Strengths and limitations
UTIs, fever, pain
UTI
The main strength of our study comprises data for regional resistance rates as a component of the multimodal intervention that has not been used in intervention studies to improve prescribing patterns in UTI before.Our rigorous data extraction allowed us to focus on uncomplicated UTIs by using additional information from the electronic medical record to exclude complicating factors such as fever, flank pain, or immunosuppression at inclusion. This method is an advantage over routinely collected data that does not allow for accurate identification of uncomplicated UTIs. However, implementation of the project on a larger scale is limited because of the resources needed.During the intervention phase, 18 (14%) of 128 practices could not be included in the full analysis set because of insufficient or no data extraction. The most frequent reason for this was the high workload during the pandemic.The lack of blinding in the practice teams may have affected the validity of the results. Reporting bias and a contamination bias were possible because intervention and control practices were located in the same area. We believe the reporting bias was minimised by use of an objective endpoint, training of medical practice assistants, and validation of data by the study team. Contamination could have influenced prescribing behaviour. The prescription proportions in the control group, however, argue against a substantial impact from no blinding because this would have tended to reduce the effect size of the intervention. To mitigate the risk of systematic errors in data extraction, research nurses randomly checked data in the practices. Nevertheless, we cannot rule out an observer bias due to awareness of the intervention. Blinding of the statistician was also not possible at the analysis stage, because the regular analysis of the data was necessary to implement components of the interventions, for example, for the feedback analysis described. However, all statistical analyses were agreed on in advance in a detailed statistical analysis plan. Moreover suggestions from current practice and guidelines of blinding statisticians in clinical trials varies widely and is uncertain.We acknowledge the reliance on expert opinion for the estimation of the standard deviation as a limitation of our study. Yet, the standard deviation observed in our study was found to be consistent with the estimated standard deviation, suggesting that the expert opinion was reasonably aligned with the actual variability in the prescription proportions among the participating practices. This alignment indicates that the estimated standard deviation accurately captured the dispersion of the data and supports the validity of the power calculation.Another limitation is that we could not identify delayed prescriptions because these were not routinely documented in the electronic medical record. Therefore, the proportion of non-antibiotic treatment may be different than reported because delayed prescriptions are an accepted treatment option for women with uncomplicated UTI.We were not able to consider the number of patients in the practice and could not determine a denominator for our analyses because no fixed patient lists are available in German general practices and the number of patients varies from quarter to quarter. Numbers are likely to have varied at random in both groups of practices. Therefore, we could not gain information about whether consultation rates changed with the intervention or not.