Some PLWH also face HIV related stigma which can perpetuate disparities driven by race, gender, or sexuality leading to intersectional disadvantages. Intersectionality theory suggests that multiple social identities can intersect in a way that reflects multiple social structural disadvantages . Applied to emotional health, for example, PLWH who are also gay or bisexual may be at a higher risk of poor emotional outcomes compared to heterosexual PLWH or gay or bisexual HIV-seronegative persons due to the compounding effects of discrimination related to sexual orientation and HIV serostatus . We investigated the separate and interactive effects of HIV serostatus and sexual minority status on emotional health outcomes. We hypothesized that HIV-seropositivity and sexual minority status would be independently and interactively associated with poorer emotional health outcomes.We hypothesized that HIV-seropositivity and SM status would have adverse compounding, interactive effects on emotional health among those with dual identities. Contrary to our hypothesis, significant interactive effects of HIV-serostatus and SM status on Negative Affect revealed that PLWH had worse Negative Affect among heterosexuals, but not among sexual minorities. Moreover, among PLWH, sexual minorities had higher Social Satisfaction and marginally better Psychological Well being compared to heterosexuals. Some studies have also found evidence of a beneficial effect of SM status on emotional health among PLWH. Older gay and bisexual men living with HIV have better mental health related quality-of-life and coping compared to heterosexuals cannabis grow equipment. Sexual minorities living with HIV may experience lower levels of internalized stigma compared to heterosexuals living with HIV . Brener et al. found that heterosexuals living with HIV perceived greater negative reactions to their HIV serostatus compared to gay men living with HIV.
Although we were unable to account for protective psychological factors in our analyses, some studies have characterized high levels of resilience among sexual minorities living with HIV. Emlet et al. found longer duration of HIV infection to be associated with higher levels of mastery among gay and bisexual men. Fumaz et al. found high levels of resilience among long-term survivors which was related to better emotional well-being. Grit, a factor related to resilience, is associated with better neurocognitive outcomes and everyday functioning among PLWH . Our findings that heterosexuals living with HIV had worse Social Satisfaction and Psychological Well-being compared to sexual minorities may also be a function of sex differences. Whereas, women comprised 23% of our total sample, they were 56% of heterosexuals. Although we covaried for gender, our models may not have fully accounted for these effects. Women living with HIV tend to experience more psychological stressors like anxiety and depression, compared to men .Although gender-stratified analyses would be informative, we were unable to do so due to sample size restrictions. We were unable to account for all psychological factors that may be protective of emotional health and analyses were cross-sectional which limits conclusions about causality.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 neurodevelopmental 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 neuro development and what children may be most vulnerable to such exposures. Moreover, although evidence has been mounting on the impact of the physical environment on neuro development 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 multi-modal 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,mobile grow system 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 , 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. The encrypted addresses were then exposed to the geocoding API for converting into longitude and latitude . 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 ad ministerial 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.