Hospital-Initiated Smoking Cessation Among Patients Admitted with Behavioral Health Conditions.

BACKGROUND: Smoking rates among people living with behavioral health conditions (BHC) range from 30 to 65% and are 2-4 times higher than rates found in the general population. Starting tobacco treatment during a hospital stay is effective for smoking cessation, but little is known regarding treatment response among inpatients with BHC. OBJECTIVE: This study pooled data across multiple clinical trials to determine the relative success in quitting among participants with BHC compared to other study participants. PARTICIPANTS: Adults who smoke (≥ 18 years old) from five hospital-based smoking cessation randomized clinical trials. DESIGN: A retrospective analysis using data from the electronic health record to identify participants with primary diagnoses related to BHC. Recruitment and data analysis were conducted from 2011 to 2016. We used propensity score matching to pair patients with BHC to those with similar characteristics and logistic regression to determine differences between groups. MEASURES: The main outcome was self-reported 30-day abstinence 6 months post-discharge. RESULTS: Of 6612 participants, 798 patients had a BHC-related primary diagnosis. The matched sample included 642 pairs. Nearly 1 in 3 reported using tobacco medications after hospitalization, with no significant difference between patients with and without BHC (29.3% vs. 31.5%; OR (95% CI) = 0.90 (0.71, 1.14), p = 0.40). Nearly 1 in 5 patients with BHC reported abstinence at 6 months; however, their odds of abstinence were 30% lower than among people without BHC (OR (95% CI) = 0.70 (0.53,0.92), p = 0.01). CONCLUSION: When offered tobacco treatment, hospitalized patients with BHC were as likely as people without BHC to accept and engage in treatment. However, patients with BHC were less likely to report abstinence compared to those without BHC. Hospitals are a feasible and promising venue for tobacco treatment among inpatients with BHC. More studies are needed to identify treatment approaches that help people with BHC achieve long-term abstinence.

Implementation of statistical features of a Bayesian two-armed responsive adaptive randomization trial with post hoc analysis of time trend drift.

Bayesian adaptive designs with response adaptive randomization (RAR) have the potential to benefit more participants in a clinical trial. While there are many papers that describe RAR designs and results, there is a scarcity of works reporting the details of RAR implementation from a statistical point exclusively. In this paper, we introduce the statistical methodology and implementation of the trial Changing the Default (CTD). CTD is a single-center prospective RAR comparative effectiveness trial to compare opt-in to opt-out tobacco treatment approaches for hospitalized patients. The design assumed an uninformative prior, conservative initial allocation ratio, and a higher threshold for stopping for success to protect results from statistical bias. A particular emerging concern of RAR designs is the possibility that time trends will occur during the implementation of a trial. If there is a time trend and the analytic plan does not prespecify an appropriate model, this could lead to a biased trial. Adjustment for time trend was not pre-specified in CTD, but post hoc time-adjusted analysis showed no presence of influential drift. This trial was an example of a successful two-armed confirmatory trial with a Bayesian adaptive design using response adaptive randomization.

Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives.

Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.

Use of pre-enrollment randomization and delayed consent to maximize participation in a clinical trial of opt-in versus opt-out tobacco treatment.

Background: Enrollment in smoking cessation trials remain sub-optimal. The aim of this analysis was to determine the effectiveness of a modified Zelen's design in engaging hospitalized patients who smoke in a pragmatic OPT-IN versus OPT-OUT tobacco treatment trial. Methods: At bedside, clinical staff screened smokers for eligibility, randomized eligible into study arms, and delivered the appropriate treatment approach. Study staff called randomized patients at one-month post-discharge, debriefed patients on the study design, and collected consent to participate. We used frequencies and percentages for categorical variables and means and standard deviations for quantitative variables to describe the characteristics of those who consented and were enrolled versus those who did not enroll. We also compared the characteristics of participants who consented and those who were reached and explicitly refused consent at one-month follow-up. We used the Cohen's d measure of effect size to evaluate differences. Results: Of the 1,000 randomized, 741 (74.1%) consented to continue in the study at one-month follow-up. One hundred and twenty-seven (12.7%) refused consent and 132 (13.2%) were unreachable. Cohen's d effect size differences between those who consented/enrolled (n = 741) and those who were not enrolled (n = 259) were negligible (<0.2) for age, gender, race/ethnicity, and most forms of insurance. The effect size was small for Medicaid (0.36), and other public insurance (0.48). After excluding those unreached at 1 month (12.7%), there were medium Cohen's d effect size differences between those who consented to participate (n = 741) and those who explicitly refused (n = 127) with respect to age (0.55) and self-pay or no insurance (0.51). There were small to negligible effect size differences with respect to sex, race/ethnicity, and other forms of health insurance. Conclusions: The modified Zelen's design resulted in successful enrollment of most participants who were initially randomized into the trial, including those not motivated to quit.

Direct observation of Medicaid beneficiary attempts to fill prescriptions for nicotine replacement medications.

OBJECTIVES: Although many states have expanded Medicaid coverage of cessation medications, utilization remains low. Anecdotal reports suggest that beneficiaries are at times denied coverage of cessation medications at the pharmacy counter. We conducted an observational community-wide case study of Medicaid beneficiary attempts to fill over-the-counter nicotine replacement therapy at pharmacies. METHODS: We recruited tobacco-using beneficiaries from a Federally Qualified Health Center, whose providers wrote paper prescriptions for nicotine patches. Study staff escorted beneficiaries to all eligible pharmacies (n = 18) in a Midwestern community to observe fill attempts. Study staff recorded encounters via smartphone into a secure database on a university server. RESULTS: Seven of 18 pharmacies (39%) did not fill the prescription on the day of the attempt. Of these, 6 offered to order the patch for pick-up at a later date. All (4/4) chain pharmacies filled the prescription; 2/3 mass merchant pharmacies failed to fill. Combining successful same-day fills with offers to order for pick-up, 17/18 (94%) would ultimately have been able to obtain patches. CONCLUSION: This pilot study found that many beneficiaries left pharmacies without a prescription in hand. Successful same-day fills varied markedly by store type. For people with low incomes, transportation presents a major barrier for delayed pick-up. In addition, delays can fuel ambivalence toward quitting. Future research based on this pilot study might address whether patients who fail to secure a same-day prescription ever fill the prescription and, if not, the degree to which this barrier contributes to success or failure in quitting.

The Effects of Opt-out vs Opt-in Tobacco Treatment on Engagement, Cessation, and Costs: A Randomized Clinical Trial.

Importance: Tobacco use causes 7 million deaths per year; most national guidelines require people who use tobacco to opt in to care by affirming they are willing to quit. Use of medications and counseling is low even in advanced economy countries. Objective: To evaluate the efficacy of opt-out care vs opt-in care for people who use tobacco. Design, Setting, and Participants: In Changing the Default (CTD), a Bayesian adaptive population-based randomization trial, eligible patients were randomized into study groups, treated according to group assignment, and debriefed and consented for participation at 1-month follow-up. A total of 1000 adult patients were treated at a tertiary care hospital in Kansas City. Patients were randomized from September 2016 to September 2020; final follow-up was in March 2021. Interventions: At bedside, counselors screened for eligibility, conducted baseline assessment, randomized patients to study group, and provided opt-out care or opt-in care. Counselors and medical staff provided opt-out patients with inpatient nicotine replacement therapy, prescriptions for postdischarge medications, a 2-week medication starter kit, treatment planning, and 4 outpatient counseling calls. Patients could opt out of any or all elements of care. Opt-in patients willing to quit were offered each element of treatment described previously. Opt-in patients who were unwilling to quit received motivational counseling. Main Outcomes and Measures: The main outcomes were biochemically verified abstinence and treatment uptake at 1 month after randomization. Results: Of a total of 1000 eligible adult patients who were randomized, most consented and enrolled (270 [78%] of opt-in patients; 469 [73%] of opt-out patients). Adaptive randomization assigned 345 (64%) to the opt-out group and 645 (36%) to the opt-in group. The mean (SD) age at enrollment was 51.70 (14.56) for opt-out patients and 51.21 (14.80) for opt-out patients. Of 270 opt-in patients, 123 (45.56%) were female, and of 469 opt-out patients, 226 (48.19%) were female. Verified quit rates for the opt-out group vs the opt-in group were 22% vs 16% at month 1 and 19% vs 18% at 6 months. The Bayesian posterior probability that opt-out care was better than opt-in care was 0.97 at 1 month and 0.59 at 6 months. Treatment use for the opt-out group vs the opt-in group was 60% vs 34% for postdischarge cessation medication (bayesian posterior probability of 1.0), and 89% vs 37% for completing at least 1 postdischarge counseling call (bayesian posterior probability of 1.0). The incremental cost-effectiveness ratio was $678.60, representing the cost of each additional quit in the opt-out group. Conclusions and Relevance: In this randomized clinical trial, opt-out care doubled treatment engagement and increased quit attempts, while enhancing patients' sense of agency and alliance with practitioners. Stronger and longer treatment could increase cessation. Trial Registration: ClinicalTrials.gov Identifier: NCT02721082.

Quitting Smoking before and after Pregnancy: Study Methods and Baseline Data from a Prospective Cohort Study.

Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d. A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.

Non-cancer clinical trials start-up metrics at an academic medical center: Implications for advancing research.

The primary goal for any clinical trial after it receives a funding notification is to receive regulatory approval and initiate the trial for recruitment. Every trial must go through documentation and regulatory process before it can start recruiting participants and collecting data; this initial process of review and approval is known as the study start-up process (SSU). We evaluated the average time taken for studies to receive approvals. Using data from clinical trials conducted at the University of Kansas Medical Center, various times to reach the start of the study were calculated based on the dates of individual study. The results of this analysis showed that chart review studies and investigator-initiated trials had a shorter time to activation than other types of studies. Additionally, single-center studies had a shorter activation time than multi-center studies. The analysis also demonstrated that the overall processing time consistently had been reduced over time.

Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD).

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.