z-logo
open-access-imgOpen Access
Tuberculosis: Old problems and new approaches
Author(s) -
Roy M. Anderson
Publication year - 1998
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.95.23.13352
Subject(s) - tuberculosis , computational biology , virology , biology , medicine , computer science , data science , pathology
In New York City from 1985 to 1992, reported cases of tuberculosis rose by more than 20%. This followed a 30-year period of decline in the incidence of the disease. The reasons for the reversal are many and include the role of HIV-1 infection in enhancing disease progression and transmission, homelessness in the city, the emergence of multi-drug-resistant strains of the etiological agent (Mycobacterium tuberculosis), and a failing public health infrastructure. Globally, the scale of the tuberculosis problem is enormous, with the World Health Organization estimating that a third of the world’s population is infected with the bacterium and predicting 90 million new cases in the decade up to the year 2000 (1). As the magnitude of the problem continues to grow, the dream of eradication fades into the distant future, despite the fact that the treatment of the disease by mass chemotherapy is a very cost-effective public health intervention. Tuberculosis is likely to remain one of the 10 most important causes of premature mortality worldwide in the coming two decades. In this issue, Murray and Salomon (2) examine, by the use of a simple mathematical model of transmission, the potential impact of different forms of intervention to control M. tuberculosis. They reach an important conclusion, namely, that active case finding followed by treatment, if orchestrated on a global scale, could save 23 million cases of tuberculosis between 1998 and 2030. Furthermore, they argue that a vaccine of moderate efficacy (e.g., 50%), again used on a global scale, could lower the incidence of disease by 36 million and save 9 million lives over the same time interval. These predictions are likely to be influential. Recent experience suggests that the production of numerical estimates of the burden of morbidity and mortality caused by a given etiological agent confer authority in …

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom