title: Treatment interruptions among patients with tuberculosis in Russian TB hospitals.
abstract: OBJECTIVE
  To evaluate risk factors for in-patient treatment interruptions (TIs) in Russian tuberculosis (TB) hospitals.
  
  
  METHODS
  The regional case-based registers for all TB patients registered in the main regional TB hospitals were analyzed for the period 1993-2002. Multivariable analysis of risk factors for TIs was performed using logistic regression. The prediction rule was developed based on the final multivariable model coefficients obtained from analysis of the largest (Lipetsk) database.
  
  
  RESULTS
  During the study period, 18-50% of new cases and 36-56% of retreatment cases had interrupted in-patient treatment. In multivariate analysis, independent predictors of treatment interruption included: male gender (odds ratios (ORs) 1.5-2.3), age group 25-50 years (ORs 1.5-1.7), alcohol abuse (ORs 1.8-4.0), imprisonment history (ORs 1.3-2.5), unemployment (ORs 1.1-2.8), being a retreatment case (ORs 1.3-2.5), and having severe forms of TB (1.4-4.0); factors protective from interruption included urban residence (ORs 0.7-0.9) and having concomitant diseases (ORs 0.6-0.8). Based on the Lipeck model, new TB cases from the four regions were divided into low, high, and very high risk groups. Proportions of TI were approximately 20-35% in the low risk group, approximately 60-75% in the high risk group, and approximately 75-85% in the very high risk group (except Orel).
  
  
  CONCLUSIONS
  We have described the independent predictors of patient TI, and a predictive rule for the in-patient TB treatment phase interruptions has been developed. Treatment interruption is a significant obstacle in the success of the National Tuberculosis Control Program in Russia. Interventions targeted at the high risk groups should be implemented in order to prevent in-patient treatment interruption.
authors.affiliation: Office of the Special Representative of the WHO Director-General in Russia, Moscow, Russian Federation.
authors.forename: Evgeny M
authors.initials: EM
authors.lastname: Belilovsky
authors.forename: Sergey E
authors.initials: SE
authors.lastname: Borisov
authors.forename: Earl Francis
authors.initials: EF
authors.lastname: Cook
authors.forename: Shimon
authors.initials: S
authors.lastname: Shaykevich
authors.forename: Wieslaw M
authors.initials: WM
authors.lastname: Jakubowiak
authors.forename: Ekaterina V
authors.initials: EV
authors.lastname: Kourbatova
mesh_terms: D000328:Adult; D000995:Antitubercular Agents / Q000008:administration & dosage*; D004334:Drug Administration Schedule; D005260:Female; D006763:Hospitals, Chronic Disease / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012307:Risk Factors; D012426:Russia / Q000453:epidemiology; D016896:Treatment Outcome; D014376:Tuberculosis / Q000188:drug therapy* / Q000453:epidemiology / Q000382:microbiology; D014397:Tuberculosis, Pulmonary / Q000188:drug therapy* / Q000453:epidemiology / Q000382:microbiology
chemical_list: D000995:Antitubercular Agents