Cargando…

Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals

The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma’s spread ac...

Descripción completa

Detalles Bibliográficos
Autores principales: Brizzi, Andrea, Whittaker, Charles, Servo, Luciana M. S., Hawryluk, Iwona, Prete, Carlos A., de Souza, William M., Aguiar, Renato S., Araujo, Leonardo J. T., Bastos, Leonardo S., Blenkinsop, Alexandra, Buss, Lewis F., Candido, Darlan, Castro, Marcia C., Costa, Silvia F., Croda, Julio, de Souza Santos, Andreza Aruska, Dye, Christopher, Flaxman, Seth, Fonseca, Paula L. C., Geddes, Victor E. V., Gutierrez, Bernardo, Lemey, Philippe, Levin, Anna S., Mellan, Thomas, Bonfim, Diego M., Miscouridou, Xenia, Mishra, Swapnil, Monod, Mélodie, Moreira, Filipe R. R., Nelson, Bruce, Pereira, Rafael H. M., Ranzani, Otavio, Schnekenberg, Ricardo P., Semenova, Elizaveta, Sonnabend, Raphael, Souza, Renan P., Xi, Xiaoyue, Sabino, Ester C., Faria, Nuno R., Bhatt, Samir, Ratmann, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575144/
https://www.ncbi.nlm.nih.gov/pubmed/34751273
http://dx.doi.org/10.1101/2021.11.01.21265731
_version_ 1784595615150243840
author Brizzi, Andrea
Whittaker, Charles
Servo, Luciana M. S.
Hawryluk, Iwona
Prete, Carlos A.
de Souza, William M.
Aguiar, Renato S.
Araujo, Leonardo J. T.
Bastos, Leonardo S.
Blenkinsop, Alexandra
Buss, Lewis F.
Candido, Darlan
Castro, Marcia C.
Costa, Silvia F.
Croda, Julio
de Souza Santos, Andreza Aruska
Dye, Christopher
Flaxman, Seth
Fonseca, Paula L. C.
Geddes, Victor E. V.
Gutierrez, Bernardo
Lemey, Philippe
Levin, Anna S.
Mellan, Thomas
Bonfim, Diego M.
Miscouridou, Xenia
Mishra, Swapnil
Monod, Mélodie
Moreira, Filipe R. R.
Nelson, Bruce
Pereira, Rafael H. M.
Ranzani, Otavio
Schnekenberg, Ricardo P.
Semenova, Elizaveta
Sonnabend, Raphael
Souza, Renan P.
Xi, Xiaoyue
Sabino, Ester C.
Faria, Nuno R.
Bhatt, Samir
Ratmann, Oliver
author_facet Brizzi, Andrea
Whittaker, Charles
Servo, Luciana M. S.
Hawryluk, Iwona
Prete, Carlos A.
de Souza, William M.
Aguiar, Renato S.
Araujo, Leonardo J. T.
Bastos, Leonardo S.
Blenkinsop, Alexandra
Buss, Lewis F.
Candido, Darlan
Castro, Marcia C.
Costa, Silvia F.
Croda, Julio
de Souza Santos, Andreza Aruska
Dye, Christopher
Flaxman, Seth
Fonseca, Paula L. C.
Geddes, Victor E. V.
Gutierrez, Bernardo
Lemey, Philippe
Levin, Anna S.
Mellan, Thomas
Bonfim, Diego M.
Miscouridou, Xenia
Mishra, Swapnil
Monod, Mélodie
Moreira, Filipe R. R.
Nelson, Bruce
Pereira, Rafael H. M.
Ranzani, Otavio
Schnekenberg, Ricardo P.
Semenova, Elizaveta
Sonnabend, Raphael
Souza, Renan P.
Xi, Xiaoyue
Sabino, Ester C.
Faria, Nuno R.
Bhatt, Samir
Ratmann, Oliver
author_sort Brizzi, Andrea
collection PubMed
description The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma’s spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma’s detection, and were largely transient after Gamma’s detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil’s COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil’s COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.
format Online
Article
Text
id pubmed-8575144
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Cold Spring Harbor Laboratory
record_format MEDLINE/PubMed
spelling pubmed-85751442021-11-09 Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals Brizzi, Andrea Whittaker, Charles Servo, Luciana M. S. Hawryluk, Iwona Prete, Carlos A. de Souza, William M. Aguiar, Renato S. Araujo, Leonardo J. T. Bastos, Leonardo S. Blenkinsop, Alexandra Buss, Lewis F. Candido, Darlan Castro, Marcia C. Costa, Silvia F. Croda, Julio de Souza Santos, Andreza Aruska Dye, Christopher Flaxman, Seth Fonseca, Paula L. C. Geddes, Victor E. V. Gutierrez, Bernardo Lemey, Philippe Levin, Anna S. Mellan, Thomas Bonfim, Diego M. Miscouridou, Xenia Mishra, Swapnil Monod, Mélodie Moreira, Filipe R. R. Nelson, Bruce Pereira, Rafael H. M. Ranzani, Otavio Schnekenberg, Ricardo P. Semenova, Elizaveta Sonnabend, Raphael Souza, Renan P. Xi, Xiaoyue Sabino, Ester C. Faria, Nuno R. Bhatt, Samir Ratmann, Oliver medRxiv Article The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma’s spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma’s detection, and were largely transient after Gamma’s detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil’s COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil’s COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. Cold Spring Harbor Laboratory 2021-11-02 /pmc/articles/PMC8575144/ /pubmed/34751273 http://dx.doi.org/10.1101/2021.11.01.21265731 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Brizzi, Andrea
Whittaker, Charles
Servo, Luciana M. S.
Hawryluk, Iwona
Prete, Carlos A.
de Souza, William M.
Aguiar, Renato S.
Araujo, Leonardo J. T.
Bastos, Leonardo S.
Blenkinsop, Alexandra
Buss, Lewis F.
Candido, Darlan
Castro, Marcia C.
Costa, Silvia F.
Croda, Julio
de Souza Santos, Andreza Aruska
Dye, Christopher
Flaxman, Seth
Fonseca, Paula L. C.
Geddes, Victor E. V.
Gutierrez, Bernardo
Lemey, Philippe
Levin, Anna S.
Mellan, Thomas
Bonfim, Diego M.
Miscouridou, Xenia
Mishra, Swapnil
Monod, Mélodie
Moreira, Filipe R. R.
Nelson, Bruce
Pereira, Rafael H. M.
Ranzani, Otavio
Schnekenberg, Ricardo P.
Semenova, Elizaveta
Sonnabend, Raphael
Souza, Renan P.
Xi, Xiaoyue
Sabino, Ester C.
Faria, Nuno R.
Bhatt, Samir
Ratmann, Oliver
Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title_full Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title_fullStr Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title_full_unstemmed Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title_short Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
title_sort report 46: factors driving extensive spatial and temporal fluctuations in covid-19 fatality rates in brazilian hospitals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575144/
https://www.ncbi.nlm.nih.gov/pubmed/34751273
http://dx.doi.org/10.1101/2021.11.01.21265731
work_keys_str_mv AT brizziandrea report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT whittakercharles report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT servolucianams report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT hawrylukiwona report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT pretecarlosa report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT desouzawilliamm report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT aguiarrenatos report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT araujoleonardojt report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT bastosleonardos report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT blenkinsopalexandra report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT busslewisf report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT candidodarlan report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT castromarciac report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT costasilviaf report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT crodajulio report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT desouzasantosandrezaaruska report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT dyechristopher report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT flaxmanseth report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT fonsecapaulalc report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT geddesvictorev report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT gutierrezbernardo report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT lemeyphilippe report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT levinannas report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT mellanthomas report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT bonfimdiegom report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT miscouridouxenia report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT mishraswapnil report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT monodmelodie report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT moreirafiliperr report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT nelsonbruce report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT pereirarafaelhm report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT ranzaniotavio report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT schnekenbergricardop report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT semenovaelizaveta report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT sonnabendraphael report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT souzarenanp report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT xixiaoyue report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT sabinoesterc report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT farianunor report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT bhattsamir report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals
AT ratmannoliver report46factorsdrivingextensivespatialandtemporalfluctuationsincovid19fatalityratesinbrazilianhospitals