OP388 17-Year Disease Reduction Predicted By A Transmission Dynamic Model After Pneumococcal Conjugate Vaccine Introduction In The United States

Abstract

Introduction

After the introduction of the seven-valent pneumococcal conjugate vaccine (PCV7) in the United States (US) in the year 2000, the incidence of invasive pneumococcal disease (IPD) caused by the seven vaccine serotypes declined by 80 percent in vaccinated children and 30 percent in unvaccinated adults. A transmission dynamic equation model developed in 2009 captured the direct and indirect effects of vaccination in the early years after vaccination. Subsequently, the vaccine program switched to the 13-valent PCV and adult PCV13 vaccination. This work explores the accuracy of the mathematical model to predict long-term IPD due to changes in US immunization practices.

Methods

The model simulates the acquisition of asymptomatic carriage of pneumococci and the development of IPD among individuals aged <2, 2–4, 5–17, 18–49, 50–64, and ≥65 years. Pneumococcal serotypes were stratified into three categories: PCV7-type (4, 6B, 9V, 14, 18C, 19F, and 23F), PCV6-type (1, 3, 5, 6A, 7F, and 19A), and non-PCV-type (all others). Model parameters were calibrated using US IPD surveillance data from 1998–2006. Model results were compared to observed epidemiology.

Results

The model was previously shown to predict observed IPD well through 2007. After adjusting model parameters for PCV13 efficacy and adult vaccine coverage, modeled IPD closely replicated observed IPD. Observed baseline pre-vaccine incidence for children <2 years of age was 192 cases/100,000 and 13.5 cases in 2016, versus 18.5 cases estimated by the model. Similarly, observed versus modeled cases in the ≥65-year-old age group were 24 and 23.6 cases.

Conclusions

This epidemiologic model accurately simulates the observed US IPD surveillance data 17 years after initial introduction of PCV, highlighting the direct and indirect benefits of vaccination. Well-constructed mathematical models can accurately replicate real-world scenarios. Key input parameters of these models can then be modified to predict the impact of alternate scenarios, providing insights to inform public health policy-making.