Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system

Abstract

Evidence is accumulating that universal vaccination of schoolchildren would reduce the transmission of influenza. The authors sought to identify target age groups within the pediatric population that develop influenza the earliest and are most strongly linked with mortality in the population. Patient visits for respiratory illness were monitored, using real-time syndromic surveillance systems, in six Massachusetts health-care settings, including ambulatory care sites and emergency departments at tertiary-care and community hospitals. Visits from January 1, 2000, to September 30, 2004, were segmented into age group subpopulations. Timeliness and prediction of each subpopulation were measured against pneumonia and influenza mortality in New England with time-series analyses and regression models. Study results show that patient age significantly influences timeliness (p = 0.026), with pediatric age groups arriving first (p < 0.001); children aged 3-4 years are consistently the earliest (p = 0.0058). Age also influences the degree of prediction of mortality (p = 0.036), with illness among children under age 5 years, compared with all other patients, most strongly associated with mortality (p < 0.001). Study findings add to a growing body of support for a strategy to vaccinate children older than the currently targeted age of 6-23 months and specifically suggest that there may be value in vaccinating preschool-age children.

Figure 1. Predicted yearly seasonality of respiratory illness from five retrospective patient populations with respiratory illness syndromes presenting to healthcare sites and the CDC sentinel influenza-like illness surveillance system, Eastern Massachusetts, USA, 2000–2004.

Yearly cycles were obtained by cross-spectral analysis performed on each data stream after linear detrending and standardization (standardized regression residuals). The phase shift of each data stream with the CDC influenza mortality surveillance represents the timeliness of the data stream.

Figure 2. Prediction and timeliness of patients with respiratory illness syndromes for signaling pneumonia and influenza mortality by age group and site of care, Eastern Massachusetts, USA, 2000–2003.

Timeliness is the lead time to influenza mortality, obtained by cross-spectral analysis. Mortality prediction is proportion of variance explained by each healthcare population calculated by Poisson regression. The plot reveals the pediatric age groups to have the best combination of timeliness and prediction.

Figure 3. Prediction and timeliness of patients with respiratory illness syndromes for signaling pneumonia and influenza mortality summarized by age group, Eastern Massachusetts, USA, 2000–2003.

Timeliness is the average lead time to influenza mortality for each age group across all sites of care obtained by cross-spectral analysis. Mortality prediction is the average proportion of variance explained by each age group across healthcare sites calculated by Poisson regression. The plot reveals the two youngest age groups to have the best combination of timeliness and prediction.