ArticlePain medicine (Malden, Mass.)2024
Identifying patterns of pain, depression, anxiety, interpersonal trauma exposure, and nonmedical prescription opioid use: Latent class analysis among patients with chronic pain.
Article in Pain medicine (Malden, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundChronic pain in the context of certain factors may be associated with potential for nonmedical prescription opioid use; however, identifying this risk can be challenging and complex. Several variables alone have been associated with non-prescribed opioid use, including depression, anxiety, pain interference, and trauma exposure. Prior research has often failed to integrate these assessments together, which is important as these factors may cluster together in important and complex ways. The current study aimed to identify classes of patients with chronic pain who have differential risk for use of nonmedical prescription opioid use, depression and anxiety, and pain severity, interference, and catastrophizing, and interpersonal violence exposure.
methodsSelf-report and medical record data from patients (N = 211; Mage = 48, 69.0% women, 69.0% white) at a pain management center were collected.
resultsLatent class analysis revealed 3 classes with (1) low probability of clinically significant depression, anxiety, pain, and nonmedical prescription opioid use (44.7%), (2) high probability of clinically significant depression, anxiety, pain, pain catastrophizing, trauma, and nonmedical prescription opioid use (41.3%), and (3) high probability of severe pain and nonmedical prescription opioid use (14.0%).
conclusionsHigh-risk classes had either high levels of depression and anxiety, pain catastrophizing, and interpersonal violence exposure, or pain severity and interference. Future research should continue to explore these classes in large, diverse samples, and prospective study designs. Finally, results underscore that opioid use is complex, not easily identified by a single factor, and may be motivated by complex unmet clinical needs.
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