DOI: 10.1017/s204579602610095x ISSN: 2045-7960

Burden and temporal trends of major depressive disorder in older adults: a 10-year population-based longitudinal study on interlinked region-wide health registries, 2013–2023

Paola Bertuccio, Lorenzo Blandi, Mattia Celebrin, Andrea Amerio, Anna Odone

Abstract

Aims

Population-based longitudinal evidence using administrative healthcare registries remains limited. We aimed to estimate the burden and temporal trends of major depressive disorder (MDD) among individuals aged ≥65 in Lombardy, Italy, 2013 to 2023.

Methods

We conducted a retrospective population-based longitudinal study using multi-source administrative healthcare registries (Lombardy region, Italy), including an open cohort of individuals aged ≥65 years. Incident and prevalent MDD cases were identified from 2013 to 2023, through an algorithm combining hospital discharge records, administrative exemptions, long-term care facility admissions and community psychiatric services. Age-standardised incidence rates per 100,000 person-years and annual prevalence (%) were estimated overall, by sex and age, using the age-truncated (≥65) WHO standard population. Annual percent changes (APCs) and 95% confidence intervals (CIs) were estimated by Poisson regressions.

Results

We identified 19,851 incident MDD cases during the study period. Age-standardised incidence declined from 126.5/100,000 person-years (95% CI 121.7–131.3) in 2013 to 50.6 (47.6–53.7) in 2023, with an APC of 10.8% (−11.8 to −9.7). Age-standardised annual prevalence decreased from 0.94% (0.93–0.95) in 2013 to 0.63% (0.62–0.64) in 2023, corresponding to an annual decline of 3.0% (−4.7 to −1.3). Rates were higher among females. Burden and trends did not differ significantly by sex but across age groups, with the largest reductions among individuals aged 80 years or older.

Conclusions

Administrative data capture clinically diagnosed MDD within the healthcare system, rather than the broader burden in the community. Comparable epidemiological estimates are needed to interpret trends across different settings.