Therapeutic Inertia in Prescribing Biologics for Patients with Moderate-to-Severe Asthma: Workshop Summary.

Moderate-to-severe asthma represents about a quarter of the nearly 10% of Americans diagnosed with asthma. Many patients with moderate-to-severe asthma have uncontrolled symptoms that lead to exacerbations requiring oral corticosteroids. There are many factors contributing to poor asthma control, including poor adherence to prescribed therapies, the under-prescribing of biologics and therapeutic inertia. We convened an eight-member panel from fields of primary care, pulmonology, immunology, health services and clinical research, behavioral science and pharmaceutical medical affairs, with the goal of identifying contributing factors and solutions to therapeutic inertia with asthma biologics. We used the Capability, Opportunity, and Motivation (COM-B) model to classify patient and provider behavior towards therapeutic inertia. The model incorporates existing behavior theories and is driven by the interaction of capability, opportunity, and motivation. We used a Delphi method to identify and develop six primary solutions: 1) integration of patient-centered outcomes into asthma management practice; 2) provider education about asthma treatment; 3) moderate-to-severe asthma care delivery redesign; 4) harmonized, evidence-based protocol for the management of moderate-to-severe asthma; 5) designated coordinator approach for optimal asthma management; and 6) a case coordination digital support tool. Integration of patient-centered outcomes into asthma management practice and provider education were identified as having the highest potential to impact therapeutic and clinical inertia. The COM-B model is effective in identifying improvement within therapeutic inertia targeting the capabilities, opportunities, and motivations of patients, providers, and payer systems.

Novel application of approaches to predicting medication adherence using medical claims data.

OBJECTIVE: To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. DATA SOURCES/STUDY SETTING: Medicare Parts A, B, and D claims from 2007 to 2013. STUDY DESIGN: We evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ≥ 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, least absolute shrinkage and selection operator (LASSO), and random forest. We used the C-index to assess model discrimination and decile plots comparing predicted values to observed event rates to evaluate model performance. DATA EXTRACTION: We identified 11 969 beneficiaries with an acute myocardial infarction (MI)-related admission from 2007 to 2012, who filled a statin prescription at, or shortly after, discharge. PRINCIPAL FINDINGS: In all models, prior statin use was the most important predictor of future adherence (OR = 3.65, 95% CI: 3.34-3.98; OR = 3.55). Although the LASSO regression model selected nearly 90 percent of all candidate predictors, all three analytic approaches had moderate discrimination (C-index ranging from 0.664 to 0.673). CONCLUSIONS: Although none of the models emerged as clearly superior, predictive analytics could proactively determine which patients are at risk of nonadherence, thus allowing for timely engagement in adherence-improving interventions.

Preferred drug lists and Medicaid prescriptions.

BACKGROUND: When Medicaid preferred drug lists (PDLs) are implemented, they may impose indirect costs on prescribing physicians and Medicaid patients, leading to an unintended reduction in the number of Medicaid prescriptions filled. OBJECTIVE: To test retrospectively the proposition that PDLs adversely affect the number of Medicaid prescriptions filled. DATA AND METHODS: We compared three 'test' states (Alabama, Texas, and Virginia) that implemented PDLs with restrictions on the prescription of statins with three 'control' states (New Jersey, North Carolina, and Pennsylvania) that did not implement drug access restrictions. We conducted the analysis at the county level and used a differences-in-differences approach that allows for county and time-period fixed effects. RESULTS: We found that PDLs adversely impacted several measures of filled Medicaid prescriptions in the 'test' states relative to the 'control' states. CONCLUSION: There are unintended but potentially harmful consequences to cost-focused health policy interventions.