Dataset Information

Identifying Antibiotic Prescribing Patterns Through Multi-Level Latent Profile Analyses: A Cross-Sectional Survey of Primary Care Physicians.

ABSTRACT: Background: Overuse of antibiotics significantly fuels the development of Antimicrobial resistance, which threating the global population health. Great variations existed in antibiotic prescribing practices among physicians, indicating improvement potential for rational use of antibiotics. This study aims to identify antibiotic prescribing patterns of primary care physicians and potential determinants. Methods: A cross-sectional survey was conducted on 551 physicians from 67 primary care facilities in Hubei selected through random cluster sampling, tapping into their knowledge, attitudes and prescribing practices toward antibiotics. Prescriptions (n = 501,072) made by the participants from 1 January to March 31, 2018 were extracted from the medical records system. Seven indicators were calculated for each prescriber: average number of medicines per prescription, average number of antibiotics per prescription, percentage of prescriptions containing antibiotics, percentage of antibiotic prescriptions containing broad-spectrum antibiotics, percentage of antibiotic prescriptions containing parenteral administered antibiotics, percentage of antibiotic prescriptions containing restricted antibiotics, and percentage of antibiotic prescriptions containing antibiotics included in the WHO "Watch and Reserve" list. Two-level latent profile analyses were performed to identify the antibiotic prescribing patterns of physicians based on those indicators. Multi-nominal logistic regression models were established to identify determinants with the antibiotic prescribing patterns. Results: On average, each primary care physician issued 909 (ranging from 100 to 11,941 with a median of 474) prescriptions over the study period. The mean percentage of prescriptions containing antibiotics issued by the physicians reached 52.19% (SD = 17.20%). Of those antibiotic prescriptions, an average of 82.29% (SD = 15.83%) contained broad-spectrum antibiotics; 71.92% (SD = 21.42%) contained parenteral administered antibiotics; 23.52% (SD = 19.12%) contained antibiotics restricted by the regional government; and 67.74% (SD = 20.98%) contained antibiotics listed in the WHO "Watch and Reserve" list. About 28.49% of the prescribers were identified as low antibiotic users, compared with 51.18% medium users and 20.33% high users. Higher use of antibiotics was associated with insufficient knowledge, indifference to changes, complacency with satisfied patients, low household income and rural location of the prescribers. Conclusion: Great variation in antibiotic prescribing patterns exists among primary care physicians in Hubei of China. High use of antibiotics is not only associated with knowledge shortfalls but also low socioeconomic status of prescribers.

SUBMITTER: Wang D

PROVIDER: S-EPMC7748108 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identifying Antibiotic Prescribing Patterns Through Multi-Level Latent Profile Analyses: A Cross-Sectional Survey of Primary Care Physicians.

Wang Dan D Liu Chaojie C Zhang Xinping X Liu Chenxi C

Frontiers in pharmacology 20201111

<b>Background</b>: Overuse of antibiotics significantly fuels the development of Antimicrobial resistance, which threating the global population health. Great variations existed in antibiotic prescribing practices among physicians, indicating improvement potential for rational use of antibiotics. This study aims to identify antibiotic prescribing patterns of primary care physicians and potential determinants. <b>Methods</b>: A cross-sectional survey was conducted on 551 physicians from 67 primar ...[more]

PMID: 33343361

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Project description:Background: Misuse of antibiotics is prevalent worldwide and primary care is a major contributor. Although a clear diagnosis is fundamental for rational antibiotic use, primary care physicians often struggle with diagnostic uncertainty. However, we know little about how physicians cope with this situation and its association with antibiotic prescribing. Methods: A total of 583 primary care physicians were surveyed using the Dealing with Uncertainty Questionnaire. Their prescriptions (n = 949,181) over the year 2018 were retrieved retrospectively. Two categories of behavioral patterns of participants were identified based on latent class analyses (high vs. low openness and collaborativeness) in responding to diagnostic uncertainty. Multi-level logistic regression models were established to determine the associations between these behavioral patterns and antibiotic prescribing (overall and broad-spectrum antibiotics) for illness without an indication for antibiotics and those with a conditional indication for antibiotics, respectively, after adjustment for variations of patient (level one) and physician (level two) characteristics. Results: Most physicians reported open communications with their patients (80.96%), collected further information (85.08%), and referred patients to specialists (68.95%) in dealing with diagnostic uncertainly. More than half (56.95%) sought help from colleagues. Less than 20% acted on intuition or adopted a "wait and see" strategy. About 40% participants (n = 238) were classified into the group of low openness and collaborativeness in coping with diagnostic uncertainty. They were more likely to prescribe antibiotics for the recorded illness without an indication for antibiotics (AOR = 1.013 for all antibiotics, p = 0.024; AOR = 1.047 for broad-spectrum antibiotics, p < 0.001), as well as for the recorded illness with a conditional indication for antibiotics (AOR = 1.226 for all antibiotic, p < 0.001; AOR = 1.257 for broad-spectrum antibiotics, p < 0.001). Conclusion: Low tolerance with diagnostic uncertainty is evident in primary care. Inappropriate and over antibiotic prescribing is shaped by physicians' coping methods of diagnostic uncertainty. Physicians' openness and collaborativeness in responding to diagnostic uncertainty is associated with lower antibiotic prescribing in primary care. Interventions targeting on better management of diagnostic uncertainty may offer a promising approach in reducing antibiotic use in primary care.

Project description:BackgroundInappropriate use of antibiotics promotes antibiotic resistance. Little is known about physician characteristics that may be associated with inappropriate antibiotic prescribing. Our objective was to assess whether physician knowledge, time in practice, place of training and practice volume explain the differences in antibiotic prescribing among physicians.MethodsA historical cohort of 852 primary care physicians in Quebec who became certified between 1990 and 1993 was followed for their first 6-9 years of practice (1990-1998). We evaluated whether inappropriate antibiotic prescribing had occurred during the study period (1990-1998) for viral (prescription of antibiotics) and bacterial (prescription of second-or third-line antibiotics given orally) infections. We used logistic regression to estimate the independent contributions of time in practice, practice volume, place of medical training and scores on licensure examinations. Physician sex and visit setting were controlled for, as were patient age, sex, education, income and geographic area of residence.ResultsA total of 104 230 patients who received a diagnosis of a viral infection and 65 304 who received a diagnosis of a bacterial infection were included in our study. International medical graduates were more likely than University of Montréal graduates to prescribe antibiotics for viral respiratory infections (risk ratio [RR] 1.78, 95% confidence interval [CI] 1.30-2.44). Inappropriate antibiotic prescribing increased with time in practice. Physicians with a high practice volume were more likely than those with low practice volume to prescribe antibiotics for viral respiratory infections (RR 1.27, 95% CI 1.09-1.48) and to prescribe second-and third-line antibiotics as first-line treatment (RR 1.20, 95% CI 1.06-1.37). Physician scores on licensure examinations were not predictive of inappropriate antibiotic prescribing.InterpretationInternational medical graduates, physicians with high-volume practices and those who were in practice longer were more likely to prescribe antibiotics inappropriately. Developing effective interventions will require increased knowledge of the mechanisms that underlie these predictors of inappropriate antibiotic prescribing.

Project description:Objective: The purpose of this study is to investigate the mental workload level of physicians in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China and explore the subtypes of physicians regarding their mental workload. Methods: A cross-sectional survey of 1,934 physicians primarily in 24 hospitals in 6 provinces in Eastern, Central, and Western China was conducted from November 2020 to February 2021. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of physician mental workload scale developed by our research team. Chi-square tests were performed to explore the differences in demographic characteristics of the subtypes among the subgroups, and multinomial logistic regression analysis was further conducted to identify the determinants of the subtypes of physicians. Results: Overall, the participating physicians reported high levels of task load but with high self-assessed performance (68.01 ± 14.25) while performing communication work tasks characterized by direct patient interaction in outpatient clinics. About 33.8% of the participating physicians were identified as "high workload and high self-assessment" subtype, compared to 49.7% "medium workload and medium self-assessment" subtype and 16.4% "low workload and low self-assessment" subtype. Physicians in "high workload and high self-assessment" subtype had the highest mean mental workload score. Physicians who were female, younger, married, worse health status, those who had lower educational level and an average monthly income of 5,001-10,000 RMB, those who worked in tertiary A hospitals, more hours per week and more than 40 h per week in outpatient clinics, and those who saw more outpatients per day, and spent more time per patient but with higher outpatient satisfaction were more likely to belong to "high workload and high self-assessment" subtype. Conclusion: Our findings can help provide a solid foundation for developing targeted interventions for individual differences across physicians regarding their mental workload. We suggest the hospital managers should pay more attention to those physicians with characteristics of the "high workload and high self-assessment" subtype and strengthen the management of the workload of this subtype of physicians to reduce the risks of their mental health, and to maintain their high work performance in outpatient clinics.

Dataset Information

Identifying Antibiotic Prescribing Patterns Through Multi-Level Latent Profile Analyses: A Cross-Sectional Survey of Primary Care Physicians.

Publications

Identifying Antibiotic Prescribing Patterns Through Multi-Level Latent Profile Analyses: A Cross-Sectional Survey of Primary Care Physicians.

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