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School exemptions in the United States

Citation

Zipfel, Casey; Garnier, Romain; Kuney, Madeline; Bansal, Shweta (2020), School exemptions in the United States, Dryad, Dataset, https://doi.org/10.5061/dryad.fn2z34trn

Abstract

Once-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either on too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years available. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States. 

Methods

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. 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 possible that additional data for recent years may not be available, or that data may have become available in additional states not included in our dataset. 

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 cross states. We aimed to achieve parsimonious definitions of total medical exemptions, total non-medical exemptions, and total exemptions, 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. 

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.

Usage Notes

This dataset contains: 1) a master file of cleaned and joined data. This dataset contains the year, state, county FIPS code, and pertinent age group for each record. Each county-year combination that is available has entries for total enrolled, total medical exemptions, total non-medical exemption, religious exemptions, philosophical exemptions, pertussis exemptions, measles exemptions, varicella exemptions, and flu exemptions. 

2) the raw and uncleaned files that were used to produce the master file. There is a file for each state-year combination available. The columns included are:

Year: year that data is reported for

State: name of state

County: name of county

School_district: name of school district

City: name of city

Number_reporting: number of establishments within the given reporting area (e.g., number of schools contributing data within a given county or district)

School: name of school, where data is at the school level

Public/private: whether data is reported by public schools only, private schools only, or both

Age: what age groups the data is reported for (CC: childcare/preschool, KG: kindergarten, FG: first grade, MS: middle school/6th/7th grade, K12: kindergarten-high school/mixed grades)

Total_enrolled: total number of students for the given reporting area (e.g., school, district, or county)

Total_completed: total number of students that are up-to-date/compliant for all vaccines required by a particular reporting area (e.g., county or state)

Total_exempt: total number of students exempt for 1+ vaccines for under any type of exemption within the given reporting area

ME_total: all medical exemptions for the given reporting area (e.g., both permanent and temporary)

PME_total: all permanent medical exemptions for the given reporting area

TME_total: all temporary medical exemptions for the given reporting area

PBE_total: total number of students with any type of personal belief exemption for 1+ vaccine within the given reporting area (e.g., no distinction between philosophical or religious belief exemptions)

PBE_total-philosophy: total number of students with a philosophical exemption for 1+ vaccine within the given reporting area

PBE_total-religion: total number of students with a religious belief exemption for 1+ vaccine within the given reporting area

**PBE_total does not necessarily equal (PBE_total-philosophy + PBE_total-religion). Some states report additional exemptions (e.g., McKinney-Vento exemption for children experiencing homelessness) and reasons for non-compliance (conditional school entrance), while others do not. For states that do not report these other exceptions, it is unclear what the true total number of exemptions is and so the PBE_total column is left empty.

TDaP: completion of all recommended TDaP doses

TDaP_exempt: exemptions for TDaP vaccine

Var: completion of all recommended varicalla doses

Var_exempt: exemptions for varicella vaccine

HepB: completion of all recommended HepB doses

HepB_exempt: exemptions for HepB vaccine

HepA: completion of all recommended HepA doses

HepA_exempt: exemptions for HepA vaccine

MMR: completion of all recommended MMR doses

Mumps_exempt: exemptions for mumps vaccine

Rubella_exempt: exemptions for rubella vaccine

Measles2: completion of all recommended measles vaccine doses

Measles2_exempt: exemptions for measles vaccine

Polio(IPV): completion of all recommended IPV doses

Polio(IPV)_exempt: exemptions for polio vaccine

DTaP: completion of all recommended DTaP doses

DTaP_exempt: exemptions for DTaP vaccine

HIB1: completion of all recommended HIB1 doses

HIB1_exempt: exemptions for HIB1 vaccine

MCV4: completion of all recommended MCV4 doses

MCV4_exempt: exemptions for MCV4 vaccine

PCV13: completion of all recommended PCV13 doses

PCV13_exempt: exemptions for PCV13 vaccine

Influenza: completion of all recommended influenza vaccine doses

Influenza_exempt: exemptions for influenza vaccine

No_record: the number of students for which the institution was not able to acquire/report their immunization record

States report different data in different formats. 

The code used to produce the figures included in the manuscript as well as the full cleaned and raw datasets are available on Github at https://github.com/bansallab/exemptions-landscape. The code runs in Python 3.6.

Funding

National Institutes of Health, Award: R01GM123007