Pneumococcal conjugate vaccination has proved highly effective in eliminating vaccine-type pneumococcal carriage and disease. However, the potential adverse effects of serotype replacement remain a major concern when implementing routine childhood pneumococcal conjugate vaccination programmes. Applying a concise predictive model, we present a ready-to-use quantitative tool to investigate the implications of serotype replacement on the net effectiveness of vaccination against invasive pneumococcal disease (IPD) and to guide in the selection of optimal vaccine serotype compositions. We utilise pre-vaccination data on pneumococcal carriage and IPD and assume partial or complete elimination of vaccine-type carriage, its replacement by non-vaccine-type carriage, and stable case-to-carrier ratios (probability of IPD per carriage episode). The model predicts that the post-vaccination IPD incidences in Finland for currently available vaccine serotype compositions can eventually decrease among the target age group of children <5 years of age by 75%. However, due to replacement through herd effects, the decrease among the older population is predicted to be much less (20–40%). We introduce a sequential algorithm for the search of optimal serotype compositions and assess the robustness of inferences to uncertainties in data and assumptions about carriage and IPD. The optimal serotype composition depends on the age group of interest and some serotypes may be highly beneficial vaccine types in one age category (e. g. 6B in children), while being disadvantageous in another. The net effectiveness will be improved only if the added serotype has a higher case-to-carrier ratio than the average case-to-carrier ratio of the current non-vaccine types and the degree of improvement in effectiveness depends on the carriage incidence of the serotype. The serotype compositions of currently available pneumococcal vaccines are not optimal and the effectiveness of vaccination in the population at large could be improved by including new serotypes in the vaccine (e. g. 22 and 9N).
The bacterial pathogen Streptococcus pneumoniae (the pneumococcus) is a major cause of morbidity and mortality worldwide. Pneumococcal conjugate vaccines (PCV) were introduced over a decade ago and have proved highly effective in eliminating vaccine-type (VT) pneumococcal carriage and invasive disease (IPD) in countries where PCV has been included in the infant immunisation programme [1]–[3]. So far, three different PCVs have been in use: PCV7 (with vaccine serotypes 4,14,6B, 9V, 18C, 19F and 23F), PCV10 (additional serotypes 1,5 and 7F) and PCV13 (additional serotypes 3,6A and 19A). Despite the successes of PCVs against their respective vaccine types, the overall public health impact of pneumococcal conjugate vaccination remains less clear. In particular, the lost VT carriage has almost invariably been replaced by non-vaccine-type (NVT) carriage [4], [5]. Depending on the invasive potential of the serotypes involved, replacement in carriage leads to a varying degree of replacement in disease and may have undesirable implications on the overall pneumococcal disease burden in the population at large. As population-wide changes in serotype-specific carriage and disease will not fully emerge until several years after the onset of a vaccination programme [6], mathematical models are indispensable in predicting the implications of serotype replacement on the use of pneumococcal vaccines. To date, dynamic models have been used in describing pneumococcal transmission, serotype competition as the mechanism of replacement and the net effectiveness of vaccination [7]–[9]. In addition, statistical models utilising serotype-specific carriage data and estimates of invasiveness (probability of disease per carriage episode) have been applied to predict post-vaccination disease patterns [10]–[12]. These and other authors have pointed out the importance of the invasiveness of replacing non-vaccine types when assessing the net effectiveness of PCV vaccination [3], [4], [13]. In this paper, we elaborate the above ideas to develop a concise model for serotype replacement and present a ready-to-use tool for the prediction of patterns in post-vaccination pneumococcal incidence of carriage and disease, based solely on pre-vaccination data on carriage and disease. For a given vaccine composition, corresponding either to a current or a prospective vaccine, we show in detail how the net effectiveness of vaccination under serotype replacement depends on the invasiveness of the vaccine types relative to that of the non-vaccine types. We demonstrate how differences in the invasiveness across serotypes imply that the disease incidence may either decrease or increase after vaccination and introduce a sequential algorithm for the identification of the most optimal additional serotypes to current vaccine formulations.
The data on the prevalence of pneumococcal carriage in Finland originated from Syrjänen et al. [14] (<2 year olds), Palmu et al. [15] (4–5 year olds) and Leino et al. [16] (5+ year olds). The serotype distribution in carriage for under 5 years olds was based on data from <2 year old children in Finland [14] and for the rest of the population (5+ years) on data from England and Wales [4]. The UK data were available separately for the adolescent (5–19 year olds) and the adult (20+ year olds) populations. We combined these into a single set of serotype proportions corresponding to the 5+ age class by calculating the weighted averages of the proportions in the two age groups with weights 25% and 75%, respectively. The age- and serotype-specific annual average incidence of invasive pneumococcal disease (IPD) in 2000–2009 was retrieved from the National Infectious Disease Registry data (Finland). Both carriage and IPD samples were utilised on the serogroup level, except for PCV7 serotypes, for which carriage data on the serotype level were available. IPD samples with serotype 6A were re-analysed to distinguish between serotypes 6A and 6C [17]. This information was not available from the carriage samples. The proportion of 6C carriage among 6A/C carriers was assumed to be one third in both age classes [4], [18], [19]. The sensitivity of the results on this assumption was explored by assuming alternative proportions (0% and 50%). Our analysis of serotype replacement is based solely on age-specific serotype distributions in carriage and disease in the pre-vaccination era. Here, serotype distribution refers to the stationary (steady-state) distribution, assumed to be applicable in the pre-vaccination era or under a PCV programme with the current vaccine composition. We first consider a single age stratum within which the proportion of vaccine-type (VT) carriage does not have notable trends with age. The pre-vaccination incidence of carriage and disease with serotype i are denoted by ci and di, respectively. The case-to-carrier ratio ri is defined as the probability of disease per carriage episode, i. e. ri = di/ci. At the aggregate level of all vaccine types, the corresponding quantities are For the set of non-vaccine serotypes (NVT), the quantities CNVT, DNVT and RNVT are defined similarly. We note that the aggregate case-to-carrier ratios are weighted averages of the respective serotype-specific case-to-carrier ratios, the weights being the carriage incidences. In the applications of this paper, disease is equivalent to invasive pneumococcal disease (IPD), but the proposed model is applicable to any disease outcome. Our model of serotype replacement is built on two assumptions regarding the new steady-state after vaccination: (A1) the relative serotype proportions among the non-vaccine types are not affected by vaccination (proportionality assumption); (A2) the case-to-carrier ratios remain at their pre-vaccination levels. It follows from assumptions (1) and (2) that the case-to-carrier ratios remain at their pre-vaccination values also for the aggregate VT and NVT sets. Let q be the proportion of VT carriage eliminated by vaccination and p the proportion of eliminated VT carriage that is replaced by NVT carriage in the new steady state. Under assumptions (A1) and (A2), the disease incidence after vaccination is (for clarification, see Figure 1) The reduction in the disease incidence is thus (1) As RVT = DVT/CVT and RNVT = DNVT/CNVT, the following expression is equivalent to (1): (2) According to these, the disease incidence can either decrease or increase after vaccination. In particular, whether or not vaccination will be beneficial depends on the magnitude of the case-to-carrier ratio of the vaccine types compared to that of the non-vaccine types. According to equation (1), a reduction in the disease incidence requires the (average) VT case-to-carrier ratio (RVT) be larger than an (adjusted) NVT case-to-carrier ratio (p*RNVT). An alternative characterisation follows from (2), according to which a disease reduction requires the vaccine types possess a larger share of pre-vaccination disease than their (adjusted) share of pre-vaccination carriage. These characterisations are independent of the degree of elimination q. Of note, if the mean duration of carriage is the same for all serotypes, the above expressions are equally valid if carriage prevalences are used instead of the incidences CVT and CNVT.
Rewriting equation (2) for a single serotype i with q = 1, we obtain a criterion for the serotype whose addition to the vaccine would result in the largest decrease in the disease incidence. In particular, by substituting DVT = di (disease incidence of serotype i) and DNVT = D – di, where D is the total disease incidence, and likewise CVT = ci and CNVT = C - ci, equation (2) can be written as (3) The optimal serotype is the one that maximises expression (3). Clearly, the best single vaccine type is neither necessarily the one with the highest pre-vaccination incidence of IPD nor the one with the highest case-to-carrier ratio. In fact, the optimal single serotype may not correspond to the highest value of either of these two quantities. Importantly, the optimal type may be different for different age groups. Furthermore, if ci is equal (or close) to 0, the decrease in IPD incidence (3) is equal (or close) to di, i. e. the decrease is determined solely by the IPD of the serotype. Equation (3) can be applied sequentially to find the optimal vaccine with a given number of serotypes. At each step the current vaccine composition is supplemented by the best single serotype among those not already included in the vaccine. This is repeated until a desired number of vaccine serotypes is reached. In most cases this procedure can be expected to lead either to the maximum possible reduction in IPD or at least to a reduction very close to the maximum. Note that the optimal serotype composition is independent of the assumed level of VT elimination. If the elimination of VT carriage is expected to be incomplete (q<1) and proportion (1-q) *100% of VT carriage remains after vaccination, the projected incidences of carriage and IPD are obtained as weighted averages of the model projections assuming complete elimination and of the original incidences with weights q and 1-q, respectively. A program code implementing the tools proposed above, including instructions on how to use the code, is provided in File S1. The program is written in the R programming language, which is freely available.
As small changes in the VT/NVT carriage proportions may result in notable shifts in projected IPD incidences, uncertainties in carriage data should be accounted for using sensitivity analysis. When assessing the net effectiveness of vaccination with a given vaccine composition, the effect of a change in the VT/NVT carriage proportions is obtained directly from (2). To investigate the robustness of the optimal serotype composition, we calculated the order of inclusion of individual serotypes in the optimal vaccine composition for a large number of sets of carriage proportions, which were generated from an uncertainty distribution. For more details, see File S2.
We investigated the similarity of pre- and post-vaccination serotype proportions for the non-vaccine types, based on published data from three different locations [4], [10], [19]. This analysis shows that the proportionality assumption (A1) is solid as a general rule (see Figure S1).
We applied the replacement model to the pre-vaccination IPD incidence and carriage prevalence data to predict the post-vaccination IPD incidence in Finland.
We derived a simple expression for the expected net effectiveness of childhood vaccination against invasive pneumococcal disease (IPD) under serotype replacement. This expression depends only on the pre-vaccination incidences of vaccine-type (VT) and non-vaccine-type (NVT) carriage and disease. Our analysis explicates that vaccination will result in a notable reduction in the IPD incidence only if the average case-to-carrier ratio of the vaccine types clearly exceeds that of the non-vaccine types. In Finland, this would be expected to occur among children with any of the currently available PCV formulations. However, the same might not hold to the same extent in the general population not targeted by the vaccination, and our analysis indicates there are vaccine compositions with higher expected net effectiveness. These compositions are projected to be no worse than the current ones among children while clearly outperforming them in older age categories. We formulated the expected net effectiveness of vaccination in terms of serotype-specific incidences of carriage and disease. Equivalently, one could use either of the two quantities together with the case-to-carrier ratios (i. e. disease incidences divided by carriage incidences) as any two of the three quantities determine the third one. Importantly, while beneficial vaccine serotypes can be identified using carriage and disease data, they are not necessarily those with the highest carriage incidence, disease incidence or case-to-carrier ratios. The net effectiveness will be improved only if the average VT case-to-carrier ratio is larger than the average NVT case-to-carrier ratio. Furthermore, the above rule is not transparent, unless some serotype had the largest incidence of either carriage or disease and at the same the largest case-to-carrier ratio. In addition, a trivial rule applies in two special situations. First, if all serotypes have identical case-to-carrier ratios, as may be a good first approximation in case of pneumococcal otitis media, there is essentially no change in disease incidence. Second, if there is no replacement in carriage, the expected change in disease only depends on the pre-vaccination disease incidence. Stratification of carriage and disease data by age is essential in the analysis of replacement. For example, an individual serotype included in a vaccine may decrease IPD in one age category while increasing it in another (cf. serotype 6B among <5 and 5+ year olds in Finland; Figure 2). If an optimal protection for the whole population is of interest, such opposite effects across different age categories may be partly compensated by the inclusion of a sufficient number of serotypes. In particular, while the analysis shows that among the <5 year olds in Finland PCV13 cannot be much improved, the adverse effect among adults of including 6B in the vaccine could be compensated by including additional serotypes (e. g. 9N, 12,22). In practice, evaluation of optimal PCV vaccination for the whole population should be based on a cost-effectiveness analysis that takes into account health benefits and costs in the vaccine target population as well as in the older cohorts. Of note, all of the serotype compositions we consider refer to an infant vaccination programme assuming an adequate level of coverage of vaccination to induce a substantial herd effect in the whole population. The algebraic simplicity of our model is a direct consequence of the two key assumptions that neither the serotype proportions in carriage nor the invasiveness (case-to-carrier ratios) of the non-vaccine types are altered by vaccination. Examples of post-vaccination scenarios not covered by our model are a disproportionally large increase in carriage of a previously rare invasive type or an increase in invasiveness of a commonly carried type. Either of these scenarios would increase the IPD in excess of our model predictions. However, there is some empirical evidence in support of the key assumptions. In particular, our analysis confirmed the similarity in pre- and post-vaccination serotype proportions using four different datasets (Figure S1). The unchanged invasiveness of individual non-vaccine serotypes under vaccination is suggested by their relatively stable case-to carrier ratios in different populations pre-vaccination [3]. The pre- and post-vaccination incidences of carriage and disease involved in our method correspond to the respective stationary (steady-state) serotype distributions. These distributions can be characterised by the average annual serotype-specific incidences over a period where they do not manifest any systematic trends. The post-vaccination stationary distribution is typically achieved some years after the onset of a new infant vaccination programme [1], [2], [20]. Importantly, if post-vaccination data on IPD are available, the reliability of the model predictions can be monitored at an intermediate stage after the onset of a vaccination programme even before the post-vaccination stationary distribution has been reached. This is achieved by comparing the observed NVT disease reduction to the corresponding predicted reduction calculated under the assumption that the proportion of eliminated VT carriage q in the model equals the observed proportion of eliminated VT disease. There are further assumptions that may pose limitations on the applicability of the proposed method. An identical degree of elimination and replacement in all age classes, i. e. the same values of p and q in equation (1) irrespective of age was assumed. In reality some differences across age classes may exist, but at least VT elimination has been observed to be nearly complete regardless of age class due to a strong herd effect. Of note, while full elimination in VT carriage is assumed, assumptions regarding vaccine efficacy against disease are irrelevant. We assumed the long-term impact of vaccination on VT carriage is the same for all serotypes, i. e. complete or partial elimination. In addition, NVT carriage was assumed to be affected by vaccination only through replacement and vaccine-induced cross-protection was not included (for 6A, however, see Table S1). If differences between serotypes with respect to degree of elimination are assumed to exist, they could be taken into account in our model. Furthermore, as carriage data are typically available in terms of prevalences rather than incidences, also uncertainties regarding serotype specific differences in the duration of carriage affect the reliability of model predictions. Some serotypes are identified in carriage samples very rarely (e. g. serotypes 1 and 5). The current analysis suggests that the evaluation of rarely carried serotypes can rely solely on the disease incidence data. Poor availability or reliability of serotype-specific carriage data across all age classes may limit the applicability of the model. In particular, the predictions are typically sensitive to assumptions regarding the VT carriage proportion. However, carriage data on the adult population are often sparse. Overestimating the VT proportion among adults may lead to underestimation of the effectiveness of vaccination. We demonstrated this by using two alternative VT carriage proportions (53% and 62% for PCV10) in the non-target population (individuals 5+ years of age) in the context of existing PCVs (Figure 3). In practice, the similarity of the VT proportion among children (68% UK vs. 62% in Finland for PCV10) suggests that the VT proportion among adults in the UK (53%) should be a good approximation to the corresponding proportion in Finland. Several authors have previously discussed the importance of the invasiveness and the carriage incidences of the vaccine types relative to the non-vaccine types in assessing the net effectiveness of a vaccine [3], [4], [13]. Shea et al. [10] applied a replacement model similar to ours to predict the amount of pneumococcal acute otitis media following PCV13. Another line of related work is based on regression models utilising pre- and post-vaccination IPD incidences [11] or pre- and post-vaccination distributions of carriage [12]. Our results, however, appear to be the first to express the relationship between the invasiveness, the carriage incidences and the predicted IPD incidences in an operational manner that allows for the identification of optimal vaccine types in addition to calculation of their predicted effects if added to the vaccine composition. Moreover, our formulation enables an easy and explicit assessment of the role of age stratification in evaluating different PCV programmes. Based on whole-genomic sequencing data, Croucher et al. [21] suggest that serotype replacement in carriage manifests itself within sequence clusters so that VT strains are replaced by related NVT strains while the cluster specific prevalences are largely unaffected by vaccination. Of note, taking into account within sequence cluster replacement of this type in our model is analogous to the way we have above handled replacement within age categories. We have proposed tools for the quantification of the relative importance of individual pneumococcal serotypes in conjugate vaccine compositions under serotype replacement. Our examples used IPD data from Finland and carriage data from Finland and the UK. Contingent on the availability of data, our tools are easily applicable in other settings as well. However, in contrast to the relative succinctness of the underlying model, the data requirements for a successful application of the proposed tools are not straightforward to satisfy and underline the importance of the availability of age- and serotype-specific data on both pneumococcal carriage and disease.
The bacterial pathogen Streptococcus pneumoniae (pneumococcus) is a major contributor to child mortality worldwide. Hence, effective pneumococcal vaccination programmes are globally among the most cost-effective public health interventions. Three different conjugate vaccine compositions, targeting 7,10 or 13 pneumococcal serotypes, have been used in infant vaccination programmes. The use of these vaccines has both decreased the disease burden and changed the patterns of pneumococcal carriage in locations where they have been in use. However, due to serotype replacement, where the lost vaccine serotype carriage is replaced by carriage of the non-vaccine serotypes, the net effect of vaccination on the disease burden has generally been milder than expected. Here, we apply a concise model for serotype replacement and present a ready-to-use tool for the prediction of patterns in post-vaccination pneumococcal incidence of carriage and invasive disease. We introduce a sequential algorithm for the identification of the most optimal additional serotypes to current vaccine formulations and demonstrate how differences in the invasiveness across serotypes imply that the disease incidence may either decrease or increase after vaccination. The methods we outline have direct relevance in decision making while reviewing the performance of the current pneumococcal vaccination programmes.
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Optimal Serotype Compositions for Pneumococcal Conjugate Vaccination under Serotype Replacement