Patient factors such as age, socioeconomic status, comorbidities, and initial drug class significantly influence adherence to antihypertensive therapy, highlighting targets for adherence-improving interventions.
Identifies potential adherence barriers in hypertension; leaves open whether targeting these improves outcomes in prospective trials.
OBJECTIVE: To explore factors associated with adherence to antihypertensive drugs overall (therapy adherence) and to particular classes (class adherence) in hypertensive patients. METHODS: The current retrospective cohort study included adults with primary hypertension identified in the UK Clinical Practice Research Datalink from April 2006 to March 2013. Individuals were followed from the date of first-ever antihypertensive drug class (class adherence) prescribed and from the date of the first-ever antihypertensive drug (therapy adherence) issued to the earliest of study end, patient leaving the database, or death. Prescribing episodes (time from a drug class being first prescribed to the end of follow-up time) of six antihypertensive drug classes were recorded. Proportion of days covered (PDC) was used to estimate therapeutic adherence for a patient's antihypertensive drugs therapy during follow-up period and class adherence of a specific antihypertensive class in each episode, respectively. Generalized linear modelling was used to examine factors associated with PDC. RESULTS: Median therapy and class PDC were 93.9 and 98.3% in the 176 835 patients and 371 605 prescribing episodes; 20 and 38.4% of the patients and episodes had PDC less than 80%, respectively. Higher therapy and class PDC was associated with increasing age, using renin angiotensin drugs, and being preexisting patient and user of antihypertensive drugs. Higher deprivation, multiple comorbidities, and switching of antihypertensive drugs were associated with lower PDC. CONCLUSION: Several patient factors were confirmed as determinant of adherence to antihypertensive drug classes and therapy; hence, they can assist in identifying patients at risks of nonadherence, thus targeting them for adherence improving interventions.
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Kurdi et al. (2017) studied this question.
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