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Published on: July 28, 2023
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An approach to estimating tuberculosis incidence and case detection rate from routine notification data
K K Avilov1, A A Romanyukha2, S E Borisov3
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.
Summary
This study developed a mathematical model to estimate tuberculosis (TB) incidence and case detection rate (CDR) using only routine surveillance data. The model provides reliable subnational estimates applicable to various public health settings.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health Surveillance
Background:
- Accurate estimation of tuberculosis (TB) incidence and case detection rate (CDR) is crucial for effective control programs.
- Routine TB surveillance data are often underutilized for estimating key epidemiological parameters.
Purpose of the Study:
- To develop and validate a mathematical model for estimating TB incidence and CDR solely from routine surveillance data.
- To assess the feasibility of using a multistage disease progression model for TB parameter estimation.
Main Methods:
- A mathematical model simulating disease progression (two-stage: bacillary and non-bacillary) and case finding was developed.
- The model relates the proportion of bacillary TB cases to detection effectiveness.
- Routine TB notification data from eight Russian provinces (2000-2011), stratified by bacillary status, were analyzed.
Main Results:
- Subnational estimates for TB incidence and CDR were successfully generated.
- Incidence estimates showed a two-fold variation across provinces, while corrected CDR estimates varied by 1.5 times.
- Estimated incidence trends aligned with WHO estimates, but a specific WHO CDR trend change was not supported.
Conclusions:
- The proposed multistage modeling approach, combined with stratified notification data, offers a robust method for routine TB incidence and CDR estimation.
- This methodology is adaptable to diverse settings for ongoing public health surveillance and program evaluation.
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