RESUMO
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities 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.
Assuntos
COVID-19 , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Brasil/epidemiologia , COVID-19/epidemiologia , Hospitais , Humanos , SARS-CoV-2RESUMO
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.
RESUMO
Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreover, the mathematical modelling of the pandemic generally. We determine epidemiological distributions for patients hospitalized with COVID-19 using a large dataset (N = 21 000 - 157 000) from the Brazilian Sistema de Informação de Vigilância Epidemiológica da Gripe database. A joint Bayesian subnational model with partial pooling is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2 and 17.8 days across the different states, and a mean of 15.2 days for Brazil. We find strong evidence in favour of specific probability density function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalized lognormal for onset-to-hospital-admission. Our results show that epidemiological distributions have considerable geographical variation, and provide the first estimates of these distributions in a low and middle-income setting. At the subnational level, variation in COVID-19 outcome timings are found to be correlated with poverty, deprivation and segregation levels, and weaker correlation is observed for mean age, wealth and urbanicity.
Assuntos
COVID-19/epidemiologia , Pandemias , SARS-CoV-2 , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Brasil/epidemiologia , COVID-19/mortalidade , Criança , Pré-Escolar , Feminino , Hospitalização/estatística & dados numéricos , Hospitais/estatística & dados numéricos , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Pandemias/estatística & dados numéricos , Pobreza , Probabilidade , Fatores de Tempo , Adulto JovemRESUMO
BACKGROUND: Child undernutrition affects millions of children globally. We investigated associations between suboptimal growth and mortality by pooling large studies. METHODS: Pooled analysis involving children 1 week to 59 months old in 10 prospective studies in Africa, Asia and South America. Utilizing most recent measurements, we calculated weight-for-age, height/length-for-age and weight-for-height/length Z scores, applying 2006 WHO Standards and the 1977 NCHS/WHO Reference. We estimated all-cause and cause-specific mortality hazard ratios (HR) using proportional hazards models comparing children with mild (-2≤Z<-1), moderate (-3≤Z<-2), or severe (Z<-3) anthropometric deficits with the reference category (Z≥-1). RESULTS: 53 809 children were eligible for this re-analysis and contributed a total of 55 359 person-years, during which 1315 deaths were observed. All degrees of underweight, stunting and wasting were associated with significantly higher mortality. The strength of association increased monotonically as Z scores decreased. Pooled mortality HR was 1.52 (95% Confidence Interval 1.28, 1.81) for mild underweight; 2.63 (2.20, 3.14) for moderate underweight; and 9.40 (8.02, 11.03) for severe underweight. Wasting was a stronger determinant of mortality than stunting or underweight. Mortality HR for severe wasting was 11.63 (9.84, 13.76) compared with 5.48 (4.62, 6.50) for severe stunting. Using older NCHS standards resulted in larger HRs compared with WHO standards. In cause-specific analyses, all degrees of anthropometric deficits increased the hazards of dying from respiratory tract infections and diarrheal diseases. The study had insufficient power to precisely estimate effects of undernutrition on malaria mortality. CONCLUSIONS: All degrees of anthropometric deficits are associated with increased risk of under-five mortality using the 2006 WHO Standards. Even mild deficits substantially increase mortality, especially from infectious diseases.