Evaluating Michigan’s Administrative Rule Change on Nonmedical Vaccine Exemptions

School-level vaccine exemption data on all (public, private, charter, and virtual) schools with ≥5 students from 2008 to 2018 were obtained from the Michigan Department of Health and Human Services (MDHHS). These data were aggregated and were previously publicly available on the MDHHS Web site, and thus no institutional board review approval was required. Data included school name, year, grade, number of children enrolled, number of students with up-to-date vaccinations, and number of exemption waivers issued by type. Michigan kindergarteners were required to receive a second dose of the varicella vaccine in 2010¹⁹ ; thus, we selected an analytic period of 2011–2018 to maintain consistent vaccine requirements. Data cleaning and geocoding, described in Masters et al,²⁰  resulted in a sample of 2769 schools that were spatially joined to 2010 school districts to link school district–level demographics from the 2018 American Community Survey 5-year estimate (data from 2013 to 2017).

For each year, the percentage of kindergarteners in each school with vaccine exemptions was calculated by dividing the number of students with an exemption by the number of enrolled students. Data were aggregated to the school district level by summing NME counts in schools on the basis of the school district boundary in which the school was located. Geographic trends in exemption rates were broken down by type and evaluated from 2011 to 2014 and from 2015 to 2018. Analyses were performed in R version 3.6.0. Maps were generated by using Environmental Systems Research Institute ArcMap version 10.7.1.

We used a hierarchical binomial regression model (using the R package lme4²¹ ) to understand variation in school-level NME rates. This model included random intercepts at the school district level and a binary variable indicating whether the time period was before or after the rule change. Geographic clustering of exemptions before and after the change was assessed by using the local indicator of spatial association (LISA),²²  which identifies spatial clusters of high and low exemption rates (Supplemental Equation 1). The LISA statistic was calculated for school districts (a meaningful administrative unit for policy action and parental decision-making) for each year, and clusters were evaluated for persistence over time in the pre- and postimplementation periods.

We used a hierarchical Bayesian binomial model with school district–level random intercepts (using the R package rstanarm²³ ) to evaluate predictors of NMEs over the study period, accounting for variation at the school district level inherent in the data. We used zero-mean gaussian priors for the intercept (SD = 10) and coefficients (SD = 2.5). We used ArcGIS origin-destination cost matrix calculation service to regress school-level NME rates by school district–level percentages of adults with a college education; per-capita income; and percentages of white residents (all categorized into tertiles); school type; and a continuous variable, calculated as the travel time (in hours) to the local health department from each school. Health department travel time and per-capita income were interacted with the year (centered at 2014) to evaluate whether associations changed over time. We ran a counterfactual exercise using the model output to generate the posterior mean of the marginal probability of obtaining an NME in each year, fixing the distribution of school types, travel times, and demographics. This presents the predicted probability of an NME if every kindergartener in Michigan were in each category of each variable and allows us to make counterfactual comparisons of predicted NME probabilities.

From 2011 to 2014, overall exemption rates remained stable, averaging 5.6% per year. However, the proportion of total exemptions due to NMEs increased during this period, whereas medical exemptions decreased (Fig 1). After Michigan’s rule change was implemented in 2015, exemption rates decreased to 3.6%. The unadjusted binomial model (Supplemental Table 2) revealed that the odds of obtaining an NME were significantly lower in the 4 years after the policy compared with the previous 4 years. However, after 2015, exemption rates increased each year, rising to 3.7% in 2016, 4.2% in 2017, and 4.5% in 2018 (26% higher than 2015). Since 2015, medical exemption rates stayed stable, whereas philosophical and religious exemption rates increased by 18% and 70%, respectively.

Public and charter schools had the lowest exemption rates, with ∼5% for the duration of the study period. (Table 1). On average, 91.3% of kindergarteners in this data set attended public school, 7.8% attended private school, 0.6% attended charter school, and 0.2% attended virtual school. Private schools had higher rates of exemptions from 2011 to 2018, with ∼10% before the 2015 policy, dropping to 7.3% in 2015 and increasing steadily each year, reaching 8.6% by 2018. Virtual schools had the highest exemption rates: >27% in 2012, 2013, and 2018.

Although NME rates fell in the immediate aftermath of the 2015 rule change, rates have increased steadily since and maintained relatively stable patterns of geographic clustering. School district–level clusters of persistent, high vaccine exemption rates from 2011 to 2014 and from 2015 to 2018 are shown in Fig 2. Different types of NMEs followed distinct clustering patterns. Figure 2A reveals that philosophical exemption clusters persisted in rural, remote regions of the Upper Peninsula, a cluster in northwestern Michigan disappeared after 2015, and a new cluster in mid-Michigan emerged after 2015. There were 56 school districts in a high philosophical exemption cluster from 2011 to 2014, decreasing to 26 for the period from 2015 to 2018, although the number of persistent clusters (>3 years) was unchanged (Supplemental Table 3). Religious exemption clusters revealed little change (Fig 2B): there were 32 religious exemption school district clusters in both time periods, although fewer persistent clusters afterward. Finally, for medical exemptions (Fig 2C), some persistent clusters in northeastern Michigan disappeared, yet a large, more persistent cluster appeared in southeast Michigan, overlapping with a philosophical exemption cluster. The number of medical exemption clusters dropped from 43 to 25. Overall, the 2015 rule change appeared to reduce the number of philosophical exemption clusters (although not persistent clusters) and diminished the spatial distribution of some medical exemption clusters. Religious exemption clusters were largely unchanged.

Our counterfactual analysis of the predicted probability of an NME if every kindergartener in Michigan were in each category of select demographic variables revealed that the average marginal probability of obtaining an NME was similar for kindergarteners whose school district was in the 2 lowest tertiles of per-capita income but was higher for school districts in the highest tertile of per-capita income (Fig 3, Supplemental Table 4). After the rule change, the discrepancy in the posterior mean average probability of an NME diminished between those in the wealthiest tertile and those in the poorer 2 tertiles. School district–level percentage of white residents was monotonically associated with NME rates: kindergarteners in the lowest tertile had the lowest NME probability, and those in the highest tertile had the highest (Supplemental Table 4). For percentage of adults with a college education, a different association was observed; kindergarteners in the middle tertile had the highest probability of an NME versus those in the other tertiles (Supplemental Table 4). Increased travel time to the local health department did not have a predictive effect on the probability of an NME (Supplemental Tables 5 and 6; Supplemental Figs 4–10).

High rates of NMEs are a critical public health challenge that has begun to reverse decades of public health success in the control of VPDs. Although New York, Maine, and Washington tightened NME regulations in the wake of the 2019 measles outbreaks, Michigan passed no comparable legislation. In fact, even minor policy remedies presented in the state have been stymied: Michigan introduced HB4610 in May 2019 to make schools with >5% exemption rates publicly post such information, but there has been no movement on this bill.²⁴  As such, Michigan’s 2015 Administrative Rule change, which did not require legislative action, was a strategic avenue for MDHHS to attempt to reduce NMEs.¹¹ 

Although the rule change induced a sharp decline in the number of NMEs in the year after it went into effect, NME rates in Michigan have since rebounded nearly to pre-2015 levels, suggesting that this change has not had an enduring impact. Interestingly, medical exemption rates in Michigan decreased after the rule change and have remained at these reduced levels since 2015. This is in contrast to what was observed in California after SB277, in which medical exemption rates increased 250%, indicative of a replacement effect in which parents were obtaining medical exemptions in place of nonmedical ones.²⁵  The rate of religious exemptions in Michigan has rebounded faster than philosophical exemptions, which is concerning given that the 2019 outbreaks were primarily driven by religious exemption clusters.

Michigan’s increasing exemption rates mirror national trends: from 1991 to 2004, the mean state-level NME rate increased from 0.98% to 1.48%,²  and from 2011 to 2016, the national rate of NMEs increased from 1.75% to 2.25%.²⁶  Michigan’s high NME rate has put the state at risk: Olive et al²⁷  identified Michigan’s Oakland, Macomb, and Wayne counties to be among the 10 counties in the United States with the highest numbers of NMEs, foreshadowing the 2019 measles outbreak in Oakland County. Our analysis confirmed high NME rates in these counties, and identified school district–level clustering of religious exemptions in Macomb County, indicating regions in which VPD risk may also be high at a finer scale. Overall, the fact that in 4 years, exemption rates have already rebounded to nearly pre–rule change levels indicates that stronger action, combined with multifaceted approaches that do not rely exclusively on legislative and administrative changes, is needed to curb increasing outbreak risk in Michigan.

Our findings that private schools had approximately twice the rate of exemptions as public and charter school align with research from California.¹¹  Although the vast majority of kindergarteners in this data set attended public school, ∼8% of the students attended private schools, and thus the increase in private school exemption rates is concerning. Virtual schools had extremely high exemption rates, providing a glimpse of nonvaccination among homeschooled children. The number of kindergarteners enrolled in virtual schools increased nearly 10-fold over the study period, from 57 students in 2011 to 487 in 2018; thus, the high rates of exemptions in virtual schools is particularly worrying if these numbers continue to rise, which may occur especially in the aftermath of the coronavirus disease 2019 pandemic.

We also identified spatial clusters with persistently high exemption rates both before and after the rule change. This is important because spatial clustering of nonvaccination may dramatically increase the risk of outbreaks at the local and population level.²⁸  Given that students who obtain kindergarten vaccine exemptions age through the educational system, a region with persistently high kindergarten exemption rates is likely to have markedly reduced vaccination levels among students, accumulating children susceptible to VPDs.¹⁸  Our analysis revealed that many exemption clusters remained persistent after the rule change, which more strongly aligns with the aftermath of California’s AB2109 (which decreased exemption rates but not clustering)^11,17  than with Washington’s SB5005. Additionally, the geography of philosophical and religious NMEs were distinct. These findings generally concur with Mashinini et al.²⁹  However, our analysis used school districts as the clustering unit and evaluated clusters for their persistence over time, providing results at an actionable geographic scale. The distinct patterns of spatial clustering observed for different exemption types indicate that the downstream impacts of policy changes to further restrict access to NMEs would likely play out heterogeneously across a state characterized by a diverse socioeconomic, racial, and religious landscape.

Results from our counterfactual exercise by using Bayesian regression output underscored that school type was a predictor of NME rates, with virtual and private schoolers having higher probabilities of exemptions than their public school counterparts. Distance to the local health department was not a strong predictor of school-level exemption rates, highlighting that this policy likely only reduced exemptions because of convenience rather than conviction and potentially indicating that those who pursued an NME after 2015 were sufficiently motivated that the opportunity cost associated with traveling to the health department was not a high barrier. We found nonlinear patterns across school district–level percentage of white residents, percentage of college-educated adults, and per-capita income. This generally concurs with previous research in which undervaccinated children are often minorities of lower socioeconomic status and educational attainment, whereas completely nonvaccinated children are often white, wealthy, educated, and privately insured.^30–32 

A notable strength of this study is the use of school-level data to identify potential geographic clustering and regions in which herd immunity might be broken because of high exemption rates. This data source represents all schools with at least 5 students in Michigan, providing a near-complete assessment of kindergarten vaccination. Additionally, school-level data are appropriate here given that school is the unit of aggregation at which much of transmission occurs. Geocoding these schools allowed for linkage of sociodemographic variables from the American Community Survey, permitting measures of community-level demographics while using transmission-level vaccination data.

This study also has some limitations, most importantly that we only analyzed exemption data for kindergarteners, thus creating an incomplete picture of the vaccination status of the full student population in these schools. Additionally, it is possible that there are missing data if not all students were present when schools were surveyed for vaccination and enrollment records. Assigning school districts to schools on the basis of spatial location alone may not perfectly represent the body of students in virtual and private schools, which may draw from a broader geographic area. Finally, using school district–level demographics may not be a perfect match to the student body from each school, as school catchment areas may extend beyond the boundaries of the units chosen or be specific subsegments of a geographic unit, introducing the possibility of ecological bias.

Michigan’s Administrative Rule change reduced the number of NMEs immediately after its implementation, yet NMEs have since rebounded, and many school district–level NME clusters have persisted, illustrating that this change did not have a lasting impact on NMEs in Michigan. Navin et al³³  found that Michigan’s vaccine waiver educators rarely convinced parents to vaccinate their children, underscoring that such a policy is effectively imposing a cost to reduce convenience exemptions, yet unlikely to change perception. They also found there may be a threshold of burden beyond which increasing inconvenience does not further reduce exemptions.³⁴  As a result, it is important to balance the implementation of stronger policies to curb NME rates to reduce the frequency of outbreaks with the possibility of backlash against restrictions of individual liberty.^35,36 

Michigan’s administrative policy should be viewed within the larger context of the interventions available to reduce incentives to obtain exemptions. In addition to state regulation of vaccine exemptions, interventions should be aimed to counter growing levels of vaccine hesitancy through education, building confidence in vaccines and government, curbing misinformation, educating doctors about the importance of vaccination, minimizing missed opportunities, and increasing affordability.³⁷  These recommendations are particularly important against a backdrop of the coronavirus disease 2019 pandemic, which has led to reduced ambulatory care visits, causing a precipitous drop in pediatric vaccination rates.³⁸  At this critical juncture, we must use new tactics (more than the usual slate of interventions) to fight vaccine misinformation, build back trust, increase vaccine uptake, and minimize the risk of additional outbreaks.

LISA

local indicator of spatial association

MDHHS

Michigan Department of Health and Human Services

NME

nonmedical exemption

VPD

vaccine-preventable disease