Understanding spatial heterogeneity of drug resistant tuberculosis

描述(由申请人提供)：本申请表旨在为布里格姆妇女医院(BWH)全球卫生公平部研究员Helen Jenkins博士颁发K01奖。结核分枝杆菌(TB)耐药株威胁结核病控制。耐药结核病(耐药结核病)的发现和成功治疗受到目前大多数环境中可用的昂贵和耗时的检测方法的影响。对这一流行病的严重低估是有效控制的障碍。未确诊的DR-TB病例的预后较差，并且可能传播疾病的时间更长。在大多数情况下，当获得或使用药敏试验受到限制时，很难确定耐药热点。该项目旨在开发对耐药结核病数据进行空间分析的方法，以考虑诊断测试中的地理差异，并能够确定耐药结核病的高发病率。来自世界上两个最令人担忧的地区的数据集 关于耐药结核病，将使用东欧和撒哈拉以南非洲。具体目标是：(A) 描述和了解诊断水平高的地区耐药结核病的异质性和聚集性，(B)制定在诊断用途具有地理异质性的地区估计耐药结核病负担的方法，(C)描述和了解耐药结核病负担的异质性，以说明诊断检测中的巨大地理差异，(D)确定结核病治疗期间发展和获得耐药性的风险因素。这项工作将为分析这些地区的类似数据集提供一个方法学框架。拟议的项目可以帮助确定耐药结核病出现的机制，从而促进确定干预措施，以确保更多的患者得到及时和适当的治疗，并限制抗药性疾病的传播。詹金斯博士的导师将是哈佛大学公共卫生学院(HSPH)生物统计系统计计算教授马塞洛·帕加诺博士，他的研究重点是 传染病监测和统计方法博士和泰德·科恩博士，医学博士，公共卫生硕士，博士，HSPH流行病学助理教授和BWH医学副教授，从事结核病流行病学和耐药结核病监测研究。詹金斯博士的顾问委员会将包括HSPH的流行病学教授马克·利普西奇博士、哈佛大学地理分析中心的地理信息系统专家杰夫·布洛瑟姆博士和BWH的副内科医生、医学博士、公共卫生硕士Sonya Shin博士。詹金斯博士的培训目标是(1)成为空间数据分析方面的专家，(2)对传染病生物学有扎实的了解，她将参加课程来实现这些目标。詹金斯博士所接受的研究经验和培训将为她提供必要的技能和初步数据，使她能够在美国申请R01基金，获奖期即将结束。从长远来看，詹金斯博士的目标是成为一名独立的研究员，在传染病流行病学中开发和应用生物统计空间方法。

DESCRIPTION (provided by applicant): This application is for a K01 Award for Dr. Helen Jenkins, a Research Fellow in the Global Health Equity Division at Brigham and Women's Hospital (BWH). Drug resistant strains of Mycobacterium tuberculosis (TB) threaten TB control. The detection and successful treatment of drug resistant TB (DR-TB) are compromised by the costly and time-consuming detection methods that are currently available in most settings. The severe under-estimation of this epidemic is a barrier to effective control. Undiagnosed DR-TB cases have poorer outcomes and may transmit disease for longer. Identifying hotspots of drug resistance is difficult when either access to or usage of drug susceptibility testing is limited, a in most settings. This project aims to develop methods for the spatial analysis of DR-TB data that account for geographic variation in diagnostic testing and allow identification of high DR-TB incidence. Datasets from two of the most concerning regions of the world with regard to DR-TB, Eastern Europe and sub-Saharan Africa, will be used. The specific aims are: (A) Describe and understand heterogeneity and clustering of DR-TB in an area with high levels of diagnostics, (B) Develop methods for estimating DR-TB burden in an area with geographic heterogeneity in diagnostic use, (C) Describe and understand heterogeneity in DR-TB burden accounting for substantial geographic variation in diagnostic testing, (D) Identify risk factors fo the development and acquisition of drug resistance during TB treatment. This work will provide a methodological framework for analyzing similar datasets in these regions. The proposed project can help identify the mechanisms by which DR-TB emerges thus facilitating identification of interventions that ensure more patients receive timely and appropriate treatment and limit spread of resistant forms of disease. Dr. Jenkins' mentors will be Dr. Marcello Pagano, PhD, Professor of Statistical Computing in the Department of Biostatistics, Harvard School of Public Health (HSPH) whose research focuses on surveillance and statistical methods for infectious diseases and Dr. Ted Cohen, MD, MPH, DrPH, Assistant Professor of Epidemiology at HSPH and Associate Professor of Medicine at BWH who conducts research on TB epidemiology and DR-TB surveillance. Dr. Jenkins's advisory committee will include Dr. Marc Lipsitch, DPhil, Professor of Epidemiology at HSPH, Jeff Blossom, MSc, GIS expert at the Center for Geographic Analysis, Harvard University and Dr. Sonya Shin, MD, MPH, Associate Physician at BWH. Dr. Jenkins' training goals are (1) to become an expert in the analysis of spatial data and (2) obtain a solid understanding of the biology of infectious disease and she will take courses to achieve these. The research experience and training that Dr. Jenkins receives will provide her with the skills and preliminary data necessary to apply for R01 funding in the U.S. towards the end of the Award period. In the long-term, Dr. Jenkins aims to become an independent researcher developing and applying bio-statistical spatial methods in infectious disease epidemiology.