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Int. J. Environ. Res. Public Health 2014, 11(10), 10559-10586; doi:10.3390/ijerph111010559

Article
Predictors of Healthcare Service Utilization for Mental Health Reasons
Marie-Josée Fleury 1,2,3,*, André Ngamini Ngui 2, Jean-Marie Bamvita 3, Guy Grenier 3 and Jean Caron 1,3
1
Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada
2
Montreal Addiction Rehabilitation Centre—University Institute (CRDM-IU), Montreal, QC H2M 2E8 Canada
3
Psychosocial Division, Douglas Hospital Research Centre, Montreal, QC H4H 1R3, Canada
*
Author to whom correspondence should be addressed; Tel.: +1-514-761-6131 (ext. 4344); Fax: +1-514-762-3049.
External Editors: Luis Salvador-Carulla, Alan Rosen and Ana Fernandez Sanchez
Received: 6 June 2014; in revised form: 24 September 2014 / Accepted: 25 September 2014 / Published: 15 October 2014

Abstract

: This study was designed to identify: (1) predictors of 12-month healthcare service utilization for mental health reasons, framed by the Andersen model, among a population cohort in an epidemiological catchment area; and (2) correlates associated with healthcare service utilization for mental health reasons among individuals with and without mental disorders respectively. Analyses comprised univariate, bivariate, and multiple regression analyses. Being male, having poor quality of life, possessing better self-perception of physical health, and suffering from major depressive episodes, panic disorder, social phobia, and emotional problems predicted healthcare service utilization for mental health reasons. Among individuals with mental disorders, needs factors (psychological distress, impulsiveness, emotional problems, victim of violence, and aggressive behavior) and visits to healthcare professionals were associated with healthcare service utilization for mental health reasons. Among individuals without mental disorders, healthcare service utilization for mental health reasons is strongly associated with enabling factors such as social support, income, environmental variables, and self-perception of the neighborhood. Interventions facilitating social cohesion and social solidarity in neighborhood settings may reduce the need to seek help among individuals without mental disorders. Furthermore, in their capacity as frontline professionals, general practitioners should be more sensitive in preventing, detecting, and treating mental disorders in routine primary care.
Keywords:
mental health; service utilization; Andersen behavioral model; longitudinal study; catchment area research

1. Introduction

According to the World Health Organization, mental disorders are the world’s leading cause of disability after cardiovascular disease [1,2]. According to a recent study, mental disorders account for approximately 33% of time lost to disability worldwide [3]. Excluding neurological conditions affecting the brain, the rates of lifetime prevalence of mental disorders among adults worldwide ranges from 12.2% to 48.6%, and 12-month prevalence from 8.4% to 29.1% [4]. In the United States, community epidemiological surveys estimate that about 30% of the adult population meet “the criteria for a 12-month mental disorder” [5]. As for the 2012 Canadian Community Health Survey among Canadians aged 15 years or more, 10% experienced at least one mental disorder in the previous 12 months [6]. In the province of Quebec, the estimated prevalence of mental disorders was 12% in the general population in 2009–2010 [7].

Mental disorders are associated with major social and economic consequences. Patients with mental disorders have high mortality rates [8], poor quality of life [9], lower self-esteem [10], and lack educational and income-generating opportunities, thus limiting their chances of economic development and depriving them of social networks and status within the community [11]. They also experience a variety of chronic physical health problems such as hypertensive and cerebrovascular diseases [12]. Among individuals with major depression in a 12-month period, 66% are also affected by chronic physical disease [13]. According to a WHO survey, 52% of individuals with heart diseases also experience symptoms of depression and 30% meet the diagnostic criteria for major depression [14].

Despite the pervasive need for mental health treatment among individuals with mental disorders, it is generally acknowledged that a great proportion of them do not use healthcare services [15,16,17,18,19]. Canadian studies report that less than 40% of Canadians suffering from mental disorders consult a healthcare professional or services for mental health reasons [20,21]. Thus, there is a pressing need to identify factors that foster healthcare service utilization for mental health reasons. The decision to seek help for mental disorders is a complex process that involves personal socio-demographic characteristics, culturally mediated interpretations of symptoms, availability of healthcare services, economic and socio-structural factors, and healthcare service organization [22]. A number of healthcare service utilization models exist [23,24,25,26,27,28,29], but most studies of healthcare service utilization for mental health reasons have used the behavioral model developed by Andersen in the 1960s [15,16,17,18,30,31,32]. This model conceptualized healthcare service utilization as a function of individuals’ predisposing, enabling, and needs characteristics [33,34].

The predisposing factors center on individual characteristics prior to an illness episode (for example, age, gender, life satisfaction, marital status, self-rated health). Studies have revealed that a high level of education [35], being a woman [36] and young [37] were variables correlated with seeking help for mental disorders. The enabling factors centers on the idea that variables such as income, community and system resources (including social support and service availability and accessibility) are main determinants of healthcare service utilization. A study using an administrative database found that in central Toronto, lower socioeconomic status was associated with a lesser likelihood of consulting a psychiatrist or a general practitioner for mental health reasons [38]. Previous studies have found a positive correlation between good attitude toward health care providers [39], good perceived social support [40] or social ties [41,42], high income [43,44,45] and healthcare service utilization for mental health reasons. Needs factors refer mainly to the type and number of symptoms and the degree of severity of an illness. Previous studies have shown that a diagnosis of mental disorder and the severity and duration of mental health symptoms were associated with seeking help for mental health reasons [46,47]. Numerous authors have also reported that patients with co-occurring disorders are more likely to use healthcare service utilization for mental health reasons [48,49,50]. Most researchers using the behavioral model concluded that needs were the main correlates of healthcare service utilization [51,52]. The relative importance of the contribution of each component, however, varies by type of healthcare services used [53,54,55].

In addition, most studies have focused on cross-sectional population surveys [56] or on patients recruited in hospital settings (clinical sample) [57]. Few have assessed predictors associated with healthcare service utilization for mental health reasons in a catchment area, using a comprehensive framework [58,59]. Moreover, several factors such as environmental variables and self-perception of the neighborhood, which may be predictors of healthcare service utilization for mental health reasons, have received little or no attention in the literature.

Furthermore, most epidemiological studies have found that nearly 50% of individuals who use healthcare services for mental health reasons do not have a diagnosis of mental disorder in the 12 months before the interview [60,61,62,63,64]. This situation may be the result of overuse or misuse of healthcare services [60]. Some individuals may be affected by psychological distress or emotional problems resulting from bereavement, divorce, financial problems or other stressful events. Others may have previously been affected by a mental disorder and consulted a professional to determine if they had suffered a relapse. Accordingly, it is possible that individuals without mental disorders use specialized mental healthcare services rather than primary care. Better knowledge of factors distinguishing users of services for mental health reasons with and without mental disorders would be useful in improving the distribution of healthcare services.

The purpose of this study was to: (1) assess longitudinal predictors of 12-month healthcare service utilization for mental health reasons among a population cohort in an epidemiological catchment area, framed by the Andersen model; and (2) identify variables associated with healthcare service utilization for mental health reasons among individuals with and without mental disorders, respectively.

2. Methods

2.1. Study Design and Setting

Our research focused on an epidemiological catchment area located in southwestern Montreal. Greater Montreal is Canada’s second-largest urban center, with a population of 3.6 million. The catchment area is home to 269,720 people and includes four neighborhoods, which range in population from 29,680 to 72,420. Immigrants in the catchment area represent 17% of the population (vs. 26% in Montreal). The proportion of low-income households is 36% (vs. 23% in the province of Quebec and 35% in Montreal). Low-income households are located mainly in two of the four neighborhoods where close to half of the residents are low-income earners. The catchment area included a diversity of services, mainly in healthcare and mental healthcare. The latter services and the socioeconomic characteristics of the area are described in detail in other publications [17,65].

2.2. Selection Criteria and Survey Sample

For inclusion in the survey, participants had to be aged between 15 and 65 and reside in the study catchment area. The objective was to obtain a representative sample of the target population, both geographically (that is, recruiting participants from all areas of the territory) and proportionally to population density and socio-economic status (representative of educational level). Data were collected by specially trained interviewers on two separate occasions at a two-year interval.

At T1 (June 2007 to December 2008), 2434 individuals were randomly selected for the survey. On average, 600 individuals were selected in each area: Saint-Henri/Pointe-St-Charles: 612; Lachine/Dorval: 603; Lasalle: 584; Verdun: 635. The mean age of the sample was 42.4 (SD: 13.3). Sixty-three percent were female. Forty-five percent were married or common-law spouses vs. 17% divorced or separated and 37% single. Seventy-two percent had post-secondary education and 77% held a job in the last 12 months. French was the first language for 54% of participants and English for 22%. Eighty-two percent were Caucasian. Twenty-four percent of participants were non-European immigrants. Average personal income was CA$28,688 (SD: 31,061) and average household income was CA$49,566 (SD: 51,057).

All of the participants were contacted for a second interview (T2) from June 2009 to December 2010. Only 611 were lost during the follow-up, for a retention rate of 74.9%, that is, 1823 participants (Figure 1). The attrition rate at T2 (25.1%) included only 138 (5.7%) who refused to participate, 230 (9.4%) who had moved outside the catchment area, 231 (9.4%) who could not be reached, and 12 (0.5%) who had died. This attrition rate after two years was lower than that observed in the American epidemiological catchment areas [66] after one year (20.4%, including 12.6% refusals). The attrition rate was higher among youths, singles, individuals with poorer education, those with lower individual income, and those with substance dependence, which is similar to characteristics previously identified in other epidemiological catchment area studies [67,68,69,70]. The research was approved by the relevant ethics boards. The sampling strategy and data collection (especially at T1) are described in detail in related publications [58,65].

Figure 1. Flowchart of the sample from baseline (T1) to second survey (T2).
Figure 1. Flowchart of the sample from baseline (T1) to second survey (T2).
Ijerph 11 10559 g001 1024

2.3. Variables and Measuring Instruments

The dependent variable was “12-month healthcare service utilization for mental health reasons prior to the interview” (yes/no). Healthcare service utilization was measured with the Canadian Community Health Survey questionnaire [71]. It included: psychiatrists, general practitioners, psychologists, social workers, nurses and other health professionals, hospitalizations, rehabilitation centers, and community-based organization services.

Independent variables were measured at baseline (T1) and organized according to the Andersen behavioral model of healthcare service utilization, comprising predisposing factors, enabling factors, needs factors, and type of healthcare service professionals’ utilization (i.e., the practitioners listed in the paragraph above). Predisposing factors included socio-demographic variables (age, gender, education level, problems with the law in the past 12 months, lifelong of problems with the law), and beliefs (self-perception of physical and mental health, satisfaction with life, importance attributed to spirituality, number of children in the household). Enabling factors included source of income, household and personal income, quality of life, self-perception of the neighborhood (physical conditions of the neighborhood score, security score, community involvement scale score, sense of collective efficacy score, resident disempowerment scale score, neighboring behavior scale score), social support and environmental variables (driving distance to the neighborhood community health center, proportion of immigrant population in the neighborhood, mean household income in the neighborhood before income tax). Needs factors included having an mental disorder, number and types of mental disorders (major depression, mania, panic disorder, social phobia, agoraphobia, post-traumatic syndrome disorder (PTSD), alcohol dependence, and drug dependence), having emotional problems, being a victim of violence, and exhibiting aggressive behaviors (all in the past 12 months) and psychological distress score. Variables assessed in this study were measured with the instruments listed in Table 1.

Table 1. Measuring Instruments.
Table 1. Measuring Instruments.
FactorsNo.NameDescription
Predisposing factors1CCHS 1.2 [71]Canadian Community Health Survey (CCHS): survey questionnaire for socio-demographic characteristics
2Satisfaction with Life Domains Scale (SLDS) [72]20 items organized in 5 domains: daily living and social relationships, living environment, autonomy, intimate relationships, and leisure
Enabling factors3Sense of Community Scale (SCS) [73]8 items
4Community Participation Scale (CPS) [74]6 items Measures association between crime victimization, social organization, and participation in neighborhood organization
5Resident Disempowerment Scale (RDS) [75]3 items
6Sense of Collective Efficacy (SCE) [76]Evaluates the effect of social and institutional mechanisms on people living in the neighborhood
7Neighborhood Disorder Scale (NDS) [75]9 items
8Physical Conditions of the Neighborhood (PCN) [73]7 items
9Facility in Neighborhood (FN) [77]13 items; measures 3 domains: availability, utilization and quality
Enabling factors10Social Provisions Scale (SPS) [78]Measures six items: emotional support, social integration, reassurance about his value, material help, counselling and information, need to feel useful
Needs factors11Composite International Diagnostic Interview (CIDI) and CIDI-SF [71,79]Screening of mental disorders; was used in the World Mental Health 2000 (WMH 2000); included the most frequent mental disorders (mood disorders: depression, mania; anxiety disorders: social phobia, agoraphobia, and panic disorder, post-traumatic stress disorder (PTSD)). Screening of substance disorders (alcohol and drugs were based on the CIDI-Short Form (SF)
12Modified Observed Aggression Scale (MOAS) for aggressive behaviors [80]Assess 4 categories of aggressive behavior: verbal aggression, aggression to propriety, self-inflicted aggression, physical aggression
17K-10 psychological distress scale (K-10 PDS) [81]10 five-point Likert items; was used in the World Mental Health survey 2000 (WMH2000)
18Barratt Impulsivity Scale (BIS) [82]30 four-point scale items organized in three categories: motor impulsivity, cognitive impulsivity, impulsivity due to lack of planning

2.4. Analyses

Analyses comprised univariate analyses, bivariate analyses, and multiple regression analyses. Univariate analyses entailed frequency distribution for categorical variables and mean values along with standard deviations for continuous variables. Bivariate analyses were carried out to assess variables significantly associated with healthcare service utilization for mental health reasons during the follow-up period, for an Alpha value of 0.010. Variables that passed this test were then used in a multiple regression model, for an Alpha value set at 0.05. Goodness-of-fit and total variance explained were generated. Comparison analyses were made on participant characteristics at T1 according to the presence of mental disorders at T1 and gender and healthcare service utilization for mental health reasons at T2.

3. Results and Discussion

3.1. Descriptive Analyses

The sample was described according to categorical variables (Table 2) and continuous variables (Table 3) at T1 for the 1823 remaining participants in the study at T2. There were twice as many females as males. The mean age was 43 years. Most participants had received education beyond secondary school level. The majority reported being dissatisfied or very dissatisfied with life and having a poor or fair perception of their physical and mental health. More than half attributed importance to spirituality. The most prevalent mental disorder was major depression.

3.2. Comparison Analyses according to the Presence of Mental Disorders and Gender Difference

Comparison analyses were made on participants’ general characteristics according to the presence of mental disorders and their gender at T1 and according to service utilization and gender at T2. As regards predisposing factors, being female, younger, having received less education, being dissatisfied with life, having a poor or fair perception of one’s physical and mental health, and problems with the law in the past 12 months and over a lifetime were found to be significantly associated with the presence of mental disorders. Enabling factors associated with the presence of mental disorders were unemployment, lower household and personal income, lower quality of life and physical conditions of the neighborhood, higher security, community involvement, sense of collective efficacy, resident disempowerment, and neighboring behavior scale score, and lower social support. Finally, needs factors associated with the presence of mental disorders were emotional problems, being a victim of violence, exhibiting aggressive behavior, and higher psychological distress score.

Concerning predisposing factors, females and males were significantly different with respect to self-perception of mental health (better for females), importance attributed to spirituality (more females than males), and problems with the law in the past 12 months and over a lifetime (mainly in males). Enabling factors were significantly different with respect to personal income (lower in females), security score (higher for females), community involvement score, sense of collective efficacy score, resident disempowerment score (lower for females), and social support score (higher for females).

Table 2. Participant characteristics by mental health status, gender and healthcare service utilization: categorical variables.
Table 2. Participant characteristics by mental health status, gender and healthcare service utilization: categorical variables.
FactorsVariablesCategoriesParticipant Characteristics at T1
Total SampleMental DisordersGenderHealthcare Service Utilization at T2Healthcare Services Users by Gender (n = 243)
n = 1823No.YesFemaleMaleNoYesFemaleMale
n = 1580n = 243n = 1147n = 676n = 1581n = 243n = 159n = 84
%%%%%%%%%
Predisposing factorsGenderFemale62.661.469.1 * 62.265.4
Male37.438.630.9 37.834.6
EducationSecondary school or less18.917.625.8 *19.418.218.024.7 *25.822.6
Over secondary school81.182.474.280.681.882.075.374.277.4
Satisfaction with lifeSatisfied or Very satisfied82.387.256.7 **81.783.485.760.5 **61.658.3
Neither satisfied nor dissatisfied11.69.423.412.210.710.022.219.527.4
Very dissatisfied or dissatisfied6.03.419.96.15.94.317.318.914.3
Self-perception of physical healthExcellent or very good45.649.525.1 *45.446.148.328.4 **27.030.9
Good36.736.736.836.736.736.239.940.339.3
Poor or fair17.713.838.118.017.215.531.732.729.8
Self-perception of mental healthExcellent or very good58.665.522.3 **55.963.1 *63.427.2 **26.428.6
Good30.628.342.332.127.929.040.739.642.9
Poor or fair10.96.235.412.09.07.632.134.028.6
Importance attributed to spirituality57.857.161.260.952.6 *57.360.964.254.8
Problems with the law in past 12 months1.10.63.8 **.42.2 **1.01.60.04.8 *
Lifelong history of problems with the law5.43.813.7 **2.89.7 **4.312.3 **6.922.6 *
Enabling factors Source of incomeFrom job58.961.147.1 **58.559.560.746.9 **54.051.2
Others41.138.952.941.540.539.353.145.948.8
Needs factorsMental disordersin previous 12 monthsMajor depression8.60.054.09.66.9 *5.429.2 **28.929.8
Mania1.50.09.61.41.81.04.9 **2.59.5 *
Panic Disorder1.80.011.32.40.9 *1.34.9 **5.73.6
Social Phobia3.30.021.04.41.6 *2.310.3 **12.65.9
Agoraphobia1.40.08.91.80.7 *0.85.8 **6.34.8
Alcohol Dependence2.70.017.21.94.1 *1.411.5 **8.217.9 *
Drug Dependence2.20.013.71.53.4 *1.19.1 **6.314.3 *
PTSD0.80.04.81.20 *0.42.9 **4.40.0 *
Mental disorders in past 12 months (Yes/No)16.017.613.2 *10.949.0 **30.820.2
Emotional problems in past 12 months33.228.955.7 **36.927.0 **31.444.9 **50.334.5 *
Victim of violence in past 12 months5.14.011.0 **5.05.34.310.3 **8.214.3
Aggressive behaviors in past 12 months13.411.225.1 **13.313.713.016.515.717.9

Notes: * p ≤ 0.5; ** p < 0.001.

Table 3. Participant characteristic by mental health status, gender and healthcare service utilization: continuous variables.
Table 3. Participant characteristic by mental health status, gender and healthcare service utilization: continuous variables.
FactorsParticipant Characteristics at T1
Total SampleMental DisordersGenderHealthcare Service Utilization at T2Healthcare Services Users by Gender n = 243
n = 1823NoYesFemaleMaleNoYesFemaleMale
n = 1580n = 243n = 1147n = 676n = 1581n = 243n = 159n = 84
MeanSDMeanSDMeanSDMeanSDMeanSDMeanSDMeanSDMeanSDMeanSD
Predisposing factors
Age42.613.242.913.341.012.5 *42.713.142.313.342.713.241.512.942.53.039.612.5
Number of children in household1.70.61.70.61.70.51.70.71.70.61.70.61.70.51.70.51.60.5
Enabling factors
Household income60,803.549,127.463,746.250,791.245,311.435,462.8 **59,848.448,790.562,405.149,682.262,832.350,392.247,612.037,424.9 **48,70839,28245,53833,759
Personal income33879.131075.035515.832681.825262.818,425.6 **30,996.023,895.738,713.939,898.7 **34,974.532,182.126,756.721,305.6 **26,02720,44128,13722,915
Quality of life109.415.9111.914.396.517.4 **109.815.6108.916.3111.214.797.817.9 **98.617.996.318.0
Physical Conditions of the Neighborhood score45.011.145.610.941.611.4 **45.111.644.810.345.310.942.911.8 *44.012.040.8 *11.3
Security score3.71.33.61.34.01.4 **3.81.43.61.3 *3.71.33.91.4 *4.01.53.71.3
Community involvement scale score9.11.19.11.19.21.09.11.19.21.0 *9.11.19.31.0 *9.21.19.41.0
Sense of collective efficacy score26.46.026.25.927.56.5 **26.06.027.05.9 *26.25.927.66.4 *26.96.529.0 *6.1
Resident disempowerment scale score11.56.011.25.912.96.4 **11.16.112.05.9 *11.36.012.56.2 *22.16.413.35.8
Neighboring behavior scale score14.48.514.28.415.49.0 *14.78.614.08.314.38.515.18.415.38.414.98.3
Social support score80.79.081.38.777.29.8 **81.68.779.19.3 **81.18.777.610.4 **77.810.277.310.9
Needs factors
Number of mental disorders in past 12 months0.20.60.00.01.40.7 **0.20.60.20.60.10.40.81.0 **1.00.01.00.0
Psychological distress score8.26.56.85.315.47.6 **8.56.87.56.0 *7.25.714.28.0 **13.47.911.78.3

Notes: * p ≤ 0.5; ** p < 0.001.

Concerning needs factors, they were significantly different as regards major depression, panic disorder, social phobia, agoraphobia, and PTSD (mostly in females), drug and alcohol dependence (mostly in males), mental disorders and emotional problems in the past 12 months (predominantly in females), and psychological distress score (higher for females).

Participants who used healthcare services for mental health reasons at T2 had the following characteristics at T1: lower education level and history of problems with the law (predisposing factors); unemployed, lower household and personal income, quality of life score, and physical conditions of the neighborhood score; higher security score, community involvement score, sense of collective efficacy score, and disempowerment score; and lower social support score (enabling factors); dissatisfied with their physical and mental health and their life, presence of mental disorders and emotional problems, and higher psychological distress score and number of mental disorders (needs factors). Females using healthcare services for mental health reasons were significantly less numerous than males in terms of having problems with the law both in the past 12 months and over their lifetime (predisposing factors) The physical conditions of the neighborhood score was significantly higher but their sense of collectivity efficacy score was significantly lower than for males (enabling factors). Finally, they were significantly less numerous than males to have mania and drug and alcohol dependence but significantly more numerous to have emotional problems (need factors).

3.3. Variables Associated with Healthcare Service Utilization for Mental Health Reasons

Variables significantly associated with healthcare service utilization for mental health reasons during the follow-up period in bivariate analyses were used to build the multiple logistic regression model presented in Table 4. As shown in this model, seven variables were found to be independently associated. Two of them were negatively associated: male gender and quality of life. Five others were positively associated: self-perception of physical health, major depressive episode, panic disorder, social phobia, and emotional problems, all in the past 12 months. This model yielded an acceptable goodness-of-fit and explained 66% of the total variance.

Table 4. Predictors of healthcare service utilization in the general population: multiple logistic regression (n = 1.823).
Table 4. Predictors of healthcare service utilization in the general population: multiple logistic regression (n = 1.823).
PredictorsBetaSEWalddfpOR95% CI
LLUL
Gender (males)−0.9330.17129.69110.0000.3930.2810.550
Self-perception of physical health0.2110.0709.07810.0031.2351.0771.417
Major Depressive Episode in past 12 months0.5840.2157.36910.0071.7931.1762.733
Panic Disorder in past 12 months0.7200.3913.39810.0652.0550.9564.419
Social Phobia in past 12 months0.8440.2978.10410.0042.3261.3014.159
Emotional problems in past 12 months0.4310.1498.34810.0041.5391.1492.062
Quality of life score−0.0220.001217.46610.0000.9780.9760.981

Notes: Goodness of fit: Hosmer-Lemeshow test: Khi-square = 3.522; p = 0.897; Total variance explained: Nagelkerke R2 = 65.5%.

3.4. Comparison Analyses between Participants with and without Mental Disorders

Table 5 displays comparison analyses between participants with mental disorders (n = 119) and without mental disorders (n = 124), among the subsample of 243 subjects who used healthcare services for mental health reasons at T2. Comparison characteristics were variables measured at T1. Among predisposing factors, six variables were found to be significantly discriminating between the two groups: participants without mental disorders having used services for mental health reasons were significantly better educated, reported higher quality of life score, had an excellent or very good self-perception of both physical and mental health, had fewer problems with the law in the past 12 months and, marginally, over their lifetime. Among enabling factors, 11 variables were significantly discriminating: participants without mental disorders with greater household income, greater social support, residing in neighborhoods with better physical and lower social cohesion, and earning a higher mean of household income before income tax.

Table 5. Comparative analyses between participants with vs. without mental disorders using mental healthcare services (n = 243).
Table 5. Comparative analyses between participants with vs. without mental disorders using mental healthcare services (n = 243).
FactorsVariablesCategoriesTotal SampleNo Mental DisordersMental Disorders p value
(n = 243)(n = 124)(n = 119)
n (%)/(Mean (SD))n (%)/(Mean (SD))n (%)/(Mean (SD))
Predisposing factorsEducation (n (%))Secondary or more183 (75.3)101 (81.5)82 (68.9)0.023 PCT
Quality of life (Mean (SD))97.8 (17.9)105.2 (15)90 (17.5)0.000 StT
Self-perception of physical health (n (%))Excellent or very good69 (28.4)41 (33.1)28 (23.5)0.001 PCT
Good97 (39.9)57 (46)40 (33.6)
Poor or Fair77 (31.7)26 (21)51 (42.9)
Self-perception of mental health (n (%))Excellent or very good66 (27.2)50 (40.3)16 (13.4)0.000 PCT
Good99 (40.7)52 (41.9)47 (39.5)
Poor or Fair78 (32.1)22 (17.7)56 (47.1)
Lifelong history of problems with the law (n (%))30 (12.3)7 (5.6)23 (19.3)0.001 PCT
Enabling factorsHousehold income (Mean (SD))47,612.0 (37,424.9)54,305.3 (42,077.7)40,637.5 (30,508.7)0.004 StT
Social support score (Mean (SD))77.6 (10.4)80 (9.6)75.2 (10.6)0.001 StT
Environmental variablesNeighborhood physical status (Mean (SD))42.9 (11.8)45.3 (11.3)40.3 (11.9)0.027 StT
Participation in activities in the neighborhood (Mean (SD))9.3 (1.0)9.2 (1.2)9.4 (0.9)0.012 StT
Score of social cohesion: readiness to protect neighbor’s home when absent (Mean (SD))15.1 (8.4)14.6 (8)15.7 (8.8)0.000 StT
Driving distance to the neighborhood community health center (in meters) (Mean (SD))2099.5 (1329.9)2270 (1448.9)1921.8 (1173.3)0.041 StT
Proportion of immigrant population in the neighborhood (Mean (SD))22.7 (9.8)24.6 (10.3)20.8 (9)0.003
Mean household income in the neighborhood before income tax (Mean (SD))61,932.5 (23,475.9)65,022.4 (25,139)58,712.9 (21,237.4)0.036 StT
NeedsPsychological distress score (Mean (SD))14.2 (8.0)10.4 (6.5)18.3 (7.5)0.000 StT
Impulsiveness score (Mean (SD))64.2 (12.6)59.9 (10.3)68.6 (13.2)0.000 StT
Emotional problems in the 12 past months (n (%))109 (44.9)41 (33.1)68 (57.1)0.000 PCT
Victim of violence in the 12 past months (n (%))25 (10.3)4 (3.2)21 (17.6)0.000 FET
Aggressive behaviors in the 12 past months (n (%))40 (16.5)9 (7.3)31 (26.1)0.000 PCT
Healthcare service professionals utilizationVisited a psychiatrist in the past 12 months (n (%))64 (26.3)25 (20.2)39 (32.8)0.026 PCT

Notes: FET Fisher Exact Test; PCT Pearson Chi-square test; StT Student t test.

Those with mental disorders were more likely to live in shorter walking and driving distance from community health centers. Neighborhoods with a higher proportion of immigrants and, marginally, lower proportion of working population aged 15 years or more, were less likely to be home to individuals with mental disorders using services for mental health reasons. All needs factors analyzed were associated with the presence of mental disorders: psychological distress, impulsiveness, emotional problems, being a victim of violence, and displaying aggressive behavior. Finally, participants with mental disorders were more likely to visit healthcare service professionals—especially psychiatrists and, marginally less so, psychologists—than those without mental disorders.

3.5. Discussion

3.5.1. Predictors of Healthcare Service Utilization for Mental Health Reasons

The first purpose of this longitudinal study was to identify predictors of healthcare service utilization for mental health reasons in a population cohort. Using the Andersen behavioral model and a comprehensive set of variables influencing healthcare service utilization for mental health reasons, we found that two predisposing factors (gender and self-perception of physical health); one enabling variable (quality of life) and four need factors (major depressive episode, panic disorder, social phobia and emotional problems) predicted healthcare service utilization for mental health reasons. These findings are consistent with previous cross-sectional studies that have highlighted the predominance of need factors as determinants of healthcare service utilization for mental health reasons [16,65,83].

Previous studies have shown that females have a higher rate of healthcare service utilization for mental health reasons than males [84,85]. This difference has been explained by the social anchorage theory [86,87]. According to this theory, the gender difference in healthcare service utilization for mental health reasons may be explained either by the cultural values and expectations associated with a specific gender or by the specific roles endorsed by males and females. It has also been suggested that females have a greater tendency to confide in friends and family; this may explain the likelihood that females with mental disorders seek help as soon as they are diagnosed [88]. This study also confirms other research showing that females reported more mental disorders than males [89,90,91,92]. In fact, as shown in Table 2, males and females who used services for mental health reasons generally differed significantly in many ways. Males had significantly more mania, alcohol and drug dependence (need factors), problems with the law (predisposing factors) than females and lived in neighborhoods with worse physical conditions but with a greater sense of collective efficacy (enabling factors). In contrast, there were significantly more females than males with PTSD (needs factor). Furthermore, our study is the first that shows that social cohesion increases the likelihood of healthcare service utilization for mental health reasons, particularly among males. In line with the social anchorage theory, we can hypothesize that males with more severe mental disorders (such as mania) or social problems (for example, problems with the law) together with the pressure from their social network are more likely to seek help. Social cohesion probably acts as a kind of social support, and numerous studies indicate that patients with more social support tend to seek help to meet their need for care [93].

To our knowledge, ours is the first study to show clearly that positive self-rated physical health predicts healthcare service utilization for mental health reasons. There have been numerous indications that poor or fair self-perception of health resulted in increased healthcare service utilization in general [94,95,96]. Many studies have also shown correlations between poor self-rated health and physical health conditions [97,98,99,100], chronic diseases [101], frequent hospitalizations [102] and greater mortality [103,104,105,106]. However, few studies have assessed the influence of self-rated physical health on service utilization for mental health reasons [65,107].

With regard to needs factors, our results were also consistent with the literature concerning factors correlated with healthcare service utilization for mental health reasons. Previous studies have shown that patients with more mental disorders used more healthcare services [108,109]. One study on healthcare service utilization for mental health reasons in Canada and in the United States reported that among the needs factors, depression was the most significant and common predictor of overall use of services for mental health reasons [21]. Other studies showed that patients with depression used different types of health professionals. In Australia, it has been reported that 60% of patients with depression were seen in a general medical clinic, 21% were seen by a psychologist or other nonmedical therapist, and 29% were seen by a psychiatrist. Patients with depressive disorders were also most likely to be seen by both a psychiatrist and another mental health specialty provider, such as a nurse or social worker (44%), but 40% were seen only by a mental health specialty provider other than a psychiatrist [110]. One study involving 1572 Dutch subjects in an adult population with major or minor lifetime depression showed that 73% of subjects with depression had sought specialized mental healthcare or, to a lesser extent, primary care [111]. In another study, authors found that depressed elderly medical in-patients used more hospital and out-patient medical services than non-depressed patients [112].

Some authors have reported that patients with social phobia consult more specialist physicians [113] because their condition is either undiagnosed or deemed by physicians to be unlikely to benefit from early treatment designed to alter the course of the illness [114].

Although we found a correlation between panic disorder and healthcare service utilization for mental health reasons, one study conducted in the United States revealed that a minority of patients with this condition used healthcare services [61]. Another research drawn from the Healthcare for Communities study, a national household survey of the adult population in the United States, showed that panic disorder was also associated with a greater likelihood of healthcare service utilization, but not with the intensity of mental healthcare services. According to these authors, the elevated rate of healthcare service utilization among patients with panic disorder may be explained by the fact that panic disorder seems to be associated with increased odds of several comorbid disorders, including depression, dysthymia, psychosis, generalized anxiety disorder, bipolar disorder, and alcohol and drug use disorders [115].

Generally, cross-sectional and epidemiological studies have found healthcare service utilization to be associated with mental disorders, but not with emotional problems. In our study, emotional problems are not synonymous with psychological distress, which is usually reported to be strongly associated with healthcare service utilization for mental health reasons [116,117], or with poor self-perception of mental health. Some studies revealed that healthcare service utilization for mental health reasons among children with emotional problems begins at a very young age and occurs in multiple service sectors [118,119]. It is possible that individuals who experience emotional problems at a younger age are more likely to consult healthcare professionals as a preventive measure.

Finally, the only enabling factor that predicts healthcare service utilization for mental health reasons was low quality of life. In fact, according to numerous authors, low quality of life appeared to be a powerful indicator for healthcare service utilization for mental health reasons [120,121,122] and has been found to be a significant predictor of 30-day and one-year hospitalization [123].

3.5.2. Correlates Associated with Healthcare Service Utilization for Mental Health Reasons among Individuals with and without Mental Disorders Respectively

The second purpose of this longitudinal study was to identify correlates associated with healthcare service utilization for mental health reasons among individuals with and without mental disorders respectively. The proportion of users without mental disorders (51%) in our study is similar to that found in previous studies [60,61,62,63,64]. Using the Andersen behavioral model, we found that differences among the two groups were mainly associated with enabling factors (n = 11), followed by predisposing factors (n = 6), needs factors (n = 5), and, finally, variables relating to healthcare service professionals (n = 4). The results show that healthcare service utilization for mental health reasons among individuals without mental disorders was strongly associated with enabling factors such as social support, income, environmental variables, and self-perception of the neighborhood.

Social support is acknowledged to be both a protecting factor against mental disorders and a strong predictor of healthcare service utilization for mental health reasons [124,125,126,127]. Spouses and relatives can help individuals recognize their problems and seek help from mental healthcare services [127]. Furthermore, since household and not personal income was associated with healthcare service utilization for mental health reasons among individuals without mental disorders, this may indicate that they don’t live alone. A higher income is associated with healthcare service utilization for mental health reasons mainly for services from psychologists, a class of professionals that is not usually covered by public healthcare systems [46,47]. In previous research undertaken in the same epidemiological catchment area, we found that among individuals with mental disorders, those with the highest household income at T1 also favored psychologists as the professionals most often consulted after general practitioners [16]. For individuals with low income, cost is the most important barrier to access to psychotherapy [128]. Moreover, individuals without mental disorders living in neighborhoods with better physical conditions and a higher mean household income before income tax perceived social cohesion and participation in activities in their neighborhood less favorably than individuals with mental disorders. This apparent contradiction may reflect greater individualism and loneliness among individuals without mental disorders [129,130]. In addition, according to the literature, social cohesion and social solidarity are weaker in neighborhoods with stronger ethnic diversity [131,132]. When the population is too heterogeneous, individuals are less likely to trust their neighbors [131]. In our study, neighborhoods with a higher proportion of immigrants were more often associated with individuals without mental disorders using services for mental health reasons. These conditions may be the source of emotional problems and stress among individuals without mental disorders and account for their healthcare service utilization for mental health reasons. Conversely, the fact that individuals with mental disorders using services for mental health reasons were more likely to live in neighborhoods with a lower proportion of immigrants makes sense. First, mental disorder is less prevalent among immigrants because potential new arrivals presenting with chronic disease are generally not admitted into the country [38]. Secondly, immigrants of the same ethnic group generally engage in mutual aid, which helps to protect them from mental disorders regardless of their socio-economic conditions [132,133]. Furthermore, it is logical to posit that individuals with mental disorders live closer to a community health center (offering primary healthcare and mental healthcare services), especially if they were more likely to be low-income earners with limited access to transportation. Individuals with mental disorders tend to live near their treatment center [65].

It was also expected that participants without mental disorders would be less likely to have need factors and visit professionals than individuals with mental disorders. Emotional problems were the most prevalent needs domains among individuals without mental disorders. As indicated previously, emotional problems are usually detected at a young age [118,119]. It is possible that some individuals without mental disorders consult professionals for mental health reasons over the long term for their emotional problems or to prevent relapses. Furthermore, the relatively high proportion of individuals without mental disorders (20%) who consulted a psychiatrist in the past 12 months seems to confirm the existence of previous diagnoses, including diagnoses of severe mental disorders or personality disorders that are not reported in the study.

3.6. Study Limitations

Several limitations to the present study must be acknowledged. The first limitation stems from the sampling design, which enrolled subjects in a catchment area. This may limit the generalizability of the results. However, the high heterogeneity of the population within the catchment area may offset these limitations. The second limitation is that the Composite International Diagnostic Interview (CIDI) and the Composite International Diagnostic Interview Short Form (CIDI-SF) report only mood disorders, anxiety disorders, and drug and alcohol dependence; as a result, some mental disorders (for example, schizophrenia, personality disorders, and eating disorders) could not be included in the analysis. Previous studies have shown that patients with severe mental disorders, mainly schizophrenia, use more healthcare services than patients with disorders of moderate-to-low severity [134,135]. The third limitation is that we can make hypotheses only regarding the type of care used by participants. Even if females seem to be more likely to use healthcare services for mental health reasons, studies consistently show that men use more specialized care services than females [136,137]. Finally, the fourth limitation is that we did not have information concerning the frequency of visits to professionals. According to Druss et al., a lower proportion of individuals without mental disorders use services [60].

4. Conclusions

The strengths and originality of this study are found in its methodology: an epidemiological catchment area study was used, including a longitudinal survey (measured on two separate occasions), based on a comprehensive framework (Andersen model). There is a lack of literature on longitudinal predictors of healthcare service utilization for mental health reasons; as a result, the findings of the present study are of great significance. In addition, better knowledge of factors distinguishing users of healthcare service utilization for mental health reasons with and without mental disorders would be useful in improving the distribution of healthcare services.

Consistent with past cross-sectional research, the study showed that all three components of the behavioral model contributed to healthcare service utilization for mental health reasons and that needs factors (namely, mental disorders and emotional problems) were the major predictors. Males also appear to be less likely to seek care until their illness or perceived health concern is more severe. This may increase the risk of crisis and/or suicide. Consequently, outreach initiatives should aim to improve service utilization among males. Two other predictors of healthcare service utilization for mental health reasons were also uncovered: emotional problems and good self-rated physical health. These results suggest that general practitioners, in their role as the point of entry into the healthcare system (as well as access to other primary care providers), should be more sensitive to these variables in routine primary care if they are to prevent and treat mental disorders more effectively. Accordingly, healthcare service utilization for mental health reasons may be fostered through more effective screening of patients entering the system with less than optimal self-perception of physical health or other emotional and quality of life issues.

Furthermore, our study results show that enabling factors, mainly geographical variables and self-perception of the neighborhood, are key to distinguishing users of healthcare services for mental health reasons among individuals with and without mental disorders, respectively. More specifically, our study found that lower social cohesion and social solidarity in neighborhood settings are two original variables that contribute to healthcare service utilization for mental health reasons among individuals without mental disorders. These results confirm the importance for mental healthcare services to take neighborhoods into account and adjust care provision accordingly. Initiatives that facilitate the integration of immigrants and participation in activities in neighborhood settings may reduce the need, among individuals without mental disorders, to seek help. Finally, as our results indicate, we need to understand why individuals without a mental disorder consult psychiatrists. Accordingly, more concerted efforts to direct these individuals to appropriate primary-care services may be needed.

Acknowledgments

The study was funded by the Canadian Institute of Health Research (CTP-79839). We would like to thank this grant agency and all the individuals who participated in the research.

Author Contributions

Marie-Josée Fleury and Jean Caron designed the study. Jean-Marie Bamvita carried out the statistical analyses with assistance from Jean Caron. André Ngamini Ngui, Guy Grenier, and Marie-Josée Fleury wrote the article. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Michaud, C.M.; Maurray, C.J.; Blonn, B.R. Burden of disease: Implications for future research. JAMA 2001, 285, 535–539. [Google Scholar] [CrossRef] [PubMed]
  2. Warner, R.; de Girolamo, G. Épidémiologie des Troubles Mentaux et des Problèmes Psychosociaux: Schizophrénie; Organisation Mondiale de la Santé: Geneve, Switzerland, 1995. (In French) [Google Scholar]
  3. Honey, A.; Emerson, E.; Llewellyn, G.; Kariuki, M. Mental health and disability. In International Encyclopedia of Rehabilitation; Stone, J., Blouin, M., Eds.; CIRRIE: Buffalo, New York, NY, USA, 2013. [Google Scholar]
  4. Kohn, R.; Saxena, S.; Levav, I.; Saraceno, B. The treatment gap in mental health care. Bull. WHO 2004, 82, 858–866. [Google Scholar] [PubMed]
  5. Kessler, R.C.; Chiu, W.T.; Demler, O.; Walters, E.E. Prevalence, severity, and comorbidity of twelve-month dsm-iv disorders in the National Comorbidity Survey Replication (NCS-R). Arch. Gen. Psychiat. 2005, 62, 617–627. [Google Scholar] [CrossRef] [PubMed]
  6. Sunderland, A.; Findlay, L.C. Perceived need for mental health care in Canada: Results from the 2012 Canadian community health survey—Mental health. Health Rep. 2013, 24, 3–9. [Google Scholar] [PubMed]
  7. Lesage, A.; Émond, V. Surveillance of Mental Disorders in Québec: Prevalence, Mortality and Service Utilization Profile; Intitut National de Santé Publique: Quebec, Canada, 2013; Volume 6, p. 16. [Google Scholar]
  8. Thornicroft, G. Premature death among people with mental illness. BMJ 2013, 346. [Google Scholar] [CrossRef] [PubMed]
  9. Wilson, I.B.; Cleary, P.D. Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. JAMA 1995, 273, 59–65. [Google Scholar] [CrossRef] [PubMed]
  10. Myers, A.; Rosen, J.C. Obesity stigmatization and coping: Relation to mental health symptoms, body image, and self-esteem. Int. J. Obes. Relat. Metab. Disord. 1999, 23, 221–230. [Google Scholar] [CrossRef] [PubMed]
  11. Morgan, C.; Burns, T.; Fitzpatrick, R.; Pinfold, V.; Priebe, S. Social exclusion and mental health: Conceptual and methodological review. Brit. J. Psychiat. 2007, 191, 477–483. [Google Scholar] [CrossRef] [PubMed]
  12. Scott, K.M.; von Korff, M.; Angermeyer, M.C.; Benjet, C.; Bruffaerts, R.; de Girolamo, G.; Haro, J.M.; Lepine, J.P.; Ormel, J.; Posada-Villa, J.; et al. Association of childhood adversities and early-onset mental disorders with adult-onset chronic physical conditions. Arch. Gen. Psychiat. 2011, 68, 838–844. [Google Scholar] [CrossRef]
  13. Schmitz, N.; Wang, J.; Malla, A.; Lesage, A. Joint effect of depression and chronic conditions on disability: Results from a population-based study. Psychosom. Med. 2007, 69, 332–338. [Google Scholar] [CrossRef] [PubMed]
  14. Wonca: Integrating Mental Health into Primary Care; World Health Organization and World Organization of Family Doctors (Wonca): Singapore, Singapore, 2008.
  15. Fleury, M.J.; Grenier, G.; Bamvita, J.M.; Caron, J. Mental health service utilization among patients with severe mental disorders. Community Ment. Health J. 2011, 47, 365–377. [Google Scholar] [CrossRef] [PubMed]
  16. Fleury, M.J.; Grenier, G.; Bamvita, J.M.; Perreault, M.; Caron, J. Determinants of the utilization of diversified types of professionals for mental health reasons in a Montreal (Canadian) catchment area. Glob. J. Health Sci. 2012, 4, 13–29. [Google Scholar] [PubMed]
  17. Fleury, M.J.; Grenier, G.; Bamvita, J.M.; Perreault, M.; Caron, J. Determinants associated with the utilization of primary and specialized mental health services. Psychiat. Quart. 2012, 83, 41–51. [Google Scholar] [CrossRef] [PubMed]
  18. Ngamini Ngui, A.; Perreault, M.; Fleury, M.J.; Caron, J. A multi-level study of the determinants of mental health service utilization. Rev. Epidemiol. Sante Publ. 2012, 60, 85–93. [Google Scholar]
  19. Bonin, J.P.; Fournier, L.; Blais, R. Predictors of mental health service utilization by people using resources for homeless people in Canada. Psychiatr. Services 2007, 58, 936–941. [Google Scholar] [CrossRef]
  20. Vasiliadis, H.M.; Lesage, A.; Adair, C.; Boyer, R. Service use for mental health reasons: Cross-provincial differences in rates, determinants, and equity of access. Can. J. Psychiatry 2005, 50, 614–619. [Google Scholar] [PubMed]
  21. Vasiliadis, H.M.; Lesage, A.; Adair, C.; Wang, P.S.; Kessler, R.C. Do Canada and the United States differ in prevalence of depression and utilization of services? Psychiatr. Serv. 2007, 58, 63–71. [Google Scholar]
  22. Crabb, R.; Hunsley, J. Utilization of mental health care services among older adults with depression. J. Clin. Psychol. 2006, 62, 299–312. [Google Scholar] [CrossRef] [PubMed]
  23. Faith, J.; Thorburn, S.; Tippens, K.M. Examining CAM use disclosure using the behavioral model of health services use. Complement. Ther. Med. 2013, 21, 501–508. [Google Scholar] [CrossRef] [PubMed]
  24. Grant, J.; Westhues, A. Mental health respite services: A grounded service delivery model. Can. J. Commun. Ment. Health 2005, 24, 63–78. [Google Scholar] [PubMed]
  25. Grigoletti, L.; Amaddeo, F.; Grassi, A.; Boldrini, M.; Chiappelli, M.; Percudani, M.; Catapano, F.; Fiorillo, A.; Perris, F.; Bacigalupi, M.; et al. A predictive model to allocate frequent service users of community-based mental health services to different packages of care. Epidemiol. Psichiatr. Soc. 2010, 19, 168–177. [Google Scholar]
  26. Najimi, A.; Golshiri, P. Knowledge, beliefs and preventive behaviors regarding influenza A in students: A test of the health belief model. J. Educ. Health Promot. 2013, 2. [Google Scholar] [CrossRef]
  27. Montanaro, E.A.; Bryan, A.D. Comparing theory-based condom interventions: Health belief model vs. theory of planned behavior. Health Psychol. 2013, 33, 1251–1260. [Google Scholar] [CrossRef] [PubMed]
  28. Wong, R.K.; Wong, M.L.; Chan, Y.H.; Feng, Z.; Wai, C.T.; Yeoh, K.G. Gender differences in predictors of colorectal cancer screening uptake: A national cross sectional study based on the health belief model. BMC Public Health 2013, 13. [Google Scholar] [CrossRef] [PubMed]
  29. Baghianimoghadam, M.H.; Shogafard, G.; Sanati, H.R.; Baghianimoghadam, B.; Mazloomy, S.S.; Askarshahi, M. Application of the health belief model in promotion of self-care in heart failure patients. Acta Med. Iran. 2013, 51, 52–58. [Google Scholar] [PubMed]
  30. Ngamini Ngui, A.; Fleury, M.J.; Perreault, M.; Caron, J. Mental health services utilization in an inner-city of Montreal: A causal model approach. CJRS 2011, 33, 35–48. [Google Scholar]
  31. Andersen, R.; Harada, N.; Chiu, V.; Makinodan, T. Application of the behavioral model to health studies of Asian and Pacific Islander Americans. Asian Am. Pac. Isl. J. Health 1995, 3, 128–141. [Google Scholar] [PubMed]
  32. Andersen, R.M. Revisiting the behavioral model and access to medical care: Does it matter? J. Health Soc. Behav. 1995, 36, 1–10. [Google Scholar] [CrossRef]
  33. Corso, K.A.; Bryan, C.J.; Corso, M.L.; Kanzler, K.E.; Houghton, D.C.; Ray-Sannerud, B.; Morrow, C.E. Therapeutic alliance and treatment outcome in the primary care behavioral health model. Fam. Syst. Health 2012, 30, 87–100. [Google Scholar] [CrossRef] [PubMed]
  34. Smith, L.R.; Fisher, J.D.; Cunningham, C.O.; Amico, K.R. Understanding the behavioral determinants of retention in HIV care: A qualitative evaluation of a situated information, motivation, behavioral skills model of care initiation and maintenance. AIDS Patient Care STDS 2012, 26, 344–355. [Google Scholar] [CrossRef] [PubMed]
  35. Eisenberg, D.; Golberstein, E.; Gollust, S.E. Help-seeking and access to mental health care in a university student population. Med. Care 2007, 45, 594–601. [Google Scholar] [CrossRef] [PubMed]
  36. Hines-Martin, V.; Brown-Piper, A.; Kim, S.; Malone, M. Enabling factors of mental health service use among African Americans. Arch. Psychiatr. Nurs. 2003, 27, 197–294. [Google Scholar] [CrossRef]
  37. Fox, J.W. Sex, marital status, and age as social selection factors in recent psychiatric treatment. J. Health Soc. Behav. 1984, 25, 394–405. [Google Scholar] [CrossRef] [PubMed]
  38. Kirmayer, L.J.; Weinfeld, M.; Burgos, G.; Du Fort, G.G.; Lasry, J.C.; Young, A. Use of health care services for psychological distress by immigrants in an urban multicultural milieu. Can. J. Psychiatry 2007, 2, 295–304. [Google Scholar]
  39. Dutton, D. Explaining the low use of health services by poor: Costs, attitudes or delivery systems? Amer. Sociol. Rev. 1978, 43, 348–368. [Google Scholar] [CrossRef]
  40. Flett, G.L.; Blankstein, K.R.; Hicken, J.D.; Watson, M.S. Social support and help-seeking in daily hassles vs. major life events stress. J. Appl. Soc. Psychol. 1995, 25, 49–58. [Google Scholar] [CrossRef]
  41. Cannuscio, C.C.; Colditz, G.A.; Rimm, E.B.; Berkman, L.F.; Jones, C.P.; Kawachi, I. Employment status, social ties, and caregivers’ mental health. Soc. Sci. Med. 2004, 58, 1247–1256. [Google Scholar] [CrossRef] [PubMed]
  42. Kawachi, I.; Berkman, L.F. Social ties and mental health. J. Urban Health 2001, 78, 458–467. [Google Scholar] [CrossRef] [PubMed]
  43. Wray, T.B.; Dvorak, R.D.; Martin, S.L. Demographic and economic predictors of mental health problems and contact with treatment resources among adults in a low-income primary care setting. Psychol. Health Med. 2013, 18, 213–222. [Google Scholar] [CrossRef] [PubMed]
  44. Prince, M.; Livingston, G.; Katona, C. Mental health care for the elderly in low-income countries: A health systems approach. World Psychiat. 2007, 6, 5–13. [Google Scholar]
  45. Kuno, E.; Rothbard, A.B. The effect of income and race on quality of psychiatric care in community mental health centers. Community Ment. Health J. 2005, 41, 613–622. [Google Scholar] [CrossRef] [PubMed]
  46. Dhingra, S.S.; Zack, M.M.; Strine, T.W.; Druss, B.G.; Berry, J.T.; Balluz, L.S. Psychological distress severity of adults reporting receipt of treatment for mental health problems in the BRFSS. Psychiatr. Services 2011, 62, 396–403. [Google Scholar] [CrossRef]
  47. McAlpine, D.D.; Mechanic, D. Utilization of specialty mental health care among persons with severe mental illness: The roles of demographics, need, insurance, and risk. Health Serv. Res. 2000, 35, 277–292. [Google Scholar] [PubMed]
  48. Lefebvre, J.; Lesage, A.; Cyr, M.; Toupin, J.; Fournier, L. Factors related to utilization of services for mental health reasons in Montreal, Canada. Soc. Psychiat. Psychiat. Epidem. 1998, 33, 291–298. [Google Scholar]
  49. Kessler, R.C.; Zhao, S.; Katz, S.J.; Kouzis, A.C.; Frank, R.G.; Edlund, M.; Leaf, P. Past-year use of outpatient services for psychiatric problems in the National Comorbidity Survey. Amer. J. Psychiat. 1999, 156, 115–123. [Google Scholar] [PubMed]
  50. Urbanoski, K.; Rush, B.R.; Wild, T.C.; Bassani, D.G.; Castel, S. Use of mental health care services by Canadians with co-occurring substance dependence and mental disorders. Psychiatr. Serv. 2007, 58, 962–969. [Google Scholar] [CrossRef] [PubMed]
  51. Freeborn, D.K.; Pope, C.R.; Mullooly, J.P.; McFarland, B.H. Consistently high users of medical care among the elderly. Med. Care 1990, 28, 527–540. [Google Scholar] [CrossRef] [PubMed]
  52. Fasoli, D.R.; Glickman, M.E.; Eisen, S.V. Predisposing characteristics, enabling resources and need as predictors of utilization and clinical outcomes for veterans receiving mental health services. Med. Care 2010, 48, 288–295. [Google Scholar] [CrossRef] [PubMed]
  53. Handy, J.R., Jr.; Child, A.I.; Grunkemeier, G.L.; Fowler, P.; Asaph, J.W.; Douville, E.C.; Tsen, A.C.; Ott, G.Y. Hospital readmission after pulmonary resection: Prevalence, patterns, and predisposing characteristics. Ann. Thorac. Surg. 2001, 72, 1855–1859. [Google Scholar] [CrossRef] [PubMed]
  54. Atchison, K.A.; Davidson, P.L.; Nakazono, T.T. Predisposing, enabling, and need for dental treatment characteristics of ICS-II USA ethnically diverse groups. Adv. Dent. Res. 1997, 11, 223–234. [Google Scholar] [CrossRef] [PubMed]
  55. Sachdev, P.; Kruk, J. Clinical characteristics and predisposing factors in acute drug-induced akathisia. Arch. Gen. Psychiat. 1994, 51, 963–974. [Google Scholar] [PubMed]
  56. Regier, D.A.; Goldberg, I.D.; Taube, C.A. The de facto U.S. mental health services system: A public health perspective. Arch. Gen. Psychiat. 1978, 35, 685–693. [Google Scholar] [PubMed]
  57. Smith, S.L.; Melton, J.; Olyneolo, M.; Buchwald, K. Introducing trampoline use within an acute mental health care hospital setting. J. Psychiatr. Intensive Care 2009, 5, 113–121. [Google Scholar] [CrossRef]
  58. Caron, J.; Fleury, M.J.; Perreault, M.; Crocker, A.; Tremblay, J.; Tousignant, M.; Kestens, Y.; Cargo, M.; Daniel, M. Prevalence of psychological distress and mental disorders, and use of mental health services in the epidemiological catchment area of Montreal south-west. BMC Psychiat. 2012, 12. [Google Scholar] [CrossRef]
  59. Brugha, T.S.; Bebbington, P.E.; Singleton, N.; Melzer, D.; Jenkins, R.; Lewis, G.; Farrell, M.; Bhugra, D.; Lee, A.; Meltzer, H. Trends in service use and treatment for mental disorders in adults throughout Great Britain. Brit. J. Psychiat. 2004, 185, 378–384. [Google Scholar] [PubMed]
  60. Druss, B.G.; Wang, P.S.; Sampson, N.A.; Olfson, M.; Pincus, H.A.; Wells, K.B.; Kessler, R.C. Understanding mental health treatment in persons without mental diagnoses: Results from the national comorbidity survey replication. Arch. Gen. Psychiat. 2007, 64, 1196–1203. [Google Scholar] [CrossRef]
  61. Wang, P.S.; Lane, M.; Olfson, M.; Pincus, H.A.; Wells, K.B.; Kessler, R.C. Twelve-month use of mental health services in the United States: Results from the national comorbidity survey replication. Arch. Gen. Psychiat. 2005, 62, 629–640. [Google Scholar] [CrossRef] [PubMed]
  62. Kessler, R.C.; Frank, R.G.; Edlund, M.; Katz, S.J.; Lin, E.; Leaf, P. Differences in the use of psychiatric outpatient services between the United States and Ontario. N. Engl. J. Med. 1997, 336, 551–557. [Google Scholar] [CrossRef] [PubMed]
  63. Bijl, R.V.; de Graaf, R.; Hiripi, E.; Kessler, R.C.; Kohn, R.; Offord, D.R.; Ustun, T.B.; Vicente, B.; Vollebergh, W.A.; Walters, E.E.; et al. The prevalence of treated and untreated mental disorders in five countries. Health Affair 2003, 22, 122–133. [Google Scholar] [CrossRef]
  64. Demyttenaere, K.; Bruffaerts, J.; Posalada-Villa, J.; Gasquet, I.; Kovess, V.; Lepine, J.P.; Angermeyer, M.C.; Bernet, S.; de Girolamo, G.; Moriosini, P.; et al. Prevalence, severity and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. JAMA 2004, 291, 2581–2590. [Google Scholar] [CrossRef] [PubMed]
  65. Fleury, M.J.; Grenier, G.; Bamvita, J.M.; Perreault, M.; Kestens, Y.; Caron, J. Comprehensive determinants of health service utilisation for mental health reasons in a Canadian catchment area. Int. J. Equity Health 2012, 11. [Google Scholar] [CrossRef]
  66. Eaton, W.W.; Anthony, J.C.; Tepper, S.; Dryman, A. Psychopathology and attrition in the epidemiologic catchment area surveys. Amer. J. Epidemiol. 1992, 135, 1051–1059. [Google Scholar]
  67. Park, S.; Sohn, J.H.; Hong, J.P.; Chang, S.M.; Lee, Y.M.; Jeon, H.J.; Cho, S.J.; Bae, J.N.; Lee, J.Y.; Son, J.W.; et al. Prevalence, correlates, and comorbidities of four DSM-IV specific phobia subtypes: Results from the Korean epidemiological catchment area study. Psychiat. Res. 2013. [Google Scholar] [CrossRef]
  68. Larson, S.L.; Clark, M.R.; Eaton, W.W. Depressive disorder as a long-term antecedent risk factor for incident back pain: A 13-year follow-up study from the Baltimore epidemiological catchment area sample. Psychol. Med. 2004, 34, 211–219. [Google Scholar] [CrossRef] [PubMed]
  69. Pawlowski, T.; Kiejna, A. Pathways to psychiatric care. II. “Psie Pole” epidemiological catchment area. Psychiatr Pol. 2003, 37, 1063–1071. [Google Scholar] [PubMed]
  70. Messias, E.; Kirkpatrick, B. Summer birth and deficit schizophrenia in the epidemiological catchment area study. J. Nerv. Ment. Dis. 2001, 189, 608–612. [Google Scholar] [CrossRef] [PubMed]
  71. Statistiques Canada. Enquête sur la Santé Dans les Collectivités Canadiennes (ESCC-2002)—Santé Mentale et Bien-Être (Cycle 1.2); Santé Canada: Ottawa, Canada, 2003. (In French) [Google Scholar]
  72. Baker, F.; Intaglia, J. Quality of life in the evaluation of Community support systems. Eval. Program Plan. 1982, 5, 69–79. [Google Scholar] [CrossRef]
  73. Perkins, D.D.; Long, D.A. Neighborhoud sense of community and social capital: A multi-level analysis. In Psychological Sense of Community: Research, Applications and Implications; Fisher, A.T., Sonn, C.C., Bishop, B.J., Eds.; Springer: Berlin, Germany, 2002; pp. 291–318. [Google Scholar]
  74. Saegert, S.; Winke, G. Crime, social capital, and community participation. Amer. J. Community Psychol. 2004, 34, 219–233. [Google Scholar] [CrossRef]
  75. Nario-Redmond, M.; Coulton, C. Measuring resident perceptions of neighborhood conditions: Survey methodology. In Center of Urban Poverty and Social Change; Case Western Reserve University: Cleveland, OH, USA, 2000. [Google Scholar]
  76. Sampson, R.J.; Morenoff, J.D.; Gannon-Rowley, T. Assessing neighborhood effects: Social processes and new directions in research. Annu. Rev. Sociol. 2002, 28, 443–478. [Google Scholar] [CrossRef]
  77. Coulton, C.J.; Korbin, J.E.; Su, M. Measuring neighborhood context for young children in an urban area. Amer. J. Community Psychol. 1996, 24, 5–32. [Google Scholar] [CrossRef]
  78. Cutrona, C.E. Behavioral manifestation of social support: A micro-analytic study. J. Pers. Soc. Psychol. 1989, 51, 201–208. [Google Scholar]
  79. Kessler, R.C.; Andrews, G.; Mroczek, D.; Ustun, B.; Wittchen, H.U. The World Health Organization Composite International Diagnostic Interview Short Form (CIDI-SF). Int. J. Meth. Psychiatr. Res. 1998, 7, 171–185. [Google Scholar] [CrossRef]
  80. Kay, S.R.; Wolkenfied, F.; Murrill, L.M. Profiles of aggression among psychiatrist patients. I. Nature and prevalence. J. Nerv. Ment. Dis. 1988, 176, 539–546. [Google Scholar] [CrossRef] [PubMed]
  81. Kessler, R.C.; Barker, P.R.; Colpe, L.J.; Epstein, J.F.; Gfroerer, J.C.; Hiripi, E.; Howes, M.J.; Normand, S.L.; Mandercheid, R.W.; Walters, E.E.; et al. Screening for serious mental illness in the general population. Arch. Gen. Psychiat. 2003, 60, 184–189. [Google Scholar] [CrossRef] [PubMed]
  82. Barratt, E.S. Impulsiveness subtraits: Arousal and information processing. In Motivation, Emotion and Personality; Spence, J.T., Itard, C.E., Eds.; Elsevier: Amsterdam, The Netherlands, 1985; pp. 137–146. [Google Scholar]
  83. Dunlop, S.; Coyte, P.C.; McIsaac, W. Socio-economic status and the utilisation of physicians’ services: Results from the Canadian national population health survey. Soc. Sci. Med. 2000, 51, 123–133. [Google Scholar] [CrossRef] [PubMed]
  84. Walters, V.; McDonough, P.; Strohschein, L. The influence of work, household structure, and social, personal and material resources on gender differences in health: An analysis of the 1994 Canadian national population health survey. Soc. Sci. Med. 2002, 54, 677–692. [Google Scholar] [CrossRef] [PubMed]
  85. Vasiliadis, H.M.; Gagne, S.; Jozwiak, N.; Preville, M. Gender differences in health service use for mental health reasons in community dwelling older adults with suicidal ideation. Int. Psychogeriatr. 2013, 25, 374–381. [Google Scholar] [CrossRef] [PubMed]
  86. Drapeau, A.; Boyer, R.; Lesage, A. The influence of social anchorage on the gender difference in the use of mental health services. J. Behav. Health Serv. Res. 2009, 36, 372–384. [Google Scholar] [CrossRef] [PubMed]
  87. Drapeau, A.; Lesage, A.; Boyer, R. Is the statistical association between sex and the use of services for mental health reasons confounded or modified by social anchorage? Can. J. Psychiatry 2005, 50, 599–604. [Google Scholar]
  88. Bertakis, K.D.; Azari, R.; Helms, L.J.; Callahan, E.J.; Robbins, J.A. Gender differences in the utilization of health care services. J. Fam. Pract. 2000, 49, 147–152. [Google Scholar] [PubMed]
  89. Alexandrino-Silva, C.; Wang, Y.P.; Carmen Viana, M.; Bulhoes, R.S.; Martins, S.S.; Andrade, L.H. Gender differences in symptomatic profiles of depression: Results from the Sao Paulo megacity mental health survey. J. Affect. Disord. 2013, 147, 355–364. [Google Scholar] [CrossRef] [PubMed]
  90. Swami, V. Mental health literacy of depression: Gender differences and attitudinal antecedents in a representative British sample. PLoS One 2012, 7. [Google Scholar] [CrossRef] [PubMed]
  91. Maguen, S.; Luxton, D.D.; Skopp, N.A.; Madden, E. Gender differences in traumatic experiences and mental health in active duty soldiers redeployed from Iraq and Afghanistan. J. Psychiatr. Res. 2012, 46, 311–316. [Google Scholar] [CrossRef] [PubMed]
  92. Grella, C.E.; Lovinger, K. Gender differences in physical and mental health outcomes among an aging cohort of individuals with a history of heroin dependence. Addict. Behav. 2012, 37, 306–312. [Google Scholar] [CrossRef] [PubMed]
  93. Tamers, S.L.; Beresford, S.A.; Thompson, B.; Zheng, Y.; Cheadle, A.D. Exploring the role of co-worker social support on health care utilization and sickness absence. J. Occup. Environ. Med. 2011, 53, 751–757. [Google Scholar] [CrossRef] [PubMed]
  94. Smith, P.M.; Glazier, R.H.; Sibley, L.M. The predictors of self-rated health and the relationship between self-rated health and health service needs are similar across socioeconomic groups in Canada. J. Clin. Epidemiol. 2010, 63, 412–421. [Google Scholar] [CrossRef] [PubMed]
  95. Trump, D. Self-rated health and health care utilization after military deployments. Mil. Med. 2006, 171, 662–668. [Google Scholar]
  96. Kim, C.; Vahratian, A. Self-rated health and health care use among women with histories of gestational diabetes mellitus. Diabetes Care 2010, 33, 41–42. [Google Scholar] [CrossRef] [PubMed]
  97. Pattussi, M.P.; Peres, K.G.; Boing, A.F.; Peres, M.A.; da Costa, J.S. Self-rated oral health and associated factors in Brazilian elders. Community Dent. Oral Epidemiol. 2010, 38, 348–359. [Google Scholar] [CrossRef] [PubMed]
  98. Ostile, I.L.; Johansson, I.; Aasland, A.; Flato, B.; Moller, A. Self-rated physical and psychosocial health in a cohort of young adults with juvenile idiopathic arthritis. Scand. J. Rheumatol. 2010, 39, 318–325. [Google Scholar] [CrossRef] [PubMed]
  99. Giatti, L.; Barreto, S.M.; Cesar, C.C. Unemployment and self-rated health: Neighborhood influence. Soc. Sci. Med. 2010, 71, 815–823. [Google Scholar] [CrossRef] [PubMed]
  100. Toft, A.M.; Moller, H.; Laursen, B. The years after an injury: Long-term consequences of injury on self-rated health. J. Trauma 2010, 69, 26–30. [Google Scholar] [CrossRef] [PubMed]
  101. Sondergaard, H.; Juul, S. Self-rated health and functioning in patients with chronic renal disease. Dan. Med. Bull. 2010, 57, A4220. [Google Scholar] [PubMed]
  102. Farkas, J.; Kosnik, M.; Flezar, M.; Suskovic, S.; Lainscak, M. Self-rated health predicts acute exacerbations and hospitalizations in patients with COPD. Chest 2010, 138, 323–330. [Google Scholar] [CrossRef] [PubMed]
  103. Galenkamp, H.; Deeg, D.J.; Braam, A.W.; Huisman, M. “How was your health 3 years ago”? Predicting mortality in older adults using a retrospective change measure of self-rated health. Geriatr. Gerontol. Int. 2013, 13, 678–686. [Google Scholar] [CrossRef] [PubMed]
  104. Ng, N.; Hakimi, M.; Santosa, A.; Byass, P.; Wilopo, S.A.; Wall, S. Is self-rated health an independent index for mortality among older people in Indonesia? PLoS One 2012, 7. [Google Scholar] [CrossRef]
  105. Wennberg, P.; Rolandsson, O.; Jerden, L.; Boeing, H.; Sluik, D.; Kaaks, R.; Teucher, B.; Spijkerman, A.; Bueno de Mesquita, B.; Dethlefsen, C.; et al. Self-rated health and mortality in individuals with diabetes mellitus: Prospective cohort study. BMJ Open 2012, 2. [Google Scholar] [CrossRef] [PubMed]
  106. Giltay, E.J.; Vollaard, A.M.; Kromhout, D. Self-rated health and physician-rated health as independent predictors of mortality in elderly men. Age Ageing 2012, 41, 165–171. [Google Scholar] [CrossRef] [PubMed]
  107. Hansen, M.S.; Fink, P.; Frydenberg, M.; Oxhøj, M.L. Use of health services, mental illness, and self-rated disability and health in medical inpatients. Psychosom. Med. 2002, 64, 668–675. [Google Scholar] [PubMed]
  108. Valderas, J.M.; Starfield, B.; Sibbald, B.; Salisbury, C.; Roland, M. Defining comorbidity: Implications for understanding health and health services. Ann. Fam. Med. 2009, 7, 357–363. [Google Scholar] [CrossRef] [PubMed]
  109. Bijl, R.V.; Ravelli, A. Psychiatric morbidity, service use, and need for care in the general population: Results of the Netherlands mental health survey and incidence study. Amer. J. Public Health 2000, 90, 602–607. [Google Scholar] [CrossRef]
  110. Jorm, A.F.; Christensen, H.; Griffiths, K.M.; Rodgers, B. Effectiveness of complementary and self-help treatments for depression. Med. J. Aust. 2002, 176, S84–S96. [Google Scholar] [PubMed]
  111. Ten Have, M.; de Graaf, R.; Vollebergh, W.; Beekman, A. What depressive symptoms are associated with the use of care services? Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). J. Affect. Disord. 2004, 80, 239–248. [Google Scholar] [PubMed]
  112. Koenig, H.G.; Kuchibhatla, M. Use of health services by hospitalized medically ill depressed elderly patients. Amer. J. Psychiat. 1998, 155, 871–877. [Google Scholar] [PubMed]
  113. Deacon, B.; Lickel, J.; Abramowitz, J.S. Medical utilization across the anxiety disorders. J. Anxiety Disord. 2008, 22, 344–350. [Google Scholar] [CrossRef] [PubMed]
  114. Stein, M.B.; McQuaid, J.R.; Laffaye, C.; McCahill, M.E. Social phobia in the primary care medical setting. J. Fam. Pract. 1999, 48, 514–519. [Google Scholar] [PubMed]
  115. Bystritsky, A.; Kerwin, L.; Niv, N.; Natoli, J.L.; Abrahami, N.; Klap, R.; Wells, K.; Young, A.S. Clinical and subthreshold panic disorder. Depress. Anxiety 2010, 27, 381–389. [Google Scholar] [CrossRef] [PubMed]
  116. Dhingra, S.S.; Zack, M.; Strine, T.; Pearson, W.S.; Balluz, L. Determining prevalence and correlates of psychiatric treatment with Andersen’s behavorial model of health services use. Psychiatr. Serv. 2010, 61, 514–528. [Google Scholar] [CrossRef]
  117. Parslow, R.A.; Jorm, A.F. Who uses mental health services in Australia? An analysis of data from the national survey of mental health and wellbeing. Aust. N. Z. J. Psychiat. 2000, 34, 997–1008. [Google Scholar] [CrossRef]
  118. Mendenhall, A.N.; Demeter, C.; Findling, R.L.; Frazier, T.W.; Fristad, M.A.; Youngstrom, E.A.; Arnold, L.E.; Birmaher, B.; Gill, M.K.; Axelson, D.; et al. Factors influencing mental health service utilization by children with serious emotional and behavioral disturbance: Results from the LAMS study. Psychiatr. Serv. 2011, 62, 650–658. [Google Scholar] [CrossRef] [PubMed]
  119. Teich, J.L.; Buck, J.A.; Graver, L.; Schroeder, D.; Zheng, D. Utilization of public mental health services by children with serious emotional disturbances. Adm. Policy Ment. Health 2003, 30, 523–534. [Google Scholar] [CrossRef] [PubMed]
  120. Kehusmaa, S.; Autti-Ramo, I.; Helenius, H.; Hinkka, K.; Valaste, M.; Rissanen, P. Factors associated with the utilization and costs of health and social services in frail elderly patients. BMC Health Serv. Res. 2012, 12. [Google Scholar] [CrossRef]
  121. Saarela, T.M.; Finne-Soveri, H.; Liedenpohja, A.M.; Noro, A. Comparing psychogeriatric units to ordinary long-term care units—Are there differences in case-mix or clinical symptoms? Nord. J. Psychiatr. 2008, 62, 32–38. [Google Scholar]
  122. Chen, T.; Li, L. Influence of health-related quality of life on health service utilization in addition to socio-demographic and morbidity variables among primary care patients in China. Int. J. Public Health 2009, 54, 325–332. [Google Scholar] [CrossRef] [PubMed]
  123. Dominick, K.L.; Ahern, F.M.; Gold, C.H.; Heller, D.A. Relationship of health-related quality of life to health care utilization and mortality among older adults. Aging Clin. Exp. Res. 2002, 14, 499–508. [Google Scholar] [CrossRef] [PubMed]
  124. Albert, M.; Becker, T.; McCrone, P.; Thornicroft, G. Social networks and mental health service utilisation. A literature review. Int. J. Soc. Psychiatr. 1998, 44, 248–266. [Google Scholar] [CrossRef]
  125. Pescosolido, B.A.; Gardner, C.B.; Lubell, K.M. How people get into mental health services: Stories of choice, coercion and “muddling through” from “first-timers”. Soc. Sci. Med. 1998, 46, 275–286. [Google Scholar] [CrossRef] [PubMed]
  126. Pescosolido, B.A.; Wright, E.R.; Alegria, M.; Vera, M. Social networks and patterns of use among the poor with mental health problems in Puerto Rico. Med. Care 1998, 36, 1057–1072. [Google Scholar] [CrossRef] [PubMed]
  127. Howard, K.I.; Cornille, T.A.; Lyons, J.S.; Vessey, J.T.; Lueger, R.J.; Saunders, S.M. Patterns of mental health service utilization. Arch. Gen. Psychiat. 1996, 53, 696–703. [Google Scholar] [CrossRef] [PubMed]
  128. Chartier-Otis, M.; Perreault, M.; Bélanger, C. Determinants of barriers to treatment for anxiety disorders. Psychiat. Quart. 2010, 81, 127–138. [Google Scholar] [CrossRef] [PubMed]
  129. Whitley, R.; McKenzie, K. Social capital and psychiatry: Review of the literature. Harv. Rev. Psychiat. 2005, 13, 71–84. [Google Scholar] [CrossRef]
  130. Browning, C.R.; Cagney, K.A. Neighborhood structural disavantage, collective efficacy, and self-rated health in an urban setting. J. Health Soc. Behav. 2002, 43, 383–399. [Google Scholar] [CrossRef] [PubMed]
  131. Putnam, R.D. E Pluribus Unum: Diversity and community in the twenty-first century. The 2006 Johan Skytte Prize lecture. Scand. Polit. Stud. 2007, 30, 137–174. [Google Scholar] [CrossRef]
  132. Rios, R.; Aiken, L.S.; Zautra, A.J. Neighborhood contexts and the mediating role of neighborhood social cohesion on health and psychological distress among Hispanic and non-Hispanic residents. Ann. Behav. Med. 2012, 43, 50–61. [Google Scholar] [CrossRef] [PubMed]
  133. Caron, J.; Liu, A. Factors associated with psychological distress in the Canadian population: A comparison of low-income and non low-income sub-groups. Community Ment. Health J. 2011, 47, 318–330. [Google Scholar] [CrossRef] [PubMed]
  134. Harris, K.; Edlund, M. Use of mental health care and substance abuse treatment among adults with co-occurring disorders. Psychiatr. Services 2005, 56, 954–959. [Google Scholar] [CrossRef]
  135. Tempier, R.; Meadows, G.N.; Vasiliadis, H.M.; Mosier, K.E.; Lesage, A.; Stiller, A.; Graham, A.; Lepnurm, M. Mental disorders and mental health care in Canada and Australia: Comparative epidemiological findings. Soc. Psychiat. Psychiat. Epidem. 2009, 44, 63–72. [Google Scholar] [CrossRef]
  136. Keene, J.; Li, X. Age and gender differences in health service utilization. J. Public Health 2005, 27, 74–79. [Google Scholar] [CrossRef]
  137. Elnitsky, C.; Chapman, P.; Thurman, R.; Pitts, B.; Figley, C.; Unwin, B. Gender differences in combat medic mental health services utilization, barriers, and stigma. Mil. Med. 2013, 178, 775–784. [Google Scholar] [CrossRef] [PubMed]
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