BJC has received research funding from MedImmune Inc., a manufacturer of influenza vaccines. JSMP declares research support from GlaxoSmithKline, Baxter, Cruxell, Combinatorix, and DIVA Solutions. No other conflicts were declared. Dr J.S Malik Peiris is a
Steven Riley and colleagues analyze a community cohort study from the 2009 (H1N1) influenza pandemic in Hong Kong, and found that more children than adults were infected with H1N1, but children were less likely to progress to severe disease than adults.
While patterns of incidence of clinical influenza have been well described, much uncertainty remains over patterns of incidence of infection. The 2009 pandemic provided both the motivation and opportunity to investigate patterns of mild and asymptomatic infection using serological techniques. However, to date, only broad epidemiological patterns have been defined, based on largely cross-sectional study designs with convenience sampling frameworks.
We conducted a paired serological survey of a cohort of households in Hong Kong, recruited using random digit dialing, and gathered data on severe confirmed cases from the public hospital system (>90% inpatient days). Paired sera were obtained from 770 individuals, aged 3 to 103, along with detailed individual-level and household-level risk factors for infection. Also, we extrapolated beyond the period of our study using time series of severe cases and we simulated alternate study designs using epidemiological parameters obtained from our data. Rates of infection during the period of our study decreased substantially with age: for 3–19 years, the attack rate was 39% (31%–49%); 20–39 years, 8.9% (5.3%–14.7%); 40–59 years, 5.3% (3.5%–8.0%); and 60 years or older, 0.77% (0.18%–4.2%). We estimated parameters for a parsimonious model of infection in which a linear age term and the presence of a child in the household were used to predict the log odds of infection. Patterns of symptom reporting suggested that children experienced symptoms more often than adults. The overall rate of confirmed pandemic (H1N1) 2009 influenza (H1N1pdm) deaths was 7.6 (6.2–9.5) per 100,000 infections. However, there was substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 (0.65–0.86) for 3–19 years up to 220 (50–4,000) for 60 years and older. Extrapolating beyond the period of our study using rates of severe disease, we estimated that 56% (43%–69%) of 3–19 year olds and 16% (13%–18%) of people overall were infected by the pandemic strain up to the end of January 2010. Using simulation, we found that, during 2009, larger cohorts with shorter follow-up times could have rapidly provided similar data to those presented here.
Should H1N1pdm evolve to be more infectious in older adults, average rates of severe disease per infection could be higher in future waves: measuring such changes in severity requires studies similar to that described here. The benefit of effective vaccination against H1N1pdm infection is likely to be substantial for older individuals. Revised pandemic influenza preparedness plans should include prospective serological cohort studies. Many individuals, of all ages, remained susceptible to H1N1pdm after the main 2009 wave in Hong Kong.
From June 2009 to August 2010, the world was officially (according to specific WHO criteria—WHO phase 6 pandemic alert) in the grip of an Influenza A pandemic with a new strain of the H1N1 virus. During this time, more than 214 countries and overseas territories reported laboratory confirmed cases of pandemic influenza H1N1 2009 with almost 20,000 deaths.
While much is already known about patterns of incidence of clinical influenza, the patterns of infection incidence are much more uncertain, because many influenza infections are either asymptomatic or cause only mild symptoms. This means that it is difficult to obtain accurate estimates of risk factors for infection and the overall burden of disease using only clinical surveillance. However, without accurate estimates of infection incidence across different risk groups, it is not possible to establish the number of infections that give rise to severe disease (the per infection rate of severe disease). Consequently, it is difficult to give evidence-based advice for individuals, groups, and populations about the potential benefits of interventions including drugs and vaccines that might reduce the risk of influenza infection.
During the 2009 pandemic, some countries and territories, such as Hong Kong, were able to investigate patterns of mild and asymptomatic infection using serological techniques, thus providing information that may help to fill this knowledge gap. Given the high levels of polymerase chain reaction (PCR) testing and the robust reporting of hospital episodes, the main H1N1 pandemic wave in Hong Kong (during September 2009) provided an opportunity to implement a prospective cohort study to investigate the incidence of infection.
Using these methods, the researchers found that rates of H1N1 infection during the study period decreased substantially with age: for 3–19 years, the attack rate was 39%; 20–39 years, 8.9%; 40–59 years, 5.3%; and 60 years or older, 0.77%. In addition, patterns of symptom reporting indicated that children experienced symptoms more often than adults. The overall rate of confirmed H1N1 deaths was 7.6 per 100,000 infections. However, there was a substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 for 3–19 years up to 220 for 60 years and older. Statistical modeling suggested that 56% of 3–19 year olds and 16% of people overall were infected by the pandemic strain up to the end of January 2010.
The results of this study suggest that more children were infected with H1N1 than adults but most of them did not progress to severe disease. Conversely, although fewer older adults were infected with H1N1, this group was much more likely to experience severe disease. Therefore, should H1N1 infection incidence ever increase in older adults, for example by evolving to become more infectious to this group, average rates of severe disease per infection could be much higher than for the 2009 pandemic. Revised pandemic preparedness plans should include prospective serological cohort studies, such as this one, in order to be able to estimate rates of severe disease per infection.
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Influenza A infection causes substantial morbidity and mortality each year
Previous community-based serological surveys of populations outside Hong Kong have established a broad consistent pattern for the 2009 influenza pandemic, namely, high rates of infection in school-aged children relative to younger adults and lower rates in older adults: Australia
Here, we describe a longitudinal community cohort study of the main wave of the 2009 (H1N1) influenza pandemic in Hong Kong, with a design somewhat similar to the seroepidemiological components of the Tecumseh
All study protocols were approved by The Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster.
Households were approached to take part in the study on the basis of their fixed-line telephone number. Numbers were obtained from two sources, either directly from random calling of residential landline numbers for Hong Kong (the direct group), or from a subgroup of participants that had already completed a parallel study of risk behaviors
We attempted to “bracket” the main wave of the pandemic by obtaining blood samples as soon as possible and then collecting follow-up samples when the peak of transmission had passed (
Colors are coded for age groups in both charts: red, 3–19 y; green, 20–39 y; blue, 40–59 y; and magenta 60 y and older. (A) Shows the timing of recruitment of members of the study. (B) Shows the time series of hospitalized cases in Hong Kong, by week of onset.
When the phone was answered, we attempted to speak to an individual from the household who was at least 18 y old and who normally slept in the household for at least 5 nights per week. We explained the objectives of the study and asked the respondent if they and members their household were interested in participating. If the respondent agreed, we asked them to estimate the number of members of the household who would participate and we made an appointment for the household to visit the study clinic. The respondent was informed that at least one member of the household would be required to give a blood sample in order for the household to be eligible to enter the study.
On arrival at the study clinic, each individual was given an information sheet and the opportunity to question a member of the study team. Informed consent was obtained individually for either full participation in the study, or for participation without giving a blood sample. For children aged 8–17, we obtained written consent from both the child and their parent or guardian. For children aged 2–7, written consent was obtained from the parent or guardian. A questionnaire was administered and ∼8 ml whole blood was obtained. Participating households were given a tympanic thermometer. Households were allowed to keep the thermometer at the end of the study. Participants who gave a blood sample were compensated with 100 HKD (≈US$13). Incentives were given as either supermarket vouchers (adults) or book tokens (children).
Participants were asked to report when any member of the household was experiencing two or more of: fever (>37.5°C, temperature measured only when a fever was suspected), cough, sputum, sore throat, runny nose, or myalgia. Participants were offered three methods of reporting. First, we asked them to phone the study team directly to report symptoms as soon as possible. Second, we asked them to fill out a paper diary with the day and type of the symptoms. Third, during a follow-up interview, we asked them if they had experienced any symptoms between baseline and follow-up and, if so, what symptoms they had experienced. For each mode of reporting, we constructed three types of symptomatic episode: acute respiratory infection (ARI, all reported episodes of symptoms), influenza-like illness (ILI, fever plus cough or sore throat), and fever alone.
Blood samples were refrigerated at 4°C in the clinic and transferred (<1 h) using a cool box to the study laboratory later that evening. The next morning, samples were centrifuged at 1,500 rpm for 10 min and the sera extracted. The sera were frozen to −30°C for storage. For testing, sera were thawed and then heat inactivated at 56°C for 30 min. Replicate serum dilutions were mixed with 100 tissue culture infectious dose 50 (TCID50) of A/California/4/2009 (H1N1pdm) for 2 h and then transferred onto preformed monolayers of Madin-Darby Canine Kidney (MDCK) cells grown in 96-well microtitre plates. The plates were incubated at 37°C in 5% CO2 for 3 d. Neutralization of virus cytopathogenic effect (CPE) was observed under an inverted microscope to determine the highest serum dilution that neutralized ≥50% of the wells. A virus back titration, positive controls, and negative controls were included in each assay. The sensitivity of the test method was benchmarked using a standard positive control serum 09/194 provided by the National Institute for Biological Standards and Control, Centre for Health Protection, London. Neutralization tests, rather than hemagglutination tests, were chosen for assaying antibody responses to pandemic influenza H1N1 virus because neutralization tests are more sensitive for patients with virologically confirmed pandemic H1N1 infection
Individuals were classified as seroconverters if there was a 4-fold or greater rise in their neutralization titre between baseline and follow-up. Initially, all samples were screened at dilutions of 1∶20 and 1∶40 with baseline and follow-up serum samples tested in parallel in the same set of assays. If exposure status or seroconversion status from the screening dilutions was unambiguous after these screening assays, no further titrations were performed. For all other pairs of sera, antibody titration was performed in 2-fold dilutions from 1∶10 to 1∶1,280.
From the start of May 2009, patients admitted to public hospitals in Hong Kong with acute respiratory illness were routinely tested for H1N1pdm using reverse transcription (RT)-PCR. Every case for which a test was conducted was entered into an information management system administered by the Hospital Authority (eFlu). This system was integrated with the Hong-Kong–wide network for electronic notes and assigned a unique identifier based on Hong Kong identification numbers. In Hong Kong, 90% of inpatient bed days are in the public system
Estimating the number of infections per severe case would be straightforward if recruitment and follow-up had occurred during short periods of time and antibody titres rose immediately after infection. However, because of the rolling nature of recruitment and follow-up and the delay in the rise of antibody titres after infection, we developed a simple likelihood-based framework to estimate the number of infections per severe case for each age group, where a severe case could be an individual admitted to hospital, one admitted to an ICU, or a fatal case. Inference for a specific combination of age group and level of severity was independent of other combinations. Effectively, the proportion of an age group infected was equal to the number of severe cases, divided by the probability that an infection resulted in a severe case, expressed as a proportion of the total number of people in that age group (with adjustment for rising titres). Confidence intervals based on this approach reflect uncertainty arising from the size of the study and do not reflect other sources of uncertainty such as the variability in the speed with which antibody titres rise and the overall percentage of individuals whose antibodies rise after infection (we assumed 100%). Therefore, our results may slightly underestimate the number of infections. Details are given in
In order to investigate alternate study designs and to validate our estimates of the rate of severe disease per infection, we developed a simulation of exactly the stochastic process assumed by the likelihood calculation above. For any given study protocol, we summed the number of actual severe cases between baseline and follow-up (for each individual, adjusting for rising titres) and then chose randomly between individuals being infected or not infected on the basis of the probability of severe infection. Thus, we could use the same likelihood framework to analyse results from the simulation studies as was used for the actual data.
Paired sera were obtained from 770 individuals living in 469 study households. Our response rates were (for households): 1.8% of all residential landlines selected (
The infection attack rate declined with increasing age. For those aged: 3–19, the attack rate was 39% (95% confidence interval 31%–49%); 20–39 y, 8.9% (5.3%–14.7%); 40–59 y, 5.3% (3.5%–8.0%); and 60 y or older, 0.77% (0.18%–4.2%). The attack rate in the oldest group had wide confidence intervals because only a single infection was observed in 131 participants. Differences in rates of seroconversion could not be explained by baseline titres, which were similar across age groups (
In order to fully capture the influence of age on the risk of infection, we used the Akaike Information Criterion (AIC) to compare three alternative regression models: 20-y age classes, AIC = 414.1; linear age, AIC = 413.0; and a restricted cubic spline model, AIC = 407.7. We considered spline fits with between 3 and 8 knots: the 5-knot curve was best able to explain the data. The fitted spline function corresponded well with age-based rolling average of infection incidence and shows: a sharp drop in risk of infection ages older than school age, followed by a plateau for middle ages, before another sharp drop for older adults (
(A) Shows the average probability of infection for median age (
The presence of a child in the household explained the plateau in the age-risk of infection in these data. We adjusted the spline model using a binary variable for the presence or absence of a child in the household and compared the shape of the odds ratio curve (
Risk Factor | ΔAIC |
Value | Univariate Models | Model A | Model B | Model C | ||||
OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |||
Age | 7.5 | 1 y old | 1.0 | — | 1.0 | — | 1.0 | — | 1.0 | — |
Per year older than 1 | 0.93 | 0.92–0.95 | 0.94 | 0.93–0.96 | 0.94 | 0.93–0.96 | 0.94 | 0.93–0.96 | ||
Child in house | 59.6 | Present | 1.0 | — | 1.0 | — | 1.0 | — | 1.0 | — |
Not present | 0.18 | 0.098–0.32 | 0.39 | 0.21–0.75 | 0.4 | 0.21–0.75 | 0.4 | 0.21–0.75 | ||
Sex | 101.9 | Female | 1.0 | — | — | — | 1.0 | — | 1.0 | — |
Male | 1.3 | 0.79–2.0 | — | — | 0.95 | 0.56–1.6 | 0.94 | 0.55–1.6 | ||
District | 96.4 | HK Island | 1.0 | — | — | — | 1.0 | — | 1.0 | — |
KLN East | 1.2 | 0.42–3.2 | — | — | 1.2 | 0.39–3.5 | 1.2 | 0.40–3.6 | ||
KLN West | 1.9 | 0.73–4.9 | — | — | 2.7 | 0.96–7.8 | 2.8 | 0.98–7.9 | ||
NT East | 3.1 | 1.4–6.9 | — | — | 2.6 | 1.1–6.2 | 2.5 | 1.1–6.1 | ||
NT West | 1.9 | 0.81–4.7 | — | — | 1.5 | 0.56–3.8 | 1.4 | 0.55–3.7 | ||
Vaccination 2008/2009 | 102.5 | Not vaccinated | 1.0 | — | — | — | 1.0 | — | 1.0 | — |
Vaccinated | 1.2 | 0.66–2.1 | — | — | 1.5 | 0.79–3.0 | 1.4 | 0.55–3.7 | ||
Recruitment | 99.8 | Direct | 1.0 | — | — | — | — | — | 1.0 | — |
Parallel | 1.5 | 0.95–2.4 | — | — | — | — | 1.3 | 0.77–2.2 | ||
Data for one individual missing vaccination status and for another two were missing education status.
AIC for individual univariate models relative to that of the best fit multivariate model A.
AIC for mutually adjusted multivariate models, relative to that of Model A.
CI, confidence interval; HK, Hong Kong; KLN, Kowloon; NT, New Territories (outlying islands included in NT West); OR, odds ratio.
We had substantive interest in six other potential risk factors, in addition to age and the presence or absence of a child in the household. In order to efficiently prioritize model selection, we calculated the AIC for all possible regression models (63 nonempty subsets of six risk factors) combined with linear age and the presence or absence of a child in the household. None of the 63 models had a lower AIC than model A. Only three additional risk factors appeared in models not substantially different from model A on the AIC scale (ΔAIC <3): district of residence, sex, and status for 2008/2009 influenza vaccination (
Residents of New Territories East had an increased risk of infection during the study period, even after adjusting for other risk factors of interest (
In order to control for possible bias from the two alternate sources of recruitment (the direct group or the parallel group), we included the source of recruitment as a possible confounder in a final mutually adjusted regression model, model C. This model scored slightly worse on the AIC scale than did model B (ΔAIC = 3.5), with only very minor changes in estimated odds ratios and confidence intervals. Although recruitment from the parallel group was associated with an increased risk of infection, the estimated odds ratio was not significantly different from unity.
We also considered a baseline titre of 1∶40 or greater as a risk factor for infection (despite this variable being a component of the outcome variable,
Individual-level data from the serological survey are provided as
Rates of reported symptoms were low, but varied substantially by definition and by mode of reporting (
Three different definitions of symptoms were used: ILI, acute respiratory infection, or fever (see main text for details). Symptoms were reported by one of: study participants phoning into the study phone line, by symptom diary, or at follow-up interview. We also report an all-inclusive rate: the percentage of seroconverters that reported symptoms by any of the three modes. 95% confidence bounds are based on the binomial distribution.
Reporting Method | Seroconverted | Nonseroconverted | ||||
Age Groups | Age Groups | |||||
41 (48%) | 45 (52%) | 57 (8%) | 627 (92%) | |||
Phone | 7 (17%) | 1 (2%) | 0.059 |
2 (4%) | 12 (2%) | 0.335 |
Diary | 9 (22%) | 3 (7%) | 0.122 |
3 (5%) | 10 (2%) | 0.093 |
Follow-up interview | 19 (46%) | 11 (24%) | 0.206 |
7 (12%) | 44 (7%) | 0.289 |
Any of the above source | 24 (59%) | 11 (24%) | 0.059 |
11 (19%) | 57 (9%) | 0.054 |
Phone | 9 (22%) | 2 (4%) | 0.052 |
4 (7%) | 27 (4%) | 0.327 |
Diary | 18 (44%) | 11 (24%) | 0.260 |
9 (16%) | 80 (13%) | 0.716 |
Follow-up interview | 24 (59%) | 26 (58%) | 0.888 |
26 (46%) | 192 (31%) | 0.143 |
Any of the above source | 31 (76%) | 26 (58%) | 0.539 |
29 (51%) | 204 (33%) | 0.084 |
Phone | 10 (24%) | 1 (2%) | 0.009 |
2 (4%) | 14 (2%) | 0.637 |
Diary | 9 (22%) | 5 (11%) | 0.387 |
5 (9%) | 20 (3%) | 0.059 |
Follow-up interview | 21 (51%) | 13 (29%) | 0.234 |
8 (14%) | 53 (9%) | 0.302 |
Any of the above source | 27 (66%) | 14 (31%) | 0.084 |
13 (23%) | 67 (11%) | 0.034 |
ILI is defined as fever (temperature of 37.5°C or above) + cough or sore throat.
Fisher's exact test.
Chi-squared test.
Defined as any two of fevere, cough, phlegm, sore throat, running nose, and myalgia.
Temperature of 37.5°C or above.
The overall rate of confirmed H1N1pdm-associated deaths was 7.6 (6.2–9.5) per 100,000 infections. Rates of severe disease increased with age (
(B) Estimated cumulative attack rates for infection up to the end of January 2010. Three separate estimates of cumulative infection attack rate are given for each age group, on the basis of the three levels of severity, with symbols as per (A). (C) Comparison of estimates of rates of ICU admission per infection from the current study (black triangles, as per (A)) with estimates of the same statistic from ten simulations of an alternate, nonbracketing, study design (see text).
Although we aimed to obtain bracketing sera (
We simulated an alternate trial design in which 500 samples were obtained from each of the four age groups during the week containing 1 June 2009 and the 500 follow-up samples were obtained from each age group during the week containing 30 September 2009 (
The main wave of the 2009 (H1N1) pandemic infected many more children than it did adults. These differences are not explained by baseline antibody titres to H1N1pdm, but could be explained partly by social mixing patterns of the population in these different age strata. However, given that social mixing patterns within the 20–60-y age range do not exhibit substantial variation
While older adults had low infection rates, those individuals infected developed severe disease much more frequently. Further, our results suggest that individuals over 60 y experience very high absolute rates of severe outcomes, with approximately one reported and positively tested death for every 200 infections. If continuing waves of H1N1pdm infection are driven by antigenic drift, and if that drift decreases the efficiency of the cross-protection currently possessed by older adults, it is likely that future waves could have higher overall mortality than initial waves. Surveillance of clusters of severe disease in older adults should be prioritized because this may be the first clear signal of a significant antigenic evolutionary event. The efficacy of alternate vaccine formulations in preventing infection in older individuals should be assessed as a matter of priority
The low rate of ILI reported by phone and symptom diary for seroconverters in this study is consistent with results from an independent parallel study of household contacts of children in Hong Kong
Our study has a number of limitations. Firstly, we did not measure incidence in children aged 2 and lower, who are much more likely to be admitted to hospital for acute respiratory infection than other age groups, but less likely to be infected with pandemic influenza than older children
In order to extrapolate from the period of our study to the full period of the pandemic in Hong Kong, we made the assumption that the testing process for individuals who became hospitalized was consistent. This is almost certainly not the case for all hospitalized individuals. In particular, anecdotal evidence suggests that less severe hospitalized cases were less likely to be tested for H1N1pdm after the end of September. Analysis of the rate of admission to ICU per positive hospital admission supports the anecdotal evidence (unpublished data). Therefore, it is reassuring that estimates of the overall and age-specific attack rates based on the three different outcome measures (hospitalization, admission to ICU, and death) are largely consistent.
We cannot exclude the possibility of substantial sampling bias in our serological survey. We were only able to successfully obtain paired sera from an average of 1.6 individuals in ∼2% of households initially identified by random telephone number selection. Although similar in many respects, after using common demographic characteristics to compare the study population with the wider Hong Kong population (age, sex, district, and education), we cannot exclude the possibility that individuals more likely to take part in our study had a different probability of infection than the population at large. However, we suggest that the potential impact of sampling bias in our results (and the value of evidence presented here in general) should be assessed on a result-by-result basis against the background of other reported community surveys of the 2009 influenza pandemic.
For the 2009 Hong Kong pandemic season, the current results add substantially to our earlier work
The current study, by recruiting from a wide age range using the same sampling framework and a paired sera outcome, allows us to add to the available literature in a number of other ways. We present important data on infection rates and severity for those aged 60 y and older that were not reported in our previous study
With good data on other potential risk factors for infection, we were able to show how the presence of a child in the household could explain an apparent age plateau in risk of infection, while variables such as education and profession did not appear to be risk factors once adjusted for age. This type of traditional risk-factor analysis is not possible with unlinked samples for which only the following variables are usually available: age, sex, and clinic location. Similarly, our analysis of home district (using only a single clinic location) suggests that micro-scale spatial heterogeneities persisted for longer than might have been expected in a large well-connected population. For the period of our study, residents of one district (New Territories East) appeared to be at substantially greater risk of infection than were residents of other districts. It is possible that the overall level of transmission was higher in that one district than in other districts, or that the epidemic occurred sooner there than it did elsewhere. Further, it seems possible that, in Hong Kong, spatial decorrelation took a long time to occur or never did occur. Individual-based models of respiratory infections, parameterized with the commuting patterns of adults
Our results can be compared with serology-based studies of influenza incidence in other populations during the 2009 (H1N1) pandemic
It is more difficult to compare our community-wide results with historical studies such as the Tecumseh
Our simulation results show that a larger paired-sera cohort study with a shorter follow-up period could have generated—more rapidly—similar data to those presented here. We suggest that this revised design would be a valuable addition to revised pandemic preparedness plans for a small subset of large well-connected global cities. Sentinel hospitals could be established in early-affected populations to help ensure that the testing process remains consistent for ICU cases throughout the epidemic curve. Given that (a) many believe the 2009 response to have been overzealous and (b) the severity of the next pandemic strain is not known, there appears to be a substantial risk that the public health impact of the next pandemic will be underestimated. Therefore, revised preparedness plans should prioritize reactive studies that can rapidly and reliably distinguish between 2009 (H1N1)-like strains (∼1∶10,000 infection fatality rate) and more severe pandemics. If the next pandemic strain were similar in all other respects, but had an infection fatality rate of ∼1∶1,000; we could reasonably expect peak demand on key health care services such as ICU to be ten times greater than that observed during 2009/2010
Individual-level data from the serological study (see
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The timing of recruitment and follow-up for all 770 individuals for which baseline and follow-up samples were available. A small amount of random noise was added to both the
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Flow chart of study recruitment. See
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Neutralization titres against H1N1pdm. The location of each pie chart indicates neutralization tire at baseline (
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Recruitment of households by random dialing and from parallel attitude's study.
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Characteristics of the study population compared with the population of Hong Kong.
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The relationship between age group, baseline neutralization titre, and seroconversion status.
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Dictionary of field names for
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A statistical model for severity
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We thank: Chung-Hei Chan, Maying Tse, and D Heenella Nawasinghage for laboratory support; Ray Cheung, Ching-Yi Chow, Cindy Nam-Nam Hung, Edward Yu-Hang Hung, Cheril KL Ng, Thomas KK Ng, Vivian Wei, Lai-Ying Wong, and Wyman WM Wong for data collection; Choi-Hing Ko, Jane Law, Anita WH Lee, Wing-Yan Lee, Winnie Lim, Ip Wan Ki, Selina Woo, and Eileen Lai-Fong Yu for nursing support; Marie Chi for administrative support; Public Opinion Program of the University of Hong Kong for recruitment; and Christl Donnelly for helpful discussions.
Akaike Information Criterion
H1N1 pandemic influenza
intensive care unit
influenza-like illness