Generating EQ-5D-3L health utility scores from the Edinburgh Postnatal Depression Scale: a perinatal mapping study

Elizabeth M. Camacho (), Gemma E. Shields, Carolyn A. Chew-Graham, Emily Eisner, Simon Gilbody, Elizabeth Littlewood, Dean McMillan, Kylie Watson, Pasco Fearon and Deborah J. Sharp
Additional contact information
Elizabeth M. Camacho: University of Manchester
Gemma E. Shields: University of Manchester
Carolyn A. Chew-Graham: Keele University
Emily Eisner: University of Manchester
Simon Gilbody: University of York
Elizabeth Littlewood: University of York
Dean McMillan: University of York
Kylie Watson: Manchester University NHS Foundation Trust
Pasco Fearon: University College London
Deborah J. Sharp: University of Bristol

The European Journal of Health Economics, 2024, vol. 25, issue 2, No 11, 319-332

Abstract: Abstract Background Perinatal depression (PND) describes depression experienced by parents during pregnancy or in the first year after a baby is born. The EQ-5D instrument (a generic measure of health status) is not often collected in perinatal research, however disease-specific measures, such as the Edinburgh Postnatal Depression Scale (EPDS) are widely used. Mapping can be used to estimate generic health utility index values from disease-specific measures like the EPDS. Objective To develop a mapping algorithm to estimate EQ-5D utility index values from the EPDS. Methods Patient-level data from the BaBY PaNDA study (English observational cohort study) provided 1068 observations with paired EPDS and EQ-5D (3-level version; EQ-5D-3L) responses. We compared the performance of six alternative regression model types, each with four specifications of covariates (EPDS score and age: base, squared, and cubed). Model performance (ability to predict utility values) was assessed by ranking mean error, mean absolute error, and root mean square error. Algorithm performance in 3 external datasets was also evaluated. Results There was moderate correlation between EPDS score and utility values (coefficient: – 0.42). The best performing model type was a two-part model, followed by ordinary least squared. Inclusion of squared and cubed covariates improved model performance. Based on graphs of observed and predicted utility values, the algorithm performed better when utility was above 0.6. Conclusions This direct mapping algorithm allows the estimation of health utility values from EPDS scores. The algorithm has good external validity but is likely to perform better in samples with higher health status.

Keywords: Utility; Mapping; EQ-5D; Edinburgh Postnatal Depression Scale; Perinatal depression (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10198-023-01589-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:eujhec:v:25:y:2024:i:2:d:10.1007_s10198-023-01589-4

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10198/PS2

DOI: 10.1007/s10198-023-01589-4

Access Statistics for this article

The European Journal of Health Economics is currently edited by J.-M.G.v.d. Schulenburg

More articles in The European Journal of Health Economics from Springer, Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().