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27 juin 2019 Raphaël Cinotti Présentation des titres & travaux

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01 juin 2019 Caillon J, Grall-Bronnec M, Perrot B, Leboucher J, Donnio Y, Romo L, Challet G (2019) Effectiveness of At-Risk Gamblers' Temporary Self-Exclusion from Internet Gambling Sites. J Gambl Stud, 35(2): 601-615.

01 mai 2019 Karsenti D, Tharsis G, Zeitoun J, Denis P, Perrot B, Coelho J, Bellaiche G, Charbit L, Hakoune J, Doumet S, Sion-Rohart E, Cavicchi M, Zago J (2019) Comparison of 20-gauge Procore® and 22-gauge Acquire® needles for EUS-FNB of solid pancreatic masses: an observational study. Scandinavian Journal of Gastroenterology, 8: 1-7.

11 avril 2019 Montassier E, Javaudin F, Moustafa F, Nandjou D, Maignan M, Hardouin JB, Annoot C, Ogielska M, Orer PL, Schotté T, Bouget J, Agha Babaei S, Raynal PA, Eche A, Duc AT, Cojocaru RA, Benaouicha N, Potel G, Batard E, Talan DA. (2019) Guideline-Based Clinical Assessment Versus Procalcitonin-Guided Antibiotic Use in Pneumonia: A Pragmatic Randomized Trial. Ann Emerg Med(19): 30147-7.

09 avril 2019 Rouquette A, Hardouin JB, Vanhaesebrouck A, Sébille V, Coste J (2019) Differential Item Functioning (DIF) in composite health measurement scale: recommendations for characterizing DIF with meaningful consequences within the Rasch model framework.. PLOS One, 14(4): e0215073.

01 avril 2019 Guillou-Landreat M, Deriennic J, Brunault P, Challet G, Gallopel Morvan K, Grall-Bronnec M (2019) Marketing des jeux de hasard et d’argent, un enjeu de santé publique ?. La Presse médicale, 48(4): 347-352.

Updated

24 juin 2019

Typology of care trajectories of addicts in the EVALADD cohort: growth mixture modeling coupled with latent class analysis

Stagiaire

Montourcy Marion - Stagiaire M2 - M2 MPCE
Formation d'origine : M2 Modélisation en Pharmacologie Clinique et Epidémiologie (MPCE)

Résumé

Purpose: The goal of our work was to establish trajectories on patients with behavioral addictions and who beginning care in order to identify typical trajectories and to specify determining factors in the clinical evolution.

Methods: We analyzed 3 follow-ups (initial visit, 6 months and 12 months). Thus 302 patients completed these 3 follow-ups. Trajectories were realized to regroup patients according to their evolution for each score. From these trajectories and by adding comorbidities, latent classes were created to identify typologies of patients. A description of the classes was made based on socio-demographic data and more specific scores related to behavioral addictions and psychopathology.

Results: Five classes were created with one class distinguished more particularly on its addiction severity and complexity (“complex addicts”). Other 4 classes show less difference and 4 profiles emerged: “impulsive addicts”, “complex addicts”,” cooperative addicts”, “immature addicts” and “resilient addicts”.

Conclusion: Our analyzes did not show a gathering of addictions about patient profiles between classes but rather a classification based on patient’s profiles.

Keywords: behavioral addiction – growth mixture model – latent class analyses – typology

Personnes impliquées

Co-encadrant: Hardouin Jean-Benoit

Co-encadrant: Challet Gaelle

Financement

IFAC

Projet(s) associé(s)

EVALJEU : Suivi d’une cohorte de joueurs pathologiques débutant des soins dans le service d’Addictologie du CHU de Nantes. Etude clinique, monocentrique, prospective, non contrôlée

Période

Début : Avril 2015

Fin : Septembre 2015