[Identification of neurodegenerative diseases in administrative databases in France: A systematic review of the literature].
BACKGROUND
Given the health, social and economic burden of neurodegenerative diseases (ND), the development of epidemiologic studies is required. Administrative databases, such as the French national health insurance database (SNIIRAM) could represent an opportunity for researchers. ND could be presumed from drug reimbursement data, hospital stays or registration of a chronic condition. The aim of this study was to describe, in French administrative databases, algorithms used to identify Alzheimer's disease and associated disorders (ADAD), Parkinson's disease and associated disorders (PDAD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS).


METHODS
A systematic literature review was performed in Medline and gray literature through December 31th, 2015. French studies focusing on ADAD, PDAD, MS or ALS as a primary health outcome, conducted among one of the SNIIRAM data sources (outpatient reimbursements, chronic condition registration, hospital discharge) were included.


RESULTS
Thirty-four studies were included (ADAD, n=18, PDAD, n=9, MS, n=4, ALS, n=3), leading to 36 algorithms. For each studied ND, there was an important variability in the algorithms, concerning (i) the type of criteria used (administrative database versus multi-source systems); (ii) the number of criteria used; (iii) the definition used for each criteria. The extent and level of drug exposure highly varied. Identification through hospitalizations showed variations in terms of type of stay (short stay, long-term stay, psychiatric ward…), extent of diagnosis codes used, diagnosis type (principal, related, associated diagnosis) and period used. A validation study was conducted for 2 out of 36 algorithms (PDAD), and criteria completeness was estimated for 3 algorithms (MS, ALS).


CONCLUSIONS
Despite the increase in ND identification among French administrative databases, few algorithms have been validated. Validation studies should be encouraged.
Inserm UMR 1027, épidémiologie et analyses en santé publique, 37, allées Jules-Guesde, 31000 Toulouse, France; Département d'épidémiologie, économie de la santé et santé publique, faculté de médecine, université de Toulouse III, 31000 Toulouse, France; Service d'épidémiologie, CHU de Toulouse, 31000 Toulouse, France.
A
A
Gallini
Santé publique France, 94415 Saint-Maurice, France.
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F
Moisan
Département des études en santé publique, Caisse nationale d'assurance maladie des travailleurs salariés, 75986 Paris cedex 20, France.
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G
Maura
Santé publique France, 94415 Saint-Maurice, France.
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L
Carcaillon-Bentata
École des hautes études en santé publique, 35043 Rennes, France; EA 7449 REPERES, EHESP, université Rennes I, 35043 Rennes, France.
E
E
Leray
Pôle Imer, hospices civils de Lyon, 69003 Lyon, France; Hesper EA 7425, université Lyon, université Claude-Bernard Lyon 1, 69008 Lyon, France.
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J
Haesebaert
Département d'information médicale, CHRU de Lille, Lille, France.
A
A
Bruandet
Santé publique France, 94415 Saint-Maurice, France.
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E
Moutengou
Direction de la recherche, des études, de l'évaluation et des statistiques, ministères chargés de la santé, des solidarités et des comptes publics, 75014 Paris, France.
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L
Luciano
Département des études en santé publique, Caisse nationale d'assurance maladie des travailleurs salariés, 75986 Paris cedex 20, France.
A
A
Weill
Inserm UMR 1094, neuroépidémiologie tropicale, 87000 Limoges, France; UMR_S 1094, CNRS FR 3503 GEIST, neuroépidémiologie tropicale, institut d'épidémiologie neurologique et de neurologie tropicale, université de Limoges, 87000 Limoges, France.
B
B
Marin
Inserm UMR 1027, épidémiologie et analyses en santé publique, 37, allées Jules-Guesde, 31000 Toulouse, France; Département d'épidémiologie, économie de la santé et santé publique, faculté de médecine, université de Toulouse III, 31000 Toulouse, France; Service d'épidémiologie, CHU de Toulouse, 31000 Toulouse, France. Electronic address: virginie.gardette@univ-tlse3.fr.
V
V
Gardette
D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005602:France / Q000453:epidemiology; D006801:Humans; D016247:Information Storage and Retrieval; D009313:National Health Programs / Q000706:statistics & numerical data; D019636:Neurodegenerative Diseases / Q000453:epidemiology*
Administrative database; Algorithm; Algorithmes; Alzheimer; Alzheimer's Disease; Amyotrophic lateral sclerosis; Bases de données médicoadministratives; Maladies neurodégénératives; Multiple sclerosis; Neurodegenerative disorders; Parkinson; Parkinson's Disease; Sclérose en plaques; Sclérose latérale amyotrophique