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Modeling Missing Cases and Transmission Links in Networks of Extensively Drug-Resistant Tuberculosis in KwaZulu-Natal, South Africa.

Authors
  • Nelson, Kristin N1
  • Gandhi, Neel R1, 2
  • Mathema, Barun3
  • Lopman, Benjamin A1
  • Brust, James C M4
  • Auld, Sara C1, 2
  • Ismail, Nazir5, 6
  • Omar, Shaheed Vally5
  • Brown, Tyler S7
  • Allana, Salim1
  • Campbell, Angie1
  • Moodley, Pravi8, 9
  • Mlisana, Koleka8, 9
  • Shah, N Sarita10
  • Jenness, Samuel M1
  • 1 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia. , (Georgia)
  • 2 School of Medicine, Emory University, Atlanta, Georgia. , (Georgia)
  • 3 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.
  • 4 Albert Einstein College of Medicine and Montefiore Medical Center, New York, New York.
  • 5 National Institute for Communicable Diseases, Johannesburg, South Africa. , (South Africa)
  • 6 Department of Medical Microbiology, School of Medicine, University of Pretoria, Pretoria, South Africa. , (South Africa)
  • 7 Infectious Diseases Division, Massachusetts General Hospital, Boston, Massachusetts.
  • 8 National Health Laboratory Service, Johannesburg, South Africa. , (South Africa)
  • 9 School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa. , (South Africa)
  • 10 Centers for Disease Control and Prevention, Atlanta, Georgia. , (Georgia)
Type
Published Article
Journal
American journal of epidemiology
Publication Date
Jul 01, 2020
Volume
189
Issue
7
Pages
735–745
Identifiers
DOI: 10.1093/aje/kwaa028
PMID: 32242216
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011-2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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