Effects of U.S. State Medical Cannabis Laws on Treatment of Chronic Noncancer Pain.

BACKGROUND: State medical cannabis laws may lead patients with chronic noncancer pain to substitute cannabis in place of prescription opioid or clinical guideline-concordant nonopioid prescription pain medications or procedures. OBJECTIVE: To assess effects of state medical cannabis laws on receipt of prescription opioids, nonopioid prescription pain medications, and procedures for chronic noncancer pain. DESIGN: Using data from 12 states that implemented medical cannabis laws and 17 comparison states, augmented synthetic control analyses estimated laws' effects on receipt of chronic noncancer pain treatment, relative to predicted treatment receipt in the absence of the law. SETTING: United States, 2010 to 2022. PARTICIPANTS: 583 820 commercially insured adults with chronic noncancer pain. MEASUREMENTS: Proportion of patients receiving any opioid prescription, nonopioid prescription pain medication, or procedure for chronic noncancer pain; volume of each treatment type; and mean days' supply and mean morphine milligram equivalents per day of prescribed opioids, per patient in a given month. RESULTS: In a given month during the first 3 years of law implementation, medical cannabis laws led to an average difference of 0.05 percentage points (95% CI, -0.12 to 0.21 percentage points), 0.05 percentage points (CI, -0.13 to 0.23 percentage points), and -0.17 percentage points (CI, -0.42 to 0.08 percentage points) in the proportion of patients receiving any opioid prescription, any nonopioid prescription pain medication, or any chronic pain procedure, respectively, relative to what we predict would have happened in that month had the law not been implemented. LIMITATIONS: This study used a strong nonexperimental design but relies on untestable assumptions involving parallel counterfactual trends. Statistical power is limited by the finite number of states. Results may not generalize to noncommercially insured populations. CONCLUSION: This study did not identify important effects of medical cannabis laws on receipt of opioid or nonopioid pain treatment among patients with chronic noncancer pain. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Effects of State Opioid Prescribing Laws on Use of Opioid and Other Pain Treatments Among Commercially Insured U.S. Adults.

BACKGROUND: There is concern that state laws to curb opioid prescribing may adversely affect patients with chronic noncancer pain, but the laws' effects are unclear because of challenges in disentangling multiple laws implemented around the same time. OBJECTIVE: To study the association between state opioid prescribing cap laws, pill mill laws, and mandatory prescription drug monitoring program query or enrollment laws and trends in opioid and guideline-concordant nonopioid pain treatment among commercially insured adults, including a subgroup with chronic noncancer pain conditions. DESIGN: Thirteen treatment states that implemented a single law of interest in a 4-year period and unique groups of control states for each treatment state were identified. Augmented synthetic control analyses were used to estimate the association between each state law and outcomes. SETTING: United States, 2008 to 2019. PATIENTS: 7 694 514 commercially insured adults aged 18 years or older, including 1 976 355 diagnosed with arthritis, low back pain, headache, fibromyalgia, and/or neuropathic pain. MEASUREMENTS: Proportion of patients receiving any opioid prescription or guideline-concordant nonopioid pain treatment per month, and mean days' supply and morphine milligram equivalents (MME) of prescribed opioids per day, per patient, per month. RESULTS: Laws were associated with small-in-magnitude and non-statistically significant changes in outcomes, although CIs around some estimates were wide. For adults overall and those with chronic noncancer pain, the 13 state laws were each associated with a change of less than 1 percentage point in the proportion of patients receiving any opioid prescription and a change of less than 2 percentage points in the proportion receiving any guideline-concordant nonopioid treatment, per month. The laws were associated with a change of less than 1 in days' supply of opioid prescriptions and a change of less than 4 in average monthly MME per day per patient prescribed opioids. LIMITATIONS: Results may not be generalizable to non-commercially insured populations and were imprecise for some estimates. Use of claims data precluded assessment of the clinical appropriateness of pain treatments. CONCLUSION: This study did not identify changes in opioid prescribing or nonopioid pain treatment attributable to state laws. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials.

There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized interventions to individuals' smartphones. A just-in-time adaptive intervention (JITAI) adjusts to an individual's changing state and/or context to provide the right treatment, at the right time, in the right place. Micro-randomized trials (MRTs) allow for the collection of data which aid in the construction of an optimized JITAI by sequentially randomizing participants to different treatment options at each of many decision points throughout the study. Often, this data is collected passively using a mobile phone. To assess the causal effect of treatment on a near-term outcome, care must be taken when designing the data collection system to ensure it is of appropriately high quality. Here, we make several recommendations for collecting and managing data from an MRT. We provide advice on selecting which features to collect and when, choosing between "agents" to implement randomization, identifying sources of missing data, and overcoming other novel challenges. The recommendations are informed by our experience with HeartSteps, an MRT designed to test the effects of an intervention aimed at increasing physical activity in sedentary adults. We also provide a checklist which can be used in designing a data collection system so that scientists can focus more on their questions of interest, and less on cleaning data.

Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps.

BACKGROUND: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context. PURPOSE: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention. METHODS: We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS: Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION: Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION: This study was registered on ClinicalTrials.gov number NCT03225521.

Design and Analysis Considerations for Comparing Dynamic Treatment Regimens with Binary Outcomes from Sequential Multiple Assignment Randomized Trials.

In behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use 'weighted and replicated' estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the 'weighted and replicated' regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism.

Prospective assessment of patient-reported outcomes and estradiol and drug concentrations in patients experiencing toxicity from adjuvant aromatase inhibitors.

PURPOSE: Aromatase inhibitors (AI), which decrease circulating estradiol concentrations in post-menopausal women, are associated with toxicities that limit adherence. Approximately one-third of patients will tolerate a different AI after not tolerating the first. We report the effect of crossover from exemestane to letrozole or vice versa on patient-reported outcomes (PROs) and whether the success of crossover is due to lack of estrogen suppression. METHODS: Post-menopausal women enrolled on a prospective trial initiating AI therapy for early-stage breast cancer were randomized to exemestane or letrozole. Those that discontinued for intolerance were offered protocol-directed crossover to the other AI after a washout period. Changes in PROs, including pain [Visual Analog Scale (VAS)] and functional status [Health Assessment Questionnaire (HAQ)], were compared after 3 months on the first versus the second AI. Estradiol and drug concentrations were measured. RESULTS: Eighty-three patients participated in the crossover protocol, of whom 91.3% reported improvement in symptoms prior to starting the second AI. Functional status worsened less after 3 months with the second AI (HAQ mean change AI #1: 0.2 [SD 0.41] vs. AI #2: -0.05 [SD 0.36]; p = 0.001); change in pain scores was similar between the first and second AI (VAS mean change AI #1: 0.8 [SD 2.7] vs. AI #2: -0.2 [SD 2.8]; p = 0.19). No statistical differences in estradiol or drug concentrations were found between those that continued or discontinued AI after crossover. CONCLUSIONS: Although all AIs act via the same mechanism, a subset of patients intolerant to one AI report improved PROs with a different one. The mechanism of this tolerance remains unknown, but does not appear to be due to non-adherence to, or insufficient estrogen suppression by, the second AI.

Sequential Multiple Assignment Randomized Trials: An Opportunity for Improved Design of Stroke Reperfusion Trials.

BACKGROUND: Modern clinical trials in stroke reperfusion fall into 2 categories: alternative systemic pharmacological regimens to alteplase and "rescue" endovascular approaches using targeted thrombectomy devices and/or medications delivered directly for persistently occluded vessels. Clinical trials in stroke have not evaluated how initial pharmacological thrombolytic management might influence subsequent rescue strategy. A sequential multiple assignment randomized trial (SMART) is a novel trial design that can test these dynamic treatment regimens and lead to treatment guidelines that more closely mimic practice. AIM: To characterize a SMART design in comparison to traditional approaches for stroke reperfusion trials. METHODS: We conducted a numerical simulation study that evaluated the performance of contrasting acute stroke clinical trial designs of both initial reperfusion and rescue therapy. We compare a SMART design where the same patients are followed through initial reperfusion and rescue therapy within 1 trial to a standard phase III design comparing 2 reperfusion treatments and a separate phase II futility design of rescue therapy in terms of sample size, power, and ability to address particular research questions. RESULTS: Traditional trial designs can be well powered and have optimal design characteristics for independent treatment effects. When treatments, such as the reperfusion and rescue therapies, may interact, commonly used designs fail to detect this. A SMART design, with similar sample size to standard designs, can detect treatment interactions. CONCLUSIONS: The use of SMART designs to investigate effective and realistic dynamic treatment regimens is a promising way to accelerate the discovery of new, effective treatments for stroke.

Patient-Reported Outcomes and Early Discontinuation in Aromatase Inhibitor-Treated Postmenopausal Women With Early Stage Breast Cancer.

BACKGROUND: Early discontinuation of aromatase inhibitors (AIs) is common and leads to poor outcomes but is challenging to predict. In the Exemestane and Letrozole Pharmacogenetics trial, a high rate of early discontinuation due to intolerance was observed. We hypothesized that early changes in patient-reported outcomes (PROs) predict AI discontinuation and that biochemical factors are associated with changes in PROs. PATIENTS AND METHODS: Postmenopausal women with early-stage breast cancer enrolled in a prospective randomized trial of exemestane versus letrozole completed questionnaires at baseline and serially over 24 months to assess overall quality of life (EuroQOL Visual Analog Scale [VAS]); mood; and multiple symptoms, including a musculoskeletal symptom cluster. A joint mixed-effects/survival model was used to estimate the effect of the change in PROs on AI discontinuation. Associations between biochemical factors and change in PROs were examined. RESULTS: A total of 490 patients were analyzed. Worsening of EuroQOL VAS and the musculoskeletal cluster were associated with the highest risk for early discontinuation (hazard ratio [HR], 2.77 [95% confidence interval (CI), 2.72-2.81; p = .015]; HR, 4.39 [95% CI, 2.40-8.02; p < .0001], respectively). Pharmacokinetics and estrogen metabolism were not consistently associated with change in PRO measures. No clinically significant differences in any PRO between AIs were observed. CONCLUSION: Changes in PROs early during AI therapy were associated with treatment discontinuation. Identification of these changes could be used to target interventions in patients at high risk for early discontinuation. IMPLICATIONS FOR PRACTICE: Early changes in patient-reported outcomes (PROs) can predict nonpersistence to aromatase inhibitor therapy. If used in clinical practice, PROs might identify women at highest risk for early discontinuation and allow for interventions to improve tolerance before significant toxicities develop. Further research is needed to improve capturing PROs in routine clinical practice.

Evidence for association of SNPs in ABCB1 and CBR3, but not RAC2, NCF4, SLC28A3 or TOP2B, with chronic cardiotoxicity in a cohort of breast cancer patients treated with anthracyclines.

AIMS: Validation of associations for SNPs in RAC2, NCF4 and SLC28A3, identification of a novel association with a TOP2B SNP and screening 23 SNPs putatively relevant to anthracycline-induced cardiotoxicity. PATIENTS & METHODS: A total of 166 breast cancer patients treated with doxorubicin underwent echocardiogram, including 19 cases with systolic dysfunction (ejection fraction <55%) and 147 controls. Four high priority SNPs were tested in the primary analysis, with appropriate statistical correction, and 23 additional SNPs were screened in an uncorrected secondary analysis. RESULTS: Previously reported associations for RAC2, NCF4 and SLC28A3 could not be validated and a novel association with TOP2B was not discovered in this cohort (all p > 0.05), likely due to inadequate power. Two SNPs were identified in the uncorrected secondary analysis including a protective SNP in ABCB1 (3435C>T, p = 0.049) and a risk allele in CBR3 (V244M, p = 0.012). CONCLUSION: The associations reported in prior publications and those discovered in this secondary analysis require further replication in independent cohorts.

Tris(3,5-di-tert-butylcatecholato)molybdenum(VI): Lewis acidity and nonclassical oxygen atom transfer reactions.

In the solid state, tris(3,5-di-tert-butylcatecholato)molybdenum(VI) forms a dimer with seven-coordinate molybdenum and bridging catecholates. NMR spectroscopy indicates that the dimeric structure is retained in solution. The molybdenum center has a high affinity for Lewis bases such as pyridine or pyridine-N-oxide, forming seven-coordinate monomers with a capped octahedral geometry, as illustrated by the solid-state structure of (3,5-(t)Bu2Cat)3Mo(py). Structural data indicate that the complexes are best considered as Mo(VI) with substantial π donation from the nonbridging catecholates to molybdenum. Both the dimeric and the monomeric tris(catecholates) react rapidly with water to form free catechol and oxomolybdenum bis(catecholate) complexes. Monooxomolybdenum complexes are also obtained, more slowly, on reaction with dioxygen, with organic products consisting mostly of 3,5-di-tert-butyl-1,2-benzoquinone with minor amounts of the extradiol oxidation product 4,6-di-tert-butyl-1-oxacyclohepta-4,6-diene-2,3-dione. The pyridine-N-oxide complex reacts on heating (with excess pyO) to form initially (3,5-(t)Bu2Cat)2MoO(Opy) and ultimately MoO3(Opy), with quinone and free pyridine as the only organic products. The decay of (3,5-(t)Bu2Cat)3Mo(Opy) shows an accelerated, autocatalytic profile because the oxidation of its product, (3,5-(t)Bu2Cat)2MoO(Opy), produces an oxo-rich, catecholate-poor intermediate which rapidly conproportionates with (3,5-(t)Bu2Cat)3Mo(Opy), providing an additional pathway for its conversion to the mono-oxo product. The tris(catecholate) fragment Mo(3,5-(t)Bu2Cat)3 deoxygenates Opy in this nonclassical oxygen atom transfer reaction slightly less rapidly than does its oxidized product, MoO(3,5-(t)Bu2Cat)2.

Nonclassical oxygen atom transfer reactions of oxomolybdenum(VI) bis(catecholate).

Mechanistic studies indicate that the oxomolybdenum(vi) bis(3,5-di-tert-butylcatecholate) fragment deoxygenates pyridine-N-oxides in a reaction where the oxygen is delivered to molybdenum but the electrons for substrate reduction are drawn from the bound catecholate ligands, forming 3,5-di-tert-butyl-1,2-benzoquinone.