Cases of PCR-confirmed, medically attended influenza were identified from annual community cohorts based on residency in MESA. MESA is a contiguous geographic area surrounding Marshfield, Wisconsin, where nearly all 61,000 residents receive outpatient and inpatient care from the Marshfield Clinic Health System (Kieke et al., 2015). For each influenza season from 2007 to 2008 through 2017–2018, we identified MESA residents >6 months of age who received routine care from the Marshfield Clinic. These individuals were eligible for recruitment into that season’s VE study if they sought care for acute respiratory infection. Trained research coordinators recruited patients during clinical encounters in primary care departments, including urgent care, pediatrics, combined internal medicine and pediatrics, internal medicine, and family practice. Patients were enrolled on weekdays, evenings, and weekends when clinical services were provided. Research staff used an electronic appointment system to screen the chief complaints for respiratory or febrile illness. Patients were then approached in-person to assess eligibility based on specific respiratory symptoms and duration of illness. The proportion of patients with medically attended acute respiratory infection (MAARI) who were screened for enrollment varied by season and was largely determined by the volume of patients each day and staffing capacity. Only symptoms and illness duration were used to determine eligibility among those patients who were in the predefined cohort. Patients were also assessed for the presence of medical conditions that put them at high risk for complications from influenza infection, as defined by the Advisory Committee on Immunization Practice (Smith et al., 2006). These conditions included cardiovascular disease, diabetes, pulmonary disease, cancer, kidney disease, liver disease, blood disorders, immunosuppressive disorders, metabolic disorders, and neurological/musculoskeletal disorders. We considered subjects vaccinated if they received that season’s influenza vaccine ≥14 days before enrollment. For the 2009–2010 season, we only considered receipt of the 2009 monovalent vaccine. The Marshfield Clinic generally does not capture MAARI in nursing facilities with dedicated medical staff, causing undersampling of the oldest age groups. We adjusted for this (Appendix 1: ‘Age-specific rates of approachment, enrollment, and nursing home residence’).

Each season, recruitment began when influenza activity was detected in the community and usually continued for 12–15 weeks. Symptom eligibility criteria varied by season but included fever/feverishness or cough during most seasons. We retroactively standardized symptom eligibility criteria to only require cough as a symptom. Individuals with illness duration >7 days or presenting in an inpatient (hospital) setting were excluded. After obtaining informed consent, a mid-turbinate swab was obtained for influenza detection. RT-PCR was performed using CDC primers and probes to identify influenza cases, including type and subtype.

We defined the age distribution of each season as the number of cases of the dominant (more common) subtype in each of nine age groups (0–4 year-olds, 5–9 year-olds, 10–14 year-olds, 15–19 year-olds, 20–29 year-olds, 30–39 year-olds, 40–49 year-olds, 50–64 year-olds, and ≥65 years old). We excluded the subdominant subtype in each season due to concerns that short-term interference between the subtypes (Laurie et al., 2015; Goldstein et al., 2011) would affect the age distribution of the rarer subtype. The G-test of independence was used to measure differences in seasons’ age distributions.

To evaluate relative infection risk in different age groups, we measured their relative risk of infection in the first versus second half of each season. This risk is a combination of the chance of infection, conditional on infection (susceptibility), and the rate of contact with infected people. Attack rates should be higher in populations that experience more risk, and therefore these populations should be infected earlier in the epidemic (Worby et al., 2015). To calculate relative risk we used an approach similar to Worby et al., 2015. We defined the midpoint of each season as the week in which the cumulative number of cases of the dominant subtype among all people exceeded half the total for that season. Weeks before and after this point were assigned to the first and second half of the season, respectively. We assigned each case to one of the five age groups used by Worby et al., 2015 (0-4 year-olds, 5–17 year-olds, 18–49 year-olds, 50–64 year olds, and ≥65 years old). For each age group g, we defined relative risk as(1)Cfirst,t,gCsecond,t,g,where Cfirst,t,g and Csecond,t,g are the fraction of cases of the dominant subtype during influenza season t that occurred during the first or second half of the season, respectively. A relative risk >1 indicates that cases in an age group were more likely to occur during the first half of the season.

We hypothesized that the subtype of a person’s first influenza A infection affects their future susceptibility to that subtype. Testing this hypothesis requires knowing the probability that a person’s primary influenza A infection was with a particular subtype. To calculate these probabilities, we emulated the approach of Gostic et al., 2016, which assumes these probabilities are determined by a person’s year of birth and subsequent exposure to each subtype.

First, we calculated the probability that an individual born in year y received their first influenza A exposure in influenza season t. Assuming a constant per-season rate of infection i0, the probability of infection in one season (i.e., the attack rate) is given by(2)Pr(infection in single season)=1−e−i0.

By assuming that the average probability that a naive individual is infected in a single season is 0.28 (Bodewes et al., 2011; Gostic et al., 2016), we calculated the expected per-season infection rate (i0) as(3)0.28=1−e−i0,i0=−ln(0.72).

However, because the intensity of epidemics varies between seasons (It, Appendix 1: ‘Seasonal intensity’) and the fraction of the epidemic experienced by a person depends on their birth year (γy,t, Appendix 1: ‘Fraction of season experienced’), we considered the time-varying per-season infection rate,(4)iy,t=i0⁢It⁢γy,t.

Therefore, the probability that a naive individual born in year y is infected in season t is(5)ay,t=1-e-iy,t.

We used ay,t to calculate the fraction of a birth cohort y that received their first influenza A infection in season t. Let Uy,t be the fraction of people born in year y who were unexposed at the beginning of season t (Appendix 1: ‘Calculating the fraction unexposed’). The probability that a person born in year y has their first infection in season t is(6)Pr(first exposure in season t)=Pr(infected|unexposed)Pr(unexposed)=ay,tUy,t

We calculated ms,t,y, the probability that a person born in year y had their first influenza A infection with subtype s in season t, by multiplying ay,t⁢Uy,t by the frequency of subtype s in season t, ls,t (Figure 3—figure supplement 1),(7)ms,t,y=ls,t⁢ay,t⁢Uy,t.

We aimed to predict ps,t,y,v, the fraction of cases of subtype s in season t among people born in year y with vaccination status v. Our models assume that this is proportional to a combination of the following factors:

We defined models as specific combinations of the above factors. We tested a set of 10 models by pairing each of the possible implementations of HA imprinting with each implementation of VE (Figure 1). Demography, age-specific effects, and N2 imprinting were included in all these models. To test whether more complex models truly improved model fit, we also tested a simple model with constant VE and no effect of imprinting. We evaluated these 11 models by maximum likelihood and compared their performance using the corrected Akaike information criterion (cAIC, ‘Model likelihood’) and leave-one-out cross-validation.

Recall that our aim is to predict ps,t,y,v, the fraction of all PCR-confirmed influenza cases of dominant subtype s in influenza season t among people born in year y with vaccination status v. These fractions can also be interpreted as multinomial parameters that describe the probability that in season t, a medically attended influenza infection of subtype s occurs among people born in year y with vaccination status v. Each model M assumes that ps,t,y,v is proportional to a collection of model components j described above (demography, age, imprinting, and vaccination). Thus,(23)pM,s,t,y,v∝∏jϕM,j⁢ηj,s,t,y,v,where pM,s,t,y,v is a multinomial probability under model M, ϕM,j indicates whether model M contains component j, and ηj,s,t,y,v is the mathematical expression for model component j given s, t, y, and v (e.g., for HA subtype imprinting, ηj,s,t,y,v=1-hs⁢ms,t,y).

To obtain proper multinomial probabilities, we calculated a normalizing constant for each season t such that all probabilities in that season sum to 1. For convenience, let pM,s,t,y,v′=∏jϕM,j⁢ηj,s,t,y,v be the unnormalized multinomial probability for model M. Then for a specific season t, the normalized multinomial probability is(24)pM,s,t,y,v=pM,s,t,y,v′∑y′=1918ymax,tpM,s,t,y′,unvac.′+∑y′=1918ymax,tpM,s,t,y′,vac.′.where ymax,t is the maximum birth year possible for a specific season t.

To calculate the likelihood of a given model, we used the multinomial probabilities and the observed birth year distribution of cases. Let ns,t,y,v be the number of PCR-confirmed cases of dominant subtype s in influenza season t among people born in year y with vaccination status v. The total number of PCR-confirmed cases of dominant subtype s in season t is(25)Ns,t=∑y=1918ymax,tns,t,y,unvac.+∑y=1918ymax,tns,t,y,vac.

For models fitted to a restricted set of ages, we limited the cases for each season to the birth cohorts that were guaranteed to meet the age requirements in that season.

Then, the likelihood of model M in season t is given by the multinomial likelihood,(26)ℒM,t=Ns,t!⁢pM,s,t,1918,unvac.ns,t,1918,unvac.⁢pM,s,t,1918,vac.ns,t,1918,vac.⁢⋯⁢pM,s,t,ymax,t,unvac.ns,t,ymax,t,unvac.⁢pM,s,t,ymax,t,vac.ns,t,ymax,t,vac.ns,t,1918,unvac.!⁢ns,t,1918,vac.!⁢⋯⁢ns,t,ymax,t,unvac.!⁢ns,t,ymax,t,vac.!,

Finally, the full model likelihood for model M over all observed seasons is(27)ℒM=∏t=2007-20082017-2018ℒM,t.

We fitted the model to case data using the L-BFGS-B algorithm implemented in the R package optimx. We estimated 95% confidence intervals for parameters of the best-fitting model by evaluating likelihood profiles at 14 evenly spaced points and interpolating the entire profile using a smoothing spline.

The age distribution of cases varies between subtypes. The relative burden of cases is consistently higher in people ≥65 years old during H3N2-dominated seasons compared to H1N1-dominated seasons (Figure 2). The age distribution tends to vary more between subtypes than within either over time (Figure 2—figure supplement 1, off-diagonal quadrants). This is consistent with recent work showing that the ratios of H3N2 to H1N1 cases differ between age groups (Gagnon et al., 2018b).

The age distribution also varies within subtypes over time (Figure 2—figure supplement 1, diagonal quadrants). The seven H3N2-dominated seasons display three types of age distributions (Figure 2—figure supplement 1, clusters of lighter-colored cells in the upper left-hand quadrant), and two correspond to major antigenic clusters (2007–2008, Fonville et al., 2016; 2010–2012, Ann et al., 2012). These differences sometimes coincide with significant shifts in the age distribution between seasons. For instance, the highest fraction of H3N2 cases occurs in 20–29 year olds in the 2007–2008 season, but this age group has the lowest fraction of cases in the next H3N2-dominated season (2010–2011, Figure 2C). In H1N1, the shift from seasonal to pandemic strains is associated with large changes in the age distribution (Figure 2—figure supplement 1, lower right-hand quadrant).

We found further evidence that age groups differed in their susceptibility across seasons by examining the relative risk of infection during the first versus second half of each epidemic period (Materials and methods: ‘Calculating relative risk’). Individuals at greater risk of infection should be infected disproportionately early rather than late in an epidemic (Worby et al., 2015). We confirmed that an age group’s relative risk correlates with the fraction of cases within that age group in the same season (Pearson’s r = 0.58, 95% CI 0.38–0.73; Figure 2—figure supplement 2A; Appendix 1: ‘Correlation of relative risk and fraction of cases’). This trend is evident for H1N1 (Pearson’s r = 0.73, 95% CI 0.45–0.88; Figure 2—figure supplement 2A) and H3N2 seasons separately (Pearson’s r = 0.52, 95% CI 0.30–0.69; Figure 2—figure supplement 2A). The positive correlation in all seasons is robust to undersampling of cases at the start and end of seasons (Appendix 1: ‘Sensitivity to sampling effort’, Figure 2—figure supplement 2B). This provides supporting evidence that the different numbers of cases in each age group reflect underlying differences in infection risk.

Just as the age distribution of cases varies over time, the age groups with high relative risks of infection change over time. If people contact one another similarly from one season to the next, these shifting relative risks imply that age groups’ relative susceptibilities change over time. For instance, 5–17 year olds had the highest relative risk of early infection in the 2008–2009 season, whereas 50–64 year-olds had the highest relative risk in the 2013–2014 season (Figure 2—figure supplement 3). Relative risks in MESA vary more than national estimates, which show that 5–17 year-olds had the highest relative risk in all but one season from the 2009 pandemic to 2013–2014 (Worby et al., 2015). These differences may partly be due to the fact that our measurements of relative risk use outpatient visits, whereas the national estimates use hospitalizations.

Taken together, these findings suggest that the risk of influenza infection is not a simple function of age alone. Other factors, such as past influenza infections and vaccination, might explain the changing age distributions of cases in time.

We hypothesized that variation in the age distribution of cases could be explained by the aging of birth cohorts with similar early exposure histories. This would cause the early exposure history of an age group, and thus potentially its susceptibility, to change in time. To calculate the probability that people in a particular age group had their first influenza A infection with a particular subtype, we adapted the approach from Gostic et al., 2016. Briefly, we calculated the probability that an individual born in a specific year had a primary infection with H1N1, H2N2, or H3N2 using data on relative epidemic sizes and the frequencies of circulating subtypes (Figure 3—figure supplement 1; Materials and methods: ‘Calculating imprinting probabilities’).

As expected, age groups’ early exposures are not static and change over time (Figure 3). Older people nonetheless tend to be imprinted to H1N1 or H2N2, whereas younger people have higher probabilities of imprinting to H3N2. The effects of the 2009 H1N1 pandemic are evident in the three youngest age groups as a transient increase (from 2009 to approximately 2013) in their H1N1 imprinting probability. These imprinting probabilities are relatively well-constrained even after for accounting for uncertainty in epidemic size (Figure 3—figure supplement 2; Appendix 1: ‘Sensitivity to uncertainty in ILI and the frequency of influenza A’).

We fitted models to estimate the underlying effects of age, early infections, and vaccination on the age distributions of cases. As expected, the cases reveal age-specific differences in the risk of medically attended influenza A infection (Figure 4; Figure 4—figure supplement 1; Appendix 2—table 1). This risk is roughly threefold higher among children <4 years old compared to adults 20–29 years old, after adjusting for other effects (Figure 4). The decline in risk through middle age is generally consistent with attack rates estimated from serology (Monto et al., 1985; Bodewes et al., 2011; Wu et al., 2010; Huang et al., 2019) and clinical infections (Wu et al., 2017). We recently observed smaller differences in the attack rates of school-aged children and their parents when estimating infections serologically (Ranjeva et al., 2019). We hypothesize that the attack rates estimated from clinical infections might show larger differences by age due to age-related changes in infection severity and healthcare-seeking behavior. Indeed, rates of healthcare-seeking behavior have been shown to decline with age before rising in adults ≥65 years old (Biggerstaff et al., 2014; Brooks-Pollock et al., 2011; Van Cauteren et al., 2012), consistent with our results. Finally, the increased risk of medically attended influenza A infection among people ≥65 years old compared to other adults may be related to the increasing prevalence of high-risk medical conditions with age (Figure 1—figure supplement 4).

Our best-fitting model supports subtype-specific imprinting for H1N1 and H3N2 (Figure 5, top row; Appendix 2—table 1). This model also provides the best predictive power compared to other models in a leave-one-out cross-validation analysis (Figure 5—figure supplement 1; Figure 5—figure supplement 2; Appendix 1: ‘Evaluation of predictive power’). The risk of future medically attended infection by H1N1 is reduced by 66% (95% CI 53–77%) in people imprinted to H1N1, whereas the risk of future medically attended infection by H3N2 is reduced by 33% (95% CI 17–46%) in people imprinted to H3N2. We found no evidence of a protective effect from imprinting to N2 (0%, 95% CI 0–7%). These estimates of imprinting protection are insensitive to:

In theory, the estimated protective effects of imprinting could be influenced by cross-protection rather than the impact of first infection per se. Because first infections are also recent infections in children, we reasoned that the observed imprinting effects might arise from confounding with recent infections in these ages. Based on an estimated 7 year half-life of homologous protection after H1N1pdm infection in children (Ranjeva et al., 2019) and the fact that most children experience primary influenza A infection by 5 years of age (Bodewes et al., 2011), we reasoned that excluding children <15 years old would diminish the impact of protection from recent infection on our results. When we excluded the youngest age groups, our estimates of H1N1 imprinting protection decreased while H3N2 imprinting protection increased (Figure 5, second row). However, initial infection by H1N1 was still more protective than initial infection by H3N2, both imprinting effects remained positive, and there was no significant change in the values of other estimated parameters (Appendix 2—table 1 and Appendix 2—table 2).

The effects of recent infection should also manifest in the difference between the observed and estimated numbers of cases (i.e., the excess cases, Appendix 1: ‘Calculating excess cases’), since unlike typical transmission models, our model does not take prior-season infections into account when estimating cases for the current season. More infections in a birth cohort in one season should reduce susceptibility in that birth cohort at the start of the next season. We thus expect that excess cases in one season will be followed by missing cases in the next season dominated by that subtype (i.e., a negative correlation in excess cases). Instead, we observed that excess cases for each birth cohort are weakly positively correlated from season to season, suggesting that immunity from recent infections is not a major driver of temporal variation in the age distribution of cases (Figure 5—figure supplement 5).

Since older adults have the highest probability of primary infection with H1N1, we also reasoned that older adults might disproportionately drive the strong protection from H1N1 imprinting we observe. People born before 1947 were likely exposed to H1N1 strains that are antigenically similar to the post-pandemic H1N1 strains that comprise most of our H1N1 infection data (Manicassamy et al., 2010; O'Donnell et al., 2012), creating the possibility that strain-specific cross-immunity drives the pattern we attribute to subtype-specific imprinting. These people nearly all fall into the ≥65 year-old age group in the study period. The study also underenrolled medically attended infections among people in nursing facilities, which would artificially lower the case count in this age group and may affect estimates of imprinting protection. Therefore, we excluded adults ≥65 years old and refitted our models. Excluding the oldest adults does not significantly change estimated imprinting protection or other parameters (Appendix 2—table 1 and Appendix 2—table 2).

When we exclude both the youngest and oldest age groups, initial infections by H1N1 and H3N2 have similar protective effects (Figure 5, bottom row). This shows that the combined effects of cross-protection in both the youngest and oldest individuals contribute to the signal of imprinting protection we observe, but they are not its sole drivers.

The best-fitting model includes age-specific VE (Figure 4—figure supplement 1; Appendix 2—table 2). While serological responses to influenza vaccination are weakest in the young (Englund et al., 2005; Neuzil et al., 2006) and old (Lee et al., 2018; DiazGranados et al., 2014), it is unclear what age-related factors would drive variation in VE in other age groups. We hypothesized that VE in these ages varies with early exposure history, which correlates with birth year, rather than age.

To test this hypothesis, we fitted a model with birth-cohort-specific VE to the cases, excluding either children <15 years old or adults ≥65 years old. We chose birth cohorts that corresponded to the age groups of the original model in 2017–2018 (Materials and methods: ‘Vaccination’), keeping the number of parameters the same (e.g., VE in the 20–29 age group became VE in the 1988–1997 birth year cohort). We find that age-specific VE still outperforms all other models after we exclude the oldest age group (≥65 years old). In contrast, birth-cohort-specific VE performs better when we exclude children <15 years old (Figure 6—figure supplement 1). Estimates of imprinting protection and age-specific risk of medically attended influenza in the birth-cohort-specific VE models are not significantly different from estimates from the best-fitting model fitted to all ages (Appendix 2—table 1). Taken together, these results suggest that birth-cohort-specific VE best explains the case distribution in older children and adults, who have likely experienced their first influenza infection, whereas age-specific VE best explains cases in younger children, who have less influenza exposure.

VE differs between birth cohorts that have similar imprinting by subtype (Figure 6; Appendix 2—table 5). For example, the 1968–1977 and 1988–1997 cohorts have similar probabilities of primary exposure to H1N1 and H3N2, but they differ substantially in their VE to both subtypes (Figure 6). The 1988–1997 and 1998–2002 cohorts also have similar probabilities of primary exposure to each subtype and have similar H1N1 VEs, but have significantly different H3N2 VEs (Figure 6). Antigenic differences within each subtype might explain this variation.

The best-fitting model accurately reproduces the age distributions of vaccinated and unvaccinated cases of each subtype, aggregated across seasons (Figure 7A). The only exception is that it underestimates aggregate H1N1 cases in unvaccinated 5–9 year-olds. By examining the differences between predicted and observed cases for each season, we see that this is largely driven by infection during the 2009 H1N1 pandemic (Figure 7B). Such a large antigenic change may have negated any protection from previous infection in 5–9 year-olds and made them particularly susceptible to pandemic infection.

The model underestimates cases in unvaccinated individuals who were 30–39 years old and over 50 years old in the 2013–2014 season (Figure 7B), as indicated by the many excess cases in these age groups in that season. This is further evidence that subtype-specific imprinting cannot explain all age variation. As mentioned before, this season provided one of the first examples that original antigenic sin could affect protection: middle-aged adults had been targeting a familiar site on the pandemic strain that then mutated, rendering them susceptible. Other age groups were effectively blind to these changes, owing to their different exposure histories (Linderman et al., 2014; Huang et al., 2015; Arriola et al., 2014; Dávila et al., 2014; Petrie et al., 2016).

The code and data used to perform the analyses for this project are available at https://github.com/cobeylab/FluAImprinting (Arevalo et al., 2019; copy archived at https://github.com/elifesciences-publications/FluAImprinting).