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Objective: To determine whether patients could self-report physical and mental health assessments in the waiting room and whether these assessments would be associated with modified Rankin Scale (mRS) and Quality of Life in Epilepsy (QOLIE-10) scores.Methods: We offered iPad-based surveys to consecutive adult neurology patients at check-in to collect patient-reported outcome measures (PROMs). We collected demographic and clinical data on 6,075 patients through survey or administrative claims and PROMs from participating patients. We compared demographic characteristics of participants and nonparticipants and tested associations between physical and mental health scores and mRS and QOLIE-10.Results: Of 6,075 patients seen by neurologists during the study period, 2,992 (49.3%) participated in the survey. Compared to nonparticipating patients, participating patients more often were privately insured (53.5% vs 42.7%, p , 0.01), married (51.5% vs 47.9%, p , 0.01), and seen in general neurology (nonsubspecialty) clinics (53.1% vs 46.6%, p , 0.01) and more likely to report English as their preferred language (50.1% vs 38.4%, p , 0.01). Participating patients had a mean physical health T score of 28.7 (SD 15) and mental health T score of 33 (SD 15), which were 3 and 2 SD worse than the average for the US general population, respectively.

ObjectiveMany effective medical therapies are available for treating neurological diseases, but these therapies tend to be expensive and adherence is critical to their effectiveness. We used patient-reported data to examine the frequency and determinants of financial barriers to medication adherence among individuals treated for neurological disorders.Patients and methodsPatients completed cross-sectional surveys on iPads as part of routine outpatient care in a neurology clinic. Survey responses from a 3-month period were collected and merged with administrative sources of demographic and clinical information (eg, insurance type). We explored the association between patient characteristics and patient-reported failure to refill prescription medication due to cost in the previous 12 months, termed here as “nonadherence”.ResultsThe population studied comprised 6075 adults who were presented between July and September 2015 for outpatient neurology appointments. The mean age of participants was 56 (standard deviation: 18) years, and 1613 (54%) were females. The patients who participated in the surveys (2992, 49%) were comparable to nonparticipants with respect to gender and ethnicity but more often identified English as their preferred language (94% vs 6%, p<0.01). Among respondents, 9.8% (n=265) reported nonadherence that varied by condition. These patients were more frequently Hispanic (16.7% vs 9.8% white, p=0.01), living alone (13.9% vs 8.9% cohabitating, p<0.01), and preferred a language other than English (15.3% vs 9.4%, p=0.02).ConclusionOverall, the magnitude of financial barriers to medication adherence appears to vary across neurological conditions and demographic characteristics.

Introduction Longitudinal surveys provide data to estimate transition probabilities between cigarette smoking, e-cigarette use, and dual use of both, facilitating projections of future use and the impact of policies. Methods We fit a continuous time Markov multi-state model for youth (ages 12-17y) and adults (≥18y) in Waves 1-4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal survey and estimated smoking and e-cigarette transition frequencies, including initiation, cessation, and relapse. We validated transition frequency results in a microsimulation model by projecting smoking and e-cigarette use prevalence over time. Results There was more volatility in smoking and e-cigarette use among youth than among adults. For youth never smokers, annual smoking initiation among never/current/former e-cigarette users occurred in 0.4% (95% CI 0.2-0.6%)/8.8% (7.0-10.7%)/3.1% (2.1-4.2%), and current e-cigarette users were more likely to quit e-cigarettes than to initiate smoking (absolute difference in annual probability 46.5%, 38.7-54.2%). For adult current smokers, annual smoking cessation among never/current/former e-cigarette users occurred in 22.6% (20.9-24.3%)/14.5% (11.5-17.4%)/15.1% (12.1-18.2%). For adult current dual users, 14.5% quit smoking and 49.5% quit e-cigarettes annually. For adult former smokers, annual smoking relapse among never/current/former e-cigarette users occurred in 17.7% (15.8-19.6%)/29.3% (23.8-34.7%)/32.8% (27.1-38.6%). Using these transition probabilities in a microsimulation model accurately projected smoking and e-cigarette use prevalence at 12 and 24 months compared to PATH empirical data (root-mean-square error <0.7%). Discussion PATH Waves 1-4.5 contain sufficient data to generate smoking and e-cigarette use transition frequency estimates for youth and adults in a microsimulation model. E-cigarette use among youth is especially volatile.

Background Men who have sex with men (MSM) on antiretroviral therapy (ART) are at risk for multimorbidity as life expectancy increases. Simulation models can project population sizes and age distributions to assist with health policy planning. Methods We populated the CEPAC-US model with CDC data to project the HIV epidemic among MSM in the US. The PEARL model was predominantly informed by NA-ACCORD data (2009-2017). We compared projected population sizes and age distributions of MSM receiving ART (2021-2031) and investigated how parameters and assumptions affected results. Results We projected an aging and increasing population of MSM on ART: CEPAC-US: mean ± SD age, 48.6 ± 13.7y [2021] vs. 53.9 ± 15.0y [2031]; PEARL: 46.7 ± 13.2y vs. 49.2 ± 14.6y. We projected 548,800 MSM on ART (147,020 ≥ 65y) in 2031 (CEPAC-US) and 599,410 (113,400 ≥ 65y) (PEARL). Compared with PEARL, CEPAC-US projected a smaller population of MSM on ART by 2031 and a slower increase in population size, driven by higher estimates of disengagement in care and mortality. Conclusion Findings from two structurally distinct microsimulation models suggest that the MSM population receiving ART in the US will increase and age over the next decade. Subgroup-specific data regarding engagement in care and mortality can improve projections and inform health care policy planning.

Introduction Estimates of initiation, cessation, and relapse rates of tobacco cigarette smoking and e-cigarette use can facilitate projections of longer-term impact of their use. We aimed to derive transition rates and apply them to validate a microsimulation model of tobacco that newly incorporated e-cigarettes. Methods We fit a Markov multi-state model (MMSM) for participants in Waves 1–4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal study. The MMSM had nine cigarette smoking and e-cigarette use states (current/former/never use of each), 27 transitions, two sex categories, and four age categories (youth: 12-17y; adults: 18-24y/25-44y/≥45y). We estimated transition hazard rates, including initiation, cessation, and relapse. We then validated the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model, by: (a) using transition hazard rates derived from PATH Waves 1–4.5 as inputs, and (b) comparing STOP-projected prevalence of smoking and e-cigarette use at 12 and 24 months to empirical data from PATH Waves 3 and 4. We compared the goodness-of-fit of validations with “static relapse” and “time-variant relapse,” wherein relapse rates did not or did depend on abstinence duration. Results Per the MMSM, youth smoking and e-cigarette use was generally more volatile (lower probability of maintaining the same e-cigarette use status over time) than that of adults. Root-mean-squared error (RMSE) for STOP-projected versus empirical prevalence of smoking and e-cigarette use was <0.7% for both static and time-variant relapse simulations, with similar goodness-of-fit (static relapse: RMSE 0.69%, CI 0.38–0.99%; time-variant relapse: RMSE 0.65%, CI 0.42–0.87%). PATH empirical estimates of prevalence of smoking and e-cigarette use were mostly within the margins of error estimated by both simulations. Discussion A microsimulation model incorporating smoking and e-cigarette use transition rates from a MMSM accurately projected downstream prevalence of product use. The microsimulation model structure and parameters provide a foundation for estimating the behavioral and clinical impact of tobacco and e-cigarette policies.

326 Background: The immune checkpoint inhibitors (ICIs) confer a risk of unique inflammatory immune-related adverse events (irAEs), which are highly distinct from the adverse events historically observed with cytotoxic therapy. To develop a strategy for easier identification and mitigation of irAEs, we sought to understand the frequency of ED visits and hospitalizations in 90 days following ICI start by implementing a rapid learning system (RLS). Methods: We convened an Immunotherapy Toxicities Management Committee with representatives from the Center for Immuno-Oncology, Quality and Patient Safety, and Informatics to draft a series of recommendations for the development of an irAE rapid learning system. The Committee requested an audit of all irAEs between June 2015 and April 2018 by drug, event type (clinical diagnosis), outcomes (including ED visits, hospitalization with length of stay, and death), and time-frame (days since beginning ICI course). An automated pipeline was created to merge structured data from the Electronic Health Record (Epic) and billing system (EPSi). This data was used to design a tool which consisted of an automated dashboard to monitor patient and enable interventions. Results: Over the course of 3 years, a total of 2,020 unique patients receiving ICIs were seen. 918 were treated with Pembrolizumab (45.4%), 768 with Nivolumab (38.0%), 234 with Atezolizumab (11.6%), 111 with Nivolumab & Ipilmumab (5.6%), 68 with Ipilimumab (3.4%), 9 with Durvalumab (0.4%), and 9 with Avelumab (0.4%). ED visits and hospitalization rates over 90 days were similar among the three most prescribed therapies, ranging from 332 unique patient events in the Pembrolizumab Cohort (36.1%) to 96 unique patient events in the Atezolizumab cohort (41.0%). Conclusions: The dashboard is effective tool to build a RLS for irAEs. The immediate output of this tool is using natural language processing (NLP) to distinguish between irAEs related ED visits and hospitalizations or regular disease progression, and measure the impact of interventions including (a) developing standardized algorithms for monitoring for irAEs, (b) designing an educational program for providers, and (c) developing an inpatient and outpatient immunotherapy toxicity management service.