Control strategies for tuberculosis epidemics: New models for old problems

Science

Volumn 273, Issue 5274, 1996, Pages 497-500

  Blower, S M ^a,b   Small, P M ^c   Hopewell, P C ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b SAN FRANCISCO GENERAL HOSPITAL   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ISONIAZID;

ARTICLE; CHEMOPROPHYLAXIS; DISEASE CONTROL; DRUG RESISTANCE; EPIDEMIC; HUMAN; MATHEMATICAL MODEL; MYCOBACTERIUM TUBERCULOSIS; PRIORITY JOURNAL; THEORY; TREATMENT FAILURE; TUBERCULOSIS; WORLD HEALTH ORGANIZATION;

EID: 0029821283     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.273.5274.497     Document Type: Article

References (32)

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6. T. C. Porco and S. M. Blower, in preparation.
7. The structure and the assumptions of the model, without chemoprophylaxis and treatment, have been described elsewhere (4). The model structure allows that (i) the majority of latently infected individuals will never develop disease, and (ii) those infected individuals who develop disease will do so by either of two pathogenic mechanisms: primary progression (soon after infection with M. tuberculosis) or reactivation of a contained infection (several years to decades after infection). Consequently, two types of disease are modeled: primary progressive tuberculosis and reactivation tuberculosis. The two pathogenic mechanisms are modeled by allowing a proportion p of the newly infected individuals to develop disease directly and a proportion 1 - p of the newly infected individuals to enter the latent group. Over time (t), the number of susceptibles (X) increases due to the recruitment rate (II) (that is, due to birth and to immigration) and decreases due to the incidence rate of infection (λX, where λ is the per capita force of infection) and to the per capita nontuberculosis mortality rate (μ). The number of individuals with latent infections (L) increases due to the number of individuals who enter a latent phase [which is a constant proportion (1 - p) of the incidence rate of infection] and decreases due to individuals with latent infections who either progress to disease (at a per capita rate v), die of other causes (at a per capita rate μ), or receive effective chemoprophylaxis (at a per capita rate σ). The number of cases (T) increases due to individuals with latent infections who slowly develop tuberculosis (at a per capita rate v) and to recently infected individuals who develop disease by direct progression (pλX). The number of cases decreases as a result of effective treatment (at a per capita rate Φ) or mortality [due either to tuberculosis (at a per capita rate μr) or due to other causes (at a per capita rate μ)].
8. o is the basic reproductive number of tuberculosis (4).
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12. For these calculations we assumed that 50% of untreated cases die within 5 years (18).
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17. The stated goal of the WHO for global tuberculosis control is to reduce significantly mortality and morbidity. Elimination of this disease is defined as an incidence of <1 case per million population (16).
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19. For example, the cumulative fraction of cases that are treated can exceed 80% even if only 50% of the cases are treated per year.
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22. MAX is the maximum probability of treatment failure at which one case of drug resistance is generated for every two drug-sensitive cases treated.
23. Acquired drug resistance initiates an epidemic of drug-resistant tuberculosis; as the epidemic progresses, the significance of primary drug resistance to the transmission dynamics increases. Tuberculosis epidemics have slow dynamics (4); it can take many decades for drug-resistant tuberculosis to reach substantial levels (S. M. Blower, T. C. Porco, P. C. Hopewell, P. M. Small, in preparation).
24. The value of δ is determined by both patient compliance and the antibacterial activity of the drugs.
25. M. Goble et al., N. Engl. J. Med. 328, 527 (1993).
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28. The suggested levels for treatment failure rates are intended to prevent perverse outcomes; hence, significantly lower treatment failure rates are desirable.
29. Transmission models have been used previously to suggest rational control strategies for many other infectious diseases [for example, A. R. McLean and R. M. Anderson, Epidemiol. Infect. 100, 419 (1988); A. R. McLean and S. M. Blower, Proc. R. Soc. London Ser. B 253, 9 (1993); and S. M. Blower and A. R. McLean, Science 265, 1451 (1994)].
30. Transmission models have been used previously to suggest rational control strategies for many other infectious diseases [for example, A. R. McLean and R. M. Anderson, Epidemiol. Infect. 100, 419 (1988); A. R. McLean and S. M. Blower, Proc. R. Soc. London Ser. B 253, 9 (1993); and S. M. Blower and A. R. McLean, Science 265, 1451 (1994)].
31. Transmission models have been used previously to suggest rational control strategies for many other infectious diseases [for example, A. R. McLean and R. M. Anderson, Epidemiol. Infect. 100, 419 (1988); A. R. McLean and S. M. Blower, Proc. R. Soc. London Ser. B 253, 9 (1993); and S. M. Blower and A. R. McLean, Science 265, 1451 (1994)].
32. We are grateful to A. McLean for insightful comments and interesting ideas during the initial stages of this work. We also thank T. Porco, T. Lietman, C. Daley, H. Hethcote, C. Castillo-Chavez, A. Moss, N. Freimer, J. Freimer, and D. Freimer for useful comments during the course of this work. We are grateful to the National Institute on Drug Abuse (grant 1R29DA08153), the National Institute of Allergy and Infectious Diseases (grants A133831 and A135969), and the Robert Wood Johnson Foundation for financial support.