Cora Lee Wetherington, Ph.D. Women & Gender Research Coordinator National Institute on Drug Abuse National Advisory

1. Cora Lee Wetherington, Ph.D. Women & Gender Research Coordinator National Institute on Drug Abuse National Advisory Council on Drug Abuse September 20, 2006 The Pervasiveness of Sex/Gender Differences in Drug Abuse

2. Gender Differences in Drug Abuse • The Numbers • Animal Models • Menstrual Cycle • Predictors • Prevention • Brain Imaging • Prenatal Drug Exposure • Treatment

3. Gender Differences in Drug Abuse • The Numbers • Animal Models • Menstrual Cycle • Predictors • Prevention • Brain Imaging • Prenatal Drug Exposure • Treatment

4. Gender Differences: The Numbers Prevalence Data • Drug Use • Drug Dependence

5. “Any Illicit Drug Use” among Ages 12 & Older Is Higher among Males than Females An exception: Prescription Drug Abuse where Past Month rates are similar 60 Males Females 50 40 Percent 30 20 10 0 Lifetime Past Year Past Month Prescription Drug Abuse 2004 National Survey on Drug Use and Health, SAMHSA, 2005

6. Gender Differences: The Numbers Prevalence of Drug Dependence:greater for males than females • 9.2% Males • 5.6% Females (1994 Nat’l Comorbidity Survey)

7. Gender Differences: The Numbers Prevalence of Drug Dependence: greater for males than females • 9.2% Males • 5.6% Females (1994 Nat’l Comorbidity Survey) Arefemales less vulnerable to drug abuse than males?

8. Gender Differences: The Numbers • Population prevalence • vs • Conditional prevalence

9. Opportunity to Use Drugs 70 60 Male 50 Female 40 Percent 30 20 10 0 Marijuana Cocaine Hallucinogens Heroin Van Etten et al. (1999) -- 1993 NHSDA

10. Percent Use Given an Opportunity Van Etten et al. (1999) -- 1993 NHSDA

11. Gender Differences: The Numbers Calculate Dependence Only among Users: • Males and females = likely to become dependent on cocaine tobacco heroin inhalants hallucinogens analgesics Anthony et al. (1994) Data Source: National Comorbidity Survey

12. Gender Differences: The Numbers Calculate Dependence Only among Users: • Males more likely than females to become dependent on marijuana alcohol Anthony et al. (1994) Data Source: National Comorbidity Survey

13. Gender Differences: The Numbers Calculate Dependence Only among Users: • Females more likely than males to become dependent on the cluster of drugs • anxiolytics sedatives hypnotics Anthony et al. (1994) Data Source: National Comorbidity Survey

14. Gender Differences: The Numbers All Age Groups vs. Adolescents

15. Gender Differences: The Numbers Monitoring the Future Survey 1975 - Present Annual prevalence of “illicit drug use other than marijuana” • 12th graders: > for boys than girls • (exceptions: 1975 & 1981 girls > boys) • 10thgraders: > for girls than boys (since 1991) • 8th graders: > for girls than boys (since 1991)

19. Gender Differences: The Numbers Dependence Among Adolescents Users Aged 12-17 Alcohol: boys = girls Marijuana: boys = girls Nicotine: boys = girls • Cocaine : girls > boys 17.4% vs. 4.7% Kandel et al. (1997 ) Data Source: 1991, 1992, 1993 NHSDA

20. Gender Differences: The Numbers Why do adolescent girls have higher rates of cocaine dependence than boys? • They use cocaine more frequently • They use a greater quantity of cocaine • They report more symptoms at low use frequency Chen & Kandel (2002) Data Source: 1991, 1992, 1993 NHSDA

21. Gender Differences: The Numbers Risk of Becoming Cocaine-Dependent within 24 Months of First Use Females were 3-4 times more likely than males to become dependent within 24 months of cocaine use onset. O’Brien & Anthony (2005) Data Source: NHSDA 2000-2001

22. Gender Differences: The Numbers Girls Develop Tobacco Dependence Symptoms Faster than Boys • Time from onset of monthly use to symptoms: • Girls: 3 weeks • Boys: 6 months • # Symptoms among monthly users • Girls: 5.7 • Boys: 4.0 DiFranza et al. (2002)

23. Gender Differences: The Numbers • Are females are less vulnerable to drugs than males? • If given the opportunity, females are as likely as males • to use drugs • to become dependent • Adolescent females, compared to males, • in 8th and 10th grades are more likely to use “any illicit • drugs other than marijuana” • are more likely to become dependent on cocaine • use more cocaine • use more frequently • develop nicotine dependency symptoms faster

24. Gender Differences in Drug Abuse • The Numbers • Animal Models • Menstrual Cycle • Predictors • Prevention • Brain Imaging • Prenatal Drug Exposure • Treatment

25. Gender Differences: Animal Models Do data from animal behavioral models suggest that males are more vulnerable to drugs than females?

26. Gender Differences: Animal Models • Drug Self-Administration Studies: • Amount of Drug Self-Administered • Reinforcing Effectiveness • Speed of Acquisition of Self-Administration • “Prevalence” of Self-Administration

27. Gender Differences: Animal Models • 1. Amount of Drug Self-Administered • Females compared to males, self-administer more • alcohol Hill, 1978; Lancaster & Spiegel, 1992 caffeine Heppner et al., 1986 cocaine Morse et al., 1993; Matthews et al., 1999; Lynch & Carroll,1999; Hu et al., 2004 fentanyl Klein et al., 1997 heroin Carroll et al., 2001 morphine Alexander et al, 1978; Hill, 1978; Cicero et al, 2000 nicotine Donny et al., 2000

28. Gender Differences: Animal Models • 2. Reinforcing Effectiveness (cocaine & nicotine) ·Females reach higher progressive ratio breakpoint for  cocaine (Roberts et al., 1989)  nicotine (Donny et al., 2000) ·Females acquire stronger cocaine-induced conditioned place preference quicker and at lower doses (Russo et al., 2003a; Russo et al., 2003b)

29. Gender Differences: Animal Models • 3. Speed of Acquisition of Self-Administration • Females acquire self-administration faster than males ·cocaine- approx 1/2 the # sessions (Lynch & Carroll, 1999) ·heroin- approx 2/3 the # sessions (Lynch & Carroll, 1999) • ·nicotine- at lowest dose only (Donny et al., 2000)

30. Gender Differences: Animal Models • 4. “Prevalence” of Self-Administration (SA) ·More female rats acquire cocaine SA: 70% females vs. 30% males(Lynch & Carroll, 1999) ·More female Rhesus monkeys acquirePCP SA: • 100% females vs. 36.4% males(Carroll et al., 2000) • Similarpercentage of female rats acquire heroin SA: • 90.0% females vs. 91.7% males(Lynch & Carroll, 1999)

31. Gender Differences: Animal Models • Drug Self-Administration Studies: • Amount of Drug Self-Administered • Reinforcing Effectiveness • Speed of Acquisition of Self-Administration • “Prevalence” of Self-Administration

32. Gender Differences: Animal Models • Drug Self-Administration Studies: • Amount of Drug Self-Administered • Reinforcing Effectiveness • Speed of Acquisition of Self-Administration • “Prevalence” of Self-Administration • Escalation of Cocaine Self-Administration

33. Gender Differences: Animal Models • Biological Basis? • Circulating Gonadal Hormones • Estrogen  • Progesterone 

34. Gender Differences: Animal Models • Biological Basis? • It’s more than circulating gonadal hormones • Gonadectomized rats: Females (vs Males) • acquire cocaine self-administration faster • self-administer more cocaine • Hu et al. (2004)

35. Gender Differences: Animal Models • Biological Basis? • If not circulating gonadal hormones, then what? • Sexual dimorphism in brain organization during early development: • driven by gonadal hormones • driven by non-hormonal factors

36. Gender Differences: Animal Models • Biological Basis? • Non-hormonal factors • e.g., in mice & rats chromosomal sex (XX vs XY), not gonadal secretions, determines sexual dimorphism in embryonic mesencephalic dopamine neurons • Reisert et al. (1990) • Kolbinger et al. (1991) • Carruth et al. (2002)

37. Gender Differences: Animal Models • Biological Basis? Emerging research suggests that study of sexual dimorphismin • brain organization • gene expression • knockout mice are very promising areas to pursue.

38. Gender Differences in Drug Abuse • The Numbers • Animal Models • Menstrual Cycle • Predictors • Prevention • Brain Imaging • Prenatal Drug Exposure • Treatment

39. Hormonal Changes During the Menstrual Cycle

40. Gender Differences: Menstrual Cycle • SMOKED COCAINE • Repeated doses smoked cocaine (0, 6, 12.5 or 25 mg) • In follicular phase (v. luteal phase) • Higher ratings of “high” • Higher ratings of “good drug effect” Evans et al. (2002)

41. Gender Differences: Menstrual Cycle • ORAL d-AMPHETAMINE • Positive subjective effects > follicular than luteal: • > feeling of “high” • > euphoria (ARCI MBG) • > energy & intellectual efficiency (ARCI BG) • > liking the drug • > wanting the drug • Justice & de Wit (1999)

42. Gender Differences: Menstrual Cycle Can these findings be capitalized on in treatment settings?

43. Gender Differences: Menstrual Cycle NICOTINE

44. Difference Scores in Cue-Induced Craving C R A V I N G S C O R E p < .04 All Males Females F = Follicular L = Luteal F L On a scale of 1 to 10, how much do you desire a cigarette at this moment? Franklin et al. (2002)

45. Gender Differences: Menstrual Cycle • Nicotine: In the luteal phase • More smoking • More cue-induced craving • During short-term abstinence • more withdrawal symptoms • more depressive symptomatology • more desire to smoke • more desire to relieve negative affect • more weight gain • Quitting during luteal phase produces poorer • short-termabstinence (preliminary data) Implications: Quit smoking during the follicular phase

46. Gender Differences in Drug Abuse • The Numbers • Animal Models • Menstrual Cycle • Predictors • Prevention • Brain Imaging • Prenatal Drug Exposure • Treatment

47. Gender Differences: Predictors • Aggressiveness: • Predictor of drug use by boys, but not girls (Ensminger, 1992) Conduct Disorder: • Stronger predictor of drug use and dependence by female than by male adolescents (Costello et al., 1999)

48. Gender Differences: Predictors Smoking during pregnancy: • associated with smoking by preadolescent female offspring, but not male (Kandel et al., 1994; Weissman et al., 1999) Cigarette use: • stronger predictor of progression to illegal drug use by girls than by boys (Kandel et al., 1992,1998)

49. Gender Differences: Predictors Family characteristics more predictive of drug use in females than males: • Maternal • alcoholism (Boyd et al., 1993) • drug abuse (Boyd et al., 1993) • Low parental • attachment (Ensminger et al., 1982; Brook et al., 1993) • monitoring (Krohn et al., 1986) • concern (Murray et al., 1983) • Unstructured home environment (Block et al., 1988) • Dysfunctional family (Chatham et al., 1999)

50. Gender Differences: Predictors Peer Difficulties & Parental Stress: Predictors of Monthly “Bursts” in Use of Tobacco, Marijuana & Alcohol 181 Oregon youth ages 11-14 in 1- vs. 2-parent families • RESULTS: Gender-Specific • Peer Difficulties • Predictor for boys (in both family types) • Not a predictor for girls • Parental stress • Predictor for girls in 1-parent, but not 2-parent, families • Not a predictor for boys • Dishion & Skaggs (2000)