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The analgesic metamizole, which has been withdrawn from the market in several countries due to the risk of agranulocytosis but is still available on the market in Germany and some other countries, has been associated with liver injury in published case reports; however, epidemiological studies on the risk of liver injury are limited. The aim of this study was to compare the risk of liver injury up to 270 days after the first start of treatment with metamizole with the corresponding risk in patients starting treatment with paracetamol, using a retrospective cohort incident user design. The first prescription for either metamizole or paracetamol in the Intercontinental Medical Statistics (IMS)® Disease Analyzer Germany database during the study period (2009–2018) was identified in patients with at least 365 days of observation and no prior diagnosis of liver events, cancer or HIV, or treatment within the last 6 months with hepatotoxic drugs typically administered for chronic conditions. Each patient was followed for specific liver events for 90 days after the prescription. In case of a new prescription within 90 days, a new 90-day observation period started, up to a maximum of 270 days. Cox regression was used to compare the risk of liver injury in the two groups. Metamizole was associated with a higher risk of liver injury compared with paracetamol (adjusted hazard ratio 1.69, 95% confidence interval 1.46–1.97). Sensitivity analyses were performed to evaluate the robustness of these findings. In all the sensitivity analyses, metamizole was still associated with a higher risk of liver injury, including an analysis where naproxen was used as a comparator instead of paracetamol. Results from this study support previous studies suggesting that metamizole is associated with a significant risk of liver injury. Nevertheless, a possible impact of residual confounding cannot be excluded.

Although postmarketing studies conducted in population‐based databases often contain information on patients in the order of millions, they can still be underpowered if outcomes or exposure of interest is rare, or the interest is in subgroup effects. Combining several databases might provide the statistical power needed. A multi‐database study (MDS) uses at least two healthcare databases, which are not linked with each other at an individual person level, with analyses carried out in parallel across each database applying a common study protocol. Although many MDSs have been performed in Europe in the past 10 years, there is a lack of clarity on the peculiarities and implications of the existing strategies to conduct them. In this review, we identify four strategies to execute MDSs, classified according to specific choices in the execution: (A) local analyses , where data are extracted and analyzed locally, with programs developed by each site; (B) sharing of raw data, where raw data are locally extracted and transferred without analysis to a central partner, where all the data are pooled and analyzed; (C) use of a common data model with study‐specific data, where study‐specific data are locally extracted, loaded into a common data model, and processed locally with centrally developed programs; and (D) use of general common data model, where all local data are extracted and loaded into a common data model, prior to and independent of any study protocol, and protocols are incorporated in centrally developed programs that run locally. We illustrate differences between strategies and analyze potential implications.

This study measured the exposure to different categories of medicinal products discussed by the European Union (EU) Pharmacovigilance Risk Assessment Committee from September to November 2018 in four electronic primary care health databases: IQVIA Medical Research Data-UK, IQVIA Medical Research Data-France, IQVIA Medical Research Data-Germany, and Clinical Practice Research Datalink Aurum, in the entire lifespan of each database until August 31, 2018. The assessment of 83 centrally authorized products and 45 nationally authorized products showed that coverage was better for products marketed for longer duration and worse for orphan drugs. The ability to detect associations against hypothetical comparators was better for more common events and for larger effect sizes. Coverage of advanced therapies was worse for those typically administered in a specialized rather than primary care setting. This study shows that to enable better informed regulatory decisions there is a need to access complementary data sources, particularly capturing secondary care prescribing.

As the world continues to urbanize, it is necessary to identify and implement new urban development models and strategies in order to meet the challenges of sustainable development. As cities continue to face challenges in becoming fully circular, the need to establish a framework to measure the circular economy in urban areas grows. Many definitions for circular cities have been developed and addressed in recent years, as have numerous indicators. To make the transition to a circular city, we must integrate the findings and develop a general definition and measurement framework. This article aims at outlining a framework for circular cities indicators based on their key characteristics, as well providing directions for fostering circularity at the city level. To accomplish this goal, we conducted a systematic review and analyzed key papers published in the field of circular economy to determine how circular cities are measured. Choosing the right indicators to use for developing, monitoring, and evaluating circular cities is a difficult task for urban policymakers, managers, and planners. This highlights the significance of standardized frameworks for urban indicators. As a result, the authors propose a framework and highlight some key points about circular cities and smart urban metabolism.