RESUMEN
OBJECTIVE: The aims of this study were to provide population-based estimates of prevalence and incidence of any dementia and Alzheimer's dementia (AD) in the Campania region (South Italy) and to validate towards a clinical registry. METHODS: This was a population-based study, using routinely collected healthcare data of individuals living in the Campania region (South Italy) from 2015 to 2020. We included individuals aged ≥65 years alive at the prevalence day (January 1, 2021) who had at least one administrative record for dementia and/or AD from 2015 to 2020. Age-and sex-standardised prevalence rates were calculated using direct standardisation method (European population in 2020 as the reference population). To estimate the incidence, we tested three possible algorithms, which differed for the duration of the time interval between study baseline (January 1, 2015) and index date (first record for dementia and/or AD in administrative databases). We employed a clinical database for the validation of our algorithms towards neuropsychological test results. RESULTS: Among individuals aged over 65 years, 80,392 had dementia, of which 35,748 had AD. The age- and sex-standardised prevalence rates per 1,000 individuals for any dementia and AD were 77.64 (95% confidence interval [CI] = 77.57; 77.68) and 34.05 (95% CI = 34.01; 34.09), respectively. There were 82.10 incident cases of any dementia per 100,000 per year (0.79 sensitivity and 0.62 specificity) and 59.89 incident cases of AD per 100,000 per year (0.80 sensitivity and 0.59 specificity). The capture-recapture method showed a very low number of undetected cases (1.7% for any dementia and 3.0% for AD). Our algorithms showed acceptable performance with the area under the curve ranging from 0.59 to 0.72 and a double likelihood ratio of correctly identifying individuals above and below mini-mental status examination (MMSE) standard cut-offs (24 and 26). CONCLUSIONS: Prevalence and incidence of any dementia and AD in the Campania region (South Italy) from 2015 to 2020 are in line with previous estimates from other countries. Our algorithm, integrating administrative and clinical data, holds potential for assessing dementia's epidemiological burden, identifying risk factors, planning healthcare access, and developing prevention strategies.
