title: Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission.
abstract: Household contact studies, a mainstay of tuberculosis transmission research, often assume that tuberculosis-infected household contacts of an index case were infected within the household. However, strain genotyping has provided evidence against this assumption. Understanding the household versus community infection dynamic is essential for designing interventions. The misattribution of infection sources can also bias household transmission predictor estimates. We present a household-community transmission model that estimates the probability of community infection, that is, the probability that a household contact of an index case was actually infected from a source outside the home and simultaneously estimates transmission predictors. We show through simulation that our method accurately predicts the probability of community infection in several scenarios and that not accounting for community-acquired infection in household contact studies can bias risk factor estimates. Applying the model to data from Vitória, Brazil, produced household risk factor estimates similar to two other standard methods for age and sex. However, our model gave different estimates for sleeping proximity to index case and disease severity score. These results show that estimating both the probability of community infection and household transmission predictors is feasible and that standard tuberculosis transmission models likely underestimate the risk for two important transmission predictors. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Boston University, Boston, Massachusetts, U.S.A.|Section of Infectious Diseases, Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Avery I
authors.identifier: http://orcid.org/0000-0002-4164-502X
authors.initials: AI
authors.lastname: McIntosh
authors.affiliation: Department of Biostatistics, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Gheorghe
authors.initials: G
authors.lastname: Doros
authors.affiliation: Section of Infectious Diseases, Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Edward C
authors.initials: EC
authors.lastname: Jones-López
authors.affiliation: Section of Infectious Diseases, Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Mary
authors.initials: M
authors.lastname: Gaeddert
authors.affiliation: Department of Biostatistics, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Helen E
authors.initials: HE
authors.lastname: Jenkins
authors.affiliation: Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo (UFES), Vitória, Brazil.
authors.forename: Patricia
authors.initials: P
authors.lastname: Marques-Rodrigues
authors.affiliation: Section of Infectious Diseases, Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Jerrold J
authors.initials: JJ
authors.lastname: Ellner
authors.affiliation: Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo (UFES), Vitória, Brazil.
authors.forename: Reynaldo
authors.initials: R
authors.lastname: Dietze
authors.affiliation: Department of Biostatistics, Boston University, Boston, Massachusetts, U.S.A.
authors.forename: Laura F
authors.identifier: http://orcid.org/0000-0002-0588-8235
authors.initials: LF
authors.lastname: White
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D001938:Brazil / Q000453:epidemiology; D017714:Community-Acquired Infections / Q000453:epidemiology / Q000635:transmission; D003198:Computer Simulation; D016358:Contact Tracing / Q000706:statistics & numerical data; D005191:Family Characteristics; D006801:Humans; D016014:Linear Models; D011336:Probability; D012307:Risk Factors; D014397:Tuberculosis, Pulmonary / Q000453:epidemiology / Q000635:transmission*
keywords: Bayesian; bias; community; hierarchical models; household; infection; mixed effects models; risk factor; tuberculosis