A multi-methods and longitudinal study of patients' perceptions in injectable opioid agonist treatment: Implications for advancing patient-centered methodologies in substance use research

BACKGROUND

Patients' perceptions are vital to the delivery and evaluation of substance use treatment. They are most frequently collected at one time-point and measured using patient satisfaction questionnaires or qualitative methodologies. Interestingly, the findings of these studies often diverge, as satisfaction scores tend to be highly positive, while qualitative findings suggest dissatisfaction and areas for improvement. This divergence limits current understandings of patients' perceptions and their potential change over time in treatment.

OBJECTIVE

This study explores the relationship between open-ended positive and negative perceptions of treatment and patient satisfaction scores over time.

METHODS

The RUTH (Research on the Utilization of Therapeutic Hydromorphone) prospective cohort study included 131 participants receiving injectable diacetylmorphine or hydromorphone in Canada's first injectable opioid agonist treatment (iOAT) program. The study collected the Client Satisfaction Questionnaire (CSQ-8) at eight time-points over an 18-month period. Following a multi-methods approach, the study complemented the CSQ-8 with open-ended positive and negative comments of iOAT. The research team analyzed these comments thematically at each time-point to develop positive and negative perception themes. We then used growth curve modeling to explore the relationship between positive and negative perception themes and patient satisfaction over time.

FINDINGS

Over the eight time-points, six positive and eight negative perception themes emerged, broadly reflecting structural (e.g., expansion of iOAT), process (e.g., schedules), relational (e.g., interactions with providers), and outcome-related (e.g., met/unmet needs) perceptions of iOAT. On average, participants reported high satisfaction (grand mean = 29.2 out of 32), and scores did not significantly change over time. However, we did find significant unexplained variation within participants in their satisfaction trajectories and between participants in their initial satisfaction scores. In conditional growth curve models, the theme "unfavorable interactions with providers" had the strongest independent effect on overall satisfaction trajectories.

CONCLUSIONS

This study provides an example of how open-ended comments can be integrated with patient satisfaction questionnaire data to gather a comprehensive and patient-centered evaluation of substance use treatment. Considering the iOAT context specifically, relational dynamics and daily treatment access were significant predictors of patient satisfaction over time and may be attributes of iOAT that require further investigation.

Conducting sensitivity analyses to identify and buffer power vulnerabilities in studies examining substance use over time

INTRODUCTION

A priori power analysis is increasingly being recognized as a useful tool for designing efficient research studies that improve the probability of robust and publishable results. However, power analyses for many empirical designs in the addiction sciences require consideration of numerous parameters. Identifying appropriate parameter estimates is challenging due to multiple sources of uncertainty, which can limit power analyses' utility.

METHOD

We demonstrate a sensitivity analysis approach for systematically investigating the impact of various model parameters on power. We illustrate this approach using three design aspects of importance for substance use researchers conducting longitudinal studies base rates, individual differences (i.e., random slopes), and correlated predictors (e.g., co-use) and examine how sensitivity analyses can illuminate strategies for controlling power vulnerabilities in such parameters.

RESULTS

Even large numbers of participants and/or repeated assessments can be insufficient to observe associations when substance use base rates are too low or too high. Large individual differences can adversely affect power, even with increased assessments. Collinear predictors are rarely detrimental unless the correlation is high.

CONCLUSIONS

Increasing participants is usually more effective at buffering power than increasing assessments. Research designs can often enhance power by assessing participants twice as frequently as substance use occurs. Heterogeneity should be carefully estimated or empirically controlled, whereas collinearity infrequently impacts power significantly. Sensitivity analyses can identify regions of model parameter spaces that are vulnerable to bad guesses or sampling variability. These insights can be used to design robust studies that make optimal use of limited resources.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

•

Across datasets, subjects’ effective connectivity patterns in the core default mode network showed hemispheric asymmetry.

•

Differences in hemispheric asymmetry was found to be a major source of between-subject variability.

•

In contrast, most subjects showed reliable within-subject hemispheric asymmetry.

•

Differences in preprocessing methods had little effect on connectivity estimates.

•

Bayesian model reduction increased the within- and between-subject consistency of connectivity patterns.

Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder

The ability to predict relapse is a major goal of drug addiction research. Clinical and diagnostic measures are useful in this regard, but these measures do not fully and consistently identify who will relapse and who will remain abstinent. Neuroimaging approaches have the potential to complement these standard clinical measures to optimize relapse prediction. The goal of this review was to survey the existing drug addiction literature that either used a baseline functional or structural neuroimaging phenotype to longitudinally predict a clinical outcome, or that examined test-retest of a neuroimaging phenotype during a course of abstinence or treatment. Results broadly suggested that, relative to individuals who sustained abstinence, individuals who relapsed had (1) enhanced activation to drug-related cues and rewards, but reduced activation to non-drug-related cues and rewards, in multiple corticolimbic and corticostriatal brain regions; (2) weakened functional connectivity of these same corticolimbic and corticostriatal regions; and (3) reduced gray and white matter volume and connectivity in prefrontal regions. Thus, beyond these regions showing baseline group differences, reviewed evidence indicates that function and structure of these regions can prospectively predict - and normalization of these regions can longitudinally track - important clinical outcomes including relapse and adherence to treatment. Future clinical studies can leverage this information to develop novel treatment strategies, and to tailor scarce therapeutic resources toward individuals most susceptible to relapse.

Neuroscience of inhibition for addiction medicine: from prediction of initiation to prediction of relapse

A core deficit in drug addiction is the inability to inhibit maladaptive drug-seeking behavior. Consistent with this deficit, drug-addicted individuals show reliable cross-sectional differences from healthy nonaddicted controls during tasks of response inhibition accompanied by brain activation abnormalities as revealed by functional neuroimaging. However, it is less clear whether inhibition-related deficits predate the transition to problematic use, and, in turn, whether these deficits predict the transition out of problematic substance use. Here, we review longitudinal studies of response inhibition in children/adolescents with little substance experience and longitudinal studies of already addicted individuals attempting to sustain abstinence. Results show that response inhibition and its underlying neural correlates predict both substance use outcomes (onset and abstinence). Neurally, key roles were observed for multiple regions of the frontal cortex (e.g., inferior frontal gyrus, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex). In general, less activation of these regions during response inhibition predicted not only the onset of substance use, but interestingly also better abstinence-related outcomes among individuals already addicted. The role of subcortical areas, although potentially important, is less clear because of inconsistent results and because these regions are less classically reported in studies of healthy response inhibition. Overall, this review indicates that response inhibition is not simply a manifestation of current drug addiction, but rather a core neurocognitive dimension that predicts key substance use outcomes. Early intervention in inhibitory deficits could have high clinical and public health relevance.

Nonparametric Estimation of a Recurrent Survival Function

Recurrent event data are frequently encountered in studies with longitudinal designs. Let the recurrence time be the time between two successive recurrent events. Recurrence times can be treated as a type of correlated survival data in statistical analysis. In general, because of the ordinal nature of recurrence times, statistical methods that are appropriate for standard correlated survival data in marginal models may not be applicable to recurrence time data. Specifically, for estimating the marginal survival function, the Kaplan-Meier estimator derived from the pooled recurrence times serves as a consistent estimator for standard correlated survival data but not for recurrence time data. In this article we consider the problem of how to estimate the marginal survival function in nonparametric models. A class of nonparametric estimators is introduced. The appropriateness of the estimators is confirmed by statistical theory and simulations. Simulation and analysis from schizophrenia data are presented to illustrate the estimators' performance.