Seasonal influenza and the 2009 pandemic strain were characterised by similar high rates of mainly asymptomatic infection with most symptomatic cases self-managing without medical consultation. In the community the 2009 pandemic strain caused milder symptoms than seasonal H3N2.

Based on four-fold titre rises in strain-specific serology, on average influenza infected 18% (95% CI 16–22) of unvaccinated people each winter. Of those infected there were 69 respiratory illnesses per 100 person-influenza-seasons compared with 44 per 100 in those not infected with influenza. The age-adjusted attributable rate of illness if infected was 23 illnesses per 100 person-seasons (13–34), suggesting most influenza infections are asymptomatic. 25% (18–35) of all people with serologically confirmed infections had PCR-confirmed disease. 17% (10–26) of people with PCR-confirmed influenza had medically attended illness. These figures did not differ significantly when comparing pandemic with seasonal influenza. Of PCR-confirmed cases, people infected with the 2009 pandemic strain had markedly less severe symptoms than those infected with seasonal H3N2.

Using preseason and postseason serology, weekly illness reporting, and RT-PCR identification of influenza from nasal swabs, we tracked the course of seasonal and pandemic influenza over five successive cohorts (England 2006–11; 5448 person-seasons' follow-up). We compared burden and severity of seasonal and pandemic strains. We weighted analyses to the age and regional structure of England to give nationally representative estimates. We compared symptom profiles over the first week of illness for different strains of PCR-confirmed influenza and non-influenza viruses using ordinal logistic regression with symptom severity grade as the outcome variable.

Assessment of the effect of influenza on populations, including risk of infection, illness if infected, illness severity, and consultation rates, is essential to inform future control and prevention. We aimed to compare the community burden and severity of seasonal and pandemic influenza across different age groups and study years and gain insight into the extent to which traditional surveillance underestimates this burden.

We aimed to compare the community burden and severity of seasonal and pandemic influenza across different age groups and study years and gain insight into the extent to which traditional surveillance underestimates this burden. Our specific objectives were to measure the proportion of the population infected each season, the proportion of those infected who developed symptomatic disease attributable to influenza, the proportion who had detectable nasal shedding of influenza virus, the symptoms among those with confirmed influenza, and the proportion who were medically attended. During the pandemic we also aimed to measure the development of type-specific immunity to the pandemic strain.

Internationally, influenza activity surveillance provides real-time information to inform prevention and control policy.Surveillance focuses on cases seeking medical attention: the so-called tip of the iceberg of infection. Underestimation of the number of community cases leads to overestimates of severity.Heightened concern during a pandemic can change patient consultation thresholds and clinician recording and investigation behaviour, thus distorting surveillance information.Information on the community burden of influenza is key to informing control,but is not routinely collected. For example, influenza transmission models, which are widely used to consider the efficacy and cost-effectiveness of vaccines, antivirals, and non-pharmaceutical countermeasures, depend on valid epidemiological estimates of the community occurrence of disease. The available data for periods of seasonal influenza are largely derived historically from household cohort studies of families with children in communities in the USA between 1948 and 1981,and a more recent study from rural Vietnam.There have also been some cohort studies reporting on the 2009 pandemic from Hong Kong, southeast Asia, and Malias well as several cross-sectional serosurveys from this period.Case-ascertained household transmission studies can estimate the secondary attack proportion and effects of interventions within households; however, they are not designed to estimate community burden of influenza infection and disease.The Flu Watch study is the first national community cohort study of influenza occurrence enrolling households with and without children, with the additional benefit of modern molecular diagnostic techniques.

The Virus Watch program: a continuing surveillance of viral infections in metropolitan New York families. 3. Preliminary report on association of infections with disease.

Using an online survey of healthcare-seeking behaviour to estimate the magnitude and severity of the 2009 H1N1v influenza epidemic in England.

Influenza causes roughly 250 000–500 000 deaths worldwide each year.In the 20th century there were three influenza pandemics for which there are varying mortality estimates: 1918 A/H1N1 at least 20–40 million excess deaths, 1957 A/H2N2 about 4 million excess deaths, and 1968 A/H3N2 about 2 million excess deaths.In 2009 a new pandemic virus,influenza A(H1N1)pdm09, emerged in Mexicoand spread globally over 2009–10, causing an estimated 200 000 respiratory deaths and 83 000 cardiovascular deaths during the first 12 months of circulation.WHO declared an end to the pandemic on Aug 10, 2010.However, a further pandemic wave occurred in some European and other countries outside North Americain 2010–11 with reports of excess deaths in, for example, England.

Influenza A(H1N1)pdm09 in England, 2009 to 2011: a greater burden of severe illness in the year after the pandemic than in the pandemic year.

WHO World now at the start of 2009 influenza pandemic: statement to the press by WHO Director-General Dr Margaret Chan (June 11, 2009).

The sponsors had no role in study design, collection analysis, interpretation of data, or writing of the report. ACH, EBF, and ERCM had access to the raw data. The corresponding author had full access to all data and final responsibility to submit for publication.

We compared symptom profiles (an ordered categorical variable) over the first week of illness for different strains of PCR-confirmed influenza and non-influenza viruses using ordinal logistic regression with symptom severity grade as the outcome variable, adjusting for age group and strain type and accounting for repeated measures in individuals using robust standard errors (Stata ologit commands with cluster option).

We estimated the percentage of serological infections leading to illness by two independent methods. First, we calculated age-adjusted attributable rates of illness due to infection (subtracting rates of respiratory illness in non-seroconverters from those in seroconverters).Sensitivity analyses inflated these adjusted attributable rates to account for the recorded level of under-reporting (based on the proportion of expected weekly illness status reports received during periods of influenza circulation). Second, we measured the proportion of unvaccinated seroconverters with PCR-confirmed influenza.

Participants were assumed not to have a respiratory illness in weeks with missing illness status reports. After excluding illnesses where PCR identified a non-influenza virus we plotted rates of respiratory illness, influenza-like illness, and PCR-confirmed influenza (per 100 000 person-weeks). We estimated the percentage of the population infected each season by calculating age and season-specific rates of serological infection and PCR-confirmed disease per 100 person-seasons. A person-season was defined as the time from the first PCR isolation of influenza in the cohort to the last isolation in any given season, rates therefore accounted for differential follow-up time during periods of influenza circulation. We did not undertake follow-up blood samples for serological testing for participants recruited before the first (spring/summer) pandemic wave until after the second (winter) wave had finished because we had not predicted separate summer and winter pandemic waves. Methods to derive infection rates in the first wave are described in the appendix

Analyses were done in STATA version 12. Analyses of serological data were restricted to those with serological samples available (no children younger than 5 years had serological specimens). We weighted analyses to the age and regional structure of England to give nationally representative estimates. In this weighting, children younger than 15 years were considered as a single group, so measures of age-adjusted population rates of infection (but not of PCR-confirmed disease or illness) apply the rates in the 5–15 year age group to the 0–15 population. We did not weight on ethnic origin or social deprivation because there was no evidence of a strong association with infection or disease rates (data not shown) and small or zero numbers in some groups would have led to instability of weighted measures.

Key outcomes of interest were infection with influenza, defined as a four-fold titre rise in serum samples of unvaccinated individuals (but not in vaccinated individuals since both vaccination and natural infection lead to titre rises); occurrence of any acute respiratory illness (self-reported “cough, cold, sore throat, or flu-like illness”); occurrence of influenza-like illness, defined according to the US Centers for Disease Control and Prevention (CDC) definition of fever (temperature ≥37·8°C) and a cough or a sore throat in the absence of a known cause other than influenza;occurrence of PCR-confirmed influenza; symptom severity over the first week of illness in PCR-confirmed cases; and consultation with primary care. During the pandemic additional outcomes included monitoring the development of immunity (defined as antibody titre to influenza A H1N1 pdm2009 of ≥32). In this analysis key predictors of interest are age, study year, and circulating strain of influenza. The study size was chosen to give accurate annual estimates of infection and disease rates such that a sample size of 800 per year would allow a 25% risk of infection to be estimated within 95% CIs of 22–25 and a 10% risk of influenza-like illness within 95% CIs of 8–12. The study was expanded in the pandemic to provide accurate real-time measures of influenza-like illness rates recruiting as many participants as was practically possible.

We collected demographic and medical history data at baseline and self-reported vaccination status at baseline and end of follow-up. Admissions to hospital and deaths during follow-up were recorded with the end of season follow-up questionnaire completed by the lead householder, with deaths among participants also being reported to the study by participating practices and directly by families. We minimised recall bias of illness through weekly telephone or online surveys to record any “cough, cold, sore throat, or flu-like illness” among household members. In addition to weekly surveys, participants were asked to complete detailed daily symptom diaries for the duration of any acute respiratory illness, including daily temperature measurement and reporting of several symptoms: feeling feverish, headache, having muscle aches, cough, sore throat, runny nose, blocked nose, and sneezing. Symptoms were allocated a numerical score on the basis of severity (0=absent, 1=mild, 2=moderate, 3=severe or for fever <37·8°C=0, 37·8–38·9°C=1, 39·0–39·9°C=2, ≥40°C=3). Review of participants' primary-care records was used to measure consultation behaviour in practices where a research nurse was available to extract the data. We asked participants to submit, by mail, nasal swabs on day 2 of any illness. These swabs were transported in viral transport medium and screened by RT-PCR for influenza A (subtypes H1, H3), influenza B, influenza A(H1N1)pdm09 (from 2009 onwards), and a panel of other respiratory viruses including respiratory syncytial virus, rhinovirus, coronavirus, adenovirus, human metapneumovirus, and parainfluenza virus with methods described elsewhere.We measured serum antibody titres against circulating influenza strains ( appendix ) in baseline and follow-up samples with haemagglutination inhibition assay using standard methods.

Diagnosis of influenza in the community: relationship of clinical diagnosis to confirmed virological, serologic, or molecular detection of influenza.

We did a household-level community cohort study of acute respiratory illness and influenza infection, recruiting households across England ( appendix ). We followed up successive cohorts over the 2006–07, 2007–08, and 2008–09 periods of seasonal influenza circulation, and the first (spring and summer 2009), second (autumn and winter 2009), and third (winter 2010–11) waves of the pandemic. Households were recruited annually through written invitation sent to a random sample of people registered with 146 volunteer general practices as well as inviting previous participants (in 2008–11). In England, most of the population is registered with a general practice.At baseline (October–December) and follow-up visits (May–July) of each year we collected blood samples for serological testing (willingness to provide samples was a condition of participation in adults, voluntary in children aged 5–15 years, and not requested in children younger than 5 years). Follow-up samples from spring 2009 acted as baseline specimens for individuals who continued to participate through the 2009–10 pandemic.

Results

Table 1 Baseline characteristics November, 2006, to March, 2007 November, 2007, to March, 2008 November, 2008, to March, 2009 May, 2009, to September, 2009 October, 2009, to February, 2010 November, 2010, to March, 2011 GP practices/households/people 42/243/602 43/310/779 37/309/729 41/332/797 127/1460/3552 51/361/901 Age group, years 0–4 (6%) 38 (6%) 42 (5 %) 37 (5%) 36 (5%) 179 (5 %) 45 (5%) 5–15 (11%) 87 (15%) 110 (14%) 99 (14%) 109 (14%) 501 (14%) 131 (15%) 16–44 (42%) 151 (25%) 258 (33%) 172 (24%) 192 (24%) 848 (24%) 206 (23%) 45–64 (25%) 203 (34%) 272 (35%) 267 (37%) 293 (37%) 1225 (35%) 344 (38%) ≥65 (16%) 123 (20%) 97 (13%) 154 (21%) 167 (21%) 799 (23%) 175 (19%) Sex Male (49%) 281 (47%) 366 (47%) 340 (47%) 377 (47%) 1740 (49%) 455 (51%) Female (51%) 321 (53%) 413 (53%) 389 (53%) 420 (53%) 1812 (51%) 446 (50%) Region North (28%) 99 (17%) 89 (11%) 100 (14%) 106 (13%) 320 (9%) 115 (13%) West Midlands (11%) 42 (7%) 96 (12%) 46 (6%) 53 (7%) 179 (5%) 53 (6%) East and east Midlands (20%) 122 (20%) 120 (15%) 124 (17%) 118 (15%) 1456 (41%) 321 (36%) London (15%) 28 (5%) 77 (10%) 26 (4%) 28 (4%) 270 (8%) 65 (7%) Southeast (16%) 100 (17%) 117 (15%) 107 (15%) 155 (20%) 319 (9%) 110 (12%) Southwest (10%) 211 (35%) 280 (36%) 326 (45%) 337 (42%) 1008 (28%) 237 (26%) Vaccine Vaccinated 115 (19%) 130 (17%) 169 (23%) 0 157 (4%) 186 (21%) Unvaccinated 462 (77%) 632 (81%) 527 (72%) 797 (100%) 3159 (89%) 715 (79%) Unknown 25 (4%) 17 (2%) 33 (5%) 0 236 (7%) 0 IMD quintile 1 (20%) 37 (6%) 39 (5%) 28 (4%) 18 (2%) 98 (3%) 29 (3%) 2 (20%) 88 (15%) 126 (16%) 91 (13%) 62 (8%) 310 (9%) 82 (9%) 3 (20%) 164 (27%) 235 (30%) 238 (33%) 146 (18%) 915 (26%) 221 (25%) 4 (20%) 162 (27%) 250 (32%) 187 (26%) 146 (18%) 938 (26%) 280 (31%) 5 (20%) 151 (25%) 129 (17%) 185 (25%) 425 (53%) 1291 (56%) 289 (32%) Ethnic origin White (75%) 557 (98%) 733 (95%) 666 (99%) 730 (99%) 3306 (98%) 846 (98%) Non-white (25%) 5 (2%) 35 (5%) 6 (1%) 7 (1%) 78 (2%) 19 (2%) Percentages given alongside the categories are the national distributions. Data are n (%). IMD=Index of Multiple Deprivation. Roughly 10% of invited households agreed to participate ( appendix ). Table 1 presents the comparison of unweighted cohort demographics with those of the England population showing good geographical spread but under-representation of young adults; people living in socially deprived areas, north England, west Midlands, and London; and people of non-white ethnic origin.

Person follow-up time (118 158 person-weeks, 5448 person-seasons), illness-status reports (102 300: 86·6% of follow-up weeks), nasal swab submissions (2941; 88·3% of 3332 illnesses recorded during periods of influenza circulation), and influenza virus detection results are given in the appendix . There were 3295 paired sera (81% of eligible adults and 27% of eligible children aged 5–15 years, in whom blood tests were optional). Of these 2737 (83 %) were in unvaccinated individuals.

Figure 1 Rates of illness or PCR-confirmed influenza standardised by age and region Show full caption Rates of acute respiratory illness, influenza-like illness, and PCR-confirmed influenza per 100 000 person-weeks. Excludes illnesses known to be due to non-influenza viruses. The highest rates of influenza-like illness and PCR-confirmed influenza were during the epidemic of H3N2 in 2008–09 before the pandemic and in the 2010–11 third pandemic wave. Compared with other seasons, illness rates in the first pandemic wave were low ( figure 1 ).

The dominant circulating strain was influenza A H3N2 in 2006–07, seasonal A H1N1 in 2007–08, A H3N2 in 2008–09, and A(H1N1)pdm09 in 2009–10 and 2010–11. Influenza B circulated in 2007–08 and 2008–09 when it peaked after the main influenza A outbreak. In 2010–11 the influenza B peak coincided with the third wave of A(H1N1)pdm09. On average, based on rates per 100 person-seasons, PCR-confirmed influenza was identified in 4% (95% CI 3–5) of the cohort each winter: 3% (2–5) during prepandemic seasons and 5% (4–6) during pandemic winter seasons. Highest rates were in the third pandemic wave when 9% (6–13) had PCR-confirmed disease (6% influenza A and 3% influenza B) and in the 2008–09 season when 6% (4–10) had PCR-confirmed disease (5% influenza A and 2% influenza B). In all seasons most PCR-confirmed cases were influenza A, although influenza B was important in 2007–08, 2008–09, and 2010–11.

Figure 2 Rates of seasonal and pandemic influenza A infection and PCR-confirmed disease Show full caption Rates of infection established through seroconversion (four-fold titre rises in unvaccinated individuals) and rates of disease established through PCR-confirmation per 100 person-seasons (95% CIs). Risk of PCR-confirmed disease tended to decrease with increasing age ( figure 2 appendix ). For influenza A this age dependence was most apparent during the H3N2 epidemic of 2008–09 and the 2009–10 second wave of the H1N1 pandemic when children had significantly higher rates of PCR-confirmed disease and serological infection with influenza A than older adults ( appendix ). For influenza B, children had significantly higher rates of PCR-confirmed disease than adults in 2008–09 and 2010–11 seasons ( appendix ). The 2010–11 third wave of the H1N1 pandemic was unusual in having markedly higher rates of influenza A in young adults than any other season. PCR-confirmed influenza was very rare in people older than 65 years in all seasons.

On average, based on rates per 100 person-seasons, influenza infected 18% (95% CI 16–22) of the unvaccinated population each winter season: 19% (15–24) during prepandemic seasons and 18% (14–22) during the pandemic. The highest infection rate (27%, 22–34; 24% influenza A, 2% influenza B) was in the season preceding the pandemic (2008–09) and then in the 2010–11 third pandemic wave (22%, 17–28; 18 influenza A, 5% influenza B).

Infection rates were typically highest in children aged 5–15 years (not measured in younger children) and decreased with age ( figure 2 appendix ). This age-dependence was most apparent during the 2009 first pandemic wave when children aged 5–15 years was the only age group with measurable risk of infection: 26% (0–58). Age dependence of influenza A was also strong during the 2009–10 second wave of the pandemic when children had significantly higher rates of serological infection with influenza A than older adults ( appendix ). The 2010–11 third pandemic wave was the only season when young adults aged 16–44 years had the highest risk of infection: 34% (26–46). During periods of seasonal influenza A age dependence was strongest during the H3N2 epidemic of 2008–09 ( appendix ). Children also had significantly higher rates of influenza B infection than older adults in 2008–09 ( appendix ).

35 Monto AS

Koopman JS

Longini IM Tecumseh study of illness. XIII. Influenza infection and disease, 1976–1981. Most infections were asymptomatic. 192 respiratory illnesses including 70 influenza-like illnesses were reported from 327 participants with serological evidence of infection over 280 person-seasons of follow-up (69 respiratory illnesses, 25 influenza-like illnesses per 100 person-seasons). There were 623 respiratory illnesses including 95 influenza-like illnesses reported from 1742 participants with no serological evidence of infection over 1423 person-seasons of follow-up (44 respiratory illnesses, seven influenza-like illnesses per 100 person-seasons). The rate of respiratory illness attributable to influenza (age-adjusted incidence rate difference)was 23 respiratory illnesses (95% CI 13–34) including 18 influenza-like illnesses per 100 person-seasons (95% CI 12–24). There was insufficient power to test the hypothesis that the asymptomatic proportion varied by age or strain type. Sensitivity analyses adjusting for the fact that 85% of illness status reports were returned during periods of influenza circulation gave estimates of the rate of respiratory illness or influenza-like illness attributable to infection of 27 respiratory illnesses and 21 influenza-like illnesses per 100 person-seasons, respectively. These estimates of infections leading to disease are similar to the 25% (18–35) of people with serological infections who had PCR-confirmed influenza from nasal swabs. PCR-confirmation levels seemed to be lower in adults aged 65 years or older (9%, 95% CI 1–60) and for influenza B infections (5%, 1–28), although this was not statistically significant.

Table 2 Comparative symptom severity for different strains of influenza and non-influenza viruses and different age groups Fever Feverish Headache Muscle ache Sore throat Cough Runny nose Sneeze Blocked nose A(H1N1)pdm09 * * Baseline group for comparisons. 1 1 1 1 1 1 1 1 1 H3N2(n=35) 1·09(0·61–1·95); 0·774 2·22(1·33–3·71); 0·002 2·72(1·60–4·63); <0·001 4·15(2·36–7·27); <0·001 1·51(0·90–2·54); 0·118 1·34(0·82–2·18); 0·245 3·18(2·12–4·77); <0·001 2·95(1·88–4·62); <0·001 2·39(1·46–3·93); 0·001 H1N1(n=10) 0·27(0·09–0·87); 0·027 0·59(0·28–1·25); 0·172 1·53(1·83–2·81); 0·173 1·23(0·49–3·08); 0·653 2·29(1·13–4·60); 0·020 0·85(0·47–1·54); 0·584 2·26(1·24–4·10); 0·007 1·74(1·00–3·02); 0·05 1·76(0·96–3·23); 0·068 Influenza B (n=35) 1·56(0·87–2·80); 0·134 1·94(1·08–3·47); 0·026 1·27(0·72–2·25); 0·411 1·79(0·91–3·52); 0·094 1·32(0·73–2·37); 0·694 0·69(0·35–1·37); 0·290 1·01(0·59–1·72); 0·972 1·0(0·56–1·78); 0·990 2·02(1·14–3·58); 0·017 Non-influenza (n=385) 0·34(0·22–0·53); <0·001 0·51(0·36–0·72); <0·001 1·02(0·74–1·41); 0·905 0·66(0·44–0·98); 0·040 1·07(0·76–1·50); 0·694 0·56(0·40–0·77); <0·001 1·95(1·45–2·62); <0·001 1·50(1·11–2·05); 0·009 1·93(1·14–3·58); 0·017 0–15 years * * Baseline group for comparisons. 1 1 1 1 1 1 1 1 1 16–44 years (n=147) 0·25(0·15–0·41); <0·001 0·95(0·66–1·39); 0·803 1·32(0·95–1·85); 0·098 1·48(0·98–2·22); 0·06 1·60(1·16–2·20); 0·005 0·68(0·50–0·94); 0·019 0·91(0·68–1·20); 0·0503 1·28(0·96–1·70); 0·09 1·18(0·85–1·63); 0·323 45–64 years (n=195) 0·31(0·20–0·50); <0·001 1·55(1·11–2·18); 0·010 1·22(0·89–1·67); 0·219 1·75(1·18–2·59); 0·005 1·35(0·99–1·87); 0·056 0·81(0·60–1·08); 1·045 0·82(0·63–1·07); 0·148 1·15(0·87–1·52); 0·324 0·97(0·71–1·32); 0·826 ≥65 years (n=70) 0·135(0·07–0·28); <0·001 1·09(0·67–1·78); 0·726 0·97(0·64–1·47); 0·888 1·67(0·94–2·97); 0·08 1·12(0·75–1·68); 0·569 1·22(0·84–1·76); 0·0294 1·08(0·74–1·58); 0·678 2·10(1·36–2·96); <0·001 0·88(0·54–1·42); 0·595 Data are adjusted OR (95% CI); p value (across the categories of the symptom severity scale, assuming proportional odds). Numbers in the left-hand column refer to the number of PCR-confirmed cases across all years with information on daily symptoms. ORs are mutually adjusted for age and strain type. OR=odds ratio. Only a minority of people with PCR-confirmed influenza had fever with a temperature greater than 37·8°C and so met the CDC definition of influenza-like illness (110/238; 46%, 95% CI 40–53). Symptoms of A(H1N1)pdm09 were milder than those of H3N2 ( appendix ). Detailed daily symptom diaries were available on 567 participants with PCR-confirmed respiratory illnesses (102 A[H1N1]pdm09, 35 H3N2, ten H1N1, 35 influenza B, 385 non-influenza viruses). Symptoms of influenza A(H1N1)pdm09 were milder than those of H3N2 for feeling feverish, headache, muscle ache, runny nose, sneezing, and blocked nose ( table 2 ). Symptoms of A(H1N1)pdm09 were significantly more severe than those of non-influenza viruses for fever, feeling feverish, muscle aches, and cough, but significantly less severe than non-influenza viruses for runny nose, sneezing, and blocked nose ( table 2 ). Children were significantly more likely than adults to have fever ( table 2 ).

Most people with PCR-confirmed influenza did not consult and among those who did, influenza or influenza-like illness was rarely recorded in medical notes. Medical record review of 93 PCR-confirmed influenza cases across all seasons and of 459 episodes of influenza-like illness showed that 16 of 93 people with PCR-confirmed influenza (17%, 10–26) and 96 of 459 people with episodes of influenza-like illness (21%, 17–25) consulted their family doctor. Of the 96 patients consulting with influenza-like illness only eight (8%, 4–16) had influenza or influenza-like illness recorded in their medical record. Of the people with respiratory illness, those younger than 5 years were most likely to have a medical consultation ( appendix ).

Of 133 PCR-confirmed cases of influenza, with data available from end of season surveys, there was one admission to hospital potentially attributable to influenza (febrile convulsions in a child younger than 5 years within 2 weeks of a positive swab for influenza A H1N1 pdm2009). There were no deaths among these 133 PCR-confirmed cases. This single admission gives a maximum estimated hospitalisation rate of 0·75% (95% CI 0·02–4·19). Of 226 seroconverters to influenza, with data available from end of season surveys, there were two admissions to hospital that were potentially attributable to influenza: one in a young adult with a four-fold titre rise to influenza A H1N1 pdm2009 admitted with a chest infection in the winter of 2010–11 and one in an individual aged 45–64 years with a four-fold rise in titre to influenza B admitted with pneumonia during the winter of 2010–11. These two admissions give a maximum estimated hospitalisation rate for serological infection of 0·88% (95% CI 0·11–3·19). This compares with three respiratory hospitalisations in 1730 participants who did not have a four-fold titre rise (0·17%, 95% CI 0·04–0·51). There were two respiratory deaths in the cohort, both of which occurred in vaccinated participants older than 65 years during the 2008–09 winter season; one was partly attributable to chest infection and the other was attributable to pneumonia. It is not possible to infer whether or not influenza contributed because there were no nasal swab samples and post mortem serum samples were not sought.

36 Health Protection Agency

Sources of UK flu data: influenza surveillance in the United Kingdom—sentinel surveillance schemes based on networks of general practitioners. Figure 3 Number of expected events in a surveillance practice serving a population of 10 000 people Show full caption Data for a typical influenza season. Primary-care-based surveillance greatly underestimated the extent of infection and illness in the community ( figure 3 ). Under ascertainment was lower during the summer wave of the pandemic. The rate of PCR-confirmed influenza across all winter seasons was on average 22-times higher (95% CI 17–28) than rates of PCR-confirmed disease from the Royal College of General Practitioners Sentinel Influenza-Like Illness/Virological Surveillance Scheme.During the pandemic summer wave the rate was only three-times higher.