Public health utility of cause of death data: applying empirical algorithms to improve data quality

BACKGROUND

Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments.

METHODS

We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings.

RESULTS

The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD.

CONCLUSIONS

We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge.

Reducing ignorance about who dies of what: research and innovation to strengthen CRVS systems

The Sustainable Development Goal (SDG) agenda offers a major impetus to consolidate and accelerate development in civil registration and vital statistics (CRVS) systems. Strengthening CRVS systems is an SDG outcome in itself. Moreover, CRVS systems are the best - if not essential - source of data to monitor and guide health policy debates and to assess progress towards numerous SDG targets and indicators. They also provide the necessary documentation and proof of identity for service access and are critical for disaster preparedness and response. While there has been impressive global momentum to improve CRVS systems over the past decade, several challenges remain. This article collection provides an overview of recent innovations, progress, viewpoints and key areas in which action is still required - notably around the need for better systems and procedures to notify the fact of death and to reliably diagnose its cause, both for deaths in hospital and elsewhere.

Assessing the quality of cause of death data in six high-income countries: Australia, Canada, Denmark, Germany, Japan and Switzerland

OBJECTIVES

To assess the policy utility of national cause of death (COD) data of six high-income countries with highly developed health information systems.

METHODS

National COD data sets from Australia, Canada, Denmark, Germany, Japan and Switzerland for 2015 or 2016 were assessed by applying the ANACONDA software tool. Levels, patterns and distributions of unusable and insufficiently specified "garbage" codes were analysed.

RESULTS

The average proportion of unusable COD was 18% across the six countries, ranging from 14% in Australia and Canada to 25% in Japan. Insufficiently specified codes accounted for a further 8% of deaths, on average, varying from 6% in Switzerland to 11% in Japan. The most commonly used garbage codes were Other ill-defined and unspecified deaths (R99), Heart failure (I50.9) and Senility (R54).

CONCLUSIONS

COD certification errors are common, even in countries with very advanced health information systems, greatly reducing the policy value of mortality data. All countries should routinely provide certification training for hospital interns and raise awareness among doctors of their public health responsibility to certify deaths correctly and usefully for public health policy.

Improving the usefulness of US mortality data: new methods for reclassification of underlying cause of death

Background

Mortality data are affected by miscertification of the medical cause of death deaths and changes to cause of death classification systems. We present both mappings of ICD9 and ICD10 to a unified list of causes, and a new statistical model for reducing the impact of misclassification of cause of death.

Methods

We propose a Bayesian mixed-effects multinomial logistic model that can be run on individual record level death certificates to reclassify “garbage-coded” deaths onto causes that are more meaningful for public health purposes. The model uses information on the contributing causes of death and demographic characteristics of each decedent to make informed predictions of the underlying cause of death. We apply our method to death certificate data in the US from 1979 to 2011, creating more directly comparable series of cause-specific mortality for 25 major causes of death.

Results

We find that many death certificates coded to garbage codes contain other information that provides strong clues about the valid underlying cause of death. In particular, a plausible underlying cause often appears in the contributing causes of death, implying that it may be incorrect ordering of the causal chain and not missed cause assignment that leads to many garbage-coded deaths. We present an example that redistributes 48 % of heart failure deaths to other cardiovascular diseases, 25 % to ischemic heart disease, and 15 % to chronic respiratory diseases.

Conclusions

Our methods take advantage of more detailed micro-level data than is typically considered in garbage code redistribution algorithms, making it a useful tool in circumstances in which detailed death certificate data needs to be aggregated for public health purposes. We find that this method gives different redistribution results than commonly used methods that only consider population-level proportions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-016-0082-4) contains supplementary material, which is available to authorized users.