Parkinson’s disease (PD) is a major worldwide public health problem with a prevalence that is expected to increase dramatically in the coming decades. Because administrative data are useful for epidemiologic and health service studies, we aimed to define procedural algorithms to identify PD patients (on a regional basis) using these data. We built two a priori algorithms, respecting privacy laws, with increasing theoretical specificity for PD including: (1) a hospital discharge diagnosis of PD; (2) PD-specific exemption; (3) a minimum of two separate prescriptions of an antiparkinsonian drug. The two algorithms differed for drugs included. Sensitivities were tested on an opportunistic sample of 319 PD patients from the databases of 5 regional movement disorders clinics. The estimated prevalence of PD in the sample population from Tuscany was 0.49 % for algorithm 1 and 0.28 % for algorithm 2. Algorithm 1 correctly identified 291 PD patients (sensitivity 91.2 %), and algorithm 2 identified 242 PD patients (sensitivity 75.9 %). We developed two reproducible algorithms demonstrating increasing theoretical specificity with good sensitivity in identifying PD patients based on an evaluation of administrative data. This may represent a low-cost strategy to reliably follow up a large number of PD patients as a whole for evaluating the effects of therapies, disease progression and prevalence.
Administrative data Algorithm Antiparkinsonian drug Disease identification ICD-9-CM Parkinson’s disease
Dorsey ER, Constantinescu R, Thompson JP et al (2007) Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68:384–386CrossRefPubMedGoogle Scholar
Szumski NR, Cheng EM (2009) Optimizing algorithms to identify Parkinson’s disease cases within an administrative database. Mov Dis 24:51–56CrossRefGoogle Scholar
Swarztrauber K, Anau J, Peters D (2005) Identifying and distinguishing cases of parkinsonism and Parkinson’s disease using ICD-9 CM codes and pharmacy data. Mov Disord 20:964–970CrossRefPubMedGoogle Scholar
Moisan F, Gouriet V, Mazurie JL et al (2011) Prediction model of Parkinson’s disease based on antiparkinsonian drug claims. Am J Epidemiol 174:354–363CrossRefPubMedGoogle Scholar
Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184CrossRefPubMedPubMedCentralGoogle Scholar
Hughes AJ, Daniel SE, Lees AJ (2001) Improved accuracy of clinical diagnosis of Lewy body Parkinson’s disease. Neurology 57:1497–1499CrossRefPubMedGoogle Scholar