Trends and Patterns of Antibiotic Prescriptions in Primary Care Institutions in Southwest China, 2017-2022

Changlan Li; Zhezhe Cui; Du Wei; Quan Zhang; Junli Yang; Wenju Wang; Xiaobo Luo; Yue Chang

doi:10.2147/IDR.S425787

Trends and Patterns of Antibiotic Prescriptions in Primary Care Institutions in Southwest China, 2017-2022

Infect Drug Resist. 2023 Sep 5:16:5833-5854. doi: 10.2147/IDR.S425787. eCollection 2023.

Authors

Changlan Li^#¹, Zhezhe Cui², Du Wei^#^{3

4}, Quan Zhang^#⁵, Junli Yang³, Wenju Wang¹, Xiaobo Luo¹, Yue Chang^{3

4}

Affiliations

¹ School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
² Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi Province, People's Republic of China.
³ School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
⁴ Center of Medicine Economics and Management Research, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
⁵ Department of Infectious Diseases, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: To explore the prescription patterns and usage trends of antibiotics within primary care institutions located in underdeveloped regions of China from 2017 to 2022.

Methods: A retrospective analysis of antibiotic prescriptions was conducted from 25 primary care institutions in Guizhou Province during the period of 2017-2022. Antibiotic prescriptions were categorized into appropriate and inappropriate use. Appropriate use is further categorized into preferred medication, and antibiotics can be used or substituted. Inappropriate use is further categorized into unnecessary use, incorrect spectrum of antibiotics and combined use of antibiotics. Factors associated with inappropriate use were investigated using generalized estimation equations. Holt-Winters and SARIMA models were employed to predict the number of inappropriate antibiotic prescriptions as the alternative model.

Results: A total of 941,924 prescriptions were included, revealing a decreasing trend in both the number and inappropriate rates of antibiotic prescriptions from 2017 to 2022. Diseases of the respiratory system (70.66%) was the most frequent target of antibiotic use, with acute upper respiratory infections of multiple and unspecified sites representing 52.04% of these cases. The most commonly used antibiotics were penicillins (64.44%). Among all prescriptions, inappropriate antibiotic prescriptions reached 66.19%. Physicians aged over 35, holding the title of associate chief physician and possessing more than 11 years of experience were more likely to prescribe antibiotics inappropriately. The phenomenon of inappropriate antibiotic use was commoner among children aged five or younger. By comparing model parameters, it was determined that the SARIMA model outperforms the Holt-Winters model in predicting the number of inappropriate antibiotic prescriptions among primary care institutions.

Conclusion: The number and inappropriate rates of antibiotic prescriptions in southwest China exhibited a downward trend from 2017 to 2022, but inappropriate prescription remains a serious problem in primary care institutions. Therefore, future efforts should focus on strengthening physician education, training, and clinical practice. Additionally, physicians' awareness of common misconceptions about inappropriate antibiotic use must be improved, and the prescribing behavior of physicians who fulfill patients' expectations by prescribing antibiotics needs to be modified.

Keywords: antibiotics; inappropriate medication; prescription patterns; primary care institution; trends.

Grants and funding

This research was supported by the National Natural Science Foundation of China Grant (71964009) for “Research on feedback intervention mode of antibiotic prescription control in primary care institutions based on the depth graph neural network technology” and the Technology Fund Project of Guizhou Provincial Health Commission Grant (gzwjkj2019-1-218) for “Application Research of Deep Learning Technology in Rational Evaluation and Intervention of Antibiotic Prescription”. Corresponding author YC is the project leader. The funders covered travel expenses incurred during the data collection process, as well as the expert’s fees for providing guidance on study design, technological support, data analysis and interpretation, and manuscript writing assistance.