BACKGROUND: The most common factor associated with poor control of hypertension is treatment nonadherence to antihypertensive drug therapy. OBJECTIVE: To measure drug nonadherence and associated factors in pharmacologically treated hypertensive patients. METHODS: A prospective observational study was carried out from March 2007 to August 2009 at a community pharmacy in Spain. A pharmacist invited a convenience sample of hypertensive patients aged 50 years and older taking antihypertensive medication for at least 3 months prior to participate in the study. Drug nonadherence was analyzed by 3 separate methods: pill count, as the gold standard method, and Haynes-Sackett and Morisky-Green questionnaires. A descriptive analysis of drug nonadherence and variables associated with nonadherence was performed. Logistic regression models were used to determine the variables associated with nonadherence. RESULTS: Data were recorded from 419 patients. The drug nonadherence ratio varied depending on the method used: 62.8% by pill count, 3.1% by the Haynes-Sackett self-report test, and 36% according to the Morisky-Green test. In the multivariate model, the variable associated with a decrease in drug nonadherence was years of known hypertension (OR = 0.962, 95% CI = 0.937-0.988), and the variables associated with an increase in drug nonadherence were loose-pill combination therapy versus fixed-dose combination therapy or monotherapy (OR = 4.099, 95% CI = 2.494-6.757) and good perception of quality of life (OR = 1.276, 95% CI = 1.109-1.471). CONCLUSIONS: The magnitude of drug nonadherence varies depending on the method of measurement. The pill count method (reference method) revealed that 2 out of 3 patients with hypertension did not have good adherence. This study highlights the lack of antihypertensive drug adherence and the pharmacist's ability to detect the associated factors in order to find the best way to deal with nonadherence.
BACKGROUND: The most common factor associated with poor control of hypertension is treatment nonadherence to antihypertensive drug therapy. OBJECTIVE: To measure drug nonadherence and associated factors in pharmacologically treated hypertensivepatients. METHODS: A prospective observational study was carried out from March 2007 to August 2009 at a community pharmacy in Spain. A pharmacist invited a convenience sample of hypertensivepatients aged 50 years and older taking antihypertensive medication for at least 3 months prior to participate in the study. Drug nonadherence was analyzed by 3 separate methods: pill count, as the gold standard method, and Haynes-Sackett and Morisky-Green questionnaires. A descriptive analysis of drug nonadherence and variables associated with nonadherence was performed. Logistic regression models were used to determine the variables associated with nonadherence. RESULTS: Data were recorded from 419 patients. The drug nonadherence ratio varied depending on the method used: 62.8% by pill count, 3.1% by the Haynes-Sackett self-report test, and 36% according to the Morisky-Green test. In the multivariate model, the variable associated with a decrease in drug nonadherence was years of known hypertension (OR = 0.962, 95% CI = 0.937-0.988), and the variables associated with an increase in drug nonadherence were loose-pill combination therapy versus fixed-dose combination therapy or monotherapy (OR = 4.099, 95% CI = 2.494-6.757) and good perception of quality of life (OR = 1.276, 95% CI = 1.109-1.471). CONCLUSIONS: The magnitude of drug nonadherence varies depending on the method of measurement. The pill count method (reference method) revealed that 2 out of 3 patients with hypertension did not have good adherence. This study highlights the lack of antihypertensive drug adherence and the pharmacist's ability to detect the associated factors in order to find the best way to deal with nonadherence.
Authors: Carmen Valdés Y Llorca; Ernesto Cortés Castell; José Manuel Ribera Casado; Pilar de Lucas Ramos; José Luis Casteig Ayestarán; Amaia Casteig Blanco; Vicente Francisco Gil Guillén; Mercedes Rizo Baeza Journal: Int J Environ Res Public Health Date: 2021-04-19 Impact factor: 3.390
Authors: Eric K P Lee; Paul Poon; Benjamin H K Yip; Yacong Bo; Meng-Ting Zhu; Chun-Pong Yu; Alfonse C H Ngai; Martin C S Wong; Samuel Y S Wong Journal: J Am Heart Assoc Date: 2022-09-03 Impact factor: 6.106
Authors: Emma Mares-García; Antonio Palazón-Bru; David Manuel Folgado-de la Rosa; Avelino Pereira-Expósito; Álvaro Martínez-Martín; Ernesto Cortés-Castell; Vicente Francisco Gil-Guillén Journal: PeerJ Date: 2017-06-30 Impact factor: 2.984