Association Between Inappropriate Prescribing According to the 2023 Beers Criteria and Different Health Outcomes: A 1-Year Longitudinal Study in Community-Dwelling Older Adults.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-12-23 DOI:10.1007/s40801-024-00474-7
Rosiane Mendes da Silva, Alessandra Lamas Granero Lucchetti, Maria Eduarda Corradi Ferreira, Leonardo Oliveira Silva, Oscarina da Silva Ezequiel, Eduardo Luiz Mendonça Martins, Giancarlo Lucchetti
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Abstract

Introduction: This study aimed to investigate the association between the 2023 Beers criteria for inappropriate prescribing and different health outcomes among community-dwelling older individuals after a 1-year follow-up period and to assess the use and factors associated with inappropriate prescribing.

Methods: This longitudinal population study spanning from 2017 to 2018 included 490 community-dwelling older adults (≥60 years old) receiving care from family medicine teams in the city of São João del-Rei, Brazil. The 2023 Beers criteria was used to identify potentially inappropriate medications (PIMs). Community health workers carried out interviews assessing different health outcomes, such as cognition, sleep, mental health, quality of life, successful aging, and life satisfaction. Generalized estimating equations were used to evaluate whether the presence of PIMs was longitudinally associated with diverse outcomes following the 1-year follow-up period.

Results: A total of 255 (52%) of the participants used at least one PIM. The most common PIMs were benzodiazepines (36.5-38.3%), followed by proton pump inhibitors (16.2-18.4%) and sulfonylureas (9.8-10.6%). Some sociodemographic factors (e.g., marital status and race) and clinical factors (e.g., difficulties in activities of daily living and the number of diseases) were associated with the presence and/or number of PIMs at baseline. In the longitudinal analysis, the presence of PIMs exhibited associations with a spectrum of outcomes observed after a 1-year follow-up period. These outcomes included diminished physical quality of life (B = -0.21; p = 0.030), disrupted sleep patterns (B = 1.14; p < 0.001), compromised mental health-depression (B = 1.04; p = 0.041), stress (B = 2.00; p = 0.001), and anxiety (B = 1.26; p = 0.004), successful aging (B = -1.92; p = 0.033), and satisfaction with life (B = -0.77; p = 0.013).

Conclusion: The use of at least one PIM, according to the 2023 Beers criteria, was high and associated with worse health outcomes. This underscores the imperative for healthcare professionals to exercise caution when prescribing medications to older patients.

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根据2023年Beers标准开具的不当处方与不同健康结果之间的关联:一项针对社区居住老年人的1年纵向研究
前言:本研究旨在通过1年的随访,调查2023 Beers处方不当标准与社区居住老年人不同健康结局之间的关系,并评估处方不当的使用和相关因素。方法:这项从2017年到2018年的纵向人口研究纳入了巴西 o jo o del-Rei市490名社区居住的老年人(≥60岁),他们接受家庭医疗团队的护理。2023年Beers标准用于识别潜在的不适当药物(PIMs)。社区卫生工作者进行了访谈,评估不同的健康结果,如认知、睡眠、心理健康、生活质量、成功老龄化和生活满意度。使用广义估计方程来评估PIMs的存在是否与1年随访期间的各种结果纵向相关。结果:共有255名(52%)参与者至少使用了一种PIM。最常见的pim是苯二氮卓类药物(36.5-38.3%),其次是质子泵抑制剂(16.2-18.4%)和磺脲类药物(9.8-10.6%)。一些社会人口因素(如婚姻状况和种族)和临床因素(如日常生活活动困难和疾病数量)与基线时pim的存在和/或数量有关。在纵向分析中,PIMs的存在与1年随访期后观察到的一系列结果相关。这些结果包括身体生活质量下降(B = -0.21;p = 0.030),睡眠模式紊乱(B = 1.14;p结论:根据2023年比尔斯标准,至少使用一种PIM的比例较高,且与较差的健康结果相关。这强调了医疗保健专业人员在给老年患者开处方时必须谨慎行事。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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