After alignment of epidemics at the provincial or community level, small differences in the age-specific timing of influenza infections have been identified. During the fall pandemic wave, infections peaked noticeably earlier among children and youth aged 10–19 years, with an average 2.8 (95% CI: 1.6, 4.0; standard error (SE): 0.6)-day lead in the timing of infections compared with younger children aged 4–9 years and an average 3.0 (95% CI: 1.6, 4.3; SE: 0.7)-day lead compared with young adults aged 20–29 years. The age pattern of the fall wave of the 2009 pandemic was fairly similar to the pattern observed in the 2 H3N2 seasons with a novel antigenic strain (A/Sydney/05/97 in the 1997/1998 season and A/Fujian/411/02 in 2003/2004), as illustrated in . In contrast, for the H3N2 seasons where the antigenic strain was not novel and the vaccine well matched, infections among young adults aged 20–29 years led infections among youth aged 10–19 years by 3.9 (95% CI: 1.7, 6.1; SE: 1.1) days. The largest observed difference of 8.6 (95% CI: 6.3, 10.9; SE: 1.2) days was between young adults aged 20–29 years and children 4–9 years of age (). The age-specific differences were usually within 1 week for most age groups. For the 2 H1 seasons, a longer delay among adults aged 45 years or older of approximately 2–3 weeks compared with youth aged 10–19 years was noted. This may have been due to the cocirculation of 2 distinct but antigenically similar strains during the 2002/2003 season. Although the hemagglutinin samples of the H1N2 strains were antigenically similar to that of the vaccine strain A/New Caledonia/20/99 (H1N1), the level of cross-protection between the H1N1 and H1N2 strains may have been less than for other antigenically similar strains. It is possible that the level of activity for these 2 strains peaked at different times with differential clinical attack rates by age.
Our results are in agreement with those of the study of social contacts by Glass and Glass (3
), where high school students and young adults were identified as the age group most likely to form the transmission backbone for the next pandemic because of the nature of their contact networks. Our results tend not to support the inclusion of younger school-age children (5–9 years) in the lead group as others have suggested (4
) despite their high attack rate (25
). The relative timing for children aged 10–13 years appears to be intermediate relative to older and younger age groups. Variability in the timing of peak influenza activity by age was noted in an earlier study by Monto et al. (26
) on the basis of influenza-like illness consultation rates, virus isolation, and serology in a single community. The results of studies assessing age-specific differences in the timing of peak influenza-like illness consultation rates, emergency room visits, or pneumonia and influenza admissions are mixed (1
). Although statistical methods exist to estimate a seasonal baseline for time series that are not specific to influenza, such as pneumonia and influenza admissions, and attribute the excess to influenza or other respiratory virus (13
), these statistical estimates do not have the precision of methods using laboratory-confirmed cases. With less statistical power, influenza-like illness-based studies would be less likely to find age-specific differences. Although it is reasonable to assume that the peak in adult pneumonia and influenza admissions corresponds to the peak in influenza activity, this is not the case for children, where parainfluenza virus and respiratory syncytial virus were found to account for more admissions than influenza and where peaks in respiratory admissions did not correspond to peaks in influenza activity (13
). The higher transmission rate observed during the fall pandemic wave compared with the spring wave (inferred from differences in the epidemic growth rate) () was expected and is consistent with the seasonal trend in transmission rates that was observed for seasonal influenza (21
The 1-week difference in the timing of infections between youth and older adults is very short compared with differences in the timing of peak activity across Canada, which has been observed to be as long as 13 weeks (21
). The age-specific differences are slight, and as laboratory confirmation is not considered geographically representative, it was essential to control for these geographic differences in order to identify the age-specific differences. The date of symptom onset was available for most of the hospital admissions with pandemic H1N1/09; however, laboratory-confirmed cases for seasonal influenza were identified only by the date of specimen collection. Thus, we were unable to account for any age-specific differences in the time from symptom onset to presentation for medical care and specimen collection in the analysis of the seasonal influenza data. Persons presenting for medical care because of complications more than a week after onset of symptoms are, however, likely to have already cleared the virus, and positive findings are expected to be less likely at that time (29
). The average delay from symptom onset to hospital admission with laboratory-confirmed H1N1/09 was 3.7 (95% CI: 3.2, 4.1) days, increasing with increasing age from 2.7 to 4.7 days. Based on a very small proportion of cases of seasonal influenza in 2007/2008, the average delay from symptom onset to specimen procurement was 3 (95% CI: 2.8, 3.2) days, although the age trend was reversed. Although we used 2 different proxies for the level of influenza activity (laboratory-confirmed admissions and laboratory-confirmed cases representing a mix of inpatient and outpatients), the results appear to be in good agreement, and the generalization of age-specific differences in the relative timing of laboratory-confirmed admissions to the relative timing of infection appears reasonable. The potential impact of the various study limitations is likely in days rather than weeks and less than the observed variation from season to season. Each season is different, and although we compared the age-specific differences in the timing of infections by strain subtype and vaccine match/previous circulation of the antigenic strain, the number of seasons included in this study was too small to fully assess possible reasons for the observed differences in the age structure of the epidemic curves.
During the pandemic period, clinical and laboratory testing procedures varied in response to public health and clinical needs and laboratory capacity, so that detection rates were not constant over the pandemic period. The initial objective was to document the first 100 cases of the pandemic strain, as our ability to assess the virulence of the pandemic strain was otherwise limited. As many of the early cases that were identified among recent travelers to Mexico were relatively mild, intense surveillance continued beyond the first 100 cases before becoming more restrictive. Hence, the number of laboratory-confirmed hospital admissions was used, rather than the number of laboratory-confirmed cases, to describe the epidemic curve for the pandemic period. The roll out of the vaccination campaign as the epidemic peaked in November in many jurisdictions appears to have had a limited impact on the study results. Infants under the age of 6 months were not vaccinated, and persons over the age of 65 years were not among the priority groups that received the vaccine during the period of peak activity (24
). Although priority was given to all children aged from 6 months to 5 years because of their higher risk for severe outcomes, this was not the lead age group, and calculations (not shown) based on estimates of weekly vaccination rates suggest that the direct effects of the vaccination effort would have minimal effect on the average timeliness of influenza infections by age.
Many studies have identified schoolchildren as the drivers of the local spread of influenza, prompting considerations of influenza vaccination for all schoolchildren and the use of school closures to mitigate the effects of a pandemic (15
). Not all studies agree on the likely benefits of closing schools (35
), and our study of the timing of laboratory-confirmed cases of influenza A suggests that the effect of age on timing may be smaller than predicted by various models (31
). Mitigation of influenza epidemics remains complex, although the role of youth and young adults as potential drivers of the epidemic waves of seasonal and pandemic influenza should also be considered. Although this study casts doubt on the hypothesis that younger school-age children lead influenza epidemic waves, interventions targeting young children may still have a significant impact on the size of the epidemic. Even though the age-specific lead times are short, these differences in timing are sufficient to bias age-specific relative risks calculated early in the course of an epidemic or pandemic wave.