Regional Distribution of Causes of Death for Small Areas in Brazil, 1998–2017

Baptista, Emerson Augusto; Queiroz, Bernardo Lanza; Pinheiro, Pedro Cisalpino

doi:10.3389/fpubh.2021.601980

Cited by 6 publications

(10 citation statements)

References 40 publications

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“…In this study, we find that COVID-19 deaths are concentrated at ages 40 and older; that is, that there is little variation with regard to the visual reference point, which is the threshold that marks perfectly balanced proportions (Baptista et al, 2021;Kashnitsky and Aburto, 2019;Schöley, 2021). In addition, we have also limited the lower bound age group, which starts at age 25 instead of at zero years old.…”

Section: Ternary Color-codingmentioning

confidence: 73%

“…The previous health conditions of the population also play an important role, and are related to the observed results. Baptista et al (2021) pointed out that, until 2019, areas in the Northern and Northeastern regions of Brazil still had a high prevalence of infectious diseases, although a rapid increase in mortality from chronic and degenerative diseases, such as cardiovascular diseases, has also been observed.…”

Section: Discussionmentioning

confidence: 99%

“…These geographical areas are statistical constructions that are aggregated and defined by regional and socioeconomic similarities, and that have not changed their boundaries over time. In addition, they have been used elsewhere (Baptista and Queiroz, 2019a,b;Baptista et al, 2021;Lima and de Queiroz, 2014;Lima et al, 2021a). We also produced estimates using standardized rates to enable us to compare COVID-19 mortality levels, thereby eliminating the effects of the population age structure (Dowd et al, 2020).…”

Section: Data Source and Level Of Analysismentioning

confidence: 99%

“…In this study, we use ternary color-coding (Baptista et al, 2021;Kashnitsky and Schöley, 2018;Schöley, 2021) to visualize and compare the age structure of deaths from COVID-19 in Brazilian meso-regions in two different stages of the pandemic. This technique encodes the relative shares in three parts of a whole -in our case, in three age groups -and provides a visualization of the distribution of data marginalized over the geographical surface (Schöley, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Regional COVID-19 mortality in Brazil by age

Baptista¹,

Queiroz²,

Lima³

2022

Populationyearbook

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In this study, we use ternary color-coding to visualize and compare the age structure of deaths from COVID-19 in Brazilian meso-regions using the tricolore package in R, in two different phases of the pandemic. The analysis of the age profile is important to better understand the dynamics of the pandemic, and how it has affected the population over age 25, according to age groups (25–59, 60–79 and >80 years) and subpopulations of the country. The analysis focuses on the first wave of the pandemic, until the end of 2020, and the more recent wave. Overall, the results suggest that when the two recent waves of the pandemic are compared, different spatial patterns in the distribution of deaths across the country by sex and by age emerge. While the distribution of deaths is found to be concentrated at older ages, we also observe in the more recent period some areas of the country with a concentration of deaths among younger adults. The analysis further indicates that even in areas with a younger population age structure, which could act as a protective factor against complications, the age pattern of mortality is very heterogeneous, and we do not find a clearly defined age and spatial pattern. Our results highlight the importance of looking at the distribution of COVID-19 mortality across small areas, and show that there are many different levels of the pandemic in Brazil at the same time, rather than just one.

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Section: Ternary Color-codingmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Data Source and Level Of Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regional COVID-19 mortality in Brazil by age

Baptista¹,

Queiroz²,

Lima³

2022

Populationyearbook

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“…Os cânceres do TGI podem estar associados a fatores de risco, a exemplo da dieta, uso de drogas lícitas e ilícitas, sedentarismo, metástase de órgãos adjacentes e má qualidade de vida (Antonio, 2019;Bezerra et al, 2018;Melo et al, 2017). Os fatores supracitados podem variar entre as diversas regiões brasileiras, de modo que os cânceres do TGI apresentam maiores incidências nas regiões Norte e Nordeste, o que sugere uma correlação com a situação socioeconômica e cultural específica de cada região, bem como, com as distintas qualidades de serviços de saúde ofertados (Baptista et al, 2021;Bigoni et al, 2019;dos Santos et al, 2018; Instituto Nacional de Câncer [INCA], 2018).…”

Section: Introductionunclassified

Correlação da morbimortalidade de Neoplasias Malignas do Trato Gastrointestinal na população do Curimataú Paraibano Ocidental

Santos

Nascimento

Dantas

2022

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Os indicadores de saúde -morbidade e mortalidade -por câncer crescem exponencialmente, configurando um problema de saúde pública. Assim, o objetivo deste estudo foi caracterizar o perfil de morbimortalidade de Neoplasias Malignas do Trato Gastrointestinal em uma microrregião do Curimatau Paraíbano Ocidental. Realizou-se uma pesquisa documental com amostragem de uma microrregião do Curimatau Paraíbano Ocidental, mediante a utilização do DataSUS, sendo a pesquisa dividida em três etapas: coleta dos dados sociodemográficos, estratificação da morbidade e mortalidade por NMTGI e análise do coeficiente de correlação segundo Pearson. Foi possível observar que houve um crescimento populacional, com predominância da população urbana, equidade de sexos e aumento da população adulta. Quanto as condições sócio-econômicas, houve aumento da renda per capta com consequente redução da população de baixa renda. No que concerne a morbidade e mortalidade por NMTGI, as definições de neoplasias apresentam evoluções convergentes, representadas por correlações positivas (≥0,80; p<0,01), em contrapartida, quando correlaciona-se à morbidade e a mortalidade dos diferentes cânceres do TGI, observa-se correlações negativas (≥-0,80; p>0,05), todavia ambas correlações mostram uma variância compartilhada de até 83% entre as NM. Dessa forma, são existentes fatores predisponentes como fatores de riscos, ambientais, genéticos ou sociais que contribuem para o surgimento de NM, sendo necessário uma pesquisa aprofundada sobre esses fatores causais.

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Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries

Wang

Liu

et al. 2022

BMC Med

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Background Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies. Methods By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives. Results During 2006–2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83–0.94). Conclusions Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden.

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Regional Distribution of Causes of Death for Small Areas in Brazil, 1998–2017

Cited by 6 publications

References 40 publications

Regional COVID-19 mortality in Brazil by age

Regional COVID-19 mortality in Brazil by age

Correlação da morbimortalidade de Neoplasias Malignas do Trato Gastrointestinal na população do Curimataú Paraibano Ocidental

Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries

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