Use of a Refined Drug Tracer Algorithm to Estimate Prevalence and Incidence of Parkinson's Disease in a Large Israeli Population

Abstract

Estimating rates of Parkinson's disease (PD) is essential for health services planning and studies of disease determinants. However, few PD registries exist. We aimed to estimate annual prevalence and incidence of PD in a large Israeli population over the past decade using computerized drug purchase data. Based on profiles of anti-parkinsonian drugs, age at first purchase, purchase density, and follow-up time, we developed a refined algorithm for PD assessment (definite, probable or possible) and validated it against clinical diagnoses. We used the prescription database of the second largest Health Maintenance Organization in Israel (covers ~25% of population), for the years 1998–2008. PD rates by age, gender and year were calculated and compared using Poisson models. The algorithm was found to be highly sensitive (96%) for detecting PD cases. We identified 7,134 prevalent cases (67% definite/probable), and 5,288 incident cases (65% definite/probable), with mean age at first purchase 69 ± 13 years. Over the years 2000–2007, PD incidence rate of 33/100,000 was stable, and the prevalence rate increased from 170/100,000 to 256/100,000. For ages 50+, 60+, 70+, median prevalence rates were 1%, 2%, 3%, respectively. Incidence rates also increased with age (RR = 1.76, 95%CI 1.75–1.77, ages 50+, 5-year interval). For ages 50+, rates were higher among men for both prevalence (RR = 1.38, 95%CI 1.37–1.39) and incidence (RR = 1.45, 95%CI 1.42–1.48). In conclusion, our refined algorithm for PD assessment, based on computerized drug purchases data, may be a reliable tool for population-based studies. The findings indicate a burden of PD in Israel higher than previously assumed.