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A Bayesian implementation of backcalculation to estimate historical tuberculosis incidence

Abstract Number:

2571 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Anne Shapiro (1), Shariq Mohammed (1), C. Robert Horsburgh (1), Helen Jenkins (1), Laura White (1)

Institutions:

(1) Boston University School of Public Health, Boston, MA

Co-Author(s):

Shariq Mohammed  
Boston University School of Public Health
C. Robert Horsburgh  
Boston University School of Public Health
Helen Jenkins  
Boston University School of Public Health
Laura White  
Boston University School of Public Health

First Author:

Anne Shapiro  
Boston University School of Public Health

Presenting Author:

Anne Shapiro  
N/A

Abstract Text:

Despite being a leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource intensive and often imprecise. Backcalculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian backcalculation methods for TB to appropriately assign case notification data to the time point of onset of disease. Recorded counts of TB cases are known to be underestimates of the true burden of disease, so we develop a cure model formulation of the TB disease duration distribution to account for underreporting. We assume a Poisson distribution for case counts and incidence and use a penalized likelihood prior to smooth estimates. We estimated TB incidence for Viet Nam, Cambodia, and The Philippines from 1995-2019 via Markov Chain Monte Carlo. Estimated TB incidence in a given year was on average 19% greater than recorded notifications. These estimates require fewer assumptions than existing methods.

Keywords:

Bayesian estimation|MCMC|Epidemiology|Biostatistics| |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Miscellaneous

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