan’s green space on bipolar disorder found that the socio-economic level system using a composite index is more comprehensive than the single socio-economic level system, and the results of comparing the different levels of each socio-economic level system also show that the benefits brought by green space are indeed unequal in Taiwan.
The study1 of this paper provides the specific benefits of green space for bipolar disorder. The study 2 interprets this benefit from the perspective of green space inequality. All the results will provide the government with a reference for the allocation of green space in future urban planning.
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