Psychotropic Drug Use and Associated Factors Among Acutely Admitted Older People: A Cross-Sectional Study of a Clinical Sample.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY
Ema Gasi, Maria Gustafsson, Jonas Kindstedt
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引用次数: 0

Abstract

Introduction: Older people are on average more susceptible to the adverse effects of psychotropic drugs, but addressing older people as a homogenous group based on age alone can be misleading when exploring psychotropic drug use. This study aimed to describe psychotropic drug use and associated factors among community-dwelling older people who had been acutely admitted to hospital.

Methods: This cross-sectional study was based on a sample of 300 community-dwelling people 75 years or older who had been admitted to the acute medical ward at Umeå University Hospital at any time from September 2018 to October 2021. Data on medication use were obtained from electronic medical charts, and psychotropic drug use was presented as user proportions, both in terms of individual substances and drug classes. Associations between psychotropic drug use and factors comprising sex, age, cohabitation, comorbidities and multi-dose dispensing (MDD) of medicines were analysed through logistic regression.

Results: Approximately 50% of the individuals used at least one psychotropic drug, and 18% used two or more such medicines. Zopiclone displayed the highest user proportion of all psychotropics (18.3%), followed by mirtazapine (11.3%) and zolpidem (9.7%). Of note, zolpidem was more prevalent among the females than among the males (p = 0.006). Regarding other sex differences, 55 and 38% of the females and males, respectively, used at least one psychotropic drug (p = 0.004). A similar pattern was observed regarding sedatives and hypnotic drugs exclusively (p = 0.048). In the regression analysis, female sex (adjusted odds ratio [OR] 2.05 [95% confidence interval {CI} 1.22-3.42]) and MDD (adjusted OR 2.20 [95% CI 1.23-3.93]) were positively associated with psychotropic drug use.

Conclusion: The most common psychotropic drugs used by community-dwelling older people admitted to the acute medical ward were hypnotic drugs and antidepressants. Regarding patient factors, female sex and MDD system were positively associated with psychotropic drug use. Further studies concerning those two factors in relation to potential overprescribing could provide a better picture on how to optimize psychotropic drug use among acutely admitted vulnerable older people.

急诊入院老年人的精神药物使用情况及相关因素:临床样本横断面研究》。
导言:平均而言,老年人更容易受到精神药物的不良影响,但在探讨精神药物的使用时,仅根据年龄将老年人作为一个同质群体可能会产生误导。本研究旨在描述急性入院的社区老年人使用精神药物的情况及相关因素:这项横断面研究的样本为 300 名 75 岁或以上的社区居民,他们在 2018 年 9 月至 2021 年 10 月期间的任何时候曾入住于于默奥大学医院的急诊病房。药物使用数据来自电子病历,精神药物使用情况以使用者比例呈现,包括单个药物和药物类别。通过逻辑回归分析了精神药物使用与性别、年龄、同居、合并症和多剂量配药(MDD)等因素之间的关系:结果:约 50%的人至少使用一种精神药物,18%的人使用两种或两种以上此类药物。在所有精神药物中,佐匹克隆的使用比例最高(18.3%),其次是米氮平(11.3%)和唑吡坦(9.7%)。值得注意的是,唑吡坦在女性中的使用率高于男性(P = 0.006)。在其他性别差异方面,分别有 55% 和 38% 的女性和男性至少使用过一种精神药物(p = 0.004)。在镇静剂和催眠药方面也观察到类似的情况(p = 0.048)。在回归分析中,女性(调整后比值比 [OR] 2.05 [95% 置信区间 {CI}1.22-3.42])和 MDD(调整后比值比 2.20 [95% 置信区间 {CI}1.23-3.93])与精神药物的使用呈正相关:结论:入住急症病房的社区老年人最常使用的精神药物是催眠药和抗抑郁药。在患者因素方面,女性性别和 MDD 系统与精神药物的使用呈正相关。进一步研究这两个因素与潜在的过量用药之间的关系,可以更好地了解如何在急诊住院的弱势老年人中优化精神药物的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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