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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳秀熙 | |
dc.contributor.author | Chia-Hung Liu | en |
dc.contributor.author | 劉家鴻 | zh_TW |
dc.date.accessioned | 2021-06-08T07:10:57Z | - |
dc.date.copyright | 2008-09-11 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-30 | |
dc.identifier.citation | 1. Pneumonocystis pneumonia-Los Angeles. 1981, Morbidity and Mortality Weekly Reportp. 250-2.
2. 行政院衛生署疾病管制局. [cited; Available from: http://www.cdc.gov.tw. 3. 謝英恆, 台灣地區愛滋病流行病學之數學及統計研究. 2002, 行政院衛生署疾病管制局九十一年度科技研究發展計畫. 4. 黃彥芳, et al., 台灣2003年底15-49歲愛滋病毒感染估計盛行率. 臺灣醫學: p. 713-721. 5. Fang, C.T., et al., Decreased HIV transmission after a policy of providing free access to highly active antiretroviral therapy in Taiwan. J Infect Dis, 2004. 190(5): p. 879-85. 6. McQuillan, G.M., et al., Prevalence of HIV in the US household population: the National Health and Nutrition Examination Surveys, 1988 to 2002. J Acquir Immune Defic Syndr, 2006. 41(5): p. 651-6. 7. McGarrigle, C.A., et al., Estimating adult HIV prevalence in the UK in 2003: the direct method of estimation. Sex Transm Infect, 2006. 82 Suppl 3: p. iii78-86. 8. Rothman, K.J. and S. Greenland, Modern epidemiology. 1998, Philadelphia, PA: Lippincott-Raven. 9. Zill, D.G. and M.R. Cullen, Differential equations with boundary-value problems. 1997, Pacific Grove: Brooks/Cole Pub. Co. 10. Anderson, R.M. and R.M. May, Infectious diseases of humans : dynamics and control. Oxford science publications. 1991, Oxford; New York: Oxford University Press. 11. Gail, M.H. and R. Brookmeyer, Methods for projecting course of acquired immunodeficiency syndrome epidemic. J Natl Cancer Inst, 1988. 80(12): p. 900-11. 12. Boily, M.C., et al., Changes in the transmission dynamics of the HIV epidemic after the wide-scale use of antiretroviral therapy could explain increases in sexually transmitted infections: results from mathematical models. Sex Transm Dis, 2004. 31(2): p. 100-13. 13. Brookmeyer, R. and M.H. Gail, Minimum size of the acquired immunodeficiency syndrome (AIDS) epidemic in the United States. Lancet, 1986. 2(8519): p. 1320-2. 14. Brookmeyer, R. and M.H. Gail, Methods for projecting the AIDS epidemic. Lancet, 1987. 2(8550): p. 99. 15. Brookmeyer, R. and M.H. Gail, AIDS epidemiology : a quantitative approach. Monographs in epidemiology and biostatistics, v. 22. 1994, New York: Oxford University Press. 16. Karon, J.M., M. Khare, and P.S. Rosenberg, The current status of methods for estimating the prevalence of human immunodeficiency virus in the United States of America. Stat Med, 1998. 17(2): p. 127-42. 17. Chen, H.H., et al., Evaluation by Markov chain models of a non-randomised breast cancer screening programme in women aged under 50 years in Sweden. J Epidemiol Community Health, 1998. 52(5): p. 329-35. 18. Aalen, O.O., et al., A Markov model for HIV disease progression including the effect of HIV diagnosis and treatment: application to AIDS prediction in England and Wales. Stat Med, 1997. 16(19): p. 2191-210. 19. Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Collaborative Group on AIDS Incubation and HIV Survival including the CASCADE EU Concerted Action. Concerted Action on SeroConversion to AIDS and Death in Europe. Lancet, 2000. 355(9210): p. 1131-7. 20. van der Steen, J.T., et al., Predictors of mortality for lower respiratory infections in nursing home residents with dementia were validated transnationally. J Clin Epidemiol, 2006. 59(9): p. 970-9. 21. The UK register of HIV seroconverters: methods and analytical issues. UK register of HIV seroconverters (UKRHS) Steering Committee. Epidemiol Infect, 1996. 117(2): p. 305-12. 22. Ewings, F.M., et al., Survival following HIV infection of a cohort followed up from seroconversion in the UK. Aids, 2008. 22(1): p. 89-95. 23. Sterne, J.A., et al., Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study. Lancet, 2005. 366(9483): p. 378-84. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26456 | - |
dc.description.abstract | 研究背景與目的
HIV/AIDS(human immunodeficiency virus/ human immunodeficiency syndrome)傳染病監測的困難,在於無法正確記錄到可感受者受傳染病感染的時間點,往往受感染者被觀察記錄到的時間,已是有症狀的發病期;如果沒有主動的傳染病篩檢系統,此種左設限(left censoring)的問題,將造成不易從現有的傳染病的通報資料來檢視傳染病之自然病程,亦造成傳染病流行病學監測的錯估。因此利用統計模式來預估HIV感染率及HIV到AIDS的轉移機率,即可克服左設限造成的困境。目前台灣少有研究是以–傳染病動力學來長期的預測HIV/AIDS的感染發生。HAART(Highly active anti-retroviral therapy)的介入更會使HIV感染及AIDS病例的預測更加複雜化。 研究背景與目的 本研究的目的在探討HIV及AIDS的描述性流行病學,預估每年HIV的感染率和HIV到AIDS的轉移率及探求HAART對死亡或AIDS死亡的影響。 研究方法 利用HAART介入前所以HIV感染者及AIDS發病者的資料。馬可夫三階段模式(Markov three-state model)可以用來推測及模擬HIV及AIDS的自然病程動態。邏輯式成長模式(logistic growth model)可推估HIV在群族人口的成長速率。HAART對HIV感染者的死亡影響,採用時間相依的proportional hazard Cox regression model. 馬可夫三階段模式及邏輯式成長模式可以進一步用來預估在HAART實行後的HIV及AIDS變化。 主要研究結果 就現有完整資料顯示HIV感染的年發生率是0.0000006519(1/人年)。HIV感染者轉移到AIDS的速率0.139(/年),也就是進展到AIDS需要平均7.58年的時間。就不同的傳染模式而言,HIV發生率在同性間性行為族群及異性間性行為族群是靜脈毒癮注射及血友病患者的二倍;但從HIV轉換到AIDS的速率,則是相反結果。利用邏輯式迴歸模式預估出HAART的實行可以減少50%HIV及AIDS的病例。利用存活資料分析,則是HIV感染者會因HAART的介入,減少67%死亡的危險。假使沒有HAART的作用下,依建立的馬可夫三階段模式預估HIV開始流行後20年,HIV人數應有9540人,AIDS應有4164人。 結論 在不受HAART影響之下,三階段隨機模式和邏輯式成長模式都能夠用來闡明HIV及AIDS的自然病程。也證明了HAART會減少HIV感染者67%的死亡及50%的HIV或AIDS病例的發生。 | zh_TW |
dc.description.abstract | Background It is a challenging task to estimate natural course of HIV and AIDS using empirical data because subjects infected with HIV are often asymptomatic and have remained occult for a long time (left-censored problem) without active surveillance system. It is imperative to apply a statistical model to estimate infection rate and the rate of conversion from HIV to AIDS making allowance for left-censored problem. In the view of infectious epidemiology, few studies in Taiwan considered using a dynamic concept to estimate and predict HIV infection and occurrence of AIDS with long-term follow-up. Furthermore, the administration of HAART further complicates the projection of HIV infection and AIDS cases.
Objects The present study aimed to investigate descriptive epidemiology of HIV and AIDS cases, to estimate annual infection rate and conversion rate from HIV to AIDS, to investigate the effect of HARRT on total death and death due to AIDS. Methods Data used for the current study were derived from registered system of HIV and AIDS before the administration of HAART. A three-state Markov model was used to estimate the parameters pertaining to the natural history of HIV and AIDS. Logistic growth model was also used to estimate the growth rate of HIV. The effect of HARRT on reducing total death was investigated by the application of time-dependent proportional hazards Cox regression model. Both three-state-state Markov model and logistic growth model are further used to project HIV and AIDS after the HAART study. Results Annual infection rate for HIV infection was 0.0000006519 per person year. Annual conversion rate form HIV to AIDS for the overall group was 0.139 per year, namely 7.58 years of the average dwelling time staying in HIV state. By looking at different transmission modes, annual infection rate for homosexual and heterosexual modes was approximately two-fold compared with intravenous drug users and hemophiliacs. However, the opposite results were noted for annual conversion rates in terms of transmission modes. Using logistic growth model, we estimated 50% reduction of HIV and AIDS after the administration of HAART. By using survival data, we found 67% reduction in total death among HIV cohorts. The numbers of projection of HIV and AIDS in the absence of HAART intervention based on our three-state model were 9540 and 4164. Conclusion Stochastic model and logistic growth model together with data before the administration of HAART were used to elucidate the natural history of HIV and AIDS in the absence of HAART intervention. We also found HAART can reduce 67% of total death and 50% of HIV or AIDS cases. | en |
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dc.description.tableofcontents | 中文摘要………………………………………………………………II
Abstract………………………………………………………………IV 目錄………………………………………………………………VI 表目錄………………………………………………………………VIII 圖目錄………………………………………………………………X 第一章 前言 1 第一節 研究背景 1 第二節 研究目的 2 第二章 文獻回顧 3 第一節 國內HIV/AIDS感染狀況及防治政策 3 第二節 台灣HIV/AIDS的描述性流行病學 4 一、HIV/AIDS分布及危險因子 4 二、診斷病例年代趨勢 5 三、HIV累積危險率(cumulative risk) 5 第三節 HIV/AIDS流行病學推估方法及可能的困難 5 第三章 材料與方法 9 第一節 研究材料 9 一、HIV/AIDS病例定義 9 二、通報病例管道 9 第二節 研究方法 10 一、研究架構 10 二、存活分析 11 三、HIV/AIDS自然病程三階段模式 11 四、邏輯式成長模式(logistic growth model) 15 五、統計分析 16 第四章 研究結果 17 第一節 描述性結果 17 第二節 HIV/AIDS病患存活分析 18 一、總合分析 18 二、性別變項分析 19 三、傳染模式分析 19 四、多變項分析 19 五、HAART對存活的影響 19 第三節 估計HIV/AIDS疾病自然史參數 20 一、三階段模式 20 二、不同傳染模式介入 20 三、年代因素 21 四、模擬 21 第四節 邏輯式成長模式 22 一、1984年至1997年 22 二、1984年至2006年 22 三、馬可夫模式和邏輯式成長模式模擬應用 23 第五章 討論 23 一、估計HIV/AIDS自然病史之困難及解決途徑 23 二、研究方法探討 23 三、主要發現 24 四、HAART成效評估 25 五、研究限制 27 參考文獻 28 表目錄 表 1 Cases numbers of HIV/AIDS by status, sex, diagnosed age and risk factors 30 表 2 HIV/AIDS cases ratio 31 表 3 HIV/AIDS reported cases by calendar year 32 表 4 Drawbacks of different methods to evaluate and project AIDS epidemiology 32 表 5 Summary of 1489 HIV cases 33 表 6 Summary of 1401 HIV cases 33 表 7 Summary of 1489 HIV cases 34 表 8 Summary of 1489 HIV cases by risk factors 34 表 9 Age of being diagnosed as each status by risk factors 35 表 10 Survival analysis of factors affecting HIV developing to AIDS 36 表 11 Survival analysis of factors affecting HIV developing to AIDS stratified by birth cohort 36 表 12 Survival analysis of factors affecting HIV developing to death 37 表 13 Survival analysis of factors affecting HIV developing to death stratified by birth cohort 37 表 14 Survival analysis of factors affecting HIV developing to AIDS death 38 表 15 Survival analysis of factors affecting HIV developing to AIDS death stratified by birth cohort 38 表 16 The effect of HAART on total death 39 表 17 Survival analysis of factors with HAART effect of affecting HIV developing to death 40 表 18 Survival analysis of factors with HAART effect of affecting HIV developing to death stratified by birth cohort 41 表 19 Survival analysis of factors with HAART effect of affecting HIV developing to AIDS death events 42 表 20 Survival analysis of factors with HAART effect of affecting HIV developing to AIDS death events stratified by birth cohort 43 表 21 Estimate of parameters of three state Markov model of HIV/AIDS by risk factors 44 表 22 Transition rate and mean period from HIV to AIDS events: 44 表 23 Statistical results of different risk factors of transition rate from normal to HIV 45 表 24 Statistical results of different risk factors of transition rate from HIV to AIDS 45 表 25 Estimate of parameters of three state of HIV/AIDS by risk factors and year effect 46 表 26 Transition rate and mean period from HIV to AIDS events 46 圖目錄 圖 1 HIV通報人數事紀 47 圖 2 Current policy in Taiwan 48 圖 3 HIV/AIDS case ratio by age 49 圖 4 HIV incidence cases by calendar year 49 圖 5 AIDS incidence case by calendar year 50 圖 6 HIV incidence rate by calendar year 50 圖 7 AIDS case incidence rate by calendar year 51 圖 8 AIDS/HIV incidence case ratio by calendar year 51 圖 9 Cumulative risk of HIV by age 52 圖 10 研究架構 53 圖 11 AIDS 存活時間定義 54 圖 12 死亡事件時間定義 54 圖 13 The flow chart of cases numbers of HIV to AIDS and death 55 圖 14 The flow chart of cases numbers of HIV to AIDS 56 圖 15以HIV世代來看AIDS及死亡。自1990年以後診斷的HIV患者中(第七組後),進展至AIDS或死亡比例則下降。橫軸為HIV診斷年代之分組,以1984年為第一組。緃軸為各組發生橫軸為HIV診斷年代之分組,以1984年為第一組事件之人數。 57 圖 16 HIV診斷平均年齡及年代。橫軸為HIV被診斷之年代分組,以1984年為第一組。緃軸為發生事件之平均年齡。 57 圖 17 不同危險因子發生HIV之平均年齡。以HIV診斷年齡為分組,觀察不同危險因子發生HIV之平均年齡。 58 圖 18以HIV診斷年齡與事件之發生人數比。50歲以後診斷為HIV者,死亡比例高於AIDS發病比例,可歸因於競爭死因隨年紀上昇而機會增加。橫軸為HIV被診斷之年齡分組,軸縱為該組事件發生(AIDS or death)與HIV人數之比例。 58 圖 19 Cumulative survival of HIV cases until AIDS or censoring by the end of 1997/6 59 圖 20 Cumulative survival of HIV case until death or censoring by the end of 2006 59 圖 21 Cumulative survival of HIV case until AIDS death or censoring by the end of 2006 60 圖 22 Non-parametric analysis of HIV to AIDS by gender (χ2=0.7892, p=0.3742) 60 圖 23 Non-parametric analysis of HIV to AIDS by gender (χ2=1.9179, p=0.1601) 61 圖 24 Non-parametric analysis of HIV to AIDS death by gender(χ2=1.3179, p=0.2402) 61 圖 25 Non-parametric analysis of HIV to AIDS by risk factors (χ2=11.9096, 62 圖 26 Non-parametric analysis of HIV to death by risk factors (χ2=8.6202, 62 圖 27 Non-parametric analysis of HIV to AIDS death by risk factors(χ2=9.8997, p=0.0194) 63 圖 28 Simulating cumulative HIV with AIDS from 1984 to 2003. 63 圖 29 Simulating cumulative HIV with AIDS cases IDU group from 1984 to 2003 64 圖 30 Simulating cumulative HIV with AIDS cases by hemophiliacs group from 1984 to 2003 64 圖 31 Simulating cumulative HIV with AIDS cases by homosexual behavior group from 1984 to 2003 65 圖 32 Simulating cumulative HIV with AIDS cases by heterosexual behavior group from 1984 to2003 65 圖 33 Simulating HIV with AIDS cases from 1984 to 2006 with logistic growth models and Markov model 66 圖 34 Non-parametric survival analysis from HIV to AIDS by crude and adjusted results. 66 | |
dc.language.iso | zh-TW | |
dc.title | 台灣地區後天免疫缺乏症候群感染之動態流行病學 | zh_TW |
dc.title | Dynamic epidemiology of HIV/AIDS in Taiwan | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 許銘能,葉彥伯,張淑惠 | |
dc.subject.keyword | 愛滋病毒,人類免疫缺乏症候群,馬可夫三階段模式,邏輯式成長模式,存活分析,雞尾酒療法,左設限, | zh_TW |
dc.subject.keyword | Human immunodeficiency virus,Acquired immunodeficiency syndrome,Left censoring,Markov model,Survival analysis,HAART, | en |
dc.relation.page | 66 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2008-07-31 | |
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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