Research report
Exploring the social determinants of mental health service use using intersectionality theory and CART analysis John Cairney,1 Scott Veldhuizen,2 Simone Vigod,3 David L Streiner,4,5 Terrance J Wade,6 Paul Kurdyak7,8 For numbered affiliations see end of article. 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;
[email protected] Received 16 July 2013 Revised 1 September 2013 Accepted 4 September 2013 Published Online First 4 October 2013
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 substancerelated 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.
INTRODUCTION
To cite: Cairney J, Veldhuizen S, Vigod S, et al. J Epidemiol Community Health 2014;68:145–150.
Although effective treatments for mental health conditions such as depression and anxiety have been available for some time, fewer than half of individuals with a mental disorder seek formal care from a primary care physician or psychiatrist.1–4 Understanding the factors predicting mental healthcare-seeking behaviours is crucial for the formulation of health policy and the design of interventions to address mental health service access inequities. In what is arguably the most influential model of healthcare utilisation, Andersen5–7 proposed three sets of factors, which together can be used to predict use of services at the individual and population levels: predisposing, enabling and need factors. Predisposing and enabling factors are comprised
Cairney J, et al. J Epidemiol Community Health 2014;68:145–150. doi:10.1136/jech-2013-203120
mostly, though not exclusively, of social (eg, gender, age) and economic (eg, household income) variables, whereas need factors are indicators of objective need (eg, presence of a health condition) and perceived need (eg, self-rated health) for care. In an equitable system, need for care should be the most important determinant of service use. As such, the identification of non-need factors associated with service use serves a critical role in assessing systems of care. When factors such as insurance coverage are found to influence use, it raises questions about possible inequities. Although these factors may also reflect differences in the propensity to seek care, similar concerns may be raised when factors such as gender and socioeconomic status (SES) are found to predict service use. Examination of enabling and predisposing factors that predict mental healthcare service use is especially pertinent, given the long-standing concern over stigma associated with having a disorder8 and with seeking care for it.9 A variety of predisposing and enabling factors have been identified as non-need determinants of use of mental healthcare services. These include age,10 11 gender,2 12–14 SES,14–16 ethnicity,3 17–19 marital status,20 parental status20 21 and geographical location.10 As several of these factors are social status positions, concerns around equity appear to be legitimate. It is not clear, however, that the influence of these factors has been thoroughly examined. Existing research on social factors associated with mental health service use rely on statistical models of competing risks. Need and non-need factors are tested simultaneously, and those effects that are statistically significant become the focus of interpretation. With respect to social determinants, this approach is problematic, because it does not take into account the fact that the social positions occupied by each individual may interact in complex ways. The circumstances of a teenaged single mother, for example, may not be adequately described by the independent effects of marital status, parent status, age and gender. Intersectionality theory 21 22 challenges us to consider social determinants not in terms of single factors (eg, gender or SES), but in terms of multiple, interacting factors. In this framework, social disadvantage arises from a constellation of interrelated and intersecting social roles. This view, sometimes referred to as the ‘double’ or ‘triple’ jeopardy hypothesis, has informed considerable research on health. Applications in the mental healthcare utilisation literature include work showing that single 145
Research report mothers are more likely to seek care for mental health problem, than their married counterparts.20 While intuitive, intersectionality is difficult to explore empirically. In linear models, interactions involving more than two variables tend to require large sample sizes, and also to produce models that are complex, difficult to interpret, and often plagued by multicollinearity or other problems. Moreover, interactions involving non-linear effects are usually difficult to detect. Something as apparently simple as an age-by-sex interaction, for example, can be complicated not only by a nonlinear association between age and the outcome, but by the fact that the interaction with sex may itself vary with age in a nonlinear way. One solution to this problem is to eschew linear models entirely. Classification trees (CART) are one alternative popular in machine learning and data mining applications.22–24 The CART approach involves recursively identifying rules that distinguish between groups, usually with certain constraints to avoid overfitting. Although it does not permit the testing of hypotheses in the usual sense, CART has two important advantages: (1) it makes no assumptions about variable distributions or relationships and (2) it is capable of identifying complex and unsuspected interactions. CART results can also be useful from a clinical and health policy perspective, because they yield decision rules that can be used to identify at-risk individuals more easily than, for example, regression coefficients. In the present study, we use CART to examine the social determinants of mental health service use in a general population sample. Specifically, we are interested in exploring complex interactions between different social determinants and their impact on mental healthcare use. Our focus on social determinants is related to Andersen’s concern regarding equity: in a country with a ‘universal’ healthcare system,25 it is critical to evaluate whether factors other than need are influencing use of services.
METHODS Data come from cycle 1.2 of the Canadian Community Health Survey (CCHS 1.2), a population survey conducted by Statistics Canada in 2002/2003.26 The sampling frame of CCHS 1.2 included all Canadians aged 15 or older living in private dwellings, with the exception of full-time members of the armed forces and residents of remote areas or native reserves. The final sample size was 36 984. Statistics Canada linked participants from Ontario (n=13 184) to administrative health data; 10 600 (81%) were linked successfully. From this sample, we selected all participants (n=1213) who met past-year criteria for one or more of the five mood and anxiety disorders, with and without substance dependence. The merged dataset was accessed at the Institute for Clinical Evaluative Sciences (ICES). In order to access these data for these analyses, which included linking healthcare administrative date to survey data previously collected by Statistics Canada, independent review ( privacy impact assessment) was conducted to ensure compliance with privacy legislation governing access to personal healthcare information through the ICES at Sunnybrook Hospital. The project was approved through this process.
MEASURES Outpatient mental health service use We used physician billing records in the Ontario Health Insurance Plan (OHIP) database to determine whether each respondent used physician-provided mental health services in the year following the survey. In Ontario, medically necessary care coverage is fully financed by a government-funded health 146
insurance programme. Within this universal healthcare coverage setting, 94% of physicians have a fee-for-service practice that is captured by OHIP billing submission data,27 and physicians who provide services on a salary are mandated to submit ‘shadow billings’ for accountability purposes, resulting in a highly accurate and comprehensive physician activity data source. A visit for mental healthcare was defined as any outpatient encounter with a psychiatrist, or a visit with a primary care provider (ie, family physician) or geriatrician with both a mental health and/or addictions International Classification of Diseases-9 diagnosis and a mental health and/or addictions service billing code. Mental health visits with a primary care provider or geriatrician are defined based on a validated algorithm developed by Steele et al,28 with modifications to include contacts with specialist physicians.
Mental disorders CCHS 1.2 assessed all respondents for six conditions (major depressive disorder, bipolar disorder, social phobia, panic disorder, agoraphobia and substance abuse or dependence) based on symptom reporting in 12 months prior to the time of the survey using the World Mental Health—Composite International Diagnostic Interview (WMH-CIDI). The WMH-CIDI was developed and validated using the Structured Clinical Interview for DSM-IV (SCID) as a reference standard, and is now widely used in epidemiological surveys.2 29 In CCHS 1.2, the WMH-CIDI was administered by trained lay interviewers. Details of the administration and psychometric properties of the measure are provided in Gravel and Béland.26
Social determinants We selected eight variables capturing different social factors previously shown to be associated with the use of mental healthcare: age (in years), gender (males; females), marital status (married or cohabitating; formerly married (separated, divorced or widowed); never married), parental status ( parent living with children; other), income adequacy (low; moderate or high), education (less than secondary; secondary graduate; some postsecondary; post-secondary graduate), rurality (rural; urban) and visible minority status, according to the self-description chosen by the survey participant (‘white’; or any category other than ‘white’). Age was entered as a continuous variable; all others were dummy-coded. Income adequacy is a derived variable, produced by Statistics Canada that combines household income and household size. It is ‘low’ for household incomes, in 2002 Canadian dollars, of
