Rationale:
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.
Objectives: To compare the
influence of various modeling methods and assumptions on epidemiologic
projections of domestic latent TB infection (LTBI) control interventions
in California.
Methods: We compared model results between
2005 and 2050 under a base-case scenario representing current TB
services and alternative scenarios including: 1) sustained interruption of Mycobacterium tuberculosis (Mtb) transmission, 2) sustained resolution of LTBI and TB prior to entry of new residents, and 3) one-time targeted testing and treatment of LTBI among 25% of non–U.S.-born individuals residing in California.
Measurements and Main 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–U.S.-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–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.