Change in hospitalization rates in each year compared with the average of 1997/1998–1999/2000 among children aged 0–2 and 3–11 mo for RSV, pneumococcal pneumonia, and pneumococcal septicemia.

<p>The <i>y</i>-axis is the IRR and 95% confidence intervals, with values below one representing declines compared with the baseline period.</p

Variations between states, age groups, and years in the change in rates of hospitalization for RSV or pneumococcal pneumonia after introduction of PCV7.

<p>Decline in the rate of (A) RSV hospitalizations and (B) pneumococcal pneumonia hospitalizations in each state and each year (July−June) among children aged 0−11 mo compared to the average of 1997/1998−1999/2000 in the same state. The shaded areas indicate the 95% confidence intervals for the IRRs. The red dotted line indicates a rate ratio of one (no change). The colors differentiate the states.</p

Relationship in the timing of the average seasonal peak of RSV, pneumococcal pneumonia, and pneumococcal septicemia.

<p>Association between the average peak timing (in weeks) of RSV hospitalizations and the average peak timing (in weeks) of (A) pneumococcal pneumonia hospitalizations and (B) pneumococcal septicemia hospitalizations in each state among children aged <2 y, 1992/1993–2008/2009. Smaller values indicate earlier epidemics. The error bars indicate the 95% confidence intervals. The colors differentiate the states; labels for selected states are shown.</p

Association between Respiratory Syncytial Virus Activity and Pneumococcal Disease in Infants: A Time Series Analysis of US Hospitalization Data

<div><p>Background</p><p>The importance of bacterial infections following respiratory syncytial virus (RSV) remains unclear. We evaluated whether variations in RSV epidemic timing and magnitude are associated with variations in pneumococcal disease epidemics and whether changes in pneumococcal disease following the introduction of seven-valent pneumococcal conjugate vaccine (PCV7) were associated with changes in the rate of hospitalizations coded as RSV.</p><p>Methods and Findings</p><p>We used data from the State Inpatient Databases (Agency for Healthcare Research and Quality), including >700,000 RSV hospitalizations and >16,000 pneumococcal pneumonia hospitalizations in 36 states (1992/1993–2008/2009). Harmonic regression was used to estimate the timing of the average seasonal peak of RSV, pneumococcal pneumonia, and pneumococcal septicemia. We then estimated the association between the incidence of pneumococcal disease in children and the activity of RSV and influenza (where there is a well-established association) using Poisson regression models that controlled for shared seasonal variations. Finally, we estimated changes in the rate of hospitalizations coded as RSV following the introduction of PCV7. RSV and pneumococcal pneumonia shared a distinctive spatiotemporal pattern (correlation of peak timing: ρ = 0.70, 95% CI: 0.45, 0.84). RSV was associated with a significant increase in the incidence of pneumococcal pneumonia in children aged <1 y (attributable percent [AP]: 20.3%, 95% CI: 17.4%, 25.1%) and among children aged 1–2 y (AP: 10.1%, 95% CI: 7.6%, 13.9%). Influenza was also associated with an increase in pneumococcal pneumonia among children aged 1–2 y (AP: 3.2%, 95% CI: 1.7%, 4.7%). Finally, we observed a significant decline in RSV-coded hospitalizations in children aged <1 y following PCV7 introduction (−18.0%, 95% CI: −22.6%, −13.1%, for 2004/2005–2008/2009 versus 1997/1998–1999/2000). This study used aggregated hospitalization data, and studies with individual-level, laboratory-confirmed data could help to confirm these findings.</p><p>Conclusions</p><p>These analyses provide evidence for an interaction between RSV and pneumococcal pneumonia. Future work should evaluate whether treatment for secondary bacterial infections could be considered for pneumonia cases even if a child tests positive for RSV.</p><p><i>Please see later in the article for the Editors' Summary</i></p></div

Percent of pneumococcal pneumonia and pneumococcal septicemia cases attributable to RSV and influenza in children aged <2 y, 1997/1998–2008/2009.

<p>The estimates of the AP are calculated using the results of the regression model by dividing the number of pneumococcal disease cases predicted if RSV were not present by the number of pneumococcal disease cases predicted based on the observed incidence of RSV.</p><p>Percent of pneumococcal pneumonia and pneumococcal septicemia cases attributable to RSV and influenza in children aged <2 y, 1997/1998–2008/2009.</p

Study characteristics.

<p>Number of admissions for each condition in the included states and cumulative population residing in those states for 1997/1998–2008/2009.</p>†<p>Arizona, California, Colorado, Georgia, Iowa, Kansas, Massachusetts, New York, and South Carolina. See <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001776#pmed.1001776.s007" target="_blank">Table S1</a> for the tabulation of cases by state.</p><p>Study characteristics.</p

Total cases and incidence for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.

<p>Total cases are shown in millions and incidence is per 100,000 person-years.</p

Observed versus model-predicted incidence.

<p>(A) Posterior predictions from the null model, which only adjusts for age and the observation process. (B) Posterior predictions from the model using fixed effects for the predictors. (C) Leave-3-out validation results. The gray markers represent the density of model-predicted posterior distributions of incidence, while the red dots represent the median posterior predicted incidence. The size of the red circular markers is proportional to the number of person-years of observation in each study. All predictions are of the mean incidence and were generated using only the fixed-effect terms of the model, and hence do not account for unmeasured location-specific differences, e.g. in healthcare-seeking behavior.</p

Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.

<p>Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.</p

Model summary.

<p>A) The posterior marginal probability that each variable was excluded from the model (black) or included as a predictor of the intercept (dark grey) or intercept and slope (light grey) is shown for two chains. Our stochastic search variable selection algorithm could include variables either as a predictor of the intercept (the incidence in 5–14 year olds) or as a predictor of the intercept as well as the slopes (the incidence rate ratios between the other age groups and the referent age group of 5–14 year olds). B) Distribution of the average number of covariates in the model. Chain 1 was initiated using a model that included all the covariates as predictors of the main effect, while chain 2 was initiated as the null model. The null model was never sampled, implying that the models including at least one predictor better described the data than the null model. C) Posterior distributions of age-specific incidence rate ratios between the referent age group (5–14 years of age) and other age groups: <2 years, 2–4 years, ≥15 years old.</p