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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

Tools to assist in correctly identifying the underlying COD

In many low-resource countries, mortality statistics are collected through routine health management information system (HMIS) and COD certification is implemented in only some hospitals. In such settings, the standard medical death certificate is not used and the underlying COD is not identified properly. To address this issue, WHO – in collaboration with the University of Oslo – developed the Start up Mortality List (SMoL) for collecting COD information. SMoL contains a simplified list of 106 CODs that physicians can use to certify the COD. 

Iris is another tool that help to identify a COD. It is an automated coding tool that uses all the information entered on the medical death certificate to arrive at an underlying COD. Iris was not specifically built to check the quality of COD data, but – because it rejects all certificates from which an underlying COD cannot be arrived at – it does help to improve quality, particularly because it identifies the reason why a record was rejected. In addition, by introducing standard coding rules and applying these in the same way to all data, the result is that the quality and consistency of the data improves. Iris was developed by a group of European Union countries to improve the international comparability of ICD-coded data by providing a standardised way to select the underlying COD. 

Iris is described in more detail in topic 6.


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