Measuring Multiple Dimensions and Indices of Non-Vaccination Clustering in Michigan: 2008-2018

Abstract

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of non-medical vaccine exemptions (NMEs) and low vaccination coverage compared with the rest of the United States. There is a critical need to better understand the landscape of non-vaccination in Michigan to assess the risk of vaccine-preventable outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of non-vaccination, and numerous clustering metrics are available in the statistical, geographic, and epidemiologic literature. We used school-level NME data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan from 2008-2018 using Moran’s I, the Isolation Index, Modified Aggregation Index, and the Theil Index at four spatial scales. We also used thresholds of 5%, 10%, and 20% non-vaccination to assess the bias incurred when aggregating vaccination data. We found that aggregating school-level data to levels commonly used for public reporting can lead to large biases in identifying the number and location of at-risk students, and that different clustering metrics yielded variable interpretations of the non-vaccination landscape in Michigan. This paper shows the importance of choosing clustering metrics with their mechanistic interpretations in mind: be it large- or fine-scale heterogeneity, or between-and-within group contributions to spatial variation.

Publication
American Journal of Epidemiology
Nina Masters
Nina Masters
PhD Candidate

Nina is a doctoral candidate in Epidemiology at the University of Michigan. Her research focuses on spatial transmission models infectious diseases, the impact of clustered non-vaccination on outbreak risk, and the evolution of vaccine hesitancy.

Paul Delamater
Paul Delamater
Assistant Professor, UNC
Jon Zelner
Jon Zelner
Assistant Professor

My research interests include distributed robotics, mobile computing and programmable matter.