Baldacci Filippo, Policardo Laura, Rossi Simone, Ulivelli Monica, Ramat Silvia, Grassi Enrico, Palumbo Pasquale, Giovannelli Fabio, Cincotta Massimo, Ceravolo Roberto, Sorbi Sandro, Francesconi Paolo, Bonuccelli Ubaldo
Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Via Roma 67, Pisa, Italy.
Neurol Sci. 2015 May;36(5):783-6. doi: 10.1007/s10072-015-2062-z. Epub 2015 Feb 8.
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.
帕金森病（PD）是一个全球性的重大公共卫生问题，预计在未来几十年其患病率将急剧上升。由于行政数据对流行病学和卫生服务研究有用，我们旨在定义程序算法，以便利用这些数据（在区域范围内）识别PD患者。我们构建了两种符合隐私法的先验算法，对PD的理论特异性不断提高，包括：（1）医院出院诊断为PD；（2）特定于PD的豁免；（3）至少两种不同的抗帕金森药物处方。这两种算法在纳入的药物方面有所不同。对来自5个区域运动障碍诊所数据库的319例PD患者的机会性样本进行了敏感性测试。托斯卡纳样本人群中PD的估计患病率，算法1为0.49%，算法2为0.28%。算法1正确识别出291例PD患者（敏感性91.2%），算法2识别出242例PD患者（敏感性75.9%）。我们开发了两种可重复的算法，在基于行政数据评估识别PD患者时，其理论特异性不断提高且敏感性良好。这可能代表一种低成本策略，用于可靠地对大量PD患者进行整体随访，以评估治疗效果、疾病进展和患病率。
