Search

Background In the context of COVID-19 pandemic in Catalonia (Spain), the present study analyses respiratory samples collected by the primary care network using Acute Respiratory Infections Sentinel Surveillance System (PIDIRAC) during the 2019–2020 season to complement the pandemic surveillance system in place to detect SARS-CoV-2. The aim of the study is to describe whether SARS-CoV-2 was circulating before the first confirmed case was detected in Catalonia, on February 25th, 2020. Methods The study sample was made up of all samples collected by the PIDIRAC primary care network as part of the Influenza and Acute Respiratory Infections (ARI) surveillance system activities. The study on respiratory virus included coronavirus using multiple RT-PCR assays. All positive samples for human coronavirus were subsequently typed for HKU1, OC43, NL63, 229E. Every respiratory sample was frozen at—80°C and retrospectively studied for SARS-CoV-2 detection. A descriptive study was performed, analysing significant differences among variables related to SARS-CoV- 2 cases comparing with rest of coronaviruses cases through a bivariate study with Chi-squared test and statistical significance at 95%. Results Between October 2019 and April 2020, 878 respiratory samples from patients with acute respiratory infection or influenza syndrome obtained by PIDIRAC were analysed. 51.9% tested positive for influenza virus, 48.1% for other respiratory viruses. SARS-CoV-2 was present in 6 samples. The first positive SARS-CoV-2 case had symptom onset on 2 March 2020. These 6 cases were 3 men and 3 women, aged between 25 and 50 years old. 67% had risk factors, none had previous travel history nor presented viral coinfection. All of them recovered favourably. Conclusion Sentinel Surveillance PIDIRAC enhances global epidemiological surveillance by allowing confirmation of viral circulation and describes the epidemiology of generalized community respiratory viruses’ transmission in Catalonia. The system can provide an alert signal when identification of a virus is not achieved in order to take adequate preparedness measures.

BackgroundDisease surveillance using adequate case definitions is very important. The objective of the study was to compare the performance of influenza case definitions and influenza symptoms in the first two epidemic weeks with respect to other epidemic weeks.MethodsWe analysed cases of acute respiratory infection detected by the network of sentinel primary care physicians of Catalonia for 10 seasons. We calculated the diagnostic odds ratio (DOR) and 95% confidence intervals (CI) for the first two epidemic weeks and for other epidemic weeks.ResultsA total of 4,338 samples were collected in the epidemic weeks, of which 2,446 (56.4%) were positive for influenza. The most predictive case definition for laboratory-confirmed influenza was the WHO case definition for influenza-like illness (ILI) in the first two epidemic weeks (DOR 2.10; 95% CI 1.57-2.81) and in other epidemic weeks (DOR 2.31; 95% CI 1.96-2.72). The most predictive symptom was fever. After knowing that epidemic threshold had been reached, the DOR of the ILI WHO case definition in children aged ConclusionsDuring influenza epidemics, differences in the performance of the case definition and the discriminative ability of symptoms were found according to whether it was known that the epidemic threshold had been reached or not. This suggests that sentinel physicians are stricter in selecting samples to send to the laboratory from patients who present symptoms more specific to influenza after rather than before an influenza epidemic has been declared.

Seasonal influenza is a cause of hospitalization, especially in people with underlying disease or extreme age, and its severity may differ depending on the types and subtypes of circulating viruses. We investigated the factors associated with ICU admission or death in hospitalized patients with severe laboratory-confirmed influenza according to the viral type and subtype. An observational epidemiological study was carried out in patients aged ≥18 years from 12 Catalan hospitals between 2010 and 2016. For each reported case we collected demographic, virological and clinical characteristics. A mixed-effects logistic regression model was used to estimate crude and adjusted ORs. 1726 hospitalized patients were included: 595 (34.5%) were admitted to the ICU and 224 (13.0%) died. Lower ICU admission was associated with age ≥75 years in all influenza types and subtypes and with age 65-74 years for type A. In contrast, the 65-74 and ≥75 years age groups were associated with an increased risk of death in all types and subtypes, especially for type B (aOR 27.42, 95% CI: 4.95-151.93 and 15.96; 95% CI: 3.01-84.68). The comorbidity most closely associated with severe outcomes was immune deficiency, which was associated with death for type B (aOR 9.02, 95% CI: 3.05-26.69) and subtype A(H1N1)pdm09 (aOR 3.16, 95% CI: 1.77-5.66). Older age was a differential factor for ICU admission and death: it was associated with lower ICU admission but a risk factor for death. The comorbidity with the closest association with death was immune deficiency, mainly in influenza type B patients.

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010-2011 to 2013-2014) was created. A pilot test was conducted during the 2014-2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015-2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015-2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.