Elisabeth Barboza França

Resumen Este estudio tuvo como objetivo estimar la mortalidad prematura (30-69 años) para cuatro grupos prioritarios de ENT en Brasil de 2010 a 2019, comparando datos brutos y no ajustados del Sistema de Información de Mortalidad (SIM), datos del SIM ajustados por redistribución del CB y subregistro, y datos extraídos del estudio de la Carga Global de Enfermedad (CGE). Las tasas de mortalidad prematura por ENT disminuyeron en el período analizado. Aunque los métodos de ajuste apenas modificaron las tendencias temporales, observamos que las tasas de mortalidad calculadas con datos ajustados fueron significativamente más altas que las sin ajuste. Esta variación fue heterogénea entre las Unidades Federadas. Las tasas estimadas por el método SIM bruto variaron de 322,0 a 276,1 defunciones por 100 mil habitantes, mientras que las tasas SIM redistribuidas variaron de 340,4 a 296,8 defunciones por 100 mil habitantes. Las tasas estimadas para la CGE variaron de 371,6 a 323,0 defunciones por 100 mil habitantes. En conclusión, este estudio resalta la importancia de adoptar métodos que puedan aplicarse para lograr estadísticas de mortalidad más confiables, que mejoren continuamente la definición de las causas de defunción en el SIM.

This study aimed to estimate premature mortality (30-69 years) for four priority NCD groups in Brazil from 2010 to 2019, comparing crude data from the Mortality Information System (SIM), SIM data adjusted by GC redistribution and underreporting, and data extracted from the Global Burden of Disease (GBD) study. Premature mortality rates due to NCDs declined in the period analyzed. Although the adjustment methods hardly changed temporal trends, we observed that mortality rates calculated with adjusted data were significantly higher than those without adjustment. This variation was heterogeneous among the Federated Units. The rates estimated by the crude SIM method ranged from 322.0 to 276.1 deaths per 100 thousand inhabitants, while the redistributed SIM rates ranged from 340.4 to 296.8 deaths per 100 thousand inhabitants. The estimated rates for the GBD ranged from 371.6 to 323.0 deaths per 100 thousand inhabitants. In conclusion, this study highlights the importance of adopting methods that can be applied to achieve more reliable mortality statistics, which continuously improves the definition of death causes in the SIM.

Resumo O estudo objetivou estimar a mortalidade prematura (30 a 69 anos) por quatro grupos prioritários de DCNT no Brasil, no período de 2010 a 2019, comparando dados do Sistema de Informações sobre Mortalidade (SIM) brutos, sem correção, dados do SIM corrigidos pela redistribuição dos CG e sub-registro e dados extraídos do estudo Carga Global de Doenças (GBD). As taxas de mortalidade prematura por DCNT mostraram redução no período analisado. Embora os métodos de correção tenham modificado pouco as tendências temporais, foi observado as taxas de mortalidade calculadas com os dados corrigidos foram expressivamente maiores que as sem correção. Essa variação foi heterogênea entre as Unidades Federadas. As taxas estimadas pelo método do SIM bruto variaram de 322,0 a 276,1 óbitos por 100 mil habitantes, enquanto as taxas do SIM redistribuído foram de 340,4 a 296,8 óbitos por 100 mil habitantes. As taxas estimadas para o GDB variaram de 371,6 a 323,0 óbitos por 100 mil habitantes. Em conclusão o presente estudo destaca a importância de se adotar métodos que possam ser aplicados para que tenhamos estatísticas relacionadas à mortalidade mais fidedignas, o que contribui para a melhoria contínua da definição das causas de morte no SIM.

A qualidade insuficiente da causa básica de óbito no Sistema de Informações sobre Mortalidade (SIM) subenumera as violências, e se faz necessária a redistribuição das causas garbage (CG) em causas válidas na prevenção em saúde pública. Este estudo estimou a mortalidade de causas externas usando método de redistribuição de CG (GBD-Brasil), e comparou com dados do SIM e estimados do estudo GBD-IHME de 2010 a 2019 no Brasil e Unidades Federativas (UF). O algoritmo de redistribuição das CG do GBD-Brasil aplica etapas prévias do GBD-IHME com modificações, usando dois critérios: proporção das causas-alvo (válidas) ou reclassificação de causas investigadas. Os dados do SIM estão sem correção. Utiliza-se taxas padronizadas por método direto, regressão local na série temporal e razão das taxas GBD-Brasil e SIM como fator de correção para lesões de trânsito, quedas, suicídios e homicídios. O Brasil registrou 1,34 milhão de óbitos com causas externas válidas e 171.700 CG em 10 anos. A redistribuição de CG do GBD-Brasil aumentou em 12,2% as causas válidas, e a curva de tendência das taxas foi semelhante à encontrada com dados do SIM, mas divergem entre si e com o GBD-IHME em UFs do Norte e do Nordeste. As estimativas do GBD-Brasil mudaram o padrão das causas externas nas UFs, aplicando maiores correções em quedas nas UFs do Norte e do Nordeste e homicídios nas demais UFs. O método GBD-Brasil pode ser utilizado na análise de mortes violentas por buscar maior simplicidade metodológica, que garante tanto replicação por gestores públicos como consistência do dado estimado, considerando a composição local do dado no processo de redistribuição.

A 11^a Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde representa um avanço no enfoque do conhecimento e em novas abordagens das doenças. A Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde é utilizada para diferentes finalidades práticas, possibilitando avaliação do avanço da agenda de saúde global, alocação de recursos, segurança do paciente, qualificação da assistência à saúde e reembolso de seguros de saúde. É inteiramente digital, com recursos tecnológicos que permitem sua atualização periódica. No início de 2022, a 11^a Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde entrou em vigência oficial, tendo sido disponibilizada em vários de seus idiomas oficiais, como o árabe, chinês, espanhol, francês e inglês. Apresenta-se aqui o processo de tradução para a língua portuguesa em uso no Brasil, coordenado pela Universidade Federal de Minas Gerais, com apoio do Ministério da Saúde do Brasil e da Organização Pan-Americana da Saúde/Organização Mundial da Saúde. O trabalho foi realizado em três etapas entre agosto de 2021 e dezembro de 2022 por tradutores com diferentes formações: médicos especialistas (49), fisioterapeuta (1), farmacologista (1) e odontologista (1). Com este artigo metodológico, almeja-se ampliar a discussão de perspectivas para implementação da 11^a Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde no Brasil e construir uma oportunidade para sua adaptação e uso por outros países de língua oficial portuguesa.

The 11^th International Statistical Classification of Diseases and Related Health Problems (ICD-11) represents an advance in the focus on knowledge and new disease approaches. The ICD is used for different practical purposes, enabling assessment of progress in the global health agenda, resource allocation, patient safety, health care qualification, and health insurance reimbursement. It is entirely digital, with technological resources that allow periodic updating. In early 2022, ICD-11 entered into official force, having been made available in several official ICD languages such as Arabic, Chinese, Spanish, French, and English. The translation process into Brazilian Portuguese, coordinated by the Federal University of Minas Gerais (UFMG), with support from the Brazilian Ministry of Health (MS) and PAHO/WHO, is presented here. The work was carried out in three stages between August 2021 and December 2022 by translators with different backgrounds: medical specialists (49), physiotherapists (1), pharmacologists (1), and dentists (1). This methodological article aims to broaden the discussion of perspectives on implementing the ICD-11 in Brazil and build an opportunity for its adaptation and use by other Portuguese-speaking countries.

The purpose of this article is to quantify the amount of misclassification of the Coronavirus Disease-2019 (COVID-19) mortality occurring in hospitals and other health facilities in selected cities in Brazil, discuss potential factors contributing to this misclassification, and consider the implications for vital statistics. Hospital deaths assigned to causes classified as garbage code (GC) COVID-related cases (severe acute respiratory syndrome, pneumonia unspecified, sepsis, respiratory failure and ill-defined causes) were selected in three Brazilian state capitals. Data from medical charts and forensic reports were extracted from standard forms and analyzed by study physicians who re-assigned the underlying cause based on standardized criteria. Descriptive statistical analysis was performed and the potential impact in vital statistics in the country was also evaluated. Among 1,365 investigated deaths due to GC-COVID-related causes, COVID-19 was detected in 17.3% in the age group 0–59 years and 25.5% deaths in 60 years and over. These GCs rose substantially in 2020 in the country and were responsible for 211,611 registered deaths. Applying observed proportions by age, location and specific GC-COVID-related cause to national data, there would be an increase of 37,163 cases in the total of COVID-19 deaths, higher in the elderly. In conclusion, important undercount of deaths from COVID-19 among GC-COVID-related causes was detected in three selected capitals of Brazil. After extrapolating the study results for national GC-COVID-related deaths we infer that the burden of COVID-19 disease in Brazil in official vital statistics was probably under estimated by at least 18% in the country in 2020.

Excess Mortality by all causes considers deaths directly related to COVID-19 and those attributed to conditions caused by the pandemic. When stratified by social dimensions, such as race/color, it allows for the evaluation of more vulnerable populations. The study estimated the excess mortality by natural causes, separating the white and black populations in 2020. Public civil registration data on deaths observed in 2020, corrected for under registration, were used. The expected number of deaths was estimated based on the mortality rates observed in 2019, applied to the estimated population in 2020. The difference between the values expected and observed and the proportion of excess was considered the excess mortality. The present study found an excess of 270,321 deaths (22.2% above the expected) in 2020. Every state of Brazil reported deaths above the corresponding expected figure. The excess was higher for men (25.2%) than for women (19.0%). Blacks showed an excess of 27.8%, as compared to whites at 17.6%. In both sexes and all age groups, excess was higher in the black population, especially in the South, Southeast, and Midwest regions. São Paulo, the largest in population number, had twice as much excess death in the black population (25.1%) than in the white population (11.5%). The present study showed racial disparities in excess mortality during the COVID-19 pandemic in Brazil. The higher excess found for the black suggests an intrinsic relationship with the socioeconomic situation, further exposing the Brazilian reality, in which social and structural inequality is evident.

Musculoskeletal (MSK) disorders are a major cause of disability worldwide. Different modifiable risk factors are associated to these disorders. The objective of this study was to analyze the burden of low back pain (LBP), rheumatoid arthritis (RA), osteoarthritis (OA), and gout, attributable to risk factors, in 2017. The burden of LBP, RA, OA, and gout, and attributable risk factors were analyzed using data extracted from the Global Burden of Disease (GBD) Brasil-2017 study. Descriptive analysis was conducted to compare disability-adjusted life years (DALY) rates between sexes and age groups (15-49 and 50-69 years), in 2017. The highest rates of DALY due to LBP were attributed to occupational ergonomic factors in the 15-49-year group, regardless of sex and males aged 50-69 years, whereas smoking was the major contributor in the 50-69-year female group. RA-related DALY rates were attributed to smoking and were higher among women aged 50-69 years. High body mass index (BMI) was the most relevant risk factor for the burden of OA, with higher rates detected in the 50-69-year group, and it was the most significant risk factor for DALY rate attributed to gout, regardless of sex or age group. Occupational surveillance measures are indicated to prevent LBP. Actions to decrease smoking and overweight, and the surveillance of weight gain are warranted to decrease the burden of LBP, RA and OA, and gout, respectively. These actions will be more effective if age and sex differentials are considered in planning.