Abstract: Data are equivocal on the consequences of COVID-19 pandemic on pain and well-being for individuals with chronic pain. Furthermore, little is known regarding its impact on the health of young adults with chronic pain. We conducted a longitudinal study to compare pain, psychological functioning, and substance use before and during the pandemic of 196 young adults with chronic pain. Participants aged 18 to 24 years (M = 21.1 years; 79.6% females) reported on pain, anxiety, depression, and substance use before (October 2018-August 2019) and during the pandemic (October 2020-November 2020), in addition to the assessment of COVID-19 exposure and its impact. Before the pandemic, young adults experienced mild-to-moderate pain intensity (M = 3.75, SD = 2.33) and pain interference (M = 3.44, SD = 2.69). Findings were that pain intensity, pain interference, and depression symptoms remained stable during the pandemic. In contrast, anxiety symptoms increased significantly (M = 8.21, SD = 5.84 vs M = 8.89, SD = 5.95, P = 0.04). Tobacco, alcohol, and cannabis use were unchanged. Mixed linear models revealed that COVID-19 exposure and impact were not associated with changes in pain intensity or interference, with female sex associated with increased pain intensity ([beta] = 0.86, P = 0.02) and pain interference ([beta] = 0.87, P = 0.02). Our findings indicated relative stability of pain symptoms experienced by young adults with chronic pain. However, the increases in anxiety highlight the need to facilitate treatment access for mental health services to mitigate downstream impact.

Objective: It is well established that adolescents and young adults are increasingly vulnerable to the effects of early opioid exposures, with the emergency department (ED) playing a critical role in such introduction. Our objective was to identify predictors of ED opioid administration (ED-RX) and prescribing at discharge (DC-RX) among adolescent and young adults using a machine learning approach.

Methods: We conducted a secondary analysis of ED visit data from the National Hospital Ambulatory Medical Care Survey from 2014 to 2018. Visits where patients were aged 10 to 24 years were included. Predictors of ED-RX and DC-RX were identified via machine learning methods. Separate weighted logistic regressions were performed to determine the association between each predictor, and ED-RX and DC-RX, respectively.

Results: There were 12,693 ED visits identified within the study time frame, with the majority being female (58.6%) and White (70.7%). Approximately 12.3% of all visits were administered an opioid during the ED visit, and 11.5% were prescribed one at discharge. For ED-RX, the strongest predictors were fracture injury (odds ratio [OR], 5.24; 95% confidence interval [CI], 3.73-7.35) and Southern geographic region (OR, 3.01; 95% CI, 2.14-4.22). The use of nonopioid analgesics significantly reduced the odds of ED-RX (OR, 0.46; 95% CI, 0.37-0.57). Fracture injury was also a strong predictor of DC-RX (OR, 5.91; 95% CI, 4.24-8.25), in addition to tooth pain (OR, 5.47; 95% CI, 3.84-7.69).

Conclusions: Machine learning methodologies were able to identify predictors of ED-RX and DC-RX, which can be used to inform ED prescribing guidelines and risk mitigation efforts among adolescents and young adults.