Refocusing Attention on Opioid Use in Patients with Chronic Pain: A Predictive Latent Growth Analysis of Cannabis & Opioid Co-Use

Substance use among individuals with chronic pain represents an escalating public health crisis. Although opioid use and misuse dominate clinical attention amid the ongoing epidemic, cannabis legalization across the United States has introduced new complexities regarding cannabis use and opioid co-use patterns, potentially amplifying addiction risk in vulnerable populations. Despite growing concern, longitudinal research examining how psychosocial risk factors predict distinct patterns of opioid use, cannabis use, and co-use remains limited. This study employed latent growth modeling (LGM) to investigate trajectories of cognitive-affective risk factors and their differential prediction of substance use patterns among community-dwelling adults with chronic pain. Participants (n = 133; ages 19-67, M = 41.74, SD = 12.92; 55.88% women; 61.76% White) completed a 30-day ecological momentary assessment protocol to monitor stress, pain intensity, psychological distress, pain catastrophizing (helplessness, magnification, and rumination), substance cravings, and use behaviors. LGMs demonstrated an acceptable fit and explained a substantial variance in the observed variables. Substance use patterns remained relatively stable across the 30-day observation period. Critically, cognitive-affective pathways (both intercepts and slopes) consistently predicted opioid use across all risk factors. Cannabis use showed weaker associations, indicated only by helplessness trajectories and pain intensity intercepts. Most notably, elevated cognitive-affective pathways were inversely associated with co-use patterns across all risk factors. These findings challenge assumptions about polysubstance use primacy in chronic pain populations, suggesting that opioid use represents the predominant, persistent pattern driven by cognitive-affective mechanisms, while cannabis use and co-use appear more transient and less systematically predicted by these risk factors.