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Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical 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 nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008–2018 using Moran's I , the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% 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 nonvaccination landscape in Michigan. This study 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. [ABSTRACT FROM AUTHOR]

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical 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 nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008–2018 using Moran's I , the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% 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 nonvaccination landscape in Michigan. This study 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. [ABSTRACT FROM AUTHOR]