Linking measures from the Opportunity Atlas to the ABCD Study allows for objective measures of neighborhood economic opportunity to study in relation to health outcomes in ABCD youth. However, while the Opportunity Atlas estimates can be used as predictors of economic opportunity for children today, it is important to combine these estimates with additional data to determine applicability to neighborhoods that have undergone substantial change in the last several decades. There are vast differences in neighborhood access to opportunities and quality of conditions for children across America, including access to good schools and healthy foods, green spaces such as safe parks and playgrounds, safe housing and cleaner air. These inequitable neighborhood differences can negatively influence the current living conditions of a child, as well as development throughout childhood and subsequent health outcomes in adulthood . Children who grow up in neighborhoods with access to more educational and health opportunities are more likely to grow up to be healthy adults. The COI 2.0 is a national contemporary measure of neighborhood opportunity, comprising a comprehensive dataset that aggregates 29 indicators of neighborhood conditions for 72,000 census tracts in the United States. Beginning with the ABCD 4.0 data release, the ABCD Study provides scores for the COI 2.0 overall index, and the three domain indices that comprise the overall index: education , health and environment ,grow table and social and economic opportunities . We have also included scores for the 29 indicators that comprise the three domains. Detailed documentation describing the indicators that comprise each of the domains as well as the dataset source and year for each of the 29 indicators can be found in Supplemental Table 4 and the COI 2.0 technical documentation.

Given the diverse demographics of the ABCD Study participants, linking the COI 2.0 gives us objective measures of neighborhood opportunities for participants so that we can assess the influence of neighborhood quality on adolescent health and potential emerging health disparities. Crime rates are an important neighborhood characteristic that can cause distress on individuals’ mental well-being and has been linked with various children’s developmental outcomes . However, the impact of crime within the context of other neighborhood variables and how these impact neural mechanisms during children’s development is less clear. To empower researchers to investigate the impact of local crime rates in the broader context of the built environment, we obtained county-level crime statistics from Uniform Crime Reporting Data . In addition to the total crime rates, we also provided subcategories of the crime, including violent crimes, drug violations, drug sales, marijuana sales, drug possessions, and DUIs. The removal of lead from gasoline and house paint has been associated with dramatic declines in childhood lead exposure, which, given lead’s effects on child development , has been regarded as one of the greatest public health achievements of the 20th century . Unfortunately, exposure to lead remains a dire public health concern, as risk of exposure persists through lead-contaminated water pipes and ingestion of lead-contaminated dust and soil per leaded gas vehicular emissions and non-remediated lead-based paint . Collectively, children living in older homes are at greater risk of exposure . In 2016, Rad Cunningham at the Washington State Department of Health developed a nationwide map quantifying risk of lead exposure at the census-tract level, in which risk of lead exposure was a function of housing age and poverty rates . More specifically, “housing age” reflected the estimated number of homes in each census tract with lead-paint hazards based on decades of construction .

Marshall et al. and Wheeler et al. reported that these lead-risk estimates were valid proxies of childhood lead exposure, in that, across several states and cities, there was a greater prevalence of elevated blood lead levels in census tracts with higher risk scores; further research will permit determining the extent to which these lead-risk scores are predictive of individuals’ observed blood-lead levels. Accordingly, the ABCD Study incorporated the aforementioned lead-risk scores using code freely available on GitHub , to estimate census tract lead-risk scores of participants’ primary, secondary, and tertiary residential addresses. Air pollution, or the presence of toxic particulates and gases within the atmosphere, is one of the most widespread environmental issues affecting global health today. Adverse effects of air pollution exposure on mortality , and morbidity , as well as respiratory and cardiovascular health are well documented , but a growing body of evidence suggests that air pollution exposure may also compromise brain development with long lasting effects on cognition and mental health . However, because of challenges to accurately model air pollution exposure and a dearth of well-powered longitudinal neuroimaging studies that span adolescence, much remains unknown regarding the effects of air pollution on neurocognitive development during adolescence . ABCD provides a unique opportunity to investigate the effects of air pollution exposure during critical developmental periods on adolescent brain development and behavior. Using state-of-the-art air pollution modeling at high spatial resolution created by colleagues at Harvard University, ABCD provides a number of measures capturing participant’s residential exposure to three criteria ambient air pollutants: fine particulate , nitrous dioxide , and ozone . These ambient air pollutant exposure estimates are derived from a hybrid spatiotemporal model at the 1 × 1 km2 spatial resolution . This hybrid model combines the strengths of satellite-based aerosol optical depth models, land-use regression, and chemical transport models.

This model has previously been trained for the continental United States from 2000 to 2016 and tested with left-out monitors. Daily 1 × 1 km2 ambient exposure estimates were then averaged across the 2016 calendar year and linked to the nearest estimate of the 1 × 1 km2 grid for the latitude and longitudinal of the baseline residential addresses. In addition to computing average annual estimates for PM2.5, NO2, and O3, ABCD includes the minimum and maximum levels of all three pollutants in 2016 in the 4.0 annual release, as well as the number of days that PM2.5 levels exceeded the National Ambient Air Quality Standards threshold of 35 µg/m3 . By including this array of measures, researchers have the opportunity to gain insight into differential effects of long-term versus focal air pollution exposure, as well as the degree to which National Ambient Air Quality Standards’ thresholds are meaningful in terms of preventing adverse effects of air pollution exposure on the adolescent brain. The existing literature suggests that temperature, including heat and cold stress, can negatively impact how the human body functions, and cognitive functioning is no exception . Studies suggest heat waves can impact test scores across American high school students and that fluctuations in temperature may also increase symptom severity in individuals affected by certain neurological conditions . Moreover, climate change has already made temperatures hotter, producing more intensive heat waves in the U.S. . Thus, characterizing the climate that participants may have experienced at home prior to ABCD Study visits may be useful to determine how seasons or weather may relate to individual differences in brain functioning. By considering the climate, the ABCD Study holds the potential to answer pertinent questions regarding potential effects of hotter and/or greater fluctuations in temperature on brain function in today’s youth. Thus, to account for potential differences in climate, the ABCD Study has mapped temperature, humidity, and elevation to residential addresses as part of the 4.0 ABCD data release. Maximum daily temperature and vapor pressure deficit data derived at the 30-arcsec spatial resolution from 1981 through June of 2020 ,vertical rack were mapped to the residential address for the 7 days prior to each individual’s baseline study visit. Given that temperature and air pressure also decrease as a function of elevation, for completeness, elevation was also mapped to the residential address using the Google Maps Elevation API . The LED Environment Working Group strives to include additional information about the built and natural environments of all participants in the ABCD Study. These data provide an additional perspective about differences both between study sites and individual differences among children within even a single given study site location. Integrating these external environmental factors are likely important in considering both mediating and moderating effects and allows for important questions to be asked with implications for policies that may help ensure all children can thrive. That is, given the wealth of additional data collected in the ABCD Study, the addition of understanding the built and natural environment in ABCD provides the opportunity to think more broadly about how these factors may influence neurodevelopment of children within the established social determinants of health framework of public health . Specifically, health outcomes, including neurodevelopment, cognition, and mental health as measured extensively by the ABCD Study, have been recognized to be influenced by complex interactions among environmental, social, and economic factors that are ultimately closely tied to one another . Dahlgren and Whitehead provided a visual representation of such complex processes as a model of the main determinants of health and well-being in public health, which has since helped shape public health policy at both national and global scales . Thus, capturing the broader physical environment makes the ABCD Study an ideal resource for researchers interested in studying how various distal and proximal factors may impact developing children and their health.

While a number of development cognitive research studies have focused on individual factors, including socio-demographic factors , lifestyle , and social environments , additional natural and built environmental factors including neighborhood quality, community-level access to resources and opportunities, and exposure to harmful substances, provides an additional layer as to understanding and identifying key factors of neurodevelopment and to promote policies that lead to better health outcomes for all children across America. Specifically, these data can allow for researchers to examine if upstream built and natural factors might account for and/or moderate associations between physical activity and brain development, understanding the link between screen-time and mental health, determining how neighborhood conditions may impact the formation of peer groups, or exploring how recreational activities may moderate the relationship between adverse neighborhood conditions and mental health. In doing so, not only may we have a better understanding of the complex associations between the various factors contributing to neurodevelopment across childhood and adolescence, but research findings may also point to possible public health targets for intervention and treatment. While there are clear strengths in mapping the environmental context of today’s youth in the ABCD Study, there are also several important technical limitations as well as considerations for researchers planning to use and interpret these data. A vital consideration to this type of geospatial research and the variables derived from it, is the accuracy of the assignment of the exposure assessment at any given time.Any given geospatial database has both a spatial and temporal component. How these data were derived, and the degree of resolution is important to consider. For example, census tracts can be rather large, whereas in urban areas drastic differences in the environment can sometimes be noted to vary from street to street. Furthermore, individuals who live in the same census tract should not be considered to have the same experiences or the same amount of exposure in the neighborhood as others with similar demographics. Moreover, many times, geospatial databases are compiled after data is available from other sources, such as the American Community Survey or the Environmental Protection Agency. Thus, exposure estimates can often reflect a snapshot in time that may or may not overlap directly with the time period that the child was at that residential location; requiring the researcher to consider if the exposure of interest can or cannot be assumed to be stable beyond the temporal domains of the dataset. For example, many databases may create variables using 5-year averages that have then been linked to the baseline residential addresses which were collected in 2016–2018. Another technical challenge is that retrospective address collection is hindered by recall bias, or the differences in the accuracy or completeness of caregivers in the ABCD Study to recall address details over the 9–10 years prior to study enrollment. In addition, exposure assessment based on residential geospatial location also fails to capture individual data on percentage of time in which children in the current study spend time at their primary address versus other daily activities and/or various locations, such as in school. Of course, it is important to note that misclassification of exposure may be lower for children in that they may spend more of their time around the home, as compared to other populations such as adults who may spend more time commuting, time at work, or so forth.