Cohort Profile: The National Longitudinal Study of Adolescent to Adult Health (Add Health)

Why was the cohort set up?

The National Longitudinal Study of Adolescent Health (Add Health) was developed in the 1990s in response to a mandate from the United States Congress to fund a study of adolescent health, and was designed by a team of multidisciplinary investigators from the social, behavioural and biomedical sciences. The original purpose of Add Health was to understand the causes of adolescent health and health behaviour, with special emphasis on the multiple contexts of adolescent life. To achieve this scientific goal, Add Health sampled the school and family environments in which young people live their lives, which included data on peer relationship dyads, parents, siblings, neighbourhoods and communities, and provides independent and direct measurement of these complex environments over time. As the cohort transitioned into adulthood, research objectives turned to understanding how adolescent experiences, behaviours and contexts are linked to health and achievement outcomes in adulthood, and the name of the study was officially changed to The National Longitudinal Study of Adolescent to Adult Health in 2014.

Add Health is housed at the Carolina Population Center at the University of North Carolina (UNC) and has been led by two principal investigators and project directors: J Richard Udry from 1994–2004; and Kathleen Mullan Harris from 2004 to the present.

Who is in the cohort?

Add Health is a nationally representative cohort study of more than 20 000 adolescents in grades 7–12 (aged 12–19) in the USA in 1994–95, who have been followed through adolescence and into adulthood with five in-home interviews in 1995 (Wave I), 1996 (Wave II), 2001–02 (Wave III), 2008–09 (Wave IV) and 2016–18 (Wave V).¹Figure 1 displays the sampling design for selecting the original cohort. A school-based design selected 80 high schools and a paired feeder school from a list of all high schools in the USA in 1994. An in-school questionnaire was administered to more than 90 000 students in grades 7–12, who attended these schools during the 1994–95 school year, and school administrators also filled out a questionnaire about the school.

Figure 1.

Sampling structure.

School rosters from the 1993–94 school year provided the sampling frame for a second level of sampling for a 90-min in-home interview with an adolescent and a 30-min interview with one parent. A grade- and gender-stratified core sample was selected from each school pair, representing a self-weighting nationally representative sample of 12 105 American adolescents in grades 7–12 in 1995. Based on responses to the in-school survey, specific subpopulations were oversampled for purposes of providing sufficient numbers for research on vulnerable and otherwise rare populations, including ethnic (Cuban, Puerto Rican and Chinese), genetic relatedness to siblings (identical/fraternal twins, full/half siblings and unrelated adolescents living in the same household), adoption status and disability samples. Black adolescents with highly educated parents were also oversampled. For two large schools and fourteen small schools, interviews with all enrolled students were attempted to create a special saturation sample. The core sample plus the special samples yield a total of 20 745 adolescents. This Wave I in-home sample represents the national cohort of adolescents in grades 7–12 in the USA in 1995, which is followed prospectively. Because school rosters from the year preceding sample selection were used as the sampling frame for the prospective cohort, high school dropouts over 2 years (e.g. 1993–94; 1994–95) were eligible for sample selection, resulting in little bias due to high school dropouts.² For more details on design, see Harris 2010 and Harris et al., 2013.^3,4

How often have they been followed up?

Figure 2 shows the longitudinal design of Add Health. The Wave I in-home adolescent cohort has been followed up with four subsequent waves spanning 20+ years. In 1996, all adolescents in grades 7 through 11 in Wave I (plus 12th graders who were part of the genetic and adopted sample) were re-interviewed for Wave II (n = 14 738); the decision not to follow up the seniors who were in grade 12 at Wave I was design-based. The Wave II sample were in grades 8 through 12. A follow-up school administrator interview measured change in school context from 1995 to 1996.

Figure 2.

Longitudinal design.

The original cohort was followed through their transition to early adulthood with a Wave III in-home interview in 2001–02 when the sample was aged 18–26 years (n = 15 197). A sample of 1507 partners were randomly selected during the in-home interview and interviewed, filling quota samples of about 500 married, 500 cohabiting and 500 dating partners. Wave IV re-interviewed the original cohort as they settled into young adulthood in 2008–09 when the cohort was aged 24–32 years (n = 15 701). Wave V followed the cohort to the end of young adulthood when they were aged 32–42, with continuous interviewing using a mixed-mode protocol during 2016–18. Finally, the Add Health Parent Study completed a 20-year follow-up of a subset of the parents of Add Health respondents during 2015–17 (n = 3006). Add Health uses state-of-the-art methods and techniques for panel maintenance and tracing to locate and schedule an interview with all living eligible respondents, including those who may have been non-responsive in a preceding wave.

Table 1 presents response rates for the eligible sample at each completed wave of interviews. Response rates have been quite high, highest when the interval between interview waves is short but remarkably high even when the interval is over 5 years at Waves III and IV. The transition from adolescence to early adulthood and the young adult period is an especially transient phase of the life course and it is difficult to track and locate young people. Add Health has done exceptionally well, with response rates of 77.4% and 80.3% at Waves III and IV.

Table 1.

Response rates in Add Health for the eligible sample at each wave of in-home interviews

	Year	Total eligible	Number interviewed	Response ratec
Wave I	1995	26 271	20 745	79.0%
Wave IIa	1996	16 642	14 738	88.6%
Wave III	2001-02	19 600	15 197b	77.4%
Wave IV	2008-09	19 560	15 701	80.3%

^aBy design, respondents who were in the 12th grade at Wave I and who were not part of the genetic sample were not interviewed at Wave II.

^bResponse rate at Wave III is based on 15 170 respondents who had data at Wave I. An additional 27 respondents without Wave I data were included at Wave III as part of the genetic subsample.

^cWave V response rates are not provided because data collection is ongoing, and only a subset of Wave V respondents completed in-home interviews.

There has been differential attrition by gender, age, socioeconomic status, urban residence, immigrant status and race across time, with higher response rates for female, younger, higher socioeconomic status, urban, native-born and White respondents at Waves III and IV. These attrition patterns are consistent with most longitudinal cohort studies. Add Health response rates exceed other national studies with multiple year intervals between waves (e.g. National Survey of Families and Households 2001–03 wave had a 55% response rate; Midlife in the United States 2004–06 interview had a 75% retention rate).^5,6

At each wave, Add Health analysed whether patterns of attrition pose any bias to estimates of survey outcomes.^7–10 In general, non-response analyses compare respondents and non-respondents on a range of demographic, health, behavioural and attitudinal indicators measured at baseline, and estimate the extent to which differences between respondents and non-respondents introduce bias in study results. Results indicated that total and relative biases, remaining after study estimates were adjusted with final sampling weights, were minimal and that the sample at each wave adequately represented the same population as the Wave I sample. Analysis of bias due to attrition at Wave IV indicated low rates of bias that rarely exceeded 1%, which is small relative to the 20% to 80% prevalence rates for most of the baseline indicators. Despite common patterns of attrition over time, the design strategy to re-interview the original Wave I cohort at each follow-up wave minimizes non-response bias and continues to adequately represent the original cohort of 7-12th graders in US schools in 1995.

What has been measured?

Add Health contains unprecedented environmental, behavioural, psychosocial, biological and genetic data from early adolescence into adulthood on a large, nationally representative sample with extensive racial, ethnic, socioeconomic and geographical diversity.⁴ Longitudinal survey data on respondents’ social, economic, psychological and physical well-being is combined with contextual data on family, neighbourhood, community, school, friendships, peer groups and romantic relationships, providing unique opportunities to study how psychological characteristics, social environments and behaviours beginning in early adolescence are linked to health and well-being in adulthood. Extensive longitudinal life histories of health-related behaviour are available, including physical activity, risk behaviour, substance use, sexual behaviour, civic engagement, education and multiple longitudinal indicators of health status, such as general health, chronic illness, overweight status and obesity, mental health, disability, health promotion and sleep. Objective measures of health were collected across all waves, including anthropometrics, sexually transmitted infection (STI) test results [including human immunodeficiency virus (HIV)], DNA and an expanded set of biomarkers in adulthood (Waves IV and V) (including blood pressure and pulse, measures of glucose homeostasis, lipid metabolism, inflammation, immune and renal function and a medications inventory). Below we describe the innovative multilevel data that have provided unprecedented research opportunities for a multidisciplinary scientific community.

The clustered design of Add Health makes possible unique contextual levels of measurement, shown in Table 2. School-context data come from school administrator reports on school policies, health services and other school characteristics and from the in-school interviews of students whose aggregated responses represent school census measures. From respondent reports of colleges attended, college context data have been linked to individual records. Family-context data come from parent questionnaires, adolescent in-school and in-home questionnaires and interviews with siblings and additional adolescents living in the same household.

Table 2.

Contextual levels of measurement in Add Health

	Contexts
	Family					Dyadic relationships					Peer/social networks					School/college/workplace					Neighbourhood/community/state
	W1	W2	W3	W4	W5	W1	W2	W3	W4	W5	W1	W2	W3	W4	W5	W1	W2	W3	W4	W5	W1	W2	W3	W4	W5
Social
Household composition, roster, marital status	+	+	+	+	+	+	+	+	+	+	+	+				+					+	+	+	+	o
Relationships: parent, sibling, peer and partner	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+					+	+	+
Economic/work
Income, unemployment	+	+	+	+	+			+	+							+	+	+	+	+	+	+	+	+	o
School/education, school type, attainment	+	+	+	+				+	+		+	+	+			+	+	+	+	+	+	+	+	o	o
Race/ethnic/sex composition integration, discrimination	+	+	+	+	+	+	+	+	+	+	+					+	+	+	+		+	+	+	+	o
Legal, crime, policy	+	+	+	+	+	+	+	+			+	+						+	+	o	+	+	+	+	o
Health
Health care facilities, programmes and utilization	+	+	+	+		+	+	+	+		+	+				+	+				+	+	+	+	o
Fertility, morbidity and mortality, STD incidence	+	+	+	+	+	+	+	+	+	+	+	+	+			+					+	+	+	o	o
Alcohol and tobacco availability, prevention and control	+	+	+	+		+	+	+	+		+	+	+	+		+	+	+			+	+	+
Health behaviour, peer/parent substance use	+	+	+	+		+	+	+	o		+	+	+	o		+	+				o	o	o	o	o
Physical environment
Natural environment, distance to parks, day length	+		+			+		+			+		+			+					+		+	+	o
Built environment, urbanicity, street connectivity	+	+	+			+		+			+		+			+					+	+	+	+	o
Air quality, pollution				+					+															+
Housing type and quality	+	+	+	+		+	+	+	+		+	+	+	+		+	+				+	+	+	+	o

W1 = adolescent in- school and in-home, parent and school administrator surveys, geocodes. W2 = adolescent in-home, school administrator and geocodes. W3 = young adult in-home and partner sample surveys, geocodes, biomarkers. W4 = young adult in-home, biomarkers, geocodes. W5 = adult survey, biomarkers, geocodes. Additional variables from administrative datasets (e.g. US Census, Centers for Disease Control and Prevention, National Center for Health Statistics, Federal Bureau of Investigation, National Council of Churches, Common Core of Data, Private School Survey).

⁺, variable available; o, planned variable construction.

Adolescents were asked to nominate friends and sexual and romantic partners from the school rosters in the in-school and in-home surveys at Waves I and II. Peer networks characteristics can be constructed by linking friends’ data and constructing variables based on friends’ responses, and similar measures can be constructed for linked romantic and sexual partners. These peer- and dyad-context measures constitute the social network data, including information on friendship networks, sexual networks and friendship and relationship dyads.

Respondents’ home residences have been geocoded at each interview wave, and contextual data on the neighbourhood, community and state have been merged to all individual records. Nearly 12 000 environmental data elements at multiple geographical levels are available across waves. This includes such information as race, ethnic, foreign-born and religious denomination composition, poverty rates, crime statistics, STI prevalence, divorce and child support laws, welfare policies, cigarette taxes, the proximity and number of parks, sidewalks, recreation centres, fast food restaurants, alcohol outlets and other physical and social characteristics of the environments in which young people live.

Table 3 shows the array of survey and biological data in Add Health. The top panel lists the domains covered by the survey instruments at each wave, including individual-level data on household and family structure, personality, religiosity and spirituality, relationships, sexual behaviour, contraception, pregnancy, children and parenting, sleep patterns, physical activity, diet, substance use/abuse, violence, delinquency, involvement with the criminal justice system, education history, work experiences, military service, chronic and disabling conditions, injury, mental health, suicide and health service access and use. Even though respondents were first interviewed in early adolescence, there are data on infancy (birthweight) and childhood (e.g. maltreatment, chronic conditions, attention deficit hyperactivity disorder) and complete data on fertility outcomes (there were more than 14 500 births to Add Health respondents by Wave IV).

Table 3.

Survey and biomarker domains across Waves I-V in Add Health

Adolescence	Young Adulthood	Adulthood	Adulthood
Wave I-II (ages 12-19)	Wave III (ages 18-26)	Wave IV (ages 24-32)	Wave V (ages 32-42)
Questionnaire data
Demographic	Demographic	Demographic	Demographic
Family, siblings, friends	Family, siblings, friends	Family, siblings, friends	Family, siblings, friends
Education, work	Education, work	Education, work, military records	Education, work, military
Physical and mental health	Physical and mental health	Physical and mental health	Physical and mental health
Daily activities and sleep	Daily activities and sleep	Daily activities and sleep	Daily activities and sleep
Relationships	Relationships	Relationships	Relationships
Sexual and fertility histories	Sexual and fertility histories	Sexual and fertility histories	Sexual and fertility histories
Substance use	Substance use	Substance use and abuse	Substance use and abuse
Delinquency and violence	Involvement criminal justice	Involvement criminal justice	Involvement criminal justice
Attitudes, religion	Attitudes, religion	Work attitudes and characteristics, religion	Work attitudes and characteristics, religion
Economics, expectations	Economics, expectations	Economics, expectations	Economics, expectations
Psychological, personality	Psychological, personality	Big 5 Personality, stressors	Personality, stressors
	Children and parenting	Children and parenting	Children and parenting
	Civic participation	Civic participation	Civic participation
	Gambling	Cognitive function	Psychosocial factors, cognition
	Mentoring	Psychosocial factors	Retrospective child health and socioeconomic status
			Family health history
			Administrative linkages (in progress)
Biological data
Embedded genetic sample of 3000 ----------------------------------------------▸
Physical development
Height, weight	Height, weight	Height, weight, waist	Height, weight, waist
	STI tests (urine)	Metabolic (lipids, HbA1c, glucose)	Metabolic (lipids, HbA1c, glucose)
	HIV test (saliva)	Cardiovascular (blood pressure, pulse)	Cardiovascular (blood pressure, pulse)
	DNA (buccal cell)	Inflammation (hsCRP)	Inflammation (hsCRP)
		DNA (buccal cell); GWAS	mRNA; DNAm (venous blood)
		Immune function (EBV)	Renal (creatinine, cystatin C)
		Medications	Medications

The bottom panel of Table 3 shows the biological measures available across waves. The original study design included important features for understanding biological processes in health and developmental trajectories across the life course, including an embedded genetic sample with more than 3000 pairs of adolescents with varying biological resemblance (see Figure 1) and measurement of height and weight to track the obesity epidemic. At Wave III, urine and saliva samples were collected to test for STI and HIV,^11,12 and buccal cell saliva was collected from twins and full siblings in the genetic subsample for DNA extraction.¹³ An expanded set of biological measures were collected at Wave IV, including biomarkers of cardiovascular health (blood pressure, pulse), metabolic processes (waist circumference, glycosylated haemoglobin, blood glucose, lipids), immune function (Epstein-Barr virus), inflammation (C-reactive protein) and a medications inventory. Repeat biomarker measures were collected at Wave V, including new markers of renal disease. Saliva DNA was collected from the full sample at Wave IV. Candidate loci in the dopamine and serotonin pathways have been genotyped and disseminated to the scientific community.¹⁴ Genome-wide genotyping was completed on 10 974 Wave IV respondents who consented to archive their specimens for further testing, and genome-wide association study (GWAS) data are available from the database of Genotypes and Phenotypes (dbGaP). Add Health maintains a biospecimen archive available for ancillary studies.

What has it found? Key findings and publications

Add Health has a large and multidisciplinary user base of more than 50 000 researchers around the world, who have published over 3500 peer-reviewed articles in more than 750 different disciplinary journals, and has been the data source for more than 800 master’s theses and dissertations. Publications are listed at [https://www.cpc.unc.edu/projects/addhealth/publications].

Early publications focused on the role of social context in the development of adolescent health, behaviour, expectations and attainment, finding important influences of family and school connectedness,¹⁵ peer influence,^16–18 romantic relationships,¹⁹ and neighbourhoods.^20–22 For example, adolescents with a greater number and higher quality of connections to their school and family had better physical and mental health and higher attainment than youth with fewer connections.¹⁵ Adolescents whose friendship networks included friends with highly involved parents were less likely than those whose friends had uninvolved parents to binge-drink, smoke cigarettes or use marijuana.²³ Other studies report that romantic relationships in adolescence can increase depression among adolescent girls,¹⁹ and neighbourhood disadvantage is associated with higher rates of aggression, non-marital childbearing, obesity and weight gain in adulthood.^20,22,24

Recent publications documented an alarming emergence of chronic disease among young adults, including a 19% prevalence of hypertension²⁵ and 6% prevalence of diabetes.²⁶ Exploiting the longitudinal data, researchers have investigated the developmental and health pathways leading to young adult outcomes.^27,28 Add Health data support longitudinal studies of obesity,²⁹ intimate partner violence,³⁰ substance use³¹ and health disparities during the early life course from adolescence into young adulthood.^32,33

Add Health has mapped the obesity epidemic and documented long-term outcomes for obese adolescents. In adolescence (1995–96), 11% of the sample were obese; in 2001–02 when the cohort was aged 18–26, the percentage doubled to 22%; in 2008–09, 37% of the cohort at ages 24–32 was obese.^3,34,35 Building on these longitudinal data, The and colleagues²⁹ demonstrated the long-term impact of obesity early in life, reporting that obese adolescents were more likely to develop severe obesity in young adulthood [body mass index (BMI) ≥40.0] compared with normal-weight or overweight adolescents, by a risk ratio of 16 to 1. Harris³ categorized individual obesity trajectories from adolescence at Wave II to young adulthood at Wave III into three groups: not obese (those who were never obese or lost weight, 82%); become obese (those who became obese during the transition to young adulthood, 10%); and always obese (those who were obese throughout adolescence and young adulthood, 8%). As shown in Figure 3, greater exposure to obesity during adolescence and young adulthood is associated with a higher likelihood of diabetes, hypertension, high cholesterol and sleep problems in adulthood.

Figure 3.

Obesity trajectory from adolescence to young adulthood associated with multiple health outcomes in adulthood (n ∼10 000).

The unique design and diversity of the sample made possible health disparities research on special populations including the disabled,^36–40 adopted youth,^41–47 youth living with surrogate parents or relatives,^48,49 multiracial youth,^50–52 sexual minorities^53–57 and immigrants.^58,59 Findings show that: adopted adolescents are more likely to attempt suicide than their non-adopted peers⁴⁷; mixed-race adolescents are at higher health risk on a range of indicators compared with adolescents who report only one race⁵²; and bisexual women report more depressive symptoms and perceived stress than heterosexual women.⁶⁰

There is a large and growing body of genomic research that integrates the genetic data with the longitudinal environmental data to explore the influence of gene-by-environment interactions (GxE), gene-environment correlations (rGE) and polygenic scores (PGS) in health and behavioural outcomes. Hundreds of genetic research articles have explored these associations on a wide range of topics, including risk behaviour,⁶¹ substance use,^62–65 depression,⁶⁶ sexual behaviour,⁶⁷ BMI and obesity,^68–70 educational attainment,⁷¹ friendship networks,⁷² conduct problems, delinquency,^73,74 violence^73–75, and subjective well-being.⁷⁶

Genome-wide association study (GWAS) data were generated for both the sibling pairs sample⁷⁷ and Wave IV archive sample,⁷⁸ enabling the construction of a large number of PGSs.⁷⁹ These scores facilitated the following new research: sibling differences in the education PGS and educational attainment⁸⁰; moderating effects of school environments in the education PGS association with educational and occupational attainments (GxE)⁸¹; family structure and reproductive timing (rGE)⁸²; education PGS’s role in intergenerational social mobility⁸³; and cohort differences in the genetic relationship between education and smoking.⁸⁴ In addition, innovative new research is providing human evidence of ‘social genetic effects’ in which the genes of one’s peers influence individual behaviour, controlling for one’s own genes (Sotoudeh, Harris, and Conley: unpublished).⁸⁵

Figure 4 illustrates findings for social genetic effects of schoolmates and friends on educational attainment, BMI and height.⁸⁵ The blue (dark grey) bars show the effect of mean school and friend PGS, net of one’s own PGS, for educational attainment in the top panel, BMI in the second panel and height in the third panel. The red dashed line is the baseline effect of own PGS on outcomes in a null model with no other predictors, and the light grey bars represent the effect adjusted for individual-level covariates. The results indicate that the genetics of an individual’s schoolmates and friends predict the individual’s own educational attainment, whereas an individual’s height is unassociated with the height genetics of peers. Add Health has participated in several consortia for new GWAS, including educational attainment, height and alcohol use.^86–88

Figure 4.

Social genetic effects.

Integrative Add Health research uses biomarker data to link social and behavioural factors with objective measures of health. Illustrative research has examined the associations between a virginity pledge, childhood abuse and race/ethnicity with: STI risks^12,89,90; social status and obesity^91,92; birthweight, breastfeeding and inflammation⁹³; and life course exposures of neighbourhood disadvantage, social adversity and stressful life events for cardiometabolic risk.^94–98 The significant role of social interactions and social context in pre-disease pathways was documented for the first time among young adults, emphasizing the tremendous potential for intervening in the environment early in life before disease symptoms and biological damage are manifest. Social integration, exposure to family instability and urban residence protect young adults from disease risk,^99–101 whereas social mobility is associated with mental health benefits but physical health costs for racial/ethnic minorities young adults compared with Whites.¹⁰²

What are the main strengths and weaknesses?

The major strengths of Add Health emanate from its contextual and national design. The adolescent social context and peer network data, in particular, are unique because they do not rely on inherently biased self-reports to generate an image of an adolescent’s environment. Overall strengths include: (i) national representation of people who live in all 50 states and come from every race, ethnic, immigrant, geographical and socioeconomic subgroup; (ii) racial and ethnic diversity with sufficient numbers to allow within-group analysis of nine separate groups: Mexican, Cuban, Puerto Rican, Central-South American, Chinese, Filipino, African and African American, and European; (iii) understudied and vulnerable populations including individuals with disabilities, foster children and adopted children, mixed-race individuals, immigrants and sexual minorities; (iv) genetic sample of over 3000 pairs of individuals with varying biological resemblance; (v) multigenerational and longitudinal data from respondents and their parents; (vi) longitudinal social, behavioural and biological data beginning in early adolescence and extending into adulthood; (vii) extensive longitudinal multilevel data beginning in early adolescence on respondents' life circumstances and social and physical environments, including family, school, friends, neighbourhood, community and social relationships; (viii) objective measures of health including blood pressure, pulse rate, cholesterol, glucose, high-sensitivity C-reactive protein [hsCRP], Epstein-Barr virus [EBV], waist circumference, BMI, creatinine and cystatin C, and DNA on almost 16 000 participants; (ix) candidate gene and genome-wide genotyping on the full sample at Wave IV; and (x) repeated collection of DNA on twins and full siblings. New omics data will be available in the future including both transcriptome and methylation data (see Table 3).

Weaknesses include: (i) a lack of qualitative data; (ii) the wide breadth of survey data precluding in-depth measurement of specific standard scales; and (iii) a fairly long periodicity for repeated survey and biomarker measures.

Can I get hold of the data? Where can I find out more?

Datasets are available to researchers in several forms: (i) public-use, representing a subset of respondents; (ii) restricted-use and high security restricted-use, which are distributed only to authorized researchers; (iii) geocodes which can only be used in a secure data facility to link Add Health data to other spatially defined data; and (iv) high school transcript data, which are available in secure data enclaves. Data access limitations protect the confidentiality and identities of respondents while allowing data access to a wide range of researchers.

More information, including data access guidelines, study description, publications, documentation files and codebooks can be accessed at [http://www.cpc.unc.edu/projects/addhealth]. GWAS data can be accessed via dbGaP (Study Accession phs001367.v1.p1).

Profile in a nutshell

• Add Health is an ongoing longitudinal study of a nationally representative US cohort of 20 745 adolescents in grades 7-12 (aged 12-19 years) in 1994-95.

• Follow-up includes four in-home interviews in 1996, 2001-02, 2008-09, 2016–18.

• Sample attrition has been low, with response rates ranging from 77% to 89% across follow-up waves, and attrition bias has been minimal.

• The study obtains unprecedented environmental, behavioural, psychosocial, biological and genetic data from early adolescence into adulthood with extensive racial, ethnic, socioeconomic and geographical diversity.

• Add Health has a large, multidisciplinary user base of over 50 000 researchers around the world, who have published over 3500 research articles.

• Key findings show the Add Health cohort at the forefront of the obesity epidemic with profound consequences for cardiometabolic health risks, and significant social genetic effects of schoolmates and peers on health and behaviour.

• Add Health datasets are distributed according to a tiered data disclosure plan according to the degree of confidential information and security requirements needed in use of the data: see [http://www.cpc.unc.edu/projects/addhealth/data].

Funding

Add Health is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development [P01 HD031921] with co-funding from 23 other federal agencies and foundations. The full list of co-funders can be found at [http://www.cpc.unc.edu/projects/addhealth/about/funders].