Invasive pneumococcal disease caused by Streptococcus pneumoniae (pneumococcus) is a major cause of morbidity and mortality worldwide. There are at least 92 known serotypes, which differ in their nasopharyngeal carriage prevalence,1 their tendency to cause invasive disease,2 and the severity of disease.3,4
Until 2000, when the heptavalent pneumococcal conjugate vaccine (Prevnar-7 or PCV7) was introduced to the US childhood immunization program, the overall rank order of serotypes causing invasive pneumococcal disease was similar in locales worldwide, although the prevalence of specific serotypes varied over time, and some serotypes exhibited distinct geographic patterns.5–7 Prevnar-7 targets 7 clinically-important serotypes, and it has successfully reduced the burden of pneumococcal disease in areas where it is widely used.8,9 However, serotypes not covered by the vaccine have filled in the vacated niche, leading to an increase in carriage of these other serotypes.10 Consequently, nonvaccine serotypes have also increased in disease,8,9,11,12 although not enough to offset the benefits of vaccination. Given the possibility that serotype replacement could reduce the overall public health benefit of the vaccine, it is important to understand how the bacterial population might change following vaccination.
We use regression models to evaluate which serotypes are most likely to increase in disease following vaccination, and we discuss some potential applications of these approaches.
The incidence rate of invasive pneumococcal disease caused by a given serotype is the product of the prevalence of nasopharyngeal carriage of that serotype and its invasiveness,13 defined here as the proportion of carriage episodes (or, more accurately, new acquisitions13) that result in disease. An ideal model would use invasiveness and carriage prevalence data to predict the disease incidence for each serotype. Because direct estimates of pediatric invasiveness are unstable and available for a relatively small number of serotypes, we first used a known correlate of invasiveness—adult case-fatality ratio (CFR)14,15—to generate estimates of invasiveness for 37 serotypes. We combined the regression estimates of invasiveness with carriage prevalence data from England to fit a model to invasive disease data from England and Wales, and evaluated its performance in various settings. This first model effectively captured the observed serotype distribution in different locations, although there remained substantial unexplained variation in the data. We next fit an alternative model to the prevaccine disease data that used estimated invasiveness along with a correlate of carriage prevalence—number of carbons per polysaccharide repeat unit.16 This second model had a poor positive predictive value, but a good negative predictive value, demonstrating that a single fixed biochemical determinant has an association with the importance of a serotype in invasive disease. Although there is still a large amount of unexplained variation in the disease data, the models presented here demonstrate that the distribution of pneumococcal serotypes follows a partially predictable pattern.
Sources of Data
Recent data on pediatric invasive pneumococcal disease cases (less than 5 years old) from the pre- and post-Prevnar-7 eras were obtained from the Health Protection Agency of England and Wales and the Active Bacterial Core surveillance system of the United States Centers for Disease Control and Prevention (Table 1).17 For the English data, we focused on the epidemiologic year prior to the introduction of the vaccine (2005/2006) and the most recent epidemiologic year from the postvaccine era (2008/2009). With the US data, we focused on the 2 years prior to the introduction of Prevnar-7 (1998–1999) and the most recent 3 years from which data were available after vaccination (2005–2007). Data for serotypes 6A and 6C were combined together and classified as “6A” for all analyses because not all of the available studies distinguished between them. Pediatric carriage prevalence data from a prevaccine English study18 was used to fit the model. We also sought to evaluate whether using country-specific carriage data would improve the predictions, so we obtained data both on pediatric carriage and invasive disease of serotypes. We used prevaccine carriage and disease data from Kenya (carriage data, Abdullahi et al19; disease data, unpublished), and pre- and postvaccine carriage data from Massachusetts.10,20 In the carriage datasets from the United States and from England and Wales, serotypes 15B and 15C were not distinguished and are known to switch back and forth,21,22 so we divided the number of 15B/C cases equally between these 2 serotypes. All carriage prevalence data were natural-log transformed, with serotypes not detected in carriage assigned a value of 0.5 prior to transformation.
We obtained data on adult case-fatality ratio from a large Danish study of adult invasive pneumococcal disease (Brueggemann et al,2 and unpublished data). The case-fatality ratio was calculated for all serotypes that caused at least 20 cases of invasive pneumococcal disease. Polysaccharide structural data (carbon/repeat unit) was obtained from published reports as described previously.16 Missing polysaccharide structural data for serotypes 23A and 35F were filled in using the mean values for other serotypes in their respective serogroups.
Invasiveness estimates were obtained from Sleeman et al13 and log-transformed prior to use. These invasiveness estimates were considered only for serotypes that had at least 1 carriage and 1 disease isolate detected. Because invasiveness estimates were available for a limited number of serotypes, linear regression using the case-fatality ratio as a predictor of invasiveness was then performed. The results of the regression were then used to generate estimates of invasiveness for all 37 serotypes that had case-fatality data, including serotypes for which there was no direct estimate of invasiveness using the following formula:
Estimated invasiveness = exp [−0.12 × (case-fatality %) + 5.17]
The estimates generated by this regression were then used for all serotypes in downstream analyses, regardless of whether direct estimates were available.
Prediction of the relative abundance of serotypes among invasive pneumococcal disease isolates.
We first fit a log-linked negative binomial regression model to invasive pneumococcal disease count data from England and Wales, using log-carriage prevalence and log-estimated invasiveness as predictors. We considered only the 37 serotypes for which we estimated invasiveness using case-fatality ratios. The resulting model was as follows:
We also used the same model, with the same parameters calculated for England and Wales, to predict serotype frequencies for the United States and Kenya, replacing English carriage data with country-specific carriage data.
We next fit an alternative model that used estimated invasiveness together with a correlate of carriage prevalence—number of carbons per capsular polysaccharide repeat unit. Again, we used log-linked negative binomial regression and fit the model to pediatric invasive pneumococcal disease data from England and Wales, analyzing the 35 serotypes for which we had data on both estimated invasiveness and polysaccharide structure. The resulting model was as follows:
Evaluation of the Fit of the Models
We were primarily interested in the ability of Models 1 and 2 to predict relative contributions of serotypes to invasive disease. Spearman rank correlations were used to assess the relationship between the predicted number of invasive pneumococcal disease cases and the observed. Confidence intervals (CIs) for the correlation coefficients were determined using Fisher's transformation using the ci2 module in Stata. To further evaluate the ability of the model to predict the relative contribution of serotypes, we calculated the proportion of invasive pneumococcal disease caused by each serotype in a given population, and then generated curves that represented the cumulative proportion of invasive pneumococcal disease for each serotype based on the rank order predicted by the models. Predicted curves with a larger area under the curve (closer to the observed line) represented more accurate predictions.
All statistical analyses were performed using Intercooled Stata, version 9.2 (StataCorp, College Station, TX).
Estimation of Pediatric Invasiveness Using Case Fatality Ratio
To obtain estimates of pediatric invasiveness for a large number of serotypes, we first performed linear regression between pediatric invasiveness and adult case-fatality ratio. As reported elsewhere,23 we found a moderate association between these 2 variables for the 20 serotypes for which we had data (linear regression, r2 = 0.58; eFigure, http://links.lww.com/EDE/A448). We then used the regression coefficients to estimate invasiveness for all 37 serotypes for which we had case-fatality ratio data from a large Danish study.24 (The estimates for invasiveness are given in eTable 1, http://links.lww.com/EDE/A448.) Importantly, our estimates of invasiveness obtained with this method performed as well or better than the observed invasiveness measures in the downstream models (data not shown).
Model 1: Results for England and Wales Prevaccination
We next evaluated the ability of carriage prevalence and estimated invasiveness to predict the relative contribution of serotypes to invasive pneumococcal disease among children under the age of 5 years in England and Wales during the pre-Prevnar-7 era (2005–2006). Not surprisingly, serotypes with high carriage prevalence and high estimated invasiveness tended to be more common among invasive pneumococcal disease isolates. The coefficients near 1 indicate that the regression recovered the multiplicative relationship between prevalence, invasiveness, and invasive pneumococcal disease incidence. There was a strong correlation between the observed and expected values (Table 2, Fig. 1A, Spearman ρ = 0.82; 95% confidence interval [CI] = 0.68–0.91). Additionally, the cumulative proportion curve generated using the predicted serotype ranks closely mirrors the observed cumulative proportion curve (Fig. 1B). The predicted values generated from univariate regressions were not as strongly associated with the observed values as were the predicted values from the multivariate regression (carriage prevalence alone [ρ = 0.66] or estimated invasiveness alone [ρ = 0.24]).
Prediction of Serotype Ranks in Different Regions Pre- and Postvaccination
We evaluated whether our predictions fit the serotype distributions in different regions in the pre-Prevnar-7 era. We compared the predicted values obtained from the English data against the observed serotype distributions from Kenya and the United States. Reflecting the similar serotype rank orders worldwide, we again found a strong association between the predicted values from the model and the observed number of cases (Table 2).
Because serotype patterns in carriage are similar but not identical among regions, we sought to improve the predictions by calculating the expected invasive pneumococcal disease serotype frequencies using carriage prevalence data from the respective countries. The use of country-specific carriage prevalence data led to a stronger correlation between the observed and expected serotype frequencies in Kenya, but it did not substantially improve the correlation for the US data (Table 2). The predictions made with the US carriage data, however, did fit the US disease data better than the disease data from England and Wales or Kenya.
Next we asked whether these models would have predicted the distribution of serotypes causing invasive pneumococcal disease after the introduction of Prevnar-7. We compared the predicted frequencies calculated from the model using carriage data from England and Wales with the postvaccine serotype ranks from England and Wales and the United States (Table 3, Fig. 2A, C; nonvaccine serotypes only). There was a reasonably strong association between the predicted and observed serotype ranks, although it was weaker than for the prevaccine data. The model had a strong negative predictive value but poor positive predictive value: This is evident when looking at the predicted cumulative proportion curves (Fig. 2B, D); some of the serotypes predicted to be common contributed little to the cumulative proportion (on the left side of the graph), whereas the serotypes the serotypes that were predicted to be rare (right side of the graph) were in fact rare.
Unlike in the pre-Prevnar-7 era, the pre-Prevnar-7 carriage data from Massachusetts did not improve the predictions for the US invasive pneumococcal disease data (Table 3). Serotypes 22F, 19A, and 7F, which are among the most important serotypes in the postvaccine era, were predicted to be ranked seventh, eighth, and ninth out of 30 serotypes. Ranked above these serotypes were types 6A/C, which might cross-react with antitype 6B antibodies induced by Prevnar-7, and types 1 and 8, which have been shown to exhibit regional variation and temporal fluctuations.6,7 Also, serotypes 15C, 33F, and 38 were predicted to be ranked higher than 7F, 19A, and 22F.
Model 2: Use of Polysaccharide Structure Rather Than Prevalence as a Predictor
We next made an alternative model that uses a recently identified correlate of pre- and postvaccine carriage prevalence—number of carbons per capsular polysaccharide repeat unit16—along with our estimates of invasiveness. In the regression model, polysaccharide structure (number of carbons per polysaccharide repeat) was negatively associated with serotype frequency in disease, while estimated invasiveness was positively correlated with serotype frequency in the pediatric population in England and Wales. The expected values generated from this model were correlated with the observed pre-Prevnar-7 serotype ranks from England and Wales, the United States, and Kenya (Table 4). When plotting the cumulative proportion curves based on the predicted serotype ranks, this model again had a strong negative predictive value (right side of the graph) but relatively poor positive predictive value (left side of the graph) (Fig. 3A, B). In particular, the model predicted that several serotypes (such as 3 and 9A) would be common that were, in fact, rare among disease isolates. Additionally, serotype 1 was predicted to be the most common serotype; while type 1 is common in England and Wales, it is relatively rare in the United States. Comparing the predicted serotype frequencies from Model 1 and Model 2 (eTable 2, http://links.lww.com/EDE/A448), there is good agreement between the 2 models for most serotypes. However, Model 2 predicts that types 1, 2, 3, 4, 5, 8, 9A, and 19A will be more common than predicted by Model 1, while Model 1 predicts that serotype 6B would be more common than predicted by Model 2. In large part, this reflects the fact that polysaccharide structure is a good correlate of carriage prevalence among circulating strains, but this structure does not predict whether a particular serotype will be present in a given population.
Finally, we compared the predictions from this model to the observed serotype ranks in the post-Prevnar-7 era among nonvaccine serotypes. As with the pre-Prevnar-7 population, the model had a good negative predictive value but a poor positive predictive value (Table 4, Fig. 3C, D). Similar to the results from the carriage-prevalence-based model, we could not predict the exact post-Prevnar-7 rank order of serotypes, but the important replacing serotypes (including 7F and 19A) were all ranked in the top half. Additionally, we found that the predictions fit the post-PCV7 England and Wales data substantially better than the post-PCV7 US data. This could be attributed, in part, to serotypes 1 and 8, which are predicted by the model to be common but are rare in the United States compared with England and Wales.
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 postvaccine 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 2 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 postvaccine Massachusetts carriage data together,20 with scaled prevaccine carriage data from Norway25 (35% predicted reduction18) or with 2 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 (eg, antiserotype 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 coinfections, 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 multiyear 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 nonvaccine serotypes before the vaccine. As a result, the prevaccine 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 prevaccine 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 United States, 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.
We thank the CDC Streptococcal Laboratory for performing the serotyping of the US invasive pneumococcal disease isolates and the ABCs investigators for providing the invasive pneumococcal disease data from the United States, and Robert George and Pauline Kaye for providing the pre- and post-Prevnar-7 dataset on invasive disease for England & Wales, and Moses Ndiritu for providing the Kenya invasive pneumococcal disease data. We thank Rick Malley, Matthew Moore, and Elizabeth Miller for critical reading of the manuscript and discussions and Justin O'Hagan for helpful discussions.
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