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Transmission modeling with regression adjustment for analyzing household-based studies of infectious disease

application to tuberculosis

Crawford, Forrest W.a,b,c,d; Marx, Florian M.e; Zelner, Jonf; Cohen, Tede,*

doi: 10.1097/EDE.0000000000001143
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Background: Household contacts of people infected with a transmissible disease may be at risk due to this proximate exposure, or from other unobserved sources. Understanding variation in infection risk is essential for targeting interventions.

Methods: We develop an analytical approach to estimate household and exogenous forces of infection, while accounting for individual-level characteristics that affect susceptibility to disease and transmissibility. We apply this approach to a cohort study conducted in Lima, Peru of 18,544 subjects in 4,500 households with at least one active tuberculosis (TB) case, and compare the results to those obtained by Poisson and logistic regression.

Results: HIV-coinfected (susceptibility hazard ratio, SHR=3.80, 1.56-9.29), child (SHR=1.72, 1.32-2.23) and teenage (SHR=2.00, 1.49-2.68) household contacts of TB cases experience a higher hazard of TB than do adult contacts. Isoniazid preventive therapy (SHR=0.30, 0.21-0.42) and BCG vaccination (SHR=0.66, 0.51-0.86) reduce the risk of disease among household contacts. TB cases without microbiologically confirmation exert a smaller hazard of TB among their close contacts compared with smear- or culture-positive cases (excess HR=0.88, 0.82-0.93 for HIV- cases and 0.82, 0.57-0.94 for HIV+ cases). The extra-household force of infection results in 0.01 (95% CI: 0.004,0.028) TB cases per susceptible household contact per year, and the rate of transmission between a microbiologically-confirmed TB case and susceptible household contact at 0.08 (95% CI: 0.045,0.129) TB cases per pair, per year.

Conclusions: Accounting for exposure to infected household contacts permits estimation of risk factors for disease susceptibility and transmissibility, and comparison of within-household and exogenous forces of infection.

a. Department of Biostatistics, Yale School of Public Health

b. Department of Statistics & Data Science, Yale University

c. Department of Ecology & Evolutionary Biology, Yale University

d. Yale School of Management

e. Department of Epidemiology of Microbial Diseases, Yale School of Public Health

f. Department of Epidemiology, University of Michigan School of Public Health

Conflicts of interest: The authors have no conflicts of interest to declare.

Funding statement: This work was supported by NIH grants NIAID U19 A1076217, NICHD 1DP2HD091799, and NIAID R01 AI112438.

Acknowledgments:We are grateful to Chuan Chin Huang, Olga Morozova, Megan Murray, Laura F. White, and Zibiao Zhang for helpful comments on the manuscript. We also thank Mercedes Becerra, Leo Lecca, and all the members of Socios En Salud in Lima, Peru.

Corresponding author: Ted Cohen, Yale School of Public Health, 60 College Street, New Haven CT USA 06510. +1 203 785-5126, theodore.cohen@yale.edu

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