Characteristics of COVID-19 mortality during and after the pandemic in Peru

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Introduction. The COVID-19 emergency caused a pandemic with a great impact on mortality in Peru. Objective. To analyze the magnitude of mortality due to COVID-19 and identify some sociodemographic characteristics. Methods. A descriptive ecological study was carried out, analyzing data on deaths due...

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Detalles Bibliográficos
Autores: Arrasco, Juan, Medina, José, Ordoñez, Luis, Vargas Linares, Elena, Ramos, Willy
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:español
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/676301
Enlace del recurso:http://hdl.handle.net/10757/676301
Nivel de acceso:acceso embargado
Materia:COVID-19
Mortality
Mortality Registries
Peru
Socioeconomic Factors
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network_acronym_str UUPC
network_name_str UPC-Institucional
repository_id_str 2670
dc.title.es_PE.fl_str_mv Characteristics of COVID-19 mortality during and after the pandemic in Peru
title Characteristics of COVID-19 mortality during and after the pandemic in Peru
spellingShingle Characteristics of COVID-19 mortality during and after the pandemic in Peru
Arrasco, Juan
COVID-19
Mortality
Mortality Registries
Peru
Socioeconomic Factors
title_short Characteristics of COVID-19 mortality during and after the pandemic in Peru
title_full Characteristics of COVID-19 mortality during and after the pandemic in Peru
title_fullStr Characteristics of COVID-19 mortality during and after the pandemic in Peru
title_full_unstemmed Characteristics of COVID-19 mortality during and after the pandemic in Peru
title_sort Characteristics of COVID-19 mortality during and after the pandemic in Peru
author Arrasco, Juan
author_facet Arrasco, Juan
Medina, José
Ordoñez, Luis
Vargas Linares, Elena
Ramos, Willy
author_role author
author2 Medina, José
Ordoñez, Luis
Vargas Linares, Elena
Ramos, Willy
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Arrasco, Juan
Medina, José
Ordoñez, Luis
Vargas Linares, Elena
Ramos, Willy
dc.subject.es_PE.fl_str_mv COVID-19
Mortality
Mortality Registries
Peru
Socioeconomic Factors
topic COVID-19
Mortality
Mortality Registries
Peru
Socioeconomic Factors
description Introduction. The COVID-19 emergency caused a pandemic with a great impact on mortality in Peru. Objective. To analyze the magnitude of mortality due to COVID-19 and identify some sociodemographic characteristics. Methods. A descriptive ecological study was carried out, analyzing data on deaths due to COVID-19 during the period 2020-2023, as well as publicly accessible socioeconomic and demographic indicators. The variables included in the study were: year of death, number of deaths, sex, age group, province of origin, natural region, macroregion, human development index (HDI), unmet basic needs (UBN) and population density. Results. 220,628 deaths due to COVID-19 were reported, of which 219,374 (99.43%) occurred during the pandemic and 1,254 (0.57%) in the post-pandemic period; The cumulative mortality rate was 6.76 deaths/1000 inhabitants. The COVID-19 death curve shows that between 2020-2023, five waves occurred, with the second wave having the highest mortality, followed by the first wave. Mortality predominated in men, older adults (69.77%) and adults (28.11%). The macroregion of Metropolitan Lima and Callao had the highest mortality rates with 9.36 deaths/1000 inhabitants. The provinces with the highest cumulative mortality rates were those located on the coast, in quintiles with less NBI, in quintiles with a higher HDI and in those provinces with a higher population density. Conclusions. The COVID-19 pandemic caused high mortality in Peru, being higher in males, older adults, from coastal cities, with higher population density and better socioeconomic indicators.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-11-01T20:17:30Z
dc.date.available.none.fl_str_mv 2024-11-01T20:17:30Z
dc.date.issued.fl_str_mv 2024-01-01
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.issn.none.fl_str_mv 10255583
dc.identifier.doi.none.fl_str_mv 10.15381/anales.v85i3.28037
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/676301
dc.identifier.eissn.none.fl_str_mv 16099419
dc.identifier.journal.es_PE.fl_str_mv Anales de la Facultad de Medicina
dc.identifier.eid.none.fl_str_mv 2-s2.0-85204070289
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85204070289
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dc.publisher.es_PE.fl_str_mv Universidad Nacional Mayor de San Marcos, Facultad de Medicina
dc.source.es_PE.fl_str_mv Universidad Peruana de Ciencias Aplicadas (UPC)
Repositorio Academico - UPC
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
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dc.source.journaltitle.none.fl_str_mv Anales de la Facultad de Medicina
dc.source.volume.none.fl_str_mv 85
dc.source.issue.none.fl_str_mv 3
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/676301/1/license.txt
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
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spelling 34ac8357ed2f082c387693784f037d89500eb1a121f95905c392a841d04fac153ec23431fb141318738dc62cfe123aa52d06c6dbc8e746f32e665842f02ee1e3c0b3002061b828fd567bee2c3cf3463eda0045300Arrasco, JuanMedina, JoséOrdoñez, LuisVargas Linares, ElenaRamos, Willy2024-11-01T20:17:30Z2024-11-01T20:17:30Z2024-01-011025558310.15381/anales.v85i3.28037http://hdl.handle.net/10757/67630116099419Anales de la Facultad de Medicina2-s2.0-85204070289SCOPUS_ID:852040702890000 0001 2196 144X047xrr705Introduction. The COVID-19 emergency caused a pandemic with a great impact on mortality in Peru. Objective. To analyze the magnitude of mortality due to COVID-19 and identify some sociodemographic characteristics. Methods. A descriptive ecological study was carried out, analyzing data on deaths due to COVID-19 during the period 2020-2023, as well as publicly accessible socioeconomic and demographic indicators. The variables included in the study were: year of death, number of deaths, sex, age group, province of origin, natural region, macroregion, human development index (HDI), unmet basic needs (UBN) and population density. Results. 220,628 deaths due to COVID-19 were reported, of which 219,374 (99.43%) occurred during the pandemic and 1,254 (0.57%) in the post-pandemic period; The cumulative mortality rate was 6.76 deaths/1000 inhabitants. The COVID-19 death curve shows that between 2020-2023, five waves occurred, with the second wave having the highest mortality, followed by the first wave. Mortality predominated in men, older adults (69.77%) and adults (28.11%). The macroregion of Metropolitan Lima and Callao had the highest mortality rates with 9.36 deaths/1000 inhabitants. The provinces with the highest cumulative mortality rates were those located on the coast, in quintiles with less NBI, in quintiles with a higher HDI and in those provinces with a higher population density. Conclusions. The COVID-19 pandemic caused high mortality in Peru, being higher in males, older adults, from coastal cities, with higher population density and better socioeconomic indicators.Revisión por paresapplication/htmlspaUniversidad Nacional Mayor de San Marcos, Facultad de Medicinainfo:eu-repo/semantics/embargoedAccessUniversidad Peruana de Ciencias Aplicadas (UPC)Repositorio Academico - UPCAnales de la Facultad de Medicina853reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCCOVID-19MortalityMortality RegistriesPeruSocioeconomic FactorsCharacteristics of COVID-19 mortality during and after the pandemic in Peruinfo:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/676301/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/676301oai:repositorioacademico.upc.edu.pe:10757/6763012024-11-01 20:17:32.592Repositorio académico upcupc@openrepository.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