Comparative Modelling of Tuberculosis Epidemiology and Policy Outcomes in California
Menzies N,Parriott A,Shrestha S,Dowdy D,Cohen T,Salomon J,Marks S,Hill A,Winston C,Asay G,Barry P,Readhead A,Flood J,Kahn J,Shete P

Comparative Modelling of Tuberculosis Epidemiology and Policy Outcomes in California

American Journal of Respiratory and Critical Care Medicine | October 18, 2019

Rationale Mathematical modelling is used to understand disease dynamics, forecast trends, and inform public health prioritization. We conducted a comparative analysis of tuberculosis (TB) epidemiology and potential intervention effects in California, using three previously developed epidemiologic models of TB. Measurements and Methods We compared model results between 2005 and 2050 under a base case scenario representing current TB services, and alternative scenarios including: (i) sustained interruption of Mycobacterium tuberculosis (Mtb) transmission, (ii) sustained resolution of latent TB infection (LTBI) and TB prior to entry of new residents, and (iii) one-time targeted testing and treatment of LTBI among 25% of non-US-born individuals residing in California. Results Model estimates of TB cases and deaths in California were in close agreement over the historical period but diverged for LTBI prevalence and new Mtb infections—outcomes for which definitive data are unavailable. Between 2018 and 2050, models projected average annual declines of 0.58-1.42% in TB cases, without additional interventions. A one-time LTBI testing and treatment intervention among non-US-born residents was projected to produce sustained reductions in TB incidence. Models found prevalent Mtb infection and migration to be more significant drivers of future TB incidence than local transmission. Conclusions All models projected a stagnation in the decline of TB incidence, highlighting the need for additional interventions including greater access to LTBI diagnosis and treatment for non-US-born individuals. Differences in model results reflect gaps in historical data and uncertainty in the trends of key parameters, demonstrating the need for high-quality, up-to-date TB determinant and outcome data.

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