Gaps in the Use of Long-Acting Opioids Within Intervals of Consecutive Days Among Cancer Outpatients Using Electronic Pill Caps

Potential role of cannabis in ameliorating observed racialized disparities in cancer pain management

Cancer-related pain affects a large proportion of all patients with cancer yet remains inadequately managed, particularly among patients from certain racialized backgrounds. Recently, there has been increased research and clinical interest in the use of medical cannabis for cancer pain management, including its potential to ameliorate race-based disparities in cancer pain control. Although medical cannabis is not currently a US Food and Drug Administration-approved treatment option for cancer-related pain, many oncologists discuss and recommend its use with their patients, underscoring the need for researchers and clinicians to proactively identify barriers to cannabis for cancer pain management that may disproportionately affect patients from certain racial groups. In this commentary, we highlight challenges that patients from racialized backgrounds may face when incorporating cannabis into their palliative care regimens and discuss opportunities for researchers and clinicians to address these challenges should medical cannabis become a recommended treatment option for cancer pain management. In particular, we identify challenges at the structural (eg, lack of insurance coverage), clinician (eg, racialized stereotypes regarding addiction and pain), and individual (eg, internalized stigma) levels and emphasize the importance of multilevel approaches in combating these challenges as the evidence base regarding medical cannabis and its potential harms and therapeutic benefits continues to accumulate.

Trajectories and predictors of high-occurrence pain flares in ambulatory cancer patients on opioids

BACKGROUND

Pain flares have a substantive impact on the quality of life and well-being of patients with cancer. We identified longitudinal trajectories (clusters) of cancer pain flares in ambulatory patients and sociodemographic and clinical predictors of these trajectories.

METHODS

In a prospective cohort study using ecological momentary assessment (mEMA), we collected patient-reported daily pain flare ratings data over 5 months and identified predictors and correlates using validated measures.

RESULTS

The mean age of the sample (N = 270) was 60.9 years (SD = 11.2), 64.8% were female, and 32.6% self-identified as African American. Four pain flare clusters were identified. The "high-occurrence" cluster (23% of patients) experienced 5.5 (SD = 5.47) daily flares, whereas low-moderate clusters (77%) reported 2.4 (SD = 2.74) daily flares (P < .000). Those in the high-occurrence cluster reported higher pain scores (P = .000), increased pain-related interference (P = .000), depressive symptoms (P = .023), lower quality of life (P = .001), and reduced pain self-efficacy (P = .006). Notably, 67.2% of those prescribed opioids as needed (PRN only) were in the high-occurrence pain flare cluster, compared with 27.9% with PRN and around-the-clock opioid prescriptions (P = .024). Individual predictors of high-occurrence pain flares were income below $30 000, unemployment, being African American, lower education level, Medicaid insurance, current opioid misuse (COMM), baseline inpatient hospital stay duration, and PRN-only opioid regimen. In the multiple predictor model, lower education level, unemployment, COMM score, extended inpatient duration, and PRN-only opioid regimen remained significant.

CONCLUSION

In ambulatory patients with cancer, high occurrence of pain flares may be mitigated by attention to opioid prescription factors and addressing social determinants of health needs of underserved patients.

Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain Medication Usage

The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non-constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time, because in example analyses, it either generates better LCV scores or more parsimonious models and requires substantially less time.