Background Complex medication regimens may adversely affect compliance and treatment outcomes. Complexity can be assessed with the medication regimen complexity index (MRCI), which has proved to be a valid, reliable tool, with potential uses in both practice and research. Objective To use the MRCI to assess medication regimen complexity in institutionalized elderly people. Setting Five nursing homes in mainland Portugal. Methods A descriptive, cross-sectional study of institutionalized elderly people (n = 415) was performed from March to June 2009, including all inpatients aged 65 and over taking at least one medication per day. Main outcome measure Medication regimen complexity index. Results The mean age of the sample was 83.9 years (±6.6 years), and 60.2 % were women. The elderly patients were taking a large number of drugs, with 76.6 % taking more than five medications per day. The average medication regimen complexity was 18.2 (±SD = 9.6), and was higher in the females (p \ 0.001). The most decisive factors contributing to the complexity were the number of drugs and dosage frequency. In regimens with the same number of medications, schedule was the most relevant factor in the final score (r = 0.922), followed by pharmaceutical forms (r = 0.768) and additional instructions (r = 0.742). Conclusion Medication regimen complexity proved to be high. There is certainly potential for the pharmacist's intervention to reduce it as part as the medication review routine in all the patients.
Daily medication use can be affected by the gradual loss of functional ability. Thus, elderly patients are at risk for nonadherence due to functional decline, namely, decreases in cognitive skills and visual and manual dexterity. The main objective was to assess the ability of older people to self-manage their medication and to identify the main predictors for unintentional nonadherence. A cross-sectional study was conducted (2014–2017) in community centers and pharmacies. Functional assessment was performed with the Portuguese versions of the Drug Regimen Unassisted Grading Scale (DRUGS-PT) and the Self-Medication Assessment Tool (SMAT-PT). A purposive sample including 207 elderly patients was obtained. To identify the main predictors, binary logistic regression was performed. The average DRUGS-PT score was slightly lower than that in other studies. On the SMAT-PT, the greatest challenge for patients was identifying medications by reading labels/prescriptions. The main difficulties identified were medication memorization and correct schedule identification. The scores were higher with the real regimen than with the simulated regimen, underlining the difficulties for patients in receiving new information. Regarding the predictors of an older individual’s ability to self-manage medications, two explanatory models were obtained, with very high areas under the curve (> 90%). The main predictors identified were cognitive ability, level of schooling and daily medication consumption.
Purpose: Spontaneous notification systems are essential in a post-marketing safety context. However, using this method, only about 6% of all adverse drug reactions are notified. To overcome this sub-notification problem, new methods need to be developed to improve and facilitate reporting. In this sense, the use of digital media, mainly medical mobile apps, has been presented as a powerful tool, including in pharmacovigilance. We performed a scope review to identify the available apps used to report adverse drug reactions around the world to eventually identify which of them best fits the Portuguese pharmacovigilance system.
Methods:The Joanna Briggs Institute guidelines were considered, and the framework proposed by Arksey and O'Malley was followed. All the articles that met the inclusion criteria were examined for this review. When the studies lacked in information about the app, Google was used to enhance the search for further information.Results: A final number of five articles were included, revealing seven implemented mobile apps for adverse drug reaction report (Medwatcher, VigiBIP, Yellow Card, Bijwerking, Halmed, Med Safety, and ADR PvPi). These apps are implemented in the
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