[Study of algorithms to identify schizophrenia in the SNIIRAM database conducted by the REDSIAM network]
C Quantin1, C Collin2, M Frérot3
1Service 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.
Identifying schizophrenia patients in French healthcare databases requires combining multiple data points like disease status, medications, and hospitalizations. Algorithms using these combined criteria can accurately identify individuals with schizophrenia.
Area of Science:
- Health Informatics
- Psychiatry
- Epidemiology
Background:
- The REDSIAM network aims to improve French medico-administrative database utilization.
- A working group focused on mental disorders sought to identify schizophrenia patients in the SNIIRAM database.
Purpose of the Study:
- To develop and validate algorithms for identifying adult schizophrenia patients in the SNIIRAM database.
- To inventory and assess the utility of existing identification criteria for schizophrenia in French healthcare data.
Main Methods:
- Interviews with nine schizophrenia experts were conducted via telephone using a questionnaire.
- Experts shared their procedures for identifying schizophrenia patients in databases.
Main Results:
- SNIIRAM data includes chronic disease status, medications, and hospitalizations, which are key for identifying schizophrenia.
- No single criterion is sufficient; algorithms must combine chronic disease status, antipsychotic prescriptions, and hospitalization data.
- Outpatient care data is valuable but challenging to integrate.
Conclusions:
- Schizophrenia patients can be identified with relative accuracy using SNIIRAM data.
- Algorithm effectiveness depends on combining criteria and considering appropriate timeframes.
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