BACKGROUND: Reinfection is a major contributor to tuberculosis (TB). It seems that the higher the local incidence, the higher the proportion of reinfection. METHODS: Based on a systematic review of the literature, we established a regression model to predict the reinfection proportion from the local incidence. We then used our local data to verify the algorithm. RESULTS: Of the 23 studies addressing reinfection in recurrent TB, 6 were population based. The reinfection proportion was correlated with the local incidence (reinfection proportion=-29.7+36.8 x log Incidence) (95% confidence interval [CI] for coefficient, 15.3-58.3; R2=0.849). The reinfection proportion in Taiwan (incidence, 62.4/100,000 people) was estimated to be 36% (95% CI, 3%-69%). Of our 49 recurrent patients, 51% had reinfection. Patients with reactivation seemed more likely to have underlying diseases and less likely to be smear positive. The relapse isolates seemed more resistant than the initial isolates. CONCLUSIONS: The regression model could possibly predict the TB reinfection proportion from the local incidence. This algorithm is probably helpful in policy making for TB control programs. In areas where TB is endemic, reinfection might be responsible for >50% of TB cases, and aggressive surveillance to detect asymptomatic carriers could be an important strategy for controlling the disease.
BACKGROUND: Reinfection is a major contributor to tuberculosis (TB). It seems that the higher the local incidence, the higher the proportion of reinfection. METHODS: Based on a systematic review of the literature, we established a regression model to predict the reinfection proportion from the local incidence. We then used our local data to verify the algorithm. RESULTS: Of the 23 studies addressing reinfection in recurrent TB, 6 were population based. The reinfection proportion was correlated with the local incidence (reinfection proportion=-29.7+36.8 x log Incidence) (95% confidence interval [CI] for coefficient, 15.3-58.3; R2=0.849). The reinfection proportion in Taiwan (incidence, 62.4/100,000 people) was estimated to be 36% (95% CI, 3%-69%). Of our 49 recurrent patients, 51% had reinfection. Patients with reactivation seemed more likely to have underlying diseases and less likely to be smear positive. The relapse isolates seemed more resistant than the initial isolates. CONCLUSIONS: The regression model could possibly predict the TB reinfection proportion from the local incidence. This algorithm is probably helpful in policy making for TB control programs. In areas where TB is endemic, reinfection might be responsible for >50% of TB cases, and aggressive surveillance to detect asymptomatic carriers could be an important strategy for controlling the disease.
Authors: M Gabriela M Gomes; Ricardo Aguas; João S Lopes; Marta C Nunes; Carlota Rebelo; Paula Rodrigues; Claudio J Struchiner Journal: Proc Biol Sci Date: 2012-02-22 Impact factor: 5.349
Authors: David W Dowdy; Jonathan E Golub; Richard E Chaisson; Valeria Saraceni Journal: Proc Natl Acad Sci U S A Date: 2012-05-29 Impact factor: 11.205
Authors: Nicholas D Walter; Michael Strong; Robert Belknap; Diane J Ordway; Charles L Daley; Edward D Chan Journal: Respirology Date: 2012-07 Impact factor: 6.424
Authors: Ted Cohen; Paul D van Helden; Douglas Wilson; Caroline Colijn; Megan M McLaughlin; Ibrahim Abubakar; Robin M Warren Journal: Clin Microbiol Rev Date: 2012-10 Impact factor: 26.132
Authors: George W Reed; Hongjo Choi; So Young Lee; Myungsun Lee; Youngran Kim; Hyemi Park; Jongseok Lee; Xin Zhan; Hyeungseok Kang; SooHee Hwang; Matthew Carroll; Ying Cai; Sang-Nae Cho; Clifton E Barry; Laura E Via; Hardy Kornfeld Journal: PLoS One Date: 2013-02-28 Impact factor: 3.240