Knowledge, Attitude, and Prescription Practice on Antimicrobials Use Among Physicians: A Cross-Sectional Study in Eastern China

Xu, Rixiang; Mu, Ting-Yu; Wang, Jian; Xu, Chunfang; Shi, Jing

doi:10.1177/00469580211059984

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Firstly, physicians may not fully adhere to the recommendations provided by the CDSs due to concerns about potential adverse outcomes. It is possible that the fear of missing a bacterial infection or the fear of adverse events may influence physicians’ decision-making processes, leading to a cautious approach to antibiotic prescribing ( Ashiru-Oredope et al, 2021 ; Mmari et al, 2021 ; Xu et al, 2021 ). Secondly, patient preferences and demands may also contribute to the observed effect.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Xu,

Wu,

et al. 2023

Front. Pharmacol.

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Background: Clinical decision support tools (CDSs) have been demonstrated to enhance the accuracy of antibiotic prescribing among physicians. However, their effectiveness in reducing inappropriate antibiotic use for respiratory tract infections (RTI) is controversial.Methods: A literature search in 3 international databases (Medline, Web of science and Embase) was conducted before 31 May 2023. Relative risk (RR) and corresponding 95% confidence intervals (CI) were pooled to evaluate the effectiveness of intervention. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I2 over 50%).Results: A total of 11 cluster randomized clinical trials (RCTs) and 5 before-after studies were included in this meta-analysis, involving 900,804 patients met full inclusion criteria. Among these studies, 11 reported positive effects, 1 reported negative results, and 4 reported non-significant findings. Overall, the pooled effect size revealed that CDSs significantly reduced antibiotic use for RTIs (RR = 0.90, 95% CI = 0.85 to 0.95, I2 = 96.10%). Subgroup analysis indicated that the intervention duration may serve as a potential source of heterogeneity. Studies with interventions duration more than 2 years were found to have non-significant effects (RR = 1.00, 95% CI = 0.96 to 1.04, I2 = 0.00%). Egger’s test results indicated no evidence of potential publication bias (p = 0.287).Conclusion: This study suggests that CDSs effectively reduce inappropriate antibiotic use for RTIs among physicians. However, subgroup analysis revealed that interventions lasting more than 2 years did not yield significant effects. These findings highlight the importance of considering intervention duration when implementing CDSs.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432584, Identifier: PROSPERO (CRD42023432584).

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Section: Discussionmentioning

confidence: 99%

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Xu,

Wu,

et al. 2023

Front. Pharmacol.

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“…We observed a rebound in inappropriate antibiotic prescription rates after the transition from intervention to control. This may be due to the generally low professional and technical level of physicians in primary care institutions in China [46, 47]. Without the real-time warning pop-up message, physicians would not have been able to realize when they were prescribing inappropriate antibiotics.…”

Section: Discussionmentioning

confidence: 99%

Effects of a feedback intervention on antibiotic prescription control in primary care institutions based on depth graph neural network technology: a cluster randomized cross-over controlled trial

Yang

Cui

Liao

et al. 2022

Preprint

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Background: Overuse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of an artificial intelligence (AI)-based, automatic, and confidential antibiotic feedback intervention was evaluated to determine whether it could reduce antibiotic prescribing rates and avoid inappropriate prescribing behaviors by physicians. Methods: A randomized, cross-over, cluster-controlled trial was conducted in 77 primary care institutions of Guizhou Province, China. All institutions were randomly divided into two groups and given either a 3-month intervention followed by a 3-month period without any intervention or vice versa. The intervention consisted of 3 feedback measures: a real-time warning pop-up message of inappropriate antibiotic prescriptions on the prescribing physician’s computer screen, a 10-day antibiotic prescription feedback, and distribution of educational brochures. The primary and secondary outcomes are the 10-day antibiotic prescription rate and 10-day inappropriate antibiotic prescription rate. Results: There were 37 primary care institutions with 160 physicians in group 1 (intervention followed by control) and 40 primary care institutions with 168 physicians in group 2 (control followed by intervention). There were no significant differences in antibiotic prescription rates (32.1% vs 35.6%) and inappropriate antibiotic prescription rates (69.1% vs 72.0%) between the two groups at baseline ( p = 0.085, p = 0.072). After 3 months (cross-over point), antibiotic prescription rates and inappropriate antibiotic prescription rates decreased significantly faster in group 1 (11.9% vs 12.3%, p < 0.001) compared to group 2 (4.5% vs 3.1%, p < 0.001). At the end point, the decreases in antibiotic prescription rates were significantly lower in group 1 compared to group 2 (2.6% vs 11.7%, p < 0.001). During the same period, the inappropriate antibiotic prescription rates decreased in group 2 (15.9%, p < 0.001) while the rates increased in group 1 (7.3%, p < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates. Conclusion: The conclusion is that artificial intelligence based real-time pop-up of prescription inappropriate warning, the 10-day prescription information feedback intervention, and the distribution of educational brochures can effectively reduce the rate of antibiotic prescription and inappropriate rate.

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“…We developed a structured, standardized, and self-administered questionnaire following an extensive literature review [ 34 , 35 , 36 , 37 ]. The questionnaires were pre-tested among twenty physicians and were refined based on their inputs ( Supplementary File S1 ).…”

Section: Methodsmentioning

confidence: 99%

Antibiotic Prescribing Practices for Treating COVID-19 Patients in Bangladesh

et al. 2022

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Although national and international guidelines have strongly discouraged use of antibiotics to treat COVID-19 patients with mild or moderate symptoms, antibiotics are frequently being used. This study aimed to determine antibiotics-prescribing practices among Bangladeshi physicians in treating COVID-19 patients. We conducted a cross-sectional survey among physicians involved in treating COVID-19 patients. During September–November 2021, data were collected from 511 respondents through an online Google Form and hardcopies of self-administered questionnaires. We used descriptive statistics and a regression model to identify the prevalence of prescribing antibiotics among physicians and associated factors influencing their decision making. Out of 511 enrolled physicians, 94.13% prescribed antibiotics to COVID-19 patients irrespective of disease severity. All physicians working in COVID-19–dedicated hospitals and 87% for those working in outpatient wards used antibiotics to treat COVID-19 patients. The majority (90%) of physicians reported that antibiotics should be given to COVID-19 patients with underlying respiratory conditions. The most prescribed antibiotics were meropenem, moxifloxacin, and azithromycin. Our study demonstrated high use of antibiotics for treatment of COVID-19 patients irrespective of disease severity and the duty ward of study physicians. Evidence-based interventions to promote judicious use of antibiotics for treating COVID-19 patients in Bangladesh may help in reducing an overuse of antibiotics.

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Knowledge, Attitude, and Prescription Practice on Antimicrobials Use Among Physicians: A Cross-Sectional Study in Eastern China

Cited by 6 publications

References 33 publications

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Effects of a feedback intervention on antibiotic prescription control in primary care institutions based on depth graph neural network technology: a cluster randomized cross-over controlled trial

Antibiotic Prescribing Practices for Treating COVID-19 Patients in Bangladesh

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