Problem Gambling and Substance Use Among Missouri Youth: A Call for Prevention Renee M. Cunningham-Williams, PhD, MPE,LCSW

Transcription

1 Problem Gambling and Substance Use Among Missouri Youth: A Call for Prevention Renee M. Cunningham-Williams, PhD, MPE,LCSW Midwest Conference on Problem Gambling and Substance Abuse, August 13-15, 28

2 Disclosures Funding provided by: Greater Kansas City Community Foundation Port Authority of Kansas City #25952kc Center for Mental Health Services Research #5P3 MH68579 Earlier Version: National Council on Problem Gambling 27 (poster)

3 Background The National Research Council reports that 52%- 89% of youth have gambled in the past year. Estimates of problem gambling (PG) and pathological gambling disorder (PGD) vary due to methodological differences, but are about three times the adult rate. Youth are vulnerable for PG/PGD Due to their: developmental stage ineffective decision-making increased risk of co-occurring substance use and mood disorders

4 Aims To provide epidemiologic data on: gambling, problem gambling (PG) and pathological gambling disorder (PGD) collected for the first time among a household sample of Missouri youth

5 To describe prevalence rates in the context of: within-state variation, socio-demographics, and cooccurring substance use and depression

6 KC-MO-GAM STUDY Missouri Listed Phone Numbers (N=1,636,472) # age-eligible households (14-25) (n=19,469) Random selection (n=4) CATI & RDD Methodology Eligibility Screen: ages (Using next birthday method) Informed Consent Final Disposition NOT ATTEMPTED (n=147) INCOMPLETE (n=2242) Refused (n=149) Parental refusal (n=68) Minor child refusal (n=35) Incomplete (n=288) Age Ineligible household (n=856) Quota full (n=84) Eligibility Undetermined (n=51) Other (n=261) COMPLETED INTERVIEWS (n=351) Assessments DSM-IV Gambling Behavior & Disorder- (C-GAM ) Substance Use (CAS & GAM- DA) & Problems (CRAAFT) Distress (CES-D) Demographics SES measure Locator

7 Statistical Methods Prevalence: Estimate of presence of behavior or disorder (either lifetime or current timeframe) Conditional Prevalence: Prevalence estimate conditional upon a predetermined behavior or characteristic (e.g. among gamblers only)

8 Statistical Methods Study Design Weighting: Accounts for # of different telephone lines in household Statistical Post-stratification Weighting: accounts for the under-representation of year old in sample

9 Socio-demographics (n=351) Mean Age (SD); Range=14-25; Median=17 Female Gender Race/Ethnicity Caucasian African American Other Living with parents/guardians College or Higher Education Poverty/Asset Indicators Public assistance Food stamps Free lunch program Mean=19.17 (SE=.31) 48.3% % 81.6% 13.5% 4.9% 81.3% 36.9% 8.8% 12.2% 14.1% Home ownership (parents) 9.1% * % are result of post-stratification weighting, may not= 1%

10 Lifetime Prevalence: Gambling* by Region Males vs. Females Gambling Rate: 39.8% χ 2 =1.22 p=.55 n.s. Males Females KC St Louis Other -MO *Gambling for money or something of personal value >5 times lifetime

11 Lifetime Prevalence: Gambling Status Males vs. Females Males χ 2 =12.86 p=.2 Females Non gambler Non problem Problem *Only 1 person met DSM-IV criteria for PGD (.1%)

12 Lifetime Prevalence: Gambling Status* Missouri Region (n=351) Problem Gambling Rate: 14% χ 2 =4.18 p=.39 n.s. STL KC Other Non gambler Non problem gambler Problem gambler *Only 1 person met DSM-IV criteria for PGD (.1%)

13 Conditional Prevalence: Gambling Status* Missouri Region (among gamblers only: n=133) Problem Gambling Rate: 35% χ 2 =3.28 p=.28-n.s. STL KC Other Nonproblem Problem *Only 1 person met DSM-IV criteria for PGD (.3%)

14 χ 2 =.1 p=.92 Lifetime Prevalence: Gambling Activities Males vs. Females χ 2 =6.13 p=.1 χ 2 =8.86 p=.3 χ 2 =15.72 p<.1 χ 2 =1.7 p=.19 χ 2 =16.37 p<.1 χ 2 =1.72 p=.19 χ 2 =1.13 p=.29 χ 2 =23.29 p<.1 Males Females χ 2 =6.47 p= Slot/Fruit Lottery VDTs Card Gam Dice/Craps Casino Gam Bingo Pari-Mutue Market betting: n= Sports Other

15 Proportion of Missouri Youth Who Gamble by Age of Onset

16 Age of onset gambling by gambling type F=8.31, p<.1, df=9 o v e r a l l O t h e r s t o c k S p o r t s P a r i - M u tuels B i n g o C a s i n o G a m e s D i c e / C r a p s C a r d G a m e s V D T s L o t tery S l o t/fruit

17 Restless/Irritable Can't quit/cut dow Escape Gamblin Chasing Losses Preoccupation Lying/Deceit Bail Out Tolerance Give up/reduce Relationship Prob Gambling interfere χ 2 =3.2 p=.8 % Positive for Gambling Symptoms Males vs. Females χ 2 =2.82 p=.1 χ 2 =1.56 p=.21 χ 2 =.5 p=.48 χ 2 =6.49 p=.12 χ2=.4 p=.83 Males Females * Females did not endorse these symptoms Illegal acts: n= * * * * *

18 #Symptoms Mean Age of Onset & Mean # of Gambling Activities & Gambling Symptoms Males vs. Females For Gambling Activities Mean Age=19.76 SE=.5 M=14.2 SE=.7;F=16. SE=.9; p=.98-n.s. p< and p.= For Gambling Symptoms: Mean Age =19.35 SE=.7 M=16.9 SE=.9; F=2.22 SE=1.1; p=.2 Males Females 1 Gambling Activities Gambling Symptoms

19 Ever gambled Gambling status Tobacco Use Alcohol Use Drug Use Gambling, Substance Use, Clinical Distress by Demographics Age Region Race (white/non) (n=35) (n=351) (n=351) F p <.1 <.1.3 Χ p Χ p ** <.1.4 Depression.8.37* *Clinical Distress by age (n=318); ** Past 3 days combined with past year due to empty cell

20 Ever gambled Gambling status Tobacco Use Alcohol Use Drug Use Gambling, Substance Use, Clinical Distress by Poverty Indicators Χ 2 Public Assistance (n=325) p Food Stamps (n=342) Χ p Χ Free Lunch (n=345) p <.1 Home Ownership (n=348) Χ p Depression

21 Substance Use Frequency By Mean Age 23 χ 2 =13. p=<.1 χ 2 =18.5 p=<.1 χ 2 =4.7 p=.3 Never used Not past year Age 2 Past year,not past 3 days 17 Past 3 days 14 Tobacco Alcohol Drugs

22 Tobacco Use

23 Lifetime Gambling & Gambling Status By Tobacco Use Frequency χ 2 =11.93 p=.86 All Gamblers χ 2 =13.42 p=.422 Problem Gamblers Only Never used Not past year Past yr, not past 3 days Within past 3 days

24 Lifetime & Current Tobacco Use Frequency Males vs. Females Tobacco Use: M= 45% F=27.4% Past Year*: M=35.4% F=18% Past Month: M=32% F=17% Males χ 2 =1.51 p=.16 Females Never used Not past year Past yr, not past 3 days Within past 3 days *includes past 3 days

25 Tobacco Use Frequency By Race χ 2 =9.46; p=.95 Never used Not past year Past year/includes past 3 days* White Black/Other

26 Tobacco Use Frequency Missouri Region χ 2 =4.31 p=.643-n.s. KC STL Other Never used Not past year Past year,not past 3 days Past 3 days

27 Alcohol

28 Lifetime Gambling & Gambling Status By Alcohol Use Frequency χ 2 =44.9 p<.1 All Gamblers χ 2 =49.72 p<.1 Problem Gamblers Only Never used Not past year Past yr, not past 3 days Within past 3 days

29 Lifetime & Current Alcohol Use Frequency Males vs. Females χ 2 =7.85 p=.52 Drinking: M= 72% F=54.8% Past Year*: M=57.1% F=37.3% Past Month: M=32.1% F=24.4% Never used Not past year Past year,not past 3 days Past 3 days 1 Males Females *includes past 3 days

30 Alcohol Use Frequency By Race χ 2 =13.6; p<.1 Never used Not past year Past year,not past 3 days Past 3 days White Black/Other

31 Alcohol Use Frequency Missouri Region χ 2 =13.33 p=.435 KC STL Other Never used Not past year Past year,not past 3 days Past 3 days

32 Alcohol Use Frequency Receipt of Public Assistance χ 2 =15.6 p=.17 Never used Not past year Past year,not past 3 days Past 3 days No Pub Assistance Pub Assistance

33 Alcohol Use Frequency Receipt of Food Stamps χ 2 =8.45 p=.4 Never used Not past year Past year,not past 3 days Past 3 days No Food Stamps Food Stamps

34 Ilicit Drugs

35 Lifetime Gambling & Gambling Status By Drug Use Frequency χ 2 =3.82 p<.1 All Gamblers χ 2 =37.41 p<.1 Problem Gamblers only Never used Not past year Past yr, not past 3 days Within past 3 days

36 Lifetime & Current Drug Use Frequency Males vs. Females Drug Use: M= 4.6% F=27% Past Year*: M=26.5% F=27% Past Month: M=15.7% F=2.9% Never used Males χ 2 =13.81 p=.37 Females Not past year Past year, not past 3 days Within past 3 days *includes past 3 days

37 χ 2 =17.7; p=.4 Drug Use Frequency By Race Never used Not past year Past year,not past 3 days Past 3 days White Black/Other

38 Drug Use Frequency Missouri Region χ 2 =16.3 p=.149 KC STL Other Never used Not past year Past year,not past 3 days Past 3 days

39 Drug Use Frequency Receipt of Free Lunch Program χ 2 =25.4 p<.1 Never used Not past year Past year,not past 3 days Past 3 days No Free Lunch Free Lunch

40 Depression

41 % Depressive Symptoms Males vs. Females Distressed: M= 11.7% F=17.4% χ 2 =6.92 p=.89 Males Females No distress Distress

42 Lifetime Gambling & Gambling Status By Depression χ 2 =.3 p=.87-n.s. All Gamblers χ 2 =2.58 p=.28-n.s. Problem Gamblers only No Distress Distress

43 Summary: Missouri s First Household Study of Youth year olds (n=351) Nearly 4% youth have gambled >5X lifetime Mean Age of onset: 11.6 (personal skill games); Most games = age 15 ; Casino games: age 19-2 By age 2, 5% of Missouri youth would have placed a bet or gambled >5X Cunningham-Williams, PI

44 Summary: Disordered Gambling Lifetime Prevalence: Gambling Status* Missouri Region (n=351) DSMIV Chi=4.43,p=.37 At-risk = 28.6% STL KC Other overall criteria 1-2 criteria 3-4 criteria 5-1 criteria Problem = 6.4% Disorder =.2%

45 Summary (cont d): Males (vs. Females): Gambled more, Greater variety of games, More gambling problems, Earlier onset of symptoms ~ No regional, age, race, or poverty indicator differences in these rates ~ Youth gambling & Problem gambling related to frequency of: Smoking, Drinking, Drug Use ~ But not clinical distress ~

46 Implications Clinical: Gambling behavior may be an indicator of other risky behaviors (e.g. substance use) Early screening & prevention/intervention for gambling warranted especially among males

47 Implications Research: Disentangling gambling & co-occurring substances Assessing protective factors for underage gambling Longitudinal study of these youths through period of risk

48 Acknowledgements Funding Greater Kansas City Community Foundation Port Authority of Kansas City #25952kc Staff and the Participants of: KC-MO-GAM Study Peter M. Dore, M.A., Statistical Data Manager Peichang Shi, CMHSR Statistical Data Analyst Edward L. Spitznagel, Ph.D., Statistical Consultant Julie Kapp, Ph.D., University of Missouri-Columbia

49 Contact: