Table 2.
AUTHOR LAST NAME | WAS ILLICIT OPIOID ABUSE AN OUTCOME? | WAS ILLICIT OPIOID USE BEHAVIOR THE PRIMARY OUTCOME OF THE STUDY? | HOW WAS ILLICIT OPIOID ABUSE MEASURED? | DEFINITION OF ILLICIT OPIOID USE | OUTCOMES BY CHRONIC PAIN STATUS | STATISTICAL ANALYSIS | PROPORTION OF OPIOID USE OUTCOMES SHOWING PAIN TO IMPACT ILLICIT OPIOID USE BEHAVIOR | STUDY FINDINGS: DID PATIENTS CHARACTERIZED AS HAVING PAIN ALSO HAVE SIGNIFI-CANTLY HIGHER RATES OF ILLICIT OPIOID USE? |
---|---|---|---|---|---|---|---|---|
Peles | Yes | No | Urine toxicology screening | Participants were categorized as using opioids if ≥1 urine test in the month preceding the survey was positive. | Chronic pain 15 (16%) positive, non-chronic pain 20 (26.3%) | Chi-square | 0 | No |
Dhingra | Yes | No | Urine toxicology screening, Self-report | A positive urine toxicology screen or indication by self-report as assessed by the ASI past 30 day drug use history. | In univariate analyses, neither UDS nor self-reported drug use on the ASI was statistically associated with clinically significant pain. (report P-values) | t-Test, Chi-square | 0 | No |
Barry | Yes | No | Self-report | Participants reported any use in the past week, this was then analyzed as a binary variable. | The pain groups reported comparable levels of psychoactive substance use, illegal drug use and non-medical use of prescription drug in the past week. No specific percentages are reported per group. | ANOVA | 0 | No |
Chakrabarti | Yes | No | Urine toxicology screening | Participants showing a single positive opioid urine screen were found to be a positive for illicit opioid use behaviour, confirmed by urinalysis. | Opioid-positive urine (%)reported by Degree of pain Week 4 Week 8 Week 12 Extreme pain patients: 22.2% (2/9) 12.5% (1/8) 37.5% (3/8) Some pain patients: 31.3% (10/32) 26.7% (8/30) 37.9% (11/29) No pain: 21.4% (3/14) 20% (2/10) 66.7% (6/9) |
Chi-square | 0 | No |
Dennis | Yes | Yes | Urine toxicology screening | Continued opioid abuse (COA) was determined by calculating the percentage of positive opioid urine screens provided by participants (number of positive opioid urine screens/total number of opioid urine screens). High COA percentage is indicative of a high number of positive opioid urine screens or, alternatively, a higher rate of illicit opioid consumption. | Mean percentage of positive opioid urine screens among pain patients: 23.99 (SD 27.14) Mean percentage of positive opioid urine screens among non-pain patients: 15.82 (SD 20.11) |
Univariate analysis using only COA outcome as the predictor of comorbid pain in a logistic regression model | 1/1 | Yes |
Dreifuss | Yes | Yes | Urine toxicology screening, self-report, addiction severity tool score | The Substance Use Report, corroborated by weekly urine drug screens, was administered weekly during treatment and every two weeks during follow-up, and was used as the primary measure to determine “successful outcome.” | Successful with chronic pain: 79 (44.6%) Failure with chronic pain: 70 (38.3%) |
Chi-square | 0 | No |
Dunn | Yes | Yes | Urine toxicology screening | The mean percent of urine samples provided by each participant that tested positive for opioids, cocaine, or benzodiazepines were evaluated. | Chronic pain: 9, No chronic pain: 11 | Independent group t-tests were used to compare continuous variables | 0 | No |
Fox | Yes | No | Self-report | Self-reported opioid use was obtained from the substance use survey administered at baseline and follow-up, which inquired as to substance use in the 30 days prior to baseline (heroin, methadone, opioid analgesics, cocaine, alcohol, sedatives, hypnotics, or tranquilizers) and follow-ups. These questions were adapted from the ASI. | Not reported per pain status. However, they report that any opioid use decreased from 89% at baseline to 40% at 1 month, 33% at 3 months, and 26% at 6 months. Similar patterns were observed in those with and without baseline or persistent pain, and showed no significant association between any opioid use and baseline pain (AOR = 1.06, 95% CI: 0.27–4.17, P = 0.93) or persistent pain (AOR = 1.20, 95% CI: 0.31–4.63, P = 0.79), after adjusting for HIV status, depressive symptoms, history of IDU, history of incarceration, baseline opioid use, and time since initiating buprenorphine treatment. | Determined whether pain was associated with use of any opioids during the 6-month follow-up period using nonlinear mixed effects (NLME) models with self-reported use of any opioids as the dependent variable. The NLME approach accounts for non-independence of repeated measures of opioid use within individuals. | 0 | 0 |
Neumann | Yes | No | Urine toxicology screening | Report the number of patients (%) who have an opioid positive urine screen at 24 week follow-up | Methadone: 2 (15.4%), buprenorphine: 5 (38.5%) P.0.05 Odds ratio: 0.280, 95% CI: 0.042–1.878, P = 0.371 |
Fishers exact test | 0 | No |
Potter | Yes | No | Urine toxicology screening, Addiction severity tool score | Not described well or reported by pain status. | Not reported by pain status | n/a | n/a | n/a |
Rosenblum | Yes | No | Self-report | A checklist was used to record drugs, including alcohol, that were used during the patient’s last week of active use. | Drugs used in past 3 months (%) P = 0.05 None (reference for MMTP): CP 156 (42.9%) OR:1.00 1: CP:123 (27.6%) OR: 0.51 95%CI (0.31–0.84) 2: CP: 62 (38.7%) OR: 0.84 (0.46–1.53) $3: CP 49 (36.7%) 0.77 (0.40–1.50) |
Mantel–Haenszel was used for ordinal variables with 3 or more categories | 0 | No |
Trafton | Yes | Yes | Addiction severity tool score | Number of days of opioid use over last 30, as well as self-reported number of days of opioid use over lifetime. | Opiates GP:1.6 days, 1.9 years; NP: 0.8 days, 0.9 years, P: 2.3 days, 2.9 years 0.03/0.005 | The Kruskal–Wallis test was used to determine if variables significantly differed across pain severity ratings, followed by multiple t-tests to determine which levels of reported pain differed from the group reporting “none”. | 2/3 | Yes |