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Exploring the social determinants of mental health service use using intersectionality theory and CART analysis
  1. John Cairney1,
  2. Scott Veldhuizen2,
  3. Simone Vigod3,
  4. David L Streiner4,5,
  5. Terrance J Wade6,
  6. Paul Kurdyak7,8
  1. 1Departments of Family Medicine, Psychiatry & Behavioural Neurosciences, and Kinesiology, McMaster University, Hamilton, Ontario, Canada
  2. 2Social and Epidemiological Research Department, Centre for Addiction and Mental Health, Toronto, Canada
  3. 3Department of Psychiatry, Women's College Research Institute, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
  4. 4Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
  5. 5Department of Psychiatry, University of Toronto, Hamilton, Ontario, Canada
  6. 6Department of Community Health Sciences, Brock University, St. Catharines, Ontario, Canada
  7. 7Social and Epidemiological Research Department, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  8. 8Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr John Cairney, Departments of Family Medicine, Psychiatry & Behavioural Neurosciences, and Kinesiology, McMaster University, 175 Longwood Road South, Suite 201A, Hamilton, Ontario, Canada L8P 0A1; cairnej{at}mcmaster.ca

Abstract

BACKGROUND Fewer than half of individuals with a mental disorder seek formal care in a given year. Much research has been conducted on the factors that influence service use in this population, but the methods generally used cannot easily identify the complex interactions that are thought to exist. In this paper, we examine predictors of subsequent service use among respondents to a population health survey who met criteria for a past-year mood, anxiety or substance-related disorder.

METHODS To determine service use, we use an administrative database including all physician consultations in the period of interest. To identify predictors, we use classification tree (CART) analysis, a data mining technique with the ability to identify unsuspected interactions. We compare results to those from logistic regression models.

RESULTS We identify 1213 individuals with past-year disorder. In the year after the survey, 24% (n=312) of these had a mental health-related physician consultation. Logistic regression revealed that age, sex and marital status predicted service use. CART analysis yielded a set of rules based on age, sex, marital status and income adequacy, with marital status playing a role among men and by income adequacy important among women. CART analysis proved moderately effective overall, with agreement of 60%, sensitivity of 82% and specificity of 53%.

CONCLUSION Results highlight the potential of data-mining techniques to uncover complex interactions, and offer support to the view that the intersection of multiple statuses influence health and behaviour in ways that are difficult to identify with conventional statistics. The disadvantages of these methods are also discussed.

  • Access to Hlth Care
  • Mental Health
  • Social Inequalities
  • Social Science
  • Modelling

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