Page 1 Prescription Drug Abuse and Dependency by Adolescents and Young Adults: Treatment Need and Treatment Receipt Linda Simoni-Wastila, BSPharm, PhD Associate Professor University of Maryland Baltimore School of Pharmacy Huiwen Keri Yang, MS Doctoral Student and Graduate Research Assistant University of Maryland Baltimore School of Pharmacy Annual Meeting of the American Public Health Association Boston, Massachusetts November 6, 2006 NIDA grant R21 DA017730 This is the logo for the University of Maryland-Baltimore. Page 2 Scope of the Problem Substance use and resultant disorders have always been problems with adolescents and young adults Although recent evidence suggests that use of most substances, including alcohol, has been declining, non-medical prescription drug use (NMPDU) has been increasing, as has abuse and dependence on these substances However, there are large knowledge gaps in what we understand about problematic prescription drug use in these populations– - What constitutes need for treatment? DSM-IV criteria for abuse? For dependence? Non-medical use of prescription drugs? Perceived need for treatment? – What proportion of adolescents and young adults who non-medically use and/or abuse Rx drugs receive treatment? – What are factors associated with treatment receipt? Drug use patterns? Insurance? Income Other factors? Page 3 Scope of the Problem Q: Who is using prescription drugs in a non- medical context? A: EVERYONE!!!!!! - 6.4 percent - 15.1 Million - of US population greater than or equal to 12 years of age reported at least 1 episode of non-medical prescription drug use in past-year (2003 NSDUH) – Three age groups at particular risk: Older folks (50 and older) Younger folks (12 to 25) "smiley face" – Adolescents 12 to 17: 9.3 percent (2.3 million) – Young Adults 18 to 25: 14.7 percent (4.6 million) Page 4 Methods – Data and Study Aims Using the 2003 National Survey on Drug Use and Health (NSDUH), we addressed: Aim 1: Provide national estimates of treatment need for prescription drugs among adolescents and young adults Aim 2: Provide national estimates of treatment receipt Aim 3: Examine factors associated with treatment receipt for prescription drug problem use Page 5 Methods – Aim 1 Aim 1: Provide national estimates of treatment need for prescription drugs among adolescents and young adults – Perceived need for treatment: Self-reported perceived need for treatment for specific drug – Non-medical Use of Prescription Drugs (NMPDU): "Have you used any type of prescription _____ that was not prescribed for you or that you took only for the experience or feeling it caused?" Lifetime Past-Year – Prescription Drug Use Disorder (PDUD): Meets criteria for abuse OR dependence. Series of questions that separately approximate abuse and dependence based on criteria in the DSM-IV Abuse defined if past-year user AND met criteria for one or more abuse criteria – Serious problems at home, work or school – Did something that placed you in physical danger – Got in trouble with the law – Had problems with family or friends but continued using despite believing use caused problems Dependence defined as past-year user AND met 3 or more dependence criteria – Spent time over a month getting, using or getting over effects – Used more often than intended or unable to set limits on use – Needed to use more to get desired effects – Unable to cut down or stop using – Use reduced or eliminated involvement in important activities – Felt blue when trying to stop or cut down, as well as experienced 2 or more withdrawal symptoms Page 6 Methods – Aim 2 Aim 2: Provide national estimates of treatment receipt Measures: Received any drug or alcohol treatment in past year and used – Any prescription drug – Any prescription analgesic – Any prescription stimulant – Any prescription tranquilizer Page 7 Methods – Aim 3 Aim 3: Examine factors associated with treatment receipt for prescription drug problem use – Logistic regression analysis conducted among any lifetime prescription drug non-medical users Used weights provided in the NSDUH to provide national estimates Adjusted for the complex sampling design of the NSDUH by using PROC SURVEY commands available in SAS 9.1 "right arrow" correctly adjusted standard errors Page 8 Explanatory Variables: Individual Domain Sociodemographics: sex, race/ethnicity, age (2 year groupings), urban/rural (0/1), lives with both parents (0/1), health status (Poor/Fair, Good, Very Good/Excellent [ref]), enrolled in school (0/1), moved in past year (0/1), health insurance, income Substance use variables: – Drug use: Binary (0/1) for PY use of tobacco, marijuana, inhalants, heroin, cocaine/crack, hallucinogens – PY alcohol use Abstainer [reference] Up to one day/week (Casual) Up to two days/week (Moderate) Three or more days/week (Heavy) Page 9 Explanatory Variables: Individual Domain Attitudes toward risky behaviors: Binary (Low risk/High risk) based on two items measured on agree/disagree scale – "I get a kick out of doing dangerous things" - "I like to test myself by doing risky things" Criminal Activity: Binary (No criminal bh/1 or more criminal bhs) based on 5 items (stole, gang fight, serious fight, carried gun, attacked someone) Severe Mental Illness: Binary (0=SMI score < 13, 1=SMI score greater than or equal to 13) – Based on 6 items with values of 0 to 4 for total score of up to 24 – Items include: felt nervous, hopeless, restless/fidgety, sad/depressed, everything is an effort, no good/worthless Page 10 Explanatory Variables: Family and School Domains Family – Perceived parental support (Yes/No): Based on agree/disagree scale for 2 items School – Attitudes towards school (Positive/negative): Based on strongly agree/strongly disagree scale for 5 items – Drug environment in school (Low/High): Based on 4 items with 5 values asking number of students who smoke, drink, get drunk, and use marijuana/hash – Exposed to drug education in school (Yes/No): Based on 2 binary response items (took class, had films/discussion) Page 11 Explanatory Variables: Community Domain – Number of community-based activities: 0, 1 to 3, 4 to 6, greater than 6 – Religious support/attitudes (Agree/Disagree): Based on agree/disagree scale for 3 items – Exposed to drug education in community (0/1): Based on 2 binary response items (special class, had films/discussion) – Difficulty getting drugs in community (High/Low): Based on 5 point scale (probably impossible to very easy) on 5 items (cocaine, crack, heroin, LSD, and marijuana) Page 12 This is a table displaying population characteristics – individual domain. The first column is a list of characteristics. The second column gives the percent of adolescents in the study population for that characteristic. The third column gives the percent of young adults in the study population for that characteristic. The table can be read as follows: "Female": 56.5 percent adolescents, 46.9 percent young adults; "White": 84.3 percent adolescents, 81.2 percent young adults; "Black": 6.2 percent adolescents, 6.2 percent young adults; "Hispanic": 4.4 percent adolescents, 9.1 percent young adults; "Other race/ethnicity": 5.1 percent adolescents, 3.5 percent young adults; "Urban": 19.2 percent adolescents, 18.3 percent young adults; "Income Less Than $20 Thousand": 14.8 percent adolescents, 45.2 percent young adults; "Income $20 Thousand to $40 Thousand": 19.8 percent adolescents, 16.8 percent young adults; "Income Greater than $40 Thousand": 65.4 percent adolescents, 38.0 percent young adults; "Both parents at home": 29.2 percent adolescents, Not Applicable for young adults; "Poor or Fair Health": 7.4 percent adolescents, 3.4 percent young adults; "Good Health": 30.4 percent adolescents, 23.9 percent young adults; "Very Good or Excellent Health": 62.2 percent adolescents, 72.7 percent young adults; "Enrolled in School": 95.3 percent adolescents, 34.9 percent young adults; "Unemployed": Not Applicable for adolescents, 30.5 percent young adults; "Employed Part-Time": Not Applicable for adolescents, 32.5 percent young adults; "Employed Full-Time": Not Applicable for adolescents, 40.0 percent young adults; "Moved in Past Year": 29.5 percent adolescents, 58.1 percent young adults Page 13 This is a table displaying population characteristics – individual domain. The first column is a list of characteristics. The second column gives the percent of adolescents in the study population for that characteristic. The third column gives the percent of young adults in the study population for that characteristic. The table can be read as follows: "No Alcohol Use": 7.1 percent adolescents, 1.9 percent young adults; "Drink up to once weekly": 46.3 percent adolescents, 22.1 percent young adults; "Drink greater than 1 to 2 times weekly": 37.2 percent adolescents, 57.6 percent young adults; "Drink 3 times or more weekly": 9.4 percent adolescents, 18.4 percent young adults; "Cigarette Use": 63.3 percent adolescents, 74.5 percent young adults; "Marijuana Use": 64.3 percent adolescents, 70.5 percent young adults; "Cocaine or Crack Use": 17.7 percent adolescents, 36.2 percent young adults; "Hallucinogen Use": 26.8 percent adolescents, 36.0 percent young adults; "Inhalant Use": 28.9 percent adolescents, 13.2 percent young adults; "Heroin Use": 2,5 percent adolescents, 1.9 percent young adults; "High-Risk Individual": 79.5 percent adolescents, 36.7 percent young adults; "1 or more Criminal Activities": 63.8 percent adolescents, Not Applicable for young adults; "SMI greater than or equal to 13": Not Applicable for adolescents, 13.5 percent young adults Page 14 This is a table displaying population characteristics – family, school, and community domains. The first column is a list of characteristics. The second column gives the percent of adolescents in the study population for that characteristic. The third column gives the percent of young adults in the study population for that characteristic. The table can be read as follows: "Family – Perceived Parental Support”: 28.7 percent adolescents, Not Applicable for young adults; ”School – positive attitude”: 51.6 percent adolescents, Not Applicable for young adults; ”School – Drug Environment”: 71.3 percent adolescents, Not Applicable for young adults; ”School – Drug Education”: 77.1 percent adolescents, Not Applicable for young adults; ”Community – Religious Support”: 50.1 percent adolescents, Not Applicable for young adults; ”Community – Drug Education”: 57.0 percent adolescents, Not Applicable for young adults; ”Community – Number of Community Activities - None”: 9.3 percent adolescents, Not Applicable for young adults; ” Community – Number of Community Activities – 1 to 3”: 28.1 percent adolescents, Not Applicable for young adults; ” Community – Number of Community Activities – 4 to 6”: 31.3 percent adolescents, Not Applicable for young adults; ” Community – Number of Community Activities – Greater than 6”: 31.3 percent adolescents, Not Applicable for young adults; ” Community – Drugs Easy to Obtain”: 37.9 percent adolescents, 36.1 percent young adults Page 15 Treatment Need by Adolescents for Rx Drugs: Perceived Need (Among PDUD), Prevalence of NMPDU and PDUD (Among NMPDU) This is a clustered vertical bar chart (bars are represented with 3-dimensional pyramids) depicting treatment need by adolescents for prescription drug use, perceived need (among PDUD), and prevalence of non-medical prescription drug use and PDUD (among NMPDU). The y-axis represents the percentage of respondents. The x-axis consists of four drug categories (Any Rx Drug, Opioids, Stimulants, and Minor Tranquilizers), and each drug category has a cluster of three bars representing "Perceived Tx Need (Among PDUD),” "NMPDU,” and "PDUD (Among NMPDU).” The chart reads as follows: ”Any Rx Drug”: Perceived Tx Need (Among PDUD) equals 3.7 percent, NMPDU equals 9.3 percent, PDUD (Among NMPDU) equals 15.3 percent, "Opioids”: Perceived Tx Need (Among PDUD) equals 2.4 percent, NMPDU equals 7.8 percent, PDUD (Among NMPDU) equals 11.7 percent, ”Stimulants”: Perceived Tx Need (Among PDUD) equals 0.0 percent, NMPDU equals 2.3 percent, PDUD (Among NMPDU) equals 16.2 percent, ”Minor Tranquilizers”: Perceived Tx Need (Among PDUD) equals 7.5 percent, NMPDU equals 2.3 percent, PDUD (Among NMPDU) equals 16.1 percent Page 16 Treatment Need by Young Adults for Rx Drugs: Perceived Need (Among PDUD), Prevalence of NMPDU and PDUD (Among NMPDU) This is a clustered vertical bar chart (bars are represented with 3-dimensional pyramids) depicting treatment need by young adults for prescription drug use, perceived need (among PDUD), and prevalence of non-medical prescription drug use and PDUD (among NMPDU). The y-axis represents the percentage of respondents. The x-axis consists of four drug categories (Any Rx Drug, Opioids, Stimulants, and Minor Tranquilizers), and each drug category has a cluster of three bars representing "Perceived Tx Need (Among PDUD),” "NMPDU,” and "PDUD (Among NMPDU).” The chart reads as follows: ”Any Rx Drug”: Perceived Tx Need (Among PDUD) equals 3.2 percent, NMPDU equals 14.7 percent, PDUD (Among NMPDU) equals 11.0 percent, "Opioids”: Perceived Tx Need (Among PDUD) equals 4.0 percent, NMPDU equals 12.2 percent, PDUD (Among NMPDU) equals 9.0 percent, ”Stimulants”: Perceived Tx Need (Among PDUD) equals 0.0 percent, NMPDU equals 3.5 percent, PDUD (Among NMPDU) equals 12.3 percent, ”Minor Tranquilizers”: Perceived Tx Need (Among PDUD) equals 0.0 percent, NMPDU equals 5.2 percent, PDUD (Among NMPDU) equals 8.2 percent Page 17 Treatment Receipt by Adolescents Using Rx Drugs: Lifetime Use, Past-Year Use and PDUD This is a clustered vertical bar chart (bars are represented with 3-dimensional pyramids) depicting treatment receipt by adolescents using prescription drugs broken out by lifetime use, past-year use, and PDUD. The y-axis represents the percentage of respondents. The x-axis consists of four drug categories (Any Rx Drug, Opioids, Stimulants, and Minor Tranquilizers), and each drug category has a cluster of three bars representing "Lifetime Use,” "Past-Year Use,” and "PDUD.” The chart reads as follows: ”Any Rx Drug”: Lifetime Use equals 6.8 percent, Past-Year Use equals 6.9 percent, PDUD equals 10.2 percent ,”Opiods”: Lifetime Use equals 7.3 percent, Past-Year Use equals 7.1 percent, PDUD equals 9.3 percent, ”Stimulants”: Lifetime Use equals 12.0 percent, Past-Year Use equals 9.3 percent, PDUD equals 16.4 percent, ”Minor Tranquilizers”: Lifetime Use equals 10.3 percent, Past-Year Use equals 9.1 percent, PDUD equals 4.0 percent Page 18 Treatment Receipt by Young Adults Using Rx Drugs: Lifetime Use, Past-Year Use and PDUD This is a clustered vertical bar chart (bars are represented with 3-dimensional pyramids) depicting treatment receipt by young adults using prescription drugs broken out by lifetime use, past-year use, and PDUD. The y-axis represents the percentage of respondents. The x-axis consists of four drug categories (Any Rx Drug, Opioids, Stimulants, and Minor Tranquilizers), and each drug category has a cluster of three bars representing "Lifetime Use,” "Past-Year Use,” and "PDUD.” The chart reads as follows: ”Any Rx Drug”: Lifetime Use equals 6.8 percent, Past-Year Use equals 8.0 percent, PDUD equals 21.2 percent, ”Opiods”: Lifetime Use equals 7.3 percent, Past-Year Use equals 8.1 percent, PDUD equals 18.9 percent, ”Stimulants”: Lifetime Use equals 10.9 percent, Past-Year Use equals 10.1 percent, PDUD equals 36.5 percent, ”Minor Tranquilizers”: Lifetime Use equals 9.0 percent, Past-Year Use equals 10.9 percent, PDUD equals 25.1 percent Page 19 Correlates of Any Treatment Receipt Among Lifetime NMPDU: Adolescents This is a table displaying correlates of any treatment receipt among lifetime NMPDU for Adolescents. The first column is a list of characteristics. The second column gives the odds ratio for the characteristic. The third column gives the 95 percent CI for the characteristic. The note at the bottom of the table reads like this: "Not Statistically Significant: age, urban, both parents in HH, enrolled in school, health status, insurance, alcohol use, heroin use, inhalant use, hallucinogen use, religious involvement, community involvement, criminal activities, drugs difficult to obtain” The table can be read as follows: "Female Gender”: 0.52 percent odds ratio, 0.36, 0.75 95 percent CI; "NH White”: 1.27 percent odds ratio, 0.73, 2.20, 95 percent CI; "NH Black”: 3.16 percent odds ratio, 1.27, 7.86 95 percent CI; "Hispanic”: 2.17 percent odds ratio, 1.04, 4.54 95 percent CI; "Other Race/Ethnicity”: REF odds ratio, -- 95 percent CI; "Perceived Need for Treatment”: 0.43 percent odds ratio, 0.19, 0.97 95 percent CI; "Moved greater than or equal to 1 in Past Year”: 1.52 percent odds ratio, 1.04, 2.20 95 percent CI; "Cigarette Use”: 2.64 percent odds ratio, 1.56, 4.47 95 percent CI; "Marijuana Use”: 2.41 percent odds ratio, 1.50, 3.86 95 percent CI; "Cocaine Use”: 2.21 percent odds ratio, 1.43, 3.39 95 percent CI; "Risk-Taker”: 0.63 percent odds ratio, 0.43, 0.93 95 percent CI; "No Comm Drug Education”: 0.59 percent odds ratio, 0.40, 0.87 95 percent CI; "No School Drug Education”: 0.52 percent odds ratio, 0.32, 0.82 95 percent CI Page 20 Correlates of Any Treatment Receipt Among Lifetime NMPDU: Young Adults This is a table displaying correlates of any treatment receipt among lifetime NMPDU for Young Adults. The first column is a list of characteristics. The second column gives the odds ratio for the characteristic. The third column gives the 95 percent CI for the characteristic. The table can be read as follows: "Female Gender”: 0.58 percent odds ratio, 0.43, 0.79 95 percent CI; "Age 18 to 19”: REF odds ratio, -- 95 percent CI; "Age 20 to 21”: 0.67 percent odds ratio, 0.45, 0.98 95 percent CI; "Age 22 to 23”: 0.71 percent odds ratio, 0.47, 1.09 95 percent CI; "Age 24 to 25”: 0.83 percent odds ratio, 0.54, 1.27 95 percent CI; "Severe Mental Illness”: 1.81 percent odds ratio, 1.32, 2.49 95 percent CI; "Unemployed”: REF odds ratio, -- 95 percent CI; "Full time Employment”: 0.78 percent odds ratio, 0.57, 1.09 95 percent CI; "Part time Employment”: 0.60 percent odds ratio, 0.41, 0.89 95 percent CI; "Enrolled in College”: 0.71 percent odds ratio, 0.51, 0.99 95 percent CI; "No ETOH Use”: REF odds ratio, -- 95 percent CI; "Casual ETOH Use”: 0.47 percent odds ratio, 0.26, 0.85 95 percent CI; "Moderate ETOH Use”: 0.59 percent odds ratio, 0.34, 1.04 95 percent CI; "Heavy ETOH Use”: 0.74 percent odds ratio, 0.39, 1.39 95 percent CI; "Cigarette Use”: 2.16 percent odds ratio, 1.36, 3.41 95 percent CI; "Heroin Use”: 5.84 percent odds ratio, 2.84, 12.02 95 percent CI; "Marijuana Use”: 1.51 percent odds ratio, 1.07, 2.13 95 percent CI; "Difficult to Obtain Drugs”: 1.43 percent odds ratio, 1.06, 1.92 95 percent CI Page 21 Correlates of Any Treatment Receipt Among Lifetime NMPDU: Young Adults P < 0.10 "Right arrow" (-) Perceived need for treatment, moderate ETOH use, cocaine use, exposed to school drug education, high risk taking Not Statistically Significant: race/ethnicity, urban, income, health insurance, health status, moved greater than or equal to 1/past year, inhalant use, hallucinogen use Page 22 Conclusions Adolescents: – 9.3 percent used at least 1 Rx non-medically – "right arrow" 15.3 percent met PDUD criteria – "right arrow" 10.2 percent received any drug/ETOH treatment Young Adults: – 14.7 percent used at least 1 Rx non-medically – "right arrow" 11.0 percent met PDUD criteria – "right arrow" 21.2 percent received any drug/ETOH treatment Page 23 Conclusions Correlates of treatment among adolescents include: – (-) Female gender – (-) Perceived treatment need – (-) Lack of drug education – (-) Risk-taker – (+) Blacks > Other race/ethnicity – Use of other substances (cigarettes, cocaine, marijuana) Not significant: insurance, alcohol, others Page 24 Conclusions Correlates of treatment among young adults: – (-) Female gender – (-) Older age groups – (-) Enrolled in college – (-) Part-time employed (versus unemployed) – (-) Alcohol use – (+) Use of other substances (cigarettes, heroin, marijuana) Not significant: insurance, perceived treatment need, race/ethnicity Page 25 Strengths & Limitations First to examine treatment need and treatment receipt in context of Rx drugs Data limitations: – Cross-sectional – Self-reported data – Generalizability Page 26 Next Steps Complete similar analyses of correlates associated with PY NMPDU and PDUD Conduct analyses by therapeutic class Expand treatment to other potential treatment services, including mental health and general health – Look at patterns of treatment by source/type Further develop independent measures to better approximate domains of peer, work/school, family, and social environments – Develop and differentiate by predominant Rx use patterns (e.g., Rx-Only vs Poly-Substance use) – Use recursive partitioning techniques to examine predictors of treatment Page 27 Thank You!!!! Questions? Contact me at: lsimoniw@rx.umaryland.edu