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

1. Cora Lee Wetherington, Ph.D. Women & Gender Research Coordinator National Institute on Drug Abuse Women Across the Life Span Conference July 12-13, 2004 Baltimore Marriott Inner Harbor Gender Differences in Drug Abuse Across the Life Span

3. Gender Differences in Drug Abuse • Gender Differences: The Numbers • Gender Differences: Animal Models • Gender Differences: Menstrual Cycle • Gender Differences: Predictors & Progression • Gender Differences: Treatment

5. Gender Differences: The Numbers Population prevalence data • Drug use: greater for males than females • 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?

6. Gender Differences: The Numbers Calculate use prevalence only among individuals with opportunity to use Van Etten et al. (1999) Study drugs: Marijuana, Cocaine, Heroin, Hallucinogens Data Source: 1993 NHSDA Findings: • Opportunity to use: greater for males than for females. • Among individuals with opportunity to use : males and females are equally likely to initiate use.

7. Opportunity to Use Drugs 70 60 Male 50 Female 40 Percent 30 20 10 0 Marijuana Cocaine Hallucinogens Heroin

8. Percent Use Given an Opportunity

9. 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)

10. 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

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

12. Gender Differences: The Numbers Do prevalence data, adjusted for opportunity, suggest that females are less vulnerable to drugs than males ? No. If females are offered drugs, they are as likely as males to use them: marijuana, cocaine, heroin, hallucinogens. No. If females use drugs, they are as likely as males to become dependent; exceptions in both directions. Caveat:Females are less likely to receive drug offers.

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

14. 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)

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

16. Gender Differences: The Numbers Patterns of Drug Use

18. GenderDifferences: The Numbers • CAVEAT: Usage data are from treatment samples. • Perhaps female heavy users are more likely than male heavy users to present for treatment.

19. Gender Differences: The Numbers • DATOS Intake Data(n=10,010, 96 programs, 11 cities, 4 modalities) • Women, compared to men, were • ·less likely to have graduated from high school • ·almost half as likely to be employed • ·more likely to report • prior drug treatment • depression, suicidal attempts & thoughts • being troubled over current emotional/psychological problems • health problems • weekly or daily illegal activity (but < likely to be CJ involved) • ·more likely to report physical, sexual abuse or both • in year prior to treatment • occurring more than a year prior to treatment • Wechsberg et al. (1998)

20. Gender Differences: The Numbers • Myth: Females are less vulnerable to drugs than males • 1. If given the opportunity, females are as likely as males • to use drugs • to become dependent • 2. 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

21. Gender Differences: The Numbers • Myth: Males are more vulnerable than females • 3. Use patterns suggest that women • are more likely to use daily – cocaine, heroin, barbiturates • use more times per week – cocaine & heroin • use more grams per week – cocaine • 4. Women presenting for treatment have poorer levels of functioning. • Does this reflect a greater vulnerability to the impact of drugs • on women? (i.e., consequence) • Are women with poorer levels of functioning more vulnerable to drugs • than men with poorer levels of functioning? (i.e., etiologic)

22. Gender Differences in Drug Abuse • Gender Differences: The Numbers • Gender Differences: Animal Models • Gender Differences: Menstrual Cycle • Gender Differences: Predictors & Progression • Gender Differences: Treatment

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

24. GenderDifferences: Animal Models • Behavioral Models: • Amount of Drug Self-Administered • Reinforcing Effectiveness • Speed of Acquisition of Self-Administration • “Prevalence” of Self-Administration • Relapse: Reinstatement following Extinction

25. GenderDifferences: 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 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

26. GenderDifferences: Animal Models • 2. Reinforcing Effectiveness • Females reach higher progressive ratio breakpoint for cocaine (Roberts et al., 1989) nicotine (Donny et al., 2000) • Females have shorter latency for first nicotine infusion of the session (Donny et al., 2000)

27. Gender Differences: Animal Models • Progressive ratio breakpoint (BP) (Roberts et al., 1989) • Males: 48.2 • Females: 264.1 • Females during estrus: approx. 400 • Estrus BP > metestrous/diestrous or proestrusBP

28. GenderDifferences: 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)

29. GenderDifferences: Animal Models • 4. “Prevalence” of Self-Administration (SA) ·Similar percentage of female rats acquire heroin SA: 90.0% females vs. 91.7% males (Lynch & Carroll, 1999) ·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)

30. GenderDifferences: Animal Models • 5. Relapse: Reinstatement following Extinction of Cocaine SA • Females, compared to males, • exhibit greater reinstatement of extinguished responding • “relapse” with a lower priming dose • Lynch & Carroll (2000)

31. GenderDifferences: Animal Models • Behavioral Models: • Amount of Drug Self-Administered • Reinforcing Effectiveness • Speed of Acquisition of Self-Administration • “Prevalence” of Self-Administration • Relapse: Reinstatement following Extinction

32. Gender Differences in Drug Abuse • Gender Differences: The Numbers • Gender Differences: Animal Models • Gender Differences: Menstrual Cycle • Gender Differences: Predictors & Progression • Gender Differences: Treatment

33. Hormonal Changes During the Menstrual Cycle

34. Gender Differences: Menstrual Cycle Pharmacokinetics (Humans) : Cocaine • Pharmacokinetics of i.v. 0.2 and 0.4 mg/kg cocaine: • peak plasma levels • time to reach peak plasma level (Tmax) • elimination half life • AUC • No differences among males, females (luteal), females (follicular) Exception: Tmax for 0.4 mg/kg • Females • follicular phase: 4.0 min • luteal phase: 6.7 min •Males: 8.0 min Mendelson et al. (1999)

35. Gender Differences: Menstrual Cycle • ORAL d-AMPHETAMINE • 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)

36. 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)

37. Gender Differences: Menstrual Cycle • NICOTINE CESSATION STUDY • Quitters in the late luteal phase, vs follicular phase: • more withdrawal symptoms • more depressive symptomatology • Implications for timing of initiation of cessation • Perkins (2000)

38. Gender Differences: Menstrual Cycle • CUE-INDUCED NICOTINE CRAVING • Follicular phase females reported significantly less craving than • luteal phase females • males • Franklin et al. (2004)

39. 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 Early F Late L On a scale of 1 to 10, how much do you desire a cigarette at this moment?

40. Gender Differences: Menstrual Cycle • NICOTINE CESSATION • Greater abstinence when cessation is initiated in • the follicular phase. • Abstinence rates at 9 weeks post-quit date: • All women: 46% • Follicular phase quit date: 69% • Luteal phase quit date: 29% • Franklin et al. (CPDD, 03)

41. Gender Differences: Menstrual Cycle • SHORT-TERM ABSTINENCE & WEIGHT GAIN • 20 highly dependent women, not planning to quit • Engaged in 1 wk abstinence • Results: • Mean weight gain in abstainers: 3.1 lbs • Abstinent in luteal phase: 5.3 lbs. • Abstinent in follicular phase: 1.5 lbs • Pomerleau et al., 2000

42. Gender Differences: Menstrual Cycle • Smoking Cessation Implications: • Quit in the Follicular Phase • Less desire to smoke • Less desire to relieve negative affect • Fewer withdrawal symptoms • Less depressive symptomatology • Less cue-induced craving • Less weight gain • Better abstinence

43. Gender Differences in Drug Abuse • Gender Differences: The Numbers • Gender Differences: Animal Models • Gender Differences: Menstrual Cycle • Gender Differences: Predictors & Progression • Gender Differences: Treatment

44. Gender Differences: Predictors & Progression • Depression: greater predictor of drug use by male than by female adolescents (Costello et al., 1999) • Conduct disorders: greater predictor of drug use and dependence by female than by male adolescents (Costello et al., 1999) • Aggressiveness: predictor of drug use by boys, but not girls (Ensminger, 1992)

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

46. Gender Differences: Predictors & Progression • Early vs. Late Initiators of Drug Use • - Boys who develop abuse or dependence: • initiate drug use earlier than boys who do not • develop abuse or dependence • - Girls who develop abuse or dependence: • initiate drug use later than girls who do not • develop abuse or dependence • Costello et al. (1999)

47. Gender Differences: Predictors & Progression • Among youth who became dependent before age 16, • boys used earlier than girls: • Cannabis 2.0 years earlier • Smoking 3.5 years earlier • Any substance 2.5 years earlier • Among youth who did not become dependent before • age 16, no gender differences in age of onset of first use. • Costello et al. (1999)

48. Gender Differences: Predictors & Progression 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)

49. Gender Differences: Predictors & Progression Peer Difficulties & Parental Stress as Predictors of Monthly “Bursts” in Use of Tobacco, Marijuana & Alcohol • 181 Oregon adolescents aged 11-14 in 1- vs. 2-parent families • RESULTS • 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)

50. Gender Differences: Predictors & Progression Childhood Sexual Abuse (CSA) Very high rates of CSA reported by women in treatment. Does this mean that CSA plays an etiologic role in drug dependence?