Common combinations of medications used among oldest-old women: a population-based study over 15 years

Kaeshaelya Thiruchelvam, Julie Byles, Syed Shahzad Hasan, Nicholas Egan, Dominic Cavenagh, Therese Kairuz

Research output: Contribution to journalArticle

Abstract

Background: Older people use many medications, but combinations of medications used among the oldest old (≥80 years)are not commonly reported. Aims: This study aimed to determine common combinations of medications used among women aged 77–96 years and to describe characteristics associated with these combinations. Methods: A cohort study of older women enroled in the Australian Longitudinal Study on Women’s Health over a 15-year period was used to determine combinations of medications using latent class analysis. Multinomial logistic regression was used to determine characteristics associated with these combinations. Results: The highest medication users during the study were for the cardiovascular (2003: 80.28%; 2017: 85.63%) and nervous (2003: 66.03%; 2017: 75.41%) systems. A 3-class latent model described medication use combinations: class 1:‘Cardiovascular & neurology anatomical group’ (27.25%) included participants using medications of the cardiovascular and nervous systems in their later years; class 2: ‘Multiple anatomical group’ (16.49%) and class 3: ‘Antiinfectives & multiple anatomical group’ (56.27%). When compared to the reference class (class 1), the risk of participants being in class 3 was slightly higher than being in class 2 if they had >4 general practitioner visits (RRR 2.37; 95% CI 2.08, 2.71), Department of Veterans Affairs’ coverage (RRR 1.59; 95% CI 1.36, 1.86), ≥4 chronic diseases (RRR 3.16; 95% CI 2.56, 3.90) and were frail (RRR 1.47; 95% CI 1.27, 1.69). Conclusion: Identification of combinations of medication use may provide opportunities to develop multimorbidity guidelines and target medication reviews, and may help reduce medication load for older individuals.
Original languageEnglish
JournalAging clinical and experimental research
Early online date9 Sep 2020
DOIs
Publication statusE-pub ahead of print - 9 Sep 2020

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