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Notifiable Diseases Information System (SINAN): main features of tuberculosis notification and data analysis
Marli Souza Rocha1, Patrícia Bartholomay1, Marcela Virginnia Cavalcante2
1Secretaria de Vigilância em Saúde, Programa Nacional de Controle da Tuberculose, Brasília, DF, Brasil.
Summary
The Notifiable Diseases Information System (SINAN) tracks active tuberculosis (TB) in Brazil. This paper details SINAN-Net
Area of Science:
- Public Health
- Infectious Disease Epidemiology
- Health Information Systems
Background:
- The Notifiable Diseases Information System (SINAN) is crucial for understanding tuberculosis (TB) epidemiology in Brazil.
- SINAN's nationwide availability facilitates continuous data consolidation and TB control monitoring.
- Understanding SINAN's specific functionalities for TB is essential for effective public health strategies.
Purpose of the Study:
- To present the specific features of SINAN-Net relevant to tuberculosis surveillance.
- To detail the follow-up screen, record linkage, and reporting functionalities within SINAN-Net for TB.
- To describe key variables, indicators, challenges, and limitations of the SINAN system for TB control.
Main Methods:
- Descriptive analysis of the SINAN-Net system's architecture and functionalities for tuberculosis.
- Review of key variables and indicators used in SINAN for TB case management and monitoring.
- Identification and discussion of system-specific challenges and limitations encountered in TB data management.
Main Results:
- SINAN-Net offers specific tools for tuberculosis follow-up, including dedicated screens and reporting.
- Record linkage capabilities within SINAN-Net enhance data accuracy and completeness for TB cases.
- The system provides essential variables and indicators for evaluating TB control program effectiveness.
Conclusions:
- SINAN-Net is a vital tool for monitoring and evaluating tuberculosis control in Brazil.
- Addressing the identified challenges and limitations can further optimize the use of SINAN for TB surveillance.
- The system's specific features support data-driven decision-making in public health initiatives against TB.
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