ABSTRACT
Objetivos: Conocer el impacto de la COVID-19 en incidencia y letalidad en los centros residenciales de mayores (CRM) de Galicia. Métodos: Se trata de un estudio descriptivo en residentes y trabajadores de los CRM con COVID-19 confirmada. El análisis abarcó del 1 de marzo de 2020 al 27 de marzo de 2022 y se estratificó en 6 períodos (uno por ola). Se analizó el impacto en incidencia (tasa de ataque, número de brotes, reinfecciones, sexo, edad y técnica diagnóstica) y letalidad (por sexo, edad, lugar de fallecimiento y número de centros con fallecidos). Resultados: Hubo 15.819 personas afectadas, el 51,9% de las plazas y el 47% de los trabajadores. La tasa de ataque en residentes fue: 5,8% en la primera ola, 10,4% en la segunda, 6,3% en la tercera, 0,1% en la cuarta, 2,1% en la quinta y 27,3% en la sexta ola. En la sexta ola hubo un 11,3% de reinfecciones y el número de brotes en fue 3 veces mayor que en la segunda. La letalidad en residentes fue mayor durante la primera ola (21,8%) y menor durante la sexta (2,4%). Solo falleció un trabajador en relación a la COVID-19. Conclusiones: la vigilancia de la COVID-19 en CRM fue fundamental para conocer la dinámica de la enfermedad. La sexta ola fue la de mayor incidencia y la de menor letalidad. La letalidad fue superior en la primera ola. La cuarta y la quinta ola tuvieron menor incidencia debido a los efectos de la vacunación.
ABSTRACT
Background: Europe has had a large variability in COVID-19 incidence between and within countries, particularly after June 2020. We aim to assess the variability between European countries and regions located in a given country. Methods: We used ECDC information including countries having 7 regions or more. The metric used to assess the regional variability within a country was the intercuartilic range in a weekly basis for 32 weeks between June 29th 2020 and February 1st 2021. We also calculated each country's overall variability across the 32 weeks using the distances from the regional curves of the 14-day incidence rates to the corresponding national curve, using the L2 metric for functional data. We afterwards standardised this metric to a scale from 0 to 100 points. We repeated the calculations excluding island regions. Results: The variability between and within countries was large. Slovenia, Spain and Portugal have the greatest variability. Spain and Slovenia held also the top three places for the greatest number of weeks (Spain for 19 weeks and Slovenia for 10) with the highest variability. For variability among the incidence curves across the 32-week period, Slovenia, Portugal and Spain ranked first in functional variability, when all the regions were analysed but also when the island regions were excluded. Conclusions: These differences might be due to how countries tackled the epidemiological situation. The persistent variability in COVID-19 incidence between regions of a given country suggests that governmental action may have an important role in applying epidemiological control measures.
Subject(s)
COVID-19 , Europe , Humans , Incidence , Policy , SARS-CoV-2ABSTRACT
INTRODUCTION: The SARS-CoV-2 pandemic is the most important health challenge observed in 100 years, and since its emergence has generated the highest excess of non-war-related deaths in the western world. Since this disease is highly contagious and 33% of cases are asymptomatic, it is crucial to develop methods to predict its course. We developed a predictive model for Covid-19 infection in Spanish provinces. METHODS: We applied main components analysis to epidemiological data for Spanish provinces obtained from the National Centre of Epidemiology, based on the epidemiological curve between 24 February and 8 June 2020. Using this method, we classified provinces according to their epidemiological progress (worst, intermediate, and good). RESULTS: We identified 2 components that explained 99% of variability in the 52 epidemiological curves. The first component can be interpreted as the crude incidence rate trend and the second component as the speed of increase or decrease in the incidence rate during the period analysed. We identified 10 provinces in the group with the worst progress and 17 in the intermediate group. The threshold values for the 7-day incidence rate for an alert 1 (intermediate) were 134 cases/100,000 inhabitants, and 167 for alert 2 (high), respectively, showing a high discriminative power between provinces. CONCLUSIONS: These alert levels might be useful for deciding which measures may affect population mobility and other public health decisions when considering community transmission of SARS-CoV-2 in a given geographical area. This information would also facilitate intercomparison between healthcare areas and Autonomous Communities.