Scholarlink

Background <p>Antimicrobial resistance caused by inappropriate antibiotic use has become a global public health crisis. The majority of antibiotics are prescribed at primary care settings which often lack sufficient capacity and surveillance. This study aimed at identifying and testing pragmatic measures and models derived from routinely collected electronic records of primary care encounters to inform continuous antibiotics stewardship.</p> Methods <p>We first extracted a total of 7.097&#xa0;million records of primary care visits over 25 months from Anhui, China, through stratified random cluster sampling and a minimum set of data about related communities. We then identified pragmatic measures and models for examining antibiotic prescribing at primary care settings through repeated cycles of measure/model identification and relevance analysis. The ‘identification’ used multidisciplinary group meetings while the analysis adopted hybrid methodologies, including descriptive analysis, random forest classification modeling, and data visualization.</p> Results <p>The study revealed that: (a) antibiotic prescribing rates ranged from 36.82 to 86.25% for the 7 categories of diagnoses studied, including respiratory diseases (RD), digestive diseases (DD), urogenital diseases (UD), skin infections (SI), injuries (IJ), eye infections (EI), and oral and dental diseases (OD); (b) although overall antibiotic prescribing decreased from 67.80 to 47.11% over the study period, the proportion of broad-spectrum added up to 78.96%; (c) the top 10% and 20% clinicians prescribed 59.9% and 80.0% of all the antibiotic prescriptions; (d) 50.8% of the antibiotic recipients received 2 or more antibiotic prescriptions within the 25-months; (e) the AUC of models of antibiotic prescribing ranged from 0.92 to 0.97 for the 7-category- diagnoses, in which the patient, clinician and spatiotemporal variables contributed 0.08 ~ 0.27, 0.30 ~ 0.70 and 0.16 ~ 0.62 respectively.</p> Conclusion <p>The measures and models derived out of routinely collected electronic records of primary healthcare encounters in this study are both feasible and useful.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

In search of pragmatic measures and models from routinely collected electronic records to inform continuous optimization of antibiotics stewardship at primary care settings: preliminary findings from Anhui, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

查看全文
  • Dongying Xiao,
  • Xin Yang,
  • Ying Zheng,
  • Jia Xu,
  • Ningjing Yang,
  • Yin Li,
  • Yuning Wang,
  • Ruirui Cui,
  • Nana Li,
  • Rong Liu,
  • Manman Lu,
  • Debin Wang,
  • Xingrong Shen

摘要

Background

Antimicrobial resistance caused by inappropriate antibiotic use has become a global public health crisis. The majority of antibiotics are prescribed at primary care settings which often lack sufficient capacity and surveillance. This study aimed at identifying and testing pragmatic measures and models derived from routinely collected electronic records of primary care encounters to inform continuous antibiotics stewardship.

Methods

We first extracted a total of 7.097 million records of primary care visits over 25 months from Anhui, China, through stratified random cluster sampling and a minimum set of data about related communities. We then identified pragmatic measures and models for examining antibiotic prescribing at primary care settings through repeated cycles of measure/model identification and relevance analysis. The ‘identification’ used multidisciplinary group meetings while the analysis adopted hybrid methodologies, including descriptive analysis, random forest classification modeling, and data visualization.

Results

The study revealed that: (a) antibiotic prescribing rates ranged from 36.82 to 86.25% for the 7 categories of diagnoses studied, including respiratory diseases (RD), digestive diseases (DD), urogenital diseases (UD), skin infections (SI), injuries (IJ), eye infections (EI), and oral and dental diseases (OD); (b) although overall antibiotic prescribing decreased from 67.80 to 47.11% over the study period, the proportion of broad-spectrum added up to 78.96%; (c) the top 10% and 20% clinicians prescribed 59.9% and 80.0% of all the antibiotic prescriptions; (d) 50.8% of the antibiotic recipients received 2 or more antibiotic prescriptions within the 25-months; (e) the AUC of models of antibiotic prescribing ranged from 0.92 to 0.97 for the 7-category- diagnoses, in which the patient, clinician and spatiotemporal variables contributed 0.08 ~ 0.27, 0.30 ~ 0.70 and 0.16 ~ 0.62 respectively.

Conclusion

The measures and models derived out of routinely collected electronic records of primary healthcare encounters in this study are both feasible and useful.