Quantin C, Collin C, Frérot M, Besson J, Cottenet J, Corneloup M, Soudry-Faure A, Mariet A-S, Roussot A
Service de biostatistiques et d'information médicale (DIM), université Bourgogne Franche-Comté, CHRU Dijon, 21000 Dijon, France; Inserm, CIC 1432, Dijon University Hospital, Clinical Investigation Center, clinical epidemiology/clinical trials unit, 21000 Dijon, France; Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Inserm, UVSQ, Institut Pasteur, université Paris-Saclay, 94800 Villejuif, France.
Direction scientifique et de la stratégie européenne, Agence nationale de sécurité du médicament et des produits de santé, pôle épidémiologie des produits de santé, 143/147, boulevard Anatole-France, 93285 Saint-Denis cedex, France.
Rev Epidemiol Sante Publique. 2017 Oct;65 Suppl 4:S226-S235. doi: 10.1016/j.respe.2017.03.133. Epub 2017 May 31.
The aim of the REDSIAM network is to foster communication between users of French medico-administrative databases and to validate and promote analysis methods suitable for the data. Within this network, the working group "Mental and behavioral disorders" took an interest in algorithms to identify adult schizophrenia in the SNIIRAM database and inventoried identification criteria for patients with schizophrenia in these databases.
The methodology was based on interviews with nine experts in schizophrenia concerning the procedures they use to identify patients with schizophrenia disorders in databases. The interviews were based on a questionnaire and conducted by telephone.
The synthesis of the interviews showed that the SNIIRAM contains various tables which allow coders to identify patients suffering from schizophrenia: chronic disease status, drugs and hospitalizations. Taken separately, these criteria were not sufficient to recognize patients with schizophrenia, an algorithm should be based on all of them. Apparently, only one-third of people living with schizophrenia benefit from the longstanding disease status. Not all patients are hospitalized, and coding for diagnoses at the hospitalization, notably for short stays in medicine, surgery or obstetrics departments, is not exhaustive. As for treatment with antipsychotics, it is not specific enough as such treatments are also prescribed to patients with bipolar disorders, or even other disorders. It seems appropriate to combine these complementary criteria, while keeping in mind out-patient care (every year 80,000 patients are seen exclusively in an outpatient setting), even if these data are difficult to link with other information. Finally, the experts made three propositions for selection algorithms of patients with schizophrenia.
Patients with schizophrenia can be relatively accurately identified using SNIIRAM data. Different combinations of the selected criteria must be used depending on the objectives and they must be related to an appropriate length of time.
REDSIAM网络的目标是促进法国医疗行政数据库用户之间的交流，并验证和推广适用于这些数据的分析方法。在这个网络中，“精神和行为障碍”工作组关注了在SNIIRAM数据库中识别成人精神分裂症的算法，并梳理了这些数据库中精神分裂症患者的识别标准。
该方法基于对九位精神分裂症专家的访谈，内容涉及他们在数据库中识别精神分裂症患者所采用的程序。访谈以问卷为基础，通过电话进行。
访谈综合显示，SNIIRAM包含各种表格，使编码人员能够识别患有精神分裂症的患者：慢性病状况、药物治疗和住院情况。单独来看，这些标准不足以识别精神分裂症患者，一种算法应基于所有这些标准。显然，只有三分之一的精神分裂症患者受益于慢性病状况。并非所有患者都住院，而且住院时的诊断编码，尤其是在内科、外科或产科短期住院时，并不详尽。至于抗精神病药物治疗，其特异性不足，因为双相情感障碍患者甚至其他疾病患者也会接受此类治疗。结合这些互补标准似乎是合适的，同时要记住门诊治疗情况（每年有8万名患者仅在门诊接受治疗），即使这些数据难以与其他信息相联系。最后，专家们提出了三项精神分裂症患者选择算法的建议。
使用SNIIRAM数据可以相对准确地识别精神分裂症患者。必须根据目标使用所选标准的不同组合，并且它们必须与适当的时间段相关。
