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Obesity (body mass index [BMI]≥30kg/mWe sought to identify distinct long-term BMI trajectories and examined associations with demographic and clinical characteristics.The design was latent trajectory modeling over 10 years of a retrospective cohort. Subgroups were identified via latent class growth mixture models, separately by sex. Weighted multinomial logistic regressions identified factors associated with subgroup membership.Participants were a retrospective cohort of 292,331 males and 62,898 females enrolled in VA.The main outcome measure was 6-month average VA-measured BMI over the course of 10 years. Additional electronic health record measures on demographic, clinical, and services utilization characteristics were also used to characterize latent trajectories.Four trajectories were identified for men and for women, corresponding to standard BMI categories "normal weight" (BMI25), "overweight" (BMI 25-29.99), and "with obesity" (BMI ≥30): "normal weight" and increasing (males: 28.4%; females: 22.8%), "overweight" and increasing (36.4%; 35.6%), "with obesity" and increasing (33.6%; 40.0%), and "with obesity" and stable (males: 1.6%) or decreasing (females: 1.6%). Race, ethnicity, comorbidities, mental health diagnoses, and mental health service utilization discriminated among classes.BMI in the 10 years following VA enrollment increased modestly. VA should continue prioritizing weight management interventions to the large number of veterans with obesity upon VA enrollment, because the majority remain with obesity.

In the setting of uncertain efficacy and additional, unreimbursed cost, use of an embolic protection device (EPD) during transcatheter aortic valve replacement (TAVR) has had variable uptake. The Centers for MedicareMedicaid Services (CMS) instituted a new technology add-on payment to cover EPD use in October 2018.To evaluate the association between CMS TAVR reimbursement rates and EPD use.This cohort study used the Society for Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy registry to identify patients who underwent TAVR between January 2018 and September 2019. Analysis took place between July 2020 and February 2022.The association between EPD use and CMS reimbursement was assessed using multivariable logistic regression models adjusted for patient characteristics (model 1) and patient/hospital (annualized TAVR volume and teaching status) characteristics (model 2).Among 511 institutions, CMS reimbursement for TAVR ranged from $28 062 to $111 280 with a median (IQR) of $45 884 ($40 331-$53 627). Among 84 353 patients (median [IQR] age, 81.0 [75.0-86.0] years; 46 247 male individuals [54.8%]; 3958 [4.7%] of Hispanic or Latino ethnicity; 78 170 White individuals [92.7%]) treated at the sites, 6012 (7.1%) underwent TAVR with EPD. Patient characteristics associated with EPD use included prior stroke (adjusted odds ratio [aOR], 1.13 [95% CI, 1.00-1.27]; P = .048), female sex (aOR, 0.85 [95% CI, 0.78-0.93]; P .001), hemodialysis (aOR, 0.52 [95% CI, 0.40-0.68]; P .001), and shock (aOR, 0.62 [95% CI, 0.41-0.94]; P = .03). Higher CMS reimbursement up to $50 000 per TAVR was associated with greater likelihood of EPD use in model 1 (per $1000; aOR, 1.08 [95% CI, 1.01-1.16]; P = .02). However, this association was no longer apparent after adjusting for site characteristics (model 2; aOR, 1.03 [95% CI, 0.96-1.11]; P = .38). Higher TAVR volume was associated with increased EPD use (per 25 TAVRs; aOR, 1.15 [95% CI, 1.09-1.21]; P .001). There was no significant change in the odds of EPD uptake before vs after institution of the CMS new technology add-on payment across tertiles of CMS TAVR reimbursement (Wald χ2 = 3.59; P = .17).EPD use during TAVR remains infrequent and is associated with multiple patient and site characteristics. While CMS reimbursement varies significantly across institutions, TAVR case volume, rather than CMS TAVR reimbursement or the CMS new technology add-on payment, appears to be the predominant factor associated with EPD use. Ongoing work is needed to understand the economic drivers that contribute to the association between procedural volume and EPD use.

Polypharmacy and the inappropriate use of medications is a prevalent, potentially harmful, and costly problem that can be addressed by deprescribing, which is defined as the “systematic process of identifying and discontinuing drugs when existing or potential harms outweigh existing or potential benefits within the context of an individual patient's care goals, functional status, life expectancy, values, and preferences.”1 Evidence from clinical trials has demonstrated that deprescribing interventions can reduce the number of potentially inappropriate medications; yet the next question of whether such interventions are cost-effective is often unanswered.2 In this commentary, we describe current practices in assessing the value of deprescribing interventions and their limitations, and then suggest potential improvements.

BackgroundThe Veterans Health Administration (VHA) is the largest integrated health care system in the United States (US). Among VHA patients, the rate of use of concurrent chemoradiation therapy (CCRT) among those with unresectable, stage III non-small cell lung cancer (NSCLC) is unknown. The objective was to report recent CCRT treatment patterns in VHA patients and identify characteristics associated with receipt of CCRT.MethodsUsing Department of Veteran Affairs (VA) Cancer Registry System data linked to VA electronic medical records, we determined rates of CCRT, sequential CRT (SCRT), radiation therapy (RT) only, chemotherapy (CT) only, and neither treatment.ResultsAmong 4054 VHA patients who met study criteria, CCRT rates slightly increased from 44 to 50% between 2013 and 2017. Factors associated with decreased odds of CCRT receipt compared to any other treatment included increasing age (adjusted odds ratio [aOR] per 10 years = 0.67; 95% CI: 0.60–0.76) and Charlson-Deyo comorbidity score (aOR = 0.94; 95% CI: 0.91–0.97). White race was associated with increased odds of CCRT receipt (aOR = 1.24; 95% CI: 1.004–1.53). In a chart review sample of 200 patients, less than half (n = 85) had a documented reason for not receiving CCRT. Among these, 29% declined treatment, and 71% did not receive CCRT due to “not being a candidate” for reasons related to frailty or lung nodules being too far apart for radiation therapy.ConclusionsCCRT rates among VHA patients with unresectable, stage III NSCLC slightly increased from 2013 to 2017; however in 2017, only half were receiving CCRT. Older patients and those with multiple comorbidities were less likely to receive CCRT and even when controlling for these factors, non-white patients were less likely to receive CCRT.

BACKGROUND: The prevalence of financial medication assistance (FMA), including patient assistance programs, coupons/copayment cards, vouchers, discount cards, and programs/pharmacy services that help patients apply for such programs, has increased. The impact of FMA on medication adherence and persistence has not been synthesized. OBJECTIVE: The primary objective of this study was to review published studies evaluating the impact of FMA on the three phases of medication adherence (initiation [or primary adherence], implementation [or secondary adherence], and discontinuation) and persistence. Among these studies, the secondary objective was to report the impact of FMA on patient out-of-pocket costs and clinical outcomes. METHODS: A systematic review was performed using MEDLINE and Web of Science. RESULTS: Of 656 articles identified, eight studies met all inclusion criteria. Seven studies examined FMA for medications treating cardiovascular diseases, while one study assessed FMA for cancer medications. Among included studies, FMA had a positive impact on medication adherence or persistence, and most measured this impact over one year or less. Of the three phases of medication adherence, implementation (5 of 8) was most commonly reported, followed by discontinuation (3 of 8), and then initiation (1 of 8). Regarding implementation, users of FMA had a higher mean medication possession ratio (MPR) than nonusers, ranging from 7 to 18 percentage points higher. The percentage of patients who discontinued medication was 7 percentage points lower in users of FMA versus nonusers for cardiovascular disease states. In one cancer study, FMA had a larger impact on initiation than discontinuation, ie, compared to nonusers, users of FMA were less likely to abandon an initial prescription (risk ratio= 0.12, 95% confidence interval [CI]: 0.08–0.18), and this effect was larger than the decreased likelihood of discontinuing the medication (hazard ratio = 0.76, 95% CI: 0.66–0.88). In 3 of 8 studies reporting on medication persistence, FMA increased the odds of medication persistence for one year ranged from 11% to 47%, depending on the study. In addition to adherence, half of the studies reported on FMA impacts on patient out-of-pocket costs and 3 of 8 studies reported on clinical outcomes. Impacts on patient out-of-pocket costs were mixed; two studies reported that out-of-pocket costs were higher for users of a coupon or a voucher versus nonusers, one study reported the opposite, and one study reported null effects. Impacts on clinical outcomes were either positive or null. CONCLUSIONS: We found that FMA has positive impacts on all phases of medication adherence as well as medication persistence over one year. Future studies should assess whether FMA has differential impacts based on phase of medication adherence and report on its longer-term (ie, beyond one year) impacts on medication adherence.

BACKGROUND: Tenofovir alafenamide (TAF) is a new formulation of tenofovir disoproxil fumarate (TDF) that was approved in 2015. While clinical trial evidence suggests that TAF has more favorable outcomes related to kidney injury and loss of bone mineral density, TAF also leads to higher lipid levels compared to TDF. OBJECTIVE: To determine prescribing rates of TDF and TAF among new recipients from 2014 to 2018 in a large academic health system, and compare baseline patient characteristics of those newly prescribed TDF versus TAF before and after the approval of TAF in November 2015. METHODS: Electronic health record data were used to identify new recipients of TDF or TAF from 2014 to 2018 and describe their total monthly TDF and TAF prescriptions by indication. Patient characteristics were compared among new recipients of TDF before November 2015, new recipients of TDF after November 2015, and new recipients of TAF. RESULTS: Monthly TAF prescribing rates increased to match TDF prescribing rates by April 2018 (82 versus 88 prescriptions per month). TAF recipients and new recipients of TDF before November 2015 had similar racial distributions; both of these groups were more likely to be Black compared to new recipients of TDF after November 2015 (55% and 53% versus 37%; p

BACKGROUND: In 2014, qualified health plans sold in the Affordable Care Act (ACA) marketplaces were accused of providing drug coverage that was too restrictive and costly. After the change in admin...

There is a paucity of studies validating budget impact models. The lack of such studies may contribute to the underuse of budget impact models by payers in formulary decision making.To assess the face validity, internal verification, and predictive validity of a previously published model that assessed the budgetary impact of antidiabetic formulary changes.4 experts with diverse backgrounds were selected and asked questions regarding the face validity of the structure/conceptual model, input data, and results from the budget impact model. To assess internal verification, structured "walk-throughs," unit tests, extreme condition tests, traces, replication tests, and double programming techniques were used. The predictive validity of the model was evaluated by comparing the predicted and realized budget using mean absolute scaled error. "Realized" budgetary impact of the formulary changes was calculated by taking the difference between realized budget in the year after the formulary changes and the budget had there been no formulary changes (i.e., the counterfactual). The counterfactual budget was modeled using the best fit autoregressive integrated moving average model.When assessing the face validity of the model, the 4 experts brought up issues such as how to incorporate other health insurance, recent policy changes, cost inflation, and potential impacts on insulin use. The 6 internal verification techniques caught mistakes in equations, missing data, and misclassified data. The realized budget was found to be lower than the predicted budget, with 13% error and an absolute scaled error of 2.60. After removing the model assumption that past utilization trends would continue, the model's predictive accuracy improved (the absolute scaled error dropped below 1 to 0.48). The "realized" budgetary impact was found to be greater than the predicted budgetary impact, largely because of lower-than-expected utilization.The budget impact model overpredicted utilization in the year after the formulary changes. Discoveries through the validation process improved the accuracy and transparency of the model.This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. AHRQ had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer Ingelheim, Janssen/JJ, Regeneron, and Sanofi-Aventis, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed CareSpecialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.

Managed care organizations are growing more sophisticated in their ability to analyze data. There are increasing numbers of data analysts at managed care organizations, as well as more types of real-time, or "live," data available. These data range from pharmacy claims and enrollment files to medical claims, medical records, and linkages to external data. Moreover, the data are often curated in a way that allows for easier data analysis. Using these data, managed care residents are often required to perform a project to evaluate a utilization management policy or clinical program. Yet, there is a lack of guidance specific to managed care organizations on how to conduct such a research study using "live" claims data. This Viewpoint article provides a primer for managed care residents and other managed care professionals who are seeking to use data to help inform decisions on how to manage their beneficiaries' health and costs. DISCLOSURES: There was no funding source for this manuscript. Hung reports a grant from the Agency for Healthcare Research and Quality and personal fees from Blue Cross Blue Shield Association, outside the submitted work. Gedey, Groeneweg, and Jay have nothing to disclose.

Achieving high COVID-19 vaccination rates among employees is necessary to prevent outbreaks in health care settings. The goal of the study was to produce actionable and timely evidence about factors underlying the intention and decisions to obtain the COVID-19 vaccine by employees.The study was conducted from December 2020 - May 2021 with employees from a VA health care system in Southeastern US. The study used a convergent mixed methods design comprising two main activities: a cross-sectional survey conducted prior to COVID-19 vaccine distribution, and semi-structured interviews conducted 4-6 months after vaccine distribution. Data were collected about participant characteristics, vaccination intention prior to distribution, vaccination decision post-distribution, determinants of vaccination intention and decision, activating factors, sources of information and intervention needs. Data from the survey and interviews were analyzed separately and integrated narratively in the discussion.Prior to vaccine distribution, 77% of employees wanted to be vaccinated. Post vaccine distribution, we identified 5 distinct decision-making groups: 1) vaccine believers who actively sought vaccination and included those sometimes described as "immunization advocates", 2) go along to get along (GATGA) individuals who got vaccinated but did not actively seek it, 3) cautious acceptors who got the COVID-19 vaccine after some delay, 4) fence sitters who remained uncertain about getting vaccinated, and 5) vaccine refusers who actively rejected the COVID-19 vaccine. Participants identifying with Black or multiple races were more likely to express hesitancy in their vaccination intention.The findings of our study highlight distinct decision-making profiles associated with COVID-19 vaccination among employees of a VA health care system, and provide tailored recommendations to reduce vaccine hesitancy in this population.

To assess the within-trial cost-effectiveness of a behavioral intervention to improve glaucoma medication adherence.Prospective cost-effectiveness analysis of randomized, controlled trial data.The study setting was a Veterans Affairs (VA) eye clinic. The patient population comprised veterans with medically treated glaucoma and self-reported poor adherence. Participants were randomized to a personalized educational session with a reminder bottle to promote medication adherence or to a control session on general eye health. Costs were assessed from the perspective of the VA payor at 6 months using the VA Managerial Cost Accounting System. Probabilistic sensitivity analyses were conducted using bootstrapped samples. The main outcome measures were the proportion of participants attaining ≥80% adherence as measured by electronic monitor, total intervention and medical resource costs, and incremental cost-effectiveness ratios comparing intervention to control at 6 months.Of 200 randomized participants, 95 of 100 assigned to the intervention and 97 of 100 assigned to the control had adherence outcomes at 6 months, and the proportion of adherent patients was higher in the intervention group compared to control (0.78 vs 0.40, P.0001). All participants had costs at 6 months. The total cost at 6 months was $1,149,600 in the intervention group (n = 100) compared to $1,298,700 in the control group (n = 100). Thus, in a hypothetical cohort of 100 patients, the intervention was associated with cost savings (-$149,100) and resulted in 38 additional patients achieving medication adherence.An adherence-enhancing behavioral intervention was effective and cost saving at 6 months.

Objective Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. Study design and setting SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. Results Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). Conclusion Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.

Background: Glaucoma treatment requires patients to follow daily, often times complex, eye drop regimens, but adherence is poor for many patients, putting them at risk for irreversible vision loss. A comprehensive approach is needed to address the challenges in the self-management of glaucoma. The purpose of this study is to improve glaucoma medication adherence in Veterans with medically treated glaucoma using an education-based intervention. Methods/design: This study is a single-site randomized controlled trial enrolling 200 Veterans and their companions, if companions are involved in their care. It has two arms: an intervention group and a control group. Participants in the intervention group receive an educational session with a non-physician interventionist and are provided with an AdhereTech smart bottle with the reminder functions activated. The control group is designed as an attention control such that they have a session on general eye health and are provided with a smart bottle but without the reminder functions activated. The primary outcome is the proportion of prescribed doses taken on schedule over 6 months following randomization according to the smart bottle. Secondary outcomes include intensification of glaucoma treatment, cost of intervention delivery, and cost-effectiveness of the intervention over 12 months. Discussion: The education-based intervention that we are testing is comprehensive in scope, to encompass a variety of barriers to adherence that glaucoma patients encounter, but personalized to address issues facing individual patients. Particular attention was given to feasibility in the real-world setting, as the high throughput of patients and lack of reimbursement for educational encounters in ophthalmology would limit implementation of a resource-intensive intervention.

Background Recently developed peanut desensitization treatment reduces the incidence of allergic reactions, the anxiety associated with the risk of accidental exposure, and the burden of precautionary behavior. Eliciting parent preferences for tradeoffs involving treatment effectiveness, tolerability, costs, and convenience quantifies the burden of juvenile peanut allergy and the perceived value of peanut desensitization therapies. Objective To understand heterogeneity in parents' treatment preferences and the role of personal characteristics in explaining differences. Methods An Internet-based, discrete-choice experiment survey was administered to a national sample of 500 parents of children aged 4 to 17 years with peanut allergy to quantify parents' preferences for peanut desensitization therapies for their children. Latent-class, mixed-logit analysis estimated relative importance coefficients for groups of participants with distinctly different preferences. Results Parents' choice patterns fell into 1 of 4 preference subgroups: (1) Cost-sensitive, (2) Protreatment (but Side-Effect–Averse), (3) Trader, and (4) Inconsistent. Mode of administration had little relative importance across all subgroups. Characteristics associated with belonging to a given preference subgroup included parent age, child age, income, parent perception of child risk and ability to manage allergic reactions, past allergic reactions, and changes in precautionary behaviors posttreatment. Conclusions We found distinct differences in parent preferences for tradeoffs involving effectiveness, tolerability, and costs of peanut desensitization treatments. Parents' treatment preferences help quantify the burden of juvenile peanut allergy and the perceived value of new therapies. Such information can inform patient-centric clinical and regulatory decision making.

Protocol adherence in behavioral intervention clinical trials is critical to trial success. There is increasing interest in understanding which patients are more likely to adhere to trial protocols. The objective of this study was to demonstrate the use of a data-driven approach to explore patient characteristics associated with the lowest and highest rates of adherence in three trials assessing interventions targeting behaviors related to lifestyle and risk for cardiovascular disease. Each trial included a common set of baseline variables. Model-based recursive partitioning (MoB) was applied in each trial to identify participant characteristics of subgroups characterized by these baseline variables with differences in protocol adherence. Bootstrap resampling was conducted to provide optimism-corrected c-statistics of the final solutions. In the three trials, rates of protocol adherence varied from 56.9% to 87.5%. Evaluation of heterogeneity of protocol adherence via MoB in each trial resulted in trees with 2–4 subgroups based on splits of 1–3 variables. In two of the three trials, the first split was based on pain in the past week, and those reporting lower pain were less likely to be adherent. In one of these trials, the second and third splits were based on education and employment, where those with lower education levels and who were employed were less likely to be adherent. In the third trial, the two splits were based on smoking status and then marriage status, where smokers who were married were least likely to be adherent. Optimism-corrected c-statistics ranged from 0.54 to 0.63. Model-based recursive partitioning can be a useful approach to explore heterogeneity in protocol adherence in behavioral intervention trials. An important next step would be to assess whether patterns hold in other similar studies and samples. Identifying subgroups who are less likely to be adherent to an intervention can help inform modifications to the intervention to help tailor the intervention to these subgroups and increase future uptake and impact. Trial registration ClinicalTrials.gov identifiers: NCT01828567 , NCT02360293 , and NCT01838226 .

Multi-site clinical trials are essential within medical practice to help drive reliable and generalizable knowledge on advancing medical treatments. Although the success of multi-site trials is significantly dependent on local clinician and site research teams, best practices for engagement of site teams, or “site engagement,” has not been extensively discussed. Site engagement centers on including sites in the planning and implementation of clinical trials to promote trial enrollment, compliance, and applicability to local contexts. Using a case example from the RadComp Trial, a longitudinal, multi-site clinical trial, novel site engagement practices are provided across three major research phases. In the Planning Phase, site engagement builds partnerships and commitment by active elicitation of information on site specific processes and feedback on trial design. In the Conducting Phase, sustained engagement encourages bi-directional communication and facilitates learning networks for enhanced site performance. In the Dissemination Phase, site and community partnerships are leveraged to create locally designed dissemination plans for broader scientific reach and impact. Site engagement practices discussed in this paper can be replicated or molded for application in other multi-site clinical trials.

This survey study assesses which patients who may be eligible for a health savings account do not have one and what decisions patients with these accounts make about contributing to them in a representative sample of US adults., Key Points Question How are US adults who are enrolled in a high-deductible health plan using health savings accounts to save for health care? Findings In this national survey of 1637 respondents, approximately 1 in 3 adults enrolled in a high-deductible health plan did not have a health savings account. Among those with a health savings account, most had not contributed money into it in the last year, and less education and health insurance literacy were associated with not having made contributions. Meaning These findings suggest that few US adults enrolled in high-deductible health plans are using health savings accounts to save for health care, and targeted interventions could enhance uptake of and contributions to health savings accounts., Importance Health savings accounts (HSAs) can be used by enrollees in high-deductible health plans (HDHPs) to save for health care expenses before taxes. Expansion of and encouraging contributions to HSAs have been centerpieces of recent federal legislation. Little is known about how US residents who may be eligible for HSAs are using them to save for health care. Objective To determine which patients who may be eligible for an HSA do not have one and what decisions patients with HSAs make about contributing to them. Design, Setting, and Participants This cross-sectional national survey assessed an online survey panel representative of the US adult population. Adults aged 18 to 64 years and enrolled in an HDHP for at least 12 months were eligible to participate. Data were collected from August 26 to September 19, 2016, and analyzed from November 1, 2019, to April 30, 2020. Main Outcomes and Measures Prevalence of not having an HSA or not making HSA contributions in the last 12 months and reasons for not making the HSA contributions. Results Based on data from 1637 individuals (American Association of Public Opinion Research response rate 4, 54.8%), half (50.6% [95% CI, 47.7%-53.6%]) of US adults in HDHPs were female, and most were aged 36 to 51 (35.7% [95% CI, 32.8%-38.6%]) or 52 to 64 (36.8% [95% CI, 34.1%-39.5%]) years. Approximately 1 in 3 (32.5% [95% CI, 29.8%-35.3%]) did not have an HSA. Those who obtained their health insurance through an exchange were more likely to lack an HSA (70.3% [95% CI, 61.9%-78.6%]) than those who worked for an employer that offered only 1 health insurance plan (36.5% [95% CI, 30.9%-42.1%]; P

The number of people in the United States living with Alzheimer disease (AD) is growing, resulting in significant clinical and economic impact. Substantial research investment has led to drug development in stages of AD before symptomatic dementia, such as preclinical AD. Although there are no treatments approved for preclinical AD, there are currently 6 phase 3 clinical trials for preclinical AD treatments. In this article, we review these clinical trials and highlight considerations for future coverage decisions. In line with the definition of preclinical AD, enrollment in these trials focuses on cognitively unimpaired patients that are at high risk of AD because of family history and then genetic testing or brain imaging. Enrollment in most of these trials also allows for younger patients, including those aged under 65 years. Primary clinical trial endpoints focus on cognition often 4 or more years after treatment. Secondary endpoints include other measures of cognition and function, as well as biomarkers. Review of these trials brings to light a few potential considerations when covering these new medications in the future. First, novel and potentially costly approaches involving genetic testing and/or positron emission tomography imaging may be needed to identify appropriate patients and should be developed efficiently. Second, the long duration of these clinical trials suggest that there may be a need for alternative payment approaches in the United States that encourage early payers to pay for a medication for which the long-term benefits may not be realized until after the beneficiary is no longer with the health plan. Third, the value of AD treatments may differ across populations, creating a potential role for indication-based or population-based contracting. Finally, considering the potentially high budgetary impact and little real-world evidence for a new drug class, payers and manufacturers may want to consider outcomes-based payment approaches and coverage with evidence development to mitigate uncertainty about the value of the treatment demonstrated in well-defined populations in clinical trials versus more heterogeneous real-world settings. DISCLOSURES: This work was funded through a generous gift from the Global CEO Initiative on Alzheimer Disease. Hung reports grants from Agency for Healthcare Research and Quality and Pharmaceutical Research and Manufacturers of America outside the submitted work and past employment at CVS Health and BlueCross BlueShield Association. McClellan is an independent board member on the boards of Johnson & Johnson, Cigna, Alignment Healthcare, and Seer; co-chairs the Accountable Care Learning Collaborative and the Guiding Committee for the Health Care Payment Learning and Action Network; and receives fees for serving as an advisor for Cota and MITRE. Hamilton Lopez and Schneider have nothing to disclose. Part of this work was presented at the 2019 AMCP Nexus Meeting, October 29-November 1, 2019, in National Harbor, MD.

47 Background: Practice guidelines have been modified to recommend hereditary and tumor gene mutation testing in patients with metastatic castration-resistant prostate cancer (mCRPC) to identify patients for molecularly targeted therapies. Identifying appropriate candidates for testing can be challenging in electronic health records and claims data. In this study, we used natural language processing (NLP) algorithms to identify veterans with mCRPC, reported gene mutation testing rates and identified factors associated with testing. Methods: This is a retrospective observational cohort study using NLP to identify veterans diagnosed with mCRPC between 2016 and 2020. Patient and facility characteristics were reported descriptively. Chi-square and t-tests were used to determine whether differences were statistically significant at a significance level of 0.05 based on receipt of testing. Generalized linear mixed models with binomial error distributions and logit links accounting for clustering by facility were used to determine which factors were independently associated with testing. Results: 9,282 veterans were diagnosed with mCRPC between 2016 and 2020, as determined by NLP algorithms identifying diagnosis of metastatic disease and castration-resistant disease. Among these patients, 381 died within 45 days of their diagnosis, and were excluded from analysis. In the analytic cohort of 8,901 veterans, 1,282 (14%) patients received testing. Of these, 1,041 (81%) received tumor tissue testing and 292 (23%) received hereditary testing. In bivariate analyses, age, race, ethnicity, Commission on Cancer (COC) facility certification, and facility complexity rating differed between veterans who received the test versus who did not (mean age of 73 versus 77, p < 0.0001; 30% versus 24% Black, p < 0.0001; 93% versus 92% non-Hispanic, p = 0.04; 64% versus 63% COC-certified facility, p = 0.04; and 59% versus 52% most complex facility, p < 0.0001). In multivariate analyses, older age and lower facility complexity rating were associated with lower odds of testing (for every 10-year increase in age, adjusted odds ratio [aOR], 95% confidence interval [CI]: 0.54, 0.50-0.58; Mid-high and low complexity facilities compared to highest complexity facilities: aOR, 95% CI: 0.52, 0.32-0.85 and 0.39, 0.22-0.71, respectively). Conclusions: Gene mutation testing in veterans with mCRPC is underutilized. Older age and being seen in a lower complexity facility are independently associated with a lower odds of testing. Patient and facility barriers to testing should be identified to improve guideline concordant care.

Objectives Cost-effectiveness estimates are useful to a health plan when they are specific to a utilization management policy question. To help inform a step therapy policy decision, this study assessed the 3-year cost-effectiveness of adding a sodium-glucose cotransporter 2 (SGLT2) inhibitor versus switching to a glucagon-like peptide-1 receptor agonist (GLP-1 RA) in patients with type 2 diabetes who are on metformin and a dipeptidyl peptidase-4 (DPP-4) inhibitor from both private and public payer perspectives in the United States. Study design Cost-effectiveness analysis. Methods A decision-analytic model was built incorporating goal glycated hemoglobin (A1C) achievement as the effectiveness measure, as well as adverse effect and discontinuation rates from clinical trial data. One-way, scenario, and probabilistic sensitivity analyses were performed. Results In a cohort of 1000 patients, adding an SGLT2 inhibitor led to $3.9 million more in spending and 93 more patients reaching goal A1C compared with switching from a DPP-4 inhibitor to a GLP-1 RA. This resulted in an incremental cost-effectiveness ratio (ICER) of $42,125 per patient to achieve goal A1C from the private payer perspective. Using a public payer perspective led to an ICER of $103,829. These results were most sensitive to changes in drug costs and the proportion of patients achieving A1C goal or discontinuing. Conclusions Assuming a $50,000 willingness-to-pay threshold, adding an SGLT2 inhibitor was cost-effective compared with switching from a DPP-4 inhibitor to a GLP-1 RA from a private payer perspective but not from a public payer perspective. This study highlights how differences in payer reimbursement rates for medications can lead to contrasting results.

Historically, budget impact models have been used to demonstrate how a new drug or technology entering the market could influence payer budgets, with the hope of influencing coverage of the drug or technology. This article proposes the adaptation of a budget impact model framework to answer the payer question "What is the budget impact of our approaches to managing the utilization of drugs and technologies?" Throughout this article, we also describe lessons learned while collaborating with a U.S. payer to evaluate the budgetary impact of specific antidiabetic drug utilization management approaches. DISCLOSURES: The study discussed in this commentary was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Lugo has nothing to disclose. Mullins reports grants and other fees from AHRQ, during the conduct of the study; grants and personal fees from Bayer and Pfizer; and personal fees from Boehringer-Ingelheim, Janssen/JJ, Regeneron, and Sanofi, outside the submitted work. Parts of the study were presented as a poster presentation at the 2017 AMCP Managed CareSpecialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. The study was also published as Hung's PhD dissertation.

We explored biologics receipt in metastatic colon cancer.We used Surveillance, Epidemiology, and End Results-Medicare data of 4,545 elderly patients diagnosed with incident metastatic colon cancer from 2003 to 2009, treated with chemotherapy and/or biologics, and followed up through 2010.A total of 2,504 (55%) patients received a biologics-containing regimen. Treatment with biologics fluctuated between 46% and 63% of first-line regimens and 67% and 73% of second-line regimens. Bevacizumab accounted for 95% of first-line and 68% of second-line biologics use. Cetuximab accounted for 33% of second-line and 48% of third-line use. Panitumumab accounted for 5% of second-line and 27% of third-line use. The adjusted odds of biologics receipt decreased rapidly with age, resulting in a threefold difference between the youngest and the oldest study participants in the sample (odds ratio [OR] 0.35, p.01). African Americans (OR 0.77, p = .03) and patients with Charlson Comorbidity Index of 1 (OR 0.83, p = .02) or1 (OR 0.75, p.01) were considerably less likely to receive biologics therapy. Medicare state buy-in was associated with 2% lower odds of receiving biologics (OR 0.98, p = .04).After controlling for sociodemographic and clinical differences, age, race, comorbidities, and low income had a statistically significantly negative effect on the likelihood of receiving biologics among treated patients. Use of biologics varied over time, across the treatment continuum, and by chemotherapy regimen. Bevacizumab was most frequently used in both first- and second-line treatment. Cetuximab was the second most prescribed biologic. Panitumumab use was mostly limited to third-line treatment.It is well-known that patients in the "real world" receive cancer treatments that do not reflect the strict treatment protocols of clinical trials. This is particularly true for complex and elderly patients with metastatic disease, who are frequently underrepresented in clinical trials. Although this article does not provide any additional evidence about the effectiveness of one treatment regimen or treatment sequence over another, it enhances our understanding of oncology practice outside of the clinical trial setting and provides useful information for future health services and health economics research in metastatic colon cancer.

OBJECTIVES: This study examined ninety-day and one-year postoperative healthcare utilization and costs following total knee arthroplasty (TKA) from the health sector and patient perspectives. DESIGN: This study relied on: 1) patient-reported medical resource utilization data from diaries in the Knee Arthroplasty Pain Coping Skills Training (KASTPain) trial; and 2) Medicare fee schedules. Medicare payments, patient cost-sharing, and patient time costs were estimated. Generalized linear mixed models were used to identify baseline predictors of costs. RESULTS: In the first ninety days following TKA, patients had an average of 29.7 outpatient visits and 6% were hospitalized. Mean total costs during this period summed to $3720, the majority attributed to outpatient visit costs (84%). Over the year following TKA, patients had an average of 48.9 outpatient visits, including 33.2 for physical therapy. About a quarter (24%) of patients were hospitalized. Medical costs were incurred at a decreasing rate, from $2,428 in the first six weeks to $648 in the last six weeks. Mean total medical costs across all patients over the year were $8,930, including $5,328 in outpatient costs. Total costs were positively associated with baseline Charlson comorbidity score (p

Biosimilars undergo an abbreviated licensure pathway called 351(k), which was created by the Biologics Price Competition and Innovation Act of 2009. This approval process is different from the 351(a) pathway for original biologic approval and, as of August 2017, has been used to approve 5 biosimilars in the United States.To identify the types and quantities of evidence required by the FDA for biosimilar approval and the corresponding evidence manufacturers have provided in their 351(k) biosimilar approval applications.To collect data for this review, we searched through drug-specific FDA approval documents and approval-related FDA webcasts for approval indications and dates; reference product names; formulations; postmarketing requirements and commitments; evidence used for extrapolation claims; advisory committee votes; and evidence on similarity in analytical and functional characteristics, pharmacokinetics, pharmacodynamics, efficacy, safety, and immunogenicity.All biosimilars approved in the United States provided a large evidence base to demonstrate similarity in analytical and functional characteristics-3 to 5 clinical studies showed similarity in pharmacokinetics and pharmacodynamics, and 1 to 2 clinical studies demonstrated efficacy. Safety and immunogenicity were evaluated across all clinical studies. All biosimilars were compared with either the U.S.-licensed reference product or the reference product licensed by the European Union. Extrapolation allowed biosimilars to be approved for indications in which clinical studies were not conducted. The few indications for which biosimilars did not share approval with the reference product were due to market exclusivity protection. None of the 5 biosimilars have been approved as interchangeable in the United States.The approval process for the first 5 biosimilars on the market in the United States provides a baseline understanding of what type and degree of evidence is required for biosimilar approval.There was no external funding for this study. Hung reports employment as a pharmacist for CVS Health, an AHRQ F32 grant, and meeting/accommodation/travel support from AACP, DIA, and ISPOR, all outside the submitted work. Vu and Mostovoy have nothing to disclose. Study concept and design were contributed by Hung and Mostovoy, along with Vu. Hung and Vu collected the data, and data interpretation was performed primarily by Hung, along with Mostovoy. The manuscript was written by Hung and Vu and revised by all the authors. Some of the study results were previously presented as a poster at the ISPOR 22nd Annual International Meeting; May 20-24, 2017; in Boston, Massachusetts.

The desire for patient-centeredness and more pragmatic clinical trials is increasing. Pragmatic clinical trials are conducted in normal practices to answer whether interventions work in real-world settings, whereas explanatory clinical trials are conducted under stricter settings with tighter control to answer whether an intervention can work. While both explanatory and pragmatic trials can have patient-centered elements, pragmatic trials more directly answer patient and policymaker questions.

Medicare Prescription Drug Plans (PDPs) do not have incentives to consider long-term outcomes and costs associated with both medical and pharmacy benefits (LTOCMP) when making formulary decisions.To identify existing quality measures, payment models, and public reporting tools for PDPs that are related to formulary decision making and that could be used as potential incentives for PDPs to consider LTOCMP.PubMed, Google, and quality measure databases, as well as Center for Medicare and Medicaid Innovation and Centers for MedicareMedicaid Services (CMS) websites, were searched for appropriate quality measures, payment models, and public reporting tools.Few quality measures and other mechanisms exist that are related to formulary decision making and have the potential to be incentives for PDPs to consider LTOCMP. Only 3 such tools were identified: (1) Medicare Part D star ratings quality measures in use by CMS, (2) the Medicare Plan Finder website, and (3) URAC PBM Accreditation standards and measures. Furthermore, the majority of the quality initiatives identified were only indirectly related to motivating PDPs to consider LTOCMP.Efforts are needed to develop mechanisms to align PDP incentives with LTOCMP.No outside funding supported this research/study. Hung reports part-time employment by CVS Health and Blue Cross Blue Shield Association, as well as past paid internships with Biotechnology Industry Organization and AMCP Foundation/Allergan. Perfetto reports advisory board, consultancy, and lecture fees from Pfizer, Otsuka, Sanofi, National Pharmaceutical Council, and AMCP Foundation, as well as employment by the National Health Council and Journal of Managed CareSpecialty Pharmacy. Both authors contributed to study concept and design, data interpretation, and manuscript revision. Hung collected the data and prepared the manuscript.

To compare the rates of mammographic screening and reasons for not receiving mammography between Medicare Advantage (MA) and Medicare fee-for-service (FFS) beneficiaries.Cross-sectional study.Unadjusted rates of mammographic screening and reasons for not receiving mammography were compared between MA and FFS beneficiaries in total and within subgroups. Probabilities of mammographic screening were also compared between the MA and FFS beneficiaries after adjusting for age, education, race/ethnicity, health status, marriage status, and region of residence using modified Poisson regression models.We found significantly higher unadjusted rates of mammographic screening in the MA beneficiary population compared with the FFS population (50.3% vs 44%, respectively; P.0001). MA beneficiaries were more likely to receive a mammogram than FFS beneficiaries (unadjusted relative risk [RR], 1.13; 95% CI, 1.07-1.19; and adjusted RR, 1.06; 95% CI, 1.01-1.12). Furthermore, the top 6 reasons why MA and FFS beneficiaries did not receive mammograms were: "not needed," "doctor did not recommend," "forgot," "not recommended annually," "did not like mammograms," and "appointment due soon." Among non-Hispanic blacks and Hispanics specifically, FFS beneficiaries were more likely to report doctors not recommending mammograms than MA beneficiaries.MA beneficiaries were 1.06 times more likely to receive mammography screening compared with the FFS population. Additionally, providers were less likely to recommend mammographic screening to non-Hispanic black and Hispanic FFS beneficiaries compared with non-Hispanic black and Hispanic MA beneficiaries.

The treatment programme and literature considered in this chapter focus on child traumatic brain injury (TBI), and it is unclear to what extent the evidence can be generalised to children and youth with brain injuries of nontraumatic origin. TBI is the most common cause of acquired disability in children, affecting approximately 500,000 children in the USA each year (Langlois et al., 2004). TBI in children may result in significant impairment in cognitive, behavioural and social functioning, especially if the injury is severe. Emerging behaviour problems occur in up to 75% of children who have sustained a severe TBI (Schwartz et al., 2003). Common cognitive sequalae include impaired attention, decreased processing speed and perseveration, as well as deficits in inhibition, planning and problem solving.

Painful diabetic peripheral neuropathy is difficult to treat, partially because the underlying mechanism of pain is not fully understood. Various treatment guidelines recommend first-line agents, such as α2-δ ligands, serotonin-norepinephrine reuptake inhibitors, and tricyclic antidepressants but combination therapy of alternative agents including opiates is often warranted. Tapentadol extended-release has a novel dual mechanism of action; it is both a mu-opioid receptor agonist and a norephinephrine reuptake inhibitor. It has been in the spotlight since it was FDA-approved specifically for the treatment of painful diabetic peripheral neuropathy in 2012. Previous reviews of tapentadol have focused on chronic pain. The purpose of this review article is to assess the efficacy and safety of tapentadol extended-release in adult populations with painful diabetic peripheral neuropathy and provide guidance for formulary decisions.

CME Learning Objectives Describe evidence of differential treatment response of chemotherapy in elderly versus nonelderly stage III colon cancer patients. Synthesize differences in evidence of effectiveness and safety of chemotherapy between elderly and nonelderly stage III colon cancer patients to inform patient decision making and physician prescribing practices. Background. Chemotherapy effectiveness in clinical practice may differ from the efficacy demonstrated in clinical trials, particularly among populations underrepresented in clinical trials, such as elderly patients with cancer. This review aims to examine the relative effectiveness of chemotherapy for stage III colon cancer in elderly versus nonelderly patients. Methods. A systematic literature review was conducted using the Agency for Healthcare Research and Quality approach. Literature searches were performed in Medline and Evidence-Based Medicine Reviews databases. Chemotherapy regimens approved for stage III colon cancer were reviewed. Four effectiveness and 15 safety outcomes were extracted. Results. From 708 identified articles, 25 articles provided data on the relative effectiveness and safety of chemotherapy among elderly versus nonelderly patients. Four of 14 studies showed lower overall survival treatment effects, whereas one of five and one of four studies indicated more favorable treatment effects for time to progression and overall response rate. Grade 3 or 4 adverse events were higher among elderly patients for cardiac disorder (2/5 studies), leukopenia (1/5), neutropenia (4/16), thrombocytopenia (2/13), febrile neutropenia (1/4), infection (2/10), dehydration (2/6), diarrhea (6/20), and fatigue (6/13). Grade 3 or 4 adverse events were lower for neutropenia (2/16 studies), nausea/vomiting (1/16), and neuropathy (1/9). Conclusion. The majority of the evidence suggests that chemotherapy has similar relative effectiveness and safety for patients >65 years of age versus younger patients with stage III colon cancer. When differences are reported, treatment effects are more often worse among the elderly. This review suggests that without other reasons for withholding treatment, elderly patients should receive chemotherapy as often as nonelderly patients.

540 Background: Metastatic colon cancer (mCC) patients may receive multiple lines of treatment (Tx1, Tx2, etc.) consisting of one or more cytotoxic (CYT: 5FU/LV, oxaliplatin [OX], irinotecan [IRI]) and biologic (BIO: bevacizumab [BEV], cetuximab [CET], panitumumab [PAN]) drugs. The National Comprehensive Cancer Network (NCCN) provides evidence-based Tx recommendations for each line. The objective of this study was to examine real-world clinical practice patterns between 2002 and 2010. In particular, we compared the most common regimens across Tx lines and how Tx patterns changed over time. We also documented the uptake and use of new BIOs. Methods: We used population-based SEER-Medicare data to determine Tx1, Tx2, and Tx3 regimens of 4,616 mCC patients (the median age at diagnosis was 78) diagnosed in 2003-2009 and followed through 2010. We will use an algorithm previously developed by us to identify regimens. Results: The most common CYT backbone in Tx1 was OX (51% of patients) followed by 5FU/LV (30%). In comparison, IRI was a preferred choice in Tx2 (65%) and Tx3 (31%). In 2003, the most common Tx1 regimens were 5FU/LV- (56%) and IRI-based (35%). 5FU/LV and IRI use decreased to 22% and 9% respectively in 2009, while OX use increased from 7% in 2003 to 63% in 2009. In 2004, the FDA approved BEV for Tx1. BEV’s share increased from 9% in 2004 to 53% in 2005. BEV was used in 9% of Tx2 regimens in 2004 and 46% in 2005. CET was approved in 2004. CET was used in less than 5% of Tx1 regimens in any year up to 2010. CET use in Tx2 increased from 19% to 27% between 2005 and 2007, and declined to 23% in 2010. The FDA approved PAN in September 2006 for treatment after failure of CYT-based regimens, i.e., primarily in Tx3 and beyond. Only 350 (8%) of patients received Tx3, and of these 59 (17%) received PAN without a CYT backbone. One in three Tx3 regimens consisted of biologics only (54% CET, 43% PAN). Conclusions: This study used SEER-Medicare registry data to examine and document real-world clinical practice patterns in treatment of elderly mCC patients between 2003 and 2010. We observed that as new biologic agents were introduced to the market, variations in the combinations and the number of treatment have significantly and rapidly changed.