dataesr/scientific-paragraphs-categorization huggingface dataset:
Leur potentiel en recherche médicale est illustré par les publications les plus récentes. Ces bases de données contiennent des données démographiques, les données de remboursements des prestations ambulatoires (dont les délivrances de médicaments), les données médicales des régimes de l'assurance maladie (affections de longue durée, maladies professionnelles, arrêts de travail. ) et les données hospitalières issues du programme de médicalisation des systèmes d'information. Toutes ces données sont individuelles, prospectivement recueillies, anonymisées et chaînables. Tout cela fait du SNIIR-AM une source de données très intéressante pour la recherche épidémiologique, pharmacoépidémiologique et en économie de la santé, particulièrement pour les maladies rares. L'EGB est particulièrement utile à l'étude des maladies plus fréquentes et sur le long terme.
© 2014 Société nationale française de médecine interne (SNFMI). Publié par Elsevier Masson SAS. Tous droits réservés. As in North American and other European countries, French public authorities want to develop the use of national medicoadministrative databases for research. For that purpose, their accessibility to researchers is regularly improved  [1] [2] [3]. This article is focused on the use of the French national health insurance system databases for medical research. The French universal health insurance system manages, through public entities, all reimbursements of healthcare to all people affiliated to a health insurance scheme in France. Accounts management requires the generation of centralized databases, containing also administrative data. The result is one of the largest global database of medical and administrative data in the general population, covering nearly 65 million persons, far ahead of the UK's Clinical Practice Research Datalink (approximately five million persons), the Quebec Health Insurance Corporation (Régie de l'assurance maladie du Québec, approximately four million persons) or those of northern European countries  [1].
To facilitate the building of patient cohorts for the most common diseases, a sample of 1/97th of the SNIIR-AM has been established since 2005 in order to provide a 20-year follow-up of beneficiaries. This sample is named the permanent beneficiaries sample (échantillon généraliste de bénéficiaires [EGB])  [4, 5]. The aim of this article is to describe the main characteristics of the SNIIR-AM and the EGB architectures, to present the conditions for accessing to them and their main strengths and limitations when used for medical research. The potential of these databases in medical research is illustrated by the most recent publications.
1. The inter-scheme consumption data (données de consommation inter-régimes [DCIR]) set includes all outpatient reimbursed health expenditures. These data are transmitted in real time by the CNAMTS and the various special insurance schemes. This DCIR database is also linked with data from the health insurance medical service that includes information about long-term diseases (LTD), occupational accidents, and occupational diseases. PMSI data stem from all private or public hospitals and are provided to the CNAMTS for linkage to the SNIIR-AM. Information includes the hospital identifier, the dates of start and end of stays, the diagnostic codes (one primary diagnosis, one related diagnosis and up to 30 associated diagnoses coded with the International Classification of Diseases, version ICD-10 [8]), the diagnosis-related group (groupe homogène de malades [GHM]) coding, the procedures (Classification commune des actes médicaux [CCAM] coding), the costly drugs dispensed, whether the stays occurred in a special unit (resuscitation, intensive care, neonatology or palliative care), and a score of severity for stays in intensive care units (Simplified Acute Physiology Score II).
A database exists for Medicine, Surgery and Obstetrics hospitalisations (PMSI MCO for médecine chirurgie obstétrique), one for psychiatry (PMSI PSY for psychiatrie), one for home hospitalisations (PMSI HAD for hospitalisaiton à domicile), and one for after-care and rehabilitation (PMSI SSR for soins de suite et de réadaptation). PMSI data are updated every three months  [1, 4]. Demographics (age, gender, place of residence -municipality code, advantage of the Universal Health Coverage, affiliate scheme) are available in the DCIR and PMSI databases. Data of a given individual are linked through a unique identification number named numéro d'identification au répertoire (NIR), which is a unique identifier for each insured person. However, the NIR is not accessible in the SNIIR-AM in order to preserve the identity of patients.
It is made anonymous by a complex two-level procedure called fonction d'occultation des informations nominatives (FOIN), which is completely irreversible. This makes it difficult to match clinical cohort data with SNIIR-AM data (See Section 2)  [1]. The depth of the SNIIR-AM is three years plus the current year. PMSI data kept by the Technical Agency for Hospitalisation Information (Agence technique d'information sur l'hospitalisation [ATIH]) have a sliding depth of 10 years plus the current year. The law allows conservation of successive SNIIR-AM extractions corresponding to 10 years of follow-up  [7].
Demographic data Age, gender, place of residency, insurance scheme, benefit from the Universal Health Coverage whose data are progressively implemented. Data available are those of the SNIIR-AM. They are also anonymous. Individual data are stored and accessible for 20 years. When a patient dies, emigrates or is no longer insured, he or she is considered out of the cohort: his/her data are retained but censored on the release date. Newly insured persons are added quarterly to the EGB to keep constant the proportion of 1/97th of the insured persons in France  [4]. Table  1  compares the SNIIR-AM with EGB. Because of the depth of the database and accessibility, the EGB is a database of choice for long-term cohorts of patients with common diseases.
Conditions for accessing the SNIIR-AM and the EGB Conditions for accessing the SNIIR-AM and the EGB are strictly regulated by law  [7]. The Health Data Institute (Institut des données de santé [IDS]) ensures the consistency of medico-administrative databases, data quality and availability of databases for research  [9]. Any access request to the SNIIR-AM or the EGB for a project requires authorization from the IDS. Only non-profit research can apply  [10]. Access to the EGB requires a direct secured connection to a devoted server, with no possibility to import data. As there is no database creation, only authorization from the IDS is required. Direct access to the SNIIR-AM is not possible. Following authorization from the IDS and the National Data Protection Commission (Commission nationale de l'informatique et des libertés [CNIL]), CNAMTS engineers extract from the SNIR-AM the study population according to the inclusion and non-inclusion criteria given to them.
Data are supplied on a secure, electronic medium. As noted above, SNIIR-AM and EGB data are irreversibly anonymous. Collecting the patients' NIR in a clinical cohort (in order to link cohort data with SNIIR-AM data) currently requires a State Council (Conseil d'État) decree, making this an almost impossible strategy in practice. A second strategy is to identify the NIRs achieving a probabilistic matching. It consists in recording in the clinical cohort indirect identifying data (date and place of birth, gender), plus possibly directly identifying data (name and surname). Then, these data are searched in the NIR repertory in order to try to identify the NIR that corresponds to a given patient. However, this later strategy can lead to errors and non-completeness in NIR identification  [1].
Advantages and limitations The advantages and limitations of the SNIIR-AM and of the EGB with existing or planned solutions to the limitations are summarized in Table  2. The main uses of these databases for medical research are listed in Table  3. The main limitation of these databases is their complexity. An extraction of the SNIIR-AM has several dozen of tables, some only used for accountancy, containing hundreds of variables and millions of lines. A SNIIR-AM dictionary is available for users of the health insurance databases. As the EGB is dedicated to medical research, a "simplified EGB" emerged in late 2013, simplifying the presentation of outpatient healthcares. However, training on the complex architecture of these bases is mandatory since 2013. The EGB and the SNIIR-AM user groups meet regularly with officials of the IDS and the CNAMTS to keep abreast of updates, and communicate on secure forums.
The EMOIS (évaluation, management, organisation, information, santé) group organizes meetings and publishes reports to promote and improve research on the SNIIR-AM and the EGB  [11]. Working on these data also requires sufficient knowledge of data management using the statistical software SAS TM (SAS Institute, North Carolina, USA) used on the EGB server. Working on SNIIR-AM data extractions requires to have a secured server with sufficient capacity. As previously suggested, a second general limitation is the access to SNIIR-AM data extractions. Obtaining the necessary authorizations and data extraction by CNAMTS engineers requires 17 months on average [10].
Advantages and limitations in epidemiology The major advantage of these databases is the huge number of patients they include and the prospective data collection. Therefore, large cohorts can be built in these databases to clarify the epidemiology of diseases at a nationwide level and taking into account geographical variations. The SNIIR-AM is a very useful data source to estimate the incidence and prevalence of rare diseases, such as lupus or immune thrombocytopenia  [12, 13]. For instance, we could identify 3771 incident immune thrombocytopenia patients in the SNIIR-AM during a two-year period  [13]. For comparison, the most important cohort of immune thrombocytopenia patients ever built in medico-administrative databases was in the UK Clinical Practice Research Datalink that identified 1145 incident adults during a 15-year period.
The huge number of patients followed in the SNIIR-AM allows the assessment of variations according to age, gender, seasons or areas  [12, 13]. A possible limitation in the use of medico-administrative databases in epidemiology is the validity of diagnosis codes. Quality control before data transmission to the CNAMTS and audits involves mainly the treatment of aberrant or missing data  [4]. Moreover, financial coding of healthcare is preferentially checked. LTD attributions are not exhaustive for a given disease: for example, patients who have already obtained a LTD for diabetes mellitus and suffering from myocardial infarction do not systematically request for a new LTD for myocardial infarction  [1]. In the PMSI, the details of diagnosis codes (primary, related and associated) should be studied and not the GHMs, which form an accounting harmonization.
Only events accompanying the diagnosis or complicating the disease are encoded in the PMSI. The inability to collect the NIR prevents any reliable connection with clinical data. Recent reports of the High Council of Public Health (Haut Conseil de la Santé Publique)  [1]  and Bras-Loth report commissioned by the Minister of Social Affairs and Health  [2]  stress the urgency of drafting a State Council decree serving as a general framework for secured access to the NIR. Moreover, the PMSI database does not collect the entire hospital activities: in the absence of procedure, consultations and emergency department stays lasting less than 24 hours are not recorded. The PMSI PSY, SSR and HAD are still poorly featured in the SNIIR-AM and absent from the EGB. Patients are identified thanks to their NIR.
That makes studies carried out in pediatric populations more complex. Indeed, children do not have their own rights as insured persons. Consumption of care is through their "rights holder" (usually the legally guardian). As a child may have several rights holders (both parents, for example) to facilitate access to care, the consumption of care of one child can be linked in the SNIIR-AM with several identifiers. A national health identifier should soon be created to solve this problem  [1]. Lastly, demographic data are limited. Only all-cause mortality can now be evaluated in the SNIIR-AM as in a recent follow-up study in the general population after initial hospitalisation for heart failure  [16]. The causes of death should be specified in the SNIIR-AM in the near future by individual linkage of the causes of death from the epidemiology centre for causes of death (Centre d'épidémiologie des causes de décès [CépiDc]) of the National Institute of Health and Medical Research (Institut national de la santé et de la recherche médicale [Inserm])  [1].
Advantages and limitations in pharmacoepidemiology The field of pharmacoepidemiology assesses the use, the effectiveness and the safety of drugs in a real-world practice (after marketing authorization). Therefore, health insurance databases are frequently used as a material for pharmacoepidemiological studies. They are the most appropriate tools to evaluate the immunisation coverage in the general population  [18, 19]  or specific populations. We have estimated the exposure to pneumococcal vaccine in adult patients with primary immune thrombocytopenia treated with rituximab  [20]  as recommended by French guidelines  [21]. The EGB was also used to assess the relative frequency of prescribing biotherapies in rheumatoid arthritis  [22]. Health insurance databases are tools of choice to assess compliance with the recommendations at the population level, as shown recently by the exposure to recommended drugs after myocardial infarction  [23].
However, they cannot be used to assess accurately the reasons for non-adherence to recommendations. The depth of the EGB make possible a dynamic view of drug exposure: our team was able to show the increasing number of patients exposed to baclofen in recent years for alcohol abuse  [24]  and the impact of health authorities alerts about safety of antipsychotics in elderly patients on the rate of prescription of psychotropic drugs in this population  [25]. Moreover, the SNIIR-AM and the EGB allow the identification of consumer profiles  [26]. Drug effectiveness in real-world practice can be assessed from the SNIIR-AM or the EGB, by overall mortality (soon, cause-specific mortality), and serious events (in-hospital and LTD diagnosis codes, procedures or drugs used as proxies). These databases are a powerful material to compare the efficacy of two drugs with the same indication, but which have not been compared in clinical trials.
As regards immunopathology, these databases are appropriate for the follow-up in the long-term of cohorts of patients suffering from rare diseases. Two studies carried out in a Canadian database similar to the SNIIR-AM (the Régie de l'assurance maladie du Québec database) assessed the risk of infection on corticosteroids in older rheumatoid arthritis patients  [32, 33]. Our team is currently working in the SNIIR-AM with a similar approach on the benefit-to-risk balance of the different therapeutic strategies used in primary immune thrombocytopenia (FAITH study, No. ENCePP 4574)  [34]. Lastly, in-hospital and outhospital financial data are extremely detailed, and research in the field of health economics is rising in the SNIIR-AM and the EGB. However, some limitations should be kept in mind: as mentioned previously, the indications of treatments and of paraclinical examinations are not recorded.
They are deducted from LTD and hospital diagnosis codes, as well as from some procedures or drugs used as proxies. Only dispensing of reimbursed drugs is recorded: self-medication with over-the-counter drugs cannot be measured. Detailed information regarding the dose of pharmaceutical forms and the number of units dispensed are available. However, the prescribed dose is not recorded. This should be modelized thanks to pharmacoepidemiological methods. These approaches use most frequently the Defined Daily Dose defined for each drug by the World Health Organization  [35]. Validation of these pharmacoepidemiological methods is an important field of research, for instance regarding the modelization of drug exposure from rare dispensing  [36]  or the impact of cumulative doses  [37]. During hospital stays, only data regarding the dispensed costly drugs are available.
SNIIR-AM EGB Population All insurance schemes (since 2009) Dynamic sample of 1/97th (n = 660,000) of insured persons in France Only individuals receiving reimbursed healthcare (patients) except SLM and some rare insurance schemes (not necessarily ill) Benefiting from health care or not Rare problem of loss of follow-up Database depth 3 years + the current year; 10 years for the PMSI Recording during 20 years (start in 2005) Possibility to stock data for 10 years Data Out-hospital reimbursed healthcare Out-hospital reimbursed health cares ALD, work accidents, occupational diseases, sick-leaves ALD, work accidents, occupational diseases, sick-leaves PMSI MCO, PSY, HAD, SSR PMSI MCO only Access Non-profit research organizations Non-profit research organizations IDS authorization IDS authorization CNIL authorization Direct access by connection to EGB server Extraction by CNAMTS engineers Preferential use Studies on rare diseases Diseases with high prevalence Studies needing rapid results ALD:.
To identify the individuals feeding at a given chamber, and hence the number of helpers, we conducted a minimum of 1 h daily observations for at least three consecutive days  (Covas et al. 2006; Doutrelant & Covas 2007). Observers were situated in a hide placed at 3-5 m from the colony. We obtained data on breeding group composition for 168 breeders (85 females and 83 males). Then, from 2000 to 2005 and from 2008 to 2013, we used capture-mark-recapture data to estimate survival. Birds captured in 2006 were not considered here because no birds breeding in 1999 and 2000 were recaptured after 2005. The minimum age of the breeders varied from one to 11 years (Fig. 1 ). The exact age of some individuals (n = 28) was known if they were first ringed as nestlings or fledglings (in their first 4 months after fledging sociable weavers can be easily aged through the development of the black bib).
Rainfall influences food availability and the duration and success of the breeding season in sociable weavers  (Maclean 1973b; Dean & Milton 2001; Covas, du Plessis & Doutrelant 2008 ) and can thus influence survival  (Altwegg et al. 2013). To control this factor, we obtained rainfall data from Kimberley Airport (28°48 0 S, 24°46 0 E; c. 10 km from the centre of the study site) and included it as a covariate in our analyses. For the present analyses, we used summer rainfall (from September to June), which coincided with the breeding season. Summer rainfall during the study period ranged from 251 to 807 mm. molecular determination of the sex and identity of the parents Since sociable weavers are sexually monomorphic, sex had to be determined through molecular techniques. The breeders' sex was determined by amplification of chromo-helicase-DNA-binding genes located on the W and Z sex chromosomes using the P2 and P8 universal primers  (Griffiths et al.
1999; McRae & Amos 1999; Richardson et al. 2000; Sefc, Payne & Sorenson 2001; McRae et al. 2005; Dawson et al. 2010 Dawson et al. , 2013)). These were grouped into four primer sets using a Qiagen Mastermix kit. PCR product was sequenced using an ABI3730 capillary sequencer using the GeneScan TM 500 ROX TM Size Standard (Applied Biosystems), and results were analysed using GENEMAP- PER v3. 7 software (Applied Biosystems, Foster City, CA, USA). All of the scores were checked manually and adjusted wherever the genotype call was deemed to be in error. The program CERVUS v3. 0. 3 (Tristan Marshal, Field Genetics Ltd, London, UK) was used to quantify the number of alleles and the observed and expected heterozygosity and to check for null alleles. The program GENEPOP (http://genepop. curtin. edu.
au) was used to test each locus for conformity to Hardy-Weinberg equilibrium (HWE) and to check for linkage disequilibrium (LD) between loci. The program COLONY v2. 0. 3. 5  (Jones & Wang 2010)  was used to assign each chick a most likely mother and father through a likelihood approach. We used the genotypes of 181 offspring and used all genotyped male and female adult birds in the whole study population as parent candidates (529 females and 561 males). The proportion of candidate parents sampled was set at 75% to simulate the chance that an unknown individual might be the parent. A rate of 1% marker typing error was set. Fathers and mothers were assigned when their output parentage probability was given as 1. As previously reported  (Covas et al. 2006) , we did not find any evidence of extra-pair or extra-group paternity in this study (100% of identified incubating males and females were found to be the parents of the whole brood, and 100% of the 83 genetically assigned fathers were seen feeding the nestlings).
statistical methods We tested for differences in survival according to the number of helpers using multistate multi-event capture-recapture (CR) models with state uncertainty  (Pradel 2005) , using the software E- SURGE v1. 8. 9  (Choquet, Rouan & Pradel 2009 ) and following a maximum-likelihood procedure from the capture-recapture histories of the birds. In this type of analysis, the probability of encountering a marked individual is the product of four probabilities: the survival probability, the probability of changing status (here the number of helpers), the probability of recapture and the ability to attribute a state (number of helpers) to an individual (state uncertainty). Individual capture histories were built for 168 birds with known number of helpers (from 0 to 4) for at least one reproductive event.
Acknowledgements We first want to thank all the volunteers, ringers, students and researchers that helped with the captures and breeding monitoring. Notably, in the recent years,  Lisa Malm , C ecile Houl e,  Aurelien Prudor ,  Marie Laure Carpentier ,  Lara Broom ,  Margaux Rat ,  Franck Theron ,  Maxime Loubon ,  Ren e van Dijk ,  Jennifer Kaden ,  Graham Grieve ,  Pat Cochran ,  Colin de Kock ,  Eben Muller  and  Ronelle Visagie. Mark Anderson ,  Morn e du Plessis ,  Phil Hockey  and  Eric Herrmann  provided key support to the study over the years. Roger Pradel ,  Arnaud Gr  egoire,  Olivier Gimenez ,  R emy Choquet  and  Guillaume P eron  provided useful advice about CMR methods and the use of E-SURGE. We also thank  Paul Acker  for discussions and advice as well as two editors and two reviewers for helpful comments and suggestions that considerably improved the manuscript.
Bioinformatics analysis The generated sequences were subjected to bioinformatic analysis using the GnS-PIPE developed by the GenoSol platform (INRA, Dijon, France) and initially described by  Terrat et al. (2012). After an initial quality filtering step (>350 bp, no base ambiguity), reads were aligned with infernal alignments that use the secondary structure of the 16S rRNA gene  (Cole et al. 2009 ) and clustered at 95% sequence similarity into operational taxonomic units (OTU). This clustering step was done using a custom PERL program that does not consider differences in homopolymer lengths, which can cause the main 454-pyrosequencing errors  (Balzer et al. 2011). Each sample was then randomly rarefied at a sequencing depth of 10,800 quality sequences to allow rigorous comparison of the data.
Community structure was characterized using weighted UniFrac distance  (Lozupone and Knight 2005)  calculated with the PycoGent package  (Knight et al. 2007 ) on a phylogenetic tree computed using FastTree and the most abundant sequence to represent each OTU. Quality reads were used for taxonomy-based analysis by similarity approaches using USEARCH  (Edgar 2010)  against the corresponding Silva database  (Quast et al. 2013).