Patient-reported financial barriers to adherence to treatment in neurology

Abstract

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.

Social determinants of health associated with epilepsy treatment adherence in the United States: A scoping review

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Cost-related prescription non-adherence and patient-reported outcomes in systemic lupus erythematosus: The Michigan Lupus Epidemiology & Surveillance program

“…Despite their implementation, the benefits may not reach all of the population equally and racial and ethnic disparities in CRNA persist. [35][36][37][38] The 2015 National Healthcare Disparities Report assessed the quality of care among different populations based on using more than 260 healthcare process, outcome, and access measures, and covering a wide variety of conditions and patient settings. The results indicated that White patients receive better quality of care than that of Hispanic patients for 36.7% of quality measures, and better quality of care than that of Black patients for 41.1% of quality measures.…”

Antiepileptic drug treatment after an unprovoked first seizure

“…[26][27][28] With additional epidemiologic and imaging data available in the future, our model could also be further personalized with variables for different types of seizures (e.g., generalized tonicclonic, generalized absence, focal simple partial) and choice of AED (including polytherapy). 29,30 Finally, our model can be adjusted to account for additional patient factors that could influence choice of intervention such as genetic profiling, 31,32 machine learning-driven treatment outcome prediction, 33 treatment affordability, 34 and pregnancy planning. 35 Overall, the base model presented here is customizable and can be adapted to a wide range of clinical cases in the future, offering the potential to inform personalized therapies for epilepsy.…”