Assessment of the Quality of Cause-of-Death Data in Serbia for 2005-2019 Vital Statistics Performance Index Estimation.

Background: We aimed to evaluate the quality of the cause of death (COD) concerning mortality patterns and completeness of death registration to identify areas for improvement in Serbia. Methods: COD data collected from the mortality register in Serbia from 2005 to 2019 (1540615 deaths) were analyzed with the software Analysis of National Causes of Death for Action. The Vital Statistics Performance Index for Quality (VSPI(Q)) is estimated for the overall COD data quality. Results: The completeness of death certification was higher than 98%. Usable underlying COD was registered in 57%, 24.1% with an unusable and 18.6% with insufficiently specified COD. The VSPI(Q) was 67.2%, denoting medium quality. The typical error was using intermediate COD (24.7% of all deaths), while 13.2% and 8.5% of all garbage codes (GC) belonged to the Very High and High Severity classes. The leading underlying COD is unspecified cardiomyopathy. The analysis revealed that 39.1% of GC has been redistributed to non-communicable diseases, 2.5% to external causes and 1.1% to communicable diseases. Conclusion: In the 15 years' worth of data analyzed, the true underlying COD, in many cases, was ill-defined, indicating that COD data at the national level could be distorted. The additional and continuous professional education of medical students as well as physicians is needed. It should focus on the most common GC among the leading COD and acquiring skills in certifying external causes of death.

The trend of the quality of cause-of-death data and its association with socio-economic indicators in Serbia in the period 2005-19.

This study aims to evaluate the temporal trend in the quality of cause-of-death data and garbage code profiles and to determine its association with socio-economic status in Serbia. A longitudinal study was assessed using data from mortality registers from 2005 to 2019. Computer application Analysis of Causes of National Deaths for Action (ANACONDA) calculates the distribution of garbage codes by severity and composite quality indicator: Vital Statistics Performance Index for Quality (VSPI(Q)). A relationship between VSPI(Q) and country development was estimated by analysing two socio-economic indicators: the Socio-demographic Index and the Human Development Index (HDI). Serbia indicates progress in strengthening cause-of-death statistics. The steady upward trend of the VSPI(Q) index has risen from 55.6 (medium quality) to 70.2 (high quality) over the examined years. Significant reduction of 'Insufficiently specified causes with limited impact' (Level 4) and an increase in the trend of 'High-impact garbage codes' (Levels 1 to 3) were evident. Decreased deaths of no policy value (annual percentage change of -1.41%) have manifested since 2014. A strong positive association between VSPI(Q) and socio-economic indicators was assessed, where the HDI has shown a stronger association with VSPI(Q). Improved socio-economic conditions on the national level are followed by enhanced cause-of-death data quality. Upcoming actions to improve quality should be directed at high-impact garbage codes. The study underlines the need to prioritise the education and training of physicians with a crucial role in death certification to overcome many cause-of-death quality issues identified in this assessment.

Excesso de óbitos classificados com códigos garbage na pandemia de Covid-19, no estado da Paraíba / Excess of deaths classified with garbage codes in the COVID-19 pandemic / Exceso de muertes clasificados con códigos garbage en la pandemia COVID-19

Resumo No cenário de calamidade assistido durante a pandemia de Covid-19, o ato de definir a causa básica de um óbito não foi trivial e o aumento da utilização dos códigos garbage (códigos mal definidos ou pouco específicos) traz preocupação em relação à qualidade da informação sobre as causas de morte nos estados do Brasil. Constatou-se um aumento significativo do número de óbitos no período pandêmico no estado da Paraíba, localizado na região Nordeste do Brasil, situação que se assemelhou ao contexto nacional. Em 2020 ocorreram 31.107 óbitos na Paraíba, enquanto no período de 2015 a 2019 foi identificada uma média de 27.000 óbitos. O objetivo do presente estudo é identificar e mensurar o excesso de óbitos classificados com códigos garbage em 2020, no estado da Paraíba, durante a pandemia de Covid-19. As séries temporais de óbitos foram obtidas do Painel de Monitoramento da Mortalidade por Causas Básicas Inespecíficas ou Incompletas (garbage codes) do Ministério da Saúde, para todos os meses de 2015 a 2020. Foram calculadas as projeções de óbitos totais e dos óbitos por códigos garbage (CG). O cálculo do excesso da proporção de óbitos classificados por códigos garbage no estado da Paraíba, em 2020, resultou em 8,58%, destacando-se o mês de junho com o maior valor (19,42%). Espera-se contribuir para avanços no conhecimento da realidade da qualidade da informação da notificação dos óbitos em uma área do país que almeja avanços nesse sentido e evidenciar a necessidade da investigação em outras localidades do Brasil.

Abstract In the disaster scenario witnessed during the pandemic caused by COVID-19, the act of defining the underlying cause of a death was no trivial matter and the increased use of Garbage Codes (poorly defined or not very specific codes) raises concerns regarding the quality of the information on causes of death in the states of Brazil. There was a significant increase in the number of deaths during the pandemic in the state of Paraíba, located in the Northeast region of Brazil, a situation similar to the national context. There were 31,107 deaths in 2020 in Paraíba, while an average of 27,000 deaths were identified for the period 2015 to 2019. Our goal was to identify and measure the excess of deaths classified with Garbage Codes in the COVID-19 pandemic in the geographic space of the state of Paraíba in 2020. The time series of deaths were obtained from the Mortality Monitoring Panel for Unspecific or Incomplete Basic Causes (Garbage Codes) of the Ministry of Health, for all months of the years 2015 to 2020. Projections of total deaths and deaths by Garbage Codes (GC) were calculated. The calculation of the excess proportion of deaths classified by Garbage Codes in the state of Paraíba resulted in 8.58%, highlighting the month of June 2020, which reached the maximum value equal to 19.42%. We expect to contribute to advance knowledge regarding the current reality of quality information on death notifications in an area of the country that seeks to make progress in this direction and to highlight the need for research in other locations in Brazil.

Resumen En el escenario de desastre que se vivió durante la pandemia de COVID-19, definir la causa subyacente de una muerte no fue baladí y el aumento del uso de códigos garbage (códigos mal definidos o poco específicos) genera preocupaciones acerca la calidad de la información sobre las causas de muerte en los estados de Brasil. En cuanto al aumento del número de muertes en el período de la pandemia en el estado de Paraíba, en la región Nordeste de Brasil, la situación fue similar al contexto nacional: 31.107 muertes en 2020, mientras que durante el período 2015-2019 el promedio fue de 27.000 muertes. El objetivo de este trabajo fue identificar y medir el exceso de muertes clasificadas con códigos Garbage en la pandemia de COVID-19 en el estado de Paraíba en 2020. Las series temporales de defunciones se obtuvieron del Panel de Seguimiento de Mortalidad por Causas Básicas Inespecíficas o Incompletas del Ministerio de Salud, para todos los meses de 2015 a 2020. Se calcularon proyecciones de muertes totales y muertes por códigos basura, para los que el cálculo de la proporción de exceso de muertes resultó en 8,58 %, con un valor de 19,42 % durante junio. Se espera contribuir a la calidad de la información sobre las notificaciones de muerte en un área del país que busca avances en esa dirección y resaltar la necesidad de investigaciones en otras localidades de Brasil.

Heart failure describing the underlying cause of death: a misconception, lack of information on the true underlying causes, or both?

AIM: The underlying cause of death represents the most important information on death certificates. Often, conditions that cannot represent a true underlying cause of death are listed as such. This phenomenon affects the quality of vital statistics and results of studies using cause-specific mortality as endpoints. We aimed at exploring the magnitude and factors associated with the use of heart failure to describe the underlying cause of death. METHODS: In this cross-sectional, register based study we linked data from the Norwegian Cause of Death Registry and the Norwegian Patient Registry. We used logistic regression models to analyse the association between external factors and heart failure listed as the underlying cause of death. RESULTS: Heart failure was listed as the underlying cause of death in 3.6% of all deaths. The odds of heart failure increased: (a) by 35% for 5-year increment in age; (b) by 78% for deaths occurring at nursing homes (compared with in-hospital deaths); and (c) by 602% for deaths not followed by an autopsy (compared with those followed by an autopsy). Deceased with a previous hospitalisation with heart failure as the discharge diagnosis had 514% higher odds of having heart failure listed as their underlying cause of death. Of the deceased with heart failure listed as the underlying cause of death, 9.4% did not have any, and 69.2% had only irrelevant additional information for assessing the true underlying cause of death in their death certificates. CONCLUSIONS: Heart failure listed as the underlying cause of death was associated with age, place of death, autopsy and previous hospitalisations - all factors that should not influence coding procedures. Better completion of death certificates in accordance with the World Health Organization rules will help reduce the use of heart failure to describe the underlying cause of death.

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.

[Noninformative coding of causes of death in cardiovascular deaths: effects on the mortality rate for ischemic heart disease]. / Nichtinformative Codierungen bei kardiovaskulären Todesursachen: Auswirkungen auf die Mortalitätsrate für ischämische Herzerkrankungen.

BACKGROUND: The validity of mortality statistics is specific to causes of death and depends on the quality of death certificates. The proportion of noninformative underlying causes of death in all deaths is an indicator for the validity of a mortality statistic. The most frequent noninformative cause of death involves cardiovascular diseases (ICD-10: I00-I99). OBJECTIVES: Regional differences in the frequency and type of use of noninformative cardiovascular causes of death are investigated and their effect on the mortality rate of ischemic heart disease is presented. MATERIALS AND METHODS: Mortality rates for cardiovascular causes of death by gender, age group, and federal state were extracted from the Information System of the Federal Health Monitoring (GBE) for 2000, 2010, 2015, and 2016. The proportion of noninformative causes of death in all cardiovascular deaths, as well as the mortality rate for ischemic heart disease after recoding noninformative causes of death, were calculated. RESULTS: The proportion of noninformative causes of death in all cardiovascular deaths is high and depends on age, sex, federal state, and year of death. Regional differences in frequency and type of use were found. After recoding selected noninformative causes of death, the mean increase in the mortality rate for ischemic heart disease in all federal states was 33%. DISCUSSION: A comparison of cause-specific mortality rates between regions, sexes, and over time is affected by differences in the use of noninformative causes of death. Improving the quality of death certificates is a prerequisite for valid mortality statistics.

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.