Laboratory of Pharmaceutical & Biopharmaceutical Technology, UFR of Health, Normandy University, Unirouen, 22 Bd Gambetta, 76183 Rouen Cedex, France.
DC2N, INSERM U1239, Unirouen, Normandy University, 76128 Mont Saint Aignan, France.
Int J Environ Res Public Health. 2020 Jun 16;17(12):4301. doi: 10.3390/ijerph17124301.
The French national public health agency (Santé publique France) has used data from the national health insurance reimbursement system (SNDS) to identify medicalised acute gastroenteritis (mAGE) for more than 10 years. This paper presents the method developed to evaluate this system: performance and characteristics of the discriminatory algorithm, portability in mainland and overseas French departments, and verification of the mAGE database updating process. Pharmacy surveys with certified mAGE from 2012 to 2015 were used to characterise mAGE and to estimate the sensitivity and predictive positive value (PPV) of the algorithm. Prescription characteristics from these pharmacy surveys and from 2014 SNDS prescriptions in six mainland and overseas departments were compared. The sensitivity (0.90) and PPV (0.82) did not vary according to the age of the population or year. Prescription characteristics were similar within all studied departments. This confirms that the algorithm can be used in all French departments, for both paediatric and adult populations, with stability and durability over time. The algorithm can identify mAGE cases at a municipal level. The validated system has been implemented in a national waterborne disease outbreaks surveillance system since 2019 with the aim of improving the prevention of infectious disease risk attributable to localised tap water systems.
法国国家公共卫生署(Santé publique France)十多年来一直利用国家健康保险报销系统(SNDS)的数据来识别医学化急性肠胃炎(mAGE)。本文介绍了为此开发的评估方法:该判别算法的性能和特征、在法国大陆和海外省份的可移植性,以及对 mAGE 数据库更新过程的验证。使用经过认证的 mAGE 进行了 2012 年至 2015 年的药房调查,以描述 mAGE 的特征,并估计算法的敏感性和阳性预测值(PPV)。对这些药房调查和 2014 年六个法国大陆和海外省份 SNDS 处方中的处方特征进行了比较。敏感性(0.90)和 PPV(0.82)不随人口年龄或年份而变化。所有研究部门的处方特征均相似。这证实了该算法可用于所有法国省份的儿科和成人人群,并且随着时间的推移具有稳定性和耐久性。该算法可在市级水平识别 mAGE 病例。自 2019 年以来,该经过验证的系统已被纳入国家水源性疾病暴发监测系统,旨在改善对地方性自来水系统相关传染病风险的预防。