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  • 學位論文

統計模式應用於研究生疲勞危險因子之探討

The Associated Factors of Fatigue with Statistical Applications among Postgraduate Students

指導教授 : 陳秀熙

摘要


背景:年輕的研究生族群是疲勞發生的高危險群,除了已經知道的危險因子,共存疾病或者是代謝症候群因子對於疲勞的影響仍是未知的;疲勞因發生的原因不同及病程不同,其臨床表現為多面向性的;而我們的受試者為學校中各個學院的研究生因此資料屬於階層結構;也由於疲勞的危險因子為多因性且其相互作用相當複雜,因此我們應用了一些統計方法針對以上的特性來釐清疲勞與相關危險因子的關係。 方法:於2004年我們針對1806位新進研究生做了疲勞危險因子的調查,也收集這些參與研究受試者的前3年健保檔就醫資料。第一部分,為了建立共存疾病貢獻分數,應用就醫行為為次數資料因此使用卜瓦松回歸模式來分析共存疾病與疲勞的關係;第二部分,由於疲勞有四個面向,我們應用了多變量分析來探討疲勞與各個代謝症候群因子的關係;第三部分,針對我們的研究生分屬不同學院在校園裡是屬於階層結構,因此我們應用了多階層模式來分析疲勞與其相關因子的關係;最後,因為其多因性且複雜的交互作用,我們應用了結構方程模式來分析這樣的資料。 結果:在干擾因子校正後,曾經被診斷為呼吸道系統疾病者對未被診斷者增加14%的疲勞危險性 (第九版國際疾病分類碼 460-519 , 相對危險 (RR) =1.14, 95% 信賴區間: 1.00, 1.31), 診斷有生殖泌尿系統疾病者增加49% 危險性 (第九版國際疾病分類碼580-629, RR=1.49, 95%信賴區間: 1.01, 2.20), 診斷有皮膚及皮下組織疾病者增加30% 危險性 (第九版國際疾病分類碼680-709, RR=1.30, 95%信賴區間: 1.00, 1.69)。 依據性別分層後,在男性受試者中,四個面向的疲勞分別與腰圍(Hotelling-Lawley Trace test p=0.016),收縮壓 (p=0.022)及舒張壓(p=0.015)顯示有多變量的統計相關;而女性的多面向疲勞與代謝症候群因子則沒有統計相關。 在多階層回歸模式部分,將各種疲勞分數當作依變項,分別以身體活動量等當作自變項,在校正後的隨機截距模式中,不同學院的基礎疲勞分數並沒有統計上的差異;在隨機斜率模式及隨機截距與斜率模式中也沒有統計上的差異。 在結構方程模式中,顯著的路徑係數分別為:共存疾病貢獻分數之於健康生活型態(路徑係數-0.0294, 標準誤0.0144), 健康生活型態之於疲勞 (-0.6823, 0.0715),營養狀況之於疲勞(0.0624, 0.0298), 以及睡眠時間之於疲勞(-0.0426, 0.0183) 。 結論:本論文闡明了過去鮮少討論的疲勞相關因子中的共存疾病及代謝症候群因子,並根據疲勞有四個面向及研究生資料為階層結構的特質,使用不同的統計方法分析,進一步引入各種潛在變項及其相關外顯變項,應用結構方程模式分析以建立造成疲勞的路徑模式。

並列摘要


Background: In addition to the established risk factors, the contribution of co-morbid diseases or metabolic syndrome to fatigue, particularly for young post-graduate students who are one of groups vulnerable to fatigue, is still poorly understood. However, the measurement of fatigue may get involved with multi-dimension property on different aspects of clinical attributes. Beside, as the subjects of interest were postgraduate students, the data is a hierarchical structure such as students nested within class or colleges. Moreover, since the risk factors responsible for fatigue are multifarious and the reciprocal relationships are complex, we therefore applied several statistical techniques to accommodate these properties while the relationships between putative factors and fatigue were investigated. Methods: We conducted a survey for identifying risk factors associated with fatigue among 1806 new entrants of post-graduate students in the year 2004. We identified comorbidity information on these participants three years prior to the study. In the first part, we applied a Poisson regress model to evaluate the association between comorbid disease and fatigue by treating the visits due to comorbidity as counts in order to build up risk score taking co-morbidity into account. In the second part, multivariate analysis was adopted to evaluate the association between metabolic syndrome and four-dimension of fatigue score. In the third part we built up multi-level regression models inherent from hierarchical data structure to evaluate the association between associated factors and fatigue. Finally, we used the structural equation model making allowance for hierarchical data structure to disentangle the reciprocal relationships across these multi-attributes. Results: Students who had been diagnosed as one of three systematic diseases led to severe total fatigue by 14% for respiratory system disease (International Classification of Disease, 9th Revision, Clinical Modification code(ICD) 460-519 , relative risk (RR) =1.14, 95% CI: 1.00, 1.31), by 49% for genitourinary system disease (ICD 580-629, RR=1.49, 95%CI: 1.01, 2.20), and by 30% for skin and subcutaneous tissue disease (ICD 680-709, RR=1.30, 95%CI: 1.00, 1.69) when potential confounders were adjusted. By the stratification of gender, males with Hotelling-Lawley Trace test revealed significant multivariate effects relevant to four-dimensional fatigue for waist circumference (p=0.016), systolic blood pressure (p=0.022), and diastolic blood pressure (p=0.015) respectively. The corresponding results for females failed to show multivariate effects for each metabolic component. In multilevel regression model, while each specific fatigue score was taken as a dependent variable, the random intercept model with adjustment for baseline fatigue score across colleges shows a non-statistical significant difference for all the independent variables (such as physical activity). The results based on random slope model and both random intercept and slope models were not statistically significant. For analysis of structural equation model, the significant path coefficient included comorbidity score to healthy life style (path coefficient (standard error), -0.0294 (0.0144)), and three paths to fatigue, including nutrition status (0.0624 (0.0298)), healthy life style (-0.6823 (0.0715)), and sleep time (-0.0426 (0.0183)) It is noticed that the contribution of metabolic factors to any pathway was not statistically significant. Conclusion: This thesis elucidated the associations between fatigue and risk factors such as co-morbidity and metabolic factors that have been barely addressed before. Different statistical methods have been proposed to analyze the postgraduate data characterized by four-dimension property and also multi-level property. The structural equation model was further applied to building up the pathways leading to fatigue by considering various latent variables and their associated manifested variables.

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