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Zhang Y, Ren Y, Huang Y, Yao M, Jia Y, Wang Y, Mei F, Zou K, Tan J, Sun X. Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey. BMC Med Res Methodol 2024; 24:62. [PMID: 38461257 PMCID: PMC10924989 DOI: 10.1186/s12874-024-02184-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/20/2024] [Indexed: 03/11/2024] Open
Abstract
INTRODUCTION Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. OBJECTIVE To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. METHODS A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. RESULTS We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. CONCLUSION The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.
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Affiliation(s)
- Yuanjin Zhang
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Yan Ren
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Yunxiang Huang
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Minghong Yao
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Yulong Jia
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Yuning Wang
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Fan Mei
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Kang Zou
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China.
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China.
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China.
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China.
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China.
- Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu, China.
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Tian ZX, Yi XX, Cho A, O’Gara F, Wang YP. CpxR Activates MexAB-OprM Efflux Pump Expression and Enhances Antibiotic Resistance in Both Laboratory and Clinical nalB-Type Isolates of Pseudomonas aeruginosa. PLoS Pathog 2016; 12:e1005932. [PMID: 27736975 PMCID: PMC5063474 DOI: 10.1371/journal.ppat.1005932] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 09/13/2016] [Indexed: 12/30/2022] Open
Abstract
Resistance-Nodulation-Division (RND) efflux pumps are responsible for multidrug resistance in Pseudomonas aeruginosa. In this study, we demonstrate that CpxR, previously identified as a regulator of the cell envelope stress response in Escherichia coli, is directly involved in activation of expression of RND efflux pump MexAB-OprM in P. aeruginosa. A conserved CpxR binding site was identified upstream of the mexA promoter in all genome-sequenced P. aeruginosa strains. CpxR is required to enhance mexAB-oprM expression and drug resistance, in the absence of repressor MexR, in P. aeruginosa strains PA14. As defective mexR is a genetic trait associated with the clinical emergence of nalB-type multidrug resistance in P. aeruginosa during antibiotic treatment, we investigated the involvement of CpxR in regulating multidrug resistance among resistant isolates generated in the laboratory via antibiotic treatment and collected in clinical settings. CpxR is required to activate expression of mexAB-oprM and enhances drug resistance, in the absence or presence of MexR, in ofloxacin-cefsulodin-resistant isolates generated in the laboratory. Furthermore, CpxR was also important in the mexR-defective clinical isolates. The newly identified regulatory linkage between CpxR and the MexAB-OprM efflux pump highlights the presence of a complex regulatory network modulating multidrug resistance in P. aeruginosa. Pseudomonas aeruginosa is one of the major pathogens associated with cystic fibrosis and multidrug resistant P. aeruginosa has been listed as the Top 10 antibiotic resistance threats in the US CDC report (http://www.cdc.gov/drugresistance/biggest_threats.html). Drug efflux systems play a major role in multidrug resistance in P. aeruginosa. Currently, the regulatory networks modulating efflux pump expression are not fully understood. Here, we demonstrate that CpxR, a potentially multifaceted regulator, is directly involved in regulation of expression of MexAB-OprM, the major efflux pump in P. aeruginosa. The newly identified activator CpxR plays an important role in modulating multidrug resistance in nalB-type laboratory and clinical isolates. This work provides insight into the complex regulatory networks modulating multidrug resistance in P. aeruginosa.
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Affiliation(s)
- Zhe-Xian Tian
- State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China
- * E-mail: (ZXT); (YPW)
| | - Xue-Xian Yi
- State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China
| | - Anna Cho
- State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China
| | - Fergal O’Gara
- BIOMERIT Research Centre, Department of Microbiology, University College Cork, Cork, Ireland
- School of Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia, Australia
| | - Yi-Ping Wang
- State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China
- * E-mail: (ZXT); (YPW)
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