Development and validation of a nomogram to predict medication risk based on a knowledge, attitude and practice (KAP) survey of residents in Shanxi Province, China

• Published in: Frontiers in pharmacology Vol. 15; p. 1302274
• Main Authors: Xie, Da-Shuai, Xie, Xue-Hu, Yang, Li-Hua, Li, Na, Zhang, Xiao, Xie, Yi-Tong, Yang, Wei, Ning, Yao-Jun, Xie, Jun, Cheng, Xiao-Jun, Duan, Shao-Jun, Wang, Shi-Wei, Hao, Li-Hong, Shi, Ping
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 17.04.2024
• Subjects: China; factor; KAP; medication; nomogram; Pharmacology; resident; medication; KAP; nomogram; factor; China; resident
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2024.1302274

Unsafe medication practices and medication errors are a major cause of harm in healthcare systems around the world. This study aimed to explore the factors that influence the risk of medication and provide medication risk evaluation model for adults in Shanxi province, China. The data was obtained from the provincial questionnaire from May to December 2022, relying on the random distribution of questionnaires and online questionnaires by four hospitals in Shanxi Province. Multiple linear regression analysis was used to explore the factors affecting the KAP score of residents. Univariate and multivariate logistic regression was used to determine the independent risk factors, and the nomogram was verified by receiver operating characteristic curve, calibration and decision curve analysis. A total of 3,388 questionnaires were collected, including 3,272 valid questionnaires. The average scores of drugs KAP were 63.2 ± 23.04, 33.05 ± 9.60, 23.67 ± 6.75 and 33.16 ± 10.87, respectively. On the evaluation criteria of the questionnaire, knowledge was scored "fair", attitude and practice were scored "good". Sex, monthly income, place of residence, insurance status, education level, and employment were regarded as independent risk factors for medication and a nomogram was established by them. Males, low-income, and low-educated people are important factors affecting the risk of medication. The application of the model can help residents understand the risk of their own medication behavior and reduce the harm of medication.