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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳為堅(Wei J. Chen) | |
dc.contributor.author | Yu-Ling Huang | en |
dc.contributor.author | 黃鈺玲 | zh_TW |
dc.date.accessioned | 2021-06-08T01:16:00Z | - |
dc.date.copyright | 2014-10-20 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-13 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18631 | - |
dc.description.abstract | 背景: 大規模實施減害計畫能否有效減緩愛滋病毒在族群中的傳播,目前仍無定論;主要爭議在於:愛滋疫情下降的原因,究竟是源自減害計畫有效減少注射相關危險行為所產生的預防效果?還是疫情傳播使未感染的危險族群逐漸減少而自然導致的結果?在台灣,為控制注射藥癮族群中經由共用針具傳染而爆發的一波新興愛滋感染疫情,政府逐步實施全國性的減害計畫:在1997年起已全面提供免費雞尾酒療法的背景下,於2004年底起對注射藥癮族群擴大進行愛滋篩檢與衛教;之後,分別從2005年11月及2006年2月起於四個縣市試辦清潔針具計畫和鴉片類替代療法,並於2006年7月起全國擴大實施。本研究欲評估台灣注射藥癮族群愛滋疫情的演變,以及減害計畫對控制愛滋病毒傳播的效果。
方法: 我們依據衛福部疾管署的全國愛滋疫情監測通報資料,使用確定性均勻混合模型分析未實施減害前與全面實施減害後的愛滋傳播動力學,獲得疫情在指數變化期的愛滋傳播參數值不偏估計。進一步,以數學模型模擬無減害計畫情境預期產生的新通報愛滋感染個案數,並與實際有減害計畫情境產生的新通報個案數做比較,估計出減害計畫預防了多少在注射藥癮族群中的潛在新愛滋感染個案數。其次,為評估注射相關危險行為,包括共用針具和注射鴉片類藥物,我們依據衛福部的全國藥物濫用監測通報統計資料,估計注射藥癮族群的共用針頭比率和尿液檢體嗎啡篩檢陽性率,分析其變化趨勢及其與減害服務量和新通報愛滋感染個案數的相關性。 結果: 自2004年第四季起對注射藥癮族群擴大進行愛滋篩檢與衛教後,注射藥癮族群中的新通報愛滋感染個案數,於2005年第四季起開始下降,並於2006年7月全國擴大實施清潔針具計畫與鴉片類替代療法後,快速下降,其下降趨勢與疫情隨自然演進而預期產生的變化,有顯著不同(P=0.003)。比較減害前後的疫情指數變化期, 2007年全國達到充分減害規模後,注射藥癮族群中的愛滋病毒傳染速率比實施減害前降低至少67.5%(95%信賴區間40.9–94.1%, P<0.0001)。在每年全國醫療院所通報的注射藥癮個案中,共用針頭的比率從24.2% 顯著下降至8.6%(Ptrend<0.0001, 2003–2010);在每年全國毒品檢測單位通報的尿液檢體中,嗎啡篩檢陽性率也從25.4% 顯著下降至11.1%(Ptrend<0.0001, 2005–2010)。注射藥癮者共用針頭比率和尿液檢體嗎啡篩檢陽性率的下降,分別與清潔針具和鴉片類替代療法服務量的增加有顯著相關(Ps<0.0001);且此二種注射相關危險行為指標的合併下降程度能良好預測翌年在注射藥癮族群中的新通報愛滋感染個案數下降程度(P<0.0001, R2=0.90)。我們估計減害計畫自2004年第四季起開始實施至2011年底止,已預防注射藥癮族群中潛在的13,554–72,787例(69–92%)新通報愛滋感染個案。 結論: 本研究顯示:台灣的全國性減害計畫有效減緩注射藥癮族群中的愛滋病毒傳播速率和注射相關危險行為。這些證據使全面提供注射藥癮者減害服務的政策,具備更強固的實證基礎。 | zh_TW |
dc.description.abstract | Background: It remains controversial whether a population-level decline in HIV after implementing a harm reduction program is attributed to the successful intervention that reduces risk behaviours rather than to the natural course of the HIV epidemic. To control an injecting-drug-use transmitted (IDUT) HIV outbreak, Taiwan implemented a national harm reduction (HR) programme, in the existing context of free antiretroviral treatment. We aimed to assess evolution of the IDUT-HIV epidemic and the effect of the HR programme in reducing HIV transmission.
Methods: We estimated the HIV transmission rate from the national HIV surveillance data, using a deterministic homogeneous mixing model. The number of new IDUT-HIV cases averted was estimated using counterfactual analysis. We assessed injection-related risk behaviours at population level using the national drug-abuse surveillance data. Results: IDU-targeted HIV testing and education activities expanded from quarter four (Q4) 2004. The number of new IDUT-HIV cases started to decrease from Q4 2005. After nationwide implementation of needle-syringe programmes (NSPs) and opioid substitution therapy (OST) from July 2006, the IDUT-HIV epidemic declined rapidly, following a trajectory significantly different from its natural course (p=0.003). The fully scaled-up HR programme cut the HIV transmission rate by at least 67.5% (95% CI 40.9–94.1%, P<0.0001) from pre-HR level. The annual rate of self-reported needle-sharing among the IDU cases reported from all clinical facilities significantly decreased (from 24.2% to 8.6%, Ptrend<0.0001, 2003–2010); so did the morphine-positive rate among the urine screens reported from all testing facilities (from 25.4% to 11.1%, Ptrend<0.0001, 2005–2010). The needle-sharing and morphine-positive rates inversely correlated with the annual NSP and OST service amounts, respectively (Ps<0.0001), and jointly predicted the next year’s number of new IDUT-HIV cases (P<0.0001, R2=0.90). From Q4 2004 through 2011, an estimated 13,554–72,787 (69–92%) new IDUT-HIV cases were averted. Conclusions: The national HR programme markedly reduced the HIV transmission rate and injection-related risk behaviours among IDUs at population level. Our findings strengthen the evidence base for providing universal access to HR services for IDUs. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T01:16:00Z (GMT). No. of bitstreams: 1 ntu-103-F91846015-1.pdf: 3386705 bytes, checksum: dfb58cf64b984d53815a587b473c0268 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 I
致謝 X 中文摘要 XII ABSTRACT XIV INTRODUCTION 1 METHODS 4 1. Brief Summary 4 1.1 Study design and materials 4 1.1.1 National HIV surveillance system and its case detection rate 4 1.1.2 Detectable emergence and actual emergence of the IDUT-HIV epidemic 4 1.1.3 National drug-abuse surveillance data and injection-related risk behaviours 4 1.1.4 National NSP/OST data and service amounts 5 1.1.5 Time trends in HIV risk behaviours and association analysis 5 1.2 Effect assessment 5 1.2.1 HIV transmission dynamics 5 1.2.2 Counterfactual analysis 7 2. The National HIV/AIDS Surveillance System, HIV Data, and Analysis 8 2.1 The national HIV/AIDS surveillance system 8 2.1.1 Diagnosis of HIV and AIDS 8 2.1.2 HIV screening programmes and policy 8 2.1.3 HAART and medical care 10 2.2 The HIV surveillance data and analysis 11 2.2.1 National HIV/AIDS surveillance registry from Taiwan CDC 11 2.2.2 The IDU risk group: according to HIV risk grouping by Taiwan CDC 11 2.2.3 The sexual risk group 12 2.2.4 HIV data analysis 12 3. The National Drug-Abuse Surveillance System, Risk Behaviour Data, and Analysis 13 3.1 The national drug-abuse surveillance system 13 3.1.1 Physician reporting of clinical drug-abuse cases 13 3.1.2 Laboratory reporting of urine drug-screen samples 15 3.2 The HIV risk behaviour data and analysis 16 3.2.1 Needle-sharing rate among clinical IDU cases: trend indicator of IES behaviour 16 3.2.2 Morphine-positive rate among urine drug-screen samples: trend indicator of illicit opioid injection 17 3.2.3 Population rate of syphilis cases: trend indicator of unprotected sexual behaviour 17 3.2.4 Evaluate time trends in HIV risk behaviours 18 3.2.4.1 Injection-related: needle-sharing rate and urine morphine-positive rate 18 3.2.4.2 Sex-related: population rate of syphilis cases 19 3.2.4.3 Time trend analysis: regression-based 19 4. The National HR Programme, NSP/OST Data, and Analysis 20 4.1 The national HR programme 20 4.1.1 Background 20 4.1.2 IDU-targeted HTC and IEC 20 4.1.3 NSP 21 4.1.4 OST 22 4.1.5 Structural intervention 24 4.2 The NSP/OST data and analysis 26 4.2.1 Calculate NSP service amounts (2005–2010) using the national NSP statistics 26 4.2.2 Estimate OST service amounts (2006–2010) using the national OST dataset 26 4.2.3 Estimate the coverage rates of NSP/OST services 26 5. Analyse the Associations Between New HIV Cases, Risk Behaviours, and HR Services 28 5.1 Injection-related risk behaviours vs new IDUT-HIV cases 28 5.2 NSP/OST Services vs injection-related risk behaviours and new IDUT-HIV cases 28 5.3 Association analysis: regression-based 29 6. Estimate the HIV Infection-to-Detection Interval Distribution 31 6.1 Background 31 6.2 Basic equations 31 7. Estimate HIV Transmission Dynamics in the Increasing Exponential Phase 33 7.1 Backgound 33 7.2 Basic model 33 7.3 Basic equations 34 7.4 Estimate transmission dynamics using HIV surveillance data: mathematical basis 35 7.4.1 The HIV incidence function I(t) 35 7.4.2 The HIV surveillance function S(t) 36 7.4.3 Estimate HIV transmission dynamics from S(t) 37 7.5 Estimate Lpre: the linear regression slope of ln[S(t)] in pre-HR exponential phase 37 7.6 Estimate Bpre: the HIV transmission rate in pre-HR exponential phase 38 8. Estimate HIV Transmission Dynamics in the Decreasing Exponential Phase 39 8.1 Extended model 39 8.2 Extended equations 39 8.3 Natural-course theory 40 8.3.1 Background 40 8.3.2 Estimate R0: the basic reproductive number 40 8.3.3 Estimate the final HIV prevalence in natural-course scenario 41 8.3.4 Estimate transmission dynamics using HIV surveillance data: mathematical basis 41 8.3.4.1 The HIV incidence function I(t) 41 8.3.4.2 The HIV surveillance function S(t) 42 8.3.4.3 Estimate HIV transmission dynamics from S(t) 42 8.3.5 Test the natural-course hypothesis 43 8.4 Intervention-effect theory 44 8.4.1 Background 44 8.4.2 Estimate transmission dynamics using HIV surveillance data: mathematical basis 45 8.4.2.1 Time frames: pre/full-HR exponential phases of I(t) and the corresponding S(t) 45 8.4.2.2 The HIV incidence function I(t) 45 8.4.2.3 The HIV surveillance function S(t) 46 8.4.2.4 Estimate HIV transmission dynamics from S(t) 46 8.4.3 Estimate Lfull: the linear regression slope of ln[S(t)] in full-HR exponential phase 47 8.4.4 Estimate vfull: the removal rate of HIV(+) IES-IDUs in full-HR exponential phase 47 8.4.5 Estimate Bfull: the HIV transmission rate in full-HR exponential phase 48 8.4.6 Estimate ∆B: the reduction ratio of HIV transmission rate 49 8.4.7 Estimate R: the effective reproductive number 49 9. Estimate Pfull: the HIV Prevalence among IES-IDUs in Full-HR Exponential Phase 51 9.1 Indirect estimation of Pfull: mathematical basis 51 9.1.1 Numerator: the annual HIV prevalence among IDUs in full-HR exponential phase 51 9.1.2 Denominator: the annual IES rate among IDUs in full-HR exponential phase 51 9.1.2.1 The IES rate is higher than the needle-sharing rate 51 9.1.2.2 Low estimate of actual IES rate: prevalence of self-reported IES behaviour 52 9.1.2.3 High estimate of actual IES rate: prevalence of anti-HCV antibody positivity 52 9.2 Obtain data for the numerator and denominator: systematic review 53 9.2.1 Literature search: HIV prevalence and IES rate among IDUs since 2007 53 9.2.2 Study exclusion criteria 53 9.2.3 Calculate Pfull from extracted data 54 10. Estimate the IDU Population Size: the Number of IDUs 55 10.1 Robust estimate based on two different methods 55 10.2 Method one 55 10.3 Method two 55 11. Estimate the Number of New HIV Cases Averted (Counterfactual Analysis) 57 11.1 Background 57 11.2 Estimate the number of IDUT-HIV cases averted: mathematical basis 57 11.2.1 Estimate the counterfactual number of prevalent IDUT-HIV infections: Y(t) 57 11.2.2 Estimate the counterfactual number of new IDUT-HIV infections: I(t) 58 11.2.3 Estimate the counterfactual number of new IDUT-HIV cases: θ(t) 58 11.2.4 Estimate the number of new IDUT-HIV cases averted: θ(t) - S(t) 59 11.3 Parameterization 59 11.3.1 Determine the value of Y0 59 11.3.2 Estimate transmission parameters (L, m, B) from pre-HR HIV surveillance data 60 11.3.3 Estimate the risk population size N: the number of IES-IDUs 60 11.3.3.1 N = the IES rate among IDUs * the number of IDUs 60 11.3.3.2 The counterfactual IES rate among IDUs: plausible range of value 60 11.3.3.3 Calculate N 62 12. Statistical Analyses 63 RESULTS 64 1. Emergence, evolution, and transmission dynamics of the IDUT-HIV epidemic 64 2. Injection-related risk behaviours, NSP/OST services, and association analyses 65 3. IDU-related vs non-IDU-related outcomes 65 4. Number of IDUT-HIV cases averted (counterfactual analysis) 66 DISCUSSION 67 1. Major findings 67 2. Decline of the IDUT-HIV epidemic: underlying causes 67 3. Consistency with molecular epidemiologic research findings 68 4. Increase in IES behaviour before HR and the potential causes 68 5. Reduction in injection-related risk behaviours after HR and the potential causes 69 6. Coverage of HR Services: NSP, OST, and HTC 70 7. Contextual factors of the HR programme effect: ART and structural intervention 70 8. Conclusion 71 REFERENCE 72 APPENDIX 86 1. List of Abbreviations 87 2. Supplementary Results 88 2.1 The estimated Pfull: HIV prevalence among IES-IDUs in full-HR exponential phase 88 2.2 The estimated number of IDUs (IDU Population Size) 90 2.2.1 Result of method one 90 2.2.2 Result of method two 90 2.2.3 Robust estimate based on the results of method one and method two 90 List of Tables Table 1: Estimated quarterly case detection rate of the national HIV surveillance system, 2006–2007 92 Table 2: Estimated values of Pfull based on the data extracted from systematic review of the reported HIV prevalence and IES rates among IDUs in Taiwan since 2007 93 Table 3: Amount and estimated coverage rates of NSP and OST services 95 Table 4: Association of the NSP/OST service amounts with the injection-related risk behaviours and the next year’s new IDUT-HIV cases 100 Table 5: Characteristics of new HIV cases, by risk group 103 List of Figures Figure 1: Risk factor distribution of new HIV cases, 1984–2011 106 Figure 2: Quarterly number of new HIV cases, by risk group, 1984–2011 108 Figure 3: Estimated HIV infection-to-detection interval distribution of the IDUT-HIV epidemic and the ST-HIV epidemic 110 Figure 4: Estimating HIV transmission dynamics in the pre-HR exponential phase (Q4 2001–Q3 2003) and the full-HR exponential phase (2007–2008), using HIV surveillance data from the period Q4 2002–Q3 2004 (panel A) and 2008–2009 (panel B), respectively 112 Figure 5: Time trends in the annual number of new HIV cases and level of HIV risk behaviours 114 Figure 6: The annual morphine-positive rate and (meth)amphetamine-positive rate among urine drug-screen samples reported from all testing facilities, 2001–2010 116 Figure 7: Actual and counterfactual numbers of new IDUT-HIV cases 118 | |
dc.language.iso | en | |
dc.title | 全國性減害計畫對台灣注射藥癮族群愛滋疫情的防治成效:全人口研究 | zh_TW |
dc.title | Effect of a National Harm Reduction Programme on the
HIV Epidemic Among Injecting Drug Users in Taiwan: A Population-Based Study | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 劉仁沛(Jen-Pei Liu),周穎政(Yiing-Jenq Chou),黃景祥(Jing-Shiang Hwang),郭柏秀(Po-Hsiu Kuo),陳培哲(Pei-Jer Chen) | |
dc.subject.keyword | 人類免疫缺乏病毒,注射藥癮,減害,清潔針具計畫,替代療法,美沙冬,預防與控制, | zh_TW |
dc.subject.keyword | HIV infections,Substance abuse,intravenous,Harm reduction,Needle exchange programme,Opioid substitution treatment,Methadone,Prevention & control, | en |
dc.relation.page | 119 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2014-08-13 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | 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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