The first column of Fig. 3 illustrates this data type using the Area Deprivation Index, a measurement of neighborhood deprivation derived from the American Community Survey. The second column in Fig. 3 illustrates traffic counts, which are point data that were obtained by surveying stations at various geographical locations. In contrast, raster data are usually obtained by model estimation, incorporating multiple sources such as satellite imaging and ground station surveys, as is seen for fine particulate matter in the third column of Fig. 3. The curated GIS database compiled by the ABCD Study LED Environment Working Group includes both vector and raster data of multiple built and natural environmental contextual variables. As shown in Table 1 and outlined in greater detail below, various environmental datasets have been used to map environmental factors to the state-, census-, residential-level for ABCD Study participants to date. Youth grow up in overlapping circles of cultural and socio-political contexts, from their local family and neighborhoods to the states and countries in which they live. We typically focus on the experience of stigma and bias at a relatively local level . Critically, there are also important indicators of more systemic or structural bias reflected in social norms at the community or institutionalized laws, policies and practices that may either reflect the behavior of individuals or shape the behavior of individuals in youths’ local environment. However, we rarely directly examine the relationship between objective measures of systemic/structural bias and function in youth. The ABCD Study provides a novel opportunity to address such critical questions with empirical data, given the geographic variability of the sites involved in the ABCD Study, which affords significant divergence across youth in their exposure to such systemic biases. To address such questions, colleagues at Harvard University created state-level indicators of three types of structural stigma : gender , race , and ethnicity . This information was linked to each youth in the ABCD Study as a function of their baseline site of participation and does not yet include information about whether the child moved to a different state, which may have different state level indicators, at later visits. To create these state-level measures, they used several types of data described in detail in . First, they obtained data about implicit and explicit attitudes about each of these three identity groups aggregated at the state-level, derived from large-scale projects that spanned several years: Project Implicit , the General Social Survey , and the American National Election Survey . Second, for information on gender, they obtained state-level data of women’s economic and political statuses and information about reproductive policies, such as information about availability of abortion providers. Third, for information on attitudes towards Latinx individuals, they examined state-level policies on immigration, recognizing that many Latinx individuals are not immigrants but that such state-level policies likely influence the experience of all individuals in the community with that identity.
These data can be used to examine how these state level biases interact with youth’s identities to predict a range of factors,mobile vertical rack such as educational experience, mental health, brain development, and substance use/abuse. In the United States, public acceptance of cannabis use has increased alongside increased access because of broader cannabis legalization. Currently, 36 states have legalized either recreational or medical cannabis use.However, among younger adolescents , greater exposure to cannabis advertisements was associated with greater use, intention to use, and positive expectancies . The difference in results as a function of age highlights the importance of understanding how cannabis regulations affect younger cohorts of children and adolescents who may have greater exposure to cannabis advertisement after living in an environment with legal access to cannabis for a longer period. Furthermore, the ABCD Study is an ideal dataset to examine the effects of cannabis legalization because there are 21 sites located in 17 states with various state cannabis policies. In addition, the ABCD Study is collecting detailed substance use data unlike other national surveys. Cannabis legalization categories were assigned to participants based on their state of residence. The four cannabis legalization categories are: 1. Recreational – allows adults to use cannabis for recreational purposes, 2. Medical – allows adults to use cannabis for medical conditions, 3. Low THC/CBD – allows adults to use cannabis that is low in THC and high in CBD for medical conditions, and 4. No legal access to cannabis – forbids access to cannabis. Urbanicity can provide information as to the impact of living in urban areas. Urbanicity indices may reflect the presence of environmental and social conditions that are more common in urban areas, such as pollution, congestion, and increased rates of social interactions. To date, various health factors have been linked to urbanicity, such as increases in overweight/obesity, increased calorie intake, decreased physical activity, increased drug and alcohol use, and mental health disorders, among many others . In the ABCD Study, we have linked five measures of urbanicity to residential addresses, including two density measures , census-tract derived metrics classifying the locations as urban or non-urban areas, walk ability, and motor vehicle information including distance to roadway and traffic volumes. Population density refers to the number of people living in a given unit of area . Differences in population density have been linked to psychological and environmental quality of life , and has been shown to moderate relationships between the built environment and health outcomes .
Thus, information about variability of population density may be important for contextualizing relationships between the build environment and health outcomes in the ABCD Study. As such, the population density from the Gridded Population of the World , provided by the Socioeconomic Data and Applications Center , has been linked to ABCD individual participant address information. National-level population estimates from 2010 used in this metric have been adjusted to the United Nations World Population estimates, which can often be corrected for over- or under-reporting and mapped to an ~1-km grid. Population density values represent persons per km2 . Similarly, gross residential density is a measure of housing units per acre on unprotected land and is an alternative measure of crowding. This measure was obtained from the Smart Location Database created by the United States Environmental Protection Agency based on the 2010 Census Data and also linked to ABCD Study individual addresses. While many studies have documented the effects of increased urbanicity on child and adolescent health outcomes, few studies have focused on differential risk associated with living in a rural area relative to an urban area . Although the number of studies devoted to this topic are few, linking this information to the ABCD Study may provide an opportunity to further investigate both positive and negative impacts of living in an rural area. To classify individuals as living in a rural or urban area, urban-rural census tract variables from 2010 were mapped to each address. Based on this external database, the Census Bureau identifies two types of urban areas, including Urbanized Areas of 50,000 or more people and Urban Clusters of at least 2500, but less than 50,000 people. Rural areas are those that encompass all population, housing, and territory not included within an urban area . In urban places, city planning designs have limited the walk ability between work, home, and recreational spaces, with distances too great to walk . Such reduction in walk ability leads to fewer opportunities for physical activity and a risk for health. Understanding potential links between the walk ability of the built environment of the child and physical and mental health outcomes is important in the context of the ABCD Study . A measure of walk ability was linked to ABCD participant addresses using the National Walk ability Index from the Smart Location Database created by the United States Environmental Protection Agency based on 2010 census data. Walk ability scores were calculated at the census-tract level,vertical grow rack ranking each census tract on a range from 1 to 20 according to relative walk ability. The walk ability score is based on a weighted formula that uses ranked indicators as related to the propensity of walk trips.
The ranked-indicator scores used in the weighted formula include a combination of diversity of employment types plus the number of occupied housing, pedestrian-oriented intersections, and proportion of workers who carpool. Beyond population density and walk ability, epidemiological studies have also reported associations between road proximity and brain health. Various neuro development, cognitive functioning, and mental health outcomes have been linked to living near major roadways . As such, the ABCD Study may be valuable to help understand how the distance of a child’s home to major roadways as well as the daily traffic patterns on nearby roadways impacts cognitive and neuro developmental trajectories over time. Therefore, we have mapped road proximity and traffic volume estimates to residential addresses of the child in the ABCD Study to provide insight into both the major roadways nearby and how many cars and trucks typically utilize these roads. the geospatial coordinates of the major roads were obtained through the North American Atlas for roads, as last updated July 2012 , and the shortest distance to a major roadway in meters was linked to participant’s residential addresses. In the field of developmental cognitive neuroscience, socioeconomic status has traditionally been treated as an individual-level variable, specific to each family or person. However, socioeconomic status can also be attributed to neighborhoods and communities, which may represent an independent construct from family-level socioeconomic status with considerable effects on child development . In the ABCD Study, detailed questions are asked about socioeconomic and social factors at the family-level. Thus, the ABCD Study is an ideal dataset to examine the independent and multiplicative associations of family- and neighborhood-level socioeconomic status on adolescent health. Investigations with these ABCD data can elucidate the underlying mechanisms by which various contexts uniquely influence development and potential emerging health disparities . Accordingly, the ABCD Study has incorporated the Area Deprivation Index measure of neighborhood-level socioeconomic status in past data releases, as well as information on crime and risk of lead exposure. Moving forward, three additional metrics, including the Social Vulnerability Index, Opportunity Atlas, and the Child Opportunity Index, have been linked in the 4.0 annual data release. The neighborhoods in which children in America grow up can influence outcomes in adulthood. As such, the Opportunity Atlas estimates measures of average outcomes across 20,000 people in adulthood according to the census tracts in which they grew up . The ABCD Study includes scores from the Opportunity Atlas that indicate the predicted 2014–2015 mean income earnings of adults aged 31–37 years that grew up in that census tract as children. Scores are provided based on the childhood census tracts of the Opportunity Atlas cohort, but we also provide the adult mean earnings disaggregated by parental household income percentiles based on the national income distribution during their childhood. For example, the mean income earnings at the 25th percentile rank correspond to the mean income earnings of adults whose parents were at the 25th percentile of the national income distribution.Although the outcomes for census tracts are based on children who were born in those tracts between 1978 and 1983, Chetty et al. suggest that these longitudinal outcomes are best suited for measuring stable outcomes in earnings in adulthood. 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.