MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | preprint |
Idioma: | eng |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/1606 |
Resumo: | Objectives: the present study aims to generate estimates of mortality rates due to garbage codes (GC) for municipalities in Brazil by comparing direct and indirect methods, based on deaths registered in the Mortality Information System (SIM) between 2015 and 2017. Methods: Data from the SIM were used. The analysis was performed in groups of GC, levels 1 and 2, levels 3 and 4 and total GC. Mortality rates were estimated directly and indirectly, Empirical Bayesian Estimators. Results: about 38% of CG were estimated and regional differences in mortality rates were observed, higher in the Northeast and Southeast and lower in the South and Midwest. The Southeast presented similar rates for the two groups of CG analyzed. The smallest differences between direct and indirect estimates were observed in large cities, above 500 thousand. The municipalities in the north of Minas Gerais and the states of Rio de Janeiro, São Paulo and Bahia presented municipalities with high rates at levels 1 and 2. Conclusion: there are differences in the quality of the definition of the underlying causes of death, even with the use of indirect methodology which assists in smoothing rates. The quality of the definition of causes of death is important since they are associated with the access and quality of health services and offer subsidies for health planning. |
id |
SCI-1_b21469ffd09a896059a19ca5d74c87ad |
---|---|
oai_identifier_str |
oai:ops.preprints.scielo.org:preprint/1606 |
network_acronym_str |
SCI-1 |
network_name_str |
SciELO Preprints |
repository_id_str |
|
spelling |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017MORTALIDADE POR CAUSAS GARBAGE NOS MUNICÍPIOS BRASILEIROS: DIFERENÇAS ENTRE AS ESTIMATIVAS DIRETAS E INDIRETAS EM 2015 A 2017qualidade dos dados de mortalidadecausas mal definidasgarbage codespequenas áreasquality of data mortalityill-defined causes of degarbage codessmall areasObjectives: the present study aims to generate estimates of mortality rates due to garbage codes (GC) for municipalities in Brazil by comparing direct and indirect methods, based on deaths registered in the Mortality Information System (SIM) between 2015 and 2017. Methods: Data from the SIM were used. The analysis was performed in groups of GC, levels 1 and 2, levels 3 and 4 and total GC. Mortality rates were estimated directly and indirectly, Empirical Bayesian Estimators. Results: about 38% of CG were estimated and regional differences in mortality rates were observed, higher in the Northeast and Southeast and lower in the South and Midwest. The Southeast presented similar rates for the two groups of CG analyzed. The smallest differences between direct and indirect estimates were observed in large cities, above 500 thousand. The municipalities in the north of Minas Gerais and the states of Rio de Janeiro, São Paulo and Bahia presented municipalities with high rates at levels 1 and 2. Conclusion: there are differences in the quality of the definition of the underlying causes of death, even with the use of indirect methodology which assists in smoothing rates. The quality of the definition of causes of death is important since they are associated with the access and quality of health services and offer subsidies for health planning.Objetivos: o presente estudo tem como objetivo gerar estimativas das taxas de mortalidade por causas garbage (CG) para os municípios do Brasil fazendo a comparação entre métodos diretos e indiretos, tendo como base os óbitos registrados no SIM entre 2015 e 2017. Métodos: Os dados do Sistema de Informações sobre Mortalidade (SIM) foram utilizados. A análise foi realizada com grupos de GC, níveis 1 e 2, níveis 3 e 4 e total de GC. As taxas de mortalidade foram estimadas de forma direta e indireta, estimadores bayesianos empíricos. Resultados: observou-se cerca de 38% de CG e diferenças regionais nas taxas de mortalidade, maiores no Nordeste e Sudeste e menores no Sul e Centro-Oeste. O Sudeste apresentou taxas semelhantes para os dois grupos de CG analisados. As menores diferenças entre as estimativas diretas e indiretas foram observadas nas grandes cidades, acima de 500 mil. Os municípios do norte de Minas Gerais e estados do Rio de Janeiro, São Paulo e Bahia apresentaram municípios com altas taxas nos níveis 1 e 2. Conclusão: existem diferenças na qualidade da definição das causas básicas de morte, mesmo com o uso de metodologia indireta que auxilia na suavização das taxas. A qualidade da definição das causas de morte é importante, uma vez que se mostram associadas com o acesso e qualidade dos serviços de saúde e oferecem subsídios para o planejamento em saúde.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-12-14info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/160610.1590/1980-549720210003.supl.1enghttps://preprints.scielo.org/index.php/scielo/article/view/1606/2541Copyright (c) 2020 Renato Azeredo Teixeira , Lenice Harumi Ishitani , Elisabeth Barboza França , Pedro Cisalpino Pinheiro, Marina Martins Lobato , Deborah Carvalho Maltahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessTeixeira , Renato Azeredo Ishitani , Lenice Harumi França , Elisabeth Barboza Pinheiro, Pedro Cisalpino Lobato , Marina Martins Malta, Deborah Carvalho reponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-12-14T18:11:39Zoai:ops.preprints.scielo.org:preprint/1606Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-12-14T18:11:39SciELO Preprints - SciELOfalse |
dc.title.none.fl_str_mv |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 MORTALIDADE POR CAUSAS GARBAGE NOS MUNICÍPIOS BRASILEIROS: DIFERENÇAS ENTRE AS ESTIMATIVAS DIRETAS E INDIRETAS EM 2015 A 2017 |
title |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
spellingShingle |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 Teixeira , Renato Azeredo qualidade dos dados de mortalidade causas mal definidas garbage codes pequenas áreas quality of data mortality ill-defined causes of de garbage codes small areas |
title_short |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
title_full |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
title_fullStr |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
title_full_unstemmed |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
title_sort |
MORTALITY DUE TO GARBAGE CODES IN BRAZILIAN MUNICIPALITIES: DIFFERENCES BETWEEN DIRECT AND INDIRECT ESTIMATES IN 2015 TO 2017 |
author |
Teixeira , Renato Azeredo |
author_facet |
Teixeira , Renato Azeredo Ishitani , Lenice Harumi França , Elisabeth Barboza Pinheiro, Pedro Cisalpino Lobato , Marina Martins Malta, Deborah Carvalho |
author_role |
author |
author2 |
Ishitani , Lenice Harumi França , Elisabeth Barboza Pinheiro, Pedro Cisalpino Lobato , Marina Martins Malta, Deborah Carvalho |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Teixeira , Renato Azeredo Ishitani , Lenice Harumi França , Elisabeth Barboza Pinheiro, Pedro Cisalpino Lobato , Marina Martins Malta, Deborah Carvalho |
dc.subject.por.fl_str_mv |
qualidade dos dados de mortalidade causas mal definidas garbage codes pequenas áreas quality of data mortality ill-defined causes of de garbage codes small areas |
topic |
qualidade dos dados de mortalidade causas mal definidas garbage codes pequenas áreas quality of data mortality ill-defined causes of de garbage codes small areas |
description |
Objectives: the present study aims to generate estimates of mortality rates due to garbage codes (GC) for municipalities in Brazil by comparing direct and indirect methods, based on deaths registered in the Mortality Information System (SIM) between 2015 and 2017. Methods: Data from the SIM were used. The analysis was performed in groups of GC, levels 1 and 2, levels 3 and 4 and total GC. Mortality rates were estimated directly and indirectly, Empirical Bayesian Estimators. Results: about 38% of CG were estimated and regional differences in mortality rates were observed, higher in the Northeast and Southeast and lower in the South and Midwest. The Southeast presented similar rates for the two groups of CG analyzed. The smallest differences between direct and indirect estimates were observed in large cities, above 500 thousand. The municipalities in the north of Minas Gerais and the states of Rio de Janeiro, São Paulo and Bahia presented municipalities with high rates at levels 1 and 2. Conclusion: there are differences in the quality of the definition of the underlying causes of death, even with the use of indirect methodology which assists in smoothing rates. The quality of the definition of causes of death is important since they are associated with the access and quality of health services and offer subsidies for health planning. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-14 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/1606 10.1590/1980-549720210003.supl.1 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/1606 |
identifier_str_mv |
10.1590/1980-549720210003.supl.1 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/1606/2541 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
dc.source.none.fl_str_mv |
reponame:SciELO Preprints instname:SciELO instacron:SCI |
instname_str |
SciELO |
instacron_str |
SCI |
institution |
SCI |
reponame_str |
SciELO Preprints |
collection |
SciELO Preprints |
repository.name.fl_str_mv |
SciELO Preprints - SciELO |
repository.mail.fl_str_mv |
scielo.submission@scielo.org |
_version_ |
1797047821392674816 |