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.

Public Health Relevance

Under-detection of drug-resistant tuberculosis (DR-TB) compromises the control of tuberculosis in areas where highly resistant forms of disease are emerging. The proposed research will develop methods to estimate DRTB incidence that account for spatial variation in drug susceptibility testing and thus facilitate the identificationof true DR-TB 'hot-spots'. In turn, this will allow for investigation of local drivers of DR-TB, infor the design of control interventions, and ultimately ensure that more patients receive appropriate treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
7K01AI102944-04
Application #
9187139
Study Section
Microbiology and Infectious Diseases B Subcommittee (MID)
Program Officer
Kraigsley, Alison
Project Start
2013-06-06
Project End
2018-05-31
Budget Start
2015-09-01
Budget End
2016-05-31
Support Year
4
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Boston University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
604483045
City
Boston
State
MA
Country
United States
Zip Code
McIntosh, Avery I; Jenkins, Helen E; White, Laura F et al. (2018) Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis. PLoS Med 15:e1002638
Dodd, Peter J; Yuen, Courtney M; Becerra, Mercedes C et al. (2018) Potential effect of household contact management on childhood tuberculosis: a mathematical modelling study. Lancet Glob Health 6:e1329-e1338
Jenkins, H E; Yuen, C M (2018) The burden of multidrug-resistant tuberculosis in children. Int J Tuberc Lung Dis 22:3-6
Ma, Y; Horsburgh, C R; White, L F et al. (2018) Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis. Epidemiol Infect 146:1478-1494
Stagg, H R; Lipman, M C; McHugh, T D et al. (2017) Isoniazid-resistant tuberculosis: a cause for concern? Int J Tuberc Lung Dis 21:129-139
Van Ness, Sarah E; Chandra, Ankit; Sarkar, Sonali et al. (2017) Predictors of delayed care seeking for tuberculosis in southern India: an observational study. BMC Infect Dis 17:567
Dodd, Peter J; Yuen, Courtney M; Sismanidis, Charalambos et al. (2017) The global burden of tuberculosis mortality in children: a mathematical modelling study. Lancet Glob Health 5:e898-e906
Nourzad, S; Jenkins, H E; Milstein, M et al. (2017) Estimating the global burden of multidrug-resistant tuberculosis among prevalent cases of tuberculosis. Int J Tuberc Lung Dis 21:6-11
Jenkins, Helen E; Yuen, Courtney M; Rodriguez, Carly A et al. (2017) Mortality in children diagnosed with tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis 17:285-295
McIntosh, Avery I; Doros, Gheorghe; Jones-López, Edward C et al. (2017) Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission. Stat Med 36:2522-2532

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