We are pleased to announce the publication of “Are cause of death data fit for purpose? evidence from 20 countries at different levels of socio-economic development” in PLoS One.
While many countries have used our ANACONDA (Analysis of Causes of National Deaths for Action) tool to assess the quality of their cause of death (COD) data, no cross-country analysis had previously been conducted to explore patterns in diagnostic errors and data quality, or to examine the relationship between these patterns and other factors such as level of socio-economic development.
The study confirmed that although there is a correlation between socio-economic development and the amount of ill-defined (garbage code) causes of death, there are notable exceptions, including a significant amount of poor diagnosis in some high Socio-demographic Index (SDI) countries.
A snapshot of the ANACONDA software. To access ANACONDA, follow the link at the bottom of the page.
Our objective was to measure whether the usability of COD data and the patterns of unusable codes are related to a country’s level of socio-economic development.
Lene Mikkelsen, Senior Technical Advisor, Bloomberg Philanthropies Data for Health at the University of Melbourne
ANACONDA is the most advanced mortality data quality assessment platform that exists. It is easy to navigate and provides a comprehensive step-by-step framework for interrogating mortality data. Featuring a detailed guide for users, it offers a series of data checks and analysis that are based on decades of epidemiological and demographic research into mortality patterns in different populations.
ANACONDA includes country-specific comparator data and resources from the Global Burden of Disease Study against which the country input data can be checked for plausibility. Once downloaded, it can be used without an Internet connection.
The primary purpose of ANACONDA is to provide detailed information about the pattern and extent of the main types of errors affecting the accuracy of mortality statistics, in order to guide improvement efforts using established interventions. Better quality data will support better decision making and better population health.