14 Chapter 1 affected8,14 and can also be used to test the effect of different implementations, such as vaccination8. Moreover, the output from these surveillance systems should be communicated to health care providers and the community in general8. Possible limitations are due to privacy concerns and high costs when working with commercial partners8. A potentially under-used source of data for infectious disease surveillance is routinely collected data from hospitals. Every day, large amounts of clinical, microbiological and laboratory data are stored in hospital systems. Frequently, it is difficult to (rapidly) extract this data in an automated manner, which is essential for disease surveillance. In this first section, routinely collected hospital data is used to answer questions about the incidence of disease and population level surveillance for viral diseases. An example is the use of routinely collected data for the surveillance of Severe Acute Respiratory Infections (SARI). After the 2009 Influenza A pandemic, the WHO recommended that all countries develop national surveillance systems42. In Chapter 2, three different outcome measures are compared that can be used for the SARI surveillance: ICD-10 diagnostic code registration, Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests and registration of contact and droplet precautions. A data-mining tool was used to collect data from EHRs and assess which outcome measure would be most useful in a future, prospective setting. Shortly after the COVID-19 pandemic, an outbreak of severe hepatitis in young children was first established in Glasgow, Scotland. Since then, cases were reported in 35 countries, and the causative agent is thought to be adenovirus in combination with AAV-243. However, it was unclear whether there was also a group of children with milder disease, i.e. if we were only seeing ‘the tip of the iceberg’. In order to determine the presence of a larger group of children who may have a milder form of hepatitis, aspartate transaminase (AST) and alanine transaminase (ALT) data from 30 hospitals across the Netherlands, the UK, Ireland and Curaçao were collected to compare the proportion of increased AST and/or ALT values over time. Moreover, the method used to collect and share this data efficiently without the risk of disclosing any identifiable information, is discussed. This is described in Chapter 3. Causal inference and real-world data Finding causal relationships is a major part of scientific research44. Defining and understanding the meaning of causality has been a topic of research for centuries45. One of the conditions for causal inference, as explained by Hernán and Robins46, is exchangeability. This means that in a perfectly executed randomised controlled trial, the treated and untreated groups are exchangeable based on clinical characteristics. If the treated and untreated group were accidentally switched, and the group who was supposed to receive no treatment got treatment, and vice versa, the proportional outcome would be the same46. In an observational study however, we have no influence on who is treated and who is not treated, and the groups are unlikely to be
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