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description Publicationkeyboard_double_arrow_right Article , Other literature type 2021MDPI AG SSHRC, NIH | Impulsivity and Stimulant...SSHRC ,NIH| Impulsivity and Stimulant Drug RewardTrevor Goodyear; Allie Slemon; Christopher D. Richardson; Anne M. Gadermann; Travis Salway; Shivinder Dhari; Rod Knight; Emily K. Jenkins;Lesbian, gay, bisexual, trans, other queer, and Two-Spirit (LGBTQ2+) people are particularly at risk for the psycho-social consequences of the COVID-19 pandemic, though population-tailored research within this context remains limited. This study examines the extent of, and associations between, increased alcohol and cannabis use and deteriorating mental health among LGBTQ2+ adults in Canada during the COVID-19 pandemic. Data are drawn from LGBTQ2+ respondents to a repeated, cross-sectional survey administered to adults living in Canada (May 2020–January 2021). Bivariate cross-tabulations and multivariable logistic regression models were utilized to examine associations between increased alcohol and cannabis use, and self-reported mental health, overall coping, and suicidal thoughts. Five-hundred and two LGBTQ2+ participants were included in this analysis. Of these, 24.5% reported increased alcohol use and 18.5% reported increased cannabis use due to the pandemic. In the adjusted analyses, increased alcohol use was associated with poor overall coping (OR = 2.28 95% CI = 1.16–4.55). These findings underscore the need for population-tailored, integrated substance use and mental health supports to address interrelated increases in alcohol/cannabis use and worsening mental health among LGBTQ2+ adults, in the context of the COVID-19 pandemic and beyond. 95% CI = 1.21–3.25), whereas increased cannabis use was associated with suicidal thoughts (OR = 2.30 95% CI = 1.28–4.07) and worse self-reported mental health (OR = 1.98
Europe PubMed Centra... arrow_drop_down International Journal of Environmental Research and Public HealthOther literature type . Article . 2021License: CC BYInternational Journal of Environmental Research and Public HealthArticleLicense: CC BYData sources: UnpayWallInternational Journal of Environmental Research and Public HealthArticle . 2021Data sources: DOAJ-Articlesadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/ijerph182212155&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down International Journal of Environmental Research and Public HealthOther literature type . Article . 2021License: CC BYInternational Journal of Environmental Research and Public HealthArticleLicense: CC BYData sources: UnpayWallInternational Journal of Environmental Research and Public HealthArticle . 2021Data sources: DOAJ-Articlesadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016Ovid Technologies (Wolters Kluwer Health) NIH | Phase 2 Study of Mexileti..., NIH | Novel Molecular Mechanism..., CIHR +24 projectsNIH| Phase 2 Study of Mexiletine for the Treatment of Myotonic Dystrophy ,NIH| Novel Molecular Mechanisms of Neuromuscular Disease: Implications for Therapy ,CIHR ,NIH| BIOEQUIVALENCE AND CLINICAL IMPLICATIONS OF GENERIC BUPROPION ,NIH| IN VIVO NMR METABOLIC STUDY OF REGIONAL CARDIAC ISCHEMIA ,NIH| Evaluation of Treatments and Services to People with Duchenne Muscular Dystrophy ,NIH| BIOLOGY OF THE BONE MARROW DERIVED 3A1 STEM CELL ,NIH| Novel Strategy for Perioperative Beta-Blocker Therapy ,NIH| GENETICS-InFORMATICS TRIAL (GIFT) OF WARFARIN TO PREVENT DVT ,NIH| 9th International Myotonic Dystrophy Consortium Meeting ,NIH| Biiostatistics ,NIH| Disease Progression in Myotonic Dystrophy ,NIH| EXPERIMENTAL THERAPEUTICS IN NEUROLOGICAL DISEASE ,NIH| Comp A-NY State Surveillance and Research of MD and Neuromuscular Disorders ,NIH| BIOENERGETIC MECHANISMS ,NIH| POSITRON EMISSION TOMOGRAPHIC IMAGING OF LUNG TRANSPLANT ,SSHRC ,NIH| Phase 2 Study of 4-Aminopyridine for the Treatment of Episodic Ataxia Type 2 ,NIH| Remediating Age Related Cognitive Decline: Mindfulness-Based Stress Reduction and Exercise ,NIH| Genetically Informed Smoking Cessation Trial ,NIH| Ocular Hypertension Treatment Study 20-Year Follow-up: Clinical Center Grant ,NIH| RNA-mediated mechanisms in the myotonic dystrophies ,NIH| 7th International Myotonic Dystrophy Consortium Meeting ,NIH| FOR-DMD: Double-blind randomized trial to optimize steroid regimen in Duchenne MD ,NIH| Washington University Institute of Clinical and Translational Sciences ,NIH| Tenth International Myotonic Dystrophy Consortium Meeting ,NIH| ANALYSIS OF THE E COLI STB HEAT STABLE ENTEROTOXINAuthors: Griggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; +11 AuthorsGriggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; Pestronk, Alan; Mendell, Jerry R.; Moxley, Richard T.; King, Wendy; Kissel, John T.; Cwik, Valerie; Vanasse, Michel; Florence, Julaine M.; Pandya, Shree; Dubow, Jordan S.; Meyer, James M.;Objective: To assess safety and efficacy of deflazacort (DFZ) and prednisone (PRED) vs placebo in Duchenne muscular dystrophy (DMD). Methods: This phase III, double-blind, randomized, placebo-controlled, multicenter study evaluated muscle strength among 196 boys aged 5–15 years with DMD during a 52-week period. In phase 1, participants were randomly assigned to receive treatment with DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, PRED 0.75 mg/kg/d, or placebo for 12 weeks. In phase 2, placebo participants were randomly assigned to 1 of the 3 active treatment groups. Participants originally assigned to an active treatment continued that treatment for an additional 40 weeks. The primary efficacy endpoint was average change in muscle strength from baseline to week 12 compared with placebo. The study was completed in 1995. Results: All treatment groups (DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, and PRED 0.75 mg/kg/d) demonstrated significant improvement in muscle strength compared with placebo at 12 weeks. Participants taking PRED had significantly more weight gain than placebo or both doses of DFZ at 12 weeks; at 52 weeks, participants taking PRED had significantly more weight gain than both DFZ doses. The most frequent adverse events in all 3 active treatment arms were Cushingoid appearance, erythema, hirsutism, increased weight, headache, and nasopharyngitis. Conclusions: After 12 weeks of treatment, PRED and both doses of DFZ improved muscle strength compared with placebo. Deflazacort was associated with less weight gain than PRED. Classification of evidence: This study provides Class I evidence that for boys with DMD, daily use of either DFZ and PRED is effective in preserving muscle strength over a 12-week period.
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For further information contact us at helpdesk@openaire.eu115 citations 115 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 2visibility views 2 download downloads 0 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Public Library of Science (PLoS) NSF | Collaborative Research: A..., NIH | Injection Risk Networks i..., SSHRCNSF| Collaborative Research: Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio ,NIH| Injection Risk Networks in Rural Puerto Rico ,SSHRCAuthors: Elspeth Ready;Elspeth Ready;pmid: 29529040
pmc: PMC5846769
Social institutions that facilitate sharing and redistribution may help mitigate the impact of resource shocks. In the North American Arctic, traditional food sharing may direct food to those who need it and provide a form of natural insurance against temporal variability in hunting returns within households. Here, network properties that facilitate resource flow (network size, quality, and density) are examined in a country food sharing network comprising 109 Inuit households from a village in Nunavik (Canada), using regressions to investigate the relationships between these network measures and household socioeconomic attributes. The results show that although single women and elders have larger networks, the sharing network is not structured to prioritize sharing towards households with low food availability. Rather, much food sharing appears to be driven by reciprocity between high-harvest households, meaning that poor, low-harvest households tend to have less sharing-based social capital than more affluent, high-harvest households. This suggests that poor, low-harvest households may be more vulnerable to disruptions in the availability of country food.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2022American Psychological Association (APA) NIH | 14/21 ABCD-USA Consortium..., NIH | Adolescent Brain Cognitiv..., NIH | ABCD-USA Consortium: Twin... +19 projectsNIH| 14/21 ABCD-USA Consortium: Research Project Site at CU Boulder ,NIH| Adolescent Brain Cognitive Development (ABCD): FIU ,NIH| ABCD-USA Consortium: Twin Research Project ,NIH| 16/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UNIVERSITY OF ROCHESTER ,NIH| 20/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT VCU ,NIH| Adolescent Brain Cognitive Development (ABCD) Prospective Research in Studies of Maturation (PRISM) Consortium ,NIH| Technology-supported sedentary behavior intervention to promote recovery from cancer surgery ,NIH| A mobile sensing system to monitor symptoms during chemotherapy ,SSHRC ,NIH| ABCD-USA Consortium: Data Analysis, Informatics and Resource Center ,NIH| 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA ,NIH| 18/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT THE UNIVERSITY OF FLORIDA ,NIH| ABCD-USA Consortium: Coordinating Center ,NIH| Prospective Research Studies of Maturation (PRISM)- Research Project ,NIH| 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL ,NIH| ABCD-USA: NYC Research Project ,NIH| 12/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U PITTSBURGH ,NIH| ABCD-USA Consortium: UWM SIte ,NIH| 13/13 ABCD-USA Consortium: Research Project ,NIH| 15/21 ABCD-USA Consortium: Research Project Site at LIBR ,NIH| ABCD-USA Consortium: Research Project ,NIH| ABCD-USA Consortium: Research ProjectAuthors: Benjamin W. Nelson; Kimberly G. Lockwood; Julio Vega; Helen M. K. Harvie;Benjamin W. Nelson; Kimberly G. Lockwood; Julio Vega; Helen M. K. Harvie;doi: 10.1037/tms0000049
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2010 EnglishInter-university Consortium for Political and Social Research (ICPSR) NIH | University of Michigan's ..., AKA | Work stress and risk of c..., NIH | Vulnerability to Drug Use... +33 projectsNIH| University of Michigan's TMJD and Orofacial Pain Interdisciplinary Consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NIH| The Brain as a Target for Pre and Essential Hypertension ,SSHRC ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NIH| Examining the Bi-directional Relationship between Sleep and Stress: A Vicious Cycle ,WT ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,CIHR ,NIH| Neurobiological pathways linking stress and emotion to atherosclerosis ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and Illness ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,UKRI| Assessment of biomedical risk factors and disease outcomes in the British 1958 cohort ,NSF| Sleep Disruption as an Amplifier of Aggressive Behavior ,NIH| UCLA Clinical Translational Science Institute ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,NIH| Health Disparities Research Scholars T32 ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Institutional Clinical and Translational Science Award ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Cardiovascular Behavioral Medicine Research Training ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| Training in Behavioral &Preventive Medicine ,NIH| Integrative Pathways to Health and Illness ,NIH| Biobehavioral Influences and the Ovarian Tumor MicroenvironmentAuthors: Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.The Biomarker study is Project 4 of the MIDUS longitudinal study, a national survey of more than 7,000 Americans (aged 25 to 74) begun in 1994. The purpose of the larger study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. With support from the National Institute on Aging, a longitudinal follow-up of the original MIDUS samples [core sample (N = 3,487), metropolitan over-samples (N = 757), twins (N = 957 pairs), and siblings (N = 950)] was conducted in 2004-2006. Guiding hypotheses, at the most general level, were that behavioral and psychosocial factors are consequential for health (physical and mental). A description of the study and findings from it are available on the MIDUS Web site. The Biomarker Project (Project 4) of MIDUS II contains data from 1,255 respondents. These respondents include two distinct subsamples, all of whom completed the Project 1 Survey: (1) longitudinal survey sample (n = 1,054) and (2) Milwaukee sample (n = 201). The Milwaukee group contained individuals who participated in the baseline MIDUS Milwaukee study, initiated in 2005. The purpose of the Biomarker Project (Project 4) was to add comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biology. The broad aim is to identify biopsychosocial pathways that contribute to diverse health outcomes. A further theme is to investigate protective roles that behavioral and psychosocial factors have in delaying morbidity and mortality, or in fostering resilience and recovery from health challenges once they occur. The research was not disease-specific, given that psychosocial factors have relevance across multiple health endpoints. Biomarker data collection was carried out at three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allow for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities, bone densitometry, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Project 1, participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor. The MIDUS and MIDJA Biomarker Clinic Visits include collection of comprehensive information about medications of all types, as well as basic information about allergic reactions to any type of medication. Respondents were instructed to bring all their medications, or information about their medications, to the clinic visit to ensure the information about those medications was recorded accurately. Information regarding Prescription Medications (FDA approved medications prescribed by someone authorized/licensed under the Western medical tradition, or medications prescribed by individuals authorized under Japanese law to prescribe Western and/or Eastern/Chinese traditional medicine), Quasi Medications (including Over the Counter Medications i.e. vitamins, minerals, non-prescription pain relief, antacids, etc. that can be purchased without a prescription) and Alternative Medications (i.e. herbs, herbal blends (excluding herbal teas), homeopathic remedies, and other alternative remedies that may be purchased over the counter or "prescribed" by a health care practitioner trained in a non-western tradition)was collected at this time.The following information was collected for each medication type Medication name, dosage, and route of administration; How often the medication is taken(frequency); How long the participant has been taking a given medication; Why they think they are taking the medication; After basic cleaning protocols were completed, standardized protocols were applied to both MIDUS and MIDJA medication data to link medications first to Generic Names and associated DrugIDs and then to therapeutic and pharmacologic class information from the Lexicomp Lexi-Data database, and also to code text data describing why participants think they are taking a given medication. The scope of this collected medication data lends itself to within person analysis of medication use, thus the medication data are also released in a standalone stacked format. The stacked file only contains data about medications used where each case represents an individual medication, thus it does not include any data about medication allergies. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. All respondents participating in MIDUS II (ICPSR 4652) or the Milwaukee study (ICPSR 22840) who completed Project 1 were eligible to participate in the Biomarker assessments. Presence of Common Scales: Data users interested in the scales used for this study should refer to the scaling documentation provided on both the ICPSR and NACDA Web site. Adult non-institutionalized population of the United States. Smallest Geographic Unit: No geographic information is included other than for the Milwaukee cases. Response Rates: The response rate was 39.3 percent for each of the 2 samples (longitudinal survey sample, and Milwaukee). Datasets: DS0: Study-Level Files DS1: Aggregated Data DS2: Stacked Medication Data Midlife in the United States (MIDUS) Series face-to-face interview on-site questionnaire mixed mode
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3886/icpsr29282.v6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018BMJ NIH | STOP HIV in DUs, SSHRC, CIHR +1 projectsNIH| STOP HIV in DUs ,SSHRC ,CIHR ,NIH| Seek and Treat for Optimal Prevention of HIV & AIDS (STOP HIV/AIDS) in BCOghenowede Eyawo; Mark W. Hull; Kate Salters; Hasina Samji; Angela Cescon; Paul Sereda; Viviane D. Lima; Bohdan Nosyk; David G T Whitehurst; Scott A. Lear; Julio S. G. Montaner; Robert S. Hogg;PurposeThe Comparative Outcomes And Service Utilization Trends (COAST) Study in British Columbia (BC), Canada, was designed to evaluate the determinants of health outcomes and health care services use among people living with HIV (PLHIV) as they age in the period following the introduction of combination antiretroviral therapy (cART). The study also assesses how age-associated comorbidities and health care use among PLHIV may differ from those observed in the general population.ParticipantsCOAST was established through a data linkage between two provincial data sources: The BC Centre for Excellence in HIV/AIDS Drug Treatment Program, which centrally manages cART dispensation across BC and contains prospectively collected data on demographic, immunological, virological, cART use and other clinical information for all known PLHIV in BC; and Population Data BC, a provincial data repository that holds individual event-level, longitudinal data for all 4.6 million BC residents. COAST participants include 13 907 HIV-positive adults (≥19 years of age) and a 10% random sample inclusive of 516 340 adults from the general population followed from 1996 to 2013.Findings to dateFor all participants, linked individual-level data include information on demographics, health service use (eg, inpatient care, outpatient care and prescription medication dispensations), mortality, and HIV diagnostic and clinical data. Publications from COAST have demonstrated the significant mortality reductions and dramatic changes in the causes of death among PLHIV from 1996 to 2012, differences in the amount of time spent in a healthy state by HIV status, and high levels of injury and mood disorder diagnosis among PLHIV compared with the general population.Future plansTo capture the dynamic nature of population health parameters, regular data updates and a refresh of the data linkage are planned to occur every 2 years, providing the basis for planned analysis to examine age-associated comorbidities and patterns of health service use over time.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
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For further information contact us at helpdesk@openaire.euapps Other research product2013 Canada CIHR, NIH | Initiation of injection d..., SSHRCCIHR ,NIH| Initiation of injection drug use and HIV risks among street-involved youth ,SSHRCAuthors: Cheng, Tessa Katie;Cheng, Tessa Katie;The harms of youth homelessness are well described in the academic literature, but less is known about transitions into homelessness among at risk youth. Given the importance of preventing youth homelessness, and in particular, the first incidence of homelessness, quantitative and qualitative data from street involved youth in Vancouver were analyzed in order to determine significant factors associated with this transition and generate policy options for addressing this issue. Ultimately, this study recommends placing youth workers in secondary schools to support the academic and social development of at risk youth, as well as provide connections to appropriate community supports such as housing. This is the first known study to directly ask youth for their thoughts on how to prevent the first incidence of homelessness, and the results from this Capstone provides policy makers with opportunities for targeted interventions to address youth homelessness in Vancouver.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 CanadaSpringer Science and Business Media LLC SSHRC, CIHR, NIH | Seek and Treat for Optima... +1 projectsSSHRC ,CIHR ,NIH| Seek and Treat for Optimal Prevention of HIV & AIDS (STOP HIV/AIDS) in BC ,NIH| STOP HIV in DUsOghenowede Eyawo; Conrado Franco-Villalobos; Mark W. Hull; Adriana Nohpal; Hasina Samji; Paul Sereda; Viviane D. Lima; Jeannie Shoveller; David Moore; Julio S. G. Montaner; Robert S. Hogg;Background: Non-HIV/AIDS-related diseases are gaining prominence as important causes of morbidity and mortality among people living with HIV. The purpose of this study was to characterize and compare changes over time in mortality rates and causes of death among a population-based cohort of persons living with and without HIV in British Columbia (BC), Canada. Methods: We analysed data from the Comparative Outcomes And Service Utilization Trends (COAST) study; a retrospective population-based study created via linkage between the BC Centre for Excellence in HIV/AIDS and Population Data BC, and containing data for HIV-infected individuals and the general population of BC, respectively. Our analysis included all known HIV-infected adults (≥ 20 years) in BC and a random 10% sample of uninfected BC adults followed from 1996 to 2012. Deaths were identified through Population Data BC – which contains information on all registered deaths in BC (BC Vital Statistics Agency dataset) and classified into cause of death categories using International Classification of Diseases (ICD) 9/10 codes. Age-standardized mortality rates (ASMR) and mortality rate ratios were calculated. Trend test were performed. Results: 3401 (25%), and 47,647 (9%) individuals died during the 5,620,150 person-years of follow-up among 13,729 HIV-infected and 510,313 uninfected individuals, respectively. All-cause and cause-specific mortality rates were consistently higher among HIV-infected compared to HIV-negative individuals, except for neurological disorders. All-cause ASMR decreased from 126.75 (95% CI: 84.92-168.57) per 1000 population in 1996 to 21.29 (95% CI: 17.79-24.79) in 2011-2012 (83% decline; p < 0.001 for trend), compared to a change from 7.97 (95% CI: 7.61-8.33) to 6.87 (95% CI: 6.70-7.04) among uninfected individuals (14% decline; p < 0.001). Mortality rates from HIV/AIDS-related causes decreased by 94% from 103.85 per 1000 population in 1996 to 6.72 by the 2011–2012 era (p < 0.001). Significant ASMR reductions were also observed for hepatic/liver disease and drug abuse/overdose deaths. ASMRs for neurological disorders increased significantly over time. Non-AIDS-defining cancers are currently the leading non-HIV/AIDS-related cause of death in both HIV-infected and uninfected individuals. Conclusions: Despite the significant mortality rate reductions observed among HIV-infected individuals from 1996 to 2012, they still have excess mortality risk compared to uninfected individuals. Additional efforts are needed to promote effective risk factor management and appropriate screening measures among people living with HIV.
Europe PubMed Centra... arrow_drop_down Simon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional RepositorySimon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu86 citations 86 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Simon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional RepositorySimon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Other literature type 2021MDPI AG SSHRC, NIH | Impulsivity and Stimulant...SSHRC ,NIH| Impulsivity and Stimulant Drug RewardTrevor Goodyear; Allie Slemon; Christopher D. Richardson; Anne M. Gadermann; Travis Salway; Shivinder Dhari; Rod Knight; Emily K. Jenkins;Lesbian, gay, bisexual, trans, other queer, and Two-Spirit (LGBTQ2+) people are particularly at risk for the psycho-social consequences of the COVID-19 pandemic, though population-tailored research within this context remains limited. This study examines the extent of, and associations between, increased alcohol and cannabis use and deteriorating mental health among LGBTQ2+ adults in Canada during the COVID-19 pandemic. Data are drawn from LGBTQ2+ respondents to a repeated, cross-sectional survey administered to adults living in Canada (May 2020–January 2021). Bivariate cross-tabulations and multivariable logistic regression models were utilized to examine associations between increased alcohol and cannabis use, and self-reported mental health, overall coping, and suicidal thoughts. Five-hundred and two LGBTQ2+ participants were included in this analysis. Of these, 24.5% reported increased alcohol use and 18.5% reported increased cannabis use due to the pandemic. In the adjusted analyses, increased alcohol use was associated with poor overall coping (OR = 2.28 95% CI = 1.16–4.55). These findings underscore the need for population-tailored, integrated substance use and mental health supports to address interrelated increases in alcohol/cannabis use and worsening mental health among LGBTQ2+ adults, in the context of the COVID-19 pandemic and beyond. 95% CI = 1.21–3.25), whereas increased cannabis use was associated with suicidal thoughts (OR = 2.30 95% CI = 1.28–4.07) and worse self-reported mental health (OR = 1.98
Europe PubMed Centra... arrow_drop_down International Journal of Environmental Research and Public HealthOther literature type . Article . 2021License: CC BYInternational Journal of Environmental Research and Public HealthArticleLicense: CC BYData sources: UnpayWallInternational Journal of Environmental Research and Public HealthArticle . 2021Data sources: DOAJ-Articlesadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/ijerph182212155&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down International Journal of Environmental Research and Public HealthOther literature type . Article . 2021License: CC BYInternational Journal of Environmental Research and Public HealthArticleLicense: CC BYData sources: UnpayWallInternational Journal of Environmental Research and Public HealthArticle . 2021Data sources: DOAJ-Articlesadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/ijerph182212155&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016Ovid Technologies (Wolters Kluwer Health) NIH | Phase 2 Study of Mexileti..., NIH | Novel Molecular Mechanism..., CIHR +24 projectsNIH| Phase 2 Study of Mexiletine for the Treatment of Myotonic Dystrophy ,NIH| Novel Molecular Mechanisms of Neuromuscular Disease: Implications for Therapy ,CIHR ,NIH| BIOEQUIVALENCE AND CLINICAL IMPLICATIONS OF GENERIC BUPROPION ,NIH| IN VIVO NMR METABOLIC STUDY OF REGIONAL CARDIAC ISCHEMIA ,NIH| Evaluation of Treatments and Services to People with Duchenne Muscular Dystrophy ,NIH| BIOLOGY OF THE BONE MARROW DERIVED 3A1 STEM CELL ,NIH| Novel Strategy for Perioperative Beta-Blocker Therapy ,NIH| GENETICS-InFORMATICS TRIAL (GIFT) OF WARFARIN TO PREVENT DVT ,NIH| 9th International Myotonic Dystrophy Consortium Meeting ,NIH| Biiostatistics ,NIH| Disease Progression in Myotonic Dystrophy ,NIH| EXPERIMENTAL THERAPEUTICS IN NEUROLOGICAL DISEASE ,NIH| Comp A-NY State Surveillance and Research of MD and Neuromuscular Disorders ,NIH| BIOENERGETIC MECHANISMS ,NIH| POSITRON EMISSION TOMOGRAPHIC IMAGING OF LUNG TRANSPLANT ,SSHRC ,NIH| Phase 2 Study of 4-Aminopyridine for the Treatment of Episodic Ataxia Type 2 ,NIH| Remediating Age Related Cognitive Decline: Mindfulness-Based Stress Reduction and Exercise ,NIH| Genetically Informed Smoking Cessation Trial ,NIH| Ocular Hypertension Treatment Study 20-Year Follow-up: Clinical Center Grant ,NIH| RNA-mediated mechanisms in the myotonic dystrophies ,NIH| 7th International Myotonic Dystrophy Consortium Meeting ,NIH| FOR-DMD: Double-blind randomized trial to optimize steroid regimen in Duchenne MD ,NIH| Washington University Institute of Clinical and Translational Sciences ,NIH| Tenth International Myotonic Dystrophy Consortium Meeting ,NIH| ANALYSIS OF THE E COLI STB HEAT STABLE ENTEROTOXINAuthors: Griggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; +11 AuthorsGriggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; Pestronk, Alan; Mendell, Jerry R.; Moxley, Richard T.; King, Wendy; Kissel, John T.; Cwik, Valerie; Vanasse, Michel; Florence, Julaine M.; Pandya, Shree; Dubow, Jordan S.; Meyer, James M.;Objective: To assess safety and efficacy of deflazacort (DFZ) and prednisone (PRED) vs placebo in Duchenne muscular dystrophy (DMD). Methods: This phase III, double-blind, randomized, placebo-controlled, multicenter study evaluated muscle strength among 196 boys aged 5–15 years with DMD during a 52-week period. In phase 1, participants were randomly assigned to receive treatment with DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, PRED 0.75 mg/kg/d, or placebo for 12 weeks. In phase 2, placebo participants were randomly assigned to 1 of the 3 active treatment groups. Participants originally assigned to an active treatment continued that treatment for an additional 40 weeks. The primary efficacy endpoint was average change in muscle strength from baseline to week 12 compared with placebo. The study was completed in 1995. Results: All treatment groups (DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, and PRED 0.75 mg/kg/d) demonstrated significant improvement in muscle strength compared with placebo at 12 weeks. Participants taking PRED had significantly more weight gain than placebo or both doses of DFZ at 12 weeks; at 52 weeks, participants taking PRED had significantly more weight gain than both DFZ doses. The most frequent adverse events in all 3 active treatment arms were Cushingoid appearance, erythema, hirsutism, increased weight, headache, and nasopharyngitis. Conclusions: After 12 weeks of treatment, PRED and both doses of DFZ improved muscle strength compared with placebo. Deflazacort was associated with less weight gain than PRED. Classification of evidence: This study provides Class I evidence that for boys with DMD, daily use of either DFZ and PRED is effective in preserving muscle strength over a 12-week period.
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For further information contact us at helpdesk@openaire.eu115 citations 115 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 2visibility views 2 download downloads 0 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Public Library of Science (PLoS) NSF | Collaborative Research: A..., NIH | Injection Risk Networks i..., SSHRCNSF| Collaborative Research: Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio ,NIH| Injection Risk Networks in Rural Puerto Rico ,SSHRCAuthors: Elspeth Ready;Elspeth Ready;pmid: 29529040
pmc: PMC5846769
Social institutions that facilitate sharing and redistribution may help mitigate the impact of resource shocks. In the North American Arctic, traditional food sharing may direct food to those who need it and provide a form of natural insurance against temporal variability in hunting returns within households. Here, network properties that facilitate resource flow (network size, quality, and density) are examined in a country food sharing network comprising 109 Inuit households from a village in Nunavik (Canada), using regressions to investigate the relationships between these network measures and household socioeconomic attributes. The results show that although single women and elders have larger networks, the sharing network is not structured to prioritize sharing towards households with low food availability. Rather, much food sharing appears to be driven by reciprocity between high-harvest households, meaning that poor, low-harvest households tend to have less sharing-based social capital than more affluent, high-harvest households. This suggests that poor, low-harvest households may be more vulnerable to disruptions in the availability of country food.
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For further information contact us at helpdesk@openaire.eu25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2022American Psychological Association (APA) NIH | 14/21 ABCD-USA Consortium..., NIH | Adolescent Brain Cognitiv..., NIH | ABCD-USA Consortium: Twin... +19 projectsNIH| 14/21 ABCD-USA Consortium: Research Project Site at CU Boulder ,NIH| Adolescent Brain Cognitive Development (ABCD): FIU ,NIH| ABCD-USA Consortium: Twin Research Project ,NIH| 16/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UNIVERSITY OF ROCHESTER ,NIH| 20/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT VCU ,NIH| Adolescent Brain Cognitive Development (ABCD) Prospective Research in Studies of Maturation (PRISM) Consortium ,NIH| Technology-supported sedentary behavior intervention to promote recovery from cancer surgery ,NIH| A mobile sensing system to monitor symptoms during chemotherapy ,SSHRC ,NIH| ABCD-USA Consortium: Data Analysis, Informatics and Resource Center ,NIH| 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA ,NIH| 18/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT THE UNIVERSITY OF FLORIDA ,NIH| ABCD-USA Consortium: Coordinating Center ,NIH| Prospective Research Studies of Maturation (PRISM)- Research Project ,NIH| 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL ,NIH| ABCD-USA: NYC Research Project ,NIH| 12/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U PITTSBURGH ,NIH| ABCD-USA Consortium: UWM SIte ,NIH| 13/13 ABCD-USA Consortium: Research Project ,NIH| 15/21 ABCD-USA Consortium: Research Project Site at LIBR ,NIH| ABCD-USA Consortium: Research Project ,NIH| ABCD-USA Consortium: Research ProjectAuthors: Benjamin W. Nelson; Kimberly G. Lockwood; Julio Vega; Helen M. K. Harvie;Benjamin W. Nelson; Kimberly G. Lockwood; Julio Vega; Helen M. K. Harvie;doi: 10.1037/tms0000049
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2010 EnglishInter-university Consortium for Political and Social Research (ICPSR) NIH | University of Michigan's ..., AKA | Work stress and risk of c..., NIH | Vulnerability to Drug Use... +33 projectsNIH| University of Michigan's TMJD and Orofacial Pain Interdisciplinary Consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NIH| The Brain as a Target for Pre and Essential Hypertension ,SSHRC ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NIH| Examining the Bi-directional Relationship between Sleep and Stress: A Vicious Cycle ,WT ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,CIHR ,NIH| Neurobiological pathways linking stress and emotion to atherosclerosis ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and Illness ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,UKRI| Assessment of biomedical risk factors and disease outcomes in the British 1958 cohort ,NSF| Sleep Disruption as an Amplifier of Aggressive Behavior ,NIH| UCLA Clinical Translational Science Institute ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,NIH| Health Disparities Research Scholars T32 ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Institutional Clinical and Translational Science Award ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Cardiovascular Behavioral Medicine Research Training ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| Training in Behavioral &Preventive Medicine ,NIH| Integrative Pathways to Health and Illness ,NIH| Biobehavioral Influences and the Ovarian Tumor MicroenvironmentAuthors: Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.The Biomarker study is Project 4 of the MIDUS longitudinal study, a national survey of more than 7,000 Americans (aged 25 to 74) begun in 1994. The purpose of the larger study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. With support from the National Institute on Aging, a longitudinal follow-up of the original MIDUS samples [core sample (N = 3,487), metropolitan over-samples (N = 757), twins (N = 957 pairs), and siblings (N = 950)] was conducted in 2004-2006. Guiding hypotheses, at the most general level, were that behavioral and psychosocial factors are consequential for health (physical and mental). A description of the study and findings from it are available on the MIDUS Web site. The Biomarker Project (Project 4) of MIDUS II contains data from 1,255 respondents. These respondents include two distinct subsamples, all of whom completed the Project 1 Survey: (1) longitudinal survey sample (n = 1,054) and (2) Milwaukee sample (n = 201). The Milwaukee group contained individuals who participated in the baseline MIDUS Milwaukee study, initiated in 2005. The purpose of the Biomarker Project (Project 4) was to add comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biology. The broad aim is to identify biopsychosocial pathways that contribute to diverse health outcomes. A further theme is to investigate protective roles that behavioral and psychosocial factors have in delaying morbidity and mortality, or in fostering resilience and recovery from health challenges once they occur. The research was not disease-specific, given that psychosocial factors have relevance across multiple health endpoints. Biomarker data collection was carried out at three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allow for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities, bone densitometry, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Project 1, participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor. The MIDUS and MIDJA Biomarker Clinic Visits include collection of comprehensive information about medications of all types, as well as basic information about allergic reactions to any type of medication. Respondents were instructed to bring all their medications, or information about their medications, to the clinic visit to ensure the information about those medications was recorded accurately. Information regarding Prescription Medications (FDA approved medications prescribed by someone authorized/licensed under the Western medical tradition, or medications prescribed by individuals authorized under Japanese law to prescribe Western and/or Eastern/Chinese traditional medicine), Quasi Medications (including Over the Counter Medications i.e. vitamins, minerals, non-prescription pain relief, antacids, etc. that can be purchased without a prescription) and Alternative Medications (i.e. herbs, herbal blends (excluding herbal teas), homeopathic remedies, and other alternative remedies that may be purchased over the counter or "prescribed" by a health care practitioner trained in a non-western tradition)was collected at this time.The following information was collected for each medication type Medication name, dosage, and route of administration; How often the medication is taken(frequency); How long the participant has been taking a given medication; Why they think they are taking the medication; After basic cleaning protocols were completed, standardized protocols were applied to both MIDUS and MIDJA medication data to link medications first to Generic Names and associated DrugIDs and then to therapeutic and pharmacologic class information from the Lexicomp Lexi-Data database, and also to code text data describing why participants think they are taking a given medication. The scope of this collected medication data lends itself to within person analysis of medication use, thus the medication data are also released in a standalone stacked format. The stacked file only contains data about medications used where each case represents an individual medication, thus it does not include any data about medication allergies. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. All respondents participating in MIDUS II (ICPSR 4652) or the Milwaukee study (ICPSR 22840) who completed Project 1 were eligible to participate in the Biomarker assessments. Presence of Common Scales: Data users interested in the scales used for this study should refer to the scaling documentation provided on both the ICPSR and NACDA Web site. Adult non-institutionalized population of the United States. Smallest Geographic Unit: No geographic information is included other than for the Milwaukee cases. Response Rates: The response rate was 39.3 percent for each of the 2 samples (longitudinal survey sample, and Milwaukee). Datasets: DS0: Study-Level Files DS1: Aggregated Data DS2: Stacked Medication Data Midlife in the United States (MIDUS) Series face-to-face interview on-site questionnaire mixed mode
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018BMJ NIH | STOP HIV in DUs, SSHRC, CIHR +1 projectsNIH| STOP HIV in DUs ,SSHRC ,CIHR ,NIH| Seek and Treat for Optimal Prevention of HIV & AIDS (STOP HIV/AIDS) in BCOghenowede Eyawo; Mark W. Hull; Kate Salters; Hasina Samji; Angela Cescon; Paul Sereda; Viviane D. Lima; Bohdan Nosyk; David G T Whitehurst; Scott A. Lear; Julio S. G. Montaner; Robert S. Hogg;PurposeThe Comparative Outcomes And Service Utilization Trends (COAST) Study in British Columbia (BC), Canada, was designed to evaluate the determinants of health outcomes and health care services use among people living with HIV (PLHIV) as they age in the period following the introduction of combination antiretroviral therapy (cART). The study also assesses how age-associated comorbidities and health care use among PLHIV may differ from those observed in the general population.ParticipantsCOAST was established through a data linkage between two provincial data sources: The BC Centre for Excellence in HIV/AIDS Drug Treatment Program, which centrally manages cART dispensation across BC and contains prospectively collected data on demographic, immunological, virological, cART use and other clinical information for all known PLHIV in BC; and Population Data BC, a provincial data repository that holds individual event-level, longitudinal data for all 4.6 million BC residents. COAST participants include 13 907 HIV-positive adults (≥19 years of age) and a 10% random sample inclusive of 516 340 adults from the general population followed from 1996 to 2013.Findings to dateFor all participants, linked individual-level data include information on demographics, health service use (eg, inpatient care, outpatient care and prescription medication dispensations), mortality, and HIV diagnostic and clinical data. Publications from COAST have demonstrated the significant mortality reductions and dramatic changes in the causes of death among PLHIV from 1996 to 2012, differences in the amount of time spent in a healthy state by HIV status, and high levels of injury and mood disorder diagnosis among PLHIV compared with the general population.Future plansTo capture the dynamic nature of population health parameters, regular data updates and a refresh of the data linkage are planned to occur every 2 years, providing the basis for planned analysis to examine age-associated comorbidities and patterns of health service use over time.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
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For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research product2013 Canada CIHR, NIH | Initiation of injection d..., SSHRCCIHR ,NIH| Initiation of injection drug use and HIV risks among street-involved youth ,SSHRCAuthors: Cheng, Tessa Katie;Cheng, Tessa Katie;The harms of youth homelessness are well described in the academic literature, but less is known about transitions into homelessness among at risk youth. Given the importance of preventing youth homelessness, and in particular, the first incidence of homelessness, quantitative and qualitative data from street involved youth in Vancouver were analyzed in order to determine significant factors associated with this transition and generate policy options for addressing this issue. Ultimately, this study recommends placing youth workers in secondary schools to support the academic and social development of at risk youth, as well as provide connections to appropriate community supports such as housing. This is the first known study to directly ask youth for their thoughts on how to prevent the first incidence of homelessness, and the results from this Capstone provides policy makers with opportunities for targeted interventions to address youth homelessness in Vancouver.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 CanadaSpringer Science and Business Media LLC SSHRC, CIHR, NIH | Seek and Treat for Optima... +1 projectsSSHRC ,CIHR ,NIH| Seek and Treat for Optimal Prevention of HIV & AIDS (STOP HIV/AIDS) in BC ,NIH| STOP HIV in DUsOghenowede Eyawo; Conrado Franco-Villalobos; Mark W. Hull; Adriana Nohpal; Hasina Samji; Paul Sereda; Viviane D. Lima; Jeannie Shoveller; David Moore; Julio S. G. Montaner; Robert S. Hogg;Background: Non-HIV/AIDS-related diseases are gaining prominence as important causes of morbidity and mortality among people living with HIV. The purpose of this study was to characterize and compare changes over time in mortality rates and causes of death among a population-based cohort of persons living with and without HIV in British Columbia (BC), Canada. Methods: We analysed data from the Comparative Outcomes And Service Utilization Trends (COAST) study; a retrospective population-based study created via linkage between the BC Centre for Excellence in HIV/AIDS and Population Data BC, and containing data for HIV-infected individuals and the general population of BC, respectively. Our analysis included all known HIV-infected adults (≥ 20 years) in BC and a random 10% sample of uninfected BC adults followed from 1996 to 2012. Deaths were identified through Population Data BC – which contains information on all registered deaths in BC (BC Vital Statistics Agency dataset) and classified into cause of death categories using International Classification of Diseases (ICD) 9/10 codes. Age-standardized mortality rates (ASMR) and mortality rate ratios were calculated. Trend test were performed. Results: 3401 (25%), and 47,647 (9%) individuals died during the 5,620,150 person-years of follow-up among 13,729 HIV-infected and 510,313 uninfected individuals, respectively. All-cause and cause-specific mortality rates were consistently higher among HIV-infected compared to HIV-negative individuals, except for neurological disorders. All-cause ASMR decreased from 126.75 (95% CI: 84.92-168.57) per 1000 population in 1996 to 21.29 (95% CI: 17.79-24.79) in 2011-2012 (83% decline; p < 0.001 for trend), compared to a change from 7.97 (95% CI: 7.61-8.33) to 6.87 (95% CI: 6.70-7.04) among uninfected individuals (14% decline; p < 0.001). Mortality rates from HIV/AIDS-related causes decreased by 94% from 103.85 per 1000 population in 1996 to 6.72 by the 2011–2012 era (p < 0.001). Significant ASMR reductions were also observed for hepatic/liver disease and drug abuse/overdose deaths. ASMRs for neurological disorders increased significantly over time. Non-AIDS-defining cancers are currently the leading non-HIV/AIDS-related cause of death in both HIV-infected and uninfected individuals. Conclusions: Despite the significant mortality rate reductions observed among HIV-infected individuals from 1996 to 2012, they still have excess mortality risk compared to uninfected individuals. Additional efforts are needed to promote effective risk factor management and appropriate screening measures among people living with HIV.
Europe PubMed Centra... arrow_drop_down Simon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional RepositorySimon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu86 citations 86 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Simon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional RepositorySimon Fraser University Institutional RepositoryArticle . 2017Data sources: Simon Fraser University Institutional Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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