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The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis
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[Improved quality of tuberculosis data using record linkage.]

Patricia Bartholomay, Gisele Pinto de Oliveira, Rejane Sobrino Pinheiro

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    Improving tuberculosis (TB) data quality in Brazil involved linking the Information System for Notifiable Diseases (SINAN) with the Mortality Information System (SIM). This linkage refined outcome data, revealing a higher TB death rate among new cases.

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    Area of Science:

    • Public Health
    • Epidemiology
    • Health Informatics

    Background:

    • Data quality is crucial for effective tuberculosis (TB) surveillance and control.
    • Existing health information systems like SINAN and SIM may have discrepancies in outcome reporting.
    • Accurate case outcome data is essential for understanding TB burden and treatment effectiveness.

    Purpose of the Study:

    • To enhance the quality of tuberculosis (TB) data by linking the Information System for Notifiable Diseases (SINAN) with the Mortality Information System (SIM).
    • To refine TB case outcome reporting by correcting inaccuracies through probabilistic record linkage.
    • To assess the impact of data linkage on the reported outcomes of TB cases in Brazil.

    Main Methods:

    • Probabilistic record linkage was employed between SINAN and SIM databases for TB cases in Brazil (2008-2010).
    • Databases were constructed based on case outcomes, linking SINAN records (2008-2009) with SIM data mentioning TB.
    • Routine SINAN data cleaning procedures were augmented by linkage to exclude residual incomplete notifications.

    Main Results:

    • Record linkage led to a decrease in cases closed with 'patient transfer' as the outcome, from 34.8% (2008) to 35.5% (2009).
    • Following linkage with SIM, the percentage of TB deaths among new cases increased, stabilizing around 15%.
    • The linkage process identified and corrected data inconsistencies, providing a more accurate representation of TB outcomes.

    Conclusions:

    • Probabilistic linkage between SINAN and SIM significantly improves the accuracy of TB case outcome data.
    • The findings underscore the necessity of robust data quality assessments for TB surveillance.
    • Attention to data quality in TB treatment outcomes within SINAN is critical for reliable public health monitoring.