Validation of Administrative Health Data Algorithms for Identifying Persons with Parkinson's Disease and the 10-Year Prevalence Trend in Bologna, Italy.

Introduction: Health administrative databases are widely used for the estimation of the prevalence of Parkinson's disease (PD). Few in general, and none used in Italy, have been validated by testing their diagnostic accuracy. The primary objective was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. The second objective was to estimate 10-year trends in PD prevalence in the Bologna Local Health Trust from 2010 to 2019. Methods: Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. Sensitivity and specificity with relative 95% confidence intervals (CIs) were calculated. The prevalence of PD in a specific year was estimated as the ratio between the number of subjects fulfilling any criteria of the algorithm with better diagnostic accuracy and the total population in the same year (×1,000), stratified by age, sex, and district of residence. Results: The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity of 94.2% (CI: 88.4–97.6) and the other with greater specificity of 98.1% (CI: 97.7–98.5). For the estimation of prevalence, we chose the most specific algorithm with the fewest total number of misclassified cases. We identified 3,798 people with PD as of December 31, 2019, corresponding to a prevalence of 4.3 per 1,000 inhabitants (CI: 4.2–4.4). Prevalence was higher in males (4.7, CI: 4.5–5.0) than females (3.8, CI: 3.7–4.0) and increased with age. The crude prevalence over time was slightly elevated as it followed a progressive aging of the population. When stratifying the prevalence for age groups, we did not observe a trend except in the 45–64 year category where we observed an increasing trend over time. Conclusion: Algorithms based on administrative data are accurate when detecting people with PD in the Italian public health system. In a large northern Italian population, increased prevalence of about 10% was observed in the decade 2010–2019 and is explained by increased life expectancy. These data may be useful in planning the allocation of health care resources for people with PD. [ABSTRACT FROM AUTHOR]