Abstract

Many countries have used the new ANACONDA (Analysis of Causes of National Death for Action) tool to assess the quality of their cause of death data (COD), but no cross-country analysis has been done to verify how different or similar patterns of diagnostic errors and data quality are in countries or how they are related to the local cultural or epidemiological environment or to levels of development. Our objective is 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. We have assessed the quality of 20 national COD datasets from the WHO Mortality Database by assessing their completeness of COD reporting and the extent, pattern and severity of garbage codes, i.e. codes that provide little or no information about the true underlying COD. Garbage codes were classified into four groups based on the severity of the error in the code. The Vital Statistics Performance Index for Quality (VSPI(Q)) was used to measure the overall quality of each country's mortality surveillance system. The proportion of 'garbage codes' varied from 7 to 66% across the 20 countries. Countries with a high SDI generally had a lower proportion of high impact (i.e. more severe) garbage codes than countries with low SDI. While the magnitude and pattern of garbage codes differed among countries, the specific codes commonly used did not. There is an inverse relationship between a country's socio-demographic development and the overall quality of its cause of death data, but with important exceptions. In particular, some low SDI countries have vital statistics systems that are as reliable as more developed countries. However, in low-income countries, where most people die at home, the proportion of unusable codes often exceeds 50%, implying that half of all cause-specific mortality data collected is of little or no use in guiding public policy. Moreover, the cause of death pattern identified from the data is likely to seriously under-represent the true extent of the leading causes of death in the population, with very significant consequences for health priority setting. Garbage codes are prevalent at all ages, contrary to expectations. Further research into effective strategies deployed in these countries to improve data quality can inform efforts elsewhere to improve COD reporting systems.

Highlights

  • A key source of evidence for targeting health interventions to improve population health is high quality cause of death (COD) data that reliably document trends in mortality for different diseases and injuries [1]

  • Some low Socio Demographic Index (SDI) countries have vital statistics systems that are as reliable as more developed countries

  • We investigate whether the usability of COD data and the patterns of garbage codes are related to a country’s socio-economic development using the ANACONDA software tool [8, 9, S1

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Summary

Introduction

A key source of evidence for targeting health interventions to improve population health is high quality cause of death (COD) data that reliably document trends in mortality for different diseases and injuries [1]. In order to target efforts to improve the utility of COD data for policy, it is important to first understand what the key diagnostic errors are. A major problem with COD data is poor cause of death certification practices that result in ‘garbage codes’, i.e. codes that provide little or no information about the true underlying cause of death [7]. The major consequence of garbage codes is that they obscure the true mortality pattern in a population. If the underlying cause that led to septicemia is not indicated on the death certificate, public policy to prevent these deaths would be misinformed, potentially leading to inefficient resource allocation to prevent them

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