Methods and tools to evaluate the quality of vital statistics
Tools specifically to assess mortality data quality - ANACONDA
In order to analyse the quality of mortality and COD data, the Bloomberg Philanthropies Data for Health innovation program developed a powerful tool: Analysis of Causes of (National) Death for Action (ANACONDA). ANACONDA was jointly developed by the Melbourne School of Population and Global Health, and the Swiss Tropical and Public Health Institute at the University of Basel. It is currently available in English, Portuguese, Chinese and Russian.
ANACONDA performs the calculations needed for a comprehensive data quality review, and automatically generates the associated figures and tables from which a data quality assessment report can be written. Countries that integrate ANACONDA into the vital statistics production system can perform annual assessments of their data at literally no cost.
To monitor progress of improvements in the system, an overall system indicator (the Vital Statistics Performance Index [VSPI]) has been included and is automatically calculated from the input data (see example below).
ANACONDA also builds capacity of staff working with mortality data through the guidance built into the tool, and through special trainings conducted by trained facilitators that cover the use the tool and interpretation of results.
To assist users of the tool, several supporting materials have been prepared including:
- among them a detailed guidance manual to assist in interpreting the results,
- a manual to facilitate the data entry and
- a facilitator’s guide to assist instructors
- a pre-filled template to prepare an mortality assessment report.
Screenshot of the ANACONDA tool
Screenshot of the Vital Statistics Performance Index
ANACONDA has been implemented in the countries that are part of Bloomberg’s Data for Health initiative and is now being offered to other countries through workshops hosted by WHO regional agencies and the UN Economic and Social Commissions in Asia, the Pacific and Africa.
A strength of ANACONDA is that it provides many comparators for countries to compare themselves with. These are drawn mostly from global databases, particularly the Global Burden of Disease database that has individual country data going back to the 1980s. Even though many of these data points are based on estimates, they offer a rich and reliable source of comparators for individual countries to assess the plausibility of their mortality data.
ANACONDA is an assessment tool that checks data for errors, and reveals weaknesses and gaps, but it does not correct these errors. It is therefore necessary to ensure that corrective action is taken after evaluation by concerned authorities. The tool details weaknesses or bad habits, such as the misuse of certain CODs, making it possible to focus on corrective action towards eliminating weaknesses. Countries with a large amount of unusable codes are strongly advised to integrate ANACONDA into their data production process and to use it as a monitoring tool for system improvement. The VSPI values are suitable for monitoring purposes.
Guidance for assessing and interpreting the quality of mortality data using ANACONDA
For users of ANACONDA (statisticians and/or analysts in health and statistics departments, researchers, or other experts working with mortality data).
Authors: Mikkelsen L, Lopez AD
Publication date: October 2017
Resource type: CRVS resources and tools
Related resources: Course prospectus: ANACONDA
O que é ANACONDA? Uma perspectiva brasileira / What is ANACONDA? A Brazilian perspective
The D4H team at the University of Melbourne spoke to the Brazilian ANACONDA trainer and statistician for the Ministry of Health, Ana Claúdia Medeiros de Souza, about initial feedback of the ANACONDA tool in October 2017.
What are people dying of in Greenland and why?
Greenland's cause of death data have only recently been separated from Denmark in the Global Burden of Disease Study. University of Melbourne Data for Health Initiative visiting academic Kim Moesgaard Iburg is Associate Professor at Aarhus University in Denmark, he is studying how the quality of these data can be improved to further benefit the direction of health policy in the country.