請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74158
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林先和(Hsien-Ho Lin) | |
dc.contributor.author | Chih-Chi Yang | en |
dc.contributor.author | 楊芷其 | zh_TW |
dc.date.accessioned | 2021-06-17T08:22:17Z | - |
dc.date.available | 2019-08-26 | |
dc.date.copyright | 2019-08-26 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-13 | |
dc.identifier.citation | 1. Global tuberculosis report 2018, W.H. Organization, Editor. 2018: Geneva.
2. Golub, J.E., et al., Active case finding of tuberculosis: historical perspective and future prospects. Int J Tuberc Lung Dis, 2005. 9(11): p. 1183-203. 3. Ho, J., G.J. Fox, and B.J. Marais, Passive case finding for tuberculosis is not enough. International Journal of Mycobacteriology, 2016. 5(4): p. 374-378. 4. Corbett, E.L., et al., Comparison of two active case-finding strategies for community-based diagnosis of symptomatic smear-positive tuberculosis and control of infectious tuberculosis in Harare, Zimbabwe (DETECTB): a cluster-randomised trial. Lancet (London, England), 2010. 376(9748): p. 1244-1253. 5. Organization, W.H., Systematic Screening for Active Tuberculosis: Principles and Recommendations. 2013: Geneva. 6. Organization., W.H., Automated real-time nucleic acid amplification technology for rapid and simultaneous detection of tuberculosis and rifampicin resistance: Xpert MTB/RIF assay for the diagnosis of pulmonary and extrapulmonary TB in adults and children: policy update. . 2013. 7. Getahun, H., et al., Development of a standardized screening rule for tuberculosis in people living with HIV in resource-constrained settings: individual participant data meta-analysis of observational studies. PLoS Med, 2011. 8(1): p. e1000391. 8. Van't Hoog, A.H., I. Onozaki, and K. Lonnroth, Choosing algorithms for TB screening: a modelling study to compare yield, predictive value and diagnostic burden. BMC Infect Dis, 2014. 14: p. 532. 9. Adelman, M.W., et al., Intensified tuberculosis case finding among HIV-infected persons using a WHO symptom screen and Xpert((R)) MTB/RIF. Int J Tuberc Lung Dis, 2015. 19(10): p. 1197-203. 10. Van Wyk, S.S., H.H. Lin, and M.M. Claassens, A systematic review of prediction models for prevalent pulmonary tuberculosis in adults. Int J Tuberc Lung Dis, 2017. 21(4): p. 405-411. 11. Shih, Y.-J., et al., Development and validation of a prediction model for active tuberculosis case finding among HIV-negative/unknown populations. Scientific Reports, 2019. 9(1): p. 6143. 12. Ayles, H.M., et al., ZAMSTAR, The Zambia South Africa TB and HIV Reduction study: Design of a 2 × 2 factorial community randomized trial. Trials, 2008. 9(1): p. 63. 13. Reid, M.J.A. and N.S. Shah, Approaches to tuberculosis screening and diagnosis in people with HIV in resource-limited settings. The Lancet Infectious Diseases, 2009. 9(3): p. 173-184. 14. Narasimhan, P., et al., Risk Factors for Tuberculosis. Pulmonary Medicine, 2013. 2013: p. 11. 15. Jeon, C.Y. and M.B. Murray, Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med, 2008. 5(7): p. e152. 16. Schnabel, R.B., et al., Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet, 2009. 373(9665): p. 739-45. 17. Prevention, C.f.D.C.a. CDC DIVISION OF GLOBAL HIV & TB COUNTRY PROFILE. April 29, 2019 [cited 2019 May 30]; Available from: https://www.cdc.gov/globalhivtb/where-we-work/index.html. 18. James, S.L., et al., Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2018. 392(10159): p. 1789-1858. 19. Workneh, M.H., G.A. Bjune, and S.A. Yimer, Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. PLoS One, 2017. 12(4): p. e0175925. 20. Stephani, V., D. Opoku, and D. Beran, Self-management of diabetes in Sub-Saharan Africa: a systematic review. BMC Public Health, 2018. 18(1): p. 1148. 21. Asmelash, D. and Y. Asmelash, The Burden of Undiagnosed Diabetes Mellitus in Adult African Population: A Systematic Review and Meta-Analysis. Journal of Diabetes Research, 2019. 2019: p. 8. 22. Vermund, S.H., E.K. Sheldon, and M. Sidat, Southern Africa: the Highest Priority Region for HIV Prevention and Care Interventions. Current HIV/AIDS reports, 2015. 12(2): p. 191-195. 23. Aaron, L., et al., Tuberculosis in HIV-infected patients: a comprehensive review. Clinical Microbiology and Infection, 2004. 10(5): p. 388-398. 24. Hamada, Y., et al., Sensitivity and specificity of WHO's recommended four-symptom screening rule for tuberculosis in people living with HIV: a systematic review and meta-analysis. The Lancet HIV, 2018. 5(9): p. e515-e523. 25. Nanta, S., et al., Screening scheme development for active TB prediction among HIV-infected patients. Southeast Asian J Trop Med Public Health, 2011. 42(4): p. 867-75. 26. Hanifa, Y., et al., A clinical scoring system to prioritise investigation for tuberculosis among adults attending HIV clinics in South Africa. PLoS One, 2017. 12(8): p. e0181519. 27. Manaf, R., et al., Designing and Conducting Cost-Effectiveness Analysis Studies in Healthcare. Vol. 4. 2017. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74158 | - |
dc.description.abstract | 背景:
尚未被發現的結核病個案對於結核病控制是很大的挑戰。經由主動個案發現的策略可以有效找到結核病患,但是成本高昂。資源有限的國家適用以症狀為主的篩檢模式,然而此類工具所存在的敏感度及特異度問題使得整套個案發現模式需耗費大量資源,也因此造成了相當沉重的負擔。本研究發展並驗證一套活動性結核病預測模型,並與目前世界衛生組織所建議的症狀篩檢模式比較,進行成本效果分析。 方法: 研究族群來自南非/尚比亞減少結核病與愛滋病試驗(ZAMSTAR)中收集於2010年之盛行率資料。預測模型依照研究對象的愛滋病感染有無,分開建模。模型之依變項為經痰培養之結核分枝桿菌,預測因子包含結核病相關症狀、結核病危險因子以及過去個人/家戶病史。模型以多變項羅吉斯迴歸模型建立於南非資料集,基於赤池資訊準則(AIC)經逐步向後消去法選出最適模型,並轉換為分數系統,於尚比亞資料集做外部驗證。內部驗證為重複模型建立過程於1000組經自助抽樣法抽出之樣本。此外,預測模型與現有其他以症狀為基礎的篩檢策略透過成本效果分析,計算平均成本效果比(ACER)及增加成本效果比(ICER),以選出具成本效果的策略。 結果: 針對愛滋病族群的最適模型所包含之預測因子與其相對應之分數如後:性別(1)、體重減輕(1)、近期咳嗽(2)、飲酒(2)及胸痛(1),模型之預測能力在南非資料集中優於任一結核病相關症狀,於尚比亞資料集中之預測能力也不亞於目前的症狀篩檢策略。而針對非愛滋病族群的最適模型結果如後:體重減輕(3)、夜間盜汗(2)、短期咳嗽(3)、長期咳嗽(7)、飲酒(2)、吸菸(1)、性別(1)及家戶結核病史(2),模型預測能力在南非資料集及尚比亞資料集中皆優於任一結核病相關症狀及長期咳嗽。另外,經成本效果分析發現,於所有可能的篩檢模式之中,20組分數系統切點組合及16組分數系統切點組合分別在南非資料集與尚比亞資料集被選為具成本效果的篩檢策略。當具成本效果的分數系統切點組合實際應用在結核病個案發現時,平均找到一個活動性結核病患的成本範圍將從246美金至1670美金(於南非),及從186美金至6796美金(於尚比亞)。 結論: 本研究提出一套活動性結核病預測模型,除根據研究對象之愛滋病感染狀況分別建模,也與目前世界衛生組織建議之以症狀為主的篩檢策略在成本效果的考量上做綜合性的比較。模型除在預測能力及成本效果分析中優於現行篩檢策略,也在願意投入之金錢與檢出結核病個案之成效的決策點上提供多樣且彈性的選擇。 | zh_TW |
dc.description.abstract | Background:
Undetected tuberculosis (TB) cases were a challenge to global TB control. Active case finding could effectively detect TB patients but costly. For those resource-constrained countries, symptom-based screening algorithms were applicable, but at the same time resulted in a massive burden to the screening program due to the low sensitivity or low specificity of the screening tools. This study aimed to develop and validate a score-based screening algorithm for active TB case finding in the community. Furthermore, through cost-effectiveness analysis, we aimed to determine the cost-effective strategies for active TB case finding among the competing algorithms including the model developed in this study and the symptom-based tools proposed by the World Health Organization (WHO). Methods: The study population was based on a 2010 TB prevalence survey in the Zambia/South Africa Tuberculosis and AIDS Reduction (ZAMSTAR) trial. We developed separate prediction models for HIV-positive and HIV-negative/unknown populations. The dataset was divided into two parts according to participants' countries for model development (South Africa) and external validation (Zambia). The outcome was prevalent culture-confirmed TB. The potential predictors included TB symptoms, TB risk factors, and previous TB history. The models were built on multivariable logistic regression and selected through stepwise backward elimination based on Akaike Information Criterion. The final model was converted to a scoring system. Secondly, the scoring system was compared with any TB symptom and prolonged cough, through the cost0effectiveness analysis. The Average Cost Effectiveness Ratio (ACER) and Incremental Cost Effectiveness Ratio (ICER) were computed to evaluate the cost-effective strategies among all competing choices. Results: The predictors selected in the final model for HIV population and the corresponding scores are shown as follows: gender (1), weight loss (1), ever drink (2), current cough (2), and chest pain (1). The model was presented better AUC than any TB symptoms in both South African and Zambian dataset. On the other hand, the model for non-HIV population included the predictors: weight loss (3), night sweats (2), cough less than two weeks (3), prolonged (more than two weeks) cough (7), ever drink (2), ever smoke (1), gender (1), and household TB history (2). The model also presented the higher AUC than any TB symptoms and prolonged cough among the participants from two different countries. Moreover, through cost-effectiveness analysis, twenty score cutoff combinations were selected as the cost-effective strategies among the tools proposed by WHO and other score cutoff combinations in the South African dataset. The similar results were presented in Zambian dataset, in which sisteen score cutoff combinations were cost-effective in comparison to other screening strategies. When the cost-effective score cutoff combinations were applied for TB screening, the ACER ranged from 246 USD to 1670 USD in the South African dataset, and ranged from 186 USD to 6796 USD in the Zambian dataset. Conclusion: The scoring system for active TB case finding presented higher performance than any TB symptoms and prolonged cough among study population. The results of cost-effectiveness analysis showed that both the scoring systems for HIV and the non-HIV population were cost-effective at some score cutoff combinations in comparison to the tools proposed by WHO, and therefore provided new strategies for active TB case finding with multiple options under budget consideration. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:22:17Z (GMT). No. of bitstreams: 1 ntu-108-R06849005-1.pdf: 2526016 bytes, checksum: 8bd29b05a982d17cf445c9551b228376 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iv Chapter 1. Introduction 1 Chapter 2. Methods 4 2.1 Study population 4 2.2 Measurement of outcome 4 2.3 Measurement of predictors 5 2.4 Statistical analysis of the prediction model 5 2.5 Cost-effectiveness analysis (CEA) of the integrated screening algorithms 6 2.6 Application of CEA to hypothetical populations 7 Chapter 3. Results 8 3.1 Characteristic of study participants 8 3.2 Prediction model development and validation 8 3.3 Application of the scoring system 10 3.4 CEA results of possible active TB case finding strategies 10 3.5 Application of the CEA results 11 Chapter 4. Discussion 14 4.1 Summary of the results 14 4.2 Effect of fever and DM history to TB among study population 14 4.3 Performance and predictors of the prediction model for HIV population 16 4.4 Extended application of CEA results 17 4.5 Strengths of the study 18 4.6 Limitations of the study 19 Chapter 5. Conclusion 21 Reference 44 Appendix 47 | |
dc.language.iso | en | |
dc.title | 建立並驗證結核病預測模型與結核病社區篩檢策略之成本效果分析 | zh_TW |
dc.title | Development, validation, and cost-effectiveness analysis of
a prediction model for community-based systematic screening of active tuberculosis | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 方啟泰(Chi-Tau Fang),洪弘(Hung Hung),Patou M. Musumari(Patou M. Musumari) | |
dc.subject.keyword | 活動性結核病,個案發現,系統性篩檢,預測模型,分數系統,成本效果分析, | zh_TW |
dc.subject.keyword | active tuberculosis,case finding,systematic screening,prediction model,scoring system,cost-effectiveness analysis, | en |
dc.relation.page | 49 | |
dc.identifier.doi | 10.6342/NTU201903404 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-14 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 2.47 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。