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Summary: Dr. Gene Brody and his colleagues at the University of Georgia and Northwestern University used Add Health data analysis to support their theory that striving to succeed in the face of difficult odds can have a negative effect on black adolescents. Their study, which was published in Pediatrics, looked at the health outcomes of black adolescents who indicated that they were determined to succeed. These black adolescents who were from disadvantaged families had a higher chance of developing Type 2 diabetes compared to black adolescents from more privileged families. The article notes that these findings support an effect called “John Henryism,” where individuals that devote high energy to meet challenges tend to have worse heath outcomes.

Read the story in The New York Times: Why Succeeding Against the Odds Can Make You Sick, by James Hamblin, January 27, 2017.

Excerpt:

‘The focus on black adolescents is significant. In much of this research, white Americans appeared somehow to be immune to the negative health effects that accompany relentless striving. As Dr. Brody put it when telling [the article’s author] about the Pittsburgh study, “We found this for black persons from disadvantaged backgrounds, but not white persons.”

…

It seems natural to assume that jumping from one stratum to the next — being upwardly mobile — would come with gains in health. And conceivably it could work that way — like if a person won the lottery or achieved overnight fortune from writing a truly insightful tweet. But decades of research show that when resilient people work hard within a system that has not afforded them the same opportunities as others, their physical health deteriorates.’

Scholarly Source: Brody GH, Yu T, Miller GE, Chen E. Resilience in Adolescence, Health, and Psychosocial Outcomes. Pediatrics 2016; 138(6).

 

Researchers used Add Health data to investigate the impact of volunteering on crime involvement later in life, as studies have shown that volunteerism or community service can increase levels of prosocial behavior, belonging, and happiness among adolescents. Participants reported their illegal behaviors, arrests, and convictions during Waves III and IV of Add Health. During Wave III, respondents also reported their volunteerism between ages 12 and 18, stating whether the work was strictly voluntary, ordered by a court, or required by parents, school, or religious group.

Between ages 12 and 18, 58% of respondents had not volunteered, 31% were self-volunteers, 8% were adult-required volunteers, and 2.4% were court ordered volunteers. Overall, those who self-volunteered had less involvement in crime than those who did not volunteer: “those who self-volunteered reported 11% fewer illegal behaviors, 31% fewer arrests, and 39% fewer convictions by age 18–28, and 28% fewer illegal behaviors, 53% fewer arrests, and 36% fewer convictions by age 24–34, relative to the non-volunteers.” The authors suggest that adolescent volunteering may increase resilience over time and that school-based volunteering programs may help prevent criminal involvement over the life course.

View the abstract or download the complete article from Injury Epidemiology

Authors

• Shabbar I. Ranapurwala: Injury Prevention Research Center, Department of Occupational and Environmental Health, College of Public Health, University of Iowa; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Carri Casteel: Injury Prevention Research Center, Department of Occupational and Environmental Health, College of Public Health, University of Iowa
• Corinne Peek-Asa: Injury Prevention Research Center, Department of Occupational and Environmental Health, College of Public Health, University of Iowa

 

Ranapurwala SI, Casteel C, and Peek-Asa C. Volunteering in adolescence and young adulthood crime involvement: a longitudinal analysis from the add health study. Injury Epidemiology 2016; 3 (26).

 

Using Add Health data, Dr. Thorsen investigated how an adolescent’s family environment may influence when they enter into a cohabiting relationship, including marriage, and the stability of that relationship. A number of dimensions – family belonging, parental marital quality, family structure, parental relationship history, and family SES – were used to measure a respondent’s family environment during Wave I. For respondents at Wave IV, only 18% had never been in a cohabiting relationship, while 16% of respondents’ first cohabiting relationship was a marriage. Out of those who had ever had a cohabiting relationship, 50% had broken up with their partner, 35% married their partner, and 15% were still living with the same partner.

In general, a more positive family environment during adolescence is associated with fewer cohabiting relationships and having more stable cohabiting relationships. For example, respondents who felt a lower level of family belonging were more likely to break up with their partner instead of marrying them. However, the effects of a stressful family environment became less influential on cohabiting relationships when respondents began the relationships at an older age.

Maggie L. Thorsen is an Assistant Professor of Sociology at Montana State University.

Thorsen ML. The adolescent family environment and cohabitation across the transition to adulthood. Social Science Research 2016.

 

Researchers used Add Health data to examine the relations between childhood trauma and the use of prescription pain reliever misuse (PPRM) and injection drug use (IDU). After identifying nine specific types of trauma, results showed that childhood trauma occurred among 5.13% (experienced violence) to 16.37% (experienced emotional abuse) of respondents. 20% of respondents had misused pain relievers between Waves I and III; by Wave IV, this number increased to 30%. At Wave III, 1.24% of respondents had used injection drugs.

Analyses showed a dose-response relationship: respondents who experienced a higher number of traumas had a higher risk of PPRM. For example, in emerging adulthood, the increase in the odds of PPRM was 34%, 50%, 70%, 217%, and 179% for one, two, three, four, and five or more traumas, respectively. Furthermore, this link between trauma and drug use became stronger into adulthood. Authors suggest that a history of childhood trauma has a negative effect on well-being and influences psychological and physiological functioning. Given the current public health problem in the US related to use of these drugs, a focus on trauma-informed interventions and early trauma screening are needed.

View the abstract or download the complete article from Drug and Alcohol Dependence

Authors

• Kelly Quinn: Department of Population Health, NYU School of Medicine
• Lauren Boone: Department of Health Behavior and Health Education, School of Public Health, University of Michigan
• Joy D. Scheidell: Department of Population Health, NYU School of Medicine
• Pedro Mateu-Gelabert: National Development and Research Institutes, Inc.
• Susan P. McGorray: Department of Biostatistics, College of Public Health & Health Professions, College of Medicine, University of Florida
• Nisha Beharie: National Development and Research Institutes, Inc.
• Linda B. Cottler: Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida
• Maria R. Khan: Department of Population Health, NYU School of Medicine

 

Quinn et al. The relationships of childhood trauma and adulthood prescription pain reliever misuse and injection drug use. Drug and Alcohol Dependence 2016.

November is American Diabetes Month

Add Health data include reports of diabetes status for three generations: the Add Health respondent, the respondent’s parent, and the respondent’s children. Additional measures of diabetes status for the Add Health respondent are available based on medication use and results from assayed blood spots.

As Add Health Wave V data collection continues, respondents are asked survey questions relating to their diabetes status and medication use, and the biomarker home visit will allow for the collection of objective measurements of diabetes status.

Publications

Researchers have used Add Health’s diabetes data to publish on a number of topics, including both type 1 and type 2 diabetes, finances, family history, and genetics. Please click here for a list of highlighted publications. These publications are all included in our database of over 6,000 references.

Add Health Data

Measures

Parent Questionnaire

At Wave I, parents were asked about the diabetes status of the adolescent respondent (PC49F_1), the adolescent respondent’s biological mother (PC49F_2), and the adolescent respondent’s biological father (PC49F_3).

Add Health Respondent’s Self-Reported Diabetes History

At both Waves III and IV, respondents reported if they had ever been diagnosed with diabetes (H3ID16, H4ID5D) and at what age they were diagnosed (H3ID17, H4ID6D).

Add Health Respondent’s Children

Finally, at Wave IV, respondents reported if each of their biological children has diabetes (H4KK13R).

Glucose Homeostasis

During Wave IV, Add Health collected and assayed capillary whole blood via dried blood spots and the Wave IV data includes two measures of glucose homeostasis: glucose (mg/dL) and hemoglobin A1c (HbA1c %). These measures are available in our Wave IV Biomarker glucose file, which is part of the Add Health restricted use data.  Full documentation of the Wave IV glucose measurements is available online: Measures of Glucose Homeostasis.

Medications

At Wave III, respondents reported if they took any medications for diabetes within the past 12 months (H3ID26E).  Respondents were also asked if they take pills, insulin, both, or neither to control blood sugar (H3ID18). At Wave IV, respondents were asked to provide an inventory of all prescription medications that they used in the past 4 weeks. These reported medications were then assigned codes indicating their therapeutic class, therapeutic subclass, and therapeutic subgroup. These measures are available in our Medication File, which is part of the Add Health restricted use data.

Joint Classification of Diabetes at Wave IV

Based on a combination of the above measures, Add Health has constructed a variable (C_JOINT), which classifies respondents as having diabetes based on:

1. Fasting glucose value (C_FGLU) or non-fasting glucose value (C_NFGLU), or
2. Hemoglobin A1c value (C_HBA1C), or
3. Self-reported history of diabetes at Wave IV (H4ID5D), or
4. Using anti-diabetic medication in the past 4 weeks (C_MED).

The above variables are available in the Wave IV Biomarker glucose file.

For full details on the classification of diabetes at Wave IV, see our Wave IV Documentation: Measures of Glucose Homeostasis

Add Health Wave V Survey

During the Wave V survey, respondents report on their diabetes status, if they take any medications for diabetes, and if each of their biological children has diabetes. During the both the survey and the home biomarker visit, respondents will be asked to provide an inventory of the medications they have taken in the past 4 weeks. During the home visit, respondents will provide whole venous blood, which will then be assayed for levels of glucose and HbA1c.

Measures

Intimate partner violence (IPV) is a serious public health issue. Research related to the causes and consequences of IPV is vital to stopping violence and helping survivors.  The Add Health surveys include a number of questions related to IPV, and these have been asked at Waves II, III, and IV. Respondents answered questions about partner violence victimization and perpetration, as well as some criminal activity. For a listing of these questions, please use our ACE tool topic list and select Romantic Relationships>Intimate Partner Violence.

The Add Health Wave V survey repeats many of the IPV questions asked at Waves III and IV. For more information about ongoing data collection, please see the Wave V webpage: Add Health Wave V.

Publications

Add Health data is used in nearly 150 publications related to IPV, and for Domestic Violence Awareness Month, we would like to highlight a few of these more recent publications below. For a full list of publications related to IPV, search our publications database.

Journal Articles

• Alleyne-Green, B., Grinnell-Davis, C., Clark, T. T., & Cryer-Coupet, Q. R. (2015). The role of fathers in reducing dating violence victimization and sexual risk behaviors among a national sample of Black adolescents. Children and Youth Services Review, 55, 48-55. doi: 10.1016/j.childyouth.2015.04.005. http://dx.doi.org/10.1016%2Fj.childyouth.2015.04.005
• Clark, C. J., Alonso, A., Everson-Rose, S. A., Spencer, R. A., Brady, S. S., Resnick, M. D., . . . Suglia, S. F. (2016). Intimate Partner Violence in Late Adolescence and Young Adulthood and Subsequent Cardiovascular Risk in Adulthood. Preventive Medicine, 87, 132-137. doi: 10.1016/j.ypmed.2016.02.031. http://dx.doi.org/10.1016%2Fj.ypmed.2016.02.031
• Iratzoqui, A., & Watts, S. J. (2016). Longitudinal Risks for Domestic Violence. Journal of Interpersonal Violence. doi: 10.1177/0886260516663897. http://dx.doi.org/10.1177%2F0886260516663897
• Kuhl, D. C., Warner, D. F., & Warner, T. D. (2015). Intimate Partner Violence Risk among Victims of Youth Violence: Are Early Unions Bad, Beneficial, or Benign? Criminology, 53(3), 427-456. doi: 10.1111/1745-9125.12075. http://dx.doi.org/10.1111/1745-9125.12075
• Schwab-Reese, L. M., Peek-Asa, C., & Parker, E. (2016). Associations of financial stressors and physical intimate partner violence perpetration. Injury Epidemiology, 3(1), 1-10. doi: 10.1186/s40621-016-0069-4. http://dx.doi.org/10.1186/s40621-016-0069-4
• Spencer, R. A., Renner, L. M., & Clark, C. J. (2015). Patterns of Dating Violence Perpetration and Victimization in U.S. Young Adult Males and Females. Journal of Interpersonal Violence, 31(15), 2576-2597. doi: 10.1177/0886260515579506. http://dx.doi.org/10.1177%2F0886260515579506
• Ulloa, E. C., Hammett, J. F., O’Neal, D. N., Lydston, E. E., & Aramburo, L. F. (2016). The Big Five Personality Traits and Intimate Partner Violence: Findings From a Large, Nationally Representative Sample. Violence and Victims. doi: 10.1891/0886-6708.vv-d-15-00055. http://dx.doi.org/10.1891%2F0886-6708.vv-d-15-00055

Conference Proceedings

• Dellor, E. (2015, Nov 3, 2015). Childhood Maltreatment, Risk of Intimate Partner Violence and Inflammation in Young Adults. Paper presented at the American Public Health Association 143rd Annual Meeting and Exposition, Chicago, IL. https://apha.confex.com/apha/143am/webprogram/Paper327026.html
• Manlove, J., Welti, K., & Karpilow, Q. (2015, April 30-May 2 2015). Relationship Violence Typologies and Condom Use in Young Adult Dating Relationships. Paper presented at the Population Association of America Annual Meeting, San Diego, CA. http://paa2015.princeton.edu/abstracts/152431

Thesis

• Aguirre, E. (2015). Depressive symptoms and suicidality among survivors of intimate partner violence. (1563623 M.S.W.), California State University, Los Angeles, Ann Arbor. http://libproxy.lib.unc.edu/login?url=http://search.proquest.com/docview/1611854959?accountid=14244

 

Add Health Director Kathleen Mullan Harris, Deputy Director Carolyn Halpern, and Principal Investigator Jon Hussey collaborated with other researchers at the Carolina Population Center and University of North Carolina at Chapel Hill on their recent Journal of Adolescent Health publication.  The authors used Add Health data to evaluate how marijuana use and binge drinking relate to depression from adolescence to young adulthood. These researchers were interested in testing if depressive symptoms made substance use more likely (Self-Medication Model), or if substance use predicted future depressive symptoms (Stress Model).

Depression was measured using nine items from the CES-D (Center for Epidemiologic Studies Depression Scale) included in the Add Health survey. Respondents also reported their marijuana use frequency within the past 30 days and their binge drinking frequency within the past 12 months. Researchers looked at each of these variables across three Waves of Add Health: Wave I, III, and IV.

The research team found that both males and females appear to be using marijuana, but not binge drinking, to self-medicate their depression. However, for females, both binge drinking and marijuana use increased risk of future depression, suggesting that the Stress Model may be a better fit for female behaviors. The authors suggest that substance use should be considered in relation to screening for adolescent depression: depressed teens may be at a greater risk of substance use and abuse, while substance-using teens may be more likely to become depressed. 

 

 

View the abstract or download the complete article from the Journal of Adolescent Health

Authors

• Andra L. Wilkinson: Carolina Population Center, University of North Carolina at Chapel Hill; Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Carolyn Tucker Halpern: Carolina Population Center, University of North Carolina at Chapel Hill; Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Amy H. Herring: Carolina Population Center, University of North Carolina at Chapel Hill; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Meghan Shanahan: Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Injury Prevention Research Center, University of North Carolina at Chapel Hill
• Susan T. Ennett: Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Jon M. Hussey: Carolina Population Center, University of North Carolina at Chapel Hill; Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
• Kathleen Mullan Harris: Carolina Population Center, University of North Carolina at Chapel Hill; Department of Sociology, University of North Carolina at Chapel Hill

 

Wilkinson et al. Testing Longitudinal Relationships Between Binge Drinking, Marijuana Use, and Depressive Symptoms and Moderation by Sex. Journal of Adolescent Health 2016.

Check out the October 2016 issue of the Journal of Adolescent Health to read two articles featuring Add Health research:

The Association Between Adolescent Obesity and Disability Incidence in Young Adulthood

Teen Pregnancy Among Sexual Minority Women: Results From the National Longitudinal Study of Adolescent to Adult Health

 

Former Rep. Rush Holt (D-N.J.) and Mary Sue Coleman pen blog entry for The Hill which stresses the importance of research funded by the federal government.  They describe the groundbreaking research studies which received the 2016 Golden Goose Award, including the Add Health study.  Read the full story here.  

Holt is the Chief Executive Officer of the American Association for the Advancement of Science (AAAS) and Coleman is president of the Association of American Universities.

Infant health & characteristics in Add Health

Add Health data include a variety of infant health measures for two generations: Add Health respondents and respondents’ biological children. Add Health respondent birth characteristics were reported by the Add Health respondent’s parents while the Add Health respondents provided information on birth characteristics of their own biological children. Information on prenatal care, gestational age, birth weight and breastfeeding are included in the Add Health datasets.

Current data collection efforts for Add Health Wave V and the Add Health Parent Study include survey questions concerning the pregnancy and birth of the Add Health respondents and their biological children. Please refer to the study webpages for more information on ongoing data collection: Add Health Wave V, Add Health Parent Study

This research highlight focuses on infant health as part of World Breastfeeding Week, which is August 1-7, 2016. Add Health twitter will feature research on infant health and characteristics throughout the month of August so stay tuned!

Publications

To date, Add Health researchers have published 75 journal articles, conference papers/proceedings and theses concerning pregnancy and birth characteristics. To view a full list of publications on breastfeeding, birth weight and gestational age, please click here. These publications are all included in our database of over 6,000 references.

Measures

Below are brief descriptions of measures collected at previous waves which are available in existing datasets, as well as information on measures which are collected during ongoing fieldwork. Variable names provided in parenthesis hyperlink to the corresponding ACE online codebook page for the variable.

Prenatal care

Respondents were asked about prenatal care obtained for their biological children during Waves III and IV. The Wave III interview included several questions about the respondent’s or partner’s visits to a doctor or nurse-midwife for prenatal care or pregnancy checkups (H3PG11, H3PG13, H3PG14, H3PG15, H3PG16). Wave IV data also includes measures on prenatal care and pregnancy checkups (H4PG12, H4PG13).

Gestational Age

Currently available data on gestational age refer to the respondent’s biological children. During the Wave III in-home interview, respondents were asked whether their biological child was born before 40 weeks (H3LB7) and if so, how many weeks early the child was born (H3LB8). During the Wave IV in-home interview, respondents were asked how many weeks or days before or after the due date their child was born (H4LB9W, H4LB9D). For the first time in Add Health, the Wave V and Parent Study surveys are collecting data on whether the Add Health respondent was born preterm.

Birth weight

Add Health data includes measures for the respondent’s birth weight as well as the birth weight of the respondent’s biological children. At Wave I, the respondent’s parent was asked to provide the respondent’s birth weight in pounds and ounces (PC19A_P/PC19B_O), while Add Health respondents were asked for the birth weight of each of their biological children at Waves III and IV (H3LB5A/H3LB5B, H4LB6P/H4LB6O). At Wave IV, if the respondent did not know the birth weight in pounds and ounces they received a follow up question asking if the baby weighed less than 5.5 pounds (H4LB7).

The Add Health Wave V survey asks respondents to report the birth weight of their biological children while both the Wave V and Parent Study surveys include questions concerning the Add Health respondent’s birth weight. Respondents are asked to self-report their birth weight for the first time during Wave V and the project plans to link administrative data from birth records, including birth weight, to the longitudinal record of a subset of Add Health respondents.

Breastfeeding

During the Wave I parent interview, parents reported the length of time the Add Health respondent was breastfed (PC20).