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研究生:黃千芳
研究生(外文):Huang,Chien-Fang
論文名稱:重新檢視痛風患者共病症之長期轉移結構發展:以台灣十年健保資料庫分析
論文名稱(外文):Revising comorbidities of gout: in a long-term transition 10-yr Taiwan National Health Insurance data analysis.
指導教授:林寬佳林寬佳引用關係
指導教授(外文):Lin, Kuan-Chia
口試委員:林寬佳陳正怡劉如濟莊紹源
口試日期:2014-06-27
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:2014
畢業學年度:102
語文別:中文
論文頁數:90
中文關鍵詞:痛風共病症潛在轉移模式長期轉移變化
外文關鍵詞:goutcomorbiditiesLatent Transition Analysislongitudinal transition
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背景與目的:痛風是國人常見的慢性疾病,若從流行病學年齡、年代與世代之長期趨勢來看,痛風疾病本身仍主要圍繞在中老年世代與男性族群。近年來,除了痛風疾病的本質性研究外,其所伴隨的共病症影響性也越來越受到重視。
痛風所引發的共病症,除了靜態性的橫斷面研究分析之外,動態性的長期資料分析角度,將有助於探討痛風相關共病症的長期轉移變化及其後續影響。有鑑於目前學界類似的研究仍較為缺乏,加上現代化長期資料分析方法---潛在轉移模式的發展與突破,更提供此一研究之契機。據此,本研究擬運用長期動態的觀點探討影響痛風共病症之長期發展趨勢。

研究方法:本研究樣本為全民健康保險資料庫百萬歸人檔,以2000年新發生之痛風個案且持續追蹤十年作為分析世代,結合長期潛在轉移分析技術之應用,從不同角度和群聚面向研究長時間罹患痛風所導致共病症及其後續疾病發展之影響。

研究結果:(1)痛風患者之共病潛在結構,具有三種主要型態,分別為高度共病症型態、中度共病症型態與低度共病症型態,且因性別與年齡而不同。(2)共病潛在類型之群組指標具有性別上差異,但高血壓是一項跨越男女與不同年齡層之重要共病症指標,高血壓之存在,將連動提高其他共病症產生之機率,進而成為高度共病症型態。糖尿病是男女性別差異性最大之群組指標,其主要出現於女性痛風患者之族群。(3)長期轉移結構上,痛風共病症之5種潛在轉移類組,以男性年齡超過五十歲的群體為例,表現類型分別為CT1:持續以高血壓為核心伴隨高血脂及心血管疾病;CT2:持續性以高血脂為共病核心;CT3:持續較少發生共病症者、CT4:初期以高血脂為核心進而轉移至以高血壓為核心伴隨高血脂及心血管疾病;CT5:初期為較少發生共病症但隨時間進而轉移至以高血壓為核心之共病群。因此,經由不同類型反映出痛風不同的共病症長期發展過程。(4)五種長期轉移表現類型中,痛風患者潛在轉移類組為持續高度共病症類組,有最高中風及癌症十年累積發生率,然而移轉成高度共病類組之各軌跡也有次高之發生率,其中,性別及年齡也扮演重要之分類角色。(5)最後透過各年代之縱貫轉移機率呈現,初期為較少發生共病症或初期僅以高血脂為核心之痛風患者,會隨年齡增長而提升進一步轉移至以高血壓為核心之共病群機率,並增加後續中風與癌症發生機率。

結論與建議:總體來看,痛風衍伸的共病症是相當複雜的,本研究藉由潛在類別及潛在轉移模式之應用,具體提供痛風共病症之潛在類別組態,以及長時間辨識出五種不同的長期轉移結構。研究結果指出不同共病症的結構變化發展將對於後續中風與癌症之發生,扮演著重要的角色。

Objectives:Gout is a highly prevalent chronic disease in Taiwan. Based on the epidemiology of age, generation and long-term tendency of the disease, it is mainly seen among the older aged and male populations. In addition to the nature of the gout disease studies, its accompanying comorbidities and other influential factors have received more attention in the recent years.
Gout caused by comorbidities, in addition to the static analysis of cross-sectional studies, the long-term data of the dynamic analysis will also help investigate the transfer of long-term changes in gout-related comorbidities and their subsequent impacts.
The current academic research is still relatively inadequate. However, the modernization of potential long term data analysis methods—such as the development and breakthrough of the Transfer Mode—provides us with an opportunity for further research. In accordance, this present study is made to investigate the use of long-term dynamic view on the long-term effects of gout comorbidity tendencies.

Methods:The study sample was conducted from the Longitudinal Health Insurance Database (LHID) 2010 based on the Taiwan National Health Insurance Research Database (NHIRD). The new cases of gout in 2000 was continuously tracked down for a decade as an analytical generation. It was combined with the application of the long-term potential transfer analysis techniques, based on different viewpoints and cluster-oriented researches for long-term gout sufferers which led to comorbidities and the subsequent development of the disease.

Results:The results are divided into five main points: 1) Latent structure underlying the comorbidity of gout were subdivided into three main types: highly comorbidity patterns, moderate comorbidity patterns, low comorbidity patterns. It varied based on different age and genders. 2) Comorbidity index has the potential types of group differences based on genders. Hypertension is an important comorbidity index across different ages for both men and women. The presence of hypertension will increase the chances of other linked comorbidities generated, and thus, it becomes the highly comorbidity patterns. As for diabetes, its biggest difference is between the male and female groups of indicators. It usually occurs mainly in the female population of gout patients. 3) In the long-term structural shift, gout comorbidities were classified into five kinds of potential transfer class group. Male over the age of 50, for example, the performance of each type of CT1: persistent hypertension was closely associated with high cholesterol and cardiovascular heart disease ; CT2: persistent high blood lipids were comorbid core; CT3: sustainined less frequent comorbidities, CT4: High cholesterol level as its core in the early stages; and then later advancing to hypertension as its core, which was usually accompanied with high cholesterol level and cardiovascular diseases; CT5: Early stages with less frequent comorbidity and then later transferred to the core of hypertension comorbid group after a certain amount of time. Therefore, through different types of gout were able to reflect various comorbidities in the long-term developmental process. 4) Among the long-term transfer performance type, gout patients with potential class group were of continued highly comorbid conditions. In the past decade, stroke and cancer had the highest cumulative incidence rate. However, the trajectory of each class transferred into a highly comorbid group also had the second highest of occurrence rate. Among them, age and gender also played an important role in the classification. 5) Finally, through years of longitudinal transition probabilities, gout patients with early stages presented with lesser comorbidities or high cholesterol as its only core, usually worsen with age as hypertension becomes its core group of comorbidity. It usually followed by increased incidence of subsequent stroke and cancer.

Conclusions:Overall, the links between comorbidities and gout are quite complex. This study and application of Latent Transition Analysis (LTA) provides the potential class types specific comorbidities of gout, with five well-identified different long term transition structure. This study also concluded that structural changes in the development of different comorbidities will occur subsequent to the stroke and cancer, which also plays an important role.

目 錄
目錄 VIII
表目錄 IX
圖目錄 X
第一章 1
緒論 1
第一節 研究背景與動機1
第二節 問題意識4
第三節 研究目的5
第四節 研究重要性5
第二章 7
文獻探討 7
第一節 痛風疾病之生理機轉7
第二節 痛風相關共病症之發展9
第三節 潛在分類之研究17
第四節 潛在轉移模式於分析長期資料之應用20
第五節 研究啟示23
第三章 25
研究方法 25
第一節 研究架構25
第二節 研究設計26
第三節 研究對象26
第四節 研究資料來源及變項的操作型定義27
第五節 研究處理流程38
第六節 統計分析方法41
第四章 42
研究結果 42
第一節 描述性統計42
第二節 痛風患者共病症之潛在分類44
第三節 痛風患者共病症潛在分類之長期轉移結構49
第四節 痛風患者共病症長期轉移結構與中風及癌症之關聯性62
第五章 67
討論與建議67
第一節 研究意涵67
第二節 痛風共病症的發展與重要68
第三節 痛風共病症與其他慢性共病症的比較72
第四節 研究優點與限制74
第五節 結論及未來研究方向 75
參考文獻 78
中文部分 78
英文部分 81

表目錄
表 1 探討痛風相關共病症的發生情形文獻簡表15
表 2 各種類型之潛在變項分析方法18
表 3 研究變項之操作型定義 29
表 4 痛風常見藥物治療 32
表 5 2000-2009年痛風世代基本人口學資料42
表 6 2000-2009年痛風世代伴隨相關共病症之罹病人數43
表 7 痛風共病症之潛在分類之配適度模型檢定44
表 8 痛風患者為男性且年齡≥50歲之共病症潛在類別情形45
表 9 痛風患者為男性且年齡≥50歲之潛在類別機率46
表 10 痛風患者為男性且年齡< 50歲之共病症潛在類別情形46
表 11 痛風患者為男性且年齡< 50歲之潛在類別機率47
表 12 痛風患者為女性且年齡≥ 50歲之共病症潛在類別情形47
表 13 痛風患者為女性且年齡≥ 50歲之潛在類別機率48
表 14 痛風患者為女性且年齡<50歲之共病症潛在類別情形48
表 15 痛風患者為女性且年齡<50歲之潛在類別機率49
表 16 痛風患者為男性且年齡≥50歲,第一年到第二年的轉移機率 50
表 17 痛風患者為男性且年齡≥50歲,第二年到第三年的轉移機率 50
表 18 痛風患者為男性且年齡≥50歲,第三年到第四年的轉移機率 51
表 19 痛風患者為男性且年齡≥50歲,第四年到第五年的轉移機率 51
表 20 痛風患者為男性且年齡<50歲,第一年到第二年的轉移機率 53
表 21 痛風患者為男性且年齡<50歲,第二年到第三年的轉移機率 53
表 22 痛風患者為男性且年齡<50歲,第三年到第四年的轉移機率 54
表 23 痛風患者為男性且年齡<50歲,第四年到第五年的轉移機率 54
表 24 痛風患者為女性且年齡≥50歲,第一年到第二年的轉移機率 56
表 25 痛風患者為女性且年齡≥50歲,第二年到第三年的轉移機率 56
表 26 痛風患者為女性且年齡≥50歲,第三年到第四年的轉移機率 57
表 27 痛風患者為女性且年齡≥50歲,第四年到第五年的轉移機率 57
表 28 痛風患者為女性且年齡<50歲,第一年到第二年的轉移機率 59
表 29 痛風患者為女性且年齡<50歲,第二年到第三年的轉移機率 59
表 30 痛風患者為女性且年齡<50歲,第三年到第四年的轉移機率 60
表 31 痛風患者為女性且年齡<50歲,第四年到第五年的轉移機率 60

圖目錄
圖 1 潛在類別模式圖17
圖 2 潛在轉移路徑圖21
圖 3 潛在轉移模式示意圖25
圖 4 2000-2009年研究世代觀察時間圖27
圖 5 納入條件及排除條件之觀察時間圖28
圖 6 本研究資料擷取流程圖40
圖 7 痛風患者為男性且年齡≥50歲罹患共病症之長期轉移結構52
圖 8 痛風患者為男性且年齡<50歲罹患共病症之長期轉移結構55
圖 9 痛風患者為女性且年齡≥50歲罹患共病症之長期轉移結構58
圖 10 痛風患者為女性且年齡<50歲罹患共病症之長期轉移結構61
圖 11 痛風患者為男性且年齡≥50歲,10年累積中風及癌症機率62
圖 12 痛風患者為男性且年齡<50歲,10年累積中風及癌症機率63
圖 13 痛風患者為女性且年齡≥50歲,10年累積中風及癌症機率64
圖 14 痛風患者為女性且年齡<50歲,10年累積中風及癌症機率65
圖 15 2000年痛風世代共病症長期發展之情形66

中文部分
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周玉慧、謝雨生(2009).夫妻間支持授受及其影響.中華心理學刊,51(2),215-234。
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風濕病醫學會(2013).台灣痛風與高尿酸血症2013診治指引.取自http://www.rheumatology.org.tw/news/Files/N2014217133733_%B5h%AD%B7%AB%FC%A4%DE0217.pdf
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國際糖尿病聯盟(2013).老年第二型糖尿病管理全球指引.取自http://www.idf.org/guidelines/managing-older-people-type-2-diabetes
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張德明(2006).痛風與其他疾病或問題的關連.健康世界,243,16-18。
黃麗玲、林川雄、黃建財(2005).痛風與高尿酸血症的認知與管理.輔仁醫學期刊,3(1),25-32。
楊仲凱(2011).探討痛風與慢性腎臟病患死於心血管疾病的風險(碩士論文). 亞洲大學,台中市,台灣.取自臺灣博碩士論文知識加值系統。
楊志堅、吳齊殷(2001).潛藏轉移模式在社會學縱貫研究之應用:以青少年暴力行為發展研究為例.調查研究—方法與應用,9,28。
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衛生福利部國家衛生研究院(2010).2005-2008國人高血壓之狀況.取自http://nahsit.nhri.org.tw/node/34
潘文涵、吳幸娟、葉志嶸、莊紹源、張新儀、葉乃華、謝耀德(2009).台灣人飲食與健康之趨勢:1993-1996與 2005-2008營養健康調查之比較.2005-2008台灣營養健康調查成果發表會,台北市,台灣。
葉慶輝、吳香錡(2011).台灣南部健檢成人高尿酸血症與新陳代謝症候群之相關性探討.中華職業醫學雜誌,18(3),197-210。
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英文部分
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