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Am J Respir Crit Care Med. 2020 Feb 1;201(3):356-365. doi: 10.1164/rccm.201907-1289OC.

Comparative Modeling of Tuberculosis Epidemiology and Policy Outcomes in California.

Author information

1
Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
2
Philip R. Lee Institute for Health Policy Studies.
3
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
4
Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut.
5
Department of Medicine, Stanford University, Palo Alto, California.
6
Division of Tuberculosis Elimination, CDC, Atlanta, Georgia; and.
7
Tuberculosis Control Branch, California Department of Public Health, Richmond, California.
8
Department of Epidemiology and Biostatistics, and.
9
Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, California.

Abstract

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.

KEYWORDS:

immigration; infectious disease modeling; latent tuberculosis infection; public health; tuberculosis

Comment in

PMID:
31626560
DOI:
10.1164/rccm.201907-1289OC

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