Variability in the prevalence of inappropriate medication use among older adults: A review highlighting the importance of screening methods and database types.

Aims: The global older population is growing rapidly, and the rise in polypharmacy has increased potentially inappropriate medication (PIM) encounters. PIMs pose health risks, but detecting them automatically in large medical databases is complex. This review aimed to uncover PIM prevalence in individuals aged 65 years or older using health databases and emphasized the risk of underestimating PIM prevalence due to underutilization of detection tools.

Methods: This study conducted a broad search on the Medline database to identify articles about the prevalence of PIMs in older adults using various databases. Articles published between January 2010 and June 2023 were included, and specific criteria were applied for study selection. Two literature reviews conducted before our study period were integrated to obtain a perspective from the 1990s to the present day. The selected papers were analysed for variables including database type, screening method, adaptations and PIM prevalence. The study categorized databases and original screening tools for clarity, examined adaptations and assessed concordance among different screening methods.

Results: This study encompassed 48 manuscripts, covering 58 sample evaluations. The mean prevalence of PIMs within the general population aged over 65 years was 27.8%. Relevant heterogeneity emerged in both the utilized databases and the detection methods. Adaptation of original screening tools was observed in 86.2% (50/58) of cases. Half of the original screening tools used for assessing PIMs belonged to the simple category. About a third of the studies employed less than half of the original criteria after adaptation. Only three studies used over 75% of the original criteria and more than 50 criteria.

Conclusions: This extensive review highlights PIM prevalence among the older adults, emphasizing method intricacies and the potential for underestimation due to data limitations and algorithm adjustments. The findings call for enhanced methodologies, transparent algorithms and a deeper understanding of intricate rules' impact on public health implications.