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This review article aims to perform analysis and critical discussion about the literature on methods correcting mortality from accidents and violence reported to the Brazilian Mortality Information System. We consulted Medline and SciELO databases, as well as the Global Burden of Disease site, using time filter for the 1996-2015 interval. Of the 77 studies identified, we selected 29, and 14 met the corrections production criteria for cases of underreporting: underreporting of deaths in the Mortality Information System, deaths declared as ill-defined causes or deaths from external causes declared with nonspecific codes. We found that the underreporting of external causes was not significantly different from what occurs in total deaths and sometimes was higher in small and medium-sized municipalities. The reclassification of ill-defined causes of death corrected external causes to non-negligible values. The selected studies differ on proposals for correction of unspecified external causes. Evidence supports interventions to improve the quality of data, and the availability of correction procedure of external causes that bring together application conditions.

BackgroundHomicides are a major problem in Brazil. Drugs and arms trafficking, and land conflicts are three of the many factors driving homicide rates in Brazil. Understanding long-term spatiotemporal trends and social structural factors associated with homicides in Brazil would be useful for designing policies aimed at reducing homicide rates.MethodsWe obtained data from 2000 to 2014 from the Brazil Ministry of Health (MOH) Mortality Information System and sociodemographic data from the Brazil Institute of Geography and Statistics (IBGE). First, we quantified the rate of change in homicides at the municipality and state levels. Second, we used principal component regression and k-medoids clustering to examine differences in temporal trends across municipalities. Lastly, we used Bayesian hierarchical space-time models to describe spatio-temporal patterns and to assess the contribution of structural factors.ResultsThere were significant variations in homicide rates across states and municipalities. We noted the largest decrease in homicide rates in the western and southeastern states of Sao Paulo, Rio de Janeiro and Espirito Santo, which coincided with an increase in homicide rates in the northeastern states of Ceará, Alagoas, Paraiba, Rio Grande Norte, Sergipe and Bahia during the fifteen-year period. The decrease in homicides in municipalities with populations of at least 250,000 coincided with an increase in municipalities with 25,000 people or less. Structural factors that predicted municipality-level homicide rates included crude domestic product, urbanization, border with neighboring countries and proportion of population aged fifteen to twenty-nine.ConclusionsOur findings support both a dissemination hypothesis and an interiorization hypothesis. These findings should be considered when designing interventions to curb homicide rates.

Resumo O estudo tem como objetivo analisar tendências e distribuição das taxas de mortalidade por homicídios (TMH) segundo porte populacional dos municípios brasileiros entre 2000 e 2015. Trata-se de estudo ecológico dos óbitos do Sistema de Informações sobre Mortalidade, com TMH padronizada pelo método direto e intervalo de 95% de confiança. A TMH no Brasil cresceu 6% (para 29,1/100 mil) no período, com aumento em municípios de pequeno (83%; 12,7 para 23,2/100 mil) e médio porte (52%; 19,7 para 30,1/100 mil); o que se verifica para ambos os sexos, diferentes idades, regiões e em eventos por arma de fogo. A TMH decresceu em municípios de grande porte metropolitanos (19%; 40,6 para 32,9/100 mil) e da região Sudeste (55%; 45,6 para 20,6/100 mil). O risco relativo-RR de cidades pequenas e médias em relação a grandes já é maior ou próximo de 1 em mulheres (RR 0,99; 1,03), pessoas com 60 ou mais anos (RR 1,43; 1,36) e homicídios por outros meios (RR 1,16; 1,18). As cidades de São Paulo e Rio de Janeiro foram as que mais contribuíram para a redução das TMH, em especial nas cidades grandes (-37,6 e -22,3 homicídios/100 mil hab.). Municípios de porte pequeno e médio apresentam tendências consistentes de incremento de TMH mesmo considerando subgrupos populacionais. Abstract The scope of this study is to analyze the trends and distribution of homicide mortality rates (HMR) according to the population size of Brazilian municipalities between 2000 and 2015. It is an ecological study of deaths recorded in the Mortality Information System, with HMR standardized by the direct method and 95% confidence interval. HMR in Brazil grew 6% (to 29.1/100,000) in the period, with an increase in small municipalities (83%; 12.7 to 23.2/100,000) and mediumsized cities (52%; 19.7% to 30.1/100,000); which is true for both sexes, different ages, regions and firearm-related events. HMR decreased in major cities (19%; 40.6% to 32.9/100,000) and the Southeast region (55%; 45.6% to 20.6/100,000). The relative risk (RR) of small and medium-sized cities in relation to large cities is already greater than or close to 1 among women (RR 0.99; 1.03), people aged 60 years or older (RR 1.43; 1.36) and homicides by other means (RR 1.16; 1.18). The cities of São Paulo and Rio de Janeiro contributed the most to the reduction of HMR, especially in large cities (-37.6 and -22.3 homicides/100,000 inhabitants). Small and medium-sized municipalities have consistent trends of an increase in HMR even considering population subgroups.

Resumo O objetivo do estudo é analisar a mudança do padrão espacial da taxa de mortalidade por homicídios (TMH) no Brasil, entre 2000 e 2015. Trata-se de estudo ecológico por microrregiões das TMH do Sistema de Informações sobre Mortalidade, utilizando os índices de Moran e clusters de áreas críticas (95% de confiança). A TMH cresceu 6% (para 29,1/100 mil hab.; IC95% 28,9; 29,4), e em 80% das microrregiões entre 2000 e 2015. O número de áreas com altas TMH (> 38,2/100 mil) aumentou 2,7 vezes. Em 2000, as áreas com TMH mais altas concentravam-se em Pernambuco, São Paulo, Mato Grosso e Rio de Janeiro; em 2015, passam a ocupar estados das regiões Norte e Nordeste. As áreas mais críticas estão no litoral do Nordeste e nas fronteiras do Pará e Maranhão na Amazônia Legal. As menores TMH (até 19,1/100 mil) estão mais presentes nas microrregiões de São Paulo e Santa Catarina, com aglomerados menos críticos nas regiões Sudeste e Sul. O homicídio se expandiu para dentro do território nacional, com deslocamento entre regiões, em direção principalmente às mais pobres, que mostram áreas mais críticas em cenários distintos, como fronteiras de estados e litoral. Inversamente, há contração expressiva do homicídio em estados de regiões de desenvolvimento alto, com presença de áreas menos críticas. Abstract The scope of this paper is to analyze the variation of the spatial pattern of the homicide rate in Brazil between 2000 and 2015. It is an ecological study by micro-regions of homicides taken from the Mortality Information System, using Moran indexes, and critical area clusters (95% CI). The rate increased by 6% (to 29.1/100,000 inhabitants (95% CI 28.9, 29.4), and in 80% of the micro regions between 2000 and 2015. The areas with high rates (> 38.2/100,000) increased 2.7-fold. In 2000, the highest rates were concentrated in areas in Pernambuco, São Paulo, Mato Grosso and Rio de Janeiro; by 2015, it will affect most states in the North and Northeast. The coastal regions of the Northeast and borders of Pará and Maranhão in the Amazon are critical areas. The lowest rate (19.1/100,000) is in São Paulo and Santa Catarina micro regions, with less critical clusters in the Southeast and South regions. Homicides have expanded into the interior of Brazil, with displacement between regions, mainly gravitating towards the poorest, which exhibit more critical areas in several scenarios, such as state borders and the coast. Conversely, there is marked contraction of homicides in states of highly developed regions with the presence of less critical areas. Key words Homicide

Background The COVID-19 pandemic brought countless challenges to public health and highlighted the Brazilian health system vulnerabilities in facing the emergency. In this article, we analyze data on COVID-19-related deaths in 2020-21 to show the epidemic consequences in Brazil. Methods The Mortality Information System and the Live Birth Information System were the primary information sources. We used population estimates in 2020-21 to calculate COVID-19 specific mortality rates by age, sex, and educational level. Considering the total number of COVID-19 deaths in 2020-21, the COVID-19 proportional mortality (%) was estimated for each age group and sex. A graph of the daily number of deaths from January 2020 to December 2021 by sex was elaborated to show the temporal evolution of COVID-19 deaths in Brazil. In addition, four indicators related to COVID-19 mortality were estimated: infant mortality rate (IMR); maternal mortality ratio (MMR); number and rate of orphans due to mother’s COVID-19 death; the average number of years lost. Results The overall COVID-19 mortality rate was 14.8 (/10,000). The mortality rates increase with age and show a decreasing gradient with higher schooling. The rate among illiterate people was 38.8/10,000, three times higher than a college education. Male mortality was 31% higher than female mortality. COVID-19 deaths represented 19.1% of all deaths, with the highest proportions in the age group of 40-59 years. The average number of years lost due to COVID-19 was 19 years. The MMR due to COVID-19 was 35.7 per 100,000 live births (LB), representing 37.4% of the overall MMR. Regarding the number of orphans due to COVID-19, we estimated that 40,830 children under 18 lost their mothers during the epidemic, with an orphans’ rate of 7.5/10,000 children aged 0-17 years. The IMR was 11.7 per 1000 LB, with 0.2 caused by COVID-19. The peak of COVID-19 deaths occurred in March 2021, reaching almost 4000 COVID-19 deaths per day, higher than the average number of deaths per day from all causes in 2019. Conclusions The delay in adopting public health measures necessary to control the epidemic has exacerbated the spread of the disease, resulting in several avoidable deaths.

To describe the temporal evolution of the main causes of mortality in Minas Gerais (MG), Brazil, and to verify the association with socioeconomic indicators. This is a mixed ecological study in which age-standardized mortality rates were calculated per 100,000 inhabitants due to noncommunicable diseases (NCDs), communicable, neonatal and nutritional diseases (NNDs) and external causes (ECs) for 853 municipalities in MG, according to data from the Global Burden of Disease (GBD) study, in the three-year periods 2000 to 2002 (T1), 2009 to 2011 (T2) and 2016 to 2018 (T3). Between T1 and T3, mortality due to NCDs predominated; there was a 22.4% decrease in the rates for NCDs (553.6 to 429.9) and a 29% decrease in the rates for NCDs (83 to 58.9), and a 3.5% increase in EC (62.2 to 64.4). The correlation coefficients were positive (R > 0.70; p < 0.05) and higher mortality rates were found in areas with worse socioeconomic status.

This study analyzes the spatial pattern of implementation of Primary Health Care (PHC) teams in Northern and Northeastern Brazil. This is an ecological study on the rates of Community Health Workers (ACS), Family Health Team (eSF), Oral Health Team (eSB), and Family Health Extended Center (NASF) based on data from the Ministry of Health (MoH). The analysis of the area data identified patterns of spatial dependence of the municipalities for the rates, using Moran indices and scatterplots to visualize critical areas' clusters (95% confidence). Municipalities of the North (n=450) and Northeast (n=1,794) had 132,174 ACS, 18,405 eSF, 13,017 eSB, and 2,205 NASF. The proportion of municipalities with rates within the recommended by the MoH were: ACS (>1.33), 96% in the North and 98.5% in the Northeast; eSF (>2.9/1,000), 54% and 80% in the respective regions; eSB (>2.9/10,000) 28% and 59% in these respective regions. NASF teams were deployed in 70% of the North and 89% of the Northeast. Except for ACS, the North was a critical team area, mainly in Pará, Rondônia, Amazonas, and Amapá. In the Northeast, these areas were smaller and concentrated mainly in western Bahia and eastern Maranhão. The Northeast showed a better composition of teams and a smaller extent of critical areas.

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.

Background Brazil leads the world in number of firearm deaths and ranks sixth by country in rate of firearm deaths per 100,000 people. This study aims to analyze trends in and burden of mortality by firearms, according to age and sex, for Brazil, and the association between these deaths and indicators of possession and carrying of weapons using data from the global burden of diseases, injuries, and risk factors study (GBD) 2017. Methods We used GBD 2017 estimates of mortality due to physical violence and self-harm from firearms for Brazil to analyze the association between deaths by firearms and explanatory variables. Results Deaths from firearms increased in Brazil from 25,819 in 1990 to 48,493 in 2017. Firearm mortality rates were higher among men and in the 20–24 age group; the rate was 20 times higher than for women in the same age group. Homicide rates increased during the study period, while mortality rates for suicides and accidental deaths decreased. The group of Brazilian federation units with the highest firearm collection rate (median = 7.5) showed reductions in the rate of total violent deaths by firearms. In contrast, the group with the lowest firearm collection rate (median = 2.0) showed an increase in firearm deaths from 2000 to 2017. An increase in the rate of voluntary return of firearms was associated with a reduction in mortality rates of unintentional firearm deaths ( r = −0.364, p < 0.001). An increase in socio-demographic index (SDI) was associated with a reduction in all firearm death rates ( r = −0.266, p = 0.008). An increase in the composite index of firearms seized or collected was associated with a reduction in rates of deaths by firearm in the subgroup of females, children, and the elderly ( r = −0.269, p = 0.005). Conclusions There was a change in the trend of firearms deaths after the beginning of the collection of weapons in 2004. Federation units that collected more guns have reduced rates of violent firearm deaths.

How long one lives, how many years of life are spent in good and poor health, and how the population's state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life-years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Socio-demographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Globally, from 1990 to 2017, life expectancy at birth increased by 7·4 years (95% uncertainty interval 7·1–7·8), from 65·6 years (65·3–65·8) in 1990 to 73·0 years (72·7–73·3) in 2017. The increase in years of life varied from 5·1 years (5·0–5·3) in high SDI countries to 12·0 years (11·3–12·8) in low SDI countries. Of the additional years of life expected at birth, 26·3% (20·1–33·1) were expected to be spent in poor health in high SDI countries compared with 11·7% (8·8–15·1) in low-middle SDI countries. HALE at birth increased by 6·3 years (5·9–6·7), from 57·0 years (54·6–59·1) in 1990 to 63·3 years (60·5–65·7) in 2017. The increase varied from 3·8 years (3·4–4·1) in high SDI countries to 10·5 years (9·8–11·2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1·0 year (0·4–1·7) in Saint Vincent and the Grenadines (62·4 years [59·9–64·7] in 1990 to 63·5 years [60·9–65·8] in 2017) to 23·7 years (21·9–25·6) in Eritrea (30·7 years [28·9–32·2] in 1990 to 54·4 years [51·5–57·1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1·4 years (0·6–2·3) in Algeria to 11·9 years (10·9–12·9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75·8 years [72·4–78·7]) and males (72·6 years [69·8–75·0]) and the lowest estimates were in Central African Republic (47·0 years [43·7–50·2] for females and 42·8 years [40·1–45·6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41·3% (38·8–43·5) for communicable diseases and by 49·8% (47·9–51·6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40·1% (36·8–43·0), although age-standardised DALY rates decreased by 18·1% (16·0–20·2). With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low SDI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Bill & Melinda Gates Foundation.