Given the amount of substance use in PLWH and its impact on HIV care engagement ; screening and brief intervention in HIV care settings could be a critical component of the HIV care coordination. More investigation is needed to determine how to best implement substance use screening and brief intervention within the workflows of primary care HIV clinics. Our sample was recruited from the clinic waiting room and thus represents patients that are engaged in care and may not be representative of the entire clinic. The most current patient demographic data for the clinic indicated that most were male and racially diverse with nearly half Caucasian, 24.4%, African American, and 22.6% Hispanic or Latino/a. The demographic report by the clinic also indicated that the HIV exposure category was primarily MSM but also included heterosexual exposure and injection drug use . Our study did not collect data on HIV exposure category. Our efforts to over sample women and people of color were successful as demonstrated by our participants, who were 31% women and 68% people of color, both populations that are often underrepresented. In addition, the high mean CD4+ T cell count and high level of viral suppression in our cohort while typical of this clinic and San Francisco on the whole, was atypical when compared to Gardner’s cascade , and may have indicated that, despite the prevalence of substance use in our sample,hydroponic vertical farming the participants were able to control their use well enough to remain adherent to their HIV regimens. It is also possible that patients were receiving some type of substance use treatment while enrolled in this study, although we did not ask specifically about concurrent treatment.
This suggests that the findings might not be widely generalizable to other HIV clinic populations. Another limitation of our study was that, although the current science on screening for substance use recommends using single-item screeners for clinical settings to determine whether further assessment is needed, we did not use a single-item screener to determine the presence of binge drinking. However, we did use a single item question to determine the need to administer the full ASSIST tool. As such, while we can report on moderate- or high-risk alcohol use, we cannot report our samples’ response to the single item screener, most specifically binge drinking, which is an important indicator for further assessment. The physical environment encompasses both built and natural factors that can be a major determinant of our health and well being . The built environment includes man-made spaces as well as state- and community-level conditions in which we live, learn, work, and play . The natural environment on the other hand includes land, air, and water, and includes aspects of our physical surroundings such as oceans, forests, green space, and climate . These natural environments can also include potentially harmful substances, including exposure to air pollution and other toxins. Within the realm of environmental health, an extensive literature has emerged implicating the importance of the physical environment in which individuals grow up on human neuro development. For example, living in an urban setting has been associated with mental health risk, including schizophrenia and post-traumatic stress disorder , whereas neighborhood conditions, such as growing up in lower socioeconomic neighborhoods, have been linked with children’s verbal and emotional behavioral outcomes . As for the natural environment, emerging literature has also implicated green space as a potential protective factor, with links to better childhood neuro developmental outcomes and lower risk of psychiatric disorders in adolescents and adulthood . In terms of exposure to harmful substances, air pollution and lead exposure have been widely linked to cognitive functioning during childhood and adolescents as well as increased the risk of mental health problems More recently, studies have begun to show these built and natural environmental factors during childhood and adolescence influencing brain structure and function .
Indeed, these strong links between various physical environmental factors and health outcomes has led to the strong impetus for elucidating how an individual’s exposome, or the totality of exposure experienced by an individual over their lives, may affect one’s health . Thus, questions remain as to when during development and how these various exposures may exert their unique or interactive effects on neurodevelopment and what children may be most vulnerable to such exposures. Moreover, although evidence has been mounting on the impact of the physical environment on neurodevelopment outcomes, these studies have primarily focused on single exposures, cross-sectional behavioral measurements or implemented neuroimaging methods in smaller samples and have largely focused on study participants from a single limited geographical location. Thus, future research requiring large scale, population neuroimaging and longitudinal studies are needed to identify the potential biological mechanisms that may underlie the link between physical environmental exposures and brain development. The Adolescent Brain Cognitive Development Study® provides a unique opportunity to investigate the links between exposure to multiple built and natural environmental factors and the developing child and adolescent brain in a population-based study of U.S. children. The large, diverse sample and a longitudinal design, including annual follow-up for 9 years, allows researchers to examine environmental impacts on cognitive, behavioral, and multimodal neuroimaging measurements in youth across 21 metropolitan areas in America. By linking information about the physical environment of ABCD participants through geocoding of their residential locations, the ABCD Study® holds great potential in contributing to our understanding of environmental-based changes in human brain development. Although the process of identifying and linking physical environmental exposures is an ever-evolving process, the LED Environment Working Group within the consortium has already begun to map several residential-, census-, and state-level variables to better understand the built and natural environment of ABCD participants.
Thus, the goal of the current manuscript is to serve as a resource for the field regarding the existing LED Environment measures in the ABCD Study in hopes of facilitating open science and the use of these data by researchers who are interested in how the built and natural environment impacts neuro development. In the following sections, we first discuss key aspects to geospatial mapping and data linkage efforts in the ABCD Study, including: describing our workflow for linking environmental measurements in the ABCD Study while maintaining privacy protection for our participants; reviewing the currently linked environmental measurements obtained by geospatial mapping in detail, and discussing strengths and limitations of these data, including outlining how the current environmental data may be useful towards understanding social determinants of health using the ABCD Study dataset as well as considerations for the user and future directions of the geospatial mapping and data linkage efforts in the ABCD Study. After parents/caregivers and children completed written informed consent and written assent, respectively, primary residential addresses were collected in-person from the participant’s caregiver during both the baseline study visit and at each follow-up study visit occurring approximately every 12 months. At the baseline visit, the parent or caregiver was asked, “At what address does your child live?” by the Research Assistant ; the RA recorded the answer in the secure personal identifiable information portal. If a child spent less than 80% of their time at the primary address, the RA was able to record up to 2 additional current addresses in the PII to capture time spent at several home locations. Address 1 is treated as the primary address, with the percentages of time spent in primary, secondary, and tertiary addresses also recorded if the child split their time between multiple home addresses. At the follow-up in-person visits, the RA updated the current addresses as needed. As part of the second-year follow-up visit, the RA also collected up to 10 previous lifetime addresses for the child.As pointed out in prior reviews on the applications of geocoding on health sciences ,vertical agriculture converting residential addresses to the latitude and longitude is the most basic and critical step for the subsequent geospatial data linkage. To achieve this, the latitude and longitude of baseline residential addresses were geocoded by the ABCD Data Analysis Informatics and Resource Center using the Google Maps Application Programming Interface , and each address was assigned a Status Code and/or Error Message. Status codes included “OK” or “ZERO_RESULTS” . Error messages of the geocoding issues included: “city not found”, “state not found”, “street not found”, “zip code not found”, or “geocode zip code does not match typed zip code”. Only addresses that generated an “OK” status were used for exposure assignment. Of all addresses collected at the baseline visit, 98.99% were successfully geocoded. For follow-up address data collection , the Google API was used in real-time to ensure address validity and generate a map of the location in Google Maps so the participant could verify the address’s general location ensuring appropriate longitude and latitude.One critical task for geospatial mapping in the ABCD Study is to ensure the protection of privacy of the participating individuals and their families. The policy of the ABCD Study strictly prohibits the identification of participants; therefore, we took precautions in designing our geospatial mapping pipelines. We modularized and compartmentalized the pipeline, as illustrated in Fig. 2. After PII were recorded and validated by the on-site researchers, data were automatically encrypted and stored in a secured, firewall-protected intranet server.
Participants’ identification and addresses were then dissociated and separately encrypted.In parallel, the ABCD Study researchers curated a geographic information system database, based on the initial scientific inputs from the community and the feasibility of the datatype . GIS is a general framework used for capturing, storing, managing, and displaying data related to geospatial locations on the Earth’s surface . An example of the LED Environment GIS curation and the corresponding query functions can be found in the ABCD Study’s Github page . The curated GIS database was imported into the secured server and used to query the corresponding values given longitude and latitude . After the values were assigned, the longitude and latitude were removed from the subsequent process, avoiding the leakage of PII. The assigned values and the corresponding encrypted keys were then linked back to the participant ID, producing a decrypted dataset without any PII . While the encryption and decryption in the PII server were unique to ABCD, as it was developed to bridge the need between maintaining the PII of ABCD Study as a whole and the geocoding process, the geocoding data linkage is built upon the existing code bases for assigning values given the spatial coordinates and GIS database . Currently, we adopt deterministic value assignment without considering mapping uncertainties. Although this would limit the statistical modeling for spatial inference, it was a practical solution given a wide swath of environmental variables with different spatial With every address, census tract, and city having its own longitude and latitude, GIS data can be linked to estimate participants’ physical environments. There are two primary spatial data types in GIS: vector data, which is comprised of either points, lines, or spatial polygons with associated values, and spatial data , which is represented by grid cells . Examples of vector geospatial data are shown in the first two columns of Fig. 3. Spatial polygons may be associated with data aggregated at various spatial levels, and are irregular polygonal regions defined by historical, statistical, legal, and/or administerial reasons. Example data of spatial polygons include the census tracts used by the US Census, zip codes used by the United States Postal Service, or counties by local governments. The census tracts are polygons created with the intention of having about 4000 people in each of them, although the actual number ranges . Zip codes on the other hand are clusters of lines with more than 41,000 zip codes with some populations of a single zip code exceeding 100,000. 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.