Management of Benzodiazepine Misuse and Dependence

There are well-recognised harms from long-term use of benzodiazepines. These include dependency, cognitive decline and falls. It is important to prevent and recognise benzodiazepine dependence. A thorough risk assessment guides optimal management and the necessity for referral. 

The management of dependence involves either gradual benzodiazepine withdrawal or maintenance treatment. Prescribing interventions, substitution, psychotherapies and pharmacotherapies can all contribute. Unless the patient is elderly, it is helpful to switch to a long-acting benzodiazepine in both withdrawal and maintenance therapy. 

The dose should be gradually reduced over weeks to lower the risk of seizures. Harms from drugs such as zopiclone and zolpidem are less well characterised. Dependence is managed in the same manner as benzodiazepine dependence.

Benzodiazepine and z-drugs half-life and conversion table

Drug	Approximate half-life (hours)	Dose of oral benzodiazepine approximately equivalent to diazepam 5 mg
Short- to intermediate-acting benzodiazepines
Triazolam	1–3	0.25 mg
Oxazepam	4–15	15 mg
Temazepam	5–15	10 mg
Lorazepam	12–16	1 mg
Bromazepam	20	3 mg
Alprazolam	6–25	0.5 mg
Flunitrazepam	20–30	0.5 mg
Nitrazepam	16–48	5 mg
Clobazam	17–49	10 mg

Long-acting benzodiazepines (includes effects of active metabolites)
Clonazepam	22–54	0.5 mg
Diazepam	20–80	5 mg

Z-drugs
Zolpidem	2.4	10 mg
Zopiclone	5.2	7.5 mg

Benzodiazepine withdrawal syndrome – clinical features

General

Headache

Palpitations

Sweating

Musculoskeletal

Tremor, fasciculations

Muscle pain, stiffness and aches (limbs, back, neck, jaw)

Neurological

Dizziness, light-headedness

Paraesthesia, shooting pains in neck and spine

Visual disturbances (blurred vision, diplopia, photophobia, vision lags behind eye movements)

Tinnitus

Faintness and dizziness, sense of unsteadiness

Confusion, disorientation (may be intermittent) – a common cause of confusion in older patients

Delirium (in the absence of autonomic hyperactivity) – particularly in older patients

Delusions, paranoia

Hallucinations (visual, auditory)

Grand mal seizures 1–12 days after discontinuing benzodiazepines

Gastrointestinal

Nausea

Anorexia

Diarrhoea (may resemble irritable bowel syndrome)

Psychological

Rebound insomnia, nightmares

Anxiety, panic attacks

Irritability, restlessness, agitation

Poor memory and concentration

Perceptual distortions – sensory hypersensitivity (light, sound, touch, taste), abnormal sensations (e.g. ‘cotton wool’ sensations)

Metallic taste

Distortions of body image

Feelings of unreality, depersonalisation, derealisation

Depression, dysphoria

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

By:  Brett J1, Murnion B2.

• 1Clinical Pharmacology and Addiction Medicine, Drug Health Services, Royal Prince Alfred Hospital.
• 2Clinical Pharmacology and Addiction Medicine, Drug Health Services, Royal Prince Alfred Hospital ; Concord Repatriation General Hospital, Sydney. 
• Aust Prescr. 2015 Oct;38(5):152-5. Epub 2015 Oct 1.

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Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data

BACKGROUND:

Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens.

OBJECTIVE:

In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies.

METHODS:

Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning.

RESULTS:

A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription.

CONCLUSIONS:

This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online.

Below:  Word cloud for generic instance

Below:  Word cloud for street instance

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

By:   Katsuki T1, Mackey TK, Cuomo R.

• 1Kavli Institute for Brain and Mind (http://kibm.ucsd.edu/), University of California, San Diego, La Jolla, CA, United States.
• J Med Internet Res. 2015 Dec 16;17(12):e280. doi: 10.2196/jmir.5144.

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Substance Use Disorders and Psychiatric Comorbidity in Mid and Later Life: A Review

Globally, adults aged 65 years or older will increase from 516 million in 2009 to an estimated 1.53 billion in 2050. Due to substance use at earlier ages that may continue into later life, and ageing-related changes in medical conditions, older substance users are at risk for substance-related consequences.

MEDLINE and PsychInfo databases were searched using keywords: alcohol use disorder, drug use disorder, drug misuse, substance use disorder, prescription drug abuse, and substance abuse. Using the related-articles link, additional articles were screened for inclusion. This review focused on original studies published between 2005 and 2013 to reflect recent trends in substance use disorders. Studies on psychiatric comorbidity were also reviewed to inform treatment needs for older adults with a substance use disorder.

Among community non-institutionalized adults aged 50+ years, about 60% used alcohol, 3% used illicit drugs and 1–2% used nonmedical prescription drugs in the past year. Among adults aged 50+, about 5% of men and 1.4% of women had a past-year alcohol use disorder. Among alcohol users, about one in 14 users aged 50–64 had a past-year alcohol use disorder vs one in 30 elder users aged 65+. Among drug users aged 50+, approximately 10–12% had a drug use disorder. Similar to depressive and anxiety disorders, substance use disorders were among the common psychiatric disorders among older adults. Older drug users in methadone maintenance treatment exhibited multiple psychiatric or medical conditions. There have been increases in treatment admissions for illicit and prescription drug problems in the United States.

Substance use in late life requires surveillance and research, including tracking substance use in the racial/ethnic populations and developing effective care models to address comorbid medical and mental health problems.

Below:  Age at admission for substance abuse treatments: Treatment Episode Data Set (TEDS) 1999–2009; U.S. population 2009. Source: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Service Adminstration, Treatment Episode Data Set (TEDS). Data received through 11 March 2010. U.S. Bureau of the Census. Annual State Resident Population Estimates for 6 Race Groups by Age, Sex, and Hispanic Origin: 1 April 2000 to 1 July 2009 (SC-EST2009-ALLDATA6, released June, 2010)

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

By: Li-Tzy Wu and Dan G Blazer*

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA

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Interrelationships among Parental Family History of Substance Misuse, Delay Discounting & Personal Substance Use

Despite consistent evidence of the familiality of substance misuse, the mechanisms by which family history (FH) increases the risk of addiction are not well understood. One behavioral trait that may mediate the risk for substance use and addiction is delay discounting (DD), which characterizes an individual's preferences for smaller immediate rewards compared to larger future rewards.

The aim of this study is to examine the interrelationships among FH, DD, and diverse aspects of personal substance use, and to test DD as a mediator of the relationship between FH and personal substance use.

The study used crowdsourcing to recruit a community sample of adults (N = 732). Family history was assessed using a brief assessment of perceived parental substance use problems, personal substance use was assessed using the Alcohol Use Disorders Identification Test and a measure of frequency of use, and delay discounting was assessed using a latent index of discounting preferences across six reward magnitudes.

Steeper discounting was significantly associated with personal alcohol, tobacco, and marijuana use, and level of substance experimentation. Steeper DD was also associated with a denser parental FH of alcohol, tobacco, and overall substance misuse. Parental FH density was significantly associated with several aspects of personal substance use, and these relationships were partially mediated by DD.

The current study suggests that impulsivity, as measured by DD, is one proximal mechanism by which parental FH increases substance use later in life. The causal role of DD in this relationship will need to be established in future longitudinal studies.

Via: http://ht.ly/SLsr2  Purchase full article at: http://goo.gl/Fw0KzK

By:  VanderBroek L1, Acker J1, Palmer AA2,3, de Wit H3, MacKillop J4,5.

• 1Department of Psychology, University of Georgia, Athens, GA, 30602-3013, USA.
• 2Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
• 3Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA.
• 4Department of Psychology, University of Georgia, Athens, GA, 30602-3013, USA
• 5Peter Boris Centre for Addictions Research, Department of Psychiatry and Behavioural Neurosciences, McMaster University/St. Joseph's Healthcare Hamilton, Hamilton, Ontario, L8N 3K7, Canada 

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Prescription Pain Reliever Misuse Prevalence, Correlates, and Origin of Possession Throughout the Life Course

• Among respondents, 4.7% reported past-year prescription pain reliever misuse. 
• Prevalence for individuals aged 12 to 17 was 5.9%, 
• 18 to 25 was 10.2%, 
• 26-34 was 7.7%, 
• 35 to 49 was 4.3%, 
• 50 or older was 1.7%
• While many social and clinical correlates of pain reliever misuse emerged among younger respondents, these correlates diminished in significance among older adults. 
• Only past-year illicit drug use disorders (marijuana, cocaine, crack cocaine, heroin, and hallucinogen use) was a significant predictor of pain reliever misuse among all age groups. 
• Also, older adults were more likely to report pain reliever possession from multiple medical doctors,
• Whereas younger individuals were more likely to possess pain reliever from friends/relatives or through purchase from a drug dealer/stranger.

Increased efforts to better screen for illicit drug use and greater efforts to coordinate patient prescription records among medical care providers may be high priorities in developing interventions to reduce rates of misuse of prescription pain relievers, especially among older adults.

Via: http://ht.ly/SgzeL

By: Mowbray O1, Quinn A2.

• 1University of Georgia School of Social Work, United States.
• 2University of Georgia School of Social Work, United States.

Prevalence & Factors Associated with Sex Trading in the Year Prior to Entering Treatment for Drug Misuse in England

This study estimates the past year prevalence of and factors associated with sex trading (offering sex for money, drugs or something else) among 1796 men and women presenting to 342 drug misuse treatment agencies in England, and identifies service development and delivery implications.

The estimated prevalence rate of sex trading in the past 12 months was 5.1% (15.0% for women and 2.1% for men). For women, adjusted models identified crack use, previous treatment and greater readiness for treatment as independently associated with sex trading. For men, lower mental wellbeing was independently associated and marginal effects were identified for syringe sharing and unprotected sex.

Sex trading among drug misusers is associated with additional health risks and specific treatment needs. Given the scale of the problem it is important that treatment providers have the competencies to adequately address the issue and provide accessible and appropriate services.

Via: http://ht.ly/S9Kbs 

By: Gilchrist G1, Singleton N2, Donmall M3, Jones A4.

• 1King's College London, National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, 4 Windsor Walk, London SE5 8BB, United Kingdom. Electronic address: gail.gilchrist@kcl.ac.uk.
• 2King's College London, National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, 4 Windsor Walk, London SE5 8BB, United Kingdom. Electronic address: Nicola.Singleton@kcl.ac.uk.
• 3National Drug Evidence Centre, Institute of Population Health, University of Manchester, Ellen Wilkinson Building, 4th Floor, Oxford Road, Manchester M13 9PL, United Kingdom. Electronic address: M.Donmall@manchester.ac.uk.
• 4National Drug Evidence Centre, Institute of Population Health, University of Manchester, Ellen Wilkinson Building, 4th Floor, Oxford Road, Manchester M13 9PL, United Kingdom