There were only two studies that described the appropriateness of outpatient antibiotic prescriptions in multiple provinces of China. One study estimated that in primary health care settings in China, more than 60% of antibiotic prescriptions were inappropriate by using a sample of only 7311 outpatient visits from six provinces. The other study reported the appropriateness of antibiotic prescriptions in tertiary-level hospitals in 25 provinces of Mainland China, with 0•45 million prescriptions. Both studies were conducted through manual prescription review, of which the review scheme was not clearly described and validated. No study investigated the antibiotic prescription rates for various diagnoses at the national level in China. Added value of this studyWe analyzed a prescription data of 173 million outpatient visits from 28 provinces collected during October 2014 and April 2018 in China. Using a well established and validated approach, we established the baseline of outpatient antibiotic prescription rates for various diagnoses and the proportion of inappropriate antibiotic prescribing, and our results suggested that over 50% of outpatient antibiotic prescribing in China was inappropriate. To the best of our knowledge, no previous studies analyzed such a big prescription data to investigate antibiotic prescribing in China. This study provides the most recent and comprehensive evaluation of the appropriateness of outpatient antibiotic prescriptions in China, which can be benchmark for future studies to assess the progress of China curbing antibiotic misuse and overuse. Implications of all the available evidenceAlthough years of efforts to curb antibiotic use have significantly reduced antibiotic prescription rate, the inappropriate antibiotic prescribing is still prevalent in China. Our findings inform policy makers in China, as well as other countries with high prevalence of inappropriate antibiotic prescribing, carrying out more in-depth antibiotic stewardship programs focusing on reducing inappropriate antibiotic prescribing to achieve the goals of optimizing antibiotic use and curbing antimicrobial resistance. This study also provides a precedent for the use of large-scale prescription data and a wellestablished methodological framework to evaluate the appropriateness of antibiotic prescriptions in China. Future studies focusing on antibiotic use in China can apply our methods to evaluate the appropriateness of antibiotic prescribing by using big electric medical records or administrative data. Summary BackgroundInappropriate antibiotic use greatly accelerates antimicrobial resistance. The appropriateness of antibiotic prescriptions is well evaluated, using big observational data, in some high-income countries (HICs), whereas the evidence of this appropriateness is very limited in China. We aimed to assess the appropriateness of antibiotic prescriptions in China ambulatory care settings. MethodsWe used a data from the Beijing Data Center for Rational Use of Drugs, which was a national database designed for...

ObjectiveWe aimed to evaluate the validity of an algorithm to classify diagnoses according to the appropriateness of outpatient antibiotic use in the context of Chinese free text.Setting and participantsA random sample of 10 000 outpatient visits was selected between January and April 2018 from a national database for monitoring rational use of drugs, which included data from 194 secondary and tertiary hospitals in China.Research designDiagnoses for outpatient visits were classified as tier 1 if associated with at least one condition that ‘always’ justified antibiotic use; as tier 2 if associated with at least one condition that only ‘sometimes’ justified antibiotic use but no conditions that ‘always’ justified antibiotic use; or as tier 3 if associated with only conditions that never justified antibiotic use, using a tier-fashion method and regular expression (RE)-based algorithm.MeasuresSensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the classification algorithm, using classification made by chart review as the standard reference, were calculated.ResultsThe sensitivities of the algorithm for classifying tier 1, tier 2 and tier 3 diagnoses were 98.2% (95% CI 96.4% to 99.3%), 98.4% (95% CI 97.6% to 99.1%) and 100.0% (95% CI 100.0% to 100.0%), respectively. The specificities were 100.0% (95% CI 100.0% to 100.0%), 100.0% (95% CI 99.9% to 100.0%) and 98.6% (95% CI 97.9% to 99.1%), respectively. The PPVs for classifying tier 1, tier 2 and tier 3 diagnoses were 100.0% (95% CI 99.1% to 100.0%), 99.7% (95% CI 99.2% to 99.9%) and 99.7% (95% CI 99.6% to 99.8%), respectively. The NPVs were 99.9% (95% CI 99.8% to 100.0%), 99.8% (95% CI 99.7% to 99.9%) and 100.0% (95% CI 99.8% to 100.0%), respectively.ConclusionsThe RE-based classification algorithm in the context of Chinese free text had sufficiently high validity for further evaluating the appropriateness of outpatient antibiotic prescribing.

Background: Antibiotic use in pregnant women at the national level has rarely been reported in China. Objectives: We aimed to investigate antibiotic prescriptions during pregnancy in ambulatory care settings in China. Methods: Data of 4,574,961 ambulatory care visits of pregnant women from October 2014 to April 2018 were analyzed. Percentages of Antibiotic prescriptions by different subgroups and various diagnosis categories and proportions of inappropriate antibiotic prescriptions for different subgroups were estimated. Food and Drug Administration (FDA) pregnancy categories were used to describe the antibiotic prescription patterns. The 95% confidence intervals (CIs) were estimated using the Clopper––Pearson method or Goodman method. Results: Among the 4,574,961 outpatient visits during pregnancy, 2.0% (92,514 visits; 95% CI, 2.0–2.0%) were prescribed at least one antibiotic. The percentage of antibiotic prescriptions for pregnant women aged >40 years was 4.9% (95% CI, 4.7–5.0%), whereas that for pregnant women aged 26–30 years was 1.5% (95% CI, 1.4–1.5%). In addition, percentages of antibiotic prescriptions varied among different trimesters of pregnancy, which were 5.4% (95% CI, 5.3–5.4%) for the visits in the first trimester of pregnancy and 0.5% (95% CI, 0.4–0.5%) in the third trimester of pregnancy. Furthermore, the percentages of antibiotic prescriptions substantially varied among different diagnosis categories and nearly three-quarters of antibiotic prescriptions had no clear indications and thus might be inappropriate. In total, 130,308 individual antibiotics were prescribed; among these, 60.4% (95% CI, 60.0–60.8%) belonged to FDA category B, 2.7% (95% CI, 2.1–3.5%) were classified as FDA category D and 16.8% (95% CI, 16.2–17.4%) were not assigned any FDA pregnancy category. Conclusions: Antibiotic prescriptions in ambulatory care during pregnancy were not highly prevalent in mainland China. However, a substantial proportion of antibiotics might have been prescribed without adequate indications. Antibiotics whose fetal safety has not been sufficiently illustrated were widely used in pregnant women.

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Prolonged opioid treatment leads to a comprehensive cellular adaptation mediated by opioid receptors, a basis to understand the development of opioid tolerance and dependence. However, the molecular mechanisms underlying opioid-induced cellular adaptation remain obscure. Recent advances in opioid receptor trafficking and signaling in cells have extensively increased our insight into the network of intracellular signal integration. This review focuses on those important intracellular biochemical processes that play critical roles in the development of opioid tolerance and dependence after opioid receptor activation, and tries to explain what happens after opioid receptor activation, and how the cellular adaptation develops from cell membrane to nucleus. Decades of research have delineated a network on opioid receptor trafficking and signaling, but the challenge remains to explain opioid tolerance and dependence from a single cellular signal network.