Before the introduction of Prevnar-7, the rank order of serotypes causing invasive pneumococcal disease was relatively stable worldwide and over time, but the pneumococcal population has changed considerably now that conjugate vaccines are in widespread use. Serotypes that were less common before the introduction of Prevnar-7 have now increased in carriage frequency and have crept up among invasive pneumococcal disease isolates. It is not yet clear, however, whether disease caused by replacement serotypes will constitute a major concern in the future. In this study, we evaluated whether we could predict the rank order of serotypes among disease isolates before and after the introduction of Prevnar-7 using carriage prevalence data (or a correlate of carriage prevalence) together with estimates of invasiveness. The predictions fit the data from the pre-Prevnar-7 era, and in the post-Prevnar-7 era, the predictions had a good negative but poor positive predictive value.
One potential application of this approach would be to use the estimated invasiveness data to predict how different combinations of serotypes in carriage would affect disease burden. To do this, we used the pre- and post-vaccine carriage data from Massachusetts (2001 vs 2007). The data from these time periods were scaled so that the total numbers of carriage isolates in the two populations were equal, and we used these data in model 1 to predict the disease incidence for each serotype. We predicted that pneumococcal disease in children in the United States would have declined by 28% based solely on differences in invasiveness of the colonizing serotypes. We get similar estimates when we repeat this procedure with the post-vaccine Massachusetts carriage data together20
with scaled pre-vaccine carriage data from Norway25
(35% predicted reduction18
) or with two English carriage studies (28% predicted reduction or 30% predicted reduction using carriage data26
). These estimates in the reduction of pneumococcal disease are smaller than the 60% reduction in disease among hospitalized cases in the United States,9
but are consistent with the 40% reduction in the incidence of pneumococcal disease in children in England and Wales.27
These preliminary findings demonstrate how carriage data, either from population-based studies or vaccine trials, might be used to predict changes in disease burden.
A new 13-valent conjugate vaccine that targets the current Prevnar-7 serotypes as well as serotypes 1, 3, 5, 6A, 7F, and 19A has been licensed in North America and Europe. If effective, this vaccine, PCV13, will eliminate the most common causes of disease and will provide another opportunity to study serotype replacement and to evaluate the predictions of our model. While the incidence of different serotypes will be affected by numerous forces, such as cross-reactivity between serotypes (e.g. anti-serotype 6B antibodies affecting serotype 6A), the model might help to capture some of the microbiological factors that will influence the post-PCV13 bacterial population structure.
Our model does not account for all variation in the pneumococcal population structure, and this likely reflects the fact that other host and bacterial factors can affect the serotype distribution. Incorporating such factors into future models could improve the positive predictive value of these models. Differences in immunogenicity between serotypes, the immune status of the population, socioeconomic conditions, respiratory co-infections, antibiotic use, and host population structure may all influence the observed patterns of invasive pneumococcal disease incidence. Likewise, bacterial virulence factors other than capsule could affect the ability of a serotype to emerge as a cause of invasive pneumococcal disease. Serotype 1, for example, has a distinct geographic distribution of clones,28
and recent work has started to explore noncapsular, genetic factors associated with invasiveness.29,30
Additionally, some serotypes are known to exhibit long-term multi-year fluctuations in incidence.7
The reasons for such fluctuation are not fully known but could reflect shifts in the genetic background of the dominant serotypes. Because our approach focuses only on the role of capsule—a major determinant of invasiveness—we are not able to predict such fluctuations.
The results of our model further support the notion that the relative invasiveness of serotypes is a fixed property.2
The model was initially fit using invasiveness and carriage data from England, and we were able to substitute in carriage data from different locations to improve the country-specific fit without changing the invasiveness values.
Following the introduction of Prevnar-7, the serotypes that increased in prevalence were the most common non-vaccine serotypes before the vaccine. As a result, the pre-vaccine disease data is a strong predictor of post-Prevnar-7 serotype ranks. However, while such an approach might provide accurate estimates of post-Prevnar-7 serotype ranks in some situation, it would probably not be effective at predicting the post-PCV13 population structure because the vaccine will eliminate the majority of serotypes causing invasive disease in an unvaccinated population. Our model has the advantage that, in the absence of other information, we can make predictions about which of the remaining serotypes are more or less likely to emerge.
In the pre-Prevnar-7 era, we found that the predicted values based on country-specific carriage prevalence were more tightly correlated with invasive pneumococcal disease rank for that country. However, in the post-Prevnar-7 era, the predicted values calculated with England and Wales pre-Prevnar-7 carriage data were more tightly correlated with the US invasive pneumococcal disease data than were the predicted values calculated with the country-specific prevalence data. One possible reason is that fewer serotypes were detected in the US carriage pre-Prevnar-7 studies than were detected in the England and Wales study. As a result, it is impossible to distinguish between the rare serotypes and to predict which are more likely to increase after vaccination. The pre-vaccine US carriage data were collected during a single season in a single state, so it could be more likely to detect short-term fluctuations in prevalence. Additionally, subjects in the Massachusetts carriage study included only those who visited their pediatricians, and so the serotype distribution in this population might be biased.
Previous work from our group16
suggests that there could be a biologic explanation for the relative success of different serotypes in nasopharyngeal carriage. The microbial population is not simply a random assemblage of serotypes but is influenced by microbial characteristics that affect interactions with the host. Using the model with polysaccharide structure as a predictor, a single microbial factor, along with invasiveness, can be used to predict the serotype rank order in several regions.
For the polysaccharide structure model, we found a good negative predictive value for the model but poor positive predictive value. In particular, we would expect that serotypes such as 1, 3, 5, and 9A would be among the most common serotypes in pediatric invasive pneumococcal disease in the US, while in reality they were relatively rare. Types 1 and 5 are common in some parts of the world and were formerly important in the United States, but they are now rare in North America for unknown reasons.6,31,32
It has been suggested that the difference in relative frequencies of these serotypes between regions is due to difference in blood-culturing practices,33
although socioeconomic conditions, antibiotic use, differences in the predominant clones, or other factors could also contribute.31,34
The models presented here demonstrate that the pneumococcal population structure follows a somewhat predictable pattern, with the second model suggesting that this pattern is influenced by stable microbiologic factors. These approaches also provide a framework for evaluating how serotype composition in carriage could influence the disease burden in the population. The approaches could be used to evaluate potential consequences of future pneumococcal vaccines.