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.

Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation.

This paper addresses the estimation of fuzzy Gaussian distribution mixture with applications to unsupervised statistical fuzzy image segmentation. In a general way, the fuzzy approach enriches the current statistical models by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same site. We introduce a new procedure for estimating of fuzzy mixtures, which is an adaptation of the iterative conditional estimation (ICE) algorithm to the fuzzy framework, We first describe the blind estimation, i.e., without taking into account any spatial information, valid in any context of independent noisy observations. Then we introduce, in a manner analogous to classical hard segmentation, the spatial information by two different approaches: contextual segmentation and adaptive blind segmentation. In the first case, the spatial information is taken into account at the segmentation step level, and in the second case it is taken into account at the parameter estimation step level. The results obtained with the iterative conditional estimation algorithm are compared to those obtained with expectation-maximization (EM) and the stochastic EM algorithms, on both parameter estimation and unsupervised segmentation levels, via simulations. The methods proposed appear as complementary to the fuzzy C-means algorithms.