title: Use of a multi-state model in a claims database: illustration with methadone.
abstract: OBJECTIVE
  In pharmacoepidemiology, one of the main concerns is analysis of drug exposure time. However, in real-life settings, patient's behavior is complex and characterized by drug exposure dynamics. Multi-state models allow assessing the probabilities of various patterns, instead of just continuous use and/or discontinuation. The aim of this study was to illustrate with methadone, the use of multi-state model (MSM) in a large claims database.
  
  
  METHODS
  This study is based on the French health insurance reimbursement database. Methadone exposure is defined using four states for each period of follow-up: syrup only, capsule only, syrup-capsule and no dispensing. The model considered 12 possible transitions (including reverse transitions) from one state to another. To describe these transitions a time-homogeneous Markov model was used.
  
  
  RESULTS
  A total of 1265 methadone users were included. When patients belonged to the syrup-capsule state, they had a 61.8% chance of moving to capsules the following month and 20.9% of moving to syrup. The probability of moving from the syrup to the non-user state was 13.6% (11.7% from capsule state to non-user state). The average length of stay was 5.9 months (CI95%: [5.5-6.4]) in capsule state, 4.9 (CI95%: [4.6-5.2]) in syrup state and 5.9 (CI95%: [5.5-6.3]) in non user state.
  
  
  CONCLUSIONS
  MSM provided a good description of methadone patterns of use. It outlined behaviors which have led to a rapid spread of capsule formulation among methadone users. Therefore, it illustrates the utility of MSM for modeling multiple sequences of drug use in a large claims database.
authors.affiliation: Aix Marseille Université, Institut de Neurosciences Timone, CNRS 7289, Service de Pharmacologie clinique et pharmacovigilance-, 264, rue Saint Pierre, 13385, Marseille, France.|Centre d'addictovigilance (CEIP) PACA-Corse Marseille, 264, rue Saint Pierre, 13385, Marseille, France.
authors.forename: Quentin
authors.initials: Q
authors.lastname: Boucherie
authors.affiliation: Aix Marseille Université, EA 3279 Laboratoire de Santé Publique, APHM, CEIP-Addictovigilance associé Paca Corse, 270 Boulevard de Sainte-Marguerite, 13009, Marseille, France.
authors.forename: Vanessa
authors.initials: V
authors.lastname: Pauly
authors.affiliation: Aix Marseille Université, Institut de Neurosciences Timone, CNRS 7289, Service de Pharmacologie clinique et pharmacovigilance-, 264, rue Saint Pierre, 13385, Marseille, France.|Centre d'addictovigilance (CEIP) PACA-Corse Marseille, 264, rue Saint Pierre, 13385, Marseille, France.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Frauger
authors.affiliation: Aix Marseille Université, EA 3279 Laboratoire de Santé Publique, APHM, CEIP-Addictovigilance associé Paca Corse, 270 Boulevard de Sainte-Marguerite, 13009, Marseille, France.
authors.forename: Xavier
authors.initials: X
authors.lastname: Thirion
authors.affiliation: Aix Marseille Université, Institut de Neurosciences Timone, CNRS 7289, Service de Pharmacologie clinique et pharmacovigilance-, 264, rue Saint Pierre, 13385, Marseille, France.|Aix Marseille Université, EA 3279 Laboratoire de Santé Publique, APHM, CEIP-Addictovigilance associé Paca Corse, 270 Boulevard de Sainte-Marguerite, 13009, Marseille, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Pradel
authors.affiliation: Aix Marseille Université, Institut de Neurosciences Timone, CNRS 7289, Service de Pharmacologie clinique et pharmacovigilance-, 264, rue Saint Pierre, 13385, Marseille, France.|Centre d'addictovigilance (CEIP) PACA-Corse Marseille, 264, rue Saint Pierre, 13385, Marseille, France.
authors.forename: Joëlle
authors.initials: J
authors.lastname: Micallef
mesh_terms: D000328:Adult; D002626:Chemistry, Pharmaceutical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D005602:France / Q000453:epidemiology; D006801:Humans; D007349:Insurance, Health, Reimbursement / Q000706:statistics & numerical data*; D008297:Male; D008691:Methadone / Q000008:administration & dosage* / Q000737:chemistry*; D008875:Middle Aged; D008962:Models, Theoretical; D009313:National Health Programs / Q000706:statistics & numerical data*; D009293:Opioid-Related Disorders / Q000188:drug therapy / Q000453:epidemiology
chemical_list: D008691:Methadone
keywords: Markov; formulation; methadone; multi-state model; pharmacoepidemiology