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

François, Clément; Tanasescu, Adrian; Lamy, François-Xavier; Despiegel, Nicolas; Falissard, Bruno; Chalem, Ylana; Lançon, Christophe; Llorca, Pierre‐Michel; Saragoussi, Delphine; Verpillat, Patrice; Wade, Alan G; Zighed, Djamel A.

doi:10.1080/20016689.2017.1372025

Cited by 3 publications

(14 citation statements)

References 17 publications

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“…The detailed methods used to create the DHSI, and initial results and validation have been published previously by the same research group [5,6].…”

Section: Methodsmentioning

confidence: 99%

“…As detailed in the methodological manuscript [5], the study is based on a historical cohort design using data from the CPRD. The CPRD is a database of anonymized primary care records for patients registered at general practices in the UK.…”

Section: Methodsmentioning

confidence: 99%

“…For this study, the same dataset and the same population as for the weighted DHSI was used [5,6]. Patients with at least one depressive episode during the study period (1 January 2006-31 December 2012) were included.…”

Section: Study Populationmentioning

confidence: 99%

“…Reference period: 5 months before ID to 1 month after ID Follow-up period: 3 months after ID to 9 months after ID Follow-up point: ID +6 months 5 months 1 month 3 months 3 months Figure 1. Study design (Illustration from [5]).…”

Section: Index Date (Id)mentioning

confidence: 99%

“…To have a more precise estimation of health state of patients with depression using the data contained in AHDBs, a depression health state index (DHSI) was created [5]. A unique index was created by combining all variables available in an AHDB and considered to be related to the health state of patients with depression.…”

Section: Introductionmentioning

confidence: 99%

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Creation and validation of a linear index to measure the health state of patients with depression in automated healthcare databases

Touya

Lamy²,

Tanasescu³

et al. 2019

Journal of Market Access & Health Policy

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Background and objective: We previously built a weighted Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB). We now propose a linear DHSI (L-DHSI) which is easier to use and to replicate across AHDBs.Methods: A historical cohort of patients with ≥1 episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each treated episode of depression. Validation was performed by using validated definitions of remission (proxy and Patient Health Questionnaire 9 or PHQ-9) and comparing the L-DHSI between subgroups. Reliability was assessed using Cronbach’s alpha.Results: Between 1 January 2006 and 31 December 2012, 309,279 episodes of depression were identified in the CPRD. Remission was observed in 5% of the patients with lowest L-DHSI scores and in 78% of the patients with highest L-DHSI scores. Although less sensitive than the weighted DHSI, the L-DHSI was reliable and relatively easy of use. The L-DHSI was highly correlated to the weighted DHSI (Spearman coefficient 0.790, p < 0.001).Conclusion: The L-DHSI represents a good balance between reliability, usability, and reproducibility. In addition, the linearity of this index allows for an easier interpretation than the original weighted DHSI.

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“…The detailed methods used to create the DHSI, and initial results and validation have been published previously by the same research group [5,6].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Study Populationmentioning

confidence: 99%

Section: Index Date (Id)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Creation and validation of a linear index to measure the health state of patients with depression in automated healthcare databases

Touya

Lamy²,

Tanasescu³

et al. 2019

Journal of Market Access & Health Policy

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Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

Gastel

Hendrickx

Leysen

et al. 2019

Methods in Molecular Biology

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Results and validation of an index to measure health state of patients with depression in automated healthcare databases

Lamy¹,

Falissard

François

et al. 2019

Journal of Market Access & Health Policy

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Background and objective: A Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB) was developed to evaluate the health state of depressive patients, and its evolution. The study objective was to describe and validate this DHSI.Methods: A historical cohort of patients with at least one episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each episode of depression. Validation was performed by comparing the DHSI between subgroups and using validated definitions of remission (proxy and PHQ-9). Robustness was studied by assessing the impact of modifying parameters of the DHSI.Results: 309,279 episodes of depression were identified in the CPRD between 1 January 2006 and 31 December 2012. Remission was observed in 8% of the patients showing the lower DHSI scores and in 88% of the patients showing the higher DHSI scores. The DHSI was robust to a modification of the most frequent variables and to the removal of rare parameters.Conclusion: The DHSI is specific to depression severity (with remission rates in accordance with the expected variations of the DHSI) and robust. It represents a promising tool for the analysis of AHDBs.

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

Cited by 3 publications

References 17 publications

Creation and validation of a linear index to measure the health state of patients with depression in automated healthcare databases

Creation and validation of a linear index to measure the health state of patients with depression in automated healthcare databases

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

Results and validation of an index to measure health state of patients with depression in automated healthcare databases

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