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The importance of data quality

Checking the accuracy of vital events records

Methods and tools to evaluate the quality of vital statistics

Tabulation and generation of vital statistics for national policy

Presentation, communication and dissemination of vital statistics

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Methods and tools to evaluate the quality of vital statistics

Conclusions and key actions

Quality standards for statistical data are well defined and also apply to vital statistics (refer to  Deaths cause of death statistics ). The quality indicators of the birth data are described above as well as the methods used to assess the completeness of birth registration. For mortality data, ANACONDA includes the most important indicators that evaluate the accuracy and completeness of mortality and COD data. Additionally, ANACONDA also provides a detailed analysis of the type of CODs that are not useful causes and that bias countries’ COD distributions. 

All countries should integrate data assessment tools into their annual vital statistics production to help eliminate errors and reduce the number of inconsistencies and unusable mortality causes. Data that are not sufficiently robust cannot be used for policy, for guiding health programs or for monitoring the indicators needed for the Sustainable Development Goals. Monitoring of data quality should be an ongoing, continual process to ensure data are sufficiently robust for policy use.


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