Hospitalizations from covid-19: a health planning tool
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista de Saúde Pública |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100242 |
Resumo: | ABSTRACT OBJECTIVE Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease. |
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Hospitalizations from covid-19: a health planning toolCOVID-19, complicationsHospitalizationLength of StayAdmitting Department, HospitalImmunizationRegression AnalysisABSTRACT OBJECTIVE Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.Faculdade de Saúde Pública da Universidade de São Paulo2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100242Revista de Saúde Pública v.56 2022reponame:Revista de Saúde Públicainstname:Universidade de São Paulo (USP)instacron:USP10.11606/s1518-8787.2022056004315info:eu-repo/semantics/openAccessSantolino,MiguelAlcañiz,ManuelaBolancé,Catalinaeng2022-06-08T00:00:00Zoai:scielo:S0034-89102022000100242Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0034-8910&lng=pt&nrm=isoONGhttps://old.scielo.br/oai/scielo-oai.phprevsp@org.usp.br||revsp1@usp.br1518-87870034-8910opendoar:2022-06-08T00:00Revista de Saúde Pública - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Hospitalizations from covid-19: a health planning tool |
title |
Hospitalizations from covid-19: a health planning tool |
spellingShingle |
Hospitalizations from covid-19: a health planning tool Santolino,Miguel COVID-19, complications Hospitalization Length of Stay Admitting Department, Hospital Immunization Regression Analysis |
title_short |
Hospitalizations from covid-19: a health planning tool |
title_full |
Hospitalizations from covid-19: a health planning tool |
title_fullStr |
Hospitalizations from covid-19: a health planning tool |
title_full_unstemmed |
Hospitalizations from covid-19: a health planning tool |
title_sort |
Hospitalizations from covid-19: a health planning tool |
author |
Santolino,Miguel |
author_facet |
Santolino,Miguel Alcañiz,Manuela Bolancé,Catalina |
author_role |
author |
author2 |
Alcañiz,Manuela Bolancé,Catalina |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Santolino,Miguel Alcañiz,Manuela Bolancé,Catalina |
dc.subject.por.fl_str_mv |
COVID-19, complications Hospitalization Length of Stay Admitting Department, Hospital Immunization Regression Analysis |
topic |
COVID-19, complications Hospitalization Length of Stay Admitting Department, Hospital Immunization Regression Analysis |
description |
ABSTRACT OBJECTIVE Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100242 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100242 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.11606/s1518-8787.2022056004315 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Faculdade de Saúde Pública da Universidade de São Paulo |
publisher.none.fl_str_mv |
Faculdade de Saúde Pública da Universidade de São Paulo |
dc.source.none.fl_str_mv |
Revista de Saúde Pública v.56 2022 reponame:Revista de Saúde Pública instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Revista de Saúde Pública |
collection |
Revista de Saúde Pública |
repository.name.fl_str_mv |
Revista de Saúde Pública - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
revsp@org.usp.br||revsp1@usp.br |
_version_ |
1748936506921189376 |