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Abstract Background A unique policy of perimeter closures of Basic Health Zones (small administrative health units) was implemented in the Autonomous Community of Madrid from September 21st 2020 to May 23rd 2021 to face the COVID-19 pandemic. Aim To assess the impact of local perimeter confinements on the 14-days cumulative incidence of SARS-CoV-2 during the second wave of the pandemic in Madrid, Spain. Methods We compare the errors in estimation of two families of mathematical models: ones that include the perimeter closures as explanatory covariables and ones that do not, in search of a significant improvement in estimation of one family over the other. We incorporate leave-one-out cross-validation, and at each step of this process we select the best model in AIC score from a family of 15 differently tuned ones. Results The two families of models provided very similar estimations, for a 1- to 3-weeks delay in observed cumulative incidence, and also when restricting the analysis to only those Basic Health Zones that were subject to at least one closure during the time under study. In all cases the correlation between the errors yielded by both families of models was higher than 0.98 (±10− 3 95% CI), and the average difference of estimated 14-days cumulative incidence was smaller than 1.49 (±0.33 95% CI). Conclusion Our analysis suggests that the perimeter closures by Basic Health Zone did not have a significant effect on the epidemic curve in Madrid.

Abstract Background Physicians of the Spanish Influenza Sentinel Surveillance System report and systematically swab patients attended to their practices for influenza-like illness (ILI). Within the surveillance system, some Spanish regions also participated in an observational study aiming at estimating influenza vaccine effectiveness (cycEVA study). During the season 2009-2010, we estimated pandemic influenza vaccine effectiveness using both the influenza surveillance data and the cycEVA study. Methods We conducted two case-control studies using the test-negative design, between weeks 48/2009 and 8/2010 of the pandemic season. The surveillance-based study included all swabbed patients in the sentinel surveillance system. The cycEVA study included swabbed patients from seven Spanish regions. Cases were laboratory-confirmed pandemic influenza A(H1N1)2009. Controls were ILI patients testing negative for any type of influenza. Variables collected in both studies included demographic data, vaccination status, laboratory results, chronic conditions, and pregnancy. Additionally, cycEVA questionnaire collected data on previous influenza vaccination, smoking, functional status, hospitalisations, visits to the general practitioners, and obesity. We used logistic regression to calculate adjusted odds ratios (OR), computing pandemic influenza vaccine effectiveness as (1-OR)*100. Results We included 331 cases and 995 controls in the surveillance-based study and 85 cases and 351 controls in the cycEVA study. We detected nine (2.7%) and two (2.4%) vaccine failures in the surveillance-based and cycEVA studies, respectively. Adjusting for variables collected in surveillance database and swabbing month, pandemic influenza vaccine effectiveness was 62% (95% confidence interval (CI): -5; 87). The cycEVA vaccine effectiveness was 64% (95%CI: -225; 96) when adjusting for common variables with the surveillance system and 75% (95%CI: -293; 98) adjusting for all variables collected. Conclusion Point estimates of the pandemic influenza vaccine effectiveness suggested a protective effect of the pandemic vaccine against laboratory-confirmed influenza A(H1N1)2009 in the season 2009-2010. Both studies were limited by the low vaccine coverage and the late start of the vaccination campaign. Routine influenza surveillance provides reliable estimates and could be used for influenza vaccine effectiveness studies in future seasons taken into account the surveillance system limitations.

Abstract Background Seasonal flu epidemics in the European region cause high numbers of cases and deaths. Flu-associated mortality has been estimated but morbidity studies are necessary to understand the burden of disease in the population. Our objective was to estimate the excess hospital admissions in Spain of diseases associated with influenza during four epidemic influenza periods (2000 – 2004). Methods Hospital discharge registers containing pneumonia, chronic bronchitis, heart failure and flu from all public hospitals in Spain were reviewed for the years 2000 to 2004. Epidemic periods were defined by data from the Sentinel Surveillance System. Excess hospitalisations were calculated as the difference between the average number of weekly hospitalisations/100,000 in epidemic and non-epidemic periods. Flu epidemics were defined for seasons 2001/2002, 2002/2003, 2003/2004. Results A(H3N2) was the dominant circulating serotype in 2001/2002 and 2003/2004. Negligible excess hospitalisations were observed during the 2002/2003 epidemic where A(H1N1) was circulating. During 2000/2001, flu activity remained below threshold levels and therefore no epidemic period was defined. In two epidemic periods studied a delay between the peak of the influenza epidemic and the peak of hospitalisations was observed. During flu epidemics with A(H3N2), excess hospitalisations were higher in men and in persons 64 years higher than 10 per 100,000. Pneumonia accounted for 70% of all flu associated hospitalisations followed by chronic bronchitis. No excess flu-specific hospitalisations were recorded during all seasons. Conclusion Flu epidemics have an impact on hospital morbidity in Spain. Further studies that include other variables, such as temperature and humidity, are necessary and will deepen our understanding of the role of each factor during flu epidemics and their relation with morbidity.