External-environmental and internal-health early-life predictors of adolescent development

External-environmental and internal-health early-life predictors of adolescent development Authors' preprin

Informing Public Health Approaches to Obesity and Smoking Using Genome-Wide Association Studies

Rapid advances in technology and scientific methods stimulated by the sequencing of the human genome have yielded discoveries that begin to uncover the genetic roots of common chronic health conditions. However, the implications of these discoveries for public health research and practice remain unclear. Three questions are central to building a translational pipeline that links genetic discovery research with interventions to improve health: First, when in the life course do genetic risks become manifest? Second, what are the magnitudes of risks that can be predicted using genetic information? And third, do genetic markers provide new information about risk over and above the existing technology of family health history assessment? This dissertation research seeks to address these questions for two prevalent and costly sources of morbidity and early mortality, obesity and smoking. Results reveal that (1) genetic risks manifest early in the development of obesity and smoking through processes that may be amenable to public health intervention; (2) the magnitudes of risk that can be predicted using genetic information are small; but (3) the risk information provided by genetic markers is independent of information available in a family history. These findings affirm recommendations of caution in the application of genetic information to predict health risks in individuals, but suggest promise as more powerful but less common genetic risks are discovered in the continuing evolution of genomic research. Further, these findings recommend an increased focus on childhood and adolescence in genetic discovery research and add a genetic rationale to arguments for early intervention to prevent obesity and smoking.Doctor of Philosoph

Bullying victimisation and risk of self harm in early adolescence: longitudinal cohort study

Objectives To test whether frequent bullying victimisation in childhood increases the likelihood of self harming in early adolescence, and to identify which bullied children are at highest risk of self harm

Wave V County Health and Mobility Data User Guide

The Wave V County Health and Mobility database summarizes the socioeconomic, health, and mobility characteristics of the environments in which Add Health participants were living at the time of their Wave V interview. County-level data describe (1) levels of and trends in chronic disease (hypertension, type-2 diabetes) and health risk behaviors (obesity, smoking, alcohol use); and (2) economic opportunity and inequality. This contextual database permits innovative research that investigates how place influences health, behavior, and social outcomes across the transition from adolescence to the beginning of midlife, thereby, enhancing studies of the determinants and sequelae of socio-geographic mobility

Wave V County Health and Mobility Data Documentation

The Wave V County Health and Mobility database summarizes the socioeconomic, health, and mobility characteristics of the environments in which Add Health participants were living at the time of their Wave V interview. County-level data describe (1) levels of and trends in chronic disease (hypertension, type-2 diabetes) and health risk behaviors (obesity, smoking, alcohol use); and (2) economic opportunity and inequality. This contextual database permits innovative research that investigates how place influences health, behavior, and social outcomes across the transition from adolescence to the beginning of midlife, thereby, enhancing studies of the determinants and sequelae of socio-geographic mobility

The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health

Our study reported significant findings of a “social genome” that can be quantified and studied to understand human health and behavior. In a national sample of more than 5,000 American adolescents, we found evidence of social forces that act to make friends and schoolmates more genetically similar to one another compared with random pairs of unrelated individuals. This subtle genetic similarity was observed across the entire genome and at sets of genomic locations linked with specific traits—educational attainment and body mass index—a phenomenon we term “social–genetic correlation.” We also find evidence of a “social–genetic effect” such that the genetics of a person’s friends and schoolmates influenced their own education, even after accounting for the person’s own genetics

Wave III College Mobility Data Documentation

At Wave III of the Add Health survey, respondents were asked if they were currently enrolled in a postsecondary institution. Respondents who answered in the affirmative were then asked to report the institution in which they were currently enrolled. Using this information on current enrollment, data from the Mobility Report Card: The Role of Colleges in Intergenerational Mobility (Chetty 2017) were linked to the Add Health respondents. For variables C4CMR01-C4CMR11M, data came from the Preferred Estimates of Access and Mobility by College dataset (Chetty et al. 2017). These data were collected from a sample of college students who were born between 1980 and 1982 and who attended a college or university in the early 2000’s. These students were between the ages of 19 and 22 at the time of their entry into college. Further information on how the original researchers collected the data for these variables can be found here: http://www.equality-of-opportunity.org/data/college/Codebook%20MRC%20Table%201.pdf For variables C3FIN01-C3MAJ08, Chetty and colleagues drew these data from the Integrated Postsecondary Education Data System (IPEDS). Information for each of these variables were collected for the years 2000 and 2013 (unless otherwise stated). For all variables there were some instances where colleges were grouped together, for instance when multiple colleges made up a State University-System. For these colleges, data values for the variables are enrollment-weighted means of the underlying values for each of the colleges being grouped together. Though the variables available on the College Mobility data at Wave III are the same as those on the College Mobility data at Wave IV, the way in which respondents were asked to self-report college or university attendance was different between the two waves, and interpretation of these contextual data is slightly different as a result. At Wave III, respondents were asked to report if they were currently enrolled in a college of university, and information on the institution in which they were currently enrolled was collected. Information on institutions was collected regardless of the degree that the respondent was currently seeking. At Wave IV, respondents were asked to report the name of the college or university from which they received a degree. Additionally, this question was only asked if respondents reported receiving a bachelor’s degree. See “Documentation for College Mobility Data: Wave IV” (Gaydosh et al. 2019) for more information on linked college- and university-level data for this wave. In addition to the data available here, previously created contextual data on Wave III postsecondary institutions is also available. See “Wave III Education Data: Postsecondary Contextual Component Codebook” (Riegel-Crumb et al. 2008) for further information

Wave IV County Health and Mobility Data Documentation

The following is a list of data that were collected from secondary data sources and merged to Wave IV of Add Health. These variables are available at the county or state level. Data was matched to the county or state that the Add Health respondent was living in at the time of the Wave IV interview and data was matched to respondents so as to insure that these contextual variables correspond as closely as possible to the year in which the Add Health respondents were interviewed at Wave IV (2008)