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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳秀熙 | |
| dc.contributor.author | Ping-Yun Tsai | en |
| dc.contributor.author | 蔡秉芸 | zh_TW |
| dc.date.accessioned | 2021-06-08T07:14:13Z | - |
| dc.date.copyright | 2008-08-08 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-29 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26537 | - |
| dc.description.abstract | 研究背景及目的
肥胖的盛行日益嚴重，對健康造成的影響重大，而其成因相當的複雜且尚未被充分了解。過去流行病學的研究多著重於個人危險因子的探究，但環境中存在許多群體層次因素及社會脈絡因素 (contextual factors)，經由直接或間接的方式影響肥胖是值得去研究的。而有關肥胖社會脈絡因素之研究，很少同時將家族聚集之因素一起考慮。所以本研究目的是利用多階層模式探討肥胖與家族聚集、個人層級、及區域層級因素之相關性。 材料與方法 本研究對象是從1999年到2005年參加基隆市整合式篩檢 (Keelung community-based integrated screening program, KCIS)20-69歲的民眾，共74,833人。收集的資料包括戶籍檔資料、人口特徵學資料包括個人教育程度及婚姻狀態、個人生活習慣、飲食等問卷資料，測量身高、體重及血液生化值，而區域的脈絡因子是收集基隆各行政區教育程度、婚姻狀態、及人口密度等人口社會學資料。研究設計是以病例對照指標家族研究 (case-control proband family)為主，共有4,499個病例組個案及16,932個對照組個案，利用非線性混合模式 (Nonlinear mixed model)及貝氏分析 (Bayesian Analysis)於多階層模式之統計方法，估計肥胖與家族聚集、個人層級、及區域層級因素之危險對比值 (Odds ratio)及95%信賴區間。 結果 體重過重及肥胖的盛行率在年紀大、男性、教育程度低、離婚或喪偶者較高。高教育程度比例較低的區域、人口密度較高的區域，肥胖的盛行率也較高。病例指標家族比對照指標家族肥胖聚集的相對危險值為1.29 (95%信賴區間1.29-1.30)，在高教育程度比例低的區域、離婚或喪偶率高的區域、人口密度高的區域，肥胖家族聚集的相對危險值分別為1.39 ( 95%信賴區間: 1.36-1.41)、1.36 (95%信賴區間: 1.35-1.38)、及1.34 (95%信賴區間: 1.32-1.35)。肥胖與家族聚集、個人層級因素、及區域層級因素之相關性分析中，在調整多變項後，家族聚集的危險對比值是有顯著意義的，其危險對比值為 2.31 (95%信賴區間: 1.67-3.20)，且在未婚者比已婚、離婚或喪偶者高 (危險對比值2.31(95%信賴區間: 1.67-3.02) vs.1.52 (95%信賴區間: 1.10-2.11) vs.1.44 (95%信賴區間: 1.04-2.00))；在高教育程度高的區域較低的區域高 (危險對比值2.68(95%信賴區間: 1.94-3.71) vs.2.31 (95%信賴區間: 1.67-3.20))。利用貝氏方法做多階層的分析測試不同的模式，發現在同時考慮不同家族間、不同區域間、及區域對家族聚集無法解釋的異質性影響下，所得到的模式適合度比較好。 結論 本研究結合了家族病例對照指標個案之研究設計和多階層模式結合個人及社會脈絡因素，證明在調整個人因子及社會脈絡因子下，肥胖有強烈家族聚集之現象。就脈絡層次而言，在不同的家族和不同的區域間，個人肥胖的危險是具有異質性的，而且肥胖家族聚集的情形也會受到脈絡因素的影響，如區域的教育程度。未來若能探討更多脈絡因素(如社會經濟因素指標)對於肥胖家族聚集的影響，並討論是否針對脈絡因素做介入性措施，對肥胖的防治將更有幫助。對於區域的社會相關脈絡因素之探討，若可依照區域發展的地理人文背景與有興趣研究的社會脈絡因素相互考慮，是未來可繼續努力的方向。 | zh_TW |
| dc.description.abstract | Background and Study Purpose
Obesity is increasing in prevalence worldwide and is known to be associated with morbidity and mortality in relation to cardiovascular disease. The etiology of obesity is complex and is not completely understood. A large portion of epidemiologic research put emphasis on individual-level risk factors. However, group-level or macro-level variables, so-called contextual factors, also play an important role through interaction with individual factors. There are also limited studies taking both familial aggregation and contextual factors of obesity into accounts. Therefore, the aim of the present study is to explore the association between obesity and familial aggregation and risk factors at individual-level and area-level by conducting a community-based study with multi-level model analysis. Materials and methods A total of 74,833 subjects with aged 20-69 years old are identified from Keelung community-based integrated screening program (KCIS) between 1999 and 2005. By dint of KCIS study, the study design is based on a case-control proband family sampling. A total of 4,499 cases and 16,932 controls were identified. Data of household registration, demographics including education and marital status, lifestyle, and diet were collected. Anthropometric measurements were taking and the criteria of obesity was defined as body mass index≧27 kg/m2. Area-level contextual factors including high educational rate, divorce or widowed rate, and population density separated with tertile were collected from seven administrative districts of Keelung City. We applied nonlinear mixed model and Bayesian analysis for multi-level analysis to investigate the odds ratios and 95% confidence interval of familial aggregation and different level factors for obesity. Results The prevalence rate of overweight and obesity were higher in aged, male, low educated, divorced or widowed subjects, the least tertile of high education rate, and the most tertile of population density among areas. The relative risk of familial aggregation in association with obesity among relatives in case proband families compared with control proband families was 1.29 (95% CI: 1.29-1.30). The relative risk of familial aggregation with obesity in the least tertile of high education rate, the most tertile of divorce rate, and the most tertile of population density were 1.39 (95% CI: 1.36-1.41), 1.36 (95%CI: 1.35-1.38), and 1.34 (95% CI: 1.32-1.35), respectively. The risk for obesity among relatives in case versus control proband families was 2.31 (95%CI: 1.67-3.20) after controlling for significant environmental factors, and it was modified by individual marital status and high education rate of area. The odds ratio was 1.52 (95%CI:1.10-2.11) in married subjects, 1.44 (95%CI:1.04-2.00) in divorced or widowed subjects, and 2.68 (95%CI:1.94-3.71) in the least tertile of high education rate, respectively. When Bayesian analysis for multi-level model is applied, the random effects considering unexplained heterogeneity among different families, different areas, and different area effect on familial aggregation are taken into account with better goodness of fit than others. Conclusion The present study confirmed a strong tendency to familial aggregation for obesity by using the case-control proband family study with a multi-level model approach. The risk of obesity was heterogeneous among families and areas by using multi-level analysis, and familial aggregation of obesity was also affected by contextual factors. The selection of more contextual factors from different levels and the selection of the appropriate contextual factors are needed in the future. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T07:14:13Z (GMT). No. of bitstreams: 1 ntu-97-R95846006-1.pdf: 973899 bytes, checksum: 653bd0edccf4e30d966f6062e6104eb0 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 目錄
圖目錄 2 表目錄 3 Abstract 5 中文摘要 8 第一章 前言 10 第一節 研究背景 10 第二節 研究目的 14 第二章 文獻回顧 15 第一節 肥胖家族聚集研究 15 第二節 肥胖之個人層次相關因子 16 第三節 肥胖與社會脈絡因素 20 第三章 研究材料與方法 22 第一節 研究族群 22 第二節 研究設計 22 第三節 資料收集 23 第四節 肥胖定義 24 第五節 統計方法 25 第四章 結果 30 第一節 肥胖的盛行率之描述性分析 30 第二節 比較病例組及對照組基本資料之差異 31 第三節 肥胖家族聚集的描述性分析 31 第四節 家族聚集與環境因素對肥胖的影響 32 第五節 貝氏分析 (Bayesian Analysis)於多階層模式 (Hierarchical Model)之建構 33 第五章 討論 38 第一節 家族聚集及社會經濟脈絡因子對肥胖的影響 38 第二節 方法學的探討 40 第六章 研究限制 42 第七章 結論 43 參考資料 44 圖目錄 Figure 3.1 Flow chart of study design for obesity 52 Figure 4.1 Distribution of educational level by area 53 Figure 4.2 Distribution of marital status by area 54 Figure 4.3 Distribution of population density by area 55 Figure 4.4 Model R1 : Random intercept of family 56 Figure 4.5 Model R2: Random intercept of family and area 57 Figure 4.6 Model R3: Random intercept of family and random slope of area 58 Figure 4.7 Model R4: Random intercept of family and area and random slope of area 59 表目錄 Table 3.1 Frequency of family number in each household 60 Table 4.1 Prevalence rates of overweight and obesity by age, calendar year, and gender in total attendants 61 Table 4.2 Prevalence rates of overweight and obesity by educational level and marital status 62 Table 4.3 Prevalence rates of overweight and obesity by contextual factors with education, marital status and area63 Table 4.4 Comparison of demographics between case and control group (N=21431) 64 Table 4.5 Comparison of lifestyle factors and personal medical history between case and control group (N=21431) 65 Table 4.6 Distribution of number of obesity among relatives in each household 66 Table 4.7 Distribution of number of obesity among relatives in each household by area 67 Table 4.8 Distribution of number of obesity among relatives in each household by contextual factors with area education 68 Table 4.9 Distribution of number of obesity among relatives in each household by contextual factors with marital status 69 Table 4.10 Distribution of number of obesity among relatives in each household by contextual factors with population density 70 Table 4.11 Univariate analysis for familial aggregation and environmental factors associated with obesity 71 Table 4.12 Interaction assessment of each variable at individual level with familial aggregation 72 Table 4.13 Interaction assessment of socioeconomic factors at area level with familial aggregation 73 Table 4.14 Multivariate analysis for familial aggregation, environmental factors, and interaction 74 Table 4.15 Multivariate analysis for familial aggregation, environmental factors, and interaction 75 Table 4.16(1) Multilevel analysis for familial aggregation and environmental factors 76 Table 4.16(2) Multilevel analysis for familial aggregation and environmental factors 77 Table 4.16(3) Multilevel analysis for familial aggregation and environmental factors 78 Table 5.1 Prevalence rates of overweight and obesity by age, year, and gender in at least 2 attendants in the same household 79 Table 5.2 Prevalence rates of overweight and obesity by individual education, marital status, and area in at least 2 attendants in the same household 80 | |
| dc.language.iso | zh-TW | |
| dc.subject | 肥胖 | zh_TW |
| dc.subject | 家族聚集 | zh_TW |
| dc.subject | 病例對照指標家族研究 | zh_TW |
| dc.subject | 脈絡因素 | zh_TW |
| dc.subject | 多階層分析 | zh_TW |
| dc.subject | obesity | en |
| dc.subject | multi-level analysis | en |
| dc.subject | contextual factors | en |
| dc.subject | case-control proband family study | en |
| dc.subject | familial aggregation | en |
| dc.title | 多階層模式之應用於肥胖之家族聚集 | zh_TW |
| dc.title | A Multi-level Model for Familial Aggregation of Obesity | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 黃國晉 | |
| dc.contributor.oralexamcommittee | 邱泰源,嚴明芳 | |
| dc.subject.keyword | 肥胖,家族聚集,病例對照指標家族研究,脈絡因素,多階層分析, | zh_TW |
| dc.subject.keyword | obesity,familial aggregation,case-control proband family study,contextual factors,multi-level analysis, | en |
| dc.relation.page | 80 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2008-07-30 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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