Administrative data for public health: a regional data warehouse applied to dementia epidemiology

Abstract Administrative data can represent valuable sources of information for researchers seeking to advise policymakers. Several countries have established population-based data warehouses (DWH), integrating multiple data sources in single repositories through data linkage techniques. In Lombardy (Italy), a regional population-based DWH was established in the 1980s. It collects data about citizens from multiple sources in the social and health sectors, anonymising them immediately after their acquisition. Lombardy Region signed a specific agreement with the University of Pavia to investigate the epidemiology and access to care among people living with dementia (PLWD), using the DWH. A retrospective cohort study was conducted on hospitalised PLWD, including people aged ≥ 65 years with diagnoses of dementia at discharge from 2002 to 2020. The aim was to study the trends of hospitalisations among PLWD, computing these by calendar year, age, sex and cause of hospitalisation. A total of 340,144 hospital discharges were registered, of which 211,709 (63.0%) were women and 124,195 (37.0%) were men. The rate of hospitalisation decreased over the study period, ranging from 100.6/10,000 in 2002 to 65.1/10,000 in 2020. The average age at hospitalisation increased for men and women from 78.9 and 81.8 years in 2002 to 82.0 years and 84.2 years, respectively, in 2020. Respiratory diseases caused 10.4% of all hospitalisations in 2002 and grew steadily to 26.8% in 2020, becoming the leading cause of hospital admissions since 2017. Results allowed us to elaborate epidemiological measures and to characterise our population in a wide temporal trend, quantifying access to acute care by leading causes of hospitalisation. However, the analysis of administrative data comes along with several challenges, such as data incompleteness and biases. Researchers should take advantage of these data sources while keeping in mind the limitations and constraints related to data quality.