Reasons for primary medication nonadherence : a systematic review and metric analysis

• Main Authors: Lee, Shan-Qi, Sesagiri Raamkumar, Aravind, Li, Jinhui, Cao, Yuanyuan, Chen, Luxi, Witedwittayanusat, Kanokkorn, Theng, Yin-Leng
• Other Authors: Wee Kim Wee School of Communication and Information
• Format: Article
• Language: English
• Published: 2018
• Subjects: Medication Nonadherence; Altmetrics; DRNTU::Social sciences::Communication
• Online Access: https://hdl.handle.net/10356/87540; http://hdl.handle.net/10220/46086; https://doi.org/10.21979/N9/GGQ0ZW
• Institution: Nanyang Technological University

Background: The behavior of medication nonadherence is distinguished into primary and secondary nonadherence. Primary nonadherence (PNA) is not as thoroughly studied as secondary nonadherence. Objective: To explore and synthesize contributing factors to PNA based on the existing body of literature. Methods: A search was performed on the PubMed, PsycINFO, CINAHL, and ScienceDirect databases to identify previously published scholarly articles that described the “factors,” “reasons,” “determinants” or “facilitators” of PNA. The alternate spelling of “nonadherence” was used as well. The effect that the articles had in the research community, as well as across social media, was also explored. Results: 22 studies met the inclusion criteria for this review. The PNA factors that the studies identified were diverse, spanning economic, social, and medical dimensions. A multilevel classification method was applied to categorize the factors into 5 broad groups—patient, medication, health care provider, health care system, and socioeconomic factors. Patient factors were reported the most. Some groups overlapped and shared a dynamic causal relationship where one group influenced the outcome of the other. Conclusions: Like all nonadherence behaviors, PNA is multifaceted with highly varied contributing factors that are closely associated with one another. Given the multidimensional nature of PNA, future intervention studies should focus on the dynamic relationship between these factor groups for more efficient outcomes.