Linked External Data
DOI: 10.15154/z563-zd24 (Release 5.1)
List of Instruments
General Information
An overview of the ABCD Study® can be found at abcdstudy.org and detailed descriptions of the assessment protocols are available at ABCD Protocols. This page describes the contents of various instruments available for download. To understand the context of this information, refer to the release note Start Page.
Detailed information about the instruments, the constructs they are intended to measure, and relevant citations for each measure are provided in the following:
Fan, C. C., Marshall, A., Smolker, H., Gonzalez, M. R., Tapert, S. F., Barch, D. M., Sowell, E., Dowling, G. J., Cardenas-Iniguez, C., Ross, J., Thompson, W. K., & Herting, M. M. (2021). Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data. Developmental cognitive neuroscience, 52, 101030. Find here
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Schertz, K., Gonzalez, M. R., Abad, S., & Herting, M. (2023). Building Towards an Adolescent Neural Urbanome: Expanding Environmental Measures using Linked External Data (LED) in the ABCD Study. PsyArXiv.Find Here.
The COVID-19 geocoded data can be found here.
ABCD residential histories cannot be shared with users
ABCD residential histories are considered sensitive personally identifiable information (PII). To ensure the privacy of ABCD participants, address data are not shared. Instead, external data are linked within ABCD’s secure environment to provide geocoded measures while safeguarding participant privacy. The Linked External Data (LED) workgroup remains open to considering requests for new LED projects. Visit ABCD Study External Data Link Request to learn more or to suggest additional geocoded measures for future release.
Known Issues
Data collection process
The original address data collection processes in ABCD relied on a point-in-time capture of residential addresses rather than recording longitudinal residential history. As such the assumption has been made that addresses reflected participants’ baseline addresses in all versions of the reshist releases to date (2.1, 3.0, 4.0, and 5.0).
To address this limitation, the LED Environment and Policy WG has been actively working to improve the collection of residential history data to improve the temporal and geographic accuracy of participants’ reported addresses. These updates aim to improve the collection of retrospective and prospective data in order to provide more comprehensive and accurate residential histories that correspond to both the age of the child as well as each study visit. These data are expected to be available in future releases. Until then, users should be mindful of both the assumption and limitations of the currently available data.
Urban Classification
Variables: reshist_addr1_urban_area, reshist_addr2_urban_area, reshist_addr3_urban_area)
The 4.0 version of the data dictionary contained errors in the labeling used for the urbanicity of participants’ census tracts. The correct coding, below, was found in the 4.0 release notes and is now corrected in the 5.0 data dictionary.
List of values
1: Urbanized Areas (UAs) of 50,000 or more people;
2: Urban Clusters (UCs) of at least 2,500 and less than 50,000 people.
3: “Rural” encompasses all population, housing, and territory not included within an urban area.
Temperature and VPD data
Errors were identified in the 4.0 temperature and VPD linkages and should not be used given these issues. These errors were minimized for the provided data in the 5.0 release.
Data dictionary definitions
Users noted that in some versions of the downloaded 4.0 data dictionary that definitions showed up as offset by 1 variable name beginning with “Reshist_addr1_coi_he_green”. The 5.0 version of the release notes now outline additional details to help accurately match variable names and data dictionary definitions. Moreover, this issue has been corrected in the 5.0 data dictionary.
Removal of data from 5.0
A number of changes to both the geocoding process as well as identified errors in previously included data have resulted in the removal of a number of variables in the 5.0 dataset. Previous versions of these data in other releases should not be used given their known errors.
Removal of prospective residential data in 5.0
In the release, data for events beyond the baseline event were inadvertently included but were not accurately quality controlled nor verified of being relevant addresses at the time of the annual visit per each subject. As such these address data for these events were incomplete and also are not a correct reflection of the participants geographic location at that event. Given the errors in these data, these data are not included in release 5.0 and should not be used from previous data releases.
Removal of addr1_valid data in 5.0
The original address data collection processes in ABCD relied on a point-in-time capture of residential addresses without real-time verification of the location with geocoding. Thus, addresses provided were geocoded after collection, which meant some addresses later could not be geocoded (i.e. address was not valid or included typos). This was denoted by the variables “addr1_valid”, “addr2_valid” and “addr3_valid” . Since addresses are now geocoded in real-time with informants, this variable is no longer warranted and has been removed from the 5.0 data release.
Removal of 10x10km PM2.5 and NO2 air pollution estimates in 5.0
Variables: reshist_addr1_pm25, reshist_addr2_pm25, reshist_addr3_pm25, reshist_addr1_no2, reshist_addr2_no2, reshist_addr3_no2
In quality control processes the 10x10 km estimates of NO2 and PM2.5 were found to have errors and not recommended for use. These have been removed from the 5.0 release.
Instrument Descriptions
School
Stanford Education Data Archive (SEDA)
NCES school IDs corresponding to the schools ABCD participants reported attending at the baseline ABCD visit have been linked to the SEDA 4.0 dataset (Find here) for school, district, county, metro, and commuting zone level data. For protection of participant identification and confidentiality, we do not release NCES school IDs or the name or address of the school.
In the 5.0 Release, we have subdivided the SEDA tables into logical subdivisions as compared to the 4.0 Release. Please note the table name changes.
Release 5.0 Data Table: led_l_seda_demo
Measure Description: - Measure includes SEDA 4.0 dataset demographic covariates. Sources of the SEDA covariate data include:
- S. Census Bureau’s American Community Survey (ACS) - demographic and socioeconomic characteristics of individuals and households residing in each unit (School district, County, Metro, Commute) (from National Historical Geographic Information System (NHGIS);
- S. Department of Education’s NCES Common Core of Data (CCD)- basic descriptive information on schools and school districts, including demographic characteristics; and
- S. Department of Education’s Civil Rights Data Collection (CRDC) - school demographics.
See the SEDA documentation for more details Find here.
ABCD Subdomain: School
Number of Variables: 112
Notes and special considerations: The following categorical variable values were coded to numerical responses as follows.
Variable Key:
| Variable | Value |
|---|---|
| led_sch_seda_s_type | Regular School = 1; Other/Alt School = 2 |
| led_sch_seda_s_level | High = 3; Elementary = 1; Middle = 2; Other = 4 |
| led_sch_seda_s_urbanicity | City = 1; Rural = 3; Suburb = 2 |
| ledsch_seda_d_gslo in | Kindergarten = 0; Pre-Kindergarten = -1 |
Release 5.0 Data Table: led_l_seda_read
Measure Description: - Measure includes SEDA 4.0 dataset average test scores (reading/language arts)
The average test scores (reading/language arts) are included as a measure of the reading/language arts educational opportunities at each school at the following spatial resolutions: Geographic School District, County, Commuting Zone, Metropolitan Statistical Area.
ABCD Subdomain: School
Number of Variables: 420
Notes and special considerations: See led_l_seda_pool
Release 5.0 Data Table: led_l_seda_math
Measure Description: - Measure includes SEDA 4.0 dataset average test scores (math)
The average test scores (math) are included as a measure of the math educational opportunities at each school at the following spatial resolutions: Geographic School District, County, Commuting Zone, Metropolitan Statistical Area.
ABCD Subdomain: School
Number of Variables: 420
Notes and special considerations: See led_l_seda_pool
Release 5.0 Data Table: led_l_seda_pool
Measure Description: - Measure includes SEDA 4.0 dataset average test scores (reading/language arts and math)
The average test scores (math + reading/language arts) are included as a broad and cumulative measure of the educational opportunities at each school. For the following spatial resolutions: Geographic School District, County, Commuting Zone, Metropolitan Statistical Area, we linked to two types of “pool” methods:
- “pooled overall” (or pool) files contain estimates that are averaged across grades, years, and subjects
- “pooled by subject” (or poolsub) files contain estimates that are averaged across grades and years within subjects
The linked “pool” variables have the following disaggregation: average test score mean (averaged across grades, years, and subjects), average “learning rate” across grades, average “trend” in the test scores across cohorts, and average difference between math and reading/ language arts (RLA) test scores, along with their standard errors. Estimates are reported for all students and by demographic subgroups.
The linked “poolsub” variables have the following disaggregation: average test score mean in math and in RLA (averaged across grades and years), average “learning rate” across grades in math and in RLA, and average “trend” in the test scores across cohorts in math and in RLA, along with their standard errors.
ABCD Subdomain: School
Number of Variables: 556
Notes and special considerations: Data disaggregated by race or ethnicity were intentionally not released and while there are variable names disaggregated by race and ethnicity, they do not contain any data. The following disaggregation categories are included for each geographic unit:
_all all _fem female _mal male _mfg male minus female opportunity gap _ecd economically disadvantaged _nec not disadvantaged economically _neg not disadvantaged economically minus economically disadvantaged gap
Please note that in ABCD we have linked to the cohort standardized (CS) scale scores. The “CS” SEDA 4.0 files were used as recommended for research purposes in the SEDA documentation and include the Ordinary Least Squares and Empirical Bayes estimation methods. Following is the SEDA documentation on how to interpret CS scale scores:
“The CS scale is standardized within subject and grade, relative to the average of the four cohorts in our data who were in 4th grade in 2009, 2011, 2013, and 2015. We use the average of four cohorts as our reference group because they provide a stable baseline for comparison. This metric is interpretable as an effect size, relative to the grade-specific standard deviation of student-level scores in this common, average cohort. For example, a district mean of 0.5 on the CS scale indicates that the average student scored approximately one half of a standard deviation higher than the average national reference cohort scored in that same grade. Means reported on the CS scale have an overall average near 0 as expected.” (Stanford Education Data Archive Technical Documentation. Version 4.0. February 2021, pp 29-30; Find here).
Residential History-Derived Environmental Measures
Amenities and Services
Access to a number of parks, social services and amenities, and additional neighborhood demographics from the The National Neighborhood Data Archive (NaNDA) have been linked to participants’ primary, secondary, and tertiary addresses. NaNDA is a publicly available data archive containing contextual measures for locations across the United States (Find here).
Code and source files used to derive these measures can be found here: Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
The following variables from NaNDA were linked:
Parks (NaNDa)
Release 5.0 Table: led_l_parks
Measure Description: - Measure includes: - Total number of parks (per census tract and per county) - Total number of parks per census tract (top coded at 10) - Proportion of open park land within census tract
ABCD Subdomain: Amenities & Services
Number of Variables: 12
Notes and special considerations: None
Religious/Civic Organizations (NaNDa)
Release 5.0 Data Table: led_l_relciv
Measure Description: - Measure includes: - Total count of religious organizations (per census tract and per county) - Number of all religious organizations per 1000 people (per census tract and per county) - Total count of civic/social organizations(per census tract and per county) - Number of all civic/social organizations per 1000 people (per census tract and per county)
ABCD Subdomain: Amenities & Services
Number of Variables: 24
Notes and special considerations: None
Performing Arts and Sports Recreation Organizations (NanDa)
Release 5.0 Data Table: led_l_artsports
Measure Description: - Measure includes: - Total count of performing arts organizations (per census tract and per county) - Number of all performing arts organizations per 1000 people (per census tract and per county) - Total count of spectator sports organizations (per census tract and per county) - Number of all spectator sports organizations per 1000 people (per census tract and per county)
ABCD Subdomain: Amenities & Services
Number of Variables: 24
Notes and special considerations: None
Neighborhood Socioeconomic Status and Demographics (NaNDa)
Release 5.0 Data Table: led_l_nbhsoc
Measure Description: - The following were derived from the ACS 2013-2017 5-year estimates at the census tract level: - Proportion of people who are foreign born - Proportion of families with Income greater than 75K - Proportion of families with Income greater than $100K - Proportion 16+ civ labor force unemployed
In addition, three factors associated with neighborhood sociodemographics and structural characteristics are included (based on Morenoff et et., 2007): - Neighborhood disadvantage score is characterized by high levels of poverty, unemployment, female-headed families, households receiving public assistance income, and a high proportion of African Americans in a census tract. - Neighborhood affluence score represents a mix of characteristics associated with neighborhood affluence (concentrations of adults with a college education; with incomes>75K; and employed in managerial and professional occupations). - Neighborhood ethnic/immigrant score represents ethnic and immigrant concentration. Higher values indicate more Hispanic and foreign born in the census tract.
ABCD Subdomain: Amenities & Services
Number of Variables: 24
Notes and special considerations: Population density was set to -1 for tracts with zero population in a given year. Population and area densities are missing in certain years for a few census tracts whose FIPS codes changed at some point between 2003 and 2017.
Reference:
Morenoff, J.D., House, J.S., Hansen, B.B., Williams, D.R., Kaplan, G.A., & Hunte, H.E. (2007). Understanding social disparities in hypertension prevalence, awareness, treatment, and control: the role of neighborhood context [appendix A: supplemental material]. Social Science & Medicine, 65(9), 1853-1866. Find here. Please refer to Appendix A: Supplemental Material.
Built Environment
Walkability (EPA)
Release 5.0 Data Table: led_l_walk
Measure Description: The description for the smart location database can be found here. We used estimates based on 2010 census. The resulting variables include walkability index. The resolution of this database is at the census tract level
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Building Density (EPA)
Release 5.0 Data Table: led_l_densbld
Measure Description: The description for the smart location database can be found here. We used estimates based on 2010 census. The resulting variables include gross residential density (i.e., building density). The resolution of this database is at the census tract level
Release 5.0 Data Table: led_l_densbld
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Population Density (SEDAC)
Release 5.0 Data Table: led_l_denspop
Measure Description: The estimation is based on the 2010 census tract while adjusted based on potential under-reporting across the world (UN adjusted). Data are from NASA Socioeconomic Data and Applications Center (SEDAC)
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Traffic Density (Kalibrate)
Release 5.0 Data Table: led_l_traffic, led_l_roadprox
Measure Description: Traffic counts were derived for primary, secondary, and tertiary address at 1km x 1km resolution using data from Kalibrate. These measures indicate the total volume of cars passing through a given area across the entirety of 2016.
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Road Proximity (Kalibrate)
Release 5.0 Data Table: led_l_roadprox
Measure Description: Road proximity was derived for primary, secondary, and tertiary address at 1km x 1km resolution using data from Kalibrate (https://downloads.esri.com/esri_content_doc/dbl/us/Kalibrate_TrafficMetrixManual_Version140.pdf). These measures capture the number of meters participants’ addresses were from major roads.
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Urban/Rural Area (Census)
Release 5.0 Data Table: led_l_urban
Measure Description: Urban area is a categorical variable indicating if participants’ addresses were in census tracts considered to be urban (50,000 or more people), urban clusters (at least 2,500 and less than 50,000 people) or rural (less than 2,500 people per tract, and/or not included in urban areas or clusters). These measures were derived from publicly available census data from 2010 (https://www.census.gov/programs-surveys/geography/about/faq/2010-urban-area-faq.html).
List of values 1: Urbanized Areas (UAs) of 50,000 or more people; 2: Urban Clusters (UCs) of at least 2,500 and less than 50,000 people. 3: “Rural” encompasses all population, housing, and territory not included within an urban area.
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: The 4.0 version of the data dictionary contained errors in the labeling used for the urbanicity of participants’ census tracts. The correct coding, below, was found in the 4.0 release notes and is now corrected in the 5.0 data dictionary.
Crime (ICPSR)
Release 5.0 Data Table: led_l_crime
Measure Description: The description for this database can be found here. To maintain a stability on the crime estimates, we used three-year averages, 2010 to 2012. The resolution of this database is at the county level.
ABCD Subdomain: Built Environment
Number of Variables: 24
Notes and Special Considerations: None
Lead (Pb) Risk (Vox)
Release 5.0 Data Table: led_l_leadrisk
Measure Description: All participants’ primary, secondary, and tertiary addresses were, if valid, geocoded at the census tract level with respect to an estimated risk of lead (Pb) exposure. These data are from Vox.com (https://www.vox.com/a/lead-exposure-risk-map). These risk scores (deciles, 1-10, 10 being the most at risk) were calculated for each census tract and reflect a weighted sum between two summary measures of that census tract: the age of homes and the rate of poverty. The housing age and poverty rate subcomponents of the lead risk score generally reflect an estimated probability of lead exposure given the age of homes (i.e., older homes are more likely to contain lead hazards) and the proportion of individuals living in poverty (-125% of poverty level). The housing-age and poverty-rate scores for valid primary, second, and tertiary addresses are also included.
ABCD Subdomain: Built Environment
Number of Variables: 3
Notes and Special Considerations: None
Vehicle Density (ACS)
Release 5.0 Data Table: led_l_densveh
Measure Description: Vehicle density was calculated using data from the 2014-2018 American Community Survey 5-year estimates for primary, secondary, and tertiary addresses at the census tract level. Vehicle density was calculated in two ways: 1) As an area estimate (aggregate number of variables in a census tract per square mile of land area), and 2) as a population density (aggregate number of vehicles available in a census tract per individual). Vehicle density may be associated with neighborhood residents’ levels of exposure to noxious chemicals and their vulnerability to vehicle-related injuries or fatalities.
Code and source files used to derive these measures can be found here: Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Built Environment
Number of Variables: 6
Notes and special considerations: None
Air Pollution
Satellite-based Pollution Measures for Prenatal Addresses
Release 5.0 Data Table: led_l_prenatal
Measure Description: A prenatal period of air pollution exposure was estimated from previous lifetime addresses for the child obtained at the 1-year follow-up, beginning with an approximate date of conception (calculated by subtracting 279 days from the reported birthdate) and continuing through the reported date of birth. Hybrid spatiotemporal models were used to create daily estimates for PM2.5, ozone (O3), and nitrogen dioxide (NO2) for the birth years [2004-2009] at a resolution of 1 km2 [1-3]. The daily estimates of these air pollution exposures during the prenatal period of each participant were assigned to the corresponding reported birth year address(es), and daily estimates were averaged across the entire prenatal period to represent the prenatal exposure. For study participants with multiple self-reported addresses for the same calendar year, a weighted average of air pollution exposure was calculated in order to provide a single estimated exposure value for every participant. This was calculated by weighting the prenatal average exposure values for each residence by the reported percent of time spent at that residence; the sum of these weighted exposure averages was then divided by the sum of all reported percentages (regardless of total sum of time between addresses) (Eq 1).
Weighted Average = (Air PollutionAddress_1 * Percent timeAddress_1) + (Air PollutionAddress_2 * Percent timeAddress_2) / (Percent timeAddress_1 + Percent time Address_2)
The majority of participants (7,837 [91%]) had reported an address(es) corresponding to the calendar year congruent with the child’s birth year as well as the reported percentages of time spent at each address summed to 100%. For a subset of participants (806 [9%]), errors occurred in the reporting of addresses or percentages of time spent at each address, including 386 [4%] participants who reported percentages of time at multiple addresses that did not sum to 100% and 420 [5%] participants who reported addresses with no corresponding percent of time spent living at that address. Given these reporting errors, it is unclear exactly how much time was spent at various addresses during the exposure period, thus introducing uncertainty in exposure classification.
In order to convey this uncertainty, a quality control (qc) variable was created in order to rank prenatal data according to how accurate we expect the air pollution exposure weighted average to be. This qc variable, ‘prenatal_exposure_qc’ is defined as follows:
| prenatal_exposure_qc | Meaning | Number of Participants (Percent) |
|---|---|---|
| 1 | Reported percentages at all addresses sum to 100% | 7837 (90.67%) |
| 2 | Reported percentages at all addresses sum to totals ranging from 90 to 110% | 70 (0.81%) |
| 3 | Reported percentages at all addresses sum to totals less than 90% or greater than 110% | 316 (3.66%) |
| 4 | Reported percentages at addresses were missing | 420 (4.86%) |
For more information on how residential histories were obtained see As reported in Fan et al. 2021 (Find here),
ABCD Subdomain: Air Pollution
Number of Variables: 4
Notes and Special Considerations: A number of assumptions and limitations of these data should be noted and highlighted in research and publications that use these derived ABCD data.
- First, ABCD did not specifically ask caregivers where they resided when pregnant or if addresses changed during pregnancy, but rather addresses reported during a given calendar year and coinciding with the participant’s birth year were considered representative for the pregnancy period.
- Second, caregivers were only asked to provide move-in and move-out information in terms of calendar year, without month information provided. Thus, chronological sequence of addresses that overlap within a given year can not be deciphered and this error may factor into the weight sum scores computed above.
- The weighted sum equation was applied to all individuals regardless if their provided sum living in a location did or did not add to 100%. The error inherent to this approach can be reduced by only including individuals with a prenatal_exposure_qc variable equal to 1 in analyses using these data.
- The approach used above assigns a prenatal exposure based on daily exposures over the 9-months prior to a child’s birthdate. This approach assumes all children were delivered full-term, which is not necessarily true. It may be best to limit the sample of participants in analyses using these data that can be confirmed to have been in utero for full-term using caregiver report information provided about the child’s medical history in other portions of the ABCD dataset.
References:
Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment international, 130, 104909. Find here
Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2020). Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. Environmental science & technology, 54(3), 1372–1384. Find here
Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., & Schwartz, J. (2020). An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States. Environmental science & technology, 54(18), 11037–11047. Find here
Satellite-based Particulate Measures
Release 5.0 Data Table: led_l_particulat
Measure Description: Annual estimates for 15 components were linked to baseline addresses for ABCD participants. Spatiotemporal models were used to create annual estimates for baseline years 2015-2018 at a resolution of 50m. Since the models are based on the calendar year, the number of days spent in each calendar year was calculated to determine an annual estimate for the year leading to the baseline date. For each calendar year (prior and visit year), the number of days spent in each year was divided by 365 with adjustments for leap year. These percentages were applied to each year’s estimates and summed to represent each participant’s estimated annual exposure.
Estimate = (ParticulatePrior Year * Percent timePrior Year) + (ParticulateVisit Year * Percent timeVisit Year)
Estimate = (ParticulatePrior Year * (DaysPrior Year 365 + ParticulateVisit Year) / (DaysVisit Year365)
Further details can be found here .
ABCD Subdomain: Air Pollution
Number of Variables: 45
Notes and special considerations: None
Reference:
Jin, T., Amini, H., Kosheleva, A., Danesh Yazdi, M., Wei, Y., Castro, E., Di, Q., Shi, L., & Schwartz, J. (2022). Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration. Environmental health : a global access science source, 21(1), 96. Find here
Meteorology and Exposures
Temperature Estimates (PRISM)
Release 5.0 Data Table: led_l_temp
Measure Description: Daily maximum modeled temperature (“Tmax”) for each of seven days preceding and including each participant’s baseline interview date was linked to the participants’ residential address. Meteorologic data provided for linkage ranged from January 1, 2016 to December 31, 2018 and is modeled to 800m spatial resolution from the PRISM Climate Group at Oregon State University. Tmax data is in degrees C See, e.g., Spangler K, Weinberger K & Wellenius G. (2019). Suitability of gridded climate datasets for use in environmental epidemiology. Journal of Exposure Science & Environmental Epidemiology, 29 (777-89)
ABCD Subdomain: Meteorology and Exposures
Number of Variables: 21
Notes and Special Considerations: It is important to note that the days preceding and the day of baseline interview were linked based on ‘interview_date’, which depending on the individual may or may not include the same tasks/scans across all participants. Users should confirm interview_date matches the date of any assessment (i.e. scan session day 1 or 2, or behavioral tests) at an individual level in order to accurately use these data.
It is important to note that not all participants currently have temperature data as part of 5.0 for baseline visit dates. It is highly recommended that users investigate any possible bias in who does and does not have maximum temperature if using these data.
Errors were identified in the 4.0 temperature linkages and should not be used given these issues. These errors were minimized for the provided data in the 5.0 release.
Vapor Pressure Deficit (VPD) Estimates (PRISM)
Release 5.0 Data Table: led_l_vpd
Measure Description: Daily maximum vapor pressure deficit (“VPDmax”) for each of seven days preceding and including each participant’s baseline interview date was linked to the participants’ residential address. Meteorologic data provided for linkage ranged from January 1, 2016 to December 31, 2018 and is modeled to 800m spatial resolution from the PRISM Climate Group at Oregon State University. VPDmax data is in hPA.VPDmax data can be used to calculate daily relative humidity and maximum heat index. See, e.g., Spangler K, Weinberger K & Wellenius G. (2019). Suitability of gridded climate datasets for use in environmental epidemiology. Journal of Exposure Science & Environmental Epidemiology, 29 (777-89)
ABCD Subdomain: Meteorology and Exposures
Number of Variables: 21
Notes and Special Considerations: It is important to note that the days preceding and the day of baseline interview were linked based on ‘interview_date’, which depending on the individual may or may not include the same tasks/scans across all participants. Users should confirm interview_date matches the date of any assessment (i.e. scan session day 1 or 2, or behavioral tests) at an individual level in order to accurately use these data.
It is important to note that not all participants currently have vapor pressure deficit data as part of 5.0 for baseline visit dates. It is highly recommended that users investigate any possible bias in who does and does not have maximum vapor pressure deficit values if using these data.
Errors were identified in the 4.0 VPD linkages and should not be used given these issues. These errors were minimized for the provided data in the 5.0 release.
Elevation of Address (Google API)
Release 5.0 Data Table: led_l_elevation
Measure Description: This is based on direct query to the Google map, which contains elevations given where participants live. All variables are truncated to avoid potential identification while maintaining overall variations for analytical purposes.
ABCD Subdomain: Meteorology and Exposures
Number of Variables: 3
Notes and Special Considerations: None
Estimates of Environmental Noise (Harvard)
Release 5.0 Data Table: led_l_noise
Measure Description: The spatially varying noise data were obtained from a georeferenced noise model of expected environmental sound levels created by Dr. Peter James at Harvard. The model capitalized on acoustical data from 1.5 million hours of long-term measurements from 492 urban and rural sites located across the contiguous United States during 2000-2014. Geospatial sound models were developed based on these measurements to interpret and predict sound levels across the contiguous United States (Mennitt et al. 2014; D Mennitt and K Fristrup 2016). This geospatial sound model formulated relationships between sparsely measured acoustical metrics and nonacoustic environmental factors such as topography, climate, hydrology, and anthropogenic activity. The method utilized random forest, a tree-based machine learning algorithm, to perform the regression. The resulting non-time-varying geospatial sound model enabled mapping of sound levels at 270m resolution.
All noise metrics are A-weighted. A-weighting is an adjustment that reflects how the human ear perceives sound across the frequency spectrum.
- The L10 is an exceedance metric that corresponds to the sound pressure level exceeded 10% of the time.
- The L50 is an exceedance metric that corresponds to the sound pressure level exceeded 50% of the time.
- The L90 is an exceedance metric that corresponds to the sound pressure level exceeded 90% of the time.
- Leq, or the average sound level in decibels equivalent to the total sound energy measured in a 24-hour period;
- LeqNight, or the average sound level in from the hours of 10p-7a; and
- Ldn, or the day-night average sound level, which is the average sound level over a 24-hour period where sound from 10pm-7am is upweighted by 10 dB.
- Clarification: The day-night average sound level (Ldn or DNL) is the average noise level over a 24-hour period. The noise level measurements between the hours of 10pm and 7am are artificially increased by 10 dB before averaging. This noise is weighted to take into account the decrease in community background noise of 10 dB during this period. There is a similar metric called day-evening-night average sound level (DENL) commonly used in Europe or community noise exposure level (CNEL) used in California legislation; that is, the DNL with the addition of an evening period from 7 PM to 10 PM when noise level measurements are boosted 5 dB to account for the approximate decrease in background community noise by 5 dB during this period.
- Variables ending in _ant ant is anthropogenic, and _exi is total noise (anthropogenic and natural) sources. Natural sources were not included given the limited variability seen within largely urban locations in which most participants reside.
ABCD Subdomain: Meteorology and Exposures
Number of Variables: 27
Notes and Special Considerations: A number of assumptions and limitations of these data should be noted and highlighted in research and publications that use these derived ABCD data.
- Sources of the noise are not identifiable based on these models, since the source and sound level is assumed to be homogenous.
- All levels were projected for the summer season.
References:
D. J. Mennitt D.J., K. Fristrup, K., and L. Nelson, L. (2013) Mapping the extent of noise on a national scale using geospatial models. Presented at the 166th meeting of the Acoustical Society of America, San Francisco, USA. Find here
Mennitt, Daniel J., Fristrup, Kurt M. (2016). Influence factors and spatiotemporal patterns of environmental sound levels in the contiguous United States. Noise Control Engineering Journal, 64 (3), 342-353. Find here
Selected Environmental Justice Screening and Mapping Tools (EJSCREEN) Measures
Release 5.0 Data Table: led_l_ejscreen
Measure Description: Three measures from the EJSCREEN and National Air Toxics Assessment (NATA) are available for participants’ primary, secondary, and tertiary addresses at the census tract level. These data were released in 2018, based on 2014 emissions data. Three measures include: 1) Diesel particulate matter level in the air, in g/m3; 2) lifetime cancer risk from inhalation of air toxics; and 3) ratio of exposure concentration to health-based reference concentration. Additional technical information about the EJSCREEN (including information about downloading the data) can be found here.
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Meteorology and Exposures
Number of Variables: 9
Notes and special considerations: None
Natural Space and Satellite
Measure of Land Cover and Tree Canopy (NLCD)
Release 5.0 Data Table: led_l_nlcd
Measure Description: Measures of land cover (e.g., low-, medium-, or high-density development, forest, wetland) and tree canopy derived from the National Land Cover Database (NLCD) are available for participants’ primary, secondary, and tertiary addresses at the census tract and county level of aggregation. Land cover is measured as a percentage of all land within the tract/county. Tracts/counties only include the mainland US (i.e., Alaska and Hawaii are not included).
Land cover and tree canopy measures are derived from the National Land Cover Database Find here. Measures provided here use the 2019 iteration of 2016 data. Shapefiles for census tracts were obtained from the US Census bureau TIGER/Line shapefiles for 2010 Find here.
Code used to derive county and census tract levels are here:
Schertz, K., Kardan, O., Rosenberg, M. D., & Berman, M. (2021). National Land Cover Database at Census Tract and County Level (2016, mainland US). Find here
Number of Variables: 90
Notes and special considerations: None
ABCD Subdomain: Natural Space and Satellite
Land-Use Measures (NLT)
Release 5.0 Data Table: led__urbsat
ABCD Subdomain: Natural Space and Satellite
Measure Description: Here we include 11 variables comprising an “Urban-Satellite” dataset within ABCD which quantify the percentage or quantity of urbanicity measures such as nighttime lighting, population, vegetation, water, and others, within a 100m radius. These data provide information which can be used to link neuroimaging and public health data to satellite imagery measurements of macro environmental factors.
ABCD Subdomain: Natural Space and Satellite
Number of Variables: 33
Notes and special considerations: None
References: Xu, J., Liu, X., Li, Q., Goldblatt, R., Qin, W., Liu, F., Chu, C., Luo, Q., Ing, A., Guo, L., Liu, N., Liu, H., Huang, C., Cheng, J., Wang, M., Geng, Z., Zhu, W., Zhang, B., Liao, W., Qiu, S., … IMAGEN Consortia (2022). Global urbanicity is associated with brain and behaviour in young people. Nature human behaviour, 6(2), 279–293. Find here
Residential Segregation
Dissimilarity Index (ACS)
Release 5.0 Data Table: led_l_dissim
Measure Description: The most common measure of Evenness is the Dissimilarity Index. This index measures the fraction of one group that would have to move to another area, in order to equalize the population distribution. While this measure is computed comparing the ratio of two racial/ethnic groups to each other within a census tract (derived from 2014-2018 ACS estimates), these values are aggregated for a given Metropolitan Service Area (MSA). Thus, we have linked Dissimilarity Indices for the MSA in which a participant may live, and not the census tract. The following contrasts were computed: Black vs. non-Hispanic white, Asian vs. non-Hispanic white, Hispanic vs. non-Hispanic white, nonwhite vs. non-Hispanic white.
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Residential Segregation
Number of Variables: 12
Notes and special considerations: None
Exposure/Interaction Index
Release 5.0 Data Table: led_l_expint
Measure Description: The most commonly used measures of the Exposure dimension are the Indices of Isolation and Interaction. The Exposure dimension measures the likelihood of population subgroups interacting with one another using the Index of Interaction. On the other hand, the Index of Isolation measures how likely it is that one group is isolated, or only surrounded by other members of the same group. We have linked a version of the Exposure/Interaction Index that indicates the probability that one member of a group may interact with a member of a contrast group. While the measure is computed at the census tract (derived from 2014-2018 ACS estimates), these values are aggregated for a given Metropolitan Service Area (MSA). Thus, we have linked Exposure/Interaction Indices for the MSA in which a participant may live, and not the census tract. The following contrasts were computed: Black vs. non-Hispanic white, Asian vs. non-Hispanic white, Hispanic vs. non-Hispanic white, nonwhite vs. non-Hispanic white.
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Residential Segregation
Number of Variables: 12
Notes and special considerations: None
Multi-group Entropy Index
Release 5.0 Data Table: led_l_entropy
Measure Description: Both the Index of Dissimilarity and Interaction Index can only measure the segregation of two groups compared to each other. The Multi-Group Entropy Index measures the spatial distribution of multiple groups simultaneously (see here). This measure is dependent on the number of categories included in the computation, and for the linked measures. Multi-Group Entropy Indices at the census tract were derived using the following categories:
Three measures are available as of this release: Multi-Group Entropy Scores at the census tract and metro-level, and a Multi-Group Entropy Index that standardizes the sum of the census-tract scores within a metro by the standard deviation of the metro-level distribution.
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Residential Segregation
Number of Variables: 9
Notes and special considerations: None
Index of Concentration at the Extremes
Release 5.0 Data Table: led_l_ice
Measure Description: Index of Concentration at the Extremes (ICE) quantifies how persons in a specified area are concentrated into the top vs bottom of a specified societal distribution. We’ve linked two versions of the ICE: one in which we compare the top income quintile to lowest income quintile (income ICE) as a measure of economic segregation, and one in which we compare the top income quintile for non-Hispanic white to lowest income quintile of non-Hispanic Black individuals in a census tract (income + BW ICE) as a measure of racialized economic segregation.
ABCD Subdomain: Residential Segregation
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
Number of Variables: 6
Notes and special considerations: None
Reference: Krieger, N., Kim, R., Feldman, J., & Waterman, P. D. (2018). Using the Index of Concentration at the Extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). International journal of epidemiology, 47(3), 788–819. Find here
Gi* Statistics (ICPSR)
Release 5.0 Data Table: led_l_gi
Measure Description: From ICPSR Documentation:
Local Getis-Ord Gi* statistics were calculated as a measure of residential racial segregation (Find here). Measures were calculated at the census tract level on the proportion of non-Hispanic white, non-Hispanic Black, non-Hispanic Asian and Pacific Islander for 1990 and 2000 census, and Hispanic persons per census tract. Gi* statistics are Z-scores that compare the proportion of the population in the focal tract and its neighboring tracts, to the average proportion of a larger geographic unit. For the majority of tracts, the larger geographic unit was the Core-Based Statistical Area (CBSA) these tracts belonged to, and for the minority of tracts that fell outside the boundaries of a CBSA, the County was used as the larger unit.
Data for the measures were obtained from the IPUMS National Historical Geographic Information System (NHGIS) data finder. Data were downloaded for the 1990 and 2000 census, and the 2006-2009, 2010-2014, and 2015-2019 5-year American Community Survey (ACS) estimates. Geographically standardized time series tables were used for 1990 and 2000 census data. All other ACS data were standardized to 2010 census tract boundaries.
Gi* statistics were calculated using both Rook and Queen conceptualization of spatial relationships. With Rook contiguity, neighbors are determined by those that share a common edge only, while Queen contiguity neighbors are those that share both an edge or a “corner” (common vertex). See detailed documentation for further details.
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Residential Segregation
Number of Variables: 24
Notes and special considerations: None
Neighborhood Composite Measures
Area Deprivation Index (ADI)
Release 5.0 Data Table: led_l_adi
Measure Description: The Area Deprivation Index (ADI) was calculated based on a published study for the socioeconomic inequality impact on health Find here. The database we queried is the 2011 - 2015 American Community Survey 5-year summary. Although the area deprivation index has 18 different sub-scores, the recommendation is to use the national percentiles. The resolution of this is at the census tract level.
ABCD Subdomain: Neighborhood Composite Measures
Number of Variables: 57
Notes and Special Considerations: None
Child Opportunity Index (COI) 2.0
Release 5.0 Data Table: led_l_coi
Measure Description: The COI 2.0 is a composite index derived at the census tract level that measures neighborhood resources and conditions relevant to children’s healthy development. In addition to the COI index, there are 3 domain composite scores available: education, health and environment, and social and economic. There are also scores available for the 29 indicators comprising the composite scores. Raw indicator scores and z-scores are provided. The COI 2.0 data is derived from U.S. census tracts for 2010 and 2015.
See the COI 2.0 technical documentation for more details of variable sources and computations (https://data.diversitydatakids.org/dataset/coi20-child-opportunity-index-2-0-database)
ABCD Subdomain: Neighborhood Composite Measures
Number of Variables: 262
Notes and Special Considerations: None
Community Health Burden
Behavioral Health Measures (PLACES)
Release 5.0 Data Table: led_l_places
Measure Description: Measures from the PLACES dataset are available for participants’ primary, secondary, and tertiary addresses at the census tract level. The PLACES dataset is an expansion of the 500 Cities Project and is available from the Center for Disease and Control and Prevention (CDC), the Robert Wood Johnson Foundation (RWJF) and CDC Foundation. The data linked to ABCD are from the PLACES 2020 release, which reflect measures derived using the 2017/2018 Behavioral Risk Factor Surveillance System (BRFSS) data, and reflect years 2014-2018. Included are 27 measures for the entire United States: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. Additional technical information about PLACES (including information about downloading the data) can be found here
Code and source files used to derive these measures can be found here:
Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Abad, S., & Herting, M. (2023). Social and Environmental Context Variables for ABCD Linked External Data Release 5.0. Find here
ABCD Subdomain: Community Health Burden
Number of Variables: 84
Notes and special considerations: None
Social Services (NaNDa)
Release 5.0 Data Table: led_l_socsrv
Measure Description: - Measure Includes senior centers, youth centers, food banks, job training programs, and day care centers:
ABCD Subdomain: Amenities & Services
Number of Variables: 12
Notes and special considerations: None
Source