Estimating the burden of dementia and parkinsonism through a novel identification algorithm based on healthcare administrative data

Introduction: Neurological disorders (ND), particularly dementia and parkinsonism, are major public health challenges in aging populations. Estimating their prevalence and incidence is essential for healthcare resource planning and targeted interventions. This study aims to estimate the burden of these conditions in the Marche region of Italy, using a novel identification approach applied to administrative healthcare data. Methods: A cross-sectional study was conducted using administrative databases from the Marche region (2016–2021), including drug prescriptions, hospital discharge records, and chronic condition registries. The TREND protocol was used to enhance case identification. Individuals aged 40 and older were included. Age- and sex-adjusted prevalence and incidence rates were calculated for dementia, parkinsonism, and their co-occurrence. Geographic Information Systems (GIS) were used to analyze spatial distribution. Results: In 2021, age-adjusted prevalence rates were 7.1‰ for parkinsonism and 31.2‰ for dementia among individuals aged 40 and older, rising to 22.6‰ and 65.8‰, respectively, in those aged 65 and older. Five-year incidence rates were 1.7‰ for parkinsonism and 6.9‰ for dementia. Dementia was more common in women, while parkinsonism predominated in men. GIS revealed higher parkinsonism in southern areas and higher dementia in central and inland areas of Marche. Including antipsychotic and antidepressant prescriptions improved dementia case detection sensitivity. Discussion: This study demonstrates the value of administrative data and the TREND protocol in improving case identification for neurodegenerative diseases. The observed geographical patterns provide insight for regional healthcare planning in the Marche region. The analysis of antipsychotic and antidepressant use underscores the clinical complexity and healthcare needs of affected individuals. The methodology is scalable and supports reproducible, data-driven strategies for public health policy in aging populations.