The Community Burden of Influenza

Background: Influenza causes substantial morbidity and mortality. Novel strains from animals can infect humans, but such transmission is poorly understood. Serosurveillance estimates levels of influenza population immunity and infection but obtaining representative sera is challenging. Health-related quality of life (HRQoL) and absenteeism inform cost-effectiveness models of influenza interventions but these parameters are poorly understood. The National Pandemic Flu Service (NPFS) aimed to treat community cases. Little is known about the scheme’s coverage or effectiveness. / Objectives: 1) Investigate whether occupational exposure to pigs increases risk of seasonal, pandemic and zoonotic influenza infection. 2) Describe population-level patterns of influenza infection and immunity in England during 2012/13. 3) Quantify work and school absences and HRQoL from community influenza illnesses. 4) Evaluate the success of the NPFS and propose algorithm changes to improve antiviral targeting. / Methods: Flu Watch is a prospective community cohort of influenza and included recruitment of pig workers during the 2009 pandemic. The Pandemic Immunity and Population Spread study (PIPS) is a novel, population-level, cross-sectional, pandemic serosurveillance system utilizing the Health Survey for England. / Results: Pig workers had increased odds of seropositivity to seasonal, pandemic, and zoonotic influenza compared to the general population. A(H1N1)pdm09 and A(H3N2) infected 40% and 25% of the population in 2012/13. HRQoL loss and absenteeism is low for individual community-level influenza cases. NPFS consultation was low and the case definition specificity was 51%. / Conclusions: Influenza spreads readily from pigs to pig workers, posing risks for novel virus emergence and pandemics. Representative, population-level serology show that, before COVID-19, a large proportion of the population was infected each winter. Most community influenza cases take little time off work and school and this has implications for transmission. The coverage and impact of NPFS was low. Community-based surveys are needed to inform the control of seasonal and pandemic respiratory infections

Investigating obesity as a risk factor for influenza-like illness during the 2009 H1N1 influenza pandemic using the Health Survey for England.

BACKGROUND: Following the 2009 H1N1 influenza pandemic, obesity was shown to be associated with severe influenza outcomes. It remains unclear whether obesity was a risk factor for milder influenza-like illness (ILI). OBJECTIVES: To determine whether obesity was associated with an increased risk of self-reported ILI during the 2009 H1N1 influenza pandemic using Health Survey for England (HSE) 2010 cross-sectional data. METHODS: This study used HSE data collected from English households between January and December 2010. Weight and height measurements were taken by trained fieldworkers to determine obesity. ILI was defined as a positive response to the question "Have you had a flu-like illness where you felt feverish and had a cough or sore throat?" with illness occurring between May and December 2009. Multivariable logistic regression was used to evaluate the association between obesity and ILI. RESULTS: The study comprised 8407 participants (6984 adults, 1436 children), among whom 24.7% (95% CI: 23.6-25.9) were classified as obese. Of obese participants, 12.8% (95% CI: 11.1-14.8) reported ILI compared to 11.8% (95% CI: 10.8-12.8) of non-obese participants. The adjusted OR for ILI associated with obesity was 1.16 (95% CI: 0.98-1.38, P=.093). For adults and children, the adjusted ORs were 1.16 (95% CI: 0.97-1.38, P=.101) and 1.26 (95% CI: 0.72-2.21, P=.422), respectively. CONCLUSION: Household survey data showed no evidence that obesity was associated with an increase in self-reported ILI during the 2009 H1N1 influenza pandemic in England. Further studies using active prospective ILI surveillance combined with laboratory reporting would reduce bias and improve accuracy of outcome measurements

Population-level susceptibility, severity and spread of pandemic influenza: design of, and initial results from, a pre-pandemic and hibernating pandemic phase study using cross-sectional data from the Health Survey for England (HSE)

Background Assessing severity and spread of a novel influenza strain at the start of a pandemic is critical for informing a targeted and proportional response. It requires community-level studies to estimate the burden of infection and disease. Rapidly initiating such studies in a pandemic is difficult. The study aims to establish an efficient system allowing real-time assessment of population susceptibility, spread of infection and clinical attack rates in the event of a pandemic. Methods We developed and appended additional survey questions and specimen collection to the Health Survey for England (HSE) – a large, annual, rolling nationally representative general population survey recruiting throughout the year – to enable rapid population-based surveys of influenza infection and disease during a pandemic. Using these surveys we can assess the spread of the virus geographically, by age and through time. The data generated can also provide denominators for national estimates of case fatality and hospitalisation rates.Phase 1: we compared retrospectively collected HSE illness rates during the first two infection waves of the 2009 pandemic with the Flu Watch study (a prospective community cohort). Monthly and seasonal age-specific rates of illness and proportion vaccinated were compared.Phase 2: we piloted blood specimen and data collection alongside the 2012–13 HSE. We are developing laboratory methods and protocols for real-time serological assays of a novel pandemic influenza virus using these specimens, and automated programmes for analysing and reporting illness and infection rates.Phase 3: during inter-pandemic years, the study enters a holding phase, where it is included in the yearly HSE ethics application and planning procedures, allowing rapid triggering in a pandemic.Phase 4: once retriggered, the study will utilise the methods developed in phase 2 to monitor the severity and spread of the pandemic in real time. Results Phase 1: the rates of reported illness during the first two waves in the HSE underestimated the community burden as measured by Flu Watch, but the patterns of illness by age and time were broadly comparable. The extent of underestimation was greatest for HSE participants interviewed later in the year compared with those interviewed closer to the pandemic. Vaccine uptake in the HSE study was comparable to independent national estimates and the Flu Watch study.Phases 2 and 3: illness data and serological samples from 2018 participants were collected in the 2012–13 HSE and transferred to the University College London Hospital. In the 2013 HSE and onwards, this project was included in the annual HSE ethics and planning rounds. Conclusions The HSE’s underestimation of illness rates during the first two waves of the pandemic is probably due to recall bias and the limitation of being able to report only one illness when multiple illnesses per season can occur. Changes to the illness questions (reporting only recent illnesses) should help minimise these issues. Additional prospective follow-up could improve measurement of disease incidence. The representative nature of the HSE allows accurate measurements of vaccine uptake. Study registration This study is registered as ISRCTN80214280. Funding This project was funded by the NIHR Public Health Research programme and will be published in full inPublic Health Research; Vol. 3, No. 6. See the NIHR Journals Library website for further project information

Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data

Background: International and UK data suggest that Black, Asian and Minority Ethnic (BAME) groups are at increased risk of infection and death from COVID-19. We aimed to explore the risk of death in minority ethnic groups in England using data reported by NHS England. Methods: We used NHS data on patients with a positive COVID-19 test who died in hospitals in England published on 28th April, with deaths by ethnicity available from 1st March 2020 up to 5pm on 21 April 2020. We undertook indirect standardisation of these data (using the whole population of England as the reference) to produce ethnic specific standardised mortality ratios (SMRs) adjusted for age and geographical region. Results: The largest total number of deaths in minority ethnic groups were Indian (492 deaths) and Black Caribbean (460 deaths) groups. Adjusting for region we found a lower risk of death for White Irish (SMR 0.52; 95%CIs 0.45-0.60) and White British ethnic groups (0.88; 95%CIs 0.86-0.0.89), but increased risk of death for Black African (3.24; 95%CIs 2.90-3.62), Black Caribbean (2.21; 95%CIs 2.02-2.41), Pakistani (3.29; 95%CIs 2.96-3.64), Bangladeshi (2.41; 95%CIs 1.98-2.91) and Indian (1.70; 95%CIs 1.56-1.85) minority ethnic groups. Conclusion: Our analysis adds to the evidence that BAME people are at increased risk of death from COVID-19 even after adjusting for geographical region, but was limited by the lack of data on deaths outside of NHS settings and ethnicity denominator data being based on the 2011 census. Despite these limitations, we believe there is an urgent need to take action to reduce the risk of death for BAME groups and better understand why some ethnic groups experience greater risk. Actions that are likely to reduce these inequities include ensuring adequate income protection, reducing occupational risks, reducing barriers in accessing healthcare and providing culturally and linguistically appropriate public health communications

Seasonality and immunity to laboratory-confirmed seasonal coronaviruses (HCoV-NL63, HCoV-OC43, and HCoV-229E): results from the Flu Watch cohort study

Background: There is currently a pandemic caused by the novel coronavirus SARS-CoV-2. The intensity and duration of this first wave in the UK may be dependent on whether SARS-CoV-2 transmits more effectively in the winter than the summer and the UK Government response is partially built upon the assumption that those infected will develop immunity to reinfection in the short term. In this paper we examine evidence for seasonality and immunity to laboratory-confirmed seasonal coronavirus (HCoV) from a prospective cohort study in England. Methods: In this analysis of the Flu Watch cohort, we examine seasonal trends for PCR-confirmed coronavirus infections (HCoV-NL63, HCoV-OC43, and HCoV-229E) in all participants during winter seasons (2006-2007, 2007-2008, 2008-2009) and during the first wave of the 2009 H1N1 influenza pandemic (May-Sep 2009). We also included data from the pandemic and �post-pandemic� winter seasons (2009-2010 and 2010-2011) to identify individuals with two confirmed HCoV infections and examine evidence for immunity against homologous reinfection. Results: We tested 1,104 swabs taken during respiratory illness and detected HCoV in 199 during the first four seasons. The rate of confirmed HCoV infection across all seasons was 390 (95% CI 338-448) per 100,000 person-weeks; highest in the Nov-Mar 2008/9 season at 674 (95%CI 537-835). The highest rate was in February at 759 (95% CI 580-975). Data collected during May-Sep 2009 showed there was small amounts of ongoing transmission, with four cases detected during this period. Eight participants had two confirmed infections, of which none had the same strain twice. Conclusion: Our results provide evidence that HCoV infection in England is most intense in winter, but that there is a small amount of ongoing transmission during summer periods. We found some evidence of immunity against homologous reinfection.</ns3:p

A rapid review and meta-analysis of the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections in community settings

Background: Cross-sectional studies indicate that up to 80% of active SARS-CoV-2 infections may be asymptomatic. However, accurate estimates of the asymptomatic proportion require systematic detection and follow-up to differentiate between truly asymptomatic and pre-symptomatic cases. We conducted a rapid review and meta-analysis of the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections based on methodologically appropriate studies in community settings. Methods: We searched Medline and EMBASE for peer-reviewed articles, and BioRxiv and MedRxiv for pre-prints published before 25/08/2020. We included studies based in community settings that involved systematic PCR testing on participants and follow-up symptom monitoring regardless of symptom status. We extracted data on study characteristics, frequencies of PCR-confirmed infections by symptom status, and (if available) cycle threshold/genome copy number values and/or duration of viral shedding by symptom status, and age of asymptomatic versus (pre)symptomatic cases. We computed estimates of the asymptomatic proportion and 95% confidence intervals for each study and overall using random effect meta-analysis.  Results: We screened 1138 studies and included 21. The pooled asymptomatic proportion of SARS-CoV-2 infections was 23% (95% CI 16%-30%). When stratified by testing context, the asymptomatic proportion ranged from 6% (95% CI 0-17%) for household contacts to 47% (95% CI 21-75%) for non-outbreak point prevalence surveys with follow-up symptom monitoring. Estimates of viral load and duration of viral shedding appeared to be similar for asymptomatic and symptomatic cases based on available data, though detailed reporting of viral load and natural history of viral shedding by symptom status were limited. Evidence into the relationship between age and symptom status was inconclusive. Conclusion: Asymptomatic viral shedding comprises a substantial minority of SARS-CoV-2 infections when estimated using methodologically appropriate studies. Further investigation into variation in the asymptomatic proportion by testing context, the degree and duration of infectiousness for asymptomatic infections, and demographic predictors of symptom status are warranted.</ns4:p

Antibiotic prescribing in patients with self-reported sore throat.

Objectives: To investigate the predictors of general practitioner (GP) consultation and antibiotic use in those developing sore throat. Methods: We conducted a prospective population-based cohort study on 4461 participants in two rounds (2010-11) from 1897 households. Results: Participants reported 2193 sore throat illnesses, giving a community sore throat incidence of 1.57/ person-year. 13% of sore throat illnesses led to a GP consultation and 56% of these consultations led to antibiotic use. Participants most likely to have sore throats included women and children (e.g. school compared with retirement age); adjusted incidence rate ratio (aIRR) of 1.33 and 1.52, respectively. Participants with sore throat were more likely to consult their GP if they were preschool compared with retirement age [adjusted OR (aOR) 3.22], had more days of sore throat (aOR 1.11), reported more severe pain (aOR 4.24) or reported fever (aOR 3.82). Antibiotics were more often used by chronically ill individuals (aOR 1.78), those reporting severe pain (aOR 4.14), those reporting fever (aOR 2.58) or children with earache (aOR 1.85). Among those who consulted, males and adults who reported feeling anxious were more likely to use antibiotics; aOR 1.87 and 5.36, respectively. Conclusions: Only 1 in 10 people who have a sore throat see a doctor and more than half of those attending get antibiotics. Further efforts to curb antibiotic use should focus on reducing initial GP consultations through public information promoting safe self-management, targeted at groups identified above as most likely to attend with sore throats

Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study.

BACKGROUND: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK's largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. OBJECTIVE: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. METHODS: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. RESULTS: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%). CONCLUSIONS: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity

Estimates for quality of life loss due to Respiratory Syncytial Virus

BACKGROUND: In children aged <5 years in whom severe respiratory syncytial virus (RSV) episodes predominantly occur, there are currently no appropriate standardised instruments to estimate quality of life years (QALY) loss. OBJECTIVES: We estimated the age-specific QALY loss due to RSV by developing a regression model which predicts the QALY loss without the use of standardised instruments. METHODS: We conducted a surveillance study which targeted confirmed RSV episodes in children aged <5 years (confirmed cases) and their household members who experienced symptoms of RSV during the same time (suspected cases). All participants were asked to complete questions regarding their health during the infection, with the suspected cases additionally providing health-related quality of life (HR-QoL) loss estimates by completing EQ-5D-3L-Y or EQ-5D-3L instruments. We used the responses from the suspected cases to calibrate a regression model which estimates the HR-QoL and QALY loss due to infection. FINDINGS: For confirmed RSV cases in children under 5 years of age who sought health care, our model predicted a QALY loss per RSV episode of 3.823 × 10-3 (95% CI 0.492-12.766 × 10-3 ), compared with 3.024 × 10-3 (95% CI 0.329-10.098 × 10-3 ) for under fives who did not seek health care. Quality of life years loss per episode was less for older children and adults, estimated as 1.950 × 10-3 (0.185-9.578 × 10-3 ) and 1.543 × 10-3 (0.136-6.406 × 10-3 ) for those who seek or do not seek health care, respectively. CONCLUSION: Evaluations of potential RSV vaccination programmes should consider their impact across the whole population, not just young child children

Public activities preceding the onset of acute respiratory infection syndromes in adults in England - implications for the use of social distancing to control pandemic respiratory infections.

Background: Social distancing measures may reduce the spread of emerging respiratory infections however, there is little empirical data on how exposure to crowded places affects risk of acute respiratory infection. Methods: We used a case-crossover design nested in a community cohort to compare self-reported measures of activities during the week before infection onset and baseline periods. The design eliminates the effect of non-time-varying confounders. Time-varying confounders were addressed by exclusion of illnesses around the Christmas period and seasonal adjustment.  Results: 626 participants had paired data from the week before 1005 illnesses and the week before baseline. Each additional day of undertaking the following activities in the prior week was associated with illness onset: Spending more than five minutes in a room with someone (other than a household member) who has a cold (Seasonally adjusted OR 1·15, p=0·003); use of underground trains (1·31, p=0·036); use of supermarkets (1·32, p<0·001); attending a theatre, cinema or concert (1·26, p=0·032); eating out at a café, restaurant or canteen (1·25, p=0·003); and attending parties (1·47, p<0·001). Undertaking the following activities at least once in the previous week was associated with illness onset: using a bus, (aOR 1.48, p=0.049), shopping at small shops (1.9, p<0.002) attending a place of worship (1.81, p=0.005).    Conclusions: Exposure to potentially crowded places, public transport and to individuals with a cold increases risk of acquiring circulating acute respiratory infections. This suggests social distancing measures can have an important impact on slowing transmission of emerging respiratory infections