Low interim influenza vaccine effectiveness, Australia, 1 May to 24 September 2017.

In 2017, influenza seasonal activity was high in the southern hemisphere. We present interim influenza vaccine effectiveness (VE) estimates from Australia. Adjusted VE was low overall at 33% (95% confidence interval (CI): 17 to 46), 50% (95% CI: 8 to 74) for A(H1)pdm09, 10% (95% CI: -16 to 31) for A(H3) and 57% (95% CI: 41 to 69) for influenza B. For A(H3), VE was poorer for those vaccinated in the current and prior seasons.

Influenza vaccine effectiveness during the 2012 influenza season in Victoria, Australia: influences of waning immunity and vaccine match.

Vaccine effectiveness may wane with increasing time since vaccination. This analysis used the Victorian sentinel general practitioner (GP) network to estimate vaccine effectiveness for trivalent inactivated vaccines in the 2012 season. A test-negative design was used where patients presenting to GPs with influenza-like illness who tested positive for influenza were cases and noncases were those who tested negative. Vaccination status was recorded by GPs. Vaccine effectiveness was calculated as (1-odds ratio) × 100%. Estimates were compared early versus late in the season and by time since vaccination. Virus isolates were assessed antigenically by hemagglutination inhibition assay in a selection of positive samples and viruses from healthy adults who experienced a vaccine breakthrough were analyzed genetically. The adjusted vaccine effectiveness estimate for any type of influenza was 45% (95% CI: 8,66) and for influenza A(H3) was 35% (95% CI: -11,62). A non-significant effect of waning effectiveness by time since vaccination was observed for A(H3). For those vaccinated <93 days of presentation vaccine effectiveness was 37% (95% CI: -29,69), while for those vaccinated ≥93 days before presentation it was 18% (95% CI: -83,63). Comparison of early versus late in the season estimates was very sensitive to the cut off week chosen for analysis. Antigenic data suggested that low vaccine effectiveness was not associated with poor vaccine match among the A(H3) viruses. However, genetic analysis suggested nucleotide substitutions in antigenic sites. In 2012, the trivalent influenza vaccine provided moderate protection against influenza and showed limited evidence for waning effectiveness. Antigenic and genetic data can provide additional insight into understanding these estimates.

Effectiveness of seasonal influenza vaccine against pandemic (H1N1) 2009 virus, Australia, 2010.

To estimate effectiveness of seasonal trivalent and monovalent influenza vaccines against pandemic influenza A (H1N1) 2009 virus, we conducted a test-negative case-control study in Victoria, Australia, in 2010. Patients seen for influenza-like illness by general practitioners in a sentinel surveillance network during 2010 were tested for influenza; vaccination status was recorded. Case-patients had positive PCRs for pandemic (H1N1) 2009 virus, and controls had negative influenza test results. Of 319 eligible patients, test results for 139 (44%) were pandemic (H1N1) 2009 virus positive. Adjusted effectiveness of seasonal vaccine against pandemic (H1N1) 2009 virus was 79% (95% confidence interval 33%-93%); effectiveness of monovalent vaccine was 47% and not statistically significant. Vaccine effectiveness was higher among adults. Despite some limitations, this study indicates that the first seasonal trivalent influenza vaccine to include the pandemic (H1N1) 2009 virus strain provided significant protection against laboratory-confirmed pandemic (H1N1) 2009 infection.

Estimation of type- and subtype-specific influenza vaccine effectiveness in Victoria, Australia using a test negative case control method, 2007-2008.

BACKGROUND: Antigenic variation of influenza virus necessitates annual reformulation of seasonal influenza vaccines, which contain two type A strains (H1N1 and H3N2) and one type B strain. We used a test negative case control design to estimate influenza vaccine effectiveness (VE) against influenza by type and subtype over two consecutive seasons in Victoria, Australia. METHODS: Patients presenting with influenza-like illness to general practitioners (GPs) in a sentinel surveillance network during 2007 and 2008 were tested for influenza. Cases tested positive for influenza by polymerase chain reaction and controls tested negative for influenza. Vaccination status was recorded by sentinel GPs. Vaccine effectiveness was calculated as [(1--adjusted odds ratio) × 100%]. RESULTS: There were 386 eligible study participants in 2007 of whom 50% were influenza positive and 19% were vaccinated. In 2008 there were 330 eligible study participants of whom 32% were influenza positive and 17% were vaccinated. Adjusted VE against A/H3N2 influenza in 2007 was 68% (95% CI, 32 to 85%) but VE against A/H1N1 (27%; 95% CI, -92 to 72%) and B (84%; 95% CI, -2 to 98%) were not statistically significant. In 2008, the adjusted VE estimate was positive against type B influenza (49%) but negative for A/H1N1 (-88%) and A/H3N2 (-66%); none was statistically significant. CONCLUSIONS: Type- and subtype-specific assessment of influenza VE is needed to identify variations that cannot be differentiated from a measure of VE against all influenza. Type- and subtype-specific influenza VE estimates in Victoria in 2007 and 2008 were generally consistent with strain circulation data.

Detecting the start of an influenza outbreak using exponentially weighted moving average charts.

BACKGROUND: Influenza viruses cause seasonal outbreaks in temperate climates, usually during winter and early spring, and are endemic in tropical climates. The severity and length of influenza outbreaks vary from year to year. Quick and reliable detection of the start of an outbreak is needed to promote public health measures. METHODS: We propose the use of an exponentially weighted moving average (EWMA) control chart of laboratory confirmed influenza counts to detect the start and end of influenza outbreaks. RESULTS: The chart is shown to provide timely signals in an example application with seven years of data from Victoria, Australia. CONCLUSIONS: The EWMA control chart could be applied in other applications to quickly detect influenza outbreaks.

Intraseason decline in influenza vaccine effectiveness during the 2016 southern hemisphere influenza season: A test-negative design study and phylogenetic assessment.

BACKGROUND: We estimated the effectiveness of seasonal inactivated influenza vaccine and the potential influence of timing of immunization on vaccine effectiveness (VE) using data from the 2016 southern hemisphere influenza season. METHODS: Data were pooled from three routine syndromic sentinel surveillance systems in general practices in Australia. Each system routinely collected specimens for influenza testing from patients presenting with influenza-like illness. Next generation sequencing was used to characterize viruses. Using a test-negative design, VE was estimated based on the odds of vaccination among influenza-positive cases as compared to influenza-negative controls. Subgroup analyses were used to estimate VE by type, subtype and lineage, as well as age group and time between vaccination and symptom onset. RESULTS: A total of 1085 patients tested for influenza in 2016 were included in the analysis, of whom 447 (41%) tested positive for influenza. The majority of detections were influenza A/H3N2 (74%). One-third (31%) of patients received the 2016 southern hemisphere formulation influenza vaccine. Overall, VE was estimated at 40% (95% CI: 18-56%). VE estimates were highest for patients immunized within two months prior to symptom onset (VE: 60%; 95% CI: 26-78%) and lowest for patients immunized >4 months prior to symptom onset (VE: 19%; 95% CI: -73-62%). DISCUSSION: Overall, the 2016 influenza vaccine showed good protection against laboratory-confirmed infection among general practice patients. Results by duration of vaccination suggest a significant decline in effectiveness during the 2016 influenza season, indicating immunization close to influenza season offered optimal protection.

A Narrative Review of the Prevalence and Risk Factors Associated With Development of Knee Osteoarthritis After Traumatic Unilateral Lower Limb Amputation.

INTRODUCTION: Young military Service Members with traumatic unilateral lower limb amputations may be at a high risk for developing knee osteoarthritis (OA). There is growing evidence for potential influence and predictive value of nonsystemic risk factors on development and progression of primary knee OA in older adults. Proposed factors include chronic knee pain, obesity, abnormal knee joint mechanics, muscle weakness, previous knee trauma, and altered physical activity level. However, there is limited information available regarding whether such nonsystemic risk factors could also be responsible for the increased risk of knee OA after traumatic, unilateral lower limb amputation in young military Service Members. The purpose of this narrative review is to compile and present evidence regarding prevalence of nonsystemic and potentially modifiable knee OA risk factors in Service Members with traumatic, unilateral lower limb amputation, and to identify potential strategies for intervention. MATERIALS AND METHODS: A comprehensive literature search was performed in July 2015 using structured search terms related to nonsystemic risk factors for knee OA. RESULTS: Current collective evidence does suggest an elevated prevalence of the nonsystemic knee OA risk factors in young military Service Members with unilateral lower limb amputation. In conclusion, the present state of the literature supports that young military Service Members with traumatic unilateral lower limb amputations may be at increased risk for developing knee OA compared to nonamputees. Military Service Members injured at a young age have a long life expectancy, and thus require comprehensive rehabilitation programs to prevent or delay progression of knee OA. Given the lack of strong evidence, further clinical research is needed to determine whether early identification and modification of nonsystemic risk factors for knee OA could optimize long-term function and quality of life in young Service Members after traumatic, unilateral, limb amputations.

Effectiveness of seasonal influenza vaccine in Australia, 2015: An epidemiological, antigenic and phylogenetic assessment.

BACKGROUND: A record number of laboratory-confirmed influenza cases were notified in Australia in 2015, during which type A(H3) and type B Victoria and Yamagata lineages co-circulated. We estimated effectiveness of the 2015 inactivated seasonal influenza vaccine against specific virus lineages and clades. METHODS: Three sentinel general practitioner networks conduct surveillance for laboratory-confirmed influenza amongst patients presenting with influenza-like illness in Australia. Data from the networks were pooled to estimate vaccine effectiveness (VE) for seasonal trivalent influenza vaccine in Australia in 2015 using the case test-negative study design. RESULTS: There were 2443 eligible patients included in the study, of which 857 (35%) were influenza-positive. Thirty-three and 19% of controls and cases respectively were reported as vaccinated. Adjusted VE against all influenza was 54% (95% CI: 42, 63). Antigenic characterisation data suggested good match between vaccine and circulating strains of A(H3); however VE for A(H3) was low at 44% (95% CI: 21, 60). Phylogenetic analysis indicated most circulating viruses were from clade 3C.2a, rather than the clade included in the vaccine (3C.3a). VE point estimates were higher against B/Yamagata lineage influenza (71%; 95% CI: 57, 80) than B/Victoria (42%, 95% CI: 13, 61), and in younger people. CONCLUSIONS: Overall seasonal vaccine was protective against influenza infection in Australia in 2015. Higher VE against the B/Yamagata lineage included in the trivalent vaccine suggests that more widespread use of quadrivalent vaccine could have improved overall effectiveness of influenza vaccine. Genetic characterisation suggested lower VE against A(H3) influenza was due to clade mismatch of vaccine and circulating viruses.

Understanding influenza vaccine protection in the community: an assessment of the 2013 influenza season in Victoria, Australia.

BACKGROUND: The influenza virus undergoes frequent antigenic drift, necessitating annual review of the composition of the influenza vaccine. Vaccination is an important strategy for reducing the impact and burden of influenza, and estimating vaccine effectiveness (VE) each year informs surveillance and preventative measures. We aimed to describe the influenza season and to estimate the effectiveness of the influenza vaccine in Victoria, Australia, in 2013. METHODS: Routine laboratory notifications, general practitioner sentinel surveillance (including a medical deputising service) data, and sentinel hospital admission surveillance data for the influenza season (29 April to 27 October 2013) were collated in Victoria, Australia, to describe influenza-like illness or confirmed influenza during the season. General practitioner sentinel surveillance data were used to estimate VE against medically-attended laboratory confirmed influenza. VE was estimated using the case test negative design as 1-adjusted odds ratio (odds of vaccination in cases compared with controls) × 100%. Cases tested positive for influenza while non-cases (controls) tested negative. Estimates were adjusted for age group, week of onset, time to swabbing and co-morbidities. RESULTS: The 2013 influenza season was characterised by relatively low activity with a late peak. Influenza B circulation preceded that of influenza A(H1)pdm09, with very little influenza A(H3) circulation. Adjusted VE for all influenza was 55% (95%CI: -11, 82), for influenza A(H1)pdm09 was 43% (95%CI: -132, 86), and for influenza B was 56% (95%CI: -51, 87) Imputation of missing data raised the influenza VE point estimate to 64% (95%CI: 13, 85). CONCLUSIONS: Clinicians can continue to promote a positive approach to influenza vaccination, understanding that inactivated influenza vaccines prevent at least 50% of laboratory-confirmed outcomes in hospitals and the community.

Moderate influenza vaccine effectiveness with variable effectiveness by match between circulating and vaccine strains in Australian adults aged 20-64 years, 2007-2011.

BACKGROUND: Influenza vaccines are licensed annually based on immunogenicity studies. We used five sequential years of data to estimate influenza vaccine effectiveness (VE), the critical outcome in the field. METHODS: Between 2007 and 2011, we performed annual prospective test-negative design case-control studies among adults aged 20-64 years recruited from sentinel general practices in the Australian state of Victoria. We used PCR-confirmed influenza as the endpoint to estimate influenza VE for all years. We compared annual VE estimates with the match between circulating and vaccine strains, determined by haemagglutination inhibition assays. RESULTS: The adjusted VE estimate for all years (excluding 2009) was 62% (95% CI 43, 75). By type and subtype, the point estimates of VE by year ranged between 31% for seasonal influenza A(H1N1) and 88% for influenza A(H1N1)pdm09. In 2007, when circulating strains were assessed as incompletely matched, the point estimate of the adjusted VE against all influenza was 58%. The point estimate was 59% in 2011 when all strains were assessed as well matched. CONCLUSION: Trivalent inactivated vaccines provided moderate protection against laboratory-confirmed influenza in adults of working age, although VE estimates were sensitive to the model used. VE estimates correlated poorly with circulating strain match, as assessed by haemagglutination inhibition assays, suggesting a need for VE studies that incorporate antigenic characterization data.

The significance of increased influenza notifications during spring and summer of 2010-11 in Australia.

BACKGROUND & OBJECTIVE: During the temperate out-of-season months in Australia in late 2010 and early 2011, an unprecedented high number of influenza notifications were recorded. We aimed to assess the significance of these notifications. METHODS: For Australia, we used laboratory-confirmed cases notified to the WHO FluNet surveillance tool; the percentage of these that were positive; notifications by state and influenza type and subtype; and surveillance data from Google FluTrends. For the state of Victoria, we used laboratory-confirmed notified cases and influenza-like illness (ILI) proportions. We compared virus characterisation using haemagglutination-inhibition assays and phylogenetic analysis of the haemagglutinin gene for seasonal and out-of-season notifications. RESULTS: The increase in notifications was most marked in tropical and subtropical Australia, but the number of out-of-season notifications in temperate Victoria was more than five times higher than the average of the previous three seasons. However, ILI proportions in spring-summer were not different to previous years. All out-of-season viruses tested were antigenically and genetically similar to those tested during either the 2010 or 2011 influenza seasons. An increase in the number of laboratories testing for influenza has led to an increase in the number of tests performed and cases notified. CONCLUSION: An increase in influenza infections in spring-summer of 2010-11 in tropical and temperate Australia was not associated with any differences in virus characterisation compared with viruses that circulated in the preceding and following winters. This increase probably reflected a natural variation in out-of-season virus circulation, which was amplified by increased laboratory testing.

Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance.

INTRODUCTION: Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO). METHODS: For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient. RESULTS: The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations. CONCLUSIONS: Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.

Epidemiology of the 2012 influenza season in Victoria, Australia.

OBJECTIVE: To assess the magnitude and severity of the 2012 influenza season in Victoria, Australia using surveillance data from five sources. METHODS: Data from influenza notifications, sentinel general practices, a sentinel hospital network, a sentinel locum service and strain typing databases for 2012 were descriptively analysed. RESULTS: Influenza and influenza-like illness activity was moderate compared to previous years, although a considerable increase in notified laboratory-confirmed influenza was observed. Type A influenza comprised between 83% and 87% of cases from the general practitioners, hospitals and notifiable surveillance data. Influenza A/H3 was dominant in July and August, and most tested isolates were antigenically similar to the A/Perth/16/2009 virus used in the vaccine. There was a smaller peak of influenza type B in September. No tested viruses were resistant to any neuraminidase inhibitor antivirals. Higher proportions of type A/H3, hospitalized cases and those with a comorbid condition indicated for influenza vaccination were aged 65 years or older. Influenza vaccination coverage among influenza-like illness patients was 24% in sentinel general practices and 50% in hospitals. DISCUSSION: The 2012 influenza season in Victoria was average compared to previous years, with an increased dominance of A/H3 accompanied by increases in older and hospitalized cases. Differences in magnitude and the epidemiological profile of cases detected by the different data sources demonstrate the importance of using a range of surveillance data to assess the relative severity of influenza seasons.

Pandemic influenza H1N1 2009 infection in Victoria, Australia: no evidence for harm or benefit following receipt of seasonal influenza vaccine in 2009.

Conflicting findings regarding the level of protection offered by seasonal influenza vaccination against pandemic influenza H1N1 have been reported. We performed a test-negative case control study using sentinel patients from general practices in Victoria to estimate seasonal influenza vaccine effectiveness against laboratory proven infection with pandemic influenza. Cases were defined as patients with an influenza-like illness who tested positive for influenza while controls had an influenza-like illness but tested negative. We found no evidence of significant protection from seasonal vaccine against pandemic influenza virus infection in any age group. Age-stratified point estimates, adjusted for pandemic phase, ranged from 44% in persons aged less than 5 years to -103% (odds ratio=2.03) in persons aged 50-64 years. Vaccine effectiveness, adjusted for age group and pandemic phase, was 3% (95% CI -48 to 37) for all patients. Our study confirms the results from our previous interim report, and other studies, that failed to demonstrate benefit or harm from receipt of seasonal influenza vaccine in patients with confirmed infection with pandemic influenza H1N1 2009.

Continued dominance of pandemic A(H1N1) 2009 influenza in Victoria, Australia in 2010.

The 2010 Victorian influenza season was characterized by normal seasonal influenza activity and the dominance of the pandemic A(H1N1) 2009 strain. General Practice Sentinel Surveillance rates peaked at 9.4 ILI cases per 1000 consultations in week 36 for metropolitan practices, and at 10.5 ILI cases per 1000 in the following week for rural practices. Of the 678 ILI cases, 23% were vaccinated, a significantly higher percentage than in previous years. A significantly higher percentage of ILI patients were swabbed in 2010 compared to 2003-2008, but similar to 2009, with a similar percentage being positive for influenza as in previous years. Vaccination rates increased with patient age. Melbourne Medical Deputising Service rates peaked in week 35 at 19.1 ILI cases per 1000 consultations. Of the 1914 cases of influenza notified to the Department of Health, Victoria, 1812 (95%) were influenza A infections - 1001 (55%) pandemic A(H1N1) 2009, 4 (< 1%) A(H3N2) and 807 (45%) not subtyped; 88 (5%) were influenza B; and 14 (< 1%) were influenza A and B co-infections. The World Health Organization Collaborating Centre for Reference and Research on Influenza tested 403 isolates of which 261 were positive for influenza, 250 of which were influenza A and 11 were influenza B. Ninety-two per cent of the influenza A viruses were pandemic A(H1N1) 2009, and following antigenic analysis all of these were found to be similar to the current vaccine strain. Three viruses (0.9%) were found to be oseltamivir resistant due to an H275Y mutation in the neuraminidase gene.

Influenza A (H1N1) in Victoria, Australia: a community case series and analysis of household transmission.

BACKGROUND: We characterise the clinical features and household transmission of pandemic influenza A (pH1N1) in community cases from Victoria, Australia in 2009. METHODS: Questionnaires were used to collect information on epidemiological characteristics, illness features and co-morbidities of cases identified in the 2009 Victorian Influenza Sentinel Surveillance program. RESULTS: The median age of 132 index cases was 21 years, of whom 54 (41%) were under 18 years old and 28 (21%) had medical co-morbidities. The median symptom duration was significantly shorter for children who received antivirals than in those who did not (p = 0.03). Assumed influenza transmission was observed in 63 (51%) households. Influenza-like illness (ILI) developed in 115 of 351 household contacts, a crude secondary attack rate of 33%. Increased ILI rates were seen in households with larger numbers of children but not larger numbers of adults. Multivariate analysis indicated contacts of cases with cough and diarrhoea, and contacts in quarantined households were significantly more likely to develop influenza-like symptoms. CONCLUSION: Most cases of pH1N1 in our study were mild with similar clinical characteristics to seasonal influenza. Illness and case features relating to virus excretion, age and household quarantine may have influenced secondary ILI rates within households.

Pandemic (H1N1) 2009 influenza community transmission was established in one Australian state when the virus was first identified in North America.

BACKGROUND: In mid-June 2009 the State of Victoria in Australia appeared to have the highest notification rate of pandemic (H1N1) 2009 influenza in the world. We hypothesise that this was because community transmission of pandemic influenza was already well established in Victoria at the time testing for the novel virus commenced. In contrast, this was not true for the pandemic in other parts of Australia, including Western Australia (WA). METHODS: We used data from detailed case follow-up of patients with confirmed infection in Victoria and WA to demonstrate the difference in the pandemic curve in two Australian states on opposite sides of the continent. We modelled the pandemic in both states, using a susceptible-infected-removed model with Bayesian inference accounting for imported cases. RESULTS: Epidemic transmission occurred earlier in Victoria and later in WA. Only 5% of the first 100 Victorian cases were not locally acquired and three of these were brothers in one family. By contrast, 53% of the first 102 cases in WA were associated with importation from Victoria. Using plausible model input data, estimation of the effective reproductive number for the Victorian epidemic required us to invoke an earlier date for commencement of transmission to explain the observed data. This was not required in modelling the epidemic in WA. CONCLUSION: Strong circumstantial evidence, supported by modelling, suggests community transmission of pandemic influenza was well established in Victoria, but not in WA, at the time testing for the novel virus commenced in Australia. The virus is likely to have entered Victoria and already become established around the time it was first identified in the US and Mexico.

Influenza surveillance in Australia: we need to do more than count.

Laboratory-confirmed influenza is a nationally notifiable disease in Australia. According to notification data, Queensland has experienced more severe influenza seasons than other states and territories. However, this method ignores available denominator data: the number of laboratory tests performed. We propose that negative results of laboratory tests for influenza should be made notifiable, alongside laboratory-confirmed disease, and used to calculate the proportion of positive test results in real-time. Using data from the public health pathology services of three Australian states - Queensland Health laboratories, the Victorian Infectious Diseases Reference Laboratory and Western Australia's PathWest - for 2004 to 2008, we show that incorporating laboratory-negative test data into national surveillance data would add to and improve our understanding of influenza epidemiology.

H1N1 swine origin influenza infection in the United States and Europe in 2009 may be similar to H1N1 seasonal influenza infection in two Australian states in 2007 and 2008.

BACKGROUND: The population-based impact of infection with swine origin influenza A (H1N1) virus infection was not clear in the early days of the epidemic towards the end of May 2009. Australia had seven confirmed cases by 22 May 2009. We aimed to compare available data on swine origin influenza A (H1N1) virus infection overseas with seasonal influenza A (H1N1) virus infection in Australia to assist with forward planning. METHODS: Data on infection with seasonal influenza A (H1N1) virus in patients recruited through sentinel general practices in Victoria and Western Australia in 2007 and 2008 were compared with early publications on infection with swine origin influenza A (H1N1) virus in the United States and Europe. RESULTS: Influenza A (H1N1) virus infection was predominantly a disease of younger people, regardless of whether the virus was of swine or human origin. The median age of infection with swine origin virus was 20 years in the United States and 22 years in Spain, while the median age of infection with human origin virus was 18 years in Western Australia and 23 years in Victoria. CONCLUSIONS: The median age of infection with influenza A (H1N1) virus was around 20 +/- 3 years, independent of the origin of the H1N1 virus but a higher proportion of swine origin influenza infections occurred in people aged 10-18 years. This is at least partially explained by biased sampling among surveillance patients, although it may also reflect a different infection pattern.