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Objectives: To determine the prevalence and factors associated with the use of opioids among patients with chronic non-cancer pain (CNCP).Methods: A systematic review and meta-analysis. Comprehensive literature searches in Medline-PubMed, Embase and SCOPUS databases. Original studies published between 2009 and 2019 with a cross-sectional design were included. The quality of the studies was assessed with Critical Appraisal Checklist for Studies Reporting Prevalence Data from the Joanna Briggs Institute. Protocol registered in the International Prospective Register of Systematic Reviews with reference number: CRD42019137990.Results: Out of the 1,310 potential studies found, 25 studies fulfilled the inclusion criteria. Most of the studies were of high quality. High levels of heterogeneity were found in the studies included. In the general population, the prevalence of long-term opioid use was 2.3% (95% CI: 1.5–3.6%), the prevalence of short-term opioid use was 8.1% (95% CI: 5.6–11.6%), and among people with chronic low back pain it was 5.8% (95% CI: 0.5–45.5%). The prevalence of opioid use among patients from the health records or medical surveys was 41% (95% CI: 23.3–61.3%). Finally, in patients with musculoskeletal pain, the prevalence was 20.5% (95% CI: 12.9–30.9%) and in patients with fibromyalgia, 24.5% (95% CI: 22.9–26.2%). A higher prevalence of opioid use was observed among men, younger people, patients receiving prescriptions of different types of drugs, smokers and patients without insurance or with noncommercial insurance. In addition, non-white and Asian patients were less likely to receive opioids than non-Hispanic white patients.Conclusions: The prevalence of opioid use among patients with CNCP was higher in subjects with short or occasional use compared to those with long-term use. Men, younger people, more chronic pain conditions, and patients without insurance or with noncommercial insurance were most related to opioid use. However, non-white and Asian patients, and those treated by a physician trained in complementary medicine were less likely to use opioids.

This study aims to shed light on the frequency and associated factors of self-reported adherence to analgesic treatment among chronic pain (CP) patients in the Spanish population. A nationwide cross-sectional study was performed of 1066 Spanish adults, of whom 251 suffered from CP and 168 had been prescribed analgesic treatment. Adherence was assessed using a self-reported direct questionnaire and related factors were collected. Descriptive and bivariate analyses were conducted. Among the 23.5% (95% CI: 21.0–26.2%) of the sample with CP, 66.9% (95% CI: 60.7–72.7%) were taking analgesic treatment prescribed by a doctor, and 81.0% (95% CI: 74.2–86.6%) said they took the treatment as the doctor indicated. However, 17.6% forgot to take the medication, 11% overused them when in great pain, 46.3% stopped the treatment when feeling better and 33.3% when feeling worse, and 7.3% stopped taking them for financial reasons. Higher intensity of pain, polymedication, administration route (injection/patches) and some patient-related factors were associated with self-perceived adherence to treatment. Most Spanish people with CP consider that they are adherent to their analgesic treatment. However, their behavior presents contradictions. It would be advisable for professionals to inform patients about appropriate behavior regarding their therapy recommendations, and to explore potential factors related to non-adherence. This could contribute to improving pain control.

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The aim of this paper is twofold. First, we show that the expectation of the absolute value of the difference between two copies, not necessarily independent, of a random variable is a measure of its variability in the sense of Bickel and Lehmann (1979). Moreover, if the two copies are negatively dependent through stochastic ordering, this measure is subadditive. The second purpose of this paper is to provide sufficient conditions for comparing several distances between pairs of random variables (with possibly different distribution functions) in terms of various stochastic orderings. Applications in actuarial and financial risk management are given.