The Fast and Furious: Cocaine, Amphetamines and Harm Reduction

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Clinical characteristics of alcohol combined with other substance use disorders in an American Indian community sample

HIGHLIGHTS

• Multi-substance use disorder was more prevalent than single use disorder.
• Alcohol was the most common drug followed by stimulants and cannabis.
• Multi-substance use disorder was more severe and had greater co-morbidity.

BACKGROUND:

Alcohol and other substance use disorders (SUD) pose major problems of morbidity and mortality in some American Indian communities, but little is known about the clinical characteristics, risk factors, and consequences of combined alcohol and other substance use disorders (multi-substance use disorder, MSUD) in those communities.

METHODS:

Using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), in a community sample of 876 American Indians, the clinical characteristics of lifetime DSM-5 moderate or severe alcohol use disorder alone (AUD alone) (n=146) and MSUD (defined as alcohol and ≥1 other SUD) (n=284) were evaluated and compared to 347 participants with no lifetime SUD (no SUD).

RESULTS:

The majority (57%) of participants with a SUD had multi-substance use disorder and 94% of those were with AUD. Stimulants (cocaine and/or amphetamine) and/or cannabis were the most common other SUDs. Participants with AUD alone were more likely to be male and have an earlier age of first alcohol intoxication than those with no SUD. Those with MSUD were more likely to have dropped out of high school, have antisocial personality disorder (ASPD) or conduct disorder (CD), have earlier ages of first alcohol intoxication and first use of cannabis and stimulants, an earlier age of onset of AUD, and more of several AUD symptoms than those with AUD alone, but the same temporal course and time to remission of AUD.

CONCLUSIONS:

MSUD is prevalent in this sample, is associated with multiple comorbidities and denotes a more severe alcohol syndrome than AUD alone.

Purchase full article at:   http://goo.gl/ma2Rmk

By:  Gilder DA1, Stouffer GM1, Lau P1, Ehlers CL2.

• 1Molecular and Cellular Neuroscience Department, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
• 2Molecular and Cellular Neuroscience Department, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA. Electronic address: cindye@scripps.edu.
• Drug Alcohol Depend. 2016 Apr 1;161:222-9. doi: 10.1016/j.drugalcdep.2016.02.006. Epub 2016 Feb 8. 

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Weekly Energy Drink Use Is Positively Associated with Delay Discounting and Risk Behavior in a Nationwide Sample of Young Adults

Background:

Energy drink use is associated with increased risk behavior among adolescents and college students. This study examined this relationship in a nationwide sample of young adults and also examined relations between energy drink use and delay discounting.

Methods:

Participants were 874 U.S. adults 18-28 years of age with past 30-day consumption of caffeine and alcohol. Participants completed an online survey of energy drink use, drug use, sexual activity, alcohol misuse (alcohol use disorders identification test [AUDIT]), sensation seeking (four-item Brief Sensation Seeking Scale [BSSS-4]), and delay discounting of monetary rewards and condom use.

Results:

Over one-third of participants (n = 303) reported consuming energy drinks at least once per week. Weekly energy drink users were more likely than less-than-weekly energy drink users to report a recent history of risk behaviors, including cigarette smoking (56% vs. 28%), illicit stimulant use (22% vs. 6%), and unprotected sex (63% vs. 45%). Covariate-adjusted analyses found that weekly energy drink users did not have significantly higher BSSS-4 scores (3.5 vs. 3.1), but they had higher mean AUDIT scores (8.0 vs. 4.8), and they more steeply discounted delayed monetary rewards. Although weekly energy drink users did not show steeper discounting of delayed condom use, they showed a lower likelihood of using a condom when one was immediately available.

Conclusions:

This study extends findings that energy drink use is associated with risk behavior, and it is the first study to show that energy drink use is associated with monetary delay discounting.

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By:  Meredith SE1, Sweeney MM2, Johnson PS2, Johnson MW2, Griffiths RR3.

• 1Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.; Calhoun Cardiology Center, University of Connecticut School of Medicine, Farmington, Connecticut.
• 2Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine , Baltimore, Maryland.
• 3Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland. 
• J Caffeine Res. 2016 Mar 1;6(1):10-19.

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Public Opinions About Supervised Smoking Facilities for Crack Cocaine & Other Stimulants

BACKGROUND:

The purpose of this study was to estimate awareness and opinions about supervised smoking facilities (SSFs) for smoking crack cocaine and other stimulants and make comparisons with awareness and opinions about supervised injection facilities (SIFs) in Ontario, Canada.

METHODS:

We used data from a 2009 telephone survey of a representative adult sample. The survey asked about awareness of, and level of support for, the implementation of SSFs and SIFs. Data were analysed using statistical models for complex survey data, which account for stratified sampling and incorporate sampling weights.

RESULTS:

A total of 1035 participated in the survey. Significantly fewer had knowledge about SSFs (17.9 %) than about SIFs (57.6 %). Fewer strongly agreed with implementation of SSFs (19.6 %) than SIFs (28.3 %). Just over half (51.1 %) of participants somewhat agreed or disagreed, 15.7 % strongly agreed, and 10.6 % strongly disagreed with implementing both SSFs and SIFs.

CONCLUSIONS:

Members of the public in Ontario had little knowledge of SSFs compared to SIFs. Recent federal government changes in Canada may provide the leadership environment necessary to ensure that innovative, evidence-based harm reduction programs such as SSFs are developed and implemented.

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By:  Strike C1, Rotondi NK2, Watson TM3, Kolla G4, Bayoumi AM5,6,7,8.

• 1Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Canada. carol.strike@utoronto.ca.
• 2Musculoskeletal Health and Outcomes Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond Street, Toronto, Canada. nooshin.rotondi@gmail.com.
• 3Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Canada. tara.watson@utoronto.ca.
• 4Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Canada. gillian.kolla@utoronto.ca.
• 5Centre for Research on Innercity Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond Street, Toronto, Canada. ahmed.bayoumi@utoronto.ca.
• 6Department of Medicine, University of Toronto, 1 King's College Circle, Toronto, Canada. ahmed.bayoumi@utoronto.ca.
• 7Institute of Health Policy, Management, and Evaluation, University of Toronto, 155 College Street, Toronto, Canada. ahmed.bayoumi@utoronto.ca.
• 8Division of General Internal Medicine, St. Michael's Hospital, 30 Bond Street, Toronto, Canada. ahmed.bayoumi@utoronto.ca. 
• Subst Abuse Treat Prev Policy. 2016 Feb 9;11(1):8. doi: 10.1186/s13011-016-0052-7.

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Self-Reported Sexually Transmitted Infections & Sexual Risk Behaviors in the U.S. Military: How Sex Influences Risk

BACKGROUND:

Sexually transmitted infections (STIs) are prevalent in the U.S. military. However, there are limited data on risk-factor differences between sexes.

METHODS:

We used data from the 2008 Department of Defense Survey of Health Related Behaviors among active duty military personnel to identify risk factors for self-reported STIs within the past 12 months and multiple sexual partners among sexually active unmarried service members.

RESULTS:

There were 10,250 active duty personnel, mostly white (59.3%) aged 21 to 25 years (42.6%). The prevalence of any reported STI in the past 12 months was 4.2% for men and 6.9% for women. One-fourth of men and 9.3% of women reported 5 or more sexual partners in the past 12 months. Binge drinking, illicit substance use, and unwanted sexual contact were associated with increased report of sexual partners among both sexes. Family/personal-life stress and psychological distress influenced number of partnerships more strongly for women than for men (Adjusted Odds Ratio [AOR]=1.58, 95% Confidence Interval [CI]=1.18-2.12 and AOR=1.41, 95% CI=1.14-1.76, respectively). After adjusting for potential confounders, we found that the report of multiple sexual partners was significantly associated with the report of an STI among men (AOR, 5.87 [95% CI, 3.70-9.31], for ≥5 partners; AOR, 2.35 [95% CI, 1.59-3.49], for 2-4 partners) and women (AOR, 4.78 [95% CI, 2.12-10.80], for ≥5 partners; AOR, 2.35 [95% CI, 1.30-4.25], for 2-4 partners).

CONCLUSIONS:

Factors associated with the report of increasing sexual partnerships and report of an STI differed by sex. Sex-specific intervention strategies may be most effective in mitigating the factors that influence risky sexual behaviors among military personnel.

Difference in behaviors of sexually active unmarried service members by gender, 2008 HRBS dataset (n=10,250)

Characteristics	Men (n=6,822)			Women (n=3,428)			p-value
	n	%	95% CI	n	%	95% CI
Alcohol and Drug Use
Binge drinking	4101	61.1	(57.1, 65.2)^	1290	38.1	(35.1, 41.2)	<.001***
Any illicit substance use, past 12 mo.	588	10.3	(8.5, 12.1)	215	6.6	(4.2, 9.1)	<.001***
Marijuana	466	8.3	(6.4, 10.1)	178	5.5	(3.1, 7.9)	0.007**
Cocaine	210	3.6	(2.6, 4.6)	66	2.0	(1.0, 3.0)	0.014*
Ecstasy	178	3.2	(2.5, 3.9)	66	2.3	(1.3, 3.3)	0.055
Methamphetamine	102	1.7	(1.2, 2.2)	30	0.9	(0.5, 1.2)	<.001***
Heroin	89	1.4	(1.0, 1.8)^	20	0.5	(0.1, 0.9)	0.004**
Other illicit substance	297	5.1	(4.3, 5.8)^	70	2.3	(1.3, 3.3)	<.001***
Any prescription drug use for non-medical purpose, past 12 mo.	1291	20.4	(18.7, 22.1)	771	23.8	(21.2, 26.3)	0.006**
Stimulants	188	3.3	(2.7, 3.9)	108	3.3	(2.4, 4.1)	0.993
Tranquilizers	416	6.8	(6.0, 7.7)	275	8.4	(6.9, 10.0)	0.032*
Sedatives	219	3.7	(3.0, 4.4)	142	4.4	(3.1, 5.7)	0.346
Painkillers	1199	18.9	(17.2, 20.5)	717	22.2	(19.6, 24.9)	0.009**
Steroids	156	2.6	(2.1, 3.0)	55	2.0	(1.3, 2.8)	0.248
Sexual Risks
Condom use at last sex	2685	43.0	(40.9, 45.1)^	1005	32.1	(28.8, 35.4)	<.001***
Main partner at last sex	4404	62.9	(61.1, 64.6)	2839	82.5	(80.2, 84.8)^	<.001***
No. of sex partners, past 12 mo.							<.001***
5+	1654	25.2	(23.5, 26.8)^	339	9.3	(7.8, 10.8)
2-4	2705	39.8	(38.4, 41.2)	1278	38.4	(35.2, 41.6)
1	2463	35.0	(34.0, 36.1)	1811	52.3	(48.5, 56.0)^
No. of NEW sex partners, past 12 mo.							<.001***
2+	3403	51.3	(50.2, 52.3)^	1069	30.7	(27.3, 34.1)
1	1574	23.4	(22.5, 24.3)	965	31.3	(29.4, 33.3)^
None	1799	25.3	(24.3, 26.4)	1383	37.9	(35.2, 40.7)^
Unwanted sexual contact	196	2.9	(2.3, 3.6)	515	14.2	(12.0, 16.4)^	<.001***
STI, past 12 mo.	273	4.2	(3.5, 4.8)	217	6.9	(5.7, 8.1)^	<.001***
Mental Health Indicators
Depression	1567	24.8	(23.2, 26.4)	977	28.4	(26.6, 30.2)^	0.002**
Anxiety	871	13.9	(12.2, 15.5)	678	20.3	(17.6, 22.9)^	<.001***
PTSD	762	12.2	(10.7, 13.7)	467	13.9	(12.4, 15.4)	0.097
Overall stress							<.001***
Low	3370	48.9	(46.1, 51.6)^	1421	40.9	(37.7, 44.1)
Moderate	1821	27.0	(25.6, 28.4)	1079	31.6	(29.5, 33.7)^
High	1571	24.1	(21.7, 26.5)	904	27.5	(25.1, 29.8)
High military-related stress	1765	27.1	(25.1, 29.1)	1068	31.4	(28.6, 34.2)	<.001***
High family or personal-life stress	1256	19.2	(17.7, 20.6)	815	22.9	(20.9, 25.0)^	<.001***
Any gender-Related stress	--	--	--	2887	86.0	(83.9, 88.1)	--
Psychological distress	1053	16.6	(15.1, 18.1)	782	22.2	(19.3, 25.1)^	<.001***
Suicide ideation	341	5.7	(4.9, 6.4)	193	5.4	(3.9, 7.0)	0.797
Suicide attempt	85	1.4	(1.0, 1.8)	79	2.2	(1.4, 3.0)	0.048*

^95% confidence intervals of the prevalence estimate do not overlap

*p<0.05;

**p<0.01;

***p<0.001; P-values derived by Rao-Scott Chi-Square Test Percentages shown are weighted; n’s are unweighted

Full article at:  http://goo.gl/O7oZ0n

By:  Stahlman S1, Javanbakht M, Cochran S, Hamilton AB, Shoptaw S, Gorbach PM.

• 1From the *Department of Epidemiology, Fielding School of Public Health, †Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, and ‡Department of Family Medicine, University of California, Los Angeles, Los Angeles, CA. 
• Sex Transm Dis. 2014 Jun;41(6):359-64. doi: 10.1097/OLQ.0000000000000133.

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