Conceived and designed the experiments: TT OE EAG MS HE. Analyzed the data: OE AS. Contributed reagents/materials/analysis tools: TT LV. Wrote the paper: TT OE AS EAS MS EH JE OD. Revised the manuscript and provided intellectual content: OE AS EAG MS EH JE OD LV HE.
At the time of the study, EAG was employed by ANSER (the non-profit section of the organization ‘Analytical Services Inc.’). This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427.000) to investigate whether age was a determinant for being diagnosed with influenza 2005–2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10–19 (p<0.001) and 20–29 years old (p<0.01) were found to be larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. An interaction between age and outbreak was observed (p<0.001) indicating a difference in age effects between circulating virus types; this interaction persisted for seasonal outbreaks only (p<0.001). The outbreaks also differed regarding when the age groups received their diagnosis (p<0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10–19 year olds, led outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. The validation analysis showed a strong correlation (r = 0.625;p<0.001) between the recorded numbers of clinically and microbiologically defined influenza cases. Our findings demonstrate the complexity of age effects underlying the emergence of local influenza outbreaks. Disentangling these effects on the causal pathways will require an integrated information infrastructure for data collection and repeated studies of well-defined communities.
A thorough understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures, e.g. early social distancing measures, risk communication, and vaccinations (WHO 2009). However, the relationship between age and disease transmission patterns within populations is difficult to measure. Viboud et al (2006) reported that working-age adults are responsible for the between-community transfer of influenza infection during outbreaks
Local surveillance is needed to assess community-level influenza activity, as mixing between regions appears to be too weak a variable to infer causality in the direction and timing of spread
This study uses an open cohort design to investigate the occurrence of differences between age groups with regard to the proportion of individuals receiving medical care for influenza and their comparative time of diagnosis during outbreaks. The study uses data from an electronic health data repository covering the entire population in a Scandinavian county. Clinical diagnosis of influenza is used as the case definition. Specifically, the aim is to investigate whether age was a determinant for diagnosis of influenza in the county during the period 2005–2010, either alone or in interaction with an epidemic outbreak caused by a particular circulating virus type. A secondary goal was to investigate if age was associated with the within-outbreak point in time of infection onset.
The study was performed in Östergötland County (population 427.000) (
Östergötland county consists of thirteen municipalities, of which two (Linköping and Norrköping) account for about two thirds of its population. A European highway and the main train connection between Stockholm and Copenhagen run across the county, which, outside urbanized areas, consists mainly of farmland. Employees at several large companies and one university situated in the county also use two local airports for business travel to international destinations. Comparing the demographic characteristics of Östergötland's municipalities with the corresponding statistics for the three metropolitan regions in Sweden (Stockholm, Västra Götaland, and Skåne counties; 108 municipalities), as well as the remaining seventeen counties (169 municipalities) (
For those individuals for whom seasonal influenza vaccination is found medically indicated (the elderly and immunosuppressed individuals), the vaccine is administered by a physician during an office visit free of charge. According to public health records, each year about 60% of the population above 64 years of age receives the seasonal influenza vaccine. The general population can get the vaccine for a charge of approximately $45 from their primary care centre. Unlike the seasonal vaccines, during the 2009 A/H1N1 outbreak the pandemic vaccine was provided free of charge to the general population as a part of a national mass vaccination campaign. This campaign was administered in supplementary mass vaccination sites at hospitals and public health clinics throughout the county.
The study design was based on administrative public health databases established for the purpose of systematically and continuously developing and securing the quality of service, and where according to Swedish legislation (SFS 2008:355) personal identification data had been removed from the records.
Two data sources were used for the study. Annual aggregated data on the sex, age, and residence (urban, rural) of the population were collected from Statistics Sweden and grouped into nine age groups (0–9 years, 10–19 years, etc. up to 80+years). Age and sex data from individuals clinically diagnosed with influenza were identified from the data repository connected to the electronic health record systems at Östergötland County Council
Descriptive statistical methods were applied to the clinical data to help represent influenza activity in the county during the study period. The Relative Illness Ratio (RIR), i.e. the ratio of the percentage of individuals with an influenza diagnosis in a given age group to the percentage of the general population belonging to the same age group, was computed for each age group and outbreak (circulating virus type) using the formula
In the next step of the analysis, a logistic regression analysis was carried out to compute whether the probability for an individual to be diagnosed with influenza was determined by the variables age and outbreak (circulating types of influenza virus); main effects and interactions between these. In this study, the analyses were structured to allow comparisons of coefficients and odds ratios with a neutral reference variable corresponding to a computed average. Two separate analyses were carried out, including and excluding the A pH1N1 outbreak in 2009, respectively, to examine what distinguished the pH1N1 outbreak from the seasonal influenza outbreaks. Finally, the outbreaks (circulating virus types) were tested in pairs to examine interactions between age group and outbreak with regard to the probability of being diagnosed with influenza.
To investigate whether the time of infection onset during outbreaks was determined by age, an analysis of variance (ANOVA) based on the day for diagnosis was performed in the subpopulation having received an influenza diagnosis. Mean differences in time of infection onset were then calculated for each age group. The A pH1N1 outbreak in 2009–10 had two peaks; separate analyses were performed for each of these. Finally, we investigated associations between the mean time of diagnosis and the RIR for the age group during the outbreaks. The correlation between age group effects in the analysis of time of diagnosis and age group regression coefficients in the analysis of proportion of individuals with an influenza diagnosis was calculated for all six outbreak peaks.
The level of statistical significance was set to p<0.05. To denote the strength of correlations, we used limit values suggested by the Cohen Scale
In a validation step of the analysis, the case data defined by clinical diagnoses was validated against case data from the microbiological laboratories. In these analyses, both data sets were separately adjusted for week-day effects on care resource utilization. The correlations between the number of cases reported each day in the clinical and laboratory data were analyzed with 0–6 day lag. Also, the age-related risk for receiving an influenza diagnosis was computed from both data sets and compared. The analyses were performed using Minitab Statistical Software version 16.1.1 (
Five influenza outbreaks with corresponding main circulating virus types were identified (
2006-01-01–2006-04-20 (circulating virus types B, A/H3 and H1N1),
2007-01-31–2007-04-11 (A/H3N2),
2008-01-21–2008-04-30 (B and A/H1),
2008-12-24–2009-03-30 (A/H3N2), and
2009-08-21–2009-12-22 (A pH1N1).
Influenza cases (ICD-10 codes 10.0–11.8) per day in Östergötland county 2005–2010. The influenza activity as accumulated into five outbreaks lasting between 2006-01-01–2006-04-20 (circulating virus types B, A/H3 and H1N1), 2007-01-31–2007-04-11 (A/H3N2), 2008-01-21–2008-04-30 (B and A/H1), 2008-12-24–2009-03-30 (A/H3N2), and 2009-08-21–2009-12-22 (A pH1N1).
The outbreaks differed with regard to intensity, i.e. the risk for residents to receive an influenza diagnosis. The highest intensity was recorded for the A H2N3 outbreak in 2008 (1.44 of the average risk (95% C.I. 1.30–1.60) and second highest intensity during the A pH1N1 outbreak in 2009–10 (1.23 (95% C.I. 1.08–1.40)). The lowest intensity (0.64 (95% C.I. 0.54–0.75)) was recorded for the mixed B, A H3, and A H1N1 outbreak in 2006.
Up to a ten-fold age-group difference in cumulative incidence of influenza cases was observed in the outbreaks recorded during the study period (
RIR-curves comparing the A pH1N1 outbreak in 2009 to the mean for the four seasonal outbreaks are displayed in
The RIR diagrams (95% Confidence Intervals) represent the A pH1N1 outbreak in 2009 and mean values for the seasonal outbreaks 2006–2010, respectively. * p<0.05 **p<0.01 ***p<0.001 ¤ Too few observations to allow statistical analysis.
In the logistic regression analysis that covered all five outbreaks and included combined terms, a statistically significant interaction (p<0.001) between age and outbreak (circulating virus type) was observed, indicating a difference between outbreaks (circulating virus types) regarding age effect on influenza morbidity. However, also when only seasonal influenza outbreaks were included in the analysis, an interaction was observed between age and outbreak (p<0.001). It was thus not the case that the risk associated with an age group was the same during the seasonal outbreaks. A pair-wise post-hoc analysis showed that the interaction between age and outbreak was statistically significant for all but one of the pairs, namely for the A H3N2 outbreak in 2007 and the B and A H1 outbreak in 2008. For all other outbreak pairs, the age effects on proportions of individuals diagnosed with influenza differed between the outbreaks.
There was a statistically significant difference between the outbreaks regarding when age groups received a diagnosis in relation to the mean for the outbreak (p<0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10–19 years old to lead the outbreaks with the A H1 type circulating virus (
Outbreak (influenza type) | ||||||||||||
2005–06 (B, A/H3 and H1N1) | 2006–07 (A/H3N2) | 2007–08 (B and A/H1) | 2008–09 (A/H3N2) | 2009 (pH1N1), 1st wave | 2009 (pH1N1), 2nd wave | |||||||
Age (yrs) | n | Mean day (95% C.I) | n | Mean day (95% C.I) | n | Mean day (95% C.I) | n | Mean day (95% C.I) | n | Mean day (95% C.I) | n | Mean day (95% C.I) |
|
66 | −13 (−18 –−7) | 76 | 0 (−6–5) | 56 | −6 (−12–0) | 85 | 1 (−4–6) | 11 | 1 (−10–12) | 70 | 1 (−4–5) |
|
76 | −21 (−26–−15) | 48 | 0 (−7–6) | 26 | −3 (−12–6) | 42 | −1 (−8–7) | 27 | −3 (−10–4) | 97 | −1 (−4–3) |
|
33 | −9 (−17–−1) | 46 | 2 (−5–9) | 39 | −1 (−9–6) | 62 | 1 (−5–6) | 33 | 1 (−6–7) | 69 | 0 (−4–4) |
|
43 | 3 (−4–10) | 82 | −3 (−8–2) | 106 | 1 (−4–6) | 123 | 0 (−4–4) | 51 | −1 (−6–4) | 96 | 2 (−1–6) |
|
42 | 6 (−1–13) | 76 | 1 (−5–6) | 101 | −6 (−11–−1) | 141 | 3 (−1–7) | 45 | −2 (−8–3) | 74 | 0 (−4–4) |
|
31 | −8 (−16–1) | 58 | −1 (−7–5) | 89 | 0 (−5–5) | 134 | 1 (−3–5) | 21 | 2 (−6–10) | 64 | −1 (−6–3) |
|
30 | −7 (−15–2) | 31 | 3 (−6–11) | 46 | 0 (−7–7) | 58 | 5 (−1–11) | 17 | 3 (−6–11) | 24 | −1 (−8–6) |
|
7 | 16 (−2–33) | 11 | −2 (−16–12) | 9 | 0 (−16–16) | 19 | −3 (−14–8) | 3 | 4 (−17–25) | 4 | 5 (−13–24) |
|
2 | 31 (−2–65) | 9 | 2 (−14–17) | 5 | 15 (−6–36) | 13 | −6 (−19–7) | 1 | −4 (−41–32) | 3 | −5 (−26–16) |
Outbreak (influenza type) | |||||||||||
2005–06 (B, A/H3 and H1N1) | 2006–07 (A/H3N2) | 2007–08 (B and A/H1) | 2008–09 (A/H3N2) | 2009 (pH1N1) 1st wave | 2009 (pH1N1) 2nd wave | ||||||
n | r (95% C.I.) | n | r (95% C.I.) | n | r (95% C.I.) | n | r (95% C.I.) | n | r (95% C.I.) | n | r (95% C.I.) |
330 | −0.80 (−0.96–−0.30) | 437 | −0.32 (−0.81–0.43) | 477 | −0.45 (−0.86–0.31) | 677 | 0.52 (−0.22–0.88) | 209 | −0.24 (−0.78–0.50) | 501 | 0.03 (−0.65–0.68) |
The validation analysis, where both data sets were separately adjusted for week-day effects, showed a strong correlation between the number of clinically diagnosed influenza cases per day and the corresponding number of cases verified daily by microbiological analyses during the validation period. The strongest correlation (r = 0.625; p<0.001) was observed between the clinically and the microbiologically verified cases with a 2-day lag. The risk of receiving an influenza diagnosis estimated from the clinical cases and the microbiologically-verified cases showed similar patterns with risk decreasing with age. In both data sets, a statistically significant difference was observed only between the three youngest and the two oldest age categories (
We found that the age group-related cumulative incidence of influenza cases differed both between the A pH1N1 and the seasonal outbreaks and in-between the seasonal outbreaks, and that the outbreaks differed with regard to when the age groups received diagnoses. There was modest correlation between the mean time of the diagnosis for an age group and its RIR during outbreaks. These findings exhibit the complexity of age effects on the proximal and distal causes in the emergence of local influenza outbreaks. Regarding the proximal causes, we did not collect data on individual-level social contacts or personal hygiene. However, assuming that the community remains socially stable and that the effect from differences in population immunity remains level over multiple outbreaks, exposure to infectious individuals stands out as perhaps the most important determinant of long-term influenza morbidity in the different age groups. Analogous to previous long-term studies
Consistent with a recent Canadian study based on microbiologically verified influenza cases
We observed that larger proportions of influenza cases were attributed to the ages 10–19 and 20–29 years old during the A pH1N1 outbreak than during the seasonal outbreaks, while the proportions of cases observed in the age groups 0–9 years, 50–59 years, and 60–69 years were larger during the seasonal outbreaks. Variability in influenza activity by age in a single community was early noted in a study by Monto et al.
Our study design still has several important shortcomings. First, the recorded influenza cases reflect only a small subset of the actual symptomatic cases, the majority of which is not expected to seek medical care
Apart from differences in the characteristics of the virus, several non-biological factors could account for differences in influenza surveillance data in the pandemic compared with the seasonal influenza outbreaks. These include public health organizations awareness, use of diagnostic methods, public knowledge of influenza influencing healthcare-seeking behavior, and greater sensitivity of health care professionals in pursuing a diagnosis of influenza. A particularly relevant consideration for our study refers to potential differences in vaccination coverage of different age groups of the general population between the pandemic and seasonal influenza periods. In the study county, the elderly were provided vaccine during the seasonal outbreaks, and the results of this study regarding disease incidence for these age groups should therefore be interpreted with care. For most of the A pH1N1outbreak examined in the context of this study, the specific influenza vaccine was not available, and the vaccination coverage of the general population was low. However, the degree in which these parameters could have differentially affected different age groups in the pandemic compared with seasonal influenza periods, and thus confounds our comparative analysis, is difficult to estimate.
In this open cohort study, we found an interaction between age and outbreak, indicating a difference between circulating virus types regarding age effects that persisted for seasonal outbreaks only; in particular, the proportion of cases from the age groups 10–29 years old was larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. In addition, there was a tendency for the young age groups, in particular the group 10–19 years old, to lead outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. We believe that these findings are generalizable to similar communities with a rectangular age structure. In designing future studies, researchers should carefully consider the role of age within the causal pathway in light of both social and behavioral factors and the biologic characteristics of the circulating influenza virus. The local community environment can modify the interaction between pathogen and host, sometimes influencing both proximal and distal portions of the pathway. For example, social factors, such as socioeconomic status, education and housing/neighborhoods may influence both the exposure to the virus and the probability of developing disease if exposed. Disentangling the age effects in these proximal and distal causal pathways is one of the most important challenges facing infectious disease epidemiologists: this will require an integrated information infrastructure for data collection and repeated studies of well-defined communities. Integration of investigative resources in space and time that enable epidemiological, also including seroconversion data, and prognostic (simulation) studies of the same communities are warranted. Such integrated studies would strengthen the knowledge we have on the occurrence and comparative timing of influenza infections in different age groups as a basis for developing community response and disease control measures.
Östergötland county population in numbers (percent) displayed by age group, gender, and area of residence.
(DOC)
Cumulative incidence of diagnosed influenza cases per 1000 persons population (95% confidence intervals) displayed by gender and level of care during outbreaks 2005–06 to 2009.
(DOC)
Validation of clinical case definitions. Odds ratios for receiving an influenza diagnosis relative to an average age class during the A pH1N1 outbreak in 2009 according to laboratory and clinical data sets.
(DOC)
Demographic comparison between Östergötland county, the Swedish metropolitan counties, and the rest of Sweden.
(DOC)
Details of statistical methods.
(DOC)