Search

Background During 2009‐2010, pandemic influenza A (H1N1) pdm09 virus (pH1N1) infections in England occurred in two epidemic waves. Reasons for a reported increase in case‐severity during the second wave are unclear. Methods We analysed hospital‐based surveillance for patients with pH1N1 infections in England during 2009‐2010 and linked national data sets to estimate ethnicity, socio‐economic status and death within 28 days of admission. We used multivariable logistic regression to assess whether changes in demographic, clinical and management characteristics of patients could explain an increase in ICU admission or death, and accounted for missing values using multiple imputation. Results During the first wave, 54/960 (6%) hospitalised patients required intensive care and 21/960 (2%) died; during the second wave 143/1420 (10%) required intensive care and 55/1420 (4%) died. In a multivariable model, during the second wave patients were less likely to be from an ethnic minority (OR 0.33, 95% CI 0.26‐0.42), have an elevated deprivation score (OR 0.75, 95% CI 0.68‐0.83), have known comorbidity (OR 0.78, 95% CI 0.63‐0.97) or receive antiviral therapy ≤2 days before onset (OR 0.72, 95% CI 0.56‐0.92). Increased case‐severity during the second wave was not explained by changes in demographic, clinical or management characteristics. Conclusions Monitoring changes in patient characteristics could help target interventions during multiple waves of COVID‐19 or a future influenza pandemic. To understand and respond to changes in case‐severity, surveillance is needed that includes additional factors such as admission thresholds and seasonal coinfections.

Background Across the World Health Organization European Region, there are few estimates of the proportion of people seeking medical care for influenza-like illness or acute respiratory infections and who have laboratory-confirmed seasonal influenza infection. Methods We conducted a meta-analysis of data extracted from studies published between 2004 and 2017 and from sentinel data from the European surveillance system (TESSy) between 2004 and 2018. We pooled within-season estimates by influenza type/subtype, setting (outpatient (OP)/inpatient (IP)) and age group to estimate the proportion of people tested who have laboratory-confirmed and medically-attended seasonal influenza in Europe. Results In the literature review, the pooled proportion for all influenza types was 33% (95% confidence interval (CI): 30–36), higher among OP 36% (95% CI: 33–40) than IP 24% (95% CI: 20–29). Pooled estimates for all influenza types by age group were: 0–17 years, 26% (22–31); 18–64 years, 41% (32–50); ≥ 65 years, 33% (27–40). From TESSy data, 33% (31–34) of OP and 24% (21–27) of IP were positive. The highest proportion of influenza A was in people aged 18–64 years (22%, 16–29). By subtype, A(H1N1)pdm09 was highest in 18–64 year-olds (16%, 11–21%) whereas A(H3N2) was highest in those ≥ 65 years (10%, 2–22). For influenza B, the highest proportion of infections was in those aged 18–64 years (15%, 9–24). Conclusions Laboratory-confirmed influenza accounted for approximately one third of all acute respiratory infections for which medical care was sought during the influenza season.

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.

Significant increases in excess all-cause mortality, particularly in the elderly, were observed during the winter of 2014/15 in England. With influenza A(H3N2) the dominant circulating influenza A subtype, this paper determines the contribution of influenza to this excess controlling for weather. A standardised multivariable Poisson regression model was employed with weekly all-cause deaths the dependent variable for the period 2008–2015. Adjusting for extreme temperature, a total of 26 542 (95% CI 25 301–27 804) deaths in 65+ and 1942 (95% CI 1834–2052) in 15–64-year-olds were associated with influenza from week 40, 2014 to week 20, 2015. This is compatible with the circulation of influenza A(H3N2). It is the largest estimated number of influenza-related deaths in England since prior to 2008/09. The findings highlight the potential health impact of influenza and the important role of the annual influenza vaccination programme that is required to protect the population including the elderly, who are vulnerable to a severe outcome.

Influenza and respiratory syncytial virus (RSV) are common causes of respiratory tract infections and place a burden on health services each winter. Systems to describe the timing and intensity of such activity will improve the public health response and deployment of interventions to these pressures. Here we develop early warning and activity intensity thresholds for monitoring influenza and RSV using two novel data sources: general practitioner out-of-hours consultations (GP OOH) and telehealth calls (NHS 111). Moving Epidemic Method (MEM) thresholds were developed for winter 2017–2018. The NHS 111 cold/flu threshold was breached several weeks in advance of other systems. The NHS 111 RSV epidemic threshold was breached in week 41, in advance of RSV laboratory reporting. Combining the use of MEM thresholds with daily monitoring of NHS 111 and GP OOH syndromic surveillance systems provides the potential to alert to threshold breaches in real-time. An advantage of using thresholds across different health systems is the ability to capture a range of healthcare-seeking behaviour, which may reflect differences in disease severity. This study also provides a quantifiable measure of seasonal RSV activity, which contributes to our understanding of RSV activity in advance of the potential introduction of new RSV vaccines.

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 case-severity 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., Published in at http://dx.doi.org/10.1214/14-AOAS775 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Heatwaves are predicted to increase in frequency and intensity as a result of climate change. The health impacts of these events can be significant, particularly for vulnerable populations when mortality can occur. England experienced a prolonged heatwave in summer 2013. Daily age-group and region-specific all-cause excess mortality during summer 2013 and previous heatwave periods back to 2003 was determined using the same linear regression model and heatwave definition to estimate impact and place observations from 2013 in context. Predicted excess mortality due to heat during this period was also independently estimated. Despite a sustained heatwave in England in 2013, the impact on mortality was considerably less than expected; a small cumulative excess of 195 deaths (95% confidence interval -87 to 477) in 65+ year olds and 106 deaths (95% CI -22 to 234) in

Objective To carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries. Introduction Globally, there have been various studies assessing trends in Google search terms in the context of public health surveillance 1 . However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services. Methods We sought to determine the added public health utility of Google search data alongside established syndromic surveillance systems in England 2 for a range of conditions of public health importance, including allergic rhinitis, scarlet fever, bronchitis, pertussis, measles, rotavirus and the health impact of heatwaves. Google search term selection was based on diagnostic and clinical codes underlying the syndromic indicators, with Google Trends 3 used to identify additional related internet search terms. Daily data was extracted from syndromic surveillance systems 2 and from the Google Health Trends Application Programming Interface (API) from 2012 to 2017 and a retrospective daily analysis undertaken during pre-identified public health events to identify a) whether signals were detected during these events and b) assess the correlation with analogous syndromic surveillance indicators through calculation of Spearman correlation coefficients and lag assessment to determine timeliness. Results We detected increases in Google search term frequency during public health events of interest. Good correlation was seen with comparable syndromic surveillance indicators on a daily timescale for several health outcomes, including the search terms hayfever , scarlet fever , bronchiolitis and heatstroke . Weaker correlation was seen for conditions which occur in small numbers and are vaccine preventable such as measles and pertussis . Lag analysis showed similar timeliness between daily syndromic and Google data, suggesting that, overall, Google data did not provide an earlier or delayed signal compared to syndromic surveillance indicators in England. Conclusions To the best of our knowledge this is the first time trends in Google search data have been compared against syndromic data for a range of public health conditions in England. These findings demonstrate the potential utility of internet search query data in conjunction with existing systems in England, with syndromic surveillance data found to be as timely as Google data. These findings also have important implications for countries where there are no such healthcare-based syndromic surveillance systems in place. Factors to consider with analyses of Google search trend data in the context of disease surveillance have been highlighted, including the choice of search terms and interpretation of the reasons behind searching the internet. References 1 Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, Murugiah K. The use of google trends in health care research: a systematic review. PLoS One. 2014 Oct 22;9(10):e109583. 2 Public Health England. Syndromic surveillance: systems and analyses. 2017. Available online: https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses 3 Google. 2017. Google Trends. Available online: https://trends.google.com/trends/

SUMMARYSeasonal respiratory infections place an increased burden on health services annually. We used a sentinel emergency department syndromic surveillance system to understand the factors driving respiratory attendances at emergency departments (EDs) in England. Trends in different respiratory indicators were observed to peak at different points during winter, with further variation observed in the distribution of attendances by age. Multiple linear regression analysis revealed acute respiratory infection and bronchitis/bronchiolitis ED attendances in patients aged 1–4 years were particularly sensitive indicators for increasing respiratory syncytial virus activity. Using near real-time surveillance of respiratory ED attendances may provide early warning of increased winter pressures in EDs, particularly driven by seasonal pathogens. This surveillance may provide additional intelligence about different categories of attendance, highlighting pressures in particular age groups, thereby aiding planning and preparation to respond to acute changes in EDs, and thus the health service in general.

One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684-0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.

Heatwaves are a seasonal threat to public health. During July 2013 England experienced a heatwave; we used a suite of syndromic surveillance systems to monitor the impact of the heatwave. Significant increases in heatstroke and sunstroke were observed during 7-10 July 2013. Syndromic surveillance provided an innovative and effective service, supporting heatwave planning and providing early warning of the impact of extreme heat thereby improving the public health response to heatwaves.

SUMMARYMethods for estimating vaccine effectiveness (VE) against severe influenza are not well established. We used the screening method to estimate VE against influenza resulting in intensive care unit (ICU) admission in England and Scotland in 2011/2012. We extracted data on confirmed influenza ICU cases from severe influenza surveillance systems, and obtained their 2011/2012 trivalent influenza vaccine (TIV) status from primary care. We compared case vaccine uptake with population vaccine uptake obtained from routine monitoring systems, adjusting for age group, specific risk group, region and week. Of 60 influenza ICU cases reported, vaccination status was available for 56 (93%). Adjusted VE against ICU admission for those aged ⩾65 years was −10% [95% confidence interval (CI) −207 to 60], consistent with evidence of poor protection from the 2011/2012 TIV in 2011/2012. Adjusted VE for those aged

In 2016 an outbreak of Cryptosporidium potentially linked to swimming pools occurred in the West Midlands region in England. This highlighted the importance of prompt identification of Cryptosporidium by primary care and notification to Public Health England (PHE) to assist with patient management and outbreak control. This article outlines the epidemiology and processes to be followed should suspected Cryptosporidium cases present to primary care. Infection with the protozoan parasite Cryptosporidium results from ingestion of oocysts from faecally-contaminated water or food, direct person-to-person contact, or zoonotic spread. The incubation period (the time between exposure to Cryptosporidium and development of symptoms) varies but is on average between 5–7 days.1,2 Infection usually results in an acute illness lasting up to 3 weeks in otherwise healthy people, although asymptomatic infections are common and can be a source of infection for others.2 The symptoms are characterised by an abrupt onset of profuse watery diarrhoea with mucus (rarely blood) and abdominal cramping that may be associated with nausea, anorexia, and possibly fever. Although the infection is self-limiting in most patients, in the very young, older people, and in immunosuppressed individuals it can be a debilitating illness. Cryptosporidium occurs worldwide and is the fourth most common cause of gastrointestinal …

During the first year of enhanced MERS coronavirus surveillance in England, 77 persons traveling from the Middle East had acute respiratory illness and were tested for the virus. Infection was confirmed in 2 travelers with acute respiratory distress syndrome and 2 of their contacts. Patients with less severe manifestations tested negative.

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.

The 2014/15 influenza season in the United Kingdom (UK) was characterised by circulation of predominantly antigenically and genetically drifted influenza A(H3N2) and B viruses. A universal paediatric influenza vaccination programme using a quadrivalent live attenuated influenza vaccine (LAIV) has recently been introduced in the UK. This study aims to measure the end-of-season influenza vaccine effectiveness (VE), including for LAIV, using the test negative case–control design. The overall adjusted VE against all influenza was 34.3% (95% confidence interval (CI) 17.8 to 47.5); for A(H3N2) 29.3% (95% CI: 8.6 to 45.3) and for B 46.3% (95% CI: 13.9 to 66.5). For those aged under 18 years, influenza A(H3N2) LAIV VE was 35% (95% CI: −29.9 to 67.5), whereas for influenza B the LAIV VE was 100% (95% CI:17.0 to 100.0). Although the VE against influenza A(H3N2) infection was low, there was still evidence of significant protection, together with moderate, significant protection against drifted circulating influenza B viruses. LAIV provided non-significant positive protection against influenza A, with significant protection against B. Further work to assess the population impact of the vaccine programme across the UK is underway.

SUMMARYSeveral private boarding schools in England have established universal influenza vaccination programmes for their pupils. We evaluated the impact of these programmes on the burden of respiratory illnesses in boarders. Between November 2013 and May 2014, age-specific respiratory disease incidence rates in boarders were compared between schools offering and not offering influenza vaccine to healthy boarders. We adjusted for age, sex, school size and week using negative binomial regression. Forty-three schools comprising 14 776 boarders participated. Almost all boarders (99%) were aged 11–17 years. Nineteen (44%) schools vaccinated healthy boarders against influenza, with a mean uptake of 48·5% (range 14·2–88·5%). Over the study period, 1468 respiratory illnesses were reported in boarders (5·66/1000 boarder-weeks); of these, 33 were influenza-like illnesses (ILIs, 0·26/1000 boarder-weeks) in vaccinating schools and 95 were ILIs (0·74/1000 boarder-weeks) in non-vaccinating schools. The impact of vaccinating healthy boarders was a 54% reduction in ILI in all boarders [rate ratio (RR) 0·46, 95% confidence interval (CI) 0·28–0·76]. Disease rates were also reduced for upper respiratory tract infections (RR 0·72, 95% CI 0·61–0·85) and chest infections (RR 0·18, 95% CI 0·09–0·36). These findings demonstrate a significant impact of influenza vaccination on ILI and other clinical endpoints in secondary-school boarders. Additional research is needed to investigate the impact of influenza vaccination in non-boarding secondary-school settings.

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.

In this study we used the screening method to estimate the effectiveness of seasonal influenza vaccination during pregnancy in preventing influenza virus infection and influenza-related hospitalisation in infants under?six months, in England in the 2013/14 season. Seasonal influenza vaccination in pregnancy was 71% (95% CI: 24-89%) effective in preventing infant influenza virus infection and 64% (95% CI: 6-86%) effective in preventing infant influenza hospitalisation, and should be recommended in pregnancy. .

Background Subnational variation of 2009 pandemic influenza activity in England has been reported; however, little work has been published on this topic for seasonal influenza. If variation is present, this knowledge may assist with both identifying the onset of influenza epidemics, informing community antiviral prescription and local health planning. Methods An end-of-season analysis of influenza surveillance systems (acute respiratory outbreaks, primary care consultations, virological testing, influenza-confirmed secondary care admissions and excess all-cause mortality) was undertaken at national and subnational levels for 2012/13 when influenza B and A(H3N2) dominated. Results National community antiviral prescription was recommended in Week 51 following national threshold exceedance. However, this was preceded up to 2 weeks by subnational influenza activity in 2/9 regions in England. Regional variation in circulation of influenza subtypes was observed and severe influenza surveillance data sources were able to monitor the subnational impact. Conclusions Evidence of virological activity in two or more regions above a threshold indicated the onset of the 2012/13 season. Subnational thresholds should be determined and evaluated in order to improve timeliness of the national antiviral alert. During the season, outputs should be reported at levels that can inform local public health responses and variation considered when retrospectively evaluating the impact of interventions.

SUMMARYGeneral Practitioner consultation rates for influenza-like illness (ILI) are monitored through several geographically distinct schemes in the UK, providing early warning to government and health services of community circulation and intensity of activity each winter. Following on from the 2009 pandemic, there has been a harmonization initiative to allow comparison across the distinct existing surveillance schemes each season. The moving epidemic method (MEM), proposed by the European Centre for Disease Prevention and Control for standardizing reporting of ILI rates, was piloted in 2011/12 and 2012/13 along with the previously proposed UK method of empirical percentiles. The MEM resulted in thresholds that were lower than traditional thresholds but more appropriate as indicators of the start of influenza virus circulation. The intensity of the influenza season assessed with the MEM was similar to that reported through the percentile approach. The MEM pre-epidemic threshold has now been adopted for reporting by each country of the UK. Further work will continue to assess intensity of activity and apply standardized methods to other influenza-related data sources.

As part of the introduction and roll-out of a universal childhood live-attenuated influenza vaccination programme, 4–11 year-olds were vaccinated in seven pilot areas in England in the 2013/14 influenza season. This paper presents the uptake and impact of the programme for a range of disease indicators. End-of-season uptake was defined as the number of children in the target population who received at least one dose of influenza vaccine. Between week 40 2013 and week 15 2014, cumulative disease incidence per 100,000 population (general practitioner consultations for influenza-like illness and laboratory-confirmed influenza hospitalisations), cumulative influenza swab positivity in primary and secondary care and cumulative proportion of emergency department respiratory attendances were calculated. Indicators were compared overall and by age group between pilot and non-pilot areas. Direct impact was defined as reduction in cumulative incidence based on residence in pilot relative to non-pilot areas in 4–11 year-olds. Indirect impact was reduction between pilot and non-pilot areas in4 year-olds and11 year-olds. Overall vaccine uptake of 52.5% (104,792/199,475) was achieved. Although influenza activity was low, a consistent, though not statistically significant, decrease in cumulative disease incidence and influenza positivity across different indicators was seen in pilot relative to non-pilot areas in both targeted and non-targeted age groups, except in older age groups, where no difference was observed for secondary care indicators.

SUMMARYSeveral European countries have timely all-cause mortality monitoring. However, small changes in mortality may not give rise to signals at the national level. Pooling data across countries may overcome this, particularly if changes in mortality occur simultaneously. Additionally, pooling may increase the power of monitoring populations with small numbers of expected deaths, e.g. younger age groups or fertile women. Finally, pooled analyses may reveal patterns of diseases across Europe. We describe a pooled analysis of all-cause mortality across 16 European countries. Two approaches were explored. In the ‘summarized’ approach, data across countries were summarized and analysed as one overall country. In the ‘stratified’ approach, heterogeneities between countries were taken into account. Pooling using the ‘stratified’ approach was the most appropriate as it reflects variations in mortality. Excess mortality was observed in all winter seasons albeit slightly higher in 2008/09 than 2009/10 and 2010/11. In the 2008/09 season, excess mortality was mainly in elderly adults. In 2009/10, when pandemic influenza A(H1N1) dominated, excess mortality was mainly in children. The 2010/11 season reflected a similar pattern, although increased mortality in children came later. These patterns were less clear in analyses based on data from individual countries. We have demonstrated that with stratified pooling we can combine local mortality monitoring systems and enhance monitoring of mortality across Europe.

On 22 September 2012, a novel coronavirus, very closely related to that from a fatal case in Saudi Arabia three months previously, was detected in a previously well adult transferred to intensive care in London from Qatar with severe respiratory illness. Strict respiratory isolation was instituted. Ten days after last exposure, none of 64 close contacts had developed severe disease, with 13 of 64 reporting mild respiratory symptoms. The novel coronavirus was not detected in 10 of 10 symptomatic contacts tested.

Coronaviruses have the potential to cause severe transmissible human disease, as demonstrated by the severe acute respiratory syndrome (SARS) outbreak of 2003. We describe here the clinical and virological features of a novel coronavirus infection causing severe respiratory illness in a patient transferred to London, United Kingdom, from the Gulf region of the Middle East.

SUMMARYWhere very young children come into contact with water containing schistosome cercariae, infections occur and schistosomiasis can be found. In high transmission environments, where mothers daily bathe their children with environmentally drawn water, many infants and preschool-aged children have schistosomiasis. This ‘new’ burden, inclusive of co-infections with Schistosoma haematobium and Schistosoma mansoni, is being formally explored as infected children are not presently targeted to receive praziquantel (PZQ) within current preventive chemotherapy campaigns. Thus an important PZQ treatment gap exists whereby infected children might wait up to 4–5 years before receiving first treatment in school. International treatment guidelines, set within national treatment platforms, are presently being modified to provide earlier access to medication(s). Although detailed pharmacokinetic studies are needed, to facilitate pragmatic dosing in the field, an extended ‘dose pole’ has been devised and epidemiological monitoring has shown that administration of PZQ (40 mg/kg), in either crushed tablet or liquid suspension, is both safe and effective in this younger age-class; drug efficacy, however, against S. mansoni appears to diminish after repeated rounds of treatment. Thus use of PZQ should be combined with appropriate health education/water hygiene improvements for both child and mother to bring forth a more enduring solution.

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.

SUMMARYA phased introduction of routine influenza vaccination of healthy children was recommended in the UK in 2012, with the aim of protecting both vaccinated children and the wider population through reducing transmission. In the first year of the programme in 2013–2014, 4- to 11-year-olds were targeted in pilot areas across England. This study assesses if this was associated with school absenteeism, an important societal burden of influenza. During the spring 2014 term when influenza predominantly circulated, the proportion of absence sessions due to illness was compared between vaccination pilot and non-pilot areas for primary schools (to measure overall impact) and secondary schools (to measure indirect impact). A linear multilevel regression model was applied, adjusting for clustering within schools and potential school-level confounders, including deprivation, past absenteeism, and ethnicity. Low levels of influenza activity were reported in the community in 2013–2014. Primary schools in pilot areas had a significantly adjusted decrease in illness absenteeism of 0·05% relative to non-pilot schools; equivalent to an average of 4 days per school. In secondary schools, there was no significant indirect impact of being located in a pilot area on illness absenteeism. These insights can be used in conjunction with routine healthcare surveillance data to evaluate the full benefits of such a programme.

In February and March 2012, excess deaths among the elderly have been observed in 12 European countries that carry out weekly monitoring of all-cause mortality. These preliminary data indicate that the impact of influenza in Europe differs from the recent pandemic and post-pandemic seasons. The current excess mortality among the elderly may be related to the return of influenza A(H3N2) virus, potentially with added effects of a cold snap.

In 2010/11, the influenza season in England was marked by a relative increase in impact on the population compared to that seen during the 2009/10 pandemic, with the same influenza subtype, A(H1N1)pdm09, circulating. The peaks in critical care bed occupancy in both seasons coincided with peaks in influenza A(H1N1)pdm09 activity, but onset of influenza in 2010/11 additionally coincided with notably cold weather, a comparatively smaller peak in influenza B activity and increased reports of bacterial co-infection. A bigger impact on critical care services was seen across all regions in England in 2010/11, with, compared to 2009/10, a notable age shift in critical care admissions from children to young adults. The peak of respiratory syncytial virus (RSV) activity did not coincide with critical care admissions, and regression analysis suggested only a small proportion of critical care bed days might be attributed to the virus in either season. Differences in antiviral policy and improved overall vaccine uptake in 2010/11 with an influenza A(H1N1)pdm09 strain containing vaccine between seasons are unlikely to explain the change in impact observed between the two seasons. The reasons behind the relative high level of severe disease in the 2010/11 winter are likely to have resulted from a combination of factors, including an age shift in infection, accumulation of susceptible individuals through waning immunity, new susceptible individuals from new births and cold weather. The importance of further development of severe influenza disease surveillance schemes for future seasons is reinforced.

Since December 2014 and up to February 2015, the weekly number of excess deaths from all-causes among individuals?≥?65 years of age in 14 European countries have been significantly higher than in the four previous winter seasons. The rise in unspecified excess mortality coincides with increased proportion of influenza detection in the European influenza surveillance schemes with a main predominance of influenza A(H3N2) viruses seen throughout Europe in the current season, though cold snaps and other respiratory infections may also have had an effect.