Adherence to adjuvant endocrine therapy in insured black and white breast cancer survivors: Exploring adherence measures in patient data

BACKGROUND: Adjuvant endocrine therapy (AET) is a critical therapy in that it improves survival in women with hormone receptor-positive (HR+) breast cancer (BC), but adherence to AET is suboptimal. The purpose of this study was to fill scientific gaps about predictors of adherence to AET among black and white women diagnosed with BC. OBJECTIVE: To assess AET adherence in black and white insured women using multiple measures, including one that uses an innovative statistical approach. METHODS: Black and white women newly diagnosed with HR+ BC were identified from 2 health maintenance organizations. Pharmacy records captured the type of oral AET prescriptions and all fill dates. Multivariable logistic regression was used to identify predictors of adherence defined in terms of proportion of days covered (PDC; ≥80%) and medication gap of ≤10 days. A zero-inflated negative binomial (ZINB) regression model was used to identify variables associated with the total number of days of medication gaps. RESULTS: 1,925 women met inclusion criteria. 80% were PDC adherent (>80%); 44% had a medication gap of ≤10 days; and 24% had no medication gap days. Race and age were significant in all multivariable models. Black women were less likely to be adherent based on PDC than white women (OR=0.72, 95% CI=0.57-0.90, P<0.01), and they were less likely to have a medication gap of ≤10 days (OR=0.65, 95% CI=0.54-0.79, P<0.001). Women aged 25-49 years were less likely to be PDC adherent than women aged 65-93 years (OR=0.65, 95% CI=0.48-0.87, P<0.001). In the ZINB model, women were without their medication for an average of 37 days (SD=50.5). CONCLUSIONS: Racial disparities in adherence to AET in the study highlight a need for interventions among insured women. Using various measures of adherence may help better understand this multidimensional concept. There might be benefits from using both more common dichotomous measures (e.g., PDC) and integrating novel statistical approaches to allow tailoring adherence to patterns within a specific sample.