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@techreport{Brizzi:2021:10.25561/91875,
author = {Brizzi, A and Whittaker, C and Servo, LMS and Hawryluk, I and Prete, Jr CA and de, Souza WM and Aguiar, RS and Araujo, LJT and Bastos, LS and Blenkinsop, A and Buss, LF and Candido, D and Castro, MC and Costa, SF and Croda, J and de, Souza Santos A and Dye, C and Flaxman, S and Fonseca, PLC and Geddes, VEV and Gutierrez, B and Lemey, P and Levin, AS and Mellan, T and Bonfim, DM and Miscouridou, X and Mishra, S and Monod, M and Moreira, FRR and Nelson, B and Pereira, RHM and Ranzani, O and Schnekenberg, RP and Semenova, E and Sonnabend, R and Souza, RP and Xi, X and Sabino, EC and Faria, NR and Bhatt, S and Ratmann, O},
doi = {10.25561/91875},
title = {Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals},
url = {http://dx.doi.org/10.25561/91875},
year = {2021}
}

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TY  - RPRT
AB  - The SARSCoV2 Gamma variant spread rapidly across Brazil, causing substantial infection and death wa ves. We use individuallevel patient records following hospitalisation with suspected or confirmed COVID19 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 pe riods. We show that extensive fluctuations in COVID19 inhospital fatality rates also existed prior to Gamma’s detection, and were largely transient after Gamma’s detection, subsiding with hospital d emand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil’s COVID19 inhospital fatality rates are primarily associated with geographic inequities and shortages in healthcare c apacity. We project that approximately half of Brazil’s COVID19 deaths in hospitals could have been avoided without prepandemic 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 SARSCoV2, especially in low and middleincome countries.
AU  - Brizzi,A
AU  - Whittaker,C
AU  - Servo,LMS
AU  - Hawryluk,I
AU  - Prete,Jr CA
AU  - de,Souza WM
AU  - Aguiar,RS
AU  - Araujo,LJT
AU  - Bastos,LS
AU  - Blenkinsop,A
AU  - Buss,LF
AU  - Candido,D
AU  - Castro,MC
AU  - Costa,SF
AU  - Croda,J
AU  - de,Souza Santos A
AU  - Dye,C
AU  - Flaxman,S
AU  - Fonseca,PLC
AU  - Geddes,VEV
AU  - Gutierrez,B
AU  - Lemey,P
AU  - Levin,AS
AU  - Mellan,T
AU  - Bonfim,DM
AU  - Miscouridou,X
AU  - Mishra,S
AU  - Monod,M
AU  - Moreira,FRR
AU  - Nelson,B
AU  - Pereira,RHM
AU  - Ranzani,O
AU  - Schnekenberg,RP
AU  - Semenova,E
AU  - Sonnabend,R
AU  - Souza,RP
AU  - Xi,X
AU  - Sabino,EC
AU  - Faria,NR
AU  - Bhatt,S
AU  - Ratmann,O
DO  - 10.25561/91875
PY  - 2021///
TI  - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
UR  - http://dx.doi.org/10.25561/91875
UR  - https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-46-Brazil/
UR  - http://hdl.handle.net/10044/1/91875
ER  -

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