Articles: analgesics.
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Preventive medicine · Jan 2024
Inequitable access to nonpharmacologic pain treatment providers among cancer-free U.S. adults.
Using evidence-based nonpharmacologic pain treatments may prevent opioid overuse and associated adverse outcomes. There is limited data on the impact of access-promoting social determinants of health (SDoH: education, income, transportation) on use of nonpharmacologic pain treatments. Our objective was to examine the relationship between SDoH and use of nonpharmacologic pain treatment providers. Our goal was to understand policy-actionable factors contributing to inequity in pain treatment. ⋯ These findings highlight the substantial influence access-promoting SDoH have on pain treatment utilization.
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Repeated use of opioid analgesics may cause a paradoxically exacerbated pain known as opioid-induced hyperalgesia (OIH), which hinders effective clinical intervention for severe pain. Currently, little is known about the neural circuits underlying OIH modulation. Previous studies suggest that laterocapsular division of the central nucleus of amygdala (CeLC) is critically involved in the regulation of OIH. ⋯ On the contrary, silencing this pathway by chemogenetics exacerbated OIH by activating the CeLC. Combined with the electrophysiology results, the enhanced synaptic transmission from IL to CeLC might be a cortical gain of IL to relieve OIH rather than a reason for OIH generation. Scaling up IL outputs to CeLC may be an effective neuromodulation strategy to treat OIH.
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Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain. ⋯ The best model obtained in this study, the LR model, had an AUC_ROC of 0.82, accuracy of 0.82, and specificity of 0.71. The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. The strongest predictors for adherence were, in order of importance, beliefs about medicines questionnaire (BMQ)-harm, time since the start of opioid, and BMQ-necessity. Discussion. ML algorithms can be used as an effective means of predicting adherence to opioids in patients with cancer pain, which allows for proactive clinical intervention to optimize cancer pain management. This trial is registered with ChiCTR2000033576.
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In 2016, the U.S. Food and Drug Administration (FDA) issued its strongest safety warning ("Black Box Warning") for concomitant use of prescription opioids and benzodiazepines due to overdose deaths. ⋯ Our study found that between 2012 and 2019, there was no overall reduction in co-prescribing of opioids and benzodiazepines across EDs nationwide, but a decrease after the 2016 Black Box Warning.
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Patient Prefer Adher · Jan 2024
The Effects of Trade Names on the Misuse of Some Over-The-Counter Drugs and Assessment of Community Knowledge and Attitudes in Alkarak, Jordan.
This study aimed to assess the knowledge and attitudes of the community toward the brand names of the most commonly used over-the-counter (OTC) analgesics in Alkarak, Jordan, as well as to assess community's self-medication behaviors that may lead to misuse of OTC drugs. ⋯ Low levels of knowledge and unfavorable attitudes regarding OTC drug use from different brand names were reported. Increasing the awareness of the community and enhancing the role of physicians and pharmacists in OTC drug consumption may lead to decrease the misuse of these drugs.