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研究生:王君婷
研究生(外文):WANG, JUN-TING
論文名稱:探討空氣污染物與代謝症候群之間的相關性:以社區族群為基礎的橫斷性研究
論文名稱(外文):The Effects of Ambient Air Pollution and Metabolic Syndrome: Community-based Cross-sectional Study
指導教授:李佩珍李佩珍引用關係
指導教授(外文):LEE, PEI-CHEN
口試委員:王淑麗邱尚志
口試委員(外文):WANG, SHU-LICHIOU, SHANG-JYH
口試日期:2020-07-24
學位類別:碩士
校院名稱:國立臺北護理健康大學
系所名稱:健康事業管理研究所
學門:商業及管理學門
學類:醫管學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:117
中文關鍵詞:空氣污染物代謝症候群社區族群橫斷性研究
外文關鍵詞:Air PollutionMetabolic SyndromeCommunity-basedCross-sectional Study
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  • 被引用被引用:1
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背景:根據我國衛生福利部2019年統計數據顯示,代謝症候群所衍生之糖尿病、高血壓與心血管等慢性疾病,長年位居台灣十大死因之中。回顧過去文獻代謝症候群的危險因子除了年齡、性別、吸菸行為、飲酒習慣、職業或職場暴露、家族病史等因素之外,亦可歸因於環境暴露,然而綜觀國外研究發現多數研究僅針對特定空氣污染物進行探討,而國內則尚未有關於空氣污染物與代謝症候群之相關性研究發表。

目的:
本研究旨在探討臺灣彰化縣地區居民長期暴露於空氣污染物與代謝症候群盛行率之相關性。

方法:透過以社區族群為基礎的橫斷性研究設計,探討臺灣彰化縣地區居民長期暴露於空氣污染物與代謝症候群盛行率之相關性。資料來源主要為2002-2005年彰化縣環境與健康維護計畫,最終納入1,050人,並與余化龍等學者推估1998年至計畫收案日之空氣污染物,包含:氮氧化物(Nitrogen oxides,NOx)、二氧化硫(Sulphur dioxide,SO2)、一氧化碳(Carbon monoxide,CO)、臭氧(Ozone,O3)、懸浮微粒(Particulate Matter ≤ 10μg/m3,PM10)以及細懸浮微粒(Particulate Matter ≤ 2.5μg/m3,PM2.5)之平均暴露濃度資料結合,再依線性迴歸與邏輯斯迴歸計算迴歸係數(Beta,β)、勝算比估計值(Odds ratio,OR)以及95%信賴區間(Confidence interval,CI),分析空氣污染物與代謝症候群及其核心診斷之間的相關性。

結果:在調整年齡、性別、吸菸行為、飲酒習慣、家族病史、重金屬暴露、工廠密度以及其他空氣污染物後,空氣污染物SO2每增加一個四分位差(interquartile ranges,IQR)之平均濃度,罹患三酸甘油脂異常的風險(odds ratio,OR)增加2.62倍(95% confidence interval (CI)=1.15-6.01),O3卻有相反的結果(OR=0.48,95% CI=0.25-0.94);SO2每增加一個四分位差之平均濃度,罹患高密度脂蛋白異常的風險增加3.62倍(95% CI=1.31-10.03),PM10卻相反(OR=0.57,95% CI=0.36-0.89);PM10每增加一個四分位差之平均濃度,罹患空腹血糖異常的風險增加1.92倍(95% CI=1.29-2.84),SO2卻相反(OR=0.45,95% CI=0.21-0.95),進一步分層分析後發現低密度工廠地區居民較易受到空氣污染物暴露高低而影響,在調整干擾因子後SO2每增加一個四分位差之平均濃度,會增加罹患三酸甘油脂異常的風險3.85倍(95% CI=1.10-13.42),O3則為0.48倍(95% CI=0.25-0.94);而PM10每增加一個四分位差之平均濃度,雖會降低罹患高密度脂蛋白異常的風險(OR=0.22,95% CI=0.09-0.55),但會增加罹患空腹血糖異常的風險(OR=2.71,95% CI=1.23-5.94)。

結論:本研究結果發現,空氣污染物暴露濃度的高低與彰化縣地區居民代謝症候群之盛行並無顯著相關,但與代謝症候群核心診斷之盛行具有相關性,不同的空氣污染物對於代謝相關症狀的影響不同,研究結果可提供環保相關單位參考,以加強針對空氣汙染物的來源與排放進行限制,並針對敏感或高危險族群擬定健康促進相關政策與措施。
Background: According to statistics released by the Ministry of Health and Welfare in 2019, chronic diseases such as diabetes, hypertension, and cardiovascular diseases derived from metabolic syndrome have long been among the top ten causes of death in Taiwan. Reviewing the past literature, we discover that the risk factors of metabolic syndrome is also attributed to environmental exposure in addition to age, gender, smoking behavior, drinking habits, occupational and environmental exposure, and family medical history. However, according to foreign studies, it is found that most studies only focus on specific air pollutants; however, domestic studies on the correlation between air pollutants and metabolic syndrome have not been published.

Objectives: Our study is to examine the relationship between long-term exposure to air pollution and prevalence of metabolic syndrome among residents in Changhua, located in the middle of Taiwan.

Methods: We conducted a community-based cross-sectional study to investigate associations between long-term exposure to air pollution and prevalence of metabolic syndrome in Changhua, Taiwan. According to data extracted from the The Study of Environmental Pollution and Health Effects in Changhua Residents 2002-2005, in which there are a total of 1,050 subjects who met the criteria for this study, concentrations of air pollution (NOx, SO2, CO, O3, PM10 and PM2.5) were modeled based on air quality. Mean concentration of exposure was estimated based on the participant's residential address from 1998 or 2005 of insure to index date. We used linear regression and logistic regression models to analyze the effects of air pollution on prevalence of metabolic syndrome.

Results: After adjustment of age, gender, occupation, smoking, alcohol drinking, family history for hypertension or diabetes, urinary arsenic and low or high factory density area, the odds ratio (OR) of triglyceride dyslipidemia prevalence increased by 162% (95% CI=1.15-6.01) per interquartile increase in average SO2 exposure, but O3 has the opposite result (OR=0.45, 95% CI=0.21-0.95); high‐density lipoprotein dyslipidemia prevalence increased by 262% (95% CI=1.31-10.03) per interquartile increase in average SO2 exposure, but PM10 has the opposite result (OR=0.57, 95% CI=0.36-0.89); impaired fasting glucose prevalence increased by 192% (95% CI=1.29-2.84) per interquartile increase in average PM10 exposure, but SO2 has the opposite result (OR=0.45, 95% CI=0.21-0.95). The stratification analysis showed that residents in low-density factory areas are more susceptible to exposure to air pollutants. After adjustment of individual covariates, the odds ratio (OR) of triglyceride dyslipidemia prevalence increased by 285% (95% CI=1.10-13.42) per interquartile increase in average SO2 exposure, but O3 has the opposite result (OR=0.48, 95% CI=0.25-0.94); interquartile increase in average PM10 exposure will mitigate the risk of high‐density lipoprotein dyslipidemia (OR=0.22, 95% CI=0.09-0.55), but will increase impaired fasting glucose (OR=2.71, 95% CI=1.23-5.94).

Conclusions: The study results showed that the long-term exposure to air pollution is not significantly related to metabolic syndrome prevalence of residents in Changhua County, but is significantly related to the prevalence of the metabolic risk factors. Different air pollutants have different effects on metabolic factor. These results can serve as references for the environmental protection administration to monitor and control emissions of air pollution and to formulate health promotion policies and measures for people who have sensitive or high-risk of metabolic syndrome.
致謝 I
摘要 II
Abstract V
表目錄 VIII
圖目錄 IX
第一章 緒論 1
第一節 研究背景與動機 1
第二節 本研究目的與假設 3
第三節 本研究之重要性 5
第二章 文獻探討 6
第一節 代謝症候群流行病學及其危險因子 6
第二節 空氣污染物的特性與污染源 22
第三節 空氣污染物與代謝症候群之相關 26
第四節 文獻總結 30
第三章 研究方法 33
第一節 研究架構與設計 33
第二節 研究對象 35
第三節 資料來源與操作型定義 39
第四節 統計分析 45
第四章 研究結果 49
第一節 研究對象人口學特徵分佈 49
第二節 空氣污染物之分佈情形與相關性 53
第三節 長期空氣污染暴露與代謝症候群核心診斷生理數值之相關性 55
第四節 長期空氣污染暴露與代謝症候群及其核心診斷盛行率之相關性 59
第五章 討論與建議 73
第一節 研究結果總結與討論 73
第二節 本研究之優點、限制以及對未來研究建議 77
第三節 本研究結果實務之應用 79
參考文獻 83
(一)中文部分 83
(二)英文部分 84
附錄 94

表一 代謝症候群常見定義 32
表二 彰化縣環境與健康維護計畫檢查項目 40
Table 3. Demographic characteristics of study sample (N=1,050) 65
Table 4. Descriptive statistics of average ambient air pollution exposures 66
Table 5. Associations between air pollution (per IQR) and metabolic syndrome components. 67
Table 6. Associations between air pollution (per IQR) and metabolic syndrome components by study sites. 68
Table 7. Associations between air pollution (per IQR) and metabolic syndrome and its components. 70
Table 8. Associations between air pollution (per IQR) and metabolic syndrome and its components by study sites. 71
表九 臺灣彰化縣地區居民與全國代謝症候群及其核心診斷盛行率之比較 81
表十 行政院環境保護署空氣品質指標值(AQI)標準 82

圖一:探討長期暴露於空氣污染物與臺灣彰化地區代謝症候群盛行率之架構圖。 34
圖二:研究區域地理位置。 37
圖三:研究樣本流程圖。 38
圖四:空氣污染物暴露觀察時間圖。 45
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