ABSTRACT
BackgroundPortugal took early action to control the COVID19 epidemic, imposing a lockdown on March 16 when it recorded only 62 cases of COVID-19 per million inhabitants and no reported deaths. The Portuguese people complied quickly, reducing their overall mobility by 80%. We estimate the impact of the lockdown in Portugal in terms of reducing burden on the health service. MethodsWe forecasted epidemic curves for: Cases, hospital inpatients (overall and in ICU), and deaths without lockdown, assuming that the impact of containment measures would start 14 days after lockdown was implemented. We used exponential smoothing models for deaths, intensive care (ICU) and hospitalizations and an ARIMA model for number of cases. Models were selected considering fitness to the observed data to the 31st of March 2020. We then compared observed(with intervention) and forecasted curves (without intervention). ResultsBetween April 1 and April 15, there were 146 fewer deaths(-25%), 5568 fewer cases (-23%) and, as of April 15, there were 519 fewer ICU inpatients(-69%) and 508 fewer overall hospital inpatients(-28%) than forecasted without lockdown. On April 15 the number of ICU inpatients could have reached 748, three times higher than the observed value (229) if the intervention had been delayed. ConclusionIf the lockdown had not been implemented in mid-March, Portugal ICU capacity (528 ICU beds) would likely have been breached in the first half of April. The lockdown seems to have been effective in reducing transmission of SARS-Cov-2, serious Covid-19 illness and associated mortality, thereby decreasing demand on health services. Early action allowed time for the National Health Service to acquire protective equipment, to increase capacity to test and cope with the surge in hospital and ICU demand caused by the pandemic.
ABSTRACT
BackgroundOne month after the first COVID-19 infection was recorded, Portugal counted 18 051 cases and 599 deaths from COVID-19. To understand the overall impact on mortality of the pandemic of COVID-19, we estimated the excess mortality registered in Portugal during the first month of the epidemic, from March 16 until April 14 using two different methods. MethodsWe compared the observed and expected daily deaths (historical average number from daily death registrations in the past 10 years) and used 2 standard deviations confidence limit for all-cause mortality by age and specific mortality cause, considering the last 6 years. An adapted ARIMA model was also tested to validate the estimated number of all-cause deaths during the study period. ResultsBetween March 16 and April 14, there was an excess of 1,255 all-cause deaths, 14% more than expected. The number of daily deaths often surpassed the 2 standard deviations confidence limit. The excess mortality occurred mostly in people aged 75+. Forty-nine percent (49%) of the estimated excess deaths were registered as due to COVID-19, The other 51% registered as other natural causes. ConclusionEven though Portugal took early containment measures against COVID-19, and the population complied massively with those measures, there was significant excess mortality during the first month of the pandemic, mostly among people aged 75+. Only half of the excess mortality was registered as directly due do COVID-19.
ABSTRACT
IntroductionDeterminants of hospitalization, intensive care unit (ICU) admission and death are still unclear for Covid-19 and only a few studies have adjusted for confounding for different clinical outcomes including all reported cases in a country. We used routine surveillance data from Portugal to identify risk factors for COVID-19 outcomes, in order to support risk stratification, clinical and public health interventions, and scenarios to plan health care resources. MethodsWe conducted a retrospective cohort study including 20,293 laboratory confirmed cases of COVID-19 in Portugal extracted in April 28 2020, electronically through the National Epidemic Surveillance System of the Directorate-General of Health(DGS). We calculated absolute risks, relative risks (RR) and adjusted relative risks (aRR) to identify demographic and clinical factors associated with hospitalization, admission to ICU and death using Poisson regressions. ResultsIncreasing age after 60 years was the greatest determinant for all outcomes. Assuming 0-50 years as reference, being aged 80-89 years was the strongest determinant of hospital admission (aRR-5.7), 70-79 years for ICU(aRR-10.4) and > 90 years for death(aRR-226.8) with an aRR of 112.7 in those 70-79. Among comorbidities, Immunodeficiency, cardiac disease, kidney disease, and neurologic disease were independent risk factors for hospitalization (aRR 1.83,1.79,1.56, 1.82), for ICU these were cardiac, Immunodeficiency, kidney and lung disease (aRR 4.33, 2.76, 2.43, 2.04), and for death they were kidney, cardiac and chronic neurological disease (aRR: 2.9, 2.6, 2.0) Male gender was a risk factor for all outcomes. There were small statistically significant differences for the 3 outcomes between regions. Discussion and ConclusionsOlder age stands out as the strongest risk factor for all outcomes specially for death as absolute is risk was small for those younger than 50. These findings have implications in terms of risk stratified public health measures that should prioritize protecting older people although preventive behavior is needed in all ages. Epidemiologic scenarios and clinical guidelines may consider these estimated risks, even though under-ascertainment of mild and asymptomatic cases should be considered.
ABSTRACT
Em virtude da existência da interação cultivares x ambientes, torna-se pouco provável a superioridade de um cultivar em todos os ambientes. Neste sentido, com o intuito de avaliar a similaridade de ambientes e determinar o número de ambientes necessários para avaliação de cultivares, foram realizados oito ensaios de competição de cultivares no Estado do Tocantins. Quatro ensaios foram instalados em Gurupi, nas datas de 08/11, 19/11, 28/11 e 05/12/2006, e quatro no município de Alvorada, nas datas de 09/11, 23/11, 07/12 e 21/12/2006, sendo que cada ensaio representou um ambiente. O delineamento experimental utilizado foi de blocos casualizados, com sete tratamentos e quatro repetições. Os tratamentos constaram dos cultivares BR/EMGOPA 314, BRS Sambaíba, BRS Tracajá, DM-339, MSOY 108, M-SOY 8411 e M8866. Os métodos de agrupamento de ambientes utilizados foram a estratificação ambiental e a dissimilaridade, baseada na interação de ordem simples. Na análise de variância conjunta, houve efeito significativo da interação cultivares x ambiente, indicando uma inconsistência de comportamento dos cultivares com a variação ambiental, justificando-se os estudos de agrupamento de ambientes. Os dois métodos permitiram a formação de um único grupo de ambientes similares. Não houve concordância entre os métodos quanto à composição dos grupos formados. O método de estratificação ambiental resultou na formação de um grupo compreendendo três ambientes de Gurupi (19/11, 28/11 e 05/12) e dois ambientes de Alvorada (09/11 e 21/12). Entretanto, o método de dissimilaridade ambiental permitiu a formação de um grupo contendo apenas dois ambientes em Alvorada (09/11 e 23/11). Os métodos de agrupamento foram pouco sensíveis em detectar ambientes distintos representados por épocas de semeadura em um mesmo local
Because of the existence of genotype x environment interaction, it is unlikely the superiority of one cultivar in all environments. In this sense, in order to evaluate the similarity of environments and determine the number of environments for the assessment of cultivars, eight trials were carried out in the State of Tocantins. Four trials were carried out in Gurupi-TO, in the dates of 08/11, 19/11, 28/11 and 05/12/2006, and four trials were carried out in Alvorada-TO, in the dates of 09/11, 23/11, 07/12 and 21/12/2006. Each trial represented an environment. The experiment design was a randomized blocks with seven treatments (cultivars) and four replications. The cultivars were: BR/EMGOPA 314, BRS Sambaíba, BRS Tracajá, MD-339, M-SOY 108, M-Soy 8411 and M8866. The methods used were stratification and environmental dissimilarity cluster, based on the interaction of simple order. In the jointly analysis of variance there was significant effect of cultivar x environment interaction. There these indicated an inconsistency of the superiority of cultivars to environmental variation, justifying the studies of cluster environments. Both methods led to the formation of a single group of similar environments. There was no agreement between the methods regarding the composition of the groups formed. The environmental stratification method resulted in the formation of a group comprising three environments in Gurupi (19/11, 28/11 and 05/12) and two environments in Alvorada (21/12 and 11/09). However, the environmental dissimilarity method allowed the formation of a group containing only two environments in Alvorada (11/09 and 23/11). The clustering methods were less sensitive in detecting distinct environments represented by sowing dates in one place.
