American Journal of Epidemiology
Volume 187, Issue 9, 2018, Pages 2011-2020

Prospects for tuberculosis elimination in the United States: Results of a transmission dynamic model (Article) (Open Access)

Menzies N.A.* , Cohen T. , Hill A.N. , Yaesoubi R. , Galer K. , Wolf E. , Marks S.M. , Salomon J.A.
  • a Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • b Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • c Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • d Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • e Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • f Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • g Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States
  • h Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States

Abstract

We estimated long-term tuberculosis (TB) trends in the US population and assessed prospects for TB elimination. We used a detailed simulation model allowing for changes in TB transmission, immigration, and other TB risk determinants. Five hypothetical scenarios were evaluated from 2017 to 2100: 1) maintain current TB prevention and treatment activities (base case); 2) provision of latent TB infection testing and treatment for new legal immigrants; 3) increased uptake of latent TB infection screening and treatment among high-risk populations, including a 3-month isoniazid-rifapentine regimen; 4) improved TB case detection; and 5) improved TB treatment quality. Under the base case, we estimate that by 2050, TB incidence will decline to 14 cases per million, a 52% (95% posterior interval (PI): 35, 67) reduction from 2016, and 82% (95% posterior interval: 78, 86) of incident TB will be among persons born outside of the United States. Intensified TB control could reduce incidence by 77% (95% posterior interval: 66, 85) by 2050. We predict TB may be eliminated in US-born but not non-US-born persons by 2100. Results were sensitive to numbers of people entering the United States with latent or active TB, and were robust to alternative interpretations of epidemiologic evidence. TB elimination in the United States remains a distant goal; however, strengthening TB prevention and treatment could produce important health benefits. antitubercular agents; immigration; latent tuberculosis; tuberculosis, pulmonary Abbreviations: HIV, human immunodeficiency virus; LTBI, latent tuberculosis infection; MDR-TB, multidrug resistant tuberculosis; TB, tuberculosis; TLTBI, treatment of latent tuberculosis infection. © Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2018.

Author Keywords

[No Keywords available]

Index Keywords

immigrant Caucasian trend analysis risk human immigration disease eradication Human immunodeficiency virus United States Treatment Failure Humans model latent tuberculosis tuberculosis unclassified drug rifapentine simulator scenario analysis isoniazid plus rifapentine high risk population theoretical model Models, Theoretical Incidence Article disease transmission infection prevention major clinical study tuberculin test disease elimination isoniazid simulation tuberculosis control disease incidence
Link
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054971457&doi=10.1093%2faje%2fkwy094&partnerID=40&md5=8e679975f2de16e572e38c71dfd6a408
DOI: 10.1093/aje/kwy094
ISSN: 00029262
Cited by: 10
Original Language: English