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Garbage codes, such as external causes with no specific information, indicate poor quality cause of death data. Investigation of garbage codes via an effective instrument is necessary to convert them into useful data for public health. This study analyzed the performance and suitability of the new investigation of deaths from external causes (IDEC) form to improve the quality of external cause of death data in Brazil. The performance of the IDEC form on 133 external garbage codes deaths was compared with a stratified matched sample of 992 (16%) investigated deaths that used the standard garbage codes form. Consistency between these two groups was checked. The percentage of garbage codes from external causes reclassified into valid causes with a 95% confidence interval (95%CI) was analyzed. Reclassification for specific causes has been described. Qualitative data on the feasibility of the form were recorded by field investigators. Investigation using the new form reduced all external garbage codes by -92.5% (95%CI: -97.0; -88.0), whereas the existing form decreased garbage codes by -60.5% (95%CI: -63.5; -57.4). The IDEC form presented higher effectivity for external-cause garbage codes of determined intent. Deaths that remained garbage codes mainly lacked information about the circumstances of poisoning and/or vehicle accidents. Despite the fact that field investigators considered the IDEC form feasible, they suggested modifications for further improvement. The new form was more effective than the current standard form in improving the quality of defined external causes.

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RESUMO

Garbage codes, such as external causes with no specific information, indicate poor quality cause of death data. Investigation of garbage codes via an effective instrument is necessary to convert them into useful data for public health. This study analyzed the performance and suitability of the new investigation of deaths from external causes (IDEC) form to improve the quality of external cause of death data in Brazil. The performance of the IDEC form on 133 external garbage codes deaths was compared with a stratified matched sample of 992 (16%) investigated deaths that used the standard garbage codes form. Consistency between these two groups was checked. The percentage of garbage codes from external causes reclassified into valid causes with a 95% confidence interval (95%CI) was analyzed. Reclassification for specific causes has been described. Qualitative data on the feasibility of the form were recorded by field investigators. Investigation using the new form reduced all external garbage codes by -92.5% (95%CI: -97.0; -88.0), whereas the existing form decreased garbage codes by -60.5% (95%CI: -63.5; -57.4). The IDEC form presented higher effectivity for external-cause garbage codes of determined intent. Deaths that remained garbage codes mainly lacked information about the circumstances of poisoning and/or vehicle accidents. Despite the fact that field investigators considered the IDEC form feasible, they suggested modifications for further improvement. The new form was more effective than the current standard form in improving the quality of defined external causes.

Códigos garbage (códigos inespecíficos ou incompletos), como causas externas sem informações específicas, indicam dados de má qualidade sobre a causa da morte. É necessário investigar os códigos garbage com um instrumento efetivo para convertê-los em dados úteis para a saúde pública. Este estudo analisou o desempenho e a adequação do novo formulário de investigação de óbitos por causas externas (IDEC) para melhorar a qualidade dos dados de causa externa de morte no Brasil. O desempenho deste formulário em 133 óbitos com códigos garbage de causas externas foi comparado com uma amostra estratificada e pareada de 992 (16%) óbitos investigados que utilizaram o formulário padrão de códigos garbage existente. A consistência entre esses dois grupos foi verificada. Analisou-se o percentual de códigos garbage de causas externas reclassificados em causas válidas com um intervalo de 95% de confiança (IC95%). A reclassificação para causas específicas foi descrita. Dados qualitativos sobre a viabilidade do formulário foram registrados por pesquisadores de campo. A investigação com o novo formulário reduziu todos os códigos garbage de causas externas em -92,5% (IC95%: -97,0; -88,0) enquanto o formulário existente diminuiu os códigos garbage em -60,5% (IC95%: -63,5; -57,4). O formulário IDEC foi mais eficaz para os códigos garbage de causa externa sem intenção indeterminada. As mortes que permaneceram como códigos garbage careciam principalmente de informações detalhadas sobre as circunstâncias do envenenamento e dos acidentes de trânsito. O formulário IDEC foi considerado viável pelos investigadores de campo, no entanto, eles sugeriram modificações para um maior aperfeiçoamento. O novo formulário foi mais eficaz do que o formulário padrão atual na melhoria da qualidade das causas externas definidas.

Códigos garbage (códigos inespecíficos o incompletos), como causas externas inespecíficas, son los indicadores de datos de mala calidad sobre la causa de muerte. Es necesario investigar los códigos garbage con un instrumento eficaz para convertirlos en datos útiles para la salud pública. Este estudio analizó el desempeño y la adecuación del nuevo formulario de investigación de muertes por causas externas (IDEC) para mejorar la calidad de los datos de causa externa de muerte en Brasil. El desempeño de este formulario en 133 muertes con códigos garbage de causas externas se comparó con una muestra estratificada y emparejada de 992 (16%) muertes investigadas que usaron el formulario estándar de códigos garbage existente. Se comprobó la consistencia entre estos dos grupos. Se analizó el porcentaje de códigos garbage por causas externas reclasificados en causas válidas con un intervalo del 95% de confianza (IC95%). Se procedió a una reclasificación por causas específicas. Los datos cualitativos sobre la viabilidad del formulario fueron registrados por investigadores de campo. La investigación con el nuevo formulario tuvo una reducción de todos los códigos garbage de causas externas en -92,5% (IC95%: -97,0; -88,0), mientras que el formulario existente redujo todos los códigos garbage de causas externas en -60,5% (IC95%: -63,5; -57,4). El formulario IDEC fue el más efectivo para códigos garbage de causa externa sin intención indeterminada. Las muertes que quedaron como códigos garbage carecían principalmente de información detallada sobre las circunstancias de envenenamiento y de accidentes de tránsito. Los investigadores de campo confirmaron la viabilidad del formulario IDEC, además de sugerir modificaciones para mejorarlo. El nuevo formulario fue el más efectivo que el formulario estándar actual en cuanto a la mejora de la calidad de las causas externas definidas.

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This article aims to analyze the association between characteristics of death - type of certifier and place of death - and the odds of an external cause death being certified as unspecified in Brazil. Cross-sectional study of deaths due to external causes from the Mortality Information System, 2017. Unspecified external cause (UEC) is the outcome variable in the models. Type of certifier physician, place of death and the interaction of these variables were the explanatory variables. Confounders were controlled by multiple logistic regression. UEC were the initial underlying cause for 22% of the 159,720 deaths from external causes in Brazil and 31% of hospital deaths issued by coroners. After adjustment for confounders, the odds of UEC in a hospital death certified by a coroner was 98% greater (OR=1.98; 95%CI: 1.53; 2.56) than in a home/street death issued by another certifier. This was greater than the odds for certifications by coroners (OR=1.23; 95%CI: 1.14; 1.33) and hospital deaths (OR=1.44; 95%CI: 1.32; 1.58). External causes certified by coroners and/or occurring in hospitals have a higher presence of UEC than other deaths; and indicate the need for coordinated initiatives by the health and public security sectors.

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Abstract This article aims to analyze the association between characteristics of death - type of certifier and place of death - and the odds of an external cause death being certified as unspecified in Brazil. Cross-sectional study of deaths due to external causes from the Mortality Information System, 2017. Unspecified external cause (UEC) is the outcome variable in the models. Type of certifier physician, place of death and the interaction of these variables were the explanatory variables. Confounders were controlled by multiple logistic regression. UEC were the initial underlying cause for 22% of the 159,720 deaths from external causes in Brazil and 31% of hospital deaths issued by coroners. After adjustment for confounders, the odds of UEC in a hospital death certified by a coroner was 98% greater (OR=1.98; 95%CI: 1.53; 2.56) than in a home/street death issued by another certifier. This was greater than the odds for certifications by coroners (OR=1.23; 95%CI: 1.14; 1.33) and hospital deaths (OR=1.44; 95%CI: 1.32; 1.58). External causes certified by coroners and/or occurring in hospitals have a higher presence of UEC than other deaths; and indicate the need for coordinated initiatives by the health and public security sectors.

Resumo O objetivo deste artigo é analisar a associação entre características do óbito - tipo de certificador e local do óbito - e a chance de um óbito por causa externa ser certificado como inespecífico no Brasil. Estudo transversal com dados do Sistema de Informações sobre Mortalidade de 2017. Causa externa inespecífica (CEI) é a variável desfecho nos modelos. As exposições de interesse foram tipo de médico certificador, local do óbito e a interação destas variáveis. Variáveis confundidoras foram controladas por regressão logística múltipla. As CEI foram a causa básica inicial de 22% dos 159,7 mil óbitos por causas externas no Brasil e 31% dos óbitos hospitalares emitidos por médicos-legistas. Após ajuste para confundidores, a chance de CEI em um óbito hospitalar certificado por legista foi 98% maior (OR=1,98; IC95%: 1,53; 2,56) do que em um óbito domiciliares/via pública emitido por outro certificador. Esta foi maior do que as chances para certificação por legista (OR=1,23; IC95%: 1,14; 1,33) e óbito hospitalar (OR=1,44; IC95%: 1,32; 1,58). As causas externas certificadas por médicos-legistas e/ou ocorridas em hospitais têm maior presença de CEI do que outras mortes; e indicam a necessidade de iniciativas coordenadas dos setores da saúde e segurança pública.

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Background: Accurate cause of death data are essential to guide health policy. However, mortality surveillance is limited in many low-income countries. In such settings, verbal autopsy (VA) is increasingly used to provide population-level cause of death data. VAs are now widely interpreted using the automated algorithms SmartVA and InterVA. Here we use conventional autopsy as the gold standard to validate SmartVA methodology. Methods: This study included adult deaths from natural causes in São Paulo and Recife for which conventional autopsy was indicated. VA was conducted with a relative of the deceased using an amended version of the SmartVA instrument to suit the local context. Causes of death from VA were produced using the SmartVA-Analyze program. Physician coded verbal autopsy (PCVA), conducted on the same questionnaires, and Global Burden of Disease Study data were used as additional comparators. Cause of death data were grouped into 10 broad causes for the validation due to the real-world utility of VA lying in identifying broad population cause of death patterns. Findings: The study included 2,060 deaths in São Paulo and 1,079 in Recife. The cause specific mortality fractions (CSMFs) estimated using SmartVA were broadly similar to conventional autopsy for: cardiovascular diseases (46.8% vs 54.0%, respectively), cancers (10.6% vs 11.4%), infections (7.0% vs 10.4%) and chronic respiratory disease (4.1% vs 3.7%), causes accounting for 76.1% of the autopsy dataset. The SmartVA CSMF estimates were lower than autopsy for "Other NCDs" (7.8% vs 14.6%) and higher for diabetes (13.0% vs 6.6%). CSMF accuracy of SmartVA compared to autopsy was 84.5%. CSMF accuracy for PCVA was 93.0%. Interpretation: The results suggest that SmartVA can, with reasonable accuracy, predict the broad cause of death groups important to assess a population's epidemiological transition. VA remains a useful tool for understanding causes of death where medical certification is not possible.

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OBJECTIVES: Accurate civil registration and vital statistics (CRVS) systems are the primary data source to measure the impact of the COVID-19 pandemic on mortality. This study assesses how the pandemic impacted CRVS system processes in Loreto region of Peru, one of the worst affected countries globally. DESIGN: Qualitative study. SETTING: Loreto, a remote region, which had the highest reported mortality rate in Peru during the pandemic. PARTICIPANTS: Semistructured individual interviews and documentary analysis were conducted between September 2020 and May 2021 with 28 key informants from eight institutions involved in death certification. Key informants were identified using a purposive sampling strategy commencing at the Health Directorate of Loreto, and the snowball method was used where a participant suggested another organisation or person. Information from key informants was used to compare business process maps of the CRVS system before and during the pandemic. RESULTS: During early May 2020, there were seven times more registered deaths than in earlier years, but key informants believed this underestimated mortality by 20%-30%. During the pandemic, families had to interact with more institutions during the death certification process. Several issues disrupted death certification processes, including the burden of increased deaths, the Environmental Health Directorate often removing a body without the family's express agreement, the creation of COVID-19 cemeteries where no death certificate was needed for burial, greater participation of funeral homes that often used outdated paper forms, and closure of civil registry offices. There was increased use of the online National Death System (SINADEF) but many users had problems with access. CONCLUSIONS: The pandemic substantially disrupted CRVS processes in Loreto, making death certification more difficult, placing greater burden on the family and leading to more participation from unregulated organisations such as funeral homes or cemeteries. These disruptions were impacted by limitations of the CRVS system's processes before the pandemic.

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BACKGROUND: In Brazil, both the Civil Registry (CR) and Ministry of Health (MoH) Mortality Information System (SIM) are sources of routine mortality data, but neither is 100% complete. Deaths from these two sources can be linked to facilitate estimation of completeness of mortality reporting and measurement of adjusted mortality indicators using generalized linear modeling (GLM). METHODS: The 2015 and 2016 CR and SIM data were linked using deterministic methods. GLM with covariates of the deceased's sex, age, state of residence, cause of death and place of death, and municipality-level education decile and population density decile, was used to estimate total deaths and completeness nationally, subnationally and by population sub-group, and to identify the characteristics of unreported deaths. The empirical completeness method and Global Burden of Disease (GBD) 2017 estimates were comparators at the national and state level. RESULTS: Completeness was 98% for SIM and 95% for CR. The vast majority of deaths in Brazil were captured by either system and 94% were reported by both sources. For each source, completeness was lowest in the north. SIM completeness was consistently high across all sub-groups while CR completeness was lowest for deaths at younger ages, outside facilities, and in the lowest deciles of municipality education and population density. There was no clear municipality-level relationship in SIM and CR completeness, suggesting minimal dependence between sources. The empirical completeness method model 1 and GBD completeness estimates were each, on average, less than three percentage points different from GLM estimates at the state level. Life expectancy was lowest in the northeast and 7.5 years higher in females than males. CONCLUSIONS: GLM using socio-economic and demographic covariates is a valuable tool to accurately estimate completeness from linked data sources. Close scrutiny of the quality of variables used to link deaths, targeted identification of unreported deaths in poorer, northern states, and closer coordination of the two systems will help Brazil achieve 100% death reporting completeness. The results also confirm the validity of the empirical completeness method.

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BACKGROUND: Globally, an estimated two-thirds of all deaths occur in the community, the majority of which are not attended by a physician and remain unregistered. Identifying and registering these deaths in civil registration and vital statistics (CRVS) systems, and ascertaining the cause of death, is thus a critical challenge to ensure that policy benefits from reliable evidence on mortality levels and patterns in populations. In contrast to traditional processes for registration, death notification can be faster and more efficient at informing responsible government agencies about the event and at triggering a verbal autopsy for ascertaining cause of death. Thus, innovative approaches to death notification, tailored to suit the setting, can improve the availability and quality of information on community deaths in CRVS systems. IMPROVING THE NOTIFICATION OF COMMUNITY DEATHS: Here, we present case studies in four countries (Bangladesh, Colombia, Myanmar and Papua New Guinea) that were part of the initial phases of the Bloomberg Data for Health Initiative at the University of Melbourne, each of which faces unique challenges to community death registration. The approaches taken promote improved notification of community deaths through a combination of interventions, including integration with the health sector, using various notifying agents and methods, and the application of information and communication technologies. One key factor for success has been the smoothing of processes linking notification, registration and initiation of a verbal autopsy interview. The processes implemented champion more active notification systems in relation to the passive systems commonly in place in these countries. CONCLUSIONS: The case studies demonstrate the significant potential for improving death reporting through the implementation of notification practices tailored to a country's specific circumstances, including geography, cultural factors, structure of the existing CRVS system, and available human, information and communication technology resources. Strategic deployment of some, or all, of these innovations can result in rapid improvements to death notification systems and should be trialled in other settings.

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INTRODUCTION: Reliable cause-of-death statistics are an important source of information on trends and differentials in population health. In Brazil, the Mortality Information System is responsible for compiling cause of death (CoD) data. Despite the success in reducing R-codes ill-defined causes of death, other garbage codes (GC), classified as causes that cannot be the underlying CoD, according to the Global Burden of Disease study, remain a challenge. The Ministry of Health (MoH) aims to decrease the proportion of all GCs, and a pilot study tested a comprehensive strategy to investigate GC deaths that occurred in 2015. METHODS: The research was conducted in seven Brazilian cities during five months in 2016: two rural cities, one metropolitan area, and four capitals. For all GCs selected, municipal healthcare workers collected information about the terminal disease from hospital records, autopsies, family health teams, and home investigation. The fieldwork was coordinated at Federal level in partnership with State and municipal teams. RESULTS: Out of 1,242 deaths selected, physicians analyzed the information collected and certified the CoD in 1,055 deaths, resulting in 92.6% of cases having their underlying cause changed to a usable ICD-10 code. DISCUSSION: It is noteworthy the capacity the health teams in the seven cities showed during the implementation of the pilot. CONCLUSION: After results analysis, the GC investigation protocol was modified, and the implementation scaled up to 60 cities in 2017.

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ABSTRACT Introduction: Reliable cause-of-death statistics are an important source of information on trends and differentials in population health. In Brazil, the Mortality Information System is responsible for compiling cause of death (CoD) data. Despite the success in reducing R-codes ill-defined causes of death, other garbage codes (GC), classified as causes that cannot be the underlying CoD, according to the Global Burden of Disease study, remain a challenge. The Ministry of Health (MoH) aims to decrease the proportion of all GCs, and a pilot study tested a comprehensive strategy to investigate GC deaths that occurred in 2015. Methods: The research was conducted in seven Brazilian cities during five months in 2016: two rural cities, one metropolitan area, and four capitals. For all GCs selected, municipal healthcare workers collected information about the terminal disease from hospital records, autopsies, family health teams, and home investigation. The fieldwork was coordinated at Federal level in partnership with State and municipal teams. Results: Out of 1,242 deaths selected, physicians analyzed the information collected and certified the CoD in 1,055 deaths, resulting in 92.6% of cases having their underlying cause changed to a usable ICD-10 code. Discussion: It is noteworthy the capacity the health teams in the seven cities showed during the implementation of the pilot. Conclusion: After results analysis, the GC investigation protocol was modified, and the implementation scaled up to 60 cities in 2017.

RESUMO Introdução: Estatísticas confiáveis em mortalidade são importante fonte de evidências de tendências e diferenciais na saúde da população. No Brasil, o Sistema de Informação sobre Mortalidade é responsável por compilar dados da causa de morte (CM). Embora tenha havido sucesso na redução de causas mal definidas de morte, ainda há um problema com outros códigos garbage (GC), classificados como causas de morte que não devem ser registradas como básicas, segundo o estudo de Carga Global de Doença. O Ministério da Saúde estabeleceu uma meta para diminuir a proporção de todos os GC e testou em estudo piloto uma estratégia abrangente para investigar as mortes. Métodos: A pesquisa foi realizada em 7 cidades do Brasil durante 4 meses: 2 em áreas rurais, 1 em área metropolitana e 4 em capitais. Os agentes municipais de saúde coletaram informações sobre a doença terminal obtida nos registros hospitalares, autópsias, equipes de saúde da família e investigação domiciliar. O trabalho de campo foi coordenado pelo nível federal, juntamente com as equipes estaduais e municipais. Resultados: Dos 1.242 óbitos selecionados, médicos analisaram as informações coletadas e certificaram a CM em 1.055 óbitos, resultando em 92,6% dos casos tendo sua causa subjacente alterada para código específico da CID-10. Discussão: Destaca-se a capacidade de articulação que as equipes de saúde apresentaram no cumprimento das etapas propostas para o trabalho. Conclusão: Após o estudo piloto, o protocolo de investigação foi modificado e sua implementação foi ampliada para 60 cidades em 2017.

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