Ecology

Affiliations

International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
Shigenori Fujikawa, Roman Selyanchyn & Toyoki Kunitake

NanoMembrane Technologies, Inc., 4-1, Kyudai-Shimachi, Nishi-Ku, Fukuoka, 819-0388, Japan
Shigenori Fujikawa & Toyoki Kunitake

Department of Chemistry and Biochemistry, Center for Molecular Systems (CMS), Kyushu University, 744 Motooka, Nishiku, Fukuoka, 819-0395, Japan
Shigenori Fujikawa

Authors
Shigenori Fujikawa

Roman Selyanchyn

Toyoki Kunitake

Corresponding author
Correspondence to Shigenori Fujikawa. More

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We collected data from all US states where school vaccine exemption information was freely available from the Department of Health website in any format. We were able to locate that data in 24 states (see Table 1 for a list of states included). Within these states, the number of years available varied relatively widely, between 19 years in California and a single year in 6 states. The most represented year in our dataset was 2017 (corresponding to school year 2017–2018). Because the dataset was compiled in June-July 2019, we note that it is likely that additional data for more recent years may be available, or that data may have become available in additional states not included in our dataset.
Table 1 Exemption data reporting varies widely across states.
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The data format varied widely between states, and exemptions were reported either as a number of exemptions or as a percentage of the enrolled students. We have elected to use number of students rather than percentages, and have transformed data as needed. For most states included in our dataset, the data are provided at the county level. In several states (Arizona, Colorado, Illinois, Maine, Michigan, South Dakota, Tennessee, Vermont, Oregon, and Washington), the data was provided at the school level, which we aggregated to the county.
Additional data processing was necessary in some cases. In Virginia, data was provided by school name, but county or city information was not included. We used a list of public and private schools to match school names with their respective county using fuzzy matching (with the ‘fuzzywuzzy’ Python package) with an 80% matching requirement. Our algorithm was unable to find a suitable match for between 3.8% and 6.8% of schools (depending on year), and these schools were not included in the final counts at the county level. Similarly, in Idaho, data at the school level included city information but county was not provided. We first matched city and county names, before aggregating the exemption data at the county level. Finally in New York state, exemptions were provided as percentages at the school level but enrollment information was not included. We obtained enrollment for public and private schools separately from the New York State Education Department, and used the school unique code to calculate exemption number from enrollment and exemption percentages. We then aggregated these numbers at the county level.
States reported data for exemptions based on varying definitions, so we selected data records based on data availability to make the data comparable across states. We aimed to achieve parsimonious definitions of total medical exemptions (Fig. 1a), total non-medical exemptions (Fig. 1b), and total exemptions (Fig. 1c), which includes both types of exemptions. We define medical exemptions as reported total medical exemptions. In Florida, permanent medical exemptions were reported separately from temporary medical exemptions, so permanent medical exemptions was chosen to represent total medical exemptions. To define total non-medical exemptions, we considered the state law regarding non-medical exemptions and the data availability. If the state reported total aggregated non-medical exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions and only allows religious exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions, but also allows philosophical exemptions, that was considered missing data. If the state allows philosophical exemptions and only reports philosophical exemptions, that was selected as total non-medical exemptions, as the state may not differentiate religious from philosophical. If the state allows philosophical exemptions and reports both religious and philosophical exemptions separately, these values were summed for total non-medical exemptions. To define total exemptions, if the state reported a total exemptions value, this value was used. If the state did not report a total exemptions value, but reported values for total medical exemptions and total non-medical exemptions, as defined above, these were summed for total exemptions. If the state was missing either medical or non-medical exemptions, but reported the total number of students with completed vaccinations, the total exemptions was the difference between the number of students enrolled and the number of students completed. This classification process is visualized in Fig. 1.
Fig. 1

Exemptions were classified by type to standardize reporting. Exemptions were classified as medical exemptions (a), non-medical exemptions (b), and total exemptions (c) to standardize reporting across states with different values reported.

Full size image

We also considered disease-specific exemptions reports. If a state reported the number of exemptions for a vaccine specific to a given infection, that value was used. If the state did not report exemptions, but did provide the total number complete for that disease, the difference between the enrolled students and the completed students was used. For pertussis-specific vaccination, we used DTaP exemptions where available, and TDaP exemptions where DTaP was not available. For measles-specific vaccination, if separate reports were available for measles, mumps, and rubella, the value for measles was used. If measles was not available, then the mumps or rubella exemptions were used, if available.
The data in the figures is only data reported for kindergartens in states where kindergarten-specific data was available, or K-12 data in states where kindergarten-specific data was not reported. States reported age groups heterogeneously, and data by other age groups is available in the data file. We note that Oregon reports kindergarten-specific data in 2014–2015, then K-12 data in 2016–2018. More

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