Impact of Ivacaftor on Medication Use, Hospital and Outpatient Provider Visits and Associated Costs in a Medicaid Population

BACKGROUND: Ivacaftor is the first Food and Drug Administration-approved medication to treat an underlying genetic defect in patients with cystic fibrosis (CF). With an approximate annual cost of $300,000 per patient, ivacaftor may have a profound financial impact on health systems, even when utilized by a small population. Clinical data has demonstrated that treatment with ivacaftor may reduce pulmonary exacerbations (PE) and associated hospitalizations. As a result, patients receiving ivacaftor may need less outpatient care and fewer medications to treat CF complications. Evaluating the impact of ivacaftor therapy on medication utilization, PEs and hospital/outpatient visits can aid formulary decision makers in its effective management. OBJECTIVES: The primary objective is to examine the effects of ivacaftor on patients’ overall medication regimen and associated costs within a Medicaid population. The secondary objective is to examine its effect on the rates of PEs and hospital/outpatient visits. METHODS: Pharmacy and medical claims data for Medicaid members ≥ six years of age was collected for six months before and after the first reported pharmacy claim of ivacaftor. Data included: total number of unique claims, days supply, dose, and total cost for each medication, number of short-term antibiotic and/or steroid courses, outpatient provider visits, hospitalizations, ER visits and corresponding diagnosis codes. Diagnosis codes and short-term antibiotic and/or steroid courses were reviewed to determine if a PE may have occurred. RESULTS: Ivacaftor treatment did not decrease the utilization of medications used to treat patients with CF and resulted in increased pharmacy expenditures for other medications. However, a 65% reduction in PEs as well as a reduction in hospitalizations/ER visits was observed in members receiving ivacaftor. CONCLUSIONS: This study found that while ivacaftor treatment may not decrease total medication utilization or associated costs, it may decrease the number of PEs and associated hospitalizations in patients with CF

Why Life Outcomes are Hard to Predict

Small effect sizes in even the most well-executed of longitudinal studies raise the question of how good social science will ever be at predicting important life outcomes. Here, we elaborate on Lewin (1936) and propose that life outcomes (L) are determined by the cumulative effects of the person (P) and their environment (E): Life Outcomes L = f(P, E) + e. Using this model, we propose that there are at least four reasons why life outcomes are inherently hard to predict: (1) the dynamic nature and the plurality of both personal characteristics and their environmental circumstances (i.e., P and E change over the life course and are themselves multiply determined); (2) complex interactions between P and E; (3) the substantialness of e due to different types of errors; and (4) the low base rates and range restriction in certain life outcomes. Understanding the theoretical limitations in predicting life outcomes is crucial for the interpretation and advancement of social science

The online language of work-personal conflict

Abstract With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals’ language use on social media. We apply an open-vocabulary analysis to the posts of 2810 Facebook users who also completed a survey for an established work-personal conflict scale. It was found that the language-based model can predict personal-to-work conflict (r = 0.23) and work-to-personal conflict (r = 0.15) and provide important insights into such conflicts. Specifically, we found that high personal-to-work conflict was associated with netspeak and swearing, while low personal-to-work conflict was associated with language about work and positivity. We found that high work-to-personal conflict was associated with negative emotion and negative tone, while low work-to-personal conflict was associated with positive emotion and language about birthdays.