1. 1 Sampson & Laub (2004). Criminology, 41 (3): 555-592.

2. Drug Abuse Epidemiology Datasets Wilson M. Compton, M.D., M.P.E. Director, Division of Epidemiology, Services and Prevention Research National Institute on Drug Abuse 16 May 2007

3. 3 Primary Concepts 1.Do the major datasets provide good secondary analysis scientific opportunities? 2.How can NIDA promote use of these resources? Ultimate Goal is to Enhance the Process of Scientific Discovery

4. 4 1. Cross-Sectional

5. 5 Changes in Current Smoking Among US Students Before/After Launch of the "truth" Campaign in 2000 Grade19972002Change1997-19992000-2002 All28.018.0–35.7–3.2 (–3.8, –2.6)–6.8 (–7.5, –6.1) 8th19.410.7–44.8–3.4 (–4.6, –2.1)–9.0 (–10.4, –7.6) 10th29.817.7–40.6–4.6 (–5.6, –3.6)–8.7 (–9.8, –7.5) 12th36.526.7–26.8–1.8 (–2.7, –1.0)–5.1 (–6.1, –3.9) Farrelly et al, AJPH 2005;95:425-431 Odds of smoking Media market exposure (48 states shown) of the national "truth" campaign, 2000–2002 Ave. Cumulative GRP x 10,000

6. 6 Odd Ratio for Reporting Chance to Buy Drugs Aggression, Delinquency, and Opportunity to Buy Drugs: NHSDA Rosenberg & Anthony, Drug Alc Dependence 2001;63:245-252 Delinquency Aggression Low High Low High * P <.01 Compared to Low/Low Reference Group * *

7. 7 Lifetime Prevalence of Use Among Whites and African Americans 12+ Years Old (NSDUH 2004) Denise Kandel, NIDA Trajectories Conference 11/13/2006] Percentage of Lifetime Use ***

8. 8 White 2004 Afr-Am 2004 Current Smoking by Age Among White and African American Lifetime Smokers in 2004 and in the 1973-1978 Birth Cohorts in 1990, 1996 and 2004 (NHSDA/NSDUH ) Percentage of Last Month UseAge Category Denise Kandel, NIDA Trajectories Conference 11/13/2006

9. 9 Current Smoking by Age Among White and African American Lifetime Smokers in 2004 and in the 1973-1978 Birth Cohorts in 1990, 1996 and 2004 (NHSDA/NSDUH ) Percentage of Last Month UseAge Category White 1973-78 birth cohort Afr-Am 1973-78 birth cohort 1990 1996 2004 Denise Kandel, NIDA Trajectories Conference 11/13/2006

10. 10 Current Smoking by Age Among White and African American Lifetime Smokers in 2004 and in the 1973-1978 Birth Cohorts in 1990, 1996 and 2004 (NHSDA/NSDUH ) Percentage of Last Month UseAge Category White 2004 Afr-Am 2004 White 1973-78 birth cohort Afr-Am 1973-78 birth cohort 1990 2004 1996 2004 Denise Kandel, NIDA Trajectories Conference 11/13/2006

11. 11 Mean Ages of Onset of Substances Among Whites and African Americans 18-49 Years Old (NSDUH 2004) Mean Age of Onset *** * Denise Kandel, NIDA Trajectories Conference 11/13/2006

12. 12 Last Year Dependence Among Lifetime Users Age 12+ Among Whites and African Americans (NSDUH 2004) Percent Dependent *** ** Denise Kandel, NIDA Trajectories Conference 11/13/2006

13. 13 Last Year Dependence Among Lifetime MJ Users by Age Among Whites & African Americans (NSDUH 2004) Percentage of Last Year Dependence Age Category White Afr-Am Denise Kandel, NIDA Trajectories Conference 11/13/2006 5 10 15 20 25 30 12-1718-2526-3435-49

14. 14 Last Year Dependence Among Lifetime Cocaine Users by Age Among Whites and African Americans (NSDUH 2004) Percentage of Last Year Dependence Age Category White Afr-Am Denise Kandel, NIDA Trajectories Conference 11/13/2006]

15. 15 Prescription Drug Misuse and Disorders in 1991- 1992 (NLAES) and 2001-2002 (NESARC) Blanco C et al., in press * P < 0.01 * *

16. 16 1. Examples of National U.S. Cross- Sectional Data 1)Monitoring the Future (MTF) 2)National Survey on Drug Use and Health (NSDUH) 3)National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) 4)National Comorbidity Survey (NCS) and related 5)Youth Risk Behavior Survey (YRBS) 6)Treatment Episode Data Set (TEDS) 7)Drug Abuse Warning Network (DAWN)

17. 17 Select Examples of Other Large Data Sets 1)Adolescent Treatment Studies 2)SPF-SIG data 3)Criminal Justice Datasets 4)Medicaid and Medicare Administrative Data 5)V.A. Health Care Data 6)Large Managed Care Systems (e.g. Group Health Plan, Kaiser) 7)NIDA’s Clinical Trials Network (CTN) Database: www.ctndatashare.orgwww.ctndatashare.org

18. 18 2. Longitudinal

19. 19 Jackson et al. J Abnormal Psychology 2005;114:612-626 Smoking Intensity Decreasing Across Birth Cohorts But Stable Across Time (MTF Data)

20. 20

21. 21 NSPY Study: Distinguishing Vulnerable/Resolute Non-Using Adolescents Crano WD, NSPY Users Group Meeting May 2007 Sample with complete data at all rounds, Ages 12-18 (N = 2111) Predictors: Demographic Social Academic Parental Peer Other substances Marijuana Use

22. 22 Benefit Cost Markedly Greater for Life Course View of Heroin Addiction (DATOS) Zarkin et al. Health Economics 005;14:1133-1150

23. 23

24. 24 1)Monitoring the Future (MTF) 2)National Survey of Parents and Youth (NSPY) 3)Drug Abuse Treatment Outcomes Study (DATOS) 4)Adolescent Health (AddHealth) 5)National Longitudinal Survey of Youth (NLSY) 6)National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) 2. Examples of National U.S. Longitudinal Data

25. 25 Select Examples of Other Large Data Sets 1)Adolescent Treatment Studies 2)Multi-wave locally representative data Great Smoky Mountain Study and more than 50 others….

26. 26 3. Using Data Systems: Advantages and Problems

27. 27 Access to Data 1)SAMHDA Website http://www.icpsr.umich.edu/SAMHDA/ http://www.icpsr.umich.edu/SAMHDA/

28. 28 Access to Data 1)SAMHDA Website http://www.icpsr.umich.edu/SAMHDA/ http://www.icpsr.umich.edu/SAMHDA/ 2)UCLA Data Program Project (Bentler, PI) and Penn State Prevention Methods Center (Collins, PI) 3)Confidentiality concerns

29. 29 Knowledge of data sets 1)Common problem in using data collected by someone else 2)Solutions: Seminars, publications, user- groups 1)NSPY User Group 2)NESARC User Group

30. 30 3. Improving Use of Data 1)Support for secondary work Gene Environment Development Initiative (GEDI) 2)Support for release and access to data? 3)Developing common metrics for newly proposed studies that are not primarily concerned with drug abuse/addiction?

31. 31 Conclusions Multiple rich sources of data exist and provide opportunities for developing and testing important hypotheses. Promoting the effective and efficient use of these data requires management and strategic planning.

32. 32