K K Avilov1, A A Romanyukha2, S E Borisov3, E M Belilovsky3, O B Nechaeva4, A S Karkach5. 1. <sup>*</sup>Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, <sup>†</sup>Federal Research Institute for Health Organization and Informatics, Ministry of Health of the Russian Federation, Moscow. 2. <sup>*</sup>Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, <sup>‡</sup>Lomonosov Moscow State University, Moscow, <sup>§</sup>Moscow Institute of Physics and Technology (State University), Moscow. 3. <sup>¶</sup>Moscow Scientific and Clinical Center for Tuberculosis Control, Moscow Health Department, Moscow, Russia. 4. <sup>†</sup>Federal Research Institute for Health Organization and Informatics, Ministry of Health of the Russian Federation, Moscow. 5. <sup>*</sup>Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow.
Abstract
OBJECTIVE: To estimate tuberculosis (TB) incidence and case detection rate (CDR) using routine TB surveillance data only. METHODS: A mathematical model of the case detection process, representing competition between disease progression and case finding, is proposed. The model describes disease progression as a two-stage process (bacillary and non-bacillary TB), and so relates the proportion of bacillary TB cases on detection to the effectiveness of detection. Thus, given the annual numbers of newly detected TB cases stratified by bacillary status, the model estimates detection rates, incidence and CDR. Routine notification data from eight provinces in Russia, 2000-2011, were used for the study. RESULTS: Subnational level estimates of incidence and CDR were obtained. Incidence estimates varied by two-fold among the provinces; corrected CDR estimates varied by 1.5 times. The trend in the incidence estimates was similar to that in the World Health Organization estimates for the whole of Russia. The change in the trend in WHO CDR estimates in 2008-2009 was not supported by our estimates. CONCLUSION: The general approach that uses multistage models of disease progression and accordingly stratified notification data can be applied in various settings for the routine estimation of incidence and CDR.
OBJECTIVE: To estimate tuberculosis (TB) incidence and case detection rate (CDR) using routine TB surveillance data only. METHODS: A mathematical model of the case detection process, representing competition between disease progression and case finding, is proposed. The model describes disease progression as a two-stage process (bacillary and non-bacillary TB), and so relates the proportion of bacillary TB cases on detection to the effectiveness of detection. Thus, given the annual numbers of newly detected TB cases stratified by bacillary status, the model estimates detection rates, incidence and CDR. Routine notification data from eight provinces in Russia, 2000-2011, were used for the study. RESULTS: Subnational level estimates of incidence and CDR were obtained. Incidence estimates varied by two-fold among the provinces; corrected CDR estimates varied by 1.5 times. The trend in the incidence estimates was similar to that in the World Health Organization estimates for the whole of Russia. The change in the trend in WHO CDR estimates in 2008-2009 was not supported by our estimates. CONCLUSION: The general approach that uses multistage models of disease progression and accordingly stratified notification data can be applied in various settings for the routine estimation of incidence and CDR.