Mental health services use among adults with or without mental disorders: Do development stages matter?

Abstract

Background:

Mental health services (MHS) use is a complex behaviour that does not only concern individuals with current mental disorder. To date, few studies have examined age-related contextualisation of MHS use. Reasons for seeking help may vary according to development stages in adulthood.

Aims:

This study aimed to determine which predisposing, enabling and need factors, using Andersen’s model, were associated with MHS use according to adult development stages among individuals with or without current psychiatric diagnosis.

Methods:

Three age groups were examined: 18- to 29-year-olds (n = 775), 30- to 49-year-olds (n = 1,560) and 50- to 64-year-olds (n = 960). Data were obtained from the Montreal Longitudinal Catchment Area Study. Bivariate and multivariate logistic regression analyses were conducted for each age group separately to determine which predisposing, enabling and need factors were associated with MHS use in the past 12 months.

Results:

For 18- to 29-year-olds, one enabling factor (Internet search) and two need factors (presence of major depressive disorder and number of stressful events) were positively associated with MHS use. For 30- to 49-year-olds, one predisposing factor (family history of mental disorder), four enabling factors (not currently working or in school, perceiving neighbourhood disorder, social cohesion and Internet searching) and one need factor (major depressive disorder) correlated with help seeking. For 50- to 64-year-olds, two predisposing factors (family history of mental disorder and higher self-perceived stigma), two enabling factors (low satisfaction in personal relationship and Internet searching) and one need factor (alcohol dependence) were associated with MHS use.

Conclusions:

Factors associated with MHS use differ according to adult development stages. Programmes and policies should be based on age-related contextualisation to increase MHS use.

Keywords

Service utilisation mental health Andersen’s model emerging adulthood middle adulthood development

Introduction

Although 12-month prevalence of mental disorders (MD) since 2010 is estimated to be 10.1% in Canada, 18.5% in the United States and 27.1% in Europe, less than half of individuals with MD use mental health services (MHS) (Lesage et al., 2006; Pearson, Janz, & Ali, 2013; Substance Abuse and Mental Health Services Administration, 2014; Wang et al., 2007; Wittchen et al., 2011). Epidemiological studies worldwide report also that one-third to a half of individuals using MHS have no MD in the last 12 months prior to interview (Bijl et al., 2003; Bruffaerts et al., 2015; Demyttenaere et al., 2004; Druss et al., 2007; Kessler et al., 1997; Wang et al., 2005). Reasons to seek help in the absence of an MD include high psychological distress, suicidal conducts, bodily complaints, subclinical psychiatric symptoms, stressful events and follow-ups after recovering from a prior MD (Bruffaerts et al., 2015; Fleury, Ngui, Bamvita, Grenier, & Caron, 2014). Belief that intervention is helpful to manage life problems can also encourage help seeking in individuals holding high value on MHS (Mechanic, 2003). Thus, MHS use is a complex phenomenon that does not only concern individuals with current MD.

Investigating help seeking as behaviour requires examining other factors than the sole presence of MD diagnosis. Andersen’s behavioural model of health services use allows a comprehensive understanding of MHS use and posits this behaviour as resulting from interactions between individual characteristics and social environment (Andersen, 1995). Need factors are conceptualised as the ones most closely correlated to health services use; this has been demonstrated by almost all studies (Andersen, 1995; Fleury, Grenier, & Bamvita, 2015; Lindamer et al., 2012). They refer to symptomatology, distress and other clinical characteristics requiring assessment and care. Enabling factors are the personal and social mutable resources available, such as income, occupation, social provisions, quality of life and service availability. They also include neighbourhood factors, which are important determinants of mental health (Hill & Maimon, 2013; Ngamini Ngui, Perreault, Fleury, & Caron, 2012). Predisposing factors are biological and social imperatives: sociodemographic characteristics (age, sex, country of origin), social structure (living arrangement), as well as beliefs and attitudes towards health and MHS (satisfaction of life, perception of physical and mental health, stigma and psychiatric family history).

Concerning individuals with MD who do not use MHS, they may prefer to resolve themselves their own problems, they may be unaware of available MHS, they may have difficulties accessing MHS, they may doubt about treatment effectiveness, they may not feel particularly distressed or they may not recognise that they have a MD (Fleury, Bamvita, Grenier, & Caron, 2015; Sunderland & Findlay, 2013). As such, understanding the nature of MD and recognising symptoms when they occur are necessary steps that will eventually lead MHS use (Davies, Sieber, & Hunt, 1994). Considering that need factors, such as current MD diagnosis, are not sufficient to increase MHS use, working on known predisposing and enabling factors may help outreaching individuals who do not seek help, but whose MD actually affects their everyday life functioning and creates a burden to their family and their social environment. Age is a predisposing factor that needs to be better understood if one’s wants to increase MHS. It is hypothesised that applying a developmental perspective, that is, an age-related contextualisation of affect and behaviour, may contribute in increasing MHS use among individuals who may need help, but do not perceive it. Although each individual has his/her own life narrative, there are biological imperatives, age-graded norms, social timetables and role expectations structuring lifetime development into different stages (Elder, Johnson, & Crosnoe, 2003; McAdams & Olson, 2010). Not only children and adolescents but also adults do experience meaningful changes leading to personal growth, make decisions constrained by social age-related norms and interpret meaning and relevance of events according to the timing in their lives (Elder et al., 2003). As such, MD is not experienced in the same manner depending on an individual’s perceptions on life and according to the impact on one’s psychosocial situation. Responsibility levels vary in nature and intensity according to life stages (e.g. studying in university only has a direct impact on one’s life vs. leading employees or raising a child, which has short- or long-term consequences on others). Thus, the consequences of MD are not the same depending at which life stage the person is. To increase MHS use, we need to deploy strategies that are congruent with a person’s life experience (Gaudet, 2007).

Although age is a predisposing factor which has a tremendous influence on MHS use, this factor is seldom thoroughly considered. Adult samples in research protocols either include a wide range of age (18–64 years old) or are subdivided in arbitrary clusters (every 10 years at round values). To understand the impact of adult developmental stages on help seeking, this study examines three age groups: 18- to 29-year-olds (emerging adulthood), 30- to 49-year-olds and 50- to 64-year-olds (middle adulthood). These age delimitations were defined according to development theory literature (Arnett, 2000; Erickson, 1950; Levinson, 1978) and to contemporary studies on mental health and other topics such as personality, family responsibilities and economics (Alonso et al., 2004; Arnett, 2005; Kessler et al., 2007; McCrae et al., 1999; OECD, 2015; Silver, 1998). They are recognised as pragmatic approximations. A developmental stage can extend beyond these age limits because its definition depends more on the shared characteristics within an age group than on a specific chronological age (Settersten, 2003). This explains variation in age delimitation among different studies.

Emerging adulthood (aged 18–29 years) is characterised by identity exploration, instability, self-focus, ‘in-between’ feeling and optimistic possibilities (Arnett, 2000). Between 30 and 49 years old, most individuals take on committing responsibilities, such as career, long-term relationships and parenthood; their personal and professional lives stabilise (Austrian, 2008). This age group is referred by some authors as ‘young adulthood’. To avoid confusion of associating young adulthood with emerging adulthood, this term will not be used in this article when referring to 30- to 49-year-olds. Middle adulthood (aged 50–64 years) is a period in which individuals are concerned about their legacy for society and future generations (Erickson, 1950; Levinson, 1978). This stage is marked by menopause for women, by physical health decline, by empty nest syndrome for parents and by taking on the role of leader at work and in the community (Lachman, 2001).

Studies on MHS use seldom disentangle which variables are associated specifically with a particular development stage. Four studies have examined MHS utilisation among emerging adults using Andersen’s model (Bergeron, Poirier, Fournier, Roberge, & Barrette, 2005; Brown-Ogrodnick, 2004; Findlay & Sunderland, 2014; Vanheusden et al., 2008). Three predisposing factors (female, born in the country of origin and living alone) and eight need factors (diagnosis of MD, chronic physical conditions, high psychological distress, self-perceived poor mental health, suicide ideation, social difficulties, sexual assault and high levels of disability), but no enabling factor, predict MHS use. One study shows that middle-aged adults do not differ from 35- to 49-year-olds in their pattern of MHS use (Choi, DiNitto, & Marti, 2014).

This study examines which factors, using Andersen’s model, are associated with MHS use according to adult development stages among individuals with or without MD. Emerging adults are expected to seek help when the needs become burdensome, similar to previous findings (Bergeron et al., 2005; Brown-Ogrodnick, 2004; Findlay & Sunderland, 2014; Vanheusden et al., 2008). Need factors are also expected to explain help seeking among the two other age groups, as found in other studies and as conceptualised by Andersen (Andersen, 1995; Fleury, Grenier, & Bamvita, 2015; Lindamer et al., 2012). For 30- to 49-year-old adults, difficulties associated with responsibilities (joblessness, poor social network and relationship problems) are probably strong incentives to seek help, given that these issues characterise the main concerns for this development stage (Austrian, 2008). Finally, variables associated with social isolation and occupational inactivity will probably increase MHS use among 50- to 64-year-old adults. Their legacy to future generations can be hampered by social network size reduction and by problems in finding a new job as they get closer to retirement age (Freund & Ritter, 2009; Lahey, 2005; Wrzus, Hanel, Wagner, & Neyer, 2013).

Methods

Sample and procedure

Data were obtained from an ongoing longitudinal study in an epidemiological catchment area (ECA) located in four neighbourhoods of the southwest of Montreal, Canada’s second largest city. The ECA represents 269,720 inhabitants. MHS in this area include one psychiatric hospital delivering specialised second- and third-line care, two health and social services centres offering primary and second-line care and other primary care services. The ECA study was approved by a Mental Health University Institute Ethics Committee. Residents of the ECA were first contacted by phone and, after obtaining consent, a face-to-face interview was conducted (1.5–3 hours). Sampling strategy details can be found in related publications (Caron et al., 2012; Fleury et al., 2012).

At the first wave of data collection (T1), 2,434 individuals were interviewed (June 2007–November 2008). From July 2009 to November 2010, 74.9% (n = 1,823) participated at the second wave (T2). At the third wave (T3; July 2012–July 2013), 71.6% of the T2 participants (n = 1,305) remained in the study. This retention rate was similar to that of other epidemiological studies, that is, 69%−76% after 2–5 years (Kosidou et al., 2011; Torvik, Rognmo, & Tambs, 2012). To maintain a representative sample of the population, a new cohort of 1,029 participants was recruited at T3. Given that the same research design and protocol were applied to both cohorts at T1 and T3 in the longitudinal ECA study, the datasets of T1 and T3 were merged together in the current cross-sectional analyses to increase the sample size. For this study, only data from participants aged between 18 and 64 years were used because participants aged under 18 and over 65 years have access to adapted health and social services (youth centres, long-term care facilities for elders) that were not assessed in this study.

Instruments

The dichotomised dependant variable was ‘MHS use in the 12 months prior to the interview’. MHS use is defined as any contact with a mental health professional (psychiatrist, general practitioner, psychologist, etc.) or use of any health service (hospitalisations, telephone helpline, self-help groups, medication). Independent variables were selected and grouped according to Andersen’s model. Need factors included 12-month MD diagnoses (major depressive disorder, mania, any anxiety disorder, alcohol dependence and drug dependence), impulsiveness, aggressive behaviours, suicidality, psychological distress and number of stressful events. Enabling factors encompassed occupational status, personal and household income, residential stability, neighbourhood characteristics, quality of life, social provisions and Internet search for mental health information. Predisposing factors assessed were sex, country of origin, living arrangement, satisfaction of life, perception of physical and mental health, family psychiatric history and self-perceived stigma. Table 1 describes the standardised instruments used (number of items, scoring system, psychometric properties by the original authors).

Table 1.

Independent variables and measuring instruments.

Variables measured	Instruments and references	Description	Number of items	Scoring	Original psychometric properties
Need factors
Mental disorders • Major depressive disorder • Mania • Anxiety disorders (agoraphobia, panic disorder, social phobia and post-traumatic stress disorder) • Alcohol dependence • Drug dependence	Composite International Diagnostic Interview – CIDI (Kessler et al., 1998)	Psychiatric diagnoses according to the definitions and criteria of ICD-10 and DSM-IV. A preliminary section detects the presence of mental disorders, and subsequent sections detect specific disorders.	A total of 23 screening items and 54–135 items maximum depending on diagnosis	Yes/no and multiple choice. The interview follows a skip logic, meaning that items can be skipped if participants answer negatively to some particular items.	Concordance between the CIDI and ICD-10 diagnostics = .58-.97; sensitivity = .43-1.0; specificity = .84-.99.
Impulsiveness	Barratt Impulsivity Scale (Barratt, 1985)	Motor impulsiveness refers to impulsive actions (e.g. do things without thinking, act on the spur of the moment) and lack of perseverance (e.g. change jobs). Cognitive impulsiveness is characterised by attention problems (e.g. not paying attention) and cognitive instability (e.g. racing thoughts). Planning impulsiveness is defined as difficulties in self-control (e.g. planning tasks carefully) and cognitive complexities (e.g. saving regularly, thinking about complex problems).	30	Likert-like scale (1 = rarely or never; 4 = almost always or always)	Cronbach’s α = .73-.83.
Aggressive behaviours	Modified Observed Aggression Scale (Kay et al., 1988)	Verbal aggressiveness includes shouting, cursing, and threatening others. Aggressiveness towards objects can be expressed by slamming doors, throwing objects, defacing walls, breaking objects or setting things on fire. Examples of self-harming behaviours are picking/scratching skin, pulling hair, self-cutting, head banging, self-bruising and self-burning. Physical aggressiveness towards others involves hitting, holding, grabbing, striking, kicking, pushing, scratching and causing moderate to serious wounds onto others.	16	Yes/no	Inter-raters agreement = .85-.94; temporal stability = .72–.91.
Psychological distress	K-10 Psychological Distress Scale (Kessler et al., 2003)	Frequency of distress symptoms in the past month such as nervousness, tiredness, despair, agitation, sadness and feelings of worthlessness.	10	Likert-like scale (1 = always; 5 = never)	Cronbach’s α = .93.
Number of stressful events	Lifetime Events Questionnaire (Laurin, 1998)	Stressful events in the last 12 months organized around five themes: events related to income, love, family and friends, housing and aggression experiences.	25	Yes/no	Not available
Enabling factors
Occupation	CCHS 1.2 (Statistics Canada, 2003)	Working in the last 12 months and/or studying either full time or part time.	2	Yes/no	Not available
Household and personal income	CCHS 1.2 (Statistics Canada, 2003)	Income before taxes in Canadian dollars.	2	Numerical value	Not available
Residential stability	CCHS 1.2 (Statistics Canada, 2003)	Number of times the participant moved in the past 5 years.	1	Numerical value	Not available
Neighbourhood physical conditions	Neighbourhood Physical Conditions Scale (Perkins & Long, 2002)	Physical conditions of the neighbourhood, such as housings, buildings, public spaces, streets and so on	7	Likert-like scale (1 = very bad condition; 10 = excellent condition)	Cronbach’s α = .87
Perception of neighbourhood disorder	Neighbourhood Disorder Scale (Coulton et al., 1996)	Problems within the neighbourhood such as presence of drug dealers, garbage in the streets, abandoned vehicles and buildings, street gangs and so on.	9	Likert-like scale items (1 = rarely; 10 = frequently)	Cronbach’s α = .84
Perceived safety	Neighbourhood safety subscale of the International Physical Activity Questionnaire (Alexander et al., 2006)	Criminality rate in the neighbourhood and perceived safety of walking in the streets.	2	Likert-like scale (1 = strongly disagree; 4 = strongly agree)	Cronbach’s α = .71; 2-week test–retest fidelity (r = .75)
Community participation	Community Participation Scale (Saegert & Winkel, 2004)	Involvement in neighbourhood organisations such as community associations, public hearings and religious services.	6	Yes/no	Cronbach’s α = .82
Informal social control Social cohesion	Sense of Collective Efficacy (Sampson et al., 1997)	Relationship aspects among neighbours such as readiness to help, trust, agreement and shared values.	5	Likert-like scale (1 = really agree; 5 = really disagree)	Cronbach’s α = .80–.91
Resident disempowerment	Resident Disempowerment Scale (Nario-Redmond et al., 2000)	Influence and capacity of residents to change or improve their neighbourhood.	3	Likert-like scale (1 = strongly disagree; 10 = strongly agree)
Neighbouring behaviour	Neighbouring Behaviour Scale (Long & Perkins, 2003)	Behaviours with neighbours such as house-sitting during neighbour’s absence, lending tool, giving advices, helping in case of emergency, discussing problems, concerning neighbourhood.	5	Number of times in past year	Cronbach’s α = .78
Quality of life	Satisfaction with Life Domains Scale (Andrews & Whitney, 1976; Baker & Intagliata, 1982)	Quality of life consists of five domains: (1) daily life and social relations; (2) housing neighbourhood; (3) personal relationships; (4) spare-time activities; and (5) autonomy.	29	Seven-point Likert-scale items (1 = ; 7 = )	Cronbach’s α = .71–.84; test–retest r = .86
Social provisions	Social Provisions Scale (Cutrona & Russell, 1987)	Six functions of social relationships were assessed: (1) attachment; (2) social integration; (3) reassurance of worth; (4) reliable alliance; (5) guidance; (6) opportunity for nurturance.	24	Likert scale (1 = not at all true; 4 = completely true)	Cronbach’s α = .64–.76.
Predisposing factors
Age	CCHS 1.2 (Statistics Canada, 2003)	Age at interview as calculated from date of birth and as confirmed by the participant.	2	Numerical value	Not available
Sex	CCHS 1.2 (Statistics Canada, 2003)	Sex as identified and declared by the participant.	1	1 = male;2 = female	Not available
Country of origin	CCHS 1.2 (Statistics Canada, 2003)	Country where the participant was born.	1	Multiple choice; all countries other than Canada were grouped together	Not available
Living arrangement	CCHS 1.2 (Statistics Canada, 2003)	Type of situation that correspond the most to the participant’s household organisation.	1	Multiple choice; all living arrangement that included at least one other human being was considered as ‘with someone/other people’	Not available
General satisfaction of life	CIDI (Kessler et al., 1998)	Extent of general satisfaction with life.	1	Likert scale (1 = very satisfied;5 = very dissatisfied)	Not available for this specific item
Self-perception of physical health	CIDI (Kessler et al., 1998)	Self-perception of general physical health.	1	Likert scale (1 = excellent;5 = poor)	Not available for this specific item
Self-perception of mental health	CIDI (Kessler et al., 1998)	Self-perception of general mental health.	1	Likert scale (1 = excellent;5 = poor)	Not available for this specific item
Family psychiatric history	Family History Screen (Weissman et al., 2000)	Biological family member (father, mother, brothers, sisters) who1. ever had severe mental disorders or emotional or psychological problems;2. was drinking too much;3. was taking illicit drugs regularly;4) who consulted a psychiatrist, a psychologist, a social worker, another health professional, who were hospitalised or who attended an addiction rehabilitation centre.	4	Yes/no	Sensitivity = 67.6;Specificity = 87.6;Test–retest = 0.56
High stigma	Devaluation-Discrimination Scale (Link, 1987; Link et al., 2004)	The extent to which the participant believes that ‘most people’ will devalue and discriminate against the mentally ill.	12	Likert-type scale (‘strongly disagree = 1’ to ‘strongly agree = 4’); Since the total average score was 2.51, a score equal or higher to this value was identified as perceiving high stigma.	Cronbach’s α = .76

ICD: International Classification of Diseases; DSM: Diagnostic and Statistical Manual of Mental Disorders; CIDI: CCHS: Canadian Community Health Survey.

Statistical analyses

A bivariate analysis was conducted for each age group to determine which independent variables within the same development stage were associated with MHS use in the past 12 months. Chi-square analyses were conducted for categorical variables, and Student’s t-tests were performed for continuous variables. Significant independent variables (p < .10) in the bivariate analyses were entered in a multivariate logistic regression analysis for each age group separately. The alpha value was set at p < .05 to determine which variables in the model were significant. For each model, the goodness-of-fit was determined with the Hosmer–Lemeshow test, and the variance was expressed using the Nagelkerke R².

Results

Of the 3,295 adults interviewed, 16.4% of 18- to 29-year-olds, 23.6% of 30- to 49-year-olds and 22.8% of 50- to 64-year-olds had used MHS in the past 12 months. The proportion of females was twice that of males. As assessed with the Composite International Diagnostic Interview (CIDI), a minority felt dissatisfied with life (18- to 29-year-olds: 5.0%; 30- to 49-year-olds: 8.8%; 50- to 64-year-olds: 8.2%) and reported that their mental health (18- to 29-year-olds: 14.8%; 30- to 49-year-olds: 15.2%; 50- to 64-year-olds: 12.6%) was fair or poor. The most prevalent MD was major depression for all groups. Among those using MHS, 45.0%, 47.3% and 31.1% presented a MD diagnosis among 18- to 29-years-olds, 30- to 49-years-olds and 50- to 64-year-olds, respectively.

Table 2 presents detailed results from the bivariate analyses. For 18- to 29-year-olds, all MD diagnoses (except alcohol dependence), total and motor impulsiveness, all aggressive behaviours, all suicidal conducts, high psychological distress and number of stressful events were associated with MHS use. Concerning enabling factors, 18- to 29-year-old MHS users were more likely than non-users to be inactive, to have lower personal income, to report lower neighbourhood physical conditions, higher neighbourhood disorder, higher perceived safety and higher community participation, to be less satisfied with quality of life (precisely concerning daily life and social relations, personal relationships and autonomy), to perceive less social provisions (precisely concerning attachment and social integration) and to search on the Internet for mental health information. Significant predisposing factors associated with MHS use for 18- to 29-year-olds included being female, being born in Canada, being dissatisfied/very dissatisfied with life, perceiving fair/poor physical and mental health and having a family psychiatric history.

Table 2.

Comparison analyses according to mental health services use by age group (n = 3,295).

	18- to 29-year–olds		p	30- to 49-year-olds		p	50- to 64-year-olds		p
	Services use	No services use		Services use	No services use		Services use	No services use
	n = 127	n = 648		n = 368	n = 1,192		n = 219	n = 741
	n(%)/mean(SD)	n(%)/mean(SD)		n(%)/mean(SD)	n(%)/mean(SD)		n(%)/mean(SD)	n(%)/mean(SD)
Need factors
Mental disorder in past 12 months
Major depressive disorder	35 (28.7%)	34 (5.4%)	.000	87 (24.8%)	68 (5.8%)	.000	46 (21.9%)	36 (5.0%)	.000
Mania	10 (8.2%)	5 (0.8%)	.000	22 (6.2%)	12 (1.0%)	.000	7 (3.3%)	6 (0.8%)	.007
Any anxiety disorder	19 (15.0%)	33 (5.2%)	.000	73 (21.5%)	49 (4.2%)	.000	16 (7.7%)	24 (3.4%)	.007
Alcohol dependence	9 (7.1%)	32 (5.0%)	n.s.	25 (7.0%)	28 (2.4%)	.000	14 (6.5%)	9 (1.2%)	.000
Drug dependence	14 (11.1%)	17 (2.6%)	.000	32 (8.8%)	23 (1.9%)	.000	4 (1.8%)	6 (0.8%)	n.s.
Any mental disorder	54 (45.0%)	87 (14.3%)	.000	157 (47.3%)	135 (11.6%)	.000	62 (31.3%)	64 (9.1%)	.000
Number of mental disorders	0.72 (1.00)	0.20 (0.58)	.000	0.68 (0.97)	0.16 (0.52)	.000	0.43 (0.84)	0.11 (0.42)	.000
Mental disorder without dependence	34 (28.6%)	43 (7.1%)	.000	105 (32.3%)	86 (7.6%)	.000	43 (21.9%)	44 (6.4%)	.000
Dependence only	7 (5.9%)	24 (4.0%)	n.s.	23 (7.1%)	24 (2.1%)	.000	6 (3.1%)	11 (1.6%)	n.s.
Mental disorder with dependence	12 (10.1%)	15 (2.5%)	.000	22 (6.8%)	19 (1.7%)	.000	11 (5.6%)	4 (0.6%)	.000
Impulsiveness
Total impulsiveness	65.54 (11.81)	62.70 (10.49)	.006	64.42 (12.12)	59.452 (11.49)	.000	62.12(10.54)	58.680 (11.47)	.000
Motor impulsiveness	22.95 (5.01)	21.36 (4.66)	.001	21.54(5.08)	19.708 (4.61)	.000	20.31 (4.27)	18.917 (4.42)	.000
Planning impulsiveness	24.61 (5.01)	23.90 (4.35)	n.s.	24.99 (4.92)	23.157 (4.78)	.000	24.69 (4.47)	23.333 (4.68)	.000
Cognitive impulsiveness	18.08 (4.64)	17.55 (4.11)	n.s.	17.94 (4.65)	16.605 (4.35)	.000	17.05 (4.66)	16.424 (4.66)	.084
Aggressive behaviours
Verbal aggressiveness	96 (75.6%)	358 (55.9%)	.000	206 (57.4%)	630 (53.4%)	n.s.	111 (51.4%)	279 (38.4%)	.001
Aggressiveness towards objects	67 (52.8%)	186 (29.2%)	.000	115 (32.1%)	267 (22.8%)	.000	40 (18.5%)	89 (12.3%)	.021
Physical aggressiveness	23 (18.3%)	68 (10.7%)	.017	49 (13.7%)	58 (5.0%)	.000	11 (5.1%)	15 (2.1%)	.017
Suicidal conducts
Self-harm	33 (26.2%)	59 (9.3%)	.000	46 (12.9%)	50 (4.3%)	.000	10 (4.7%)	11 (1.5%)	.007
Suicide ideation	15 (11.8%)	17 (2.6%)	.000	32 (8.7%)	30 (2.5%)	.000	21 (9.6%)	16 (2.2%)	.000
Psychological distress	12.32 (7.40)	8.25 (5.89)	.000	12.48 (7.59)	7.33 (5.75)	.000	11.56 (7.62)	7.04 (6.32)	.000
Number of stressful life events	5.55 (2.81)	3.83 (2.46)	.000	4.88 (2.91)	3.16 (2.26)	.000	3.92 (2.55)	2.66 (2.18)	.000
Enabling factors
Current occupation			.002			.000			.000
At school or at work	85 (67.5%)	516 (79.8%)		202 (55.0%)	851 (72.0%)		95 (43.8%)	423 (57.3%)
No occupation	41 (32.5%)	131 (20.2%)		165 (45.0%)	331(28.0%)		122 (56.2%)	315 (42.7%)
Income
Household income	44,066.00 (29,305.35)	56,370.97 (44,560.20)	.001	54,968.31 (53,444.03)	65,302.83 (53,417.93)	.003	46,666.65 (49,550.26)	62,568.99 (55,361.86)	.001
Personal income	20,830.30 (15,084.29)	23,466.1 (18,171.90)	n.s.	32,116.10 (32,788.02)	37,382.74 (35,779.24)	.024	30,047.22 (27,011.15)	39,281.66 (33,791.50)	.001
Residential stability (past 5 years)			n.s.			n.s.			n.s.
No moving	22 (17.3%)	134 (20.7%)		132 (36.2%)	445 (37.4%)		144 (65.8%)	490 (66.2%)
Once	25 (19.7%)	158 (24.5%)		93 (25.5%)	350 (29.4%)		44 (20.1%)	171 (23.1%)
Twice or more	80 (63.0%)	354 (54.8%)		140 (34.4%)	394 (33.1%)		31 (14.2%)	79 (10.7%)
Neighbourhood characteristics
Neighbourhood physical conditions	40.97 (10.69)	43.79 (9.93)	.004	43.26 (11.14)	45.73 (11.20)	.000	43.58 (12.07)	46.02 (11.26)	.006
Perceived neighbourhood disorder	50.98 (19.40)	44.56(20.37)	.001	42.87 (21.37)	37.34 (20.51)	.000	42.47 (23.64)	34.66 (20.60)	.000
Perceived safety	3.93 (1.22)	3.64 (1.25)	.019	3.91 (1.37)	3.60 (1.31)	.000	4.23 (1.52)	3.73 (1.35)	.000
Community participation	0.77 (0.88)	0.52 (0.81)	.002	0.92 (1.08)	0.81 (0.99)	.077	1.06 (1.17)	0.92 (1.10)	n.s.
Informal social control	16.62 (4.33)	16.47 (4.11)	n.s.	16.72 (4.54)	17.38 (4.22)	.011	16.28 (4.61)	17.45 (4.31)	.001
Social cohesion	16.67 (4.07)	16.66 (3.32)	n.s.	16.88 (3.94)	17.58 (3.50)	.002	16.86 (4.17)	17.80 (3.67)	.003
Resident disempowerment	13.08 (6.35)	12.31 (5.50)	n.s.	13.11 (6.28)	11.31 (5.82)	.000	12.80 (6.87)	11.78 (6.38)	.044
Neighbouring behaviour	13.41 (8.62)	12.74 (8.72)	n.s.	15.66 (9.71)	14.76 (8.75)	n.s.	16.61 (9.76)	15.06 (9.03)	.029
Quality of life
Total	103.06 (17.23)	110.27 (13.38)	.000	99.04 (18.83)	110.57 (13.66)	.000	100.43 (18.70)	110.39 (16.46)	.000
Housing neighbourhood	15.61 (3.16)	15.84 (2.93)	n.s.	15.47 (3.43)	16.35 (2.88)	.000	16.00 (3.30)	16.90 (3.00)	.000
Daily life and social relations	31.99 (5.67)	34.17 (4.20)	.000	30.87 (5.68)	33.92 (4.39)	.000	31.55 (5.81)	34.06 (4.92)	.000
Personal relationships	15.35 (4.30)	17.06 (3.64)	.000	13.93 (4.89)	16.77 (3.97)	.000	12.47 (4.95)	15.28 (4.75)	.000
Autonomy	29.94 (6.48)	32.58 (5.09)	.000	29.28 (6.82)	32.93 (4.99)	.000	30.46 (6.67)	33.42 (5.56)	.000
Spare time activities	10.13 (2.41)	10.62 (2.11)	n.s.	9.50 (2.85)	10.51 (2.29)	.000	9.98 (2.84)	10.83 (2.49)	.000
Social provisions
Total	82.02 (10.88)	84.00 (8.24)	.061	78.83 (10.72)	82.16 (9.52)	.000	77.61 (10.24)	79.63 (9.90)	.012
Attachment	13.50 (2.38)	13.94 (1.92)	.048	12.62 (2.41)	13.58 (2.11)	.000	12.64 (2.25)	13.15 (2.09)	.002
Social integration	13.06 (2.31)	13.73 (1.83)	.003	12.53 (2.23)	13.28 (1.96)	.000	12.25 (2.32)	12.79 (2.06)	.001
Reassurance of worth	13.33 (2.26)	13.66 (1.77)	n.s.	12.83 (2.20)	13.34 (1.88)	.000	12.75 (2.11)	13.05 (1.92)	.056
Reliable alliance	14.54 (2.11)	14.76 (1.60)	n.s.	13.95 (2.12)	14.28 (1.88)	.008	13.77 (2.04)	14.00 (1.93)	n.s.
Guidance	14.13 (2.38)	14.47 (1.78)	n.s.	13.55 (2.21)	14.00 (2.04)	.001	13.18 (2.37)	13.56 (2.16)	.032
Opportunity for nurturance	13.27 (2.21)	12.96 (1.84)	n.s.	12.74 (2.36)	13.22 (2.02)	.001	12.21 (2.05)	12.69 (2.08)	.003
Internet (information searching)			.000			.000			.000
Yes	39 (41.1%)	31 (7.2%)		68 (23.8%)	53 (6.1%)		25 (14.1%)	11 (1.9%)
No	56 (58.9%)	397 (92.8%)		218 (76.2%)	823 (93.9%)		152 (85.9%)	556 (98.1%)
Predisposing factors
Sex			.000			.000			n.s.
Male	35 (27.6%)	287 (44.3%)		113 (30.7%)	512 (43.0%)		83 (37.9%)	299 (40.4%)
Female	92 (72.4%)	361 (55.7%)		255 (69.3%)	680 (57.0%)		136 (62.1%)	442 (59.6%)
Country of origin			.001			.000			.003
Canada	104 (87.4%)	457 (73.6%)		307 (86.5%)	733 (64.5%)		191 (89.3%)	579 (80.3%)
Outside of Canada	15 (12.6%)	164 (26.4%)		48 (13.5%)	404 (35.5%)		23 (10.7%)	142 (19.7%)
Living arrangement			n.s.			.000			.000
Alone	17 (13.4%)	74 (11.4%)		113 (30.7%)	191 (16.1%)		107 (48.9%)	249 (33.6%)
With someone/other people	110 (86.6%)	574 (88.6%)		255 (69.3%)	998 (83.9%)		112 (51.5%)	492 (66.4%)
Satisfaction of life			.000			.000			.000
Very satisfied/satisfied	86 (67.7%)	558 (86.1%)		214 (58.5%)	985 (82.8%)		142 (64.8%)	621 (84.3%)
Neither satisfied nor dissatisfied	20 (15.7%)	72 (11.1%)		82 (22.4%)	137 (11.5%)		43 (19.6%)	71 (9.6%)
Dissatisfied/very dissatisfied	21 (16.5%)	18 (2.8%)		70 (19.1%)	67 (5.6%)		34 (15.5%)	45 (6.1%)
Perception of physical health			.000			.000			.000
Excellent/very good	44 (34.6%)	280 (43.2%)		99 (26.9%)	558 (46.9%)		72 (32.9%)	331 (44.7%)
Good	40 (31.5%)	266 (41.0%)		142 (38.6%)	463 (38.9%)		68 (31.1%)	258 (34.8%)
Fair/poor	43 (33.9%)	102 (15.7%)		127 (34.5%)	170 (14.3%)		79 (36.1%)	152 (20.5%)
Perception of mental health			.000			.000			.000
Excellent/very good	32 (25.2%)	397 (61.3%)		92 (25.0%)	718 (60.4%)		79 (36.1%)	470 (63.4%)
Good	49 (38.6%)	182 (28.1%)		147 (39.9%)	363 (30.5%)		82 (37.4%)	208 (28.1%)
Fair/poor	46 (36.2%)	69 (10.6%)		129 (35.1%)	108 (9.1%)		58 (26.5%)	63 (8.5%)
Family psychiatric history
Mental disorder	66 (53.7%)	205 (32.4%)	.000	176 (49.4%)	340 (29.1%)	.000	99 (46.3%)	204 (28.3%)	.000
Alcohol problem	50 (40.3%)	171 (26.8%)	.002	166 (46.9%)	394 (33.9%)	.000	123 (58.3%)	317 (44.3%)	.000
Drug problem	43 (35.0%)	122 (19.2%)	.000	90 (25.8%)	181 (15.5%)	.000	52 (25.2%)	119 (16.9%)	.007
Family mental health services use	68 (53.5%)	199 (30.7%)	.000	188 (51.1%)	348 (29.2%)	.000	95 (43.4%)	251 (33.9%)	.010
High stigma	90 (71.4%)	482 (74.8%)	n.s.	295 (80.8%)	887 (75.6%)	.037	172 (80.4%)	536 (73.2%)	.034

SD: standard deviation; n.s.: nonsignificant.

For 30- to 49-year-olds, all need factors, except verbal aggressiveness, were associated with MHS use. All enabling factors, except residential stability and neighbourhood behaviour, and all predisposing factors were significantly associated with MHS use.

For 50- to 64-year-olds, all need factors, except drug dependence diagnosis, were associated with MHS use. Apart for residential stability, community participation and reliable alliance (social provisions), all other enabling factors were associated with the dependent variable. Sex is the only predisposing factor that was not associated with MHS use in middle adulthood.

Table 3 shows results from the logistic regression analysis within the 18- to 29-year-olds group. Two need factors, having major depressive disorder and experiencing a higher number of stressful events, were both associated with MHS use. One enabling factor, searching information on the Internet, was associated with MHS use. No predisposing factor was significantly associated with MHS use. The model showed an acceptable goodness-of-fit (χ² = 6.284; df = 8; p = .615) and explained 53.8% of the variance.

Table 3.

Predictors of mental health services use in 18- to 29-year-old adults with or without a mental disorder: multiple logistic regression (n = 775).

	B	S.E.	Wald	df	OR	95% CI		p
						Lower	Upper
Need factors
Major depressive disorder	2.04	0.69	8.75	1	7.65	1.99	29.47	.003
Mania	0.60	1.20	0.25	1	1.81	0.17	18.92	.619
Drug dependence	1.18	0.99	1.42	1	3.24	0.47	22.42	.233
Anxiety disorder	−0.98	0.74	1.76	1	0.37	0.09	1.60	.185
Motor impulsiveness	−0.03	0.05	0.50	1	0.97	0.88	1.06	.481
Verbal aggressiveness	0.33	0.53	0.38	1	1.39	0.49	3.90	.537
Aggressiveness towards objects	0.82	0.47	3.04	1	2.26	0.90	5.65	.081
Self-harm	0.27	0.58	0.23	1	1.31	0.43	4.06	.635
Physical aggressiveness	0.01	0.61	0.00	1	1.01	0.31	3.34	.984
Suicidal ideation	0.68	0.89	0.58	1	1.97	0.34	11.27	.446
Psychological distress	0.02	0.05	0.19	1	1.02	0.93	1.13	.660
Number of stressful events	0.23	0.10	5.21	1	1.25	1.03	1.52	.023
Enabling factors
Occupation	−0.84	0.75	1.25	1	0.43	0.10	1.88	.264
Household income	0.00	0.00	0.21	1	1.00	1.00	1.00	.648
Neighbourhood physical conditions	0.00	0.03	0.00	1	1.00	0.95	1.06	.948
Perceived neighbourhood disorder	0.02	0.01	2.30	1	1.02	0.99	1.05	.130
Perceived safety	−0.26	0.22	1.50	1	0.77	0.50	1.17	.221
Community participation	0.19	0.22	0.74	1	1.21	0.79	1.85	.389
Daily life and social relations	−0.02	0.07	0.12	1	0.98	0.85	1.12	.732
Personal relationships	0.04	0.07	0.33	1	1.04	0.91	1.20	.567
Autonomy	0.04	0.06	0.53	1	1.04	0.93	1.18	.467
Attachment	0.08	0.17	0.25	1	1.09	0.78	1.51	.619
Social integration	−0.04	0.15	0.08	1	0.96	0.71	1.29	.772
Internet search	2.17	0.56	15.04	1	8.78	2.93	26.32	.000
Predisposing factors
Sex (1 = male)	−0.09	0.49	0.03	1	0.92	0.35	2.41	.857
Country of origin (1 = Canada)	0.63	0.69	0.85	1	1.88	0.49	7.22	.357
Satisfaction of life (1 = very satisfied/satisfied)	−0.24	1.06	0.05	1	0.78	0.10	6.31	.820
Self-perception of physical health (1 = excellent/very good)	1.02	0.80	1.65	1	2.78	0.58	13.27	.199
Self-perception of mental health (1 = excellent/very good)	−1.72	0.89	3.77	1	0.18	0.03	1.02	.052
Family history of mental disorder	1.08	0.59	3.37	1	2.93	0.93	9.26	.066
Family history of alcohol problem	−0.42	0.49	0.73	1	0.66	0.25	1.73	.393
Family history of drug problem	0.16	0.54	0.09	1	1.17	0.41	3.34	.770
Family mental health services use	−0.76	0.64	1.41	1	0.47	0.13	1.64	.235

S. E.: standard error; OR: odds ratio; CI: confidence interval.

Table 4 shows results from the logistic regression analysis within the 30- to 49-year-old group. One need factor, having major depressive disorder, was associated with MHS use. Three enabling factors, perceiving neighbourhood disorder, reporting social cohesion within the neighbourhood and searching information on the Internet, were associated with MHS use. One enabling factor, having an active occupation, was negatively associated with MHS. One predisposing factor, family history of MD, was significantly associated with MHS use. The model showed an acceptable goodness-of-fit (χ² = 1.181; df = 8; p = .997) and explained 63.0% of the variance.

Table 4.

Predictors of mental health services use in 30- to 49-year-old adults with or without a mental disorder: multiple logistic regression (n = 1,560).

	B	S.E.	Wald	df	OR	95% CI		p
						Lower	Upper
Need-related factors
Major depressive disorder	2.58	0.87	8.82	1	13.24	2.41	72.78	.003
Mania	0.71	1.67	0.18	1	2.04	0.08	53.42	.669
Alcohol dependence	−2.42	1.34	3.26	1	0.09	0.01	1.23	.071
Drug dependence	0.42	1.38	0.09	1	1.52	0.10	22.89	.761
Anxiety disorder	−0.77	0.92	0.71	1	0.46	0.08	2.81	.401
Motor impulsiveness	−0.02	0.07	0.07	1	0.98	0.85	1.13	.790
Planning impulsiveness	0.00	0.09	0.00	1	1.00	0.84	1.18	.956
Cognitive impulsiveness	0.05	0.08	0.37	1	1.05	0.90	1.21	.543
Aggressiveness towards objects	0.87	0.60	2.13	1	2.39	0.74	7.68	.145
Self-harm	0.28	0.73	0.15	1	1.32	0.32	5.48	.703
Physical aggressiveness	0.13	0.78	0.03	1	1.14	0.24	5.27	.871
Suicidal ideation	0.94	1.13	0.69	1	2.57	0.28	23.57	.405
Psychological distress	0.09	0.07	1.70	1	1.10	0.96	1.26	.193
Number of stressful events	0.20	0.11	2.94	1	1.217	0.972	1.523	.087
Enabling factors
Occupation	−2.34	0.79	8.80	1	0.10	0.02	0.45	.003
Household income	0.00	0.00	0.48	1	1.00	1.00	1.00	.490
Personal income	0.00	0.00	0.48	1	1.00	1.00	1.00	.487
Neighbourhood physical conditions	−0.03	0.03	0.99	1	0.97	0.91	1.03	.319
Perceived neighbourhood disorder	0.05	0.02	6.06	1	1.05	1.01	1.10	.014
Perceived safety	−0.38	0.27	2.01	1	0.68	0.40	1.16	.157
Community participation	0.22	0.27	0.65	1	1.25	0.73	2.13	.420
Informal social control	0.02	0.08	0.09	1	1.02	0.87	1.20	.769
Social cohesion	0.23	0.11	4.30	1	1.26	1.01	1.57	.038
Resident disempowerment	−0.03	0.06	0.34	1	0.97	0.86	1.09	.561
Housing neighbourhood	0.06	0.11	0.27	1	1.06	0.85	1.32	.605
Daily life and social relations	−0.06	0.10	0.34	1	0.94	0.77	1.15	.558
Personal relationships	−0.11	0.12	0.84	1	0.89	0.70	1.14	.360
Autonomy	0.11	0.08	1.72	1	1.12	0.95	1.32	.190
Spare-time activities	−0.11	0.15	0.56	1	0.89	0.67	1.20	.452
Reliable alliance	0.15	0.24	0.40	1	1.16	0.73	1.86	.529
Attachment	0.08	0.23	0.13	1	1.09	0.69	1.72	.715
Social integration	−0.42	0.23	3.33	1	0.66	0.42	1.03	.068
Reassurance of worth	0.37	0.25	2.12	1	1.44	0.88	2.36	.145
Guidance	0.12	0.23	0.25	1	1.12	0.71	1.77	.617
Opportunity for nurturance	0.06	0.16	0.15	1	1.06	0.78	1.44	.698
Internet search	3.11	0.79	15.51	1	22.35	4.76	104.91	.000
Predisposing factors
Sex (1 = male)	0.84	0.66	1.64	1	2.31	0.64	8.36	.200
Country of origin (1 = Canada)	1.01	0.85	1.41	1	2.74	0.52	14.42	.236
Living arrangement	−2.18	1.18	3.40	1	0.11	0.01	1.15	.065
Satisfaction of life (1 = very satisfied/satisfied)	0.51	1.36	0.14	1	1.67	0.12	24.04	.707
Self-perception of physical health (1 = excellent/very good)	1.21	0.98	1.52	1	3.34	0.49	22.73	.217
Self-perception of mental health (1 = excellent/very good)	−1.49	1.11	1.83	1	0.22	0.03	1.96	.177
Family history of mental disorder*	1.46	0.73	4.02	1	4.29	1.03	17.79	.045
Family history of alcohol problem	−0.75	0.60	1.60	1	0.47	0.15	1.51	.206
Family history of drug problem	0.99	0.66	2.23	1	2.69	0.73	9.88	.135
Family mental health services use	−1.16	0.84	1.89	1	0.31	0.06	1.64	.169
High stigma	−0.28	0.64	0.20	1	0.75	0.22	2.62	.656

S. E.: standard error; OR: odds ratio; CI: confidence interval.

Table 5 shows results from the logistic regression analysis within the 50- to 64-year-old group. One need factor, having alcohol dependence, was associated with MHS use. One enabling factor, searching information on the Internet, was associated with MHS use. One enabling factor, satisfaction with personal relationships, was negatively associated with MHS. Among predisposing factors, family history of MD was significantly associated with MHS use, while low stigma was inversely associated with MHS use. The model showed an acceptable goodness of fit (χ² = 5.831; df = 8; p = .666) and explained 39.7% of the variance.

Table 5.

Predictors of mental health services use in 50- to 64-year-old adults with or without a mental disorder: multiple logistic regression (n = 960).

	B	S.E.	Wald	df	OR	95% CI		p
						Lower	Upper
Need-related factors
Major depressive disorder	0.95	0.52	3.31	1	2.58	0.93	7.16	.069
Mania	−2.68	1.45	3.39	1	0.07	0.00	1.19	.066
Alcohol dependence	1.63	0.78	4.38	1	5.08	1.11	23.29	.036
Anxiety disorder	−0.70	0.74	0.90	1	0.49	0.12	2.12	.344
Motor impulsiveness	0.08	0.05	3.00	1	1.08	0.99	1.19	.083
Planning impulsiveness	0.01	0.04	0.03	1	1.01	0.92	1.10	.873
Cognitive impulsiveness	−0.05	0.04	1.45	1	0.95	0.87	1.03	.229
Verbal aggressiveness	0.44	0.32	1.89	1	1.56	0.83	2.93	.169
Aggressiveness towards objects	−0.03	0.43	0.01	1	0.97	0.42	2.24	.937
Self-harm	1.19	0.93	1.62	1	3.28	0.53	20.50	.204
Physical aggressiveness	0.58	0.83	0.49	1	1.78	0.35	8.96	.485
Suicidal ideation	1.99	1.11	3.18	1	7.31	0.82	65.01	.074
Psychological distress	0.03	0.03	1.02	1	1.03	0.97	1.09	.313
Number of stressful events	−0.02	0.07	0.07	1	0.98	0.85	1.13	.796
Enabling factors
Occupation	−0.03	0.32	0.01	1	0.97	0.52	1.83	.934
Household income	0.00	0.00	0.88	1	1.00	1.00	1.00	.350
Personal income	0.00	0.00	3.75	1	1.00	1.00	1.00	.053
Neighbourhood physical conditions	−0.02	0.02	1.63	1	0.98	0.94	1.01	.202
Perceived neighbourhood disorder	0.00	0.01	0.10	1	1.00	0.98	1.02	.758
Perceived safety	0.00	0.13	0.00	1	1.00	0.77	1.30	.999
Informal social control	0.03	0.04	0.48	1	1.03	0.95	1.12	.489
Social cohesion	−0.01	0.05	0.02	1	0.99	0.90	1.10	.896
Resident disempowerment	0.00	0.03	0.01	1	1.00	0.94	1.05	.943
Neighbouring behaviour	0.01	0.02	0.27	1	1.01	0.97	1.04	.604
Housing neighbourhood	0.14	0.07	3.48	1	1.15	0.99	1.33	.062
Daily life and social relations	−0.05	0.05	1.01	1	0.95	0.86	1.05	.315
Personal relationships	−0.15	0.06	6.78	1	0.86	0.76	0.96	.009
Autonomy	0.03	0.04	0.49	1	1.03	0.95	1.12	.484
Spare-time activities	0.02	0.08	0.04	1	1.02	0.87	1.20	.833
Attachment	0.01	0.12	0.01	1	1.01	0.80	1.29	.912
Social integration	−0.01	0.10	0.01	1	0.99	0.81	1.22	.941
Reassurance of worth	0.12	0.11	1.11	1	1.12	0.91	1.39	.291
Guidance	0.03	0.12	0.06	1	1.03	0.81	1.30	.812
Opportunity for nurturance	−0.07	0.09	0.66	1	0.93	0.78	1.11	.418
Internet search	3.13	0.92	11.68	1	22.98	3.81	138.74	.001
Predisposing factors
Country of origin (1 = Canada)	0.91	0.48	3.63	1	2.49	0.97	6.36	.057
Living arrangement	−0.67	0.54	1.55	1	0.51	0.18	1.47	.213
Satisfaction of life (1 = very satisfied/satisfied)	−0.14	0.62	0.05	1	0.87	0.26	2.91	.815
Self-perception of physical health (1 = excellent/very good)	0.17	0.48	0.13	1	1.19	0.46	3.02	.722
Self-perception of mental health (1 = excellent/very good)	−0.51	0.59	0.75	1	0.60	0.19	1.90	.387
Family history of mental disorder	1.14	0.41	7.90	1	3.13	1.41	6.93	.005
Family history of alcohol problem	0.35	0.34	1.05	1	1.41	0.73	2.74	.306
Family history of drug problem	−0.43	0.39	01.21	1	0.65	0.30	1.40	.272
Family mental health services use	−0.23	0.41	0.31	1	0.80	0.36	1.78	.579
High stigma	−0.82	0.39	4.33	1	0.44	0.21	0.95	.037

S. E.: standard error; OR: odds ratio; CI: confidence interval.

Discussion

This study examines which predisposing, enabling and need factors are associated with MHS use among adults with or without MD according to development stages. The MHS use rate of 18- to 29-year-old and 30- to 49-year-old participants without MD in the past year is comparable with previous studies (Bruffaerts et al., 2015; Druss et al., 2007; Wang et al., 2005). Almost 70% of 50- to 64-year-olds did not have a current MD. A recent Canadian survey reported that the rate of individuals with two chronic disorders increased from 6.5% at 35–49 years old to 16.4% at 50–64 years old (Roberts, Rao, Bennett, Loukine, & Jayaraman, 2015). The same survey showed that arthritis, diabetes mellitus and asthma were often comorbid with common MD in this age group. Mental health problems are probably revealed during treatment for a chronic physical disease, leading to MHS referral. Therefore, physical complaints increase the likelihood of being in contact with medical services, especially among 50- to 64-year-olds. Another explanation is that they have probably had one in their lifetime and are continuing treatment to prevent relapse. Moreover, they may present subclinical symptoms and use MHS to avoid health condition worsening. They may consult health professionals to cope with stressful events common at that age such as divorce and bereavement.

For all age groups, as hypothesised, need factors are associated with MHS use, which is in agreement with Andersen’s model. The pattern of significant variables varies for enabling and predisposing factors depending on the age group. This highlights the importance to assess MHS use within a developmental perspective, as different factors are involved according to life stages.

For 18- to 29-year-olds and 30- to 49-year-olds, major depressive disorder is a need factor significantly associated with MHS use, as previously reported (Wang et al., 2005). One explanation is that depression is associated with lesser stigma compared with schizophrenia and alcohol dependence (Schomerus et al., 2011). Also, this disorder is twofold more prevalent among women than men, females being higher help seekers than males (Altemus, Sarvaiya, & Neill Epperson, 2014; Kovess-Masfety et al., 2014; Seedat et al., 2009). Mental health illiteracy and masculinity stereotypes are also MHS use barriers; sensitisation campaigns targeting men can reduce their stigmatising and gendered views about help seeking (Harding & Fox, 2014; Wahto & Swift, 2014).

Number of stressful events is a need factor associated with MHS use for 18- to 29-year-olds, as previously reported (Sherbourne, 1988). Exposure to stressful situations is one of the strongest variable associated with psychological distress (Caron & Liu, 2011) and MD (Benjet, Borges, & Medina-Mora, 2010; Glover, Olfson, Gameroff, & Neria, 2010; Holman, Silver, & Waitzkin, 2000), especially in individuals with psychosocial and/or biological vulnerabilities (Ingram & Luxton, 2005). In turn, psychopathology severity after stress can lead to MHS use (Gavrilovic, Schutzwohl, Fazel, & Priebe, 2005). The impact of stressful events may be buffered by psychological traits that are more developed during middle adulthood: problem solving, positive mood and sense of mastery (Lachman, 2001). Public media and programmes on campuses can help informing young people about MHS for coping with stressful events.

Alcohol dependence is a need factor associated with MHS use for middle-aged adults. They may realise that drinking is problematic when this behaviour becomes less frequent in their social circles, triggering their need to consult. Furthermore, negative consequences impairing severely psychosocial functioning (finding/keeping a job, low work performance, spousal problems) are more apparent as an individual gets older (Levola et al., 2014). Physical diseases related to alcohol dependence, such as cardiovascular diseases, cancers and liver diseases, can be the initial reason why they seek help (Cargiulo, 2007). Alcohol dependence should be screened when they seek help for physical conditions in this age group.

Among enabling factors, as expected, occupation is negatively associated with MHS use among 30- to 49-year-olds, but contrary to hypothesis, this association was nonsignificant for middle-aged adults. Mental problems are perceived as benign by 30- to 49-year-olds as they are able to work. They may also be too busy to look for the right MHS and to go through the time-consuming process of consulting and being treated (Henderson, Harvey, Overland, Mykletun, & Hotopf, 2011). Furthermore, fear concerning losing a job/promotion and worries about others finding out are other reasons for not seeking help (American Psychiatric Association, 2010). Reducing stigma, improving mental health literacy and rising positive attitude towards treatment are recommended actions in the workplace to facilitate help seeking; benefits of this early intervention include faster recovery, improved productivity and low presenteeism rate (Moll, Patten, Stuart, Kirsh, & MacDermid, 2015).

Neighbourhood disorder and social cohesion are two other enabling factors associated with MHS use among 30- to 49-year-olds. Neighbourhood disorder can act as a stressor (i.e. poverty, unemployment) that erodes mental health, disintegrate social cohesion by discouraging residents in participating in neighbourhood activities and increase exposure to crime and violence, leading to feelings of fear, mistrust and psychological distress (Kim, 2010; Polling, Khondoker, SELCoH study team, Hatch, & Hotopf, 2014). As previously reported, neighbourhood problems are correlated with depression and anxiety, while social cohesion is inversely associated with these disorders (O’Campo et al., 2015). Living in a disordered neighbourhood may enhance needs, leading to MHS use in the absence of social support within communities (Hill & Maimon, 2013). In our study, higher social cohesion was associated with MHS use. Close-knitted neighbours are apt to detect possible psychological problems and they have the influence to urge an individual to seek help. Currently, literature is scarce concerning characteristics and impact of neighbourhood according to development stages. Nevertheless, some authors recognise that differences concerning neighbourhood context exist between age groups (Settersten & Andersson, 2002). Research is needed to help formulating an explanation on why neighbourhood disorder and social cohesion influence MHS use for 30- to 49-year-olds, but not for other age groups.

For 50- to 64-year-olds, as hypothesised, one significant enabling factor for MHS is low satisfaction about intimate and close relationships; this may indicate social isolation and lack of social support. Close friends, a romantic partner or family members may act as confidents and advisors, thus alleviate stress and buffer distress perception (Gourash, 1978; Maulik, Eaton, & Bradshaw, 2011). Absence of this protective factor can prompt individuals perceiving high levels of distress to seek professional help because they cannot find the needed support in their immediate environment. Compared with younger adults, number of persons in one’s social network is lower among middle-aged adults, given that they are more likely to be separated, divorced or widowed and they have reduced the size of their friendship network (Wrzus et al., 2013). How to build significant relationships should also be addressed in treatment for middle-aged adults.

Searching for mental health information is an enabling factor associated with MHS for all age groups. A German study conducted in 2013 shows that 57% of individuals use search engines and 20% go on Wikipedia to look for information on mental health; less than 5% go on health portals, forums or hospital websites (Kalckreuth, Trefflich, & Rummel-Kluge, 2014). Quality and objectivity of online resources consulted are uncertain. MHS providers should work closely with web developers to ensure that Internet users have access to valid information. Media technology pervasiveness in everyday life highlights the necessity for the mental health system to be up-to-date and to offer adequate online information and services. Building efficient online mental health resources can overcome the traditional barriers of help seeking: social stigma, geographic localisation, financial issues and incompatible opening hours (Burns & Birrell, 2014). Internet allows for privacy, while offering free or low-cost online resources that are available 24 hours a day and from one’s home. In the context of socioeconomic austerity, online resources constitute an interesting avenue to offer adequate services while diminishing costs on the health system.

Among predisposing factors, individuals aged over 30 years with a psychiatric family history are more likely to use MHS probably because they are knowledgeable about mental health problems and MHS (Prokofyeva, Martins, Younes, Surkan, & Melchior, 2013). They have witnessed a family member going through the process of recognising symptoms and seeking appropriate help. Positive experiences of MHS use by close relatives may incite individuals who have mental health problems to consult help (Vogel, Wade, Wester, Larson, & Hackler, 2007). Among emerging adults in our sample, holding low stigma-related attitudes due to familial exposure does not seem to be a sufficient condition to seek help. Contact-based education, consisting in direct interactions with individuals with MD, exists as a programme to reduce stigma about mental health (Corrigan et al., 2001). It could be beneficial when implemented in college and in workplaces for individuals who have not interacted with a family member with an MD.

Self-perceived stigma is a predisposing factor associated with MHS only among 50- to 64-year-olds. This result appears to contradict previous studies that have identified stigma as a barrier for MHS use (Rusch, Angermeyer, & Corrigan, 2005; Schomerus & Angermeyer, 2008). However, MHS use may have preceded stigma perception, and it is enhanced once the person has entered treatment and has received a diagnosis. Middle-aged adults may have experienced discrimination by their family, friends and colleagues after seeking professional help for mental health problems. Disclosure of having a diagnosed MD is known to be related to stigmatising reactions and to social support reduction (Bos, Kanner, Muris, Janssen, & Mayer, 2009; Wahl, 2012). Therefore, stigma should be addressed during treatment with the patient. Neglecting this issue in therapy can lead to high levels of psychological distress and to treatment discontinuation (Sirey et al., 2001; Wahl, 2012).

Limitations

This study has some limitations. First, secondary analyses imply using only data that have been already collected for other purposes. It is acknowledged that other questionnaires with detailed items or with better psychometric properties would have been more suited for this research question.

Second, not all MD were assessed by the CIDI; diagnostic rates of schizophrenia-related disorders and personality disorders are unknown among our sample. Considering that this study did not assess all MD, it is highly plausible that individuals with ‘non-assessed MD’ were considered as not having an MD. However, epidemiological studies in the general population show that these non-assessed MD are usually highly comorbid with other MD that we assessed. As such, 50% of individuals with schizophrenia have comorbid depression, 15%−29% have comorbid anxiety disorder and 47% have substance use disorder (Buckley, Miller, Lehrer, & Castle, 2009). Among individuals in the general population with antisocial personality disorder, 5% have a depressive disorder, 32% have an anxiety disorder and 32%−37% have an alcohol and/or drug dependence (Ullrich & Coid, 2009). For borderline personality disorder, 60% have concurrently a mood disorder, 58% an anxiety disorder and 51% a substance use disorder (Grant et al., 2008). Concerning adults with attention deficit hyperactivity disorder (ADHD) in the general population, 49%−54% have concurrently a mood disorder, 50% have an anxiety disorder, 41%−59% have an alcohol use disorder and 53%−63% have a drug use disorder (Dakwar et al., 2014). Therefore, this high rate of comorbidity in psychiatry suggests that by assessing common MD, the sample would have also included individuals with other non-assessed disorders. Considering that comorbid conditions probably influence MHS use differently as opposed as having only one MD, future large epidemiological studies should also examine how help seeking may vary depending on the different types of pairing of MD comorbidities. Furthermore, these non-assessed MD may be associated with higher MHS use than the assessed ones. For example, the treatment gap of schizophrenia is estimated to be around 32%, while it is over 50% for mood and anxiety disorders (Kohn, Saxena, Levav, & Saraceno, 2004). Individuals with borderline personality disorder are known to be high users of MHS (Bagge, Stepp, & Trull, 2005; Bender et al., 2001). This highlights the relevance of including severe MD such as schizophrenia and personality disorders in future epidemiological studies on MHS use.

Third, participants did not give their personal reasons why they used or did not used services. This research is based on secondary analyses of data obtained from a large epidemiological study, which purpose was to gather information on many different themes other than mental health utilisation. Questions about reasons of mental health care use in absence of current diagnosis were unfortunately not asked in the original questionnaire. Large epidemiological surveys are often limited in the type of variables available for feasibility purposes. However, reasons could be inferred by examining associated variables in logistic regression models. Using the available variables by conceptualising them within a theoretical model of health utilisation, the Andersen’s model of health service utilisation gives a conceptual framework to examine thoroughly which factors contribute in seeking or not professional help.

Fourth, causality cannot be inferred between the independent variables and MHS use because of the cross-sectional design of this study. Longitudinal analyses cannot be conducted yet to determine whether significant factors differ as an individual evolves from one development stage to another because only 5 years have passed from T1 to T3. Not enough individuals have moved to the next development stage in this period. Future research should examine how preceding stages influence help-seeking behaviour in subsequent stages. Studies have shown that past positive experiences of MHS is one of the main facilitators of help seeking (Gulliver, Griffiths, & Christensen, 2010). While individuals who were treated in Child and Adolescent Mental Healthcare often do not transition into adult services at the age of 18 years because they are perceiving that adult services do not meet their needs or they are not ready for the autonomy that is required in adult care (Singh & Tuomainen, 2015), some of them may seek help later in life, based both on their positive childhood/adolescent experiences of healthcare and on their feeling that adult services are now more adapted to their current reality.

Furthermore, variables in this study concern the previous 12 months; chronology of events cannot be ascertained within this 1-year time frame. For example, reported self-perceived stigma could have appeared before, as well as during or after, MHS use. Finally, the results may represent a regional specificity of Montreal or even the ECA. However, the study design was planned to obtain a highly socioeconomically heterogeneous population.

Conclusion

This study is the first to consider development stages in adulthood in the examination of help-seeking behaviour. Andersen’s model allows a comprehensive social, demographic and psychological assessment of MHS use. This research used multiple linear regressions to identify which variables were associated with help seeking. Further studies would be needed to gather sufficient knowledge to elaborate theoretical model of developmental determinants of help seeking. It will be of high interest to test moderation and mediation among the variables assessed in the multiple linear regressions in a future research. It should be highlighted that consideration of neighbourhood context is not systematic in help-seeking literature.

Although there are some similarities, there are also different predisposing, enabling and need factors associated with MHS use according to development stages in adults with or without MD. Age-related contextualisation should be taken into account when examining help seeking. A developmental perspective allows developing policies that target populations based on their specific life-course situations (Gaudet, 2007). For example, campaigns could be developed specifically for emerging adults that give information about availability of MHS for specific MD or for broad mental health problems caused by stressful events. For 30- to 49-year-olds, workplace programmes providing information about mental health and MHS, such as Opening Minds and Beyond Silence, are relevant considering the importance of career in this age group (Moll et al., 2015; Stuart et al., 2014). For middle-aged adults, treatment could include modules addressing stigma and personal relationships to ensure successful remission. Finally, developing efficient online mental health resources is necessary, as websites and applications are now the main platforms for obtaining information, regardless of age.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Canadian Institute of Health Research (CTP-79839) and Mitacs (IT04457).

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