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OBJECTIVES: Vaccine hesitancy is a growing threat to health in the United States. Facing the fourth highest vaccine exemption rate in the United States in 2014, Michigan changed its state Administrative Rules, effective January 1, 2015, requiring parents to attend an in-person vaccine education session at their local health department before obtaining a nonmedical exemption (NME). In this article, we evaluate the longer-term impact of this policy change on the rate, spatial distribution, and sociodemographic predictors of NMEs in Michigan. METHODS: Using school-level kindergarten vaccination data from Michigan from 2011 to 2018, we evaluated sociodemographic predictors of NMEs before and after this Administrative Rule change using Bayesian binomial regression. We measured the persistence and location of school district-level geographic clustering using local indicators of spatial association. RESULTS: Immediately after the rule change, rates of NMEs fell by 32%. However, NME rates rebounded in subsequent years, increasing by 26% by 2018, although income disparities in NME rates decreased after the rule change. Philosophical, religious, and medical vaccine exemptions exhibited distinct geographic patterns across the state, which largely persisted after 2015, illustrating that NME clusters remain a concern despite this rule change. CONCLUSIONS: Although Michigan's Administrative Rule change caused a short-term decline in NME rates, NME rates have risen dramatically in the following 4 years since the policy was implemented. Michigan's administrative effort to require parental education at the local health department before receiving an exemption did not cause a sustained reduction in the rate or spatial distribution of NMEs. [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]

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]