Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology

Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology

2017 J Mark Access Health Policy

Francois, C. | Tanasescu, A. | Lamy, F. X. | Despiegel, N. | Falissard, B. | Chalem, Y. | Lancon, C. | Llorca, P. M. | Saragoussi, D. | Verpillat, P. | Wade, A. G. | Zighed, D. A. | Volume: 5, Issue: 1, Pages: 1372025, Database, cohort, depression, health state, index, outcome, Delphine Saragoussi is a full-time employee of Lundbeck SAS. Francois-Xavier, Lamy, Ylana Chalem and Patrice Verpillat were full-time employees of Lundbeck SAS, employees at the time of the study. Adrian Tanasescu and Djamel Zighed are, employees of Rithme, a service provider consulting services working with, pharmaceutical companies including Lundbeck. Nicolas Despiegel was a full-time, employee of Optum at the time of study, a service provider consulting services, working with pharmaceutical companies including Lundbeck. Bruno Falissard has, been consultant for Lundbeck, E. Lilly, BMS, Servier, SANOFI, GSK, HRA, Roche,, Boeringer Ingelheim, Bayer, Almirall, Allergan, Stallergene, Genzyme, Pierre, Fabre, Astrazeneca, Novartis, Janssen, Astellas, Biotronik, Daiichi-Sankyo,, Gilead, MSD, Lundbeck, Stallergene, Actelion, UCB, Otsuka, Grunenthal and ViiV., Christophe Lancon has been consultant for Lundbeck, Roche and Janssen., Pierre-Michel Llorca has been consultant for Lundbeck and Servier. Alan Wade, received consultancy fees from Lundbeck.,

Background and objective: Automated healthcare databases (AHDB) are an important data source for real life drug and healthcare use. In the filed of depression, lack of detailed clinical data requires the use of binary proxies with important limitations. The study objective was to create a Depressive Health State Index (DHSI) as a continuous health state measure for depressed patients using available data in an AHDB. Methods: The study was based on historical cohort design using the UK Clinical Practice Research Datalink (CPRD). Depressive episodes (depression diagnosis with an antidepressant prescription) were used to create the DHSI through 6 successive steps: (1) Defining study design; (2) Identifying constituent parameters; (3) Assigning relative weights to the parameters; (4) Ranking based on the presence of parameters; (5) Standardizing the rank of the DHSI; (6) Developing a regression model to derive the DHSI in any other sample. Results: The DHSI ranged from 0 (worst) to 100 (best health state) comprising 29 parameters. The proportion of depressive episodes with a remission proxy increased with DHSI quartiles. Conclusion: A continuous outcome for depressed patients treated by antidepressants was created in an AHDB using several different variables and allowed more granularity than currently used proxies.

https://www.doi.org/10.1080/20016689.2017.1372025