Comparative Modeling of Tuberculosis Epidemiology and Policy Outcomes in California.

Abstract

<b>Rationale:</b> Mathematical modeling 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.<b>Objectives:</b> To compare the influence of various modeling methods and assumptions on epidemiologic projections of domestic latent TB infection (LTBI) control interventions in California.<b>Methods:</b> We compared model results between 2005 and 2050 under a base-case scenario representing current TB services and alternative scenarios including: <i>1</i>) sustained interruption of <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) transmission, <i>2</i>) sustained resolution of LTBI and TB prior to entry of new residents, and <i>3</i>) one-time targeted testing and treatment of LTBI among 25% of non-U.S.-born individuals residing in California.<b>Measurements and Main Results:</b> Model estimates of TB cases and deaths in California were in close agreement over the historical period but diverged for LTBI prevalence and new <i>Mtb</i> 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-U.S.-born residents was projected to produce sustained reductions in TB incidence. Models found prevalent <i>Mtb</i> infection and migration to be more significant drivers of future TB incidence than local transmission.<b>Conclusions:</b> 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-U.S.-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 data on TB determinants and outcomes.