This paper examines prioritization for vaccine allocation in a declining influenza epidemic. It formulates conditions under which an initial campaign to vaccinate individuals with a high risk of mortality from influenza is preferable to vaccinating a core group, like children, which has a relatively low risk of mortality but fuels transmission in the community. It is shown how those conditions can be validated in real time under a range of uncertainties in the estimates of certain quantities related to the epidemic's progression and VE. We note that, for an emerging epidemic, priority for vaccine allocation with the goal of minimizing the overall mortality burden may go to school-aged children rather than adults with underlying health conditions [9
A basic source of uncertainty in applying the prioritization criterion is the need for an estimate of vaccine efficacy against fatal outcomes in various high-risk groups. Although we are not aware of data assessing the above efficacy, several studies estimating the immunogenicity and efficacy against infection in different high-risk groups for the vaccine against the 2009 A/H1N1 influenza have been published. A case–control study has found poor immune response to vaccination in haemodialysis patients [23
]. Some observational data are now available on vaccine efficacy against infection among the high-risk groups for the 2009 H1N1 influenza pandemic [24
]. These data suggest that vaccine efficacy against infection is lower in high-risk individuals than in healthy children. However, observational studies of influenza vaccine effectiveness may be subject to significant residual confounding, especially among high-risk persons [27
]. Moreover, vaccine efficacy against fatal
outcomes among high-risk adults might be different, either affirming the rationale behind their prioritization as indicated by our approach or suggesting that low efficacy in direct protection of high-risk adults makes prioritization of school-aged children advisable because of the effect of vaccinating children on the epidemic dynamics and mortality in the whole community. Another source of uncertainty related to the impact of vaccination is the potential detrimental short-term effect that vaccination might have on susceptibility. Data from Emborg et al.
] suggest a negative and statistically significant vaccine efficacy against infection occurring within a week from vaccination. Given that, in a declining epidemic, a sizeable fraction of future infections are likely to occur within a fairly short time, such an effect may take away from the benefit of vaccinating high-risk individuals, although it may also have an impact on vaccinating children.
Another potential source of uncertainty in prioritizing high-risk adults is the feasibility of a timely implementation of a vaccination campaign for those groups. For children, rapid administration of a vaccine is possible through school-based vaccination drives. High-risk adults might be a harder target group to reach, with past efforts concentrated on their healthcare providers [28
]. A combination of risk awareness and a speedy distribution framework is needed to ensure that vaccine allocation to high-risk adults would not lag significantly behind an alternative of administering the corresponding vaccine quantity to children.
Besides vaccine efficacies in different population groups, the key quantities needed to ascertain our prioritization criterion are the epidemic's decline rate and the impact of vaccinating the core group (children) on the epidemic's reproductive number. The decline rate can be estimated from surveillance data, such as those collected by the CDC [13
], using a proxy for influenza incidence described in Goldstein et al.
]. The impact of vaccinating the core group on the reproductive number can be gauged following the method in Wallinga et al.
]. That method requires knowledge of the relative susceptibility and infectivity in different population groups, and the epidemic's incidence curve stratified by those groups. Data on relative susceptibility and infectivity in different age groups can be obtained from household studies and may, in principle, become available in real time; we have borrowed the estimates from Cauchemez et al.
], which was published at the end of 2009. We did not have a good estimate of the epidemic's incidence curve; instead, we have adapted the method in Wallinga et al.
] to give an upper bound on the impact of vaccinating children using the final attack rates in different age groups, extrapolated from Zimmer et al.
]. We want to point out that, in principle, it is possible that better surveillance data for the epidemic's progression can produce sharper, real-time estimates of the impact of vaccinating children. Such surveillance data can come from serial serological data [31
], or from age-stratified data on ILI and respiratory specimen testing, combined with a real-time serological study, or possibly from syndromic data [32
]. The practical aspects of obtaining such data in real time remain uncertain [33
An assumption we make in our approach is that, as time progresses, depletion of susceptibles owing to natural infections in high-risk groups is slower than the depletion of susceptibles in the whole population (particularly among children and the young adults); thus, the relative risk for the unvaccinated subgroup of a high-risk group increases with time. The role of young individuals during the early stages of an epidemic and their subsequent depletion is known [8
], and evidence for the decline of the relative share of the young individuals among the infected can be seen in the ILI data [14
]. More evidence for the assumption on the increasing relative risk of severe outcomes in the high-risk groups is provided by Flu.Gov [3
]. However, those considerations need not imply that relative risks in each high-risk group increase with time. Children and young adults represent a small fraction of fatal cases, and adults in certain high-risk groups might be more susceptible to infection than other adults in the corresponding age groups, experiencing a larger initial depletion of susceptibles and correspondingly lower relative risk of fatality in later stages of an epidemic. To assess this issue, one can measure the share of individuals with a particular underlying health condition for a certain recorded outcome, e.g. hospitalization, in different age groups. Changes in their relative share through time should give an indication about the change in their relative risk of fatality compared with the early stages of the epidemic.
Yet another assumption in our approach is that the impact of further vaccination and depletion of susceptible individuals is stronger than the impact of seasonality or genetic changes affecting the transmissibility of the virus. For the 2009 H1N1pdm this assumption with regard to seasonality was violated in southeastern USA owing to little willingness in the population to get vaccinated when vaccine was widely available. It is reasonable to assume that for a more pathogenic strain this factor would play a lesser role. At the same time, the decline rate of the epidemic in the southeast was low and a wintertime resurgence owing to seasonal forcing could be predicted [34
]. Moreover, under the approach of this paper, such a low decline rate would justify prioritization of risk groups whose relative risks for fatal outcomes are higher than those existing in the published data.
Some regions of the USA, such as New England, had high H1N1pdm vaccination rates and a decline in the epidemic which was sustained through the winter. We assess our criterion for New England and specify the high-risk groups which should have been initially prioritized for vaccination over school-aged children, under certain assumptions on vaccine efficacy against mortality in those high-risk groups. We note that there is a wide range of uncertainty in the available data for the 2009 H1N1 pandemic with regard to the estimates for the relative risk of mortality for individuals with various underlying conditions, as well as for the attack rates and the relative susceptibility and infectivity in the different population groups. More detailed epidemiological data would reduce the above uncertainties, and correspondingly the uncertainty in our conclusions for vaccine prioritization.