Value of a national administrative database to guide public decisions: From the système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France.

In 1999, French legislators asked health insurance funds to develop a système national d'information interrégimes de l'Assurance Maladie (SNIIRAM) [national health insurance information system] in order to more precisely determine and evaluate health care utilization and health care expenditure of beneficiaries. These data, based on almost 66 million inhabitants in 2015, have already been the subject of numerous international publications on various topics: prevalence and incidence of diseases, patient care pathways, health status and health care utilization of specific populations, real-life use of drugs, assessment of adverse effects of drugs or other health care procedures, monitoring of national health insurance expenditure, etc. SNIIRAM comprises individual information on the sociodemographic and medical characteristics of beneficiaries and all hospital care and office medicine reimbursements, coded according to various systems. Access to data is controlled by permissions dependent on the type of data requested or used, their temporality and the researcher's status. In general, data can be analyzed by accredited agencies over a period covering the last three years plus the current year, and specific requests can be submitted to extract data over longer periods. A 1/97th random sample of SNIIRAM, the échantillon généraliste des bénéficiaires (EGB), representative of the national population of health insurance beneficiaries, was composed in 2005 to allow 20-year follow-up with facilitated access for medical research. The EGB is an open cohort, which includes new beneficiaries and newborn infants. SNIIRAM has continued to grow and extend to become, in 2016, the cornerstone of the future système national des données de santé (SNDS) [national health data system], which will gradually integrate new information (causes of death, social and medical data and complementary health insurance). In parallel, the modalities of data access and protection systems have also evolved. This article describes the SNIIRAM data warehouse and its transformation into SNDS, the data collected, the tools developed in order to facilitate data analysis, the limitations encountered, and changing access permissions.

[Health care use by free complementary health insurance coverage beneficiaries in France in 2012]. / Consommations de soins des bénéficiaires de la couverture maladie universelle complémentaire (CMUC) ou de l'aide pour une complémentaire santé (ACS) en 2012.

BACKGROUND: The objective was to investigate healthcare use among people covered by one of the two complementary healthcare insurance schemes available for people with low annual income: CMUC (universal complementary healthcare insurance) and, for people whose income exceeds the CMUC ceiling, ACS (aid for complementary healthcare insurance). Comparisons were made between CMUC and ACS beneficiaries versus CMUC and ACS non-beneficiaries and between CMUC beneficiaries and ACS beneficiaries. METHODS: Using the national health insurance information system (SNIIRAM), people less than 60 years old covered by the general national health insurance (86% of the 66 million inhabitants) and with ACS or CMUC coverage in 2012 were selected. Diseases were identified using hospital diagnosis, drugs refunds and long-term chronic disease status. Hospital related diagnoses were categorized in major hospital activity groups. Sex- and age-standardized relative risk (RR) were calculated. RESULTS: There were 4.4 million (9.6%) CMUC beneficiaries and 732,000 (1.6%) ACS beneficiaries (56% and 54% women; mean age: 24 years and 29 years respectively versus 52% and 30 years for CMUC or ACS non-beneficiaries). CMUC or ACS beneficiaries had more often cardiovascular diseases (RR=1.4;2.1) and diabetes (RR=2.2;2.4). Their sex- and age-standardized hospitalisation rates for all diagnosis were higher (18%; 17%, RR=1.3;1.4) than CMUC or ACS non-beneficiaries (13%). This was especially the case for the following major groups: toxicology, intoxications, alcohol major group (RR=3.8;4.0); psychiatry (RR=2.8;4.1); respiratory disease (RR=1.9;2.3); infectious disease (RR=1.9;2.7). Compared with CMUC beneficiaries, ACS beneficiaries had more often cancer (RR=1.5), cardiovascular disease (RR=1.5), neurological disease (RR=2.7), psychiatric illness (RR=2.6), end-stage renal disease (RR=2.8), hemophilia (RR=1.4) or cystic fibrosis (RR=1.6) and they received also more often disability allowance (20%, 4%). CONCLUSION: The disease and hospitalisation rates of ACS beneficiaries are similar or higher than those of CMUC beneficiaries, especially for disabling diseases. Both CMUC and ACS beneficiaries received healthcare for chronic diseases that can be targeted by prevention and screening programs for more optimal healthcare.