Risk Factors Associated With Polypharmacy and Potentially Inappropriate Medication Use in Ambulatory Care Among the Elderly in the United States: A Cross-Sectional Study.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY
Drugs - Real World Outcomes Pub Date : 2023-09-01 Epub Date: 2023-05-26 DOI:10.1007/s40801-023-00358-2
Khue Nguyen, Vinita Subramanya, Ambar Kulshreshtha
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引用次数: 3

Abstract

Introduction: Polypharmacy, defined as the concurrent use of multiple (commonly five or more) prescription drugs, is widely prevalent among the elderly. It is a preventable and significant contributor to morbidity and mortality among older people. It is linked to prescribing potentially inappropriate medications (PIMs), which have been shown to be associated with an increased risk of adverse drug interactions and reduced compliance, and in some cases result in prescribing cascades where more drugs are prescribed to manage adverse outcomes. This study aimed to examine risk factors associated with polypharmacy and PIMs among elderly patients in outpatient settings in the US.

Methods: We conducted a cross-sectional analysis using the nationally representative National Ambulatory Medical Care Survey, between 2010 and 2016. We extracted data from all people aged 65 years or older and evaluated factors associated with polypharmacy and PIMs using multivariable logistic regression. Weights were applied to obtain national estimates.

Results: During the study period, there were a total of 81,295 ambulatory visits among adults 65 years and older. Being a woman (compared with a man) was more likely to be associated with greater prevalence of PIMs (OR: 1.31, 95% CI 1.23-1.40), and living in rural areas were more likely to be associated with both polypharmacy (OR: 1.15, 95% CI 1.07-1.23) and PIMs (OR: 1.19, 95% CI 1.09-1.29), compared with living in urban areas. Older age was positively associated with polypharmacy (OR: 1.08, 95% CI 1.06-1.10), but negatively associated with PIMs (OR: 0.97, 95% CI 0.95-0.99).

Conclusions: Our study suggests age, being a woman, and living in rural areas are risk factors for both polypharmacy and PIMs usage. Aside from primary care providers' roles in managing polypharmacy, collaborative care with other specialty providers, such as clinical pharmacists, should also be considered as an approach to improving the quality of prescribing in geriatric patients. Future research should further explore reasons for polypharmacy and focus on deprescribing and quality improvement initiatives in primary care to lower polypharmacy among the elderly.

Abstract Image

美国老年人门诊护理中与多药治疗和潜在不适当药物使用相关的风险因素:一项横断面研究。
引言:多药治疗,即同时使用多种(通常为五种或五种以上)处方药,在老年人中广泛流行。它是可预防的,也是造成老年人发病率和死亡率的重要因素。它与开可能不合适的药物(PIM)有关,已被证明与药物不良反应的风险增加和依从性降低有关,在某些情况下,还会导致开更多药物来管理不良结果的处方级联。本研究旨在检查美国门诊老年患者中与多药治疗和PIM相关的风险因素。方法:我们在2010年至2016年间使用具有全国代表性的全国门诊医疗调查进行了横断面分析。我们从所有65岁或65岁以上的人群中提取数据,并使用多变量逻辑回归评估与多药治疗和PIM相关的因素。权重用于获得国家估计数。结果:在研究期间,65岁及以上的成年人共有81295次门诊就诊。与生活在城市地区相比,女性(与男性相比)更有可能与更高的PIM患病率相关(OR:1.31,95%CI 1.23-1.40),而生活在农村地区更有可能与多药治疗(OR:1.15,95%CI 1.07-1.23)和PIM(OR:1.19,95%CI 1.05-1.29)相关。年龄较大与多药治疗呈正相关(OR:1.08,95%CI 1.06-1.10),但与PIM呈负相关(OR:0.97,95%CI 0.95-0.99)。结论:我们的研究表明,年龄、女性和生活在农村地区是多药治疗和PIM使用的风险因素。除了初级保健提供者在管理多种药物方面的作用外,与其他专业提供者(如临床药剂师)的合作护理也应被视为提高老年患者处方质量的一种方法。未来的研究应进一步探索多药治疗的原因,并将重点放在初级保健中的描述和质量改进举措上,以降低老年人的多药治疗。
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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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