[Systematic Reviews, 1 December 2023]

Background: Data extraction (DE) is a challenging step in systematic reviews (SRs). Complex SRs can involve multiple interventions and/or outcomes and encompass multiple research questions. Attempts have been made to clarify DE aspects focusing on the subsequent meta-analysis; there are, however, no guidelines for DE in complex SRs. Comparing datasets extracted independently by pairs of reviewers to detect discrepancies is also cumbersome, especially when the number of extracted variables and/or studies is colossal. This work aims to provide a set of practical steps to help SR teams design and build DE tools and compare extracted data for complex SRs.

Methods: We provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.

Results: The steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.

Conclusions: We hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.

[Preventive Veterinary Medicine, 30 May 2023]

Antimicrobial resistance (AMR) in animals, including dairy cattle, is a significant concern for animal and public health worldwide. In this study, we used data collected through the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR) to: (1) describe the proportions of AMR in fecal E. coli, and (2) investigate the relationship between antimicrobial use (AMU) (intramammary and systemic routes, while accounting for confounding by other variables) and AMR/multidrug resistance (MDR – resistance to ≥ 3 antimicrobial classes) in fecal E. coli from Canadian dairy farms. We hypothesized that an increase of the AMU was associated with an increase in AMR in E. coli isolates. A total of 140 dairy farms across five provinces in Canada were included in the study. Fecal samples from pre-weaned calves, post-weaned heifers, lactating cows, and farm manure storage were cultured, and E. coli isolates were identified using MALDI-TOF MS. The minimum inhibitory concentrations (MIC) to 14 antimicrobials were evaluated using a microbroth dilution methodology. AMU was quantified in Defined Course Dose (DCD - the dose for a standardized complete treatment course on a standard size animal) and converted to a rate indicator - DCD/100 animal-years. Of 1134 fecal samples collected, the proportion of samples positive for E. coli in 2019 and 2020 was 97.1% (544/560) and 94.4% (542/574), respectively. Overall, 24.5% (266/1086) of the E. coli isolates were resistant to at least one antimicrobial. Resistance towards tetracycline was commonly observed (20.7%), whereas resistance to third-generation cephalosporins, fluoroquinolones, and carbapenems was found in 2.2%, 1.4%, and 0.1% of E. coli isolates, respectively. E. coli isolates resistant to two or ≥ 3 antimicrobial classes (MDR) was 2.7% and 15%, respectively. Two multilevel models were built to explore risk factors associated with AMR with AMU being the main exposure. Systemic AMU was associated with increased E. coli resistance. For an increase in systemic AMU equivalent to its IQR, the odds of resistance to any antimicrobial in the model increased by 18%. Fecal samples from calves had higher odds of being resistant to any antimicrobial when compared to other production ages and farm manure storage. The samples collected in 2020 were less likely to be resistant when compared to samples collected in 2019. Compared to previous studies in dairy cattle in North America, AMR in E. coli was lower.