Since the COVID-19 outbreak, public health authorities have looked for the best evidence on infection risk and prognosis to guide their choices. The interpretation of the observational studies that variably reported increased infection rates and a poor prognosis in patients with systemic rheumatic diseases (SRD) has been limited by factors such as the selection of patients in the care of tertiary referral centres, the small available sample sizes for the less prevalent diseases, the description of SRDs as a single broad category, and the neglected influence of comorbidities [1]. The use of big healthcare data has become essential in gathering crucial information for a reliable identification of high-risk groups. The Chronic Related Group (CReG) system is an experimental approach of classification of chronic patients created to predict the medical resources needed to ensure their care. This system automatically assigns a diagnosis to a subject according to medical administrative records over a pre-set period. Specifically, CReG system relies on the registration and integration of disease-specific codes used to determine the share of healthcare costs, hospital discharge diagnoses codes and access to the prescription of drugs or therapeutic procedures uniquely associated with a specific condition [2], [3]. In this analysis, we compared incidences and 30-day outcomes of 40,490 SRD patients (Table 1) to 471,6119 subjects dwelling in the Lazio Region, the second most populated region of Italy that includes the Rome metropolitan area. SRDs and comorbidity diagnoses were derived from the CReG classification while data on COVID-19 infection from a regional digital network. The risk was expressed as incidence rate ratio adjusted for demographics and comorbidities. We focused on the period from the 20th of February 2020 to the 31st of December 2020 to selectively assess a cohort of unvaccinated patients.