Results from a European multicentre case-control study reported by Marta Valenciano and colleagues suggest good protection by the pandemic monovalent H1N1 vaccine against pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine on H1N1.

BackgroundPneumococcal conjugate vaccines (PCVs) have the potential to prevent pneumococcal disease through direct and indirect protection. This multicentre European study estimated the indirect effects of 5-year childhood PCV10 and/or PCV13 programmes on invasive pneumococcal disease (IPD) in older adults across 13 sites in 10 European countries, to support decision-making on pneumococcal vaccination policies.MethodsFor each site we calculated IPD incidence rate ratios (IRR) in people aged ≥65 years by serotype for each PCV10/13 year (2011–2015) compared with 2009 (pre-PCV10/13). We calculated pooled IRR and 95% CI using random-effects meta-analysis and PCV10/13 effect as (1 − IRR)*100.ResultsAfter five PCV10/13 years, the incidence of IPD caused by all types, PCV7 and additional PCV13 serotypes declined 9% (95% CI −4% to 19%), 77% (95% CI 67% to 84%) and 38% (95% CI 19% to 53%), respectively, while the incidence of non-PCV13 serotypes increased 63% (95% CI 39% to 91%). The incidence of serotypes included in PCV13 and not in PCV10 decreased 37% (95% CI 22% to 50%) in six PCV13 sites and increased by 50% (95% CI −8% to 146%) in the four sites using PCV10 (alone or with PCV13). In 2015, PCV13 serotypes represented 20–29% and 32–53% of IPD cases in PCV13 and PCV10 sites, respectively.ConclusionOverall IPD incidence in older adults decreased moderately after five childhood PCV10/13 years in 13 European sites. Large declines in PCV10/13 serotype IPD, due to the indirect effect of childhood vaccination, were countered by increases in non-PCV13 IPD, but these declines varied according to the childhood vaccine used. Decision-making on pneumococcal vaccination for older adults must consider the indirect effects of childhood PCV programmes. Sustained monitoring of IPD epidemiology is imperative.

BackgroundIn the third season of I-MOVE (Influenza Monitoring Vaccine Effectiveness in Europe), we undertook a multicentre case-control study based on sentinel practitioner surveillance networks in eight European Union (EU) member states to estimate 2010/11 influenza vaccine effectiveness (VE) against medically-attended influenza-like illness (ILI) laboratory-confirmed as influenza.MethodsUsing systematic sampling, practitioners swabbed ILI/ARI patients within seven days of symptom onset. We compared influenza-positive to influenza laboratory-negative patients among those meeting the EU ILI case definition. A valid vaccination corresponded to > 14 days between receiving a dose of vaccine and symptom onset. We used multiple imputation with chained equations to estimate missing values. Using logistic regression with study as fixed effect we calculated influenza VE adjusting for potential confounders. We estimated influenza VE overall, by influenza type, age group and among the target group for vaccination.ResultsWe included 2019 cases and 2391 controls in the analysis. Adjusted VE was 52% (95% CI 30-67) overall (N = 4410), 55% (95% CI 29-72) against A(H1N1) and 50% (95% CI 14-71) against influenza B. Adjusted VE against all influenza subtypes was 66% (95% CI 15-86), 41% (95% CI -3-66) and 60% (95% CI 17-81) among those aged 0-14, 15-59 and ≥60 respectively. Among target groups for vaccination (N = 1004), VE was 56% (95% CI 34-71) overall, 59% (95% CI 32-75) against A(H1N1) and 63% (95% CI 31-81) against influenza B.ConclusionsResults suggest moderate protection from 2010-11 trivalent influenza vaccines against medically-attended ILI laboratory-confirmed as influenza across Europe. Adjusted and stratified influenza VE estimates are possible with the large sample size of this multi-centre case-control. I-MOVE shows how a network can provide precise summary VE measures across Europe.

BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and FindingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (“cases”) were asked if they had exposure to other potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO’s response during the epidemic, and have been updated for publication.We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = −0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible mis...

European Centre for Disease Prevention and Control, Czech National Institute of Public Health, French National Agency for Public Health, Irish Health Services Executive, Norwegian Institute of Public Health, Public Health Agency of Catalonia, Public Health Department of Community of Madrid, Navarra Hospital Complex, Public Health Institute of Navarra, CIBER Epidemiology and Public Health, Institute of Health Carlos III, Public Health Agency of Sweden, and NHS Scotland.