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

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
• primary care institution
• trends
• prescription patterns
• inappropriate medication

Abbreviations

AIR, Antibiotic inappropriate rate; WHO, World Health Organization; ICD-10, the 10th Edition of the International Classification of Diseases; CDC, Centers for Disease Control and Prevention; HIS, Hospital Information System; LWTC, LianKe Weixin Co., LTD.; GEE, Generalized estimation equation; SARIMA, Seasonal autoregressive integrated moving average; R², Determination coefficient; BIC, Bayesian information criterion; RMSE, Rooted mean square error; MAPE, Mean absolute percentage error; MSE, Mean square error; CI, Confidence interval; Ref, Reference group; OR, Odds ratio.

Data Sharing Statement

The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding authors.

Acknowledgments

We express our gratitude to all participating institutions for their invaluable information and assistance throughout the study. The authors extend their appreciation to all members of the investigational team who diligently collected the data. We would also like to acknowledge Edward McNeil, from Prince of Songkla University in Songkhla, Thailand, for his insightful feedback on improving this manuscript.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.

Additional information

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.