Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-01-01
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| Series: | Journal of Market Access & Health Policy |
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| Online Access: | http://dx.doi.org/10.1080/20016689.2017.1372025 |
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| author | Clément François Adrian Tanasescu François-Xavier Lamy Nicolas Despiegel Bruno Falissard Ylana Chalem Christophe Lançon Pierre-Michel Llorca Delphine Saragoussi Patrice Verpillat Alan G. Wade Djamel A. Zighed |
| author_facet | Clément François Adrian Tanasescu François-Xavier Lamy Nicolas Despiegel Bruno Falissard Ylana Chalem Christophe Lançon Pierre-Michel Llorca Delphine Saragoussi Patrice Verpillat Alan G. Wade Djamel A. Zighed |
| author_sort | Clément François |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-83e4adea5c8d441a82c40199e6a2e9a3 |
| institution | OA Journals |
| issn | 2001-6689 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Market Access & Health Policy |
| spelling | doaj-art-83e4adea5c8d441a82c40199e6a2e9a32025-08-20T01:58:30ZengMDPI AGJournal of Market Access & Health Policy2001-66892017-01-015110.1080/20016689.2017.13720251372025Creating an index to measure health state of depressed patients in automated healthcare databases: the methodologyClément François0Adrian Tanasescu1François-Xavier Lamy2Nicolas Despiegel3Bruno Falissard4Ylana Chalem5Christophe Lançon6Pierre-Michel Llorca7Delphine Saragoussi8Patrice Verpillat9Alan G. Wade10Djamel A. Zighed11LundbeckRithme ConsultingLundbeck SASMapiINSERM U1018, Universitté Paris-Sud, Université Paris-Saclay, UVSQLundbeck SASMarseille University HospitalCHU Clermont FerrandLundbeck SASLundbeck SASCPS ResearchLumière Lyon 2 UniversityBackground 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.http://dx.doi.org/10.1080/20016689.2017.1372025Databasedepressionhealth stateindexoutcomecohort |
| spellingShingle | Clément François Adrian Tanasescu François-Xavier Lamy Nicolas Despiegel Bruno Falissard Ylana Chalem Christophe Lançon Pierre-Michel Llorca Delphine Saragoussi Patrice Verpillat Alan G. Wade Djamel A. Zighed Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology Journal of Market Access & Health Policy Database depression health state index outcome cohort |
| title | Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology |
| title_full | Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology |
| title_fullStr | Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology |
| title_full_unstemmed | Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology |
| title_short | Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology |
| title_sort | creating an index to measure health state of depressed patients in automated healthcare databases the methodology |
| topic | Database depression health state index outcome cohort |
| url | http://dx.doi.org/10.1080/20016689.2017.1372025 |
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