Factors associated with the use of specialized mental health services in urban Latin American young people with symptoms of anxiety and depression

doi:10.21203/rs.3.rs-4930992/v1

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Research Article

Factors associated with the use of specialized mental health services in urban Latin American young people with symptoms of anxiety and depression

https://doi.org/10.21203/rs.3.rs-4930992/v1

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Background

Studies have shown that adolescents and young adults are less likely to seek mental health (MH) services. This research aims to identify the factors associated with using specialized MH services among young people in deprived urban Latin American areas.

Methods

The study included 1345 adolescents (15–16 years) and young adults (20–24 years) with symptoms of anxiety and/or depression. We collected data on predisposing, enabling, and need factors related to using specialized MH services. We compared the differences in service use using a chi-square test and determined the association using Poisson regression.

Results

18.9% of participants used specialized MH services in the previous three months. Youth whose parents received MH treatment had a 51% and 57% higher prevalence of using MH services, as those whose parents had education beyond secondary school were 64% more likely to use these services. Participants with severe levels of anxiety symptoms and self-recognition of experiencing anxiety symptoms had an 88% and 117% higher prevalence of using MH services; otherwise, severity levels of depression were not associated with the use of MH services. Specific symptoms of depression or anxiety reported on the PHQ-8 and GAD-7 scales were linked to the use of these services. Furthermore, experiencing three or more stressful life events and low and moderate levels of resilience were associated with a 48%, 74%, and 60% greater likelihood of using MH services.

Conclusion

Less than one in five participants accessed specialized MH services in the last three months. Predisposing, enabling, and need factors influencing service use include parental history of MH treatment, parents with education beyond secondary school, severe anxiety, self-recognition of ever experiencing anxiety, severe life events, and low resilience.

Mental health services

depression

anxiety

youth

young adults

adolescents

Mental health (MH) disorders are the leading cause of disease burden, with a 14% prevalence in people aged 10–24 years worldwide [1]. Adolescence and young adulthood are critical periods for the onset of MH disorders [2]. Almost half of the population with a MH disorder experienced onset before the age of 18, increasing to 65% before the age of 25 [2]. Latin America and the Caribbean (LAC) is one of the regions with the highest prevalence of mental disorders among children and adolescents, with 14% in the case of females and 15% in males [3]. Anxiety and depression are the most common disorders, accounting for 42% of cases of MH disorders among 10-19-year-olds worldwide, a proportion that rises to 47% in LAC [3]. The situation worsened with the arrival of the COVID-19 pandemic, when depression and anxiety disorders increased by 28% globally. In 2020, these disorders were more common in the 20–35 age group worldwide, with women experiencing almost twice as many new cases as men [4].

Despite the efforts to improve access to MH services, their demand still exceeds the supply [5], leading to a treatment gap of 79% for any MH disorder in the Americas [6]. In middle-income countries, approximately one-fifth of people with depression [7] and anxiety [8] receive treatment. In Latin America, most adults with MH conditions do not have access to treatment [9–13]. Structural barriers such as inequitable access due to financial difficulties [13, 14] and insufficient availability of services (i.e., not knowing where to go, not finding an appointment) represent significant obstacles to accessing MH care [15].

Young people, including adolescents and young adults, show less intention to seek MH services than older adults [16–18] despite the available evidence of effective treatments for this age group [19, 20]. Various barriers, such as a preference for solving problems on one's own, the perception that the problem is not severe enough to seek help, and the stigma associated with MH services usage, can impede young people’s access to such services [21]. Particularly, stigma could lead young individuals to prefer seeking informal sources of help, such as family, peers, or traditional healers [22, 23] and discourage MH service use [24, 25].

Many factors can influence the utilization of MH services among young people. Previous studies have examined the role of demographic characteristics [26–28] and stressful life experiences [29]. Also, social and psychological resources [30, 31], such as social support and resilience [32, 33], have been explored in the decision to seek MH services. However, most of the evidence on determinants of MH service use comes from high-income countries [28].

One framework for understanding the use of health services is Andersen's behavioral model [34], which has wide acceptance and use in MH services research studies [27, 35–38]. It is considered a practical framework because it takes into account both individual and contextual factors. According to this model, the use of health services derives from people’s predisposition to use them, factors that enable or hinder their use, and the need to receive care [34]. Predisposing factors are exogenous and difficult to change, such as demographic characteristics; enabling factors are necessary resources for the use of services, although insufficient, such as having the means and knowledge to access health services; and need factors refer to an individual's perceived need for healthcare services [34].

This study aims to explore the factors associated with the use of MH services in young people experiencing symptoms of anxiety and depression from Latin American deprived urban areas using Andersen’s behavioral model of health service use. Our research aims to provide insight into the factors influencing young people’s use of specialized MH services, which could help identify disparities and orient strategies to increase access among groups less likely to use these services.

Study design

This cross-sectional study uses data from a larger research program called “Building resilience and resources to reduce depression and anxiety in young people from urban neighborhoods in Latin America (OLA Program)” [39, 40]. The OLA Program follows a cohort of young people with symptoms of depression and anxiety from Bogotá (Colombia), Buenos Aires (Argentina), and Lima (Peru). This article uses data from the baseline measurement of the cohort collected from April 2021 to November 2022. The data collection coincides with a time of varying degrees of COVID-19 restrictions enforced by each country’s government.

Participants and sample

Participants were required to meet the following inclusion criteria: (1) to live in the city’s 50% most deprived areas (the process to define these areas is described in the OLA protocol paper [40]); (2) to be 15–16 (adolescents) or 20–24 (young adults) years old; and (3) to provide written informed consent or assent (adolescents), plus having the parent or legal tutor informed consent in the case of adolescents. Individuals were excluded if they reported (1) a diagnosis of a severe mental disorder (e.g., psychosis, bipolar disorder, schizophrenia), (2) a diagnosis of a learning disability, or (3) illiteracy.

The OLA Program sample included participants with and without symptoms of depression and/or anxiety, who were screened using the Patient Health Questionnaire-8 (PHQ-8) [41] and the General Anxiety Disorder-7 (GAD-7) [42]. Both scales have been validated and used in our study population [43, 44]. Our analysis included only the subgroup of participants who scored ten or higher in either of the two questionnaires, which indicates having clinically significant symptoms of depression for the PHQ-8 [41] and anxiety for the GAD-7 [42]. We chose this approach as we aim to focus on understanding the factors influencing MH service use among a population who may require MH treatment due to their level of mental distress.

The OLA Program aimed to recruit 340 participants with symptoms of depression and/or anxiety (i.e., a score of 10 or more in either of the screening scales) in each of the three cities, for a total of 1020 participants. The recruitment used convenience sampling, and recruitment strategies varied across the three cities, reflecting what was feasible and effective in each setting. The recruitment process is described elsewhere [39, 40].

Variables and instruments

Outcome variable

Use of specialized MH services: Specialized MH services were assessed using two questions from an adapted version of the Client Service Receipt Inventory (CSRI) [45]. The questions were: "Have you had any remote/virtual or in-person appointments with a psychiatrist in the last three months?" and "Have you had a remote/virtual or in-person appointment with a psychologist/psychotherapist in the last three months?" An appointment with a psychiatrist, psychologist, and psychotherapist was considered a specialized MH service; responses were grouped into yes and no.

Exposure variables

Our study defines the independent variables based on Andersen's behavioral model of health service use [34]. The variables considered for each determinant are presented in Fig. 1.

Predisposing factors

Sociodemographic factors

Gender (male, female), country (Argentina, Colombia, Peru), age group (adolescents 15–16 years old and young adults, 20–24 years old), living with both parents (yes, no), having a partner (yes, no), currently employed (yes, no).

Parental history of MH treatment

Based on the participants’ report on whether any of their parents ever received MH treatment, we grouped responses into yes, no, does not know.

Enabling factors

Socioeconomic status: We used three variables as a proxy: household overcrowding, health insurance ownership, and the parents' educational level. We defined household overcrowding as having a ratio of three or more people per room [46, 47] and categorized responses as yes and no. For health insurance responses, we used a dichotomic categorization (yes/no). As for parental educational level, we only considered the highest level attained by either parent, and responses were grouped as “education up to complete secondary school” and “education beyond secondary school.”

Perceived social support: We used the 12-item Multidimensional Scale of Perceived Social Support (MSPSS) [48] to measure perceived social support from three sources: family, friends, and significant others. Each subscale contains four items, and each item can be scored with a Likert scale of 7 points ranging from "strongly disagree" to "strongly agree." For the analysis, we used the total score, calculated by averaging the total sum of the scale. According to the scores, participants were classified into three groups: low (< 3), moderate (3–5), and high perceived (> 5) social support.

Need factors

Symptoms of depression: The Patient Health Questionnaire-8 (PHQ-8) [41] is an 8-item questionnaire that measures the severity of symptoms of depression in the last two weeks. Each item corresponds to a specific symptom of depression and is rated on a 4-point Likert scale: 0 (no days), 1 (1 to 6 days), 2 (7 to 11 days), and 3 (almost every day or 12 + days). The total score can range from 0 to 24. The symptom severity levels are categorized as non-significant (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), and severe (20 at 24) [41]. For the analysis, we used three categories: no symptoms or subthreshold (0–9), moderate depression (10–14), and moderately severe and severe depression (15–24). In addition, we analyzed the influence of each symptom, for which responses were grouped into less than 7 days and 7 or more days.

Symptoms of anxiety: General Anxiety Disorder-7 (GAD-7) [42] is a 7-item questionnaire that measures the severity of anxiety symptoms in the last two weeks. Each item corresponds to a specific symptom of anxiety and is rated on a 4-point Likert scale: 0 (no days), 1 (1 to 6 days), 2 (7 to 11 days), and 3 (almost every day or 12 + days); the total score ranges from 0 to 21. The symptom severity levels are categorized as minimal anxiety (0–4), mild anxiety (5–9), moderate anxiety (10–14), and severe symptoms (15–21) [42]. For the analysis, we used three categories: minimal and mild anxiety (0–9), moderate anxiety (10–14), and severe anxiety (15–21). In addition, we analyzed the influence of each symptom, for which responses were grouped into less than 7 days and 7 or more days.

Self-recognition of experiencing anxiety: Participants were asked whether they perceived they had ever experienced symptoms of anxiety. We grouped responses into two groups: never and at some point in life.

Self-recognition of experiencing depression: Participants were asked whether they perceived they had ever experienced symptoms of depression. We grouped responses into two groups: never and at some point in life.

Resilience: The Connor-Davidson Resilience Scale (CD-RISC-10) [49] is a 10-item questionnaire that measures one’s ability to cope with adversity. Each item is assessed using a 5-point Likert scale from never to almost always. The score is calculated by adding up all the responses obtained on each item, ranging from 0 to 40. We used tercile categorization to ensure a similar proportion for the three levels of resilience: low, moderate, and high.

Stressful life events (SLEs)

We adapted the Adolescent Appropriate Life Events Scale [50]. The scale asked whether participants experienced 30 negative SLEs in the last 12 months. We dichotomized the data using the median and grouped it into 0–2 SLEs and 3 or more SLEs in the previous 12 months.

Data analysis

We used the statistical program Stata version 18.0 [51] for data cleaning, recoding, and analysis. First, we excluded all people who had missing data, as well as those who did not have clinically significant symptoms of depression or anxiety. Second, due to their qualitative nature, we described each variable through absolute frequencies and proportions. This description was performed on the total sample through cross-tabulation with the outcome. Then, we presented the proportions of predisposing, enabling, and need factors according to the use of specialized MH services, and their differences were estimated using the chi-square test.

To determine the association between predisposing, enabling, and need factors and the use of specialized MH services, we estimated crude (PR) and adjusted (aPR) prevalence ratios and their 95% confidence intervals. These effect estimates were calculated using a Poisson regression with robust variance. In the adjusted model, the confounding variables we considered were gender, age group, highest educational level attained by parents, health insurance, and household overcrowding for each model. If the model included any of these confounding variables as independent variables, we adjusted the model for the remaining ones.

When selecting confounding variables, we explored earlier studies examining the relationship of interest. These include gender, age group [28], and socioeconomic status [52, 53]. Socioeconomic status encompasses the highest education level achieved by parents, health insurance, and household overcrowding, as these are closely linked to the use of MH services. Those variables were independently associated with the exposure and outcome and not on the causal pathway.

Finally, the interpretation of the p-values of all analyses was performed through cut-off points: <0.05 (moderate evidence), < 0.01 (strong evidence), and < 0.001 (very strong evidence) against the null hypothesis. The null hypothesis varied according to the type of analysis performed. The bivariate analysis was based on the lack of differences between proportions, whereas inferential analysis was based on the lack of association between the study variables and the outcome.

Ethical considerations

This study was approved by the Institutional Review Boards of Universidad de Buenos Aires on October 2nd, 2020, Pontificia Universidad Javeriana on November 20th, 2020 (FM-CIE-1138-20), and Universidad Peruana Cayetano Heredia on November 16th, 2020 (Constancia 581-33-20), as well as by the Research Ethics Committee of Queen Mary University of London, on November 16th, 2020 (QMERC2020/02). The informed consent process was carried out across all three countries. Adolescents were required to provide assent, and their legal guardian/parent was required to provide informed consent. Participants were compensated for their time and participation with vouchers or cash equivalent to $10 US dollars.

Participant characteristics

The study sample included 1345 participants with symptoms of depression and/or anxiety. Of those, 254 participants (18.9%) used specialized MH services in the previous three months. Participants were primarily women (72.3%), young adults (56.2%), had health insurance (85.4%) and were not employed (73.4%) (Table 1). Within our sample, 88.3% of participants reported having clinically significant symptoms of depression (Table 2). The symptoms more frequently reported (i.e., experienced 7 or more days in the last 14 days) were sleeping problems (67.8%), feeling tired or with low energy (65.2%), and having low or high appetite (63.9%). In addition, 68% of the sample reported clinically significant symptoms of anxiety. Worrying too much was the most frequently reported symptom (73%), followed by getting upset or irritable (60.7%) (Table 2).

Table 1

Participant characteristics according to the use of specialized MH services
Characteristics	n (%)	Use of specialized MH services		p-value*
		Yes	No
		n (%)	n (%)
Overall	1345 (100.0)	254 (18.9)	1091 (81.1)
Predisposing factors
Gender
Male	373 (27.7)	63 (24.8)	310 (28.4)	0.247
Female	972 (72.3)	191 (75.2)	781 (71.6)
Country of residence
Argentina	333 (24.8)	66 (26.0)	267 (24.5)	0.880
Colombia	558 (41.5)	104 (40.9)	454 (41.6)
Peru	454 (33.7)	84 (33.1)	370 (33.9)
Age group
15–16	589 (43.8)	101 (39.8)	488 (44.7)	0.151
20–24	756 (56.2)	153 (60.2)	603 (55.3)
Lives with both parents
Yes	604 (44.9)	110 (43.3)	494 (45.3)	0.569
No	741 (55.1)	144 (56.7)	597 (54.7)
Has a partner
Yes	451 (33.5)	92 (36.2)	359 (32.9)	0.314
No	894 (66.5)	162 (63.8)	732 (67.1)
Currently employed
Yes	358 (26.6)	67 (26.4)	291 (26.7)	0.924
No	987 (73.4)	187 (73.6)	800 (73.3)
Mother's history of MH treatment
Yes	149 (11.1)	43 (16.9)	106 (9.7)	0.004
No	960 (71.4)	167 (65.8)	793 (72.7)
Does not know	236 (17.5)	44 (17.3)	192 (17.6)
Father's history of MH treatment
Yes	71 (5.3)	22 (8.7)	49 (4.5)	0.027
No	941 (70.0)	173 (68.1)	768 (70.4)
Does not know	333 (24.7)	59 (23.2)	274 (25.1)
Enabling factors
Higher educational level attained by parents
Complete secondary school and below	758 (56.4)	112 (44.1)	646 (59.2)	< 0.001
Education beyond secondary school	587 (43.6)	142 (55.9)	445 (40.8)
Health insurance
Yes	1148 (85.4)	221 (87.0)	927 (85.0)	0.408
No	197 (14.6)	33 (13.0)	164 (15.0)
Household overcrowding
Yes	148 (11.0)	22 (8.7)	126 (11.5)	0.185
No	1197 (89.0)	232 (91.3)	965 (88.5)
Social support
Low support	156 (11.6)	26 (10.2)	130 (11.9)	0.745
Moderate support	704 (52.3)	136 (53.5)	568 (52.1)
High support	485 (36.1)	92 (36.2)	393 (36.0)
MH: Mental health
* Calculated using the chi-square test.

Participant characteristics according to the use of specialized MH services

In terms of predisposing factors, we found significant associations between the use of specialized MH services in the last three months and parental history of MH treatment, with a strong association for the mother’s history of MH treatment and a moderate association for the father’s history of MH treatment (Table 1). Regarding enabling factors, we found a very strong significant association between the use of specialized MH services and the attainment of education beyond secondary school by either parent (Table 1).

As for need factors, we found very strong and strong significant associations between the use of specialized MH services and the severity of anxiety and depression symptoms, respectively (Table 2). Additionally, we found significant associations between the use of services and self-recognition of anxiety and a moderate relationship for self-recognition of depression. Furthermore, we found very strong and moderate associations between the use of MH services and specific symptoms of both conditions. Finally, we also found strong associations between MH service use, life events, and resilience.

Table 2

Participant need factors according to the use of specialized MH services
Characteristics	n (%)	Use of specialized MH services		p-value*
		Yes	No
		n (%)	n (%)
Need factors
Severity of depressive symptoms
No symptoms/mild	158 (11.7)	24 (9.5)	134 (12.3)	0.014
Moderate	659 (49.0)	110 (43.3)	549 (50.3)
Moderately severe/severe	528 (39.3)	120 (47.2)	408 (37.4)
Severity of anxiety symptoms
Minimal/mild	430 (32.0)	63 (24.8)	367 (33.6)	< 0.001
Moderate	616 (45.8)	110 (43.3)	506 (46.4)
Severe	299 (22.2)	81 (31.9)	218 (20.0)
Anxiety-related symptoms perceived in ≥ 7 days
Feeling nervous or on edge
Less than 7 days	754 (56.1)	115 (45.3)	639 (58.6)	< 0.001
7 or more days	591 (43.9)	139 (54.7)	452 (41.4)
Lack of control over worry
Less than 7 days	810 (60.2)	126 (49.6)	684 (62.7)	< 0.001
7 or more days	535 (39.8)	128 (50.4)	407 (37.3)
Worrying too much
Less than 7 days	365 (27.1)	57 (22.4)	308 (28.2)	0.062
7 or more days	980 (72.9)	197 (77.6)	783 (71.8)
Difficulty relaxing
Less than 7 days	632 (47.0)	92 (36.2)	540 (49.5)	< 0.001
7 or more days	713 (53.0)	162 (63.8)	551 (50.5)
Restlessness
Less than 7 days	853 (63.4)	153 (60.2)	700 (64.2)	0.231
7 or more days	492 (36.6)	101 (39.8)	391 (35.8)
Getting upset or irritable
Less than 7 days	528 (39.3)	102 (40.2)	426 (39.1)	0.744
7 or more days	817 (60.7)	152 (59.8)	665 (60.9)
Feeling fearful
Less than 7 days	695 (51.7)	116 (45.7)	579 (53.1)	0.034
7 or more days	650 (48.3)	138 (46.9)	512 (46.9)
Depression-related symptoms perceived in ≥ 7 days
Lack of interest or pleasure
Less than 7 days	571 (42.4)	90 (35.4)	481 (44.1)	0.012
7 or more days	774 (57.6)	164 (64.6)	610 (55.9)
Feeling down, depressed, or hopeless
Less than 7 days	599 (44.5)	101 (39.8)	498 (45.6)	0.089
7 or more days	746 (55.5)	153 (60.2)	593 (54.4)
Trouble falling or staying asleep
Less than 7 days	431 (32.0)	77 (30.3)	354 (32.5)	0.512
7 or more days	914 (68.0)	177 (69.7)	735 (67.5)
Tired or low energy
Less than 7 days	446 (34.7)	82 (32.3)	384 (35.2)	0.379
7 or more days	879 (65.3)	172 (67.7)	705 (64.8)
Poor appetite or overeating
Less than 7 days	485 (36.1)	85 (33.5)	400 (36.7)	0.339
7 or more days	860 (63.9)	169 (66.5)	691 (63.3)
Low self-esteem or feeling like a failure
Less than 7 days	508 (37.8)	82 (32.3)	426 (39.1)	0.045
7 or more days	837 (62.2)	172 (67.7)	665 (60.9)
Lack of concentration
Less than 7 days	720 (53.5)	114 (44.9)	606 (55.5)	0.002
7 or more days	625 (46.5)	140 (55.1)	485 (44.5)
Slow movements or restlessness
Less than 7 days	938 (69.7)	168 (66.1)	770 (70.6)	0.166
7 or more days	407 (30.3)	86 (33.9)	321 (29.4)
Self-recognition of anxiety
Never	477 (35.5)	50 (19.7)	427 (39.1)	< 0.001
At some point in life	868 (64.5)	204 (80.3)	664 (60.9)
Self-recognition of depression
Never	376 (28.0)	58 (22.8)	318 (29.1)	0.043
At some point in life	969 (72.0)	196 (77.2)	773 (70.9)
Number of stressful life events in the last 12 months
0–2	631 (46.9)	96 (37.8)	536 (49.1)	0.001
3 or more	714 (53.1)	158 (62.2)	555 (50.9)
Resilience
Low	479 (35.6)	105 (41.3)	374 (34.3)	0.002
Moderate	419 (31.2)	88 (34.7)	331 (30.3)
High	447 (33.2)	61 (24.0)	386 (35.4)
MH: Mental health
* Calculated using the chi-square test.

Factors associated with the use of specialized MH services

After adjusting for gender, age group, highest educational level attained by parents, having health insurance, and household overcrowding among the predisposing factors (see Table 3), individuals who reported that their mother or father had received MH treatment had 51% and 57% higher prevalence of using specialized MH services compared to individuals whose parents had not received MH treatment, respectively. Regarding enabling factors, individuals with parents with education beyond secondary school had a 64% higher prevalence of using specialized MH services than those whose parents had a lower level of education.

Among need factors (Table 3), individuals with a severe level of anxiety symptoms were almost twice as likely to use specialized MH services compared to those experiencing minimal or mild anxiety symptoms. People who experienced anxiety symptoms, such as feeling nervous or on edge, lack of control over worry, difficulty relaxing and feeling fearful during at least 7 out of the last 14 days, had a 49%, 50%, 53%, and 32% higher prevalence of using specialized MH services compared to their counterparts, respectively. On the other hand, having severe symptoms of depression was not associated with the use of specialized MH services compared to those experiencing minimal or mild depression (Additional File 1). Additionally, people who experienced depressive symptoms, such as lack of interest or pleasure, low self-esteem, or feeling like a failure and lack of concentration or more than 7 days in the past two weeks, had a 33%, 30% and 42% higher prevalence of using specialized MH services compared to their counterparts, respectively.

Furthermore, participants who reported experiencing anxiety at some point in their lives had a 117% higher prevalence of using specialized MH services compared to those who had reported never experiencing anxiety (Table 3). In contrast, self-recognition of depressive symptoms was not associated with the utilization of MH services. In addition, those who experienced three or more SLEs in the last 12 months and those who reported low or moderate resilience had a 48%, 74% and 60% higher prevalence of using specialized MH services compared to their counterparts, respectively.

The non-statistically significant results are shown in Additional File 1.

Table 3. Factors associated with the use of specialized MH services.
Characteristics	Crude analysis		Adjusted analysis*
Characteristics	PR (CI 95%)	p-value	aPR (CI 95%)	p-value
Predisposing factors
Mother's MH treatment history
No	Ref.		Ref.
Yes	1.66 (1.24–2.21)	0.001	1.51 (1.13–2.02)	0.005
Does not know	1.07 (0.79–1.45)	0.651	1.07 (0.79–1.44)	0.681
Father's MH treatment history
No	Ref.		Ref.
Yes	1.69 (1.16–2.45)	0.006	1.57 (1.10–2.24)	0.013
Does not know	0.97 (0.74–1.26)	0.009	1.00 (0.76–1.31)	0.993
Enabling factors
Higher educational level attained by parents**
Complete secondary school and below	Ref.		Ref.
Education beyond secondary school	1.64 (1.31–2.05)	< 0.001	1.64 (1.31–2.06)	< 0.001
Need factors
Severity of anxiety symptoms
Minimal/mild	Ref.		Ref.
Moderate	1.22 (0.92–1.62)	0.172	1.23 (0.93–1.64)	0.145
Severe	1.85 (1.38–2.48)	< 0.001	1.88 (1.40–2.52)	< 0.001
Anxiety-related symptoms perceived in ≥ 7 days
Feeling nervous or on edge
Less than 7 days	Ref.		Ref.
7 or more days	1.55 (1.24–1.93)	< 0.001	1.49 (1.19–1.86)	< 0.001
Lack of control over worry
Less than 7 days	Ref.		Ref.
7 or more days	1.53 (1.23–1.91)	< 0.001	1.50 (1.20–1.87)	< 0.001
Difficulty relaxing
Less than 7 days	Ref.		Ref.
7 or more days	1.56 (1.24–1.97)	< 0.001	1.53 (1.21–1.92)	< 0.001
Feeling fearful
Less than 7 days	Ref.		Ref.
7 or more days	1.27 (1.02–1.59)	0.034	1.32 (1.06–1.64)	0.015
Symptoms related to depression perceived in ≥ 7 days
Lack of interest or pleasure
Less than 7 days	Ref.		Ref.
7 or more days	1.34 (1.06–1.70)	0.013	1.33 (1.06–1.68)	0.014
Low self-esteem or feeling like a failure
Less than 7 days	Ref.		Ref.
7 or more days	1.28 (1.01–1.63)	0.042	1.30 (1.03–1.65)	0.027
Lack of concentration
Less than 7 days	Ref.		Ref.
7 or more days	1.41 (1.13–1.77)	0.002	1.42 (1.14–1.77)	0.002
Self-recognition of anxiety
Never	Ref.		Ref.
At some point of my life	2.32 (1.75–3.06)	< 0.001	2.17 (1.64–2.88)	< 0.001
Number of stressful events in the last year
0–2	Ref.		Ref.
3 or more	1.46 (1.16–1.84)	0.001	1.48 (1.18–1.86)	0.001
Resilience
High	Ref.		Ref.
Moderate	1.54 (1.14–2.07)	0.005	1.60 (1.18–2.15)	0.002
Low	1.61 (1.20–2.14)	0.001	1.74 (1.30–2.33)	< 0.001
PR: prevalence ratio, PRa: adjusted prevalence ratio, CI: confidence interval, Ref: reference.
* Adjusted for gender, age group, highest educational level attained by parents, health insurance, and household overcrowding.
** Adjusted for gender, age group, health insurance, and household overcrowding.

Our study found that only 18.9% of participants who scored above the threshold for depressive or anxiety symptoms in the previous two weeks had used specialized MH services in the last three months. Using Andersen’s model of healthcare use, we found that certain predisposing, enabling, and need factors were associated with a higher prevalence of using specialized MH services among young people experiencing psychological distress in three cities in South America. As the MH treatment gap is a concerning issue worldwide [54], this study sheds light on critical elements that policymakers could consider to increase access to MH services among young people in the South American region.

Comparison with literature

According to our study, more than 80% of participants do not receive specialized MH care, although they might need it due to the severity of their symptoms of depression and anxiety. While we acknowledge that having symptoms does not necessarily mean participants have a formal diagnosis and, thus, not all of them are in need of specialized treatment, our findings are similar to the treatment gap reported for middle-income countries [7, 8] and Latin America (76% and 73%, respectively) for disorders such as depression and anxiety in the general population [6]. We found no difference in the use of specialized MH services across the three cities of our study, which could indicate that the scenario is similar throughout the region despite the contrasts in how health systems work in each country. On the health services side, the MH treatment gap is related to the lack of integration of MH into primary care, as well as the limited capacity of human resources to provide care for a high demand [5, 55]. On the part of young users, the stigma towards MH and the preference to solve problems on their own is a significant barrier to accessing services [24].

Among predisposing factors, young people with symptoms of anxiety and/or depression had a higher prevalence of using specialized MH services if their parents had also received MH treatment compared to their counterparts. The fact that their parents had received MH treatment could have created more awareness and decreased the stigma around MH care. This might suggest that those parents might have more resources to identify whether their children needed treatment and that young people might feel encouraged, supported and less judged for seeking support for their MH. A systematic review of parental factors associated with service use by young people with MH disorders found that 6 out of 10 studies showed that parental psychopathology was associated with young people’s use of specialized MH services (p < 0.001); however, family history of MH service use had no statistical evidence of association [56]. In addition, a study conducted with young people, parents, and teachers in Australia showed that parental history of MH could be an asset if families open up with young people about their lived experience of MH problems [57].

Among enabling factors, we identified that having a parent with education beyond secondary school favors using specialized MH services. This is consistent with previous research conducted with Dutch adolescents using education as a proxy for socioeconomic status [58]. Nevertheless, a study in Brazil with caregivers of school-aged young people found no association between education level and the use of formal or informal MH services [59]. Achieving a higher level of education may be linked to greater MH literacy, which may favor recognition of these problems [58] and more positive attitudes toward using health services [60]. However, as education level is highly associated with socioeconomic status, it might also show a tendency for less economically advantaged groups to experience more barriers to MH care, even within a sample recruited from deprived areas in Latin America.

Among need factors, people with greater severity of anxiety symptoms in the last two weeks and those who self-recognized ever experiencing anxiety symptoms had a higher prevalence of using specialized MH services compared to their counterparts. In addition, experiencing specific anxiety symptoms (e.g., feeling nervous, worried, fearful, or unrelaxed) for more than 7 days out of the last 14 days was linked to the use of specialized MH services. Experiencing some of these symptoms for a prolonged period and being aware of it could make young people conscious of how their anxiety affects their performance and productivity at work and school. Although we did not find studies focusing on the association between the severity of anxiety symptoms and the use of MH services in young people, a study with Canadian youths found that stress, used as a proxy for impairment, was a critical driver for MH service use [36]. A comparable association could also explain our findings, as young South American people reported seeking specialized MH services only when they felt they could not handle problems independently [61].

Concerning depression, our study did not find an association between the use of MH services and the severity of depression symptoms in the last two weeks or self-recognition of ever experiencing depressive symptoms. In line with our findings, a study using machine learning techniques to predict the use of MH services among adolescents could not propose a model based on depressive symptoms severity [62]. A systematic review of qualitative studies exploring barriers to seeking help among young people found that almost half of the included studies reported difficulty in identifying symptoms of mental illness as a potential barrier [63]. Nevertheless, the treatment gap for the group of young people with severe symptoms of depression is alarming. A qualitative study found that adolescents with depression tend to view their distress as a weakness of character and that accepting they have a MH problem such as depression could be viewed as a frightening experience because their normalcy is questioned [64]. In addition, the lack of access to treatment might also be explained by the debilitating effect of severe depression symptoms on day-to-day functioning and emphasizes the need for young people with severe depression symptoms to be supported to access MH services. Nevertheless, experiencing specific symptoms of depression (e.g., lack of interest, low self-esteem, and lack of concentration) for more than 7 days out of the last 14 days was linked to a greater use of specialized MH services. As with anxiety symptoms, it may be possible that these depressive symptoms are perceived as causing more impairment in daily life; however, it could also be that among depressive symptoms, these are easier to identify compared to others.

Furthermore, in our study, experiencing three or more SLEs was associated with a higher prevalence of using specialized MH services, consistent with findings from a cohort of Swedish young people [29]. It should be noted, however, that the SLEs explored in both studies differ considerably.

Finally, individuals with low and moderate resilience levels have a higher prevalence of using specialized MH services than those with high resilience. Presumably, people who struggle to recover quickly from difficulties and emotional distress may turn to external resources for help, such as MH services [32]. Previous studies have similar findings with negative correlations between resilience and seeking help for psychological problems among Chinese young adults [33] and migrant adolescents in Australia [32].

Implications

This study has identified factors that could be relevant for promoting the use of specialized MH services among young people with depression and/or anxiety. In recent years, several efforts have been made to improve MH care in Latin America, including developing legislation and implementing specialized MH centers with a community-based approach [55]. Despite these efforts, there is still a significant gap in addressing MH disorders, especially among young people [65]. Thus, a key challenge is to increase the supply of MH services and ensure these are youth-friendly, which requires young people to be involved in the design and implementation of such services.

Interestingly, depressive symptoms did not seem to drive service use, opposite to anxiety symptoms. Future research could focus on exploring whether anxiety is more distressing than depression or whether the latter has now become more accepted and perceived as not requiring specialized care. In any case, primary and secondary prevention among adolescents are essential and could specifically target young people with severe symptoms of depression as, even though this population is experiencing increased distress, they are less likely to access care.

Policymakers could aim to raise awareness about MH among young people, parents, teachers, and health professionals, especially in lower-resourced settings where awareness might be scarce. Help-seeking campaigns could include strategies that have proven to promote resilience and help-seeking behaviors, such as ensuring confidentiality and trust in the provider, having positive relationships with service staff, acknowledging the severity of the problem, giving young people the autonomy to seek help, and promoting emotion expression and openness [21, 63]. These campaigns could also benefit from incorporating young people with lived experience of MH problems from inception.

Strengths and limitations

This study is strengthened by its large sample size of over a thousand young participants from deprived areas in three of South America's biggest capital cities and its use of standardized methods across settings.

However, this study also has some limitations. Firstly, the study's observational nature only allows for determining associations, not causality. Secondly, self-reporting may lead to inaccuracies and recall bias because some of the information collected is about third parties and past events. Thirdly, there could be differential misclassification bias, where certain exposure variables, such as the severity of depressive symptoms and anxiety, may be influenced by access to MH services, potentially leading to overestimation or underestimation of the effect estimates. Finally, the data collection was partially conducted during the social distancing restrictions due to the COVID-19 pandemic, which could have impacted access to MH services in the three settings.

Less than 1 in 5 participants accessed a specialized MH service in the last three months in Latin America. This underscores the urgency of intensifying the efforts to improve accessibility to and capacity of MH care, as well as implementing effective strategies to reach more users who could benefit from these services. Young people's use of specialized MH services in urban deprived areas in South America is associated with predisposing (parental history of MH treatment), enabling (parents with education beyond secondary school), and need factors (severe anxiety, self-recognition of ever experiencing anxiety, SLEs, and resilience). However, we also found that young people with severe symptoms of depression are not more likely to access specialized MH treatment. These results, which are aligned with findings in other settings, could be helpful to inform strategies to increase access to MH care among young people experiencing symptoms of depression and anxiety.

CD-RISC-10 Connor-Davidson Resilience Scale

CSRI Client Service Receipt Inventory

GAD-7 General Anxiety Disorder-7

LAC Latin America and the Caribbean

MH Mental health

MSPSS Multidimensional Scale of Perceived Social Support

PHQ-8 Patient Health Questionnaire-8

PR Prevalence Ratio

aPR Adjusted Prevalence Ratio

SLE Stressful Life Events

Ethics approval and consent to participate
This study was approved by the Institutional Review Boards of Universidad de Buenos Aires on October 2nd, 2020, Pontificia Universidad Javeriana on November 20th, 2020 (FM-CIE-1138-20), and Universidad Peruana Cayetano Heredia on November 16th, 2020 (Constancia 581-33-20), as well as by the Research Ethics Committee of Queen Mary University of London, on November 16th, 2020 (QMERC2020/02). The informed consent process was carried out across all three countries. Adolescents were required to provide assent, and their legal guardian/parent was required to provide informed consent.

Consent for publication
Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Medical Research Council [grant number MR/S03580X/1].

Author Contribution

LHP conceptualized and designed this work, interpreted the data and wrote the manuscript. SF collaborated on the conceptualization and design of this work, as well as on the interpretation of the data and the manuscript writing. RVF led the data analysis and collaborated in interpreting the data and writing the manuscript. KAZ, SCL, CF, ALVE, JMUR, FLC, DSS, DRM, CGR, LIB and SP substantively revised the work. FDC gave substantial feedback throughout the conceptualization and design of this work, as well as on the interpretation of the data, and collaborated in the writing and revision of the manuscript.

Acknowledgement

We acknowledge Antonio Bernabé-Ortiz for their support during the analysis plan.

Data Availability

All documents from the analysis are available upon request from the corresponding author. Due to ethical and privacy concerns for the participants, the full data set is not available.

GBD Results. Institute for Health Metrics and Evaluation. https://vizhub.healthdata.org/gbd-results/?params=gbd-api-2019-permalink/380dfa3f26639cb711d908d9a119ded2. Accessed 11 May 2022.
Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2022;27:281–95.
United Nations Publications. The State of the World’s Children 2023: On My Mind Promoting, Protecting and Caring for Children's Mental Health. United Nations Pubn; 2022.
Mental health. Institute for Health Metrics and Evaluation. https://www.healthdata.org/research-analysis/health-risks-issues/mental-health. Accessed 26 Jul 2024.
Caldas de Almeida JM. Mental health services development in Latin America and the Caribbean: achievements, barriers and facilitating factors. Int Health. 2013;5:15–8.
Kohn R, Ali AA, Puac-Polanco V, Figueroa C, López-Soto V, Morgan K, et al. Mental health in the Americas: an overview of the treatment gap. Rev Panam Salud Publica. 2018;42:e165.
Mekonen T, Chan GCK, Connor JP, Hides L, Leung J. Estimating the global treatment rates for depression: A systematic review and meta-analysis. J Affect Disord. 2021;295:1234–42.
Alonso J, Liu Z, Evans-Lacko S, Sadikova E, Sampson N, Chatterji S, et al. Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018;35:195–208.
Piazza M, Fiestas F. Estudio transversal de uso de servicios de salud mental en cinco ciudades del Perú. Salud Ment. 2015;38:337–45.
Wang Y-P, Chiavegatto Filho ADP, Campanha AM, Malik AM, Mogadouro MA, Cambraia M, et al. Patterns and predictors of health service use among people with mental disorders in São Paulo metropolitan area, Brazil. Epidemiol Psychiatr Sci. 2017;26:89–101.
Zamora-Rondón DC, Suárez-Acevedo D, Bernal-Acevedo O. Analysis of needs and use of mental healthcare services in Colombia. Rev Salud Publica. 2019;21:175–80.
General I. Estudio Epidemiológico de Salud Mental en Lima Metropolitana y Callao - replicación 2012. 2016;29.
Cia AH, Stagnaro JC, Aguilar-Gaxiola S, Sustas S, Serfaty E, Nemirovsky M, et al. Twelve-month utilization rates and adequacy of treatment for mental health and substance use disorders in Argentina. Braz J Psychiatry. 2019;41:238–44.
Lopes CS, Hellwig N, E Silva G, de Menezes A. Inequities in access to depression treatment: results of the Brazilian National Health Survey - PNS. Int J Equity Health. 2016;15:154.
Orozco R, Vigo D, Benjet C, Borges G, Aguilar-Gaxiola S, Andrade LH, et al. Barriers to treatment for mental disorders in six countries of the Americas: A regional report from the World Mental Health Surveys. J Affect Disord. 2022;303:273–85.
Magaard JL, Seeralan T, Schulz H, Brütt AL. Factors associated with help-seeking behaviour among individuals with major depression: A systematic review. PLoS ONE. 2017;12:e0176730.
Oliver MI, Pearson N, Coe N, Gunnell D. Help-seeking behaviour in men and women with common mental health problems: cross-sectional study. Br J Psychiatry. 2005;186:297–301.
Mackenzie CS, Gekoski WL, Knox VJ. Age, gender, and the underutilization of mental health services: the influence of help-seeking attitudes. Aging Ment Health. 2006;10:574–82.
Sigurvinsdóttir AL, Jensínudóttir KB, Baldvinsdóttir KD, Smárason O, Skarphedinsson G. Effectiveness of cognitive behavioral therapy (CBT) for child and adolescent anxiety disorders across different CBT modalities and comparisons: a systematic review and meta-analysis. Nord J Psychiatry. 2020;74:168–80.
Zhou X, Teng T, Zhang Y, Del Giovane C, Furukawa TA, Weisz JR, et al. Comparative efficacy and acceptability of antidepressants, psychotherapies, and their combination for acute treatment of children and adolescents with depressive disorder: a systematic review and network meta-analysis. Lancet Psychiatry. 2020;7:581–601.
Radez J, Reardon T, Creswell C, Lawrence PJ, Evdoka-Burton G, Waite P. Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies. Eur Child Adolesc Psychiatry. 2021;30:183–211.
Renwick L, Pedley R, Johnson I, Bell V, Lovell K, Bee P, et al. Mental health literacy in children and adolescents in low- and middle-income countries: a mixed studies systematic review and narrative synthesis. Eur Child Adolesc Psychiatry. 2024;33:961–85.
Singh S, Zaki RA, Farid NDN. A systematic review of depression literacy: Knowledge, help-seeking and stigmatising attitudes among adolescents. J Adolesc. 2019;74:154–72.
Georgakakou-Koutsonikou N, Williams JM. Children and young people’s conceptualizations of depression: a systematic review and narrative meta-synthesis. Child Care Health Dev. 2017;43:161–81.
Lindsey MA, Joe S, Nebbitt V. Family Matters: The Role of Mental Health Stigma and Social Support on Depressive Symptoms and Subsequent Help Seeking Among African American Boys. J Black Psychol. 2010;36:458–82.
Miller S, Ringeisen H, Munoz B, Hedden SL, Colpe LJ, Rohloff H, et al. Correlates of Mental Health Service Use Among Young Adults With Mental Illness: Results From the National Survey on Drug Use and Health. Psychiatr Serv. 2016;67:642–9.
Simo B, Bamvita J-M, Caron J, Fleury M-J. Predictors of mental health service use among individuals with high psychological distress and mental disorders. Psychiatry Res. 2018;270:1122–30.
Roberts T, Miguel Esponda G, Krupchanka D, Shidhaye R, Patel V, Rathod S. Factors associated with health service utilisation for common mental disorders: a systematic review. BMC Psychiatry. 2018;18:262.
Lundin A, Forsell Y, Dalman C. Mental health service use, depression, panic disorder and life events among Swedish young adults in 2000 and 2010: a repeated cross-sectional population study in Stockholm County, Sweden. Epidemiol Psychiatr Sci. 2018;27:510–8.
Bessaha M, Mushonga D, Fedina L, DeVylder J. Association between Loneliness, Mental Health Symptoms, and Treatment Use among Emerging Adults. Health Soc Work. 2023;48:133–42.
Niermann HCM, Voss C, Pieper L, Venz J, Ollmann TM, Beesdo-Baum K. Anxiety disorders among adolescents and young adults: Prevalence and mental health care service utilization in a regional epidemiological study in Germany. J Anxiety Disord. 2021;83:102453.
Ziaian T, de Anstiss H, Antoniou G, Baghurst P, Sawyer M. Resilience and Its Association with Depression, Emotional and Behavioural Problems, and Mental Health Service Utilisation among Refugee Adolescents Living in South Australia. Int J Popul Res. 2012;2012:485956.
Sum MY, Chan SKW, Tsui HKH, Wong GHY. Stigma towards mental illness, resilience, and help-seeking behaviours in undergraduate students in Hong Kong. Early Interv Psychiatry. 2024;18:181–9.
Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1–10.
Graham A, Hasking P, Brooker J, Clarke D, Meadows G. Mental health service use among those with depression: an exploration using Andersen’s Behavioral Model of Health Service Use. J Affect Disord. 2017;208:170–6.
Sivayoganathan T, Reid GJ. Trends in population characteristics associated with mental health service use among youth and emerging adults in Canada from 2011 to 2016. Can J Public Health. 2023;114:464–73.
Roberts T, Shidhaye R, Patel V, Rathod SD. Health care use and treatment-seeking for depression symptoms in rural India: an exploratory cross-sectional analysis. BMC Health Serv Res. 2020;20:287.
Krzyż EZ, Antunez Martinez OF, Lin H-R. Uses of Andersen health services utilization framework to determine healthcare utilization for mental health among migrants-a scoping review. Front Public Health. 2023;11:1284784.
Gomez-Restrepo C, Diez-Canseco F, Brusco LI, Jassir Acosta MP, Olivar N, Carbonetti FL et al. Risk and Protective Factors for Depression and Anxiety Among Young People from Deprived Urban Neighbourhoods in South America: A Case-Control Study in Bogotá, Buenos Aires and Lima. 2023.
Priebe S, Fung C, Brusco LI, Carbonetti F, Gómez-Restrepo C, Uribe M, et al. Which resources help young people to prevent and overcome mental distress in deprived urban areas in Latin America? A protocol for a prospective cohort study. BMJ Open. 2021;11:e052339.
Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114:163–73.
Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092–7.
Alpizar D, Plunkett SW, Whaling K. Reliability and validity of the 8-item Patient Health Questionnaire for measuring depressive symptoms of Latino emerging adults. J Latina/o Psychol. 2018;6:115–30.
Casares MÁ, Díez-Gómez A, Pérez-Albéniz A, Lucas-Molina B, Fonseca-Pedrero E. Screening for anxiety in adolescents: Validation of the Generalized Anxiety Disorder Assessment-7 in a representative sample of adolescents. J Affect Disord. 2024;354:331–8.
Beecham J, Knapp M. Costing psychiatric interventions. In: Thornicroft G, Brewin CR, Wing J, editors. Measuring mental health needs. 1992. pp. 163–83.
Instituto Nacional de Estadística e Informática, editor. Características de las Viviendas, Hogares y Población. Encuesta demográfica y de salud familiar ENDES 2021. Instituto Nacional de Estadística e Informática; 2022. pp. 53–65.
Sistema de estadísticas sociodemográficas (SESD). Definiciones y conceptos utilizados en los cuadros. Instituto Nacional de Estadística y Censos; 2018.
Zimet GD, Dahlem NW, Zimet SG, Farley GK. The Multidimensional Scale of Perceived Social Support. J Pers Assess. 1988;52:30–41.
Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. J Trauma Stress. 2007;20:1019–28.
Heubeck B, O’sullivan C. An exploration into the nature, frequency and impact of school hassles in the middle school years. Aust Psychol. 1998;33:130–7.
Stata Corp LP. Stata Statistical Software Release 18. 2023.
Roy-Byrne PP, Joesch JM, Wang PS, Kessler RC. Low socioeconomic status and mental health care use among respondents with anxiety and depression in the NCS-R. Psychiatr Serv. 2009;60:1190–7.
Steele L, Dewa C, Lee K. Socioeconomic status and self-reported barriers to mental health service use. Can J Psychiatry. 2007;52:201–6.
Evans-Lacko S, Aguilar-Gaxiola S, Al-Hamzawi A, Alonso J, Benjet C, Bruffaerts R, et al. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48:1560–71.
Cruz IJH, Yantas SRO. La salud mental en tiempos de crisis: un análisis de la depresión y el suicidio en el contexto de la pandemia de Covid-19 en el Perú. KANYÚ. 2023. https://doi.org/10.61210/rck.v1i1.18
Ryan SM, Jorm AF, Toumbourou JW, Lubman DI. Parent and family factors associated with service use by young people with mental health problems: a systematic review. Early Interv Psychiatry. 2015;9:433–46.
Klinner C, Glozier N, Yeung M, Conn K, Milton A. A qualitative exploration of young people’s mental health needs in rural and regional Australia: engagement, empowerment and integration. BMC Psychiatry. 2023;23:745.
Amone-P’Olak K, Ormel J, Oldehinkel AJ, Reijneveld SA, Verhulst FC, Burger H. Socioeconomic position predicts specialty mental health service use independent of clinical severity: the TRAILS study. J Am Acad Child Adolesc Psychiatry. 2010;49:647–55.
Paula CS, Ziebold C, Ribeiro WS, Pan PM, Mari JJ, Bressan R, et al. The influence of caregiver attitudes and socioeconomic group on formal and informal mental health service use among youth. Eur Psychiatry. 2022;65:e34.
Riedel-Heller SG, Matschinger H, Angermeyer MC. Mental disorders–who and what might help? Help-seeking and treatment preferences of the lay public. Soc Psychiatry Psychiatr Epidemiol. 2005;40:167–74.
Toyama M, Godoy-Casasbuenas N, Olivar N, Brusco LI, Carbonetti F, Diez-Canseco F, et al. Identifying resources used by young people to overcome mental distress in three Latin American cities: a qualitative study. BMJ Open. 2022;12:e060340.
Dobias ML, Sugarman MB, Mullarkey MC, Schleider JL. Predicting Mental Health Treatment Access Among Adolescents With Elevated Depressive Symptoms: Machine Learning Approaches. Adm Policy Ment Health. 2022;49:88–103.
Gulliver A, Griffiths KM, Christensen H. Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC Psychiatry. 2010;10:113.
Wisdom JP, Clarke GN, Green CA. What teens want: barriers to seeking care for depression. Adm Policy Ment Health. 2006;33:133–45.
Fusar-Poli P. Integrated Mental Health Services for the Developmental Period (0 to 25 Years): A Critical Review of the Evidence. Front Psychiatry. 2019;10:355.

No competing interests reported.

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Factors associated with the use of specialized mental health services in urban Latin American young people with symptoms of anxiety and depression

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Introduction

Method

Study design

Participants and sample

Variables and instruments

Predisposing factors

Enabling factors

Need factors

Data analysis

Ethical considerations

Results

Participant characteristics

Participant characteristics according to the use of specialized MH services

Factors associated with the use of specialized MH services

Discussion

Comparison with literature

Implications

Strengths and limitations

Conclusions

Abbreviations

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Competing interests

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Author Contribution

Acknowledgement

Data Availability

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