Neuraminidase inhibitors were widely used during the 2009-10 influenza A H1N1 pandemic, but evidence for their effectiveness in reducing mortality is uncertain. We did a meta-analysis of individual participant data to investigate the association between use of neuraminidase inhibitors and mortality in patients admitted to hospital with pandemic influenza A H1N1pdm09 virus infection. We assembled data for patients (all ages) admitted to hospital worldwide with laboratory confirmed or clinically diagnosed pandemic influenza A H1N1pdm09 virus infection. We identified potential data contributors from an earlier systematic review of reported studies addressing the same research question. In our systematic review, eligible studies were done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010); however, we continued to receive data up to March 14, 2011, from ongoing studies. We did a meta-analysis of individual participant data to assess the association between neuraminidase inhibitor treatment and mortality (primary outcome), adjusting for both treatment propensity and potential confounders, using generalised linear mixed modelling. We assessed the association with time to treatment using time-dependent Cox regression shared frailty modelling. We included data for 29,234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0·81; 95% CI 0·70-0·93; p=0·0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0·48; 95% CI 0·41-0·56; p<0·0001). Early treatment versus no treatment was also associated with a reduction in mortality (adjusted OR 0·50; 95% CI 0·37-0·67; p<0·0001). These associations with reduced mortality risk were less pronounced and not significant in children. There was an increase in the mortality hazard rate with each day's delay in initiation of treatment up to day 5 as compared with treatment initiated within 2 days of symptom onset (adjusted hazard ratio [HR 1·23] [95% CI 1·18-1·28]; p<0·0001 for the increasing HR with each day's delay). We advocate early instigation of neuraminidase inhibitor treatment in adults admitted to hospital with suspected or proven influenza infection. F Hoffmann-La Roche.

There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low.

Background. The impact of neuraminidase inhibitor (NAI) treatment on clinical outcomes of public health importance during the 2009–2010 pandemic has not been firmly established. Methods. We conducted a systematic review and meta-analysis, searching 11 databases (2009 through April 2012) for relevant studies. We used standard methods conforming to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using random effects models. Results. Regarding mortality we observed a nonsignificant reduction associated with NAI treatment (at any time) versus none (OR, 0.72 [95% CI, .51–1.01]). However we observed significant reductions for early treatment (≤48 hours after symptom onset) versus late (OR, 0.38 [95% CI, .27–.53]) and for early treatment versus none (OR, 0.35 [95% CI, .18–.71]). NAI treatment (at any time) versus none was associated with an elevated risk of severe outcome (OR, 1.76 [95% CI, 1.22–2.54]), but early versus late treatment reduced the likelihood (OR, 0.41 [95% CI, .30–.56]). Conclusions. During the 2009–2010 influenza A(H1N1) pandemic, early initiation of NAI treatment reduced the likelihood of severe outcomes compared with late or no treatment. PROSPERO Registration. CRD42011001273.

Background: The relationship between public transport use and acquisition of acute respiratory infection (ARI) is not well understood but potentially important during epidemics and pandemics. Methods: A case-control study performed during the 2008/09 influenza season. Cases (n = 72) consulted a General Practitioner with ARI, and controls with another non-respiratory acute condition (n = 66). Data were obtained on bus or tram usage in the five days preceding illness onset (cases) or the five days before consultation (controls) alongside demographic details. Multiple logistic regression modelling was used to investigate the association between bus or tram use and ARI, adjusting for potential confounders. Results: Recent bus or tram use within five days of symptom onset was associated with an almost six-fold increased risk of consulting for ARI (adjusted OR = 5.94 95% CI 1.33-26.5). The risk of ARI appeared to be modified according to the degree of habitual bus and tram use, but this was not statistically significant (1-3 times/week: adjusted OR = 0.54 (95% CI 0.15-1.95; >3 times/week: 0.37 (95% CI 0.13-1.06). Conclusions: We found a statistically significant association between ARI and bus or tram use in the five days before symptom onset. The risk appeared greatest among occasional bus or tram users, but this trend was not statistically significant. However, these data are plausible in relation to the greater likelihood of developing protective antibodies to common respiratory viruses if repeatedly exposed. The findings have differing implications for the control of seasonal acute respiratory infections and for pandemic influenza.

Objectives Benzodiazepines have been associated with an increased incidence of infections, and mortality from sepsis, in the critically ill. Here, we determined the effect of community use of benzodiazepines on the occurrence of, and mortality following, pneumonia. Methods A nested case-control study using 29 697 controls and 4964 cases of community-acquired pneumonia (CAP) from The Health Improvement Network, a UK primary care patient database (2001–2002), investigated the association between benzodiazepines and pneumonia occurrence using conditional logistic regression. Cox regression was then used to determine the impact of benzodiazepines on mortality in the 4964 cases of CAP. Results are presented as adjusted OR, adjusted HR and 95% CI. Results Exposure to benzodiazepines was associated with an increased risk of pneumonia (OR 1.54, 95% CI 1.42 to 1.67). Individually diazepam, lorazepam and temazepam, but not chlordiazepoxide, were associated with an increased incidence of CAP. As a class, benzodiazepines were associated with increased 30-day (HR 1.22 (95% CI 1.06 to 1.39)) and long-term mortality (HR 1.32 (95% CI 1.19 to 1.47)) in patients with a prior diagnosis of CAP. Individually diazepam, chlordiazepoxide, lorazepam and temazepam affected long-term mortality in these patients. Conclusions Benzodiazepines were associated with an increased risk of, and mortality from, CAP. These hypothesis generating data suggest further research is required into the immune safety profile of benzodiazepines.

Influenza is a highly infectious airborne disease with an important epidemiological and societal burden; annual epidemics and pandemics have occurred since ancient times, causing tens of millions of deaths. A hundred years after this virus was first isolated, influenza vaccines are an important influenza prevention strategy and the preparations used display good safety and tolerability profiles. Innovative tools, such as recombinant technologies and intra-dermal devices, are currently being investigated in order to improve the immunological response. The recurring mutations of influenza strains has prompted the recent introduction of a quadrivalent inactivated vaccine. In the near future, scientific research will strive to produce a long-lasting universal vaccine containing an antigen that will offer protection against all influenza virus strains.