Epidemiology and Psychiatric Sciences
Volume 27, Issue 6, 2018, Pages 589-600

Taking an intersectional approach to define latent classes of socioeconomic status, ethnicity and migration status for psychiatric epidemiological research (Article)

Goodwin L. , Gazard B.* , Aschan L. , MacCrimmon S. , Hotopf M. , Hatch S.L.
  • a King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom, Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
  • b King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom
  • c King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom
  • d King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom
  • e King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom, South London and Maudsley NHS Foundation Trust, London, United Kingdom
  • f King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, 10 Cutcombe Road, London, SE59RJ, United Kingdom

Abstract

Aims. Inequalities in mental health are well documented using individual social statuses such as socioeconomic status (SES), ethnicity and migration status. However, few studies have taken an intersectional approach to investigate inequalities in mental health using latent class analysis (LCA). This study will examine the association between multiple indicator classes of social identity with common mental disorder (CMD).Methods. Data on CMD symptoms were assessed in a diverse inner London sample of 1052 participants in the second wave of the South East London Community Health study. LCA was used to define classes of social identity using multiple indicators of SES, ethnicity and migration status. Adjusted associations between CMD and both individual indicators and multiple indicators of social identity are presented.Results. LCA identified six groups that were differentiated by varying levels of privilege and disadvantage based on multiple SES indicators. This intersectional approach highlighted nuanced differences in odds of CMD, with the economically inactive group with multiple levels of disadvantage most likely to have a CMD. Adding ethnicity and migration status further differentiated between groups. The migrant, economically inactive and White British, economically inactive classes both had increased odds of CMD.Conclusions. This is the first study to examine the intersections of SES, ethnicity and migration status with CMD using LCA. Results showed that both the migrant, economically inactive and the White British, economically inactive classes had a similarly high prevalence of CMD. Findings suggest that LCA is a useful methodology for investigating health inequalities by intersectional identities. © 2017 Cambridge University Press.

Author Keywords

research design and methods Mental health Community mental health Epidemiology Population survey

Index Keywords

latent class analysis Caucasian London mental health human epidemiology risk assessment Ethnic Groups statistics and numerical data ethnic group British citizen controlled study economic aspect Epidemiologic Studies Mental Disorders mental disease social status migrant psychology Humans male Emigrants and Immigrants England female Socioeconomic Factors risk factor socioeconomics high risk population symptom prevalence Article major clinical study adult Social Environment migration research ethnicity social class disease association
Link
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017428176&doi=10.1017%2fS2045796017000142&partnerID=40&md5=9666ba77c42c73a8207539b63c20720a
DOI: 10.1017/S2045796017000142
ISSN: 20457960
Cited by: 6
Original Language: English