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PLoS One. 2019 Apr 9;14(4):e0214532. doi: 10.1371/journal.pone.0214532. eCollection 2019.

Outlook for tuberculosis elimination in California: An individual-based stochastic model.

Author information

1
Stanford University School of Medicine, Palo Alto, CA, United States of America.
2
Consortium to Assess Prevention Economics (CAPE), University of California San Francisco, San Francisco, CA, United States of America.
3
Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, CA, United States of America.
4
Curry International Tuberculosis Center, University of California, San Francisco, San Francisco, CA, United States of America.
5
Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
6
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States of America.
7
Tuberculosis Control Branch, California Department of Public Health, Richmond, CA, United States of America.
8
Division of Tuberculosis Elimination, National Center for HIV, Hepatitis, STI, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America.
9
Philip R Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA, United States of America.

Abstract

RATIONALE:

As part of the End TB Strategy, the World Health Organization calls for low-tuberculosis (TB) incidence settings to achieve pre-elimination (<10 cases per million) and elimination (<1 case per million) by 2035 and 2050, respectively. These targets require testing and treatment for latent tuberculosis infection (LTBI).

OBJECTIVES:

To estimate the ability and costs of testing and treatment for LTBI to reach pre-elimination and elimination targets in California.

METHODS:

We created an individual-based epidemic model of TB, calibrated to historical cases. We evaluated the effects of increased testing (QuantiFERON-TB Gold) and treatment (three months of isoniazid and rifapentine). We analyzed four test and treat targeting strategies: (1) individuals with medical risk factors (MRF), (2) non-USB, (3) both non-USB and MRF, and (4) all Californians. For each strategy, we estimated the effects of increasing test and treat by a factor of 2, 4, or 10 from the base case. We estimated the number of TB cases occurring and prevented, and net and incremental costs from 2017 to 2065 in 2015 U.S. dollars. Efficacy, costs, adverse events, and treatment dropout were estimated from published data. We estimated the cost per case averted and per quality-adjusted life year (QALY) gained.

MEASUREMENTS AND MAIN RESULTS:

In the base case, 106,000 TB cases are predicted to 2065. Pre-elimination was achieved by 2065 in three scenarios: a 10-fold increase in the non-USB and persons with MRF (by 2052), and 4- or 10-fold increase in all Californians (by 2058 and 2035, respectively). TB elimination was not achieved by any intervention scenario. The most aggressive strategy, 10-fold in all Californians, achieved a case rate of 8 (95% UI 4-16) per million by 2050. Of scenarios that reached pre-elimination, the incremental net cost was $20 billion (non-USB and MRF) to $48 billion. These had an incremental cost per QALY of $657,000 to $3.1 million. A more efficient but somewhat less effective single-lifetime test strategy reached as low as $80,000 per QALY.

CONCLUSIONS:

Substantial gains can be made in TB control in coming years by scaling-up current testing and treatment in non-USB and those with medical risks.

Conflict of interest statement

The authors have declared that no competing interests exist.

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