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.
