OBJECTIVE: To assess self-reported compliance with prescribed medications in a population of elderly patients prior to their hospital admission in an attempt to understand further the factors which influence drug-taking patterns. METHODS: Information which, based on personal clinical experience and published research, may impact on compliance was collected for patients by way of a chart review within 3 days of hospital admission, a search of patient computerised hospital records and an interview. All crude data were coded and entered into a computerised relational database. Each patient's data were assessed using the Naranjo algorithm and the score was recorded. Chi-square analysis highlighted those factors which significantly influenced compliance, sub-divided into under-compliance (taking less medicine than prescribed) and over-compliance (taking more medicine than prescribed). Inter-relationships between variables were investigated using multiple-regression analysis. RESULTS: Overall, 13.7% of the population (n = 512) reported non-compliance, with 10.7% reporting under-compliance and 4.3% reporting over-compliance. A number of patients reported both under- and over-compliance. Being prescribed bronchodilators, for example, was found to be associated with under-compliance, while being prescribed analgesics (excluding non-steroidal anti-inflammatories) was associated with over-compliance using Chi-square analysis. A five-variable non-compliance risk model was obtained from logistic regression analysis. This model had a specificity of 88.9% and a sensitivity of 33.3%. The factors shown to influence compliance were the type of drug being taken (diuretics, bronchodilators and benzodiazepines), independence when taking medicines and the number of non-prescription drugs being taken. All other laboratory/test data, diseases/diagnoses, reasons for hospital admission and socio-demographic factors were not significant risk factors for self-reported non-compliance in the present model. CONCLUSIONS: Although it is accepted that self-reporting of poor compliance is generally lower than actual poor compliance, the present risk model provides further insight into the drug-taking habits of elderly patients.
OBJECTIVE: To assess self-reported compliance with prescribed medications in a population of elderly patients prior to their hospital admission in an attempt to understand further the factors which influence drug-taking patterns. METHODS: Information which, based on personal clinical experience and published research, may impact on compliance was collected for patients by way of a chart review within 3 days of hospital admission, a search of patient computerised hospital records and an interview. All crude data were coded and entered into a computerised relational database. Each patient's data were assessed using the Naranjo algorithm and the score was recorded. Chi-square analysis highlighted those factors which significantly influenced compliance, sub-divided into under-compliance (taking less medicine than prescribed) and over-compliance (taking more medicine than prescribed). Inter-relationships between variables were investigated using multiple-regression analysis. RESULTS: Overall, 13.7% of the population (n = 512) reported non-compliance, with 10.7% reporting under-compliance and 4.3% reporting over-compliance. A number of patients reported both under- and over-compliance. Being prescribed bronchodilators, for example, was found to be associated with under-compliance, while being prescribed analgesics (excluding non-steroidal anti-inflammatories) was associated with over-compliance using Chi-square analysis. A five-variable non-compliance risk model was obtained from logistic regression analysis. This model had a specificity of 88.9% and a sensitivity of 33.3%. The factors shown to influence compliance were the type of drug being taken (diuretics, bronchodilators and benzodiazepines), independence when taking medicines and the number of non-prescription drugs being taken. All other laboratory/test data, diseases/diagnoses, reasons for hospital admission and socio-demographic factors were not significant risk factors for self-reported non-compliance in the present model. CONCLUSIONS: Although it is accepted that self-reporting of poor compliance is generally lower than actual poor compliance, the present risk model provides further insight into the drug-taking habits of elderly patients.
Authors: Rachel A Elliott; Dennis Ross-Degnan; Alyce S Adams; Dana Gelb Safran; Stephen B Soumerai Journal: J Gen Intern Med Date: 2007-04-05 Impact factor: 5.128