Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data
Introduction Common mental health disorders (CMD) are significant contributors to impaired health and well-being, and drive greater health resource utilisation. Electronic health records (EHR) are increasingly used for case identification of CMD when ascertaining social determinants of mental health...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Swansea University
2020-12-01
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| Series: | International Journal of Population Data Science |
| Online Access: | https://ijpds.org/article/view/1618 |
| _version_ | 1797427633977294848 |
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| author | Daniel Thompson Ann John Richard Fry Alan Watkins |
| author_facet | Daniel Thompson Ann John Richard Fry Alan Watkins |
| author_sort | Daniel Thompson |
| collection | DOAJ |
| description | Introduction
Common mental health disorders (CMD) are significant contributors to impaired health and well-being, and drive greater health resource utilisation. Electronic health records (EHR) are increasingly used for case identification of CMD when ascertaining social determinants of mental health. We seek to compare self-reported well-being indicators in groups identified using EHR-based CMD methods.
Objectives and Approach
The National Survey for Wales (NSW) contains self-reported well-being indicators (Warwick Edinburgh Mental Well-being Scale, WEMWBS) recorded annually on ~7,000 individuals. We combined data from two NSWs and linked well-being indicators with Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage (SAIL) Databank, using individual response dates. We then used WGLP data to algorithmically derive identifiers of CMD cases within survey respondents. This individual-level linkage enables a comparison of NSW responses in CMD and non-CMD cases, and to assess sensitivity and specificity of the current CMD algorithm.
Results
Survey participants comprised 18,450 adults aged 16+ and living in Wales during 16/17 or 18/19. WEMWBS responses indicate 2,338 (12.6%) participants could be considered possibly depressed, and 2,268 (12.3%) probably depressed with low mental well-being (LMW). For participants with LMW, a 42/58 percentage split is observed between male/female respondents, compared to a 45/55 respective split of those not identified with LMW. Participants with LMW recorded low measures for overall satisfaction with life, 998 (44%) reported a value of 5 or less (/10) compared to 1123 (7%) participants not identified with LMW. Similarly, 828 (37%) participants identified with LMW reported 5 or less (/10) on the life worthwhile index, compared to 800 (5%) of non-LMW participants.
Conclusion / Implications
Linkage to the NSW provides a rich data source to compare objective well-being to algorithmically derived CMD cases from routinely collected primary care data. The individual-level linkage involved will allow for the wider determinants of mental health disorders to be examined. |
| first_indexed | 2024-03-09T08:47:11Z |
| format | Article |
| id | doaj.art-4570b951034e413f84cecaf98ff33c87 |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T08:47:11Z |
| publishDate | 2020-12-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-4570b951034e413f84cecaf98ff33c872023-12-02T15:09:10ZengSwansea UniversityInternational Journal of Population Data Science2399-49082020-12-015510.23889/ijpds.v5i5.1618Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health DataDaniel Thompson0Ann John1Richard Fry2Alan Watkins3Swansea UniversitySwansea UniversitySwansea UniversitySwansea UniversityIntroduction Common mental health disorders (CMD) are significant contributors to impaired health and well-being, and drive greater health resource utilisation. Electronic health records (EHR) are increasingly used for case identification of CMD when ascertaining social determinants of mental health. We seek to compare self-reported well-being indicators in groups identified using EHR-based CMD methods. Objectives and Approach The National Survey for Wales (NSW) contains self-reported well-being indicators (Warwick Edinburgh Mental Well-being Scale, WEMWBS) recorded annually on ~7,000 individuals. We combined data from two NSWs and linked well-being indicators with Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage (SAIL) Databank, using individual response dates. We then used WGLP data to algorithmically derive identifiers of CMD cases within survey respondents. This individual-level linkage enables a comparison of NSW responses in CMD and non-CMD cases, and to assess sensitivity and specificity of the current CMD algorithm. Results Survey participants comprised 18,450 adults aged 16+ and living in Wales during 16/17 or 18/19. WEMWBS responses indicate 2,338 (12.6%) participants could be considered possibly depressed, and 2,268 (12.3%) probably depressed with low mental well-being (LMW). For participants with LMW, a 42/58 percentage split is observed between male/female respondents, compared to a 45/55 respective split of those not identified with LMW. Participants with LMW recorded low measures for overall satisfaction with life, 998 (44%) reported a value of 5 or less (/10) compared to 1123 (7%) participants not identified with LMW. Similarly, 828 (37%) participants identified with LMW reported 5 or less (/10) on the life worthwhile index, compared to 800 (5%) of non-LMW participants. Conclusion / Implications Linkage to the NSW provides a rich data source to compare objective well-being to algorithmically derived CMD cases from routinely collected primary care data. The individual-level linkage involved will allow for the wider determinants of mental health disorders to be examined.https://ijpds.org/article/view/1618 |
| spellingShingle | Daniel Thompson Ann John Richard Fry Alan Watkins Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data International Journal of Population Data Science |
| title | Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data |
| title_full | Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data |
| title_fullStr | Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data |
| title_full_unstemmed | Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data |
| title_short | Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data |
| title_sort | self reported well being indicators and case identification of common mental health disorders in routinely collected health data |
| url | https://ijpds.org/article/view/1618 |
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