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

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

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

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

  4. 1. Cross-Sectional

  5. Odds of smoking Ave. Cumulative GRP x 10,000 Media market exposure (48 states shown) of the national "truth" campaign, 2000–2002 Changes in Current Smoking Among US Students Before/After Launch of the "truth" Campaign in 2000 Farrelly et al, AJPH 2005;95:425-431

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

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

  8. 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 ) White 2004 Afr-Am 2004 Percentage of Last Month Use Age Category Denise Kandel, NIDA Trajectories Conference 11/13/2006

  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 ) White 1973-78 birth cohort Afr-Am 1973-78 birth cohort 1996 2004 1996 2004 Percentage of Last Month Use 2004 1990 2004 1990 Age Category Denise Kandel, NIDA Trajectories Conference 11/13/2006

  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 ) White 2004 White 1973-78 birth cohort Afr-Am 2004 Afr-Am 1973-78 birth cohort 2004 1996 2004 2004 2004 1996 2004 Percentage of Last Month Use 2004 1990 2004 2004 1990 Age Category Denise Kandel, NIDA Trajectories Conference 11/13/2006

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

  13. Last Year Dependence Among Lifetime MJ Users by Age Among Whites & African Americans (NSDUH 2004) 30 White Afr-Am 25 20 Percentage of Last Year Dependence 15 10 5 12-17 18-25 26-34 35-49 Age Category Denise Kandel, NIDA Trajectories Conference 11/13/2006

  14. Last Year Dependence Among Lifetime Cocaine Users by Age Among Whites and African Americans (NSDUH 2004) 30 25 20 15 10 5 12-17 18-25 26-34 35-49 White Afr-Am Percentage of Last Year Dependence Age Category Denise Kandel, NIDA Trajectories Conference 11/13/2006]

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

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

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

  18. 2. Longitudinal

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

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

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

  22. 2. Examples of National U.S. LongitudinalData Monitoring the Future (MTF) National Survey of Parents and Youth (NSPY) Drug Abuse Treatment Outcomes Study (DATOS) Adolescent Health (AddHealth) National Longitudinal Survey of Youth (NLSY) National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)

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

  24. 3. Using Data Systems: Advantages and Problems

  25. Access to Data SAMHDA Website http://www.icpsr.umich.edu/SAMHDA/

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

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

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

  29. 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.

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