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

Abstract

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.

Note: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 .

One sentence summary: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity.

Figure 1:

Spatio-temporal expansion of SARS-CoV-2 Gamma in Brazil and associated shocks in COVID-19 fatality rates in hospitals. (A) The 14 states and state capitals in which Gamma was detected by March 31, 2021 and which were included in the analysis. (B) Time evolution of SARS-CoV-2 Gamma variant frequencies in three locations, suggesting rapid expansion. Data from GISAID [23] (dots) are shown along with the number of sequenced SARS-CoV-2 samples (text), and posterior median model fits (line) and associated 95% credible intervals (CrI) (grey ribbon). (C) Weekly COVID-19 in-hospital fatality rates among hospitalised residents in Manaus with no evidence of vaccination prior to admission (dots), by age group (facets). Posterior median estimates (line) of the Bayesian multi-strain fatality model are shown along with 95% CrIs (grey ribbon), and the expected in-hospital fatality rates of non-Gamma variants (dotted line). The date of Gamma’s first detection in each location is indicated as a vertical dotted line.

Figure 2:

Analysis flow chart. Individual-level records on hospital admissions with severe acute respiratory infection across Brazil are mandatory to report to the SIVEP-Gripe (Sistema de Informação da Vigilância Epidemiológica da Gripe) database [12, 13], and publicly available records between 20 January 2020 and 26 July 2021 were downloaded on 20 September 2021. Data used to derive COVID-19 in-hospital fatality rates are shown in blue, and data used to derive the healthcare pressure indices are shown in yellow.

Figure 3:

Time trends in age-standardised COVID-19 in-hospital fatality rates and pandemic healthcare pressure.(A) Detailed time evolution for the index of ICU admissions over three weeks per ICU bed. The non-parametric estimates of age-standardised COVID-19 in-hospital fatality rates (black, right hand side axis) are shown against the healthcare pressure index (colour, left hand side axis) and the date of Gamma’s first detection as vertical dashed line. (B) Heatmap of Pearson correlation coefficients between age-standardised in-hospital fatality rates and each pandemic healthcare pressure index. In Macapá, ventilators were particularly scarce at the beginning of the pandemic, resulting in overall poor correlations of all other healthcare pressure indices except those involving ventilators.

Figure 4:

Inferred COVID-19 attributable hospital admissions and deaths among hospitalisations by SARS-CoV-2 variant. (A) Using SARS-CoV-2 variant frequency data, COVID-19 attributable hospital admissions in each age band and state capital were decomposed by Gamma and non-Gamma variant. Posterior median estimates for each age band are shown in fill colours, while estimates attributed to non-Gamma lineages are shown in lighter shades, and estimates attributed to the Gamma lineage in darker shades. The weekly totals of observed hospital admissions are shown as diamonds, and the date of Gamma’s first detection is shown as a dotted vertical line. (B) Deaths among variant-specific hospital admissions were jointly estimated under the multi-strain fatality model. Colours and shades are as in subfigure A, while weekly totals of observed deaths among hospital admissions are shown as diamonds. Admissions and deaths closely follow infection waves of different SARS-CoV-2 lineages and variants, for example the initial variant, P.2, and then Gamma in Belo Horizonte.

Figure 5:

Estimated contribution of location inequity, Gamma’s infection severity, and pandemic healthcare pressure on COVID-19 in-hospital fatality rates. (A) Estimated, weekly age-standardised COVID-19 in-hospital fatality rates, averaged across SARS-CoV-2 variants. Posterior median estimates (line) are shown with 95%CrIs (ribbon), and the lowest estimated fatality rates during the observation period in each state capital (dotted horizontal line). (B) Estimated ratio in lowest in-hospital fatality rates in each location relative that seen in Belo Horizonte. (C) Estimated ratio in in-hospital fatality rates for Gamma versus non-Gamma lineages. (D) Estimated multiplier to the lowest age-standardised fatality rates shown in subplot A that is associated with the pandemic healthcare pressure indices. In each plot, posterior median estimates are shown as dots, 95%CrIs as linerange, and summaries across locations as boxplots.