Challenges in Estimating the Impact of Vaccination with Sparse Data

Background: The synthetic control model is a powerful tool to quantify the population-level impact of vaccines because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller, subnational datasets, we evaluated the performance of synthetic control models with sparse time series data. To obtain more robust estimates of vaccine impacts from noisy time series, we proposed a possible alternative approach, STL+PCA method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome. Methods: Using both the synthetic control and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine on pneumonia hospitalizations among cases
Source: Epidemiology - Category: Epidemiology Tags: Infectious Diseases Source Type: research