Network Models for Evaluating HIV-associated MDR TB transmission and control

评估艾滋病毒相关耐多药结核病传播和控制的网络模型

• 批准号: 9100638
• 负责人: Jason Randolph Andrews
• 金额: $ 13.72万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-22 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9100638
• 关键词: AIDS clinical trial group; Accounting; African; Age; Applied Research; Chemoprophylaxis; Clinical; Clinical Treatment; Clinical Trials; Collaborations; Communicable Diseases; Communities; Community Healthcare; Cost Effectiveness Analysis; Country; Data; Development; Disease; Doctor of Medicine; Drug resistance; Drug resistance in tuberculosis; Engineering; Epidemic; Epidemiology; Evaluation; Extreme drug resistant tuberculosis; Foundations; General Hospitals; Goals; HIV; HIV/TB; Health; Health Policy; Household; Impact evaluation; Incidence; Income; Individual; Infection; Intervention; Investigation; K-Series Research Career Programs; Life; Life Expectancy; Massachusetts; Mathematics; Measures; Mentored Research Scientist Development Award; Mentors; Mentorship; Meta-Analysis; Methods; Modeling; Morbidity - disease rate; Multi-Drug Resistance; NIH Office of AIDS Research; National Institute of Allergy and Infectious Disease; Natural History; Outcome; Pathway Analysis; Patients; Pattern; Pharmaceutical Preparations; Policies; Population; Postdoctoral Fellow; Prevalence; Prevention; Prevention strategy; Preventive therapy; Preventive treatment; Public Health; Public Health Schools; Recurrence; Regimen; Research; Research Personnel; Research Training; Resources; Rifampicin resistance; Schools; South Africa; South African; Structure; Testing; Training; Treatment Protocols; Tuberculosis; Universities; Work; antiretroviral therapy; base; career; case finding; cost; cost effectiveness; design; dynamic system; experience; extensive drug resistance; high risk; improved; interest; mathematical model; member; mortality; network models; prophylactic; respiratory; screening; social; success; transmission process; tuberculosis drugs; tuberculosis treatment

DESCRIPTION (provided by applicant): The emergence of an epidemic of multidrug resistant (MDR) and extensively drug-resistant (XDR) TB in South Africa threatens to undermine the advances made in the antiretroviral therapy (ART) rollout for HIV. While contact investigations for household members of individuals with MDR or XDR TB holds practical appeal, the potential benefit of this strategy in the control of drug-resistant tuberculosis in a high TB prevalence setting is not known. Mathematical models evaluating the control of TB have not accounted for the realistic contact structure in respiratory transmission, precluding the evaluation of contact investigation strategies. This K01 application proposes to draw upon age-structured data on close contact rates from a South African in order to model tuberculosis transmission. Using this framework, the following specific aims are proposed: 1) To design and populate a network model of tuberculosis natural history and transmission and in a high HIV prevalence setting using empirical social contact data; 2)To evaluate the effect of social contact rates and network structure on the projections of tuberculosis incidence and on the impact of enhanced case-finding and treatment success; 3) To project the clinical impact and cost-effectiveness of tuberculosis screening and provision of preventive therapy for contacts of individuals with multidrug-resistant and extensively drug- resistant tuberculosis in a high HIV prevalence setting. This work will inform public health policy as well as clinical trials for development of chemoprophylaxis for MDR/XDR TB, which is a key aim of the NIAID Research Agenda for Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis. The candidate, Jason Andrews, M.D., S.M.,DTM&H is a postdoctoral fellow in infectious diseases at Massachusetts General Hospital and will benefit from the mentorship of investigators with extensive expertise in mathematical modeling, network analysis, and statistical inference. This research will be conducted in collaboration with investigators at the Desmond Tutu HIV Centre at the University of Cape Town, where extensive clinical and community-based data on HIV and TB natural history and epidemiology are available. The Candidate will gain expertise in advanced methods for mathematical modeling, network analysis and statistical inference of dynamic systems through coursework at the Harvard School of Public Health and the Applied Mathematics Department at the Harvard School of Engineering and Applied Sciences. The proposed training and research experience will provide the foundation for a career as an independent investigator.

描述（由申请人提供）：南非出现了多药耐药（MDR）和广泛耐药（XDR）结核病的流行，这可能会破坏在艾滋病毒抗逆转录病毒治疗（ART）推广方面取得的进展。虽然对耐多药或广泛耐药结核病患者的家庭成员进行接触者调查具有实际吸引力，但尚不清楚这一战略在结核病高流行环境中控制耐药结核病方面的潜在益处。评估结核控制的数学模型没有考虑到呼吸道传播中实际的接触结构，妨碍了对接触调查策略的评估。这个K01应用程序建议利用来自南非的密切接触率的年龄结构数据，以便对结核病传播进行建模。利用这一框架，提出了以下具体目标：1)利用经验性社会接触数据，设计并构建一个结核病自然史和传播网络模型；2)评估社会接触率和网络结构对结核病发病率预测的影响，以及对提高病例发现率和治疗成功率的影响；3)预测在艾滋病毒高流行环境中，对多药耐药和广泛耐药结核病患者接触者进行结核病筛查和提供预防性治疗的临床影响和成本效益。这项工作将为公共卫生政策以及开发耐多药/广泛耐药结核病化学预防的临床试验提供信息，这是NIAID耐多药和广泛耐药结核病研究议程的一个关键目标。候选人Jason Andrews， m.d., s.m.，DTM&H是麻省总医院传染病博士后，将受益于在数学建模，网络分析和统计推断方面具有广泛专业知识的研究人员的指导。这项研究将与开普敦大学Desmond Tutu艾滋病毒中心的研究人员合作进行，该中心有关于艾滋病毒和结核病自然史和流行病学的广泛临床和社区数据。候选人将通过哈佛大学公共卫生学院和哈佛大学工程与应用科学学院应用数学系的课程获得数学建模、网络分析和动态系统统计推断的高级方法方面的专业知识。拟议的培训和研究经验将为作为独立调查员的职业生涯奠定基础。