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2016
2016
2016
2016
BACKGROUND
Patients with chronic noncancer pain treated with higher doses of opioids or concurrent substance use are at increased risk of adverse events. Although several national guidelines recommend maximum dosing thresholds and urine drug testing, adherence to these guidelines is inconsistent.
METHODS
To identify predictors of higher-risk opioid prescriptions in 2 academic primary care clinics, the authors developed a retrospective cohort of 842 patients who were prescribed ≥5 opioid prescriptions for noncancer pain between March 2012 and March 2013. The authors evaluated odds of higher-dose opioid prescriptions and urine drug testing using multivariate logistic models.
RESULTS
Among study subjects, 47% received prescriptions for the equivalent of ≥50 mg morphine per day. After adjustment for confounders, patients with a resident primary care provider were less likely to receive higher-dose prescriptions compared with faculty providers (odds ratio = 0.66, 95% confidence interval [CI]: 0.46-0.94), whereas patients with a nonlocal home address were more likely to be prescribed higher doses (odds ratio = 2.1, 95% CI: 1.5-2.9). Hispanic, Asian, and older patients were also less likely to be prescribed higher doses. Urine drug testing was not regularly completed (35% over 2 years), but odds of testing were higher for patients who self-identified as black, had resident primary care providers, lived locally, or were prescribed higher opioid doses.
CONCLUSIONS
In this academic clinical setting, patients with a resident primary care provider are less likely to receive higher-risk opioid prescriptions, as are Hispanic, Asian, and older patients. Black patients complete urine drug tests more frequently independent of other patient and provider characteristics. Additional studies are needed to assess why patients who travel larger distances to their primary care clinic are prescribed higher doses of opioids for chronic noncancer pain.
View on PubMed2016
OBJECTIVE
Fatigue is a major concern for individuals with rheumatoid arthritis (RA). However, in order to treat fatigue adequately, its sources need to be identified.
METHODS
Data were collected during a single home visit (number of participants = 158). All participants had physician-diagnosed RA. Assessments of self-reported sleep quality, depression, physical activity, RA disease activity, muscle strength, functional limitations, and body composition were made. Information on demographics, medications, and smoking was collected. The Fatigue Severity Inventory (FSI; measuring average fatigue over the past 7 days) was used as the primary outcome. Analyses were first conducted to evaluate bivariate relationships with fatigue. Correlations among risk factors were examined. Multivariate analyses identified independent predictors of fatigue.
RESULTS
The mean ± SD age was 59 ± 11 years, the mean ± SD disease duration was 21 ± 13 years, and 85% of subjects were female. The mean ± SD FSI rating was 3.8 ± 2.0 (range 0-10). In multivariate analyses, self-reported disease activity, poor sleep, depression, and obesity were independently associated with fatigue. Physical inactivity was correlated with poor sleep, depression, and obesity. Mediation analyses indicated that physical inactivity had an indirect association with fatigue, mediated by poor sleep, depression, and obesity.
CONCLUSION
This cross-sectional study suggests that fatigue may not be solely a result of RA disease activity, but may result from a constellation of factors that includes RA disease activity or pain, but also includes inactivity, depression, obesity, and poor sleep. The results suggest new avenues for interventions to improve fatigue in individuals with RA, such as increasing physical activity or addressing depression or obesity.
View on PubMed2016
2016