How does mental health stigma get under the skin? Cross-sectional analysis using the Health Survey for England

Niedzwiedz, C. L. (2019) How does mental health stigma get under the skin? Cross-sectional analysis using the Health Survey for England. SSM - Population Health, 8, 100433. (doi: 10.1016/j.ssmph.2019.100433) (PMID:31312714) (PMCID:PMC6609872)

188233.pdf - Published Version
Available under License Creative Commons Attribution.



Despite increased awareness of mental health problems, stigma persists. Little research has examined potential health and wellbeing outcomes associated with stigma. The aim of this study was to investigate relationships between mental health stigma, metabolic and cardiovascular biomarkers, as well as wellbeing and quality of life among people with no mental disorder, common mental disorders and severe mental illness. Data were taken from adults aged 16 + years participating in the Health Survey for England in 2014 (N = 5491). Mental health stigma was measured using the 12-item Community Attitudes towards the Mentally Ill (CAMI) scale, intended to measure attitudes around prejudice and exclusion, and tolerance and support for community care. Individuals were divided into six groups based on their mental health (no mental disorder, common mental disorder, severe mental illness) and whether they exhibited more (≤25th percentile) or less (>25th percentile) stigmatising attitudes. Metabolic and cardiovascular biomarker outcomes included systolic and diastolic blood pressure; total cholesterol; high-density lipoprotein (HDL) cholesterol; glycated haemoglobin, body mass index (BMI), waist-hip ratio and resting pulse rate. Biomarkers were analysed individually and as an allostatic load score. Wellbeing was measured using Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) and quality of life via Euro-QoL-5D (EQ-5D). Linear regression models were calculated adjusted for confounders. Compared to individuals with less stigmatising attitudes, results suggested that those with more negative attitudes exhibited poorer wellbeing and quality of life across all mental disorder/stigma groups, including those with no mental disorder (WEMWBS (range 14–70): b = -1.384, 95% CI: -2.107 to -0.661). People with severe mental illness generally had unhealthier biomarker profiles and allostatic load scores, but results were inconsistent for any additional influence of mental health stigma. Reducing stigma may be beneficial for population wellbeing, but further research is needed to clarify whether stigma contributes to adverse biomarkers amongst people with mental illness.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Niedzwiedz, Dr Claire
Authors: Niedzwiedz, C. L.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care
Journal Name:SSM - Population Health
ISSN (Online):2352-8273
Published Online:13 June 2019
Copyright Holders:Copyright © 2019 The Author
First Published:First published in SSM - Population Health 8:100433
Publisher Policy:Reproduced under a Creative Commons license

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3021820A machine learning approach to understanding comorbidity between mental and physical health conditionsJill PellMedical Research Council (MRC)MR/R024774/1HW - Public Health