Estimates of influenza vaccine effectiveness in primary care in Scotland vary with clinical or laboratory endpoint and method : experience across the 2010/11 season

Aim: This study examines estimation of seasonal influenza vaccine effectiveness (VE) for a cohort of patients attending general practice in Scotland in 2010/11. The study focuses on the variation in estimation of VE for both virological and clinical consultation outcomes and understanding the dependency on date of analysis during the season, methodological approach and the effect of use of a propensity score model. Methods: For the clinical outcomes, three methodological approaches were considered; adjusted Poissonmulti-level modelling splitting consultations in vaccinated individuals into those before and after vaccination, adjusted Cox proportional hazards modelling and finally the screening method. For the virological outcome, the test-negative case–control study design was employed. Results: VE was highest for the most specific outcomes of ILI (Poisson end-of-season VE = 47% (95% CI:−69%, 83%); Cox VE = 34% (95% CI: −64%, 73.2%); Screening VE = 52.8% (95% CI: 3.8%, 76.8%)) and a viro-logical diagnosis (VE = 54% (95% CI: −37%, 85%)). Using the Cox approach, adjusted for propensity scoreonly gave VE = 46.5% (95% CI: −30.4%, 78.0%). Conclusion: Our approach illustrated the ability to achieve relatively consistent estimates of seasonalinfluenza VE using both specific and less specific outcomes. Construction of a propensity score and usefor bias adjustment increased the estimate of ILI VE estimated from the Cox model and made estimatesmore similar to the Poisson approach, which models differences in consultation behaviour of vacci-nated individuals more inherently in its structure. VE estimation for the same data was found to vary bymethodology which should be noted when comparing results from different studies and countries

Laboratory-confirmed respiratory infections as triggers for acute myocardial infarction and stroke: a self-controlled case series analysis of national linked datasets from Scotland

While acute respiratory tract infections can trigger cardiovascular events, the differential effect of specific organisms is unknown. This is important to guide vaccine policy.Using national infection surveillance data linked to the Scottish Morbidity Record, we identified adults with a first myocardial infarction or stroke from January 1, 2004 to December 31, 2014 and a record of laboratory-confirmed respiratory infection during this period. Using self-controlled case series analysis, we generated age- and season-adjusted incidence ratios (IRs) for myocardial infarction (n=1227) or stroke (n=762) after infections compared with baseline time.We found substantially increased myocardial infarction rates in the week after Streptococcus pneumoniae and influenza virus infection: adjusted IRs for days 1-3 were 5.98 (95% CI 2.47-14.4) and 9.80 (95% CI 2.37-40.5), respectively. Rates of stroke after infection were similarly high and remained elevated to 28 days: day 1-3 adjusted IRs 12.3 (95% CI 5.48-27.7) and 7.82 (95% CI 1.07-56.9) for S. pneumoniae and influenza virus, respectively. Although other respiratory viruses were associated with raised point estimates for both outcomes, only the day 4-7 estimate for stroke reached statistical significance.We showed a marked cardiovascular triggering effect of S. pneumoniae and influenza virus, which highlights the need for adequate pneumococcal and influenza vaccine uptake. Further research is needed into vascular effects of noninfluenza respiratory viruses

Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilisation. The Scottish experience of the 2017/18 season to date

Scotland observed an unusual influenza A(H3N2)- dominated 2017/18 influenza season with healthcare services under significant pressure. We report the application of the moving epidemic method (MEM) to virology data as a tool to predict the influenza peak activity period and peak week of swab positivity in the current season. This novel MEM application has been successful locally and is believed to be of potential use to other countries for healthcare planning and building wider community resilience

Seasonal Influenza Vaccine Effectiveness in the community (SIVE): protocol for a cohort study exploiting a unique national linked data set

Introduction Seasonal influenza vaccination is recommended for all individuals aged 65 years and over and in individuals younger than 65 years with comorbidities. There is good evidence of vaccine effectiveness (VE) in young healthy individuals but less robust evidence for effectiveness in the populations targeted for influenza vaccination. Undertaking a randomised controlled trial to assess VE is now impractical due to the presence of national vaccination programmes. Quasi-experimental designs offer the potential to advance the evidence base in such scenarios, and the authors have therefore been commissioned to undertake a naturalistic national evaluation of seasonal influenza VE by using data derived from linkage of a number of Scottish health databases. The aim of this study is to examine the effectiveness of the seasonal influenza vaccination in the Scottish population. Methods and analysis A cohort study design will be used pooling data over nine seasons. A primary care database covering 4% of the Scottish population for the period 2000–2009 has been linked to the national database of hospital admissions and the death register and is being linked to the Health Protection Scotland virology database. The primary outcome is VE measured in terms of rate of hospital admissions due to respiratory illness. Multivariable regression will be used to produce estimates of VE adjusted for confounders. The major challenge of this approach is addressing the strong effect of confounding due to vaccinated individuals being systematically different from unvaccinated individuals. Analyses using propensity scores and instrumental variables will be undertaken, and the effect of an unknown confounder will be modelled in a sensitivity analysis to assess the robustness of the estimates

Epidemiology of seasonal coronaviruses: establishing the context for the emergence of coronavirus disease 2019

Public health preparedness for coronavirus (CoV) disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal CoVs (sCoVs) and other cocirculating viruses in the West of Scotland, United Kingdom. We analyzed routine diagnostic data for >70 000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43, and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently, and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity

Increase in Legionnaires' disease cases associated with travel to Dubai among travellers from the United Kingdom, Sweden and the Netherlands, October 2016 to end August 2017.

Between 1 October 2016 and 31 August 2017, 51 Legionnaires' disease (LD) cases from the United Kingdom, Sweden and the Netherlands were identified with associated travel to Dubai. Cases did not all stay in the same accommodation, indicating that no single accommodation could be the source for all these infections. While local investigations continue into other potential sources, clinicians should remain alert to the possibility of LD among travellers returning from Dubai with respiratory illness

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness