Latent class mediation modeling is designed to estimate the mediation effect when both the mediator and the outcome are latent class variables. We suggest using an adjusted one-step approach in which the latent class models for the mediator and the outcome are estimated first to decide on the number of classes, then the latent class models and the mediation model are jointly estimated. We present both an empirical demonstration and a simulation study to compare the performance of this one-step approach to a standard three-step approach with modal assignment (modal) and four different modern three-step approaches. Results from the study indicate that unadjusted modal, which ignores the classification errors of the latent class models, produced biased mediation effects. On the other hand, the adjusted one-step approach and the modern three-step approaches performed well with respect to bias for estimating mediation effects, regardless of measurement quality (i.e., model entropy) and latent class size. Among the three-step approaches we investigated, the maximum likelihood method with modal assignment and the BCH correction with robust standard error estimators are good alternatives to the adjusted one-step approach, given their unbiased standard error estimations.

The U.S. opioid crisis necessitates that health care providers of all types work collaboratively to manage patients taking prescription opioid medications and manage those who may be misusing prescription opioids. Musculoskeletal conditions are the most common diagnoses associated with an opioid prescription. Physical therapists commonly manage patients with musculoskeletal conditions and chronic pain. Some patients who attend physical therapy for pain management take prescription opioid medications for pain and some of these patients may be misusing prescription opioids. Physical therapists who manage patients with musculoskeletal conditions are well-positioned to help address the opioid crisis. Historically, physical therapists have not been adequately engaged in efforts to manage persons with co-occurring musculoskeletal pain and opioid misuse or OUD. The American Physical Therapy Association (APTA) has emphasized physical therapy over the use of prescription opioids for the management of painful conditions. The APTA, however, does not highlight the important role that physical therapists could play in monitoring opioid use among patients receiving treatment for pain, nor the role that physical therapists should play in screening for opioid misuse. Such screening could facilitate referral of patients suspected misuse to an appropriate provider for formal assessment and treatment. This commentary presents simulated musculoskeletal patient presentations depicting 2 common opioid use states; chronic opioid use and opioid misuse. The cases highlight and interactions that physical therapists could have with these patients and actions that the physical therapist could take when working inter-disciplinarily. Recommendations are provided that aim to increase physical therapists’ knowledge and skills related to managing patients taking prescription opioid medications for pain.

Prenatal substance use is common and can affect maternal and infant health. In addition, prenatal substance use is associated with mental health comorbidities (depression, anxiety, and posttraumatic stress disorder). Unremitting prenatal substance use disorders and mental health comorbidities are associated with poor health outcomes for mothers and exposed infants. The purpose of this study was to examine how any substance use, type of substance use (polysubstance use vs. single substance use), and combinations of mental health comorbidities predict continued use during pregnancy (i.e., use in the 30 days prior to delivery). Health records of patients enrolled in a comprehensive prenatal program for women with substance use disorders were retrospectively analyzed (N = 281). Urine drug screen records were used to determine substance use, and diagnostic codes were used to identify mental health comorbidities. Thirty-seven percent of the sample (n = 105/281) tested positive for substances at admission and 42% (n = 119/281) demonstrated continued use. 30% of the sample (n = 85/281) had depression, anxiety, and posttraumatic stress disorder, 27% (n = 76/281) had two of the three mental health comorbidities, 26% (n = 73/281) had one of the three comorbidities, and 17% (n = 47/281) did not have a mental health comorbidity. Any substance use at admission or having all three mental health conditions were associated with continued use. Substance use at admission and number of mental health conditions were independent predictors of continued use, although substance use was the stronger predictor. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder.