Study of algorithms to identify schizophrenia in the SNIIRAM database conducted by the REDSIAM network

Background. - 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. Methods. - 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. Results. - 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. Conclusion. - 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. (C) 2017 Elsevier Masson SAS. All rights reserved.