None.

The 2014/15 influenza season was the second season of roll-out of a live attenuated influenza vaccine (LAIV) programme for healthy children in England. During this season, besides offering LAIV to all two to four year olds, several areas piloted vaccination of primary (4-11 years) and secondary (11-13 years) age children. Influenza A(H3N2) circulated, with strains genetically and antigenically distinct from the 2014/15 A(H3N2) vaccine strain, followed by a drifted B strain. We assessed the overall and indirect impact of vaccinating school age children, comparing cumulative disease incidence in targeted and non-targeted age groups in vaccine pilot to non-pilot areas. Uptake levels were 56.8% and 49.8% in primary and secondary school pilot areas respectively. In primary school age pilot areas, cumulative primary care influenza-like consultation, emergency department respiratory attendance, respiratory swab positivity, hospitalisation and excess respiratory mortality were consistently lower in targeted and non-targeted age groups, though less for adults and more severe end-points, compared with non-pilot areas. There was no significant reduction for excess all-cause mortality. Little impact was seen in secondary school age pilot only areas compared with non-pilot areas. Vaccination of healthy primary school age children resulted in population-level impact despite circulation of drifted A and B influenza strains.

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Very different influenza seasons have been observed from 2008/09–2011/12 in England and Wales, with the reported burden varying overall and by age group. The objective of this study was to estimate the impact of influenza on all-cause and cause-specific mortality during this period. Age-specific generalised linear regression models fitted with an identity link were developed, modelling weekly influenza activity through multiplying clinical influenza-like illness consultation rates with proportion of samples positive for influenza A or B. To adjust for confounding factors, a similar activity indicator was calculated for Respiratory Syncytial Virus. Extreme temperature and seasonal trend were controlled for. Following a severe influenza season in 2008/09 in 65+yr olds (estimated excess of 13,058 influenza A all-cause deaths), attributed all-cause mortality was not significant during the 2009 pandemic in this age group and comparatively low levels of influenza A mortality were seen in post-pandemic seasons. The age shift of the burden of seasonal influenza from the elderly to young adults during the pandemic continued into 2010/11; a comparatively larger impact was seen with the same circulating A(H1N1)pdm09 strain, with the burden of influenza A all-cause excess mortality in 15–64 yr olds the largest reported during 2008/09–2011/12 (436 deaths in 15–44 yr olds and 1,274 in 45–64 yr olds). On average, 76% of seasonal influenza A all-age attributable deaths had a cardiovascular or respiratory cause recorded (average of 5,849 influenza A deaths per season), with nearly a quarter reported for other causes (average of 1,770 influenza A deaths per season), highlighting the importance of all-cause as well as cause-specific estimates. No significant influenza B attributable mortality was detected by season, cause or age group. This analysis forms part of the preparatory work to establish a routine mortality monitoring system ahead of introduction of the UK universal childhood seasonal influenza vaccination programme in 2013/14.

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Knowledge of the severity of an influenza outbreak is crucial for informing and monitoring appropriate public health responses, both during and after an epidemic. However, case-fatality, case-intensive care admission and case-hospitalisation risks are difficult to measure directly. Bayesian evidence synthesis methods have previously been employed to combine fragmented, under-ascertained and biased surveillance data coherently and consistently, to estimate caseseverity risks in the first two waves of the 2009 A/H1N1 influenza pandemic experienced in England. We present in detail the complex probabilistic model underlying this evidence synthesis, and extend the analysis to also estimate severity in the third wave of the pandemic strain during the 2010/2011 influenza season. We adapt the model to account for changes in the surveillance data available over the three waves. We consider two approaches: (a) a two-stage approach using posterior distributions from the model for the first two waves to inform priors for the third wave model; and (b) a one-stage approach modelling all three waves simultaneously. Both approaches result in the same key conclusions: (1) that the age-distribution of the case-severity risks is "u"-shaped, with children and older adults having the highest severity; (2) that the age-distribution of the infection attack rate changes over waves, school-age children being most affected in the first two waves and the attack rate in adults over 25 increasing from the second to third waves; and (3) that when averaged over all age groups, case-severity appears to increase over the three waves. The extent to which the final conclusion is driven by the change in age-distribution of those infected over time is subject to discussion.

Disasters exact a heavy toll globally. However, the degree to which we can accurately quantify their impact, in particular mortality, remains challenging. It is critical to ensure that disaster data reliably reflects the scale, type, and distribution of disaster impacts given the role of data in: (1) risk assessments; (2) developing disaster risk management programs; (3) determining the resources for response to emergencies; (4) the types of action undertaken in planning for prevention and preparedness; and (5) identifying research gaps. The Sendai Framework for Disaster Risk Reduction 2015-2030s seven global disaster-impact reduction targets represent the first international attempt to systematically measure the effectiveness of disaster-impact reduction as a means of better informing policy with evidence. Target A of the Sendai Framework aims to ''substantially reduce global disaster mortality by 2030, aiming to lower the average per 100,000 global mortality rate in the decade 2020-2030 compared to the period 2005-2015.'' This article provides an overview of the complexities associated with defining, reporting, and interpreting disaster mortality data used for gauging success in meeting Target A, acknowledging different challenges for different types of hazard events and subsequent disasters. It concludes with suggestions of how to address these challenges to inform the public health utility of monitoring through the Sendai Framework.

BackgroundA Heat-Health Watch system has been established in England and Wales since 2004 as part of the national heatwave plan following the 2003 European-wide heatwave. One important element of this plan has been the development of a timely mortality surveillance system. This article reports the findings and timeliness of a daily mortality model used to ‘nowcast’ excess mortality (utilising incomplete surveillance data to estimate the number of deaths in near-real time) during a heatwave alert issued by the Met Office for regions in South and East England on 24 June 2011.MethodsDaily death registrations were corrected for reporting delays with historical data supplied by the General Registry Office. These corrected counts were compared with expected counts from an age-specific linear regression model to ascertain if any excess had occurred during the heatwave.ResultsExcess mortality of 367 deaths was detected across England and Wales in ≥85-year-olds on 26 and 27 June 2011, coinciding with the period of elevated temperature. This excess was localised to the east of England and London. It was detected 3 days after the heatwave.ConclusionA daily mortality model was sensitive and timely enough to rapidly detect a small excess, both, at national and regional levels. This tool will be useful when future events of public health significance occur.