title: Heightened vulnerability to MDR-TB epidemics after controlling drug-susceptible TB.
abstract: BACKGROUND
  Prior infection with one strain TB has been linked with diminished likelihood of re-infection by a new strain. This paper attempts to determine the role of declining prevalence of drug-susceptible TB in enabling future epidemics of MDR-TB.
  
  
  METHODS
  A computer simulation of MDR-TB epidemics was developed using an agent-based model platform programmed in NetLogo (See http://mdr.tbtools.org/). Eighty-one scenarios were created, varying levels of treatment quality, diagnostic accuracy, microbial fitness cost, and the degree of immunogenicity elicited by drug-susceptible TB. Outcome measures were the number of independent MDR-TB cases per trial and the proportion of trials resulting in MDR-TB epidemics for a 500 year period after drug therapy for TB is introduced.
  
  
  RESULTS
  MDR-TB epidemics propagated more extensively after TB prevalence had fallen. At a case detection rate of 75%, improving therapeutic compliance from 50% to 75% can reduce the probability of an epidemic from 45% to 15%. Paradoxically, improving the case-detection rate from 50% to 75% when compliance with DOT is constant at 75% increases the probability of MDR-TB epidemics from 3% to 45%.
  
  
  CONCLUSIONS
  The ability of MDR-TB to spread depends on the prevalence of drug-susceptible TB. Immunologic protection conferred by exposure to drug-susceptible TB can be a crucial factor that prevents MDR-TB epidemics when TB treatment is poor. Any single population that successfully reduces its burden of drug-susceptible TB will have reduced herd immunity to externally or internally introduced strains of MDR-TB and can experience heightened vulnerability to an epidemic. Since countries with good TB control may be more vulnerable, their self interest dictates greater promotion of case detection and DOTS implementation in countries with poor control to control their risk of MDR-TB.
authors.affiliation: School of Humanities and Sciences, Stanford University, Stanford, California, USA.
authors.forename: Jason D
authors.initials: JD
authors.lastname: Bishai
authors.forename: William R
authors.initials: WR
authors.lastname: Bishai
authors.forename: David M
authors.initials: DM
authors.lastname: Bishai
mesh_terms: D000995:Antitubercular Agents / Q000627:therapeutic use*; D003198:Computer Simulation; D058872:Epidemics; D006801:Humans; D008954:Models, Biological; D015995:Prevalence; D018088:Tuberculosis, Multidrug-Resistant / Q000188:drug therapy / Q000453:epidemiology*
chemical_list: D000995:Antitubercular Agents