Prevalence and correlates of major depression in Granada,Spain: Results from the GranadΣp study

Abstract

Background:

Major depression is one of the world’s leading causes of disability. Up-to-date information about the epidemiology of this disorder is key to health care planning.

Aim:

The aim of our study is to report prevalence and correlates of current major depressive disorder (MDD) in the province of Granada, Southern Spain.

Methods:

The GranadΣp is a cross-sectional study based on a community-dwelling adult population living in the province of Granada, Southern Spain. Community-dwelling adults aged 18–80 years (n = 810) were interviewed using the Mini-International Neuropsychiatric Interview (MINI). A variety of exposure assessments were also undertaken.

Results:

Point (2 weeks) prevalence of MDD in the Granada population was 5.6%. Positive family history of mental illness, high degree of neuroticism, high number of life threatening events (LTE), poor physical health status, cognitive impairment and cannabis use were independently associated with MDD in the multivariate regression model. Being female was also associated with MDD, but the significance disappeared after adjusting for neuroticism and physical health.

Conclusion:

Prevalence of MDD in the Granada population is higher than expected. The effects of the financial crisis could be partially accountable for this excess in prevalence. Six variables were found to be independently associated with MDD. Association between female sex and depression may be partially explained by the confounding effect of neuroticism.

Keywords

Epidemiology depression cognition drug abuse gender

Introduction

Major depressive disorder (MDD) is a highly prevalent mental disease that has been associated with medical morbidity, increased mortality, functional impairment and diminishment of quality of life. It is the fourth leading cause of disability measured in disability adjusted life years (DALYs) and it also has a significant economic impact. Although its importance was recently acknowledged in the World Health Organization (WHO) report on disability, depression continues to gain less attention than its physical counterparts in terms of health care provision and population mentalization (Egede, 2007; Jonsson & Bebbington, 1994; Kessler, 2012).

Several population surveys from the 1980s onward have been carried out to determine the prevalence of MDD and other mental disorders. Some of the first large-scale studies were the Epidemiologic Catchment Area (1980–1985) and the National Comorbidity Survey (1990–1992), both in the United States, which reported a 12-month prevalence ranging from 1.7% to 8.6% (Bourdon, Rae, Locke, Narrow, & Regier, 1992; Kessler et al., 1994).

When further evidence from both Western and non-Western countries emerged, they revealed wide interregional differences and even a substantial degree of disparity was sometimes found within the same country (Bunting, Murphy, O’Neil, & Ferry, 2013; Cho et al., 2015; Goldney, Eckert, Hawthorne, & Taylor, 2010; Graaf, Have, Gool, & Dorsselaer, 2012; Kiejna et al., 2015; Liu et al., 2015; Markkula et al., 2015; Mohammadi et al., 2005). Methodological differences seemed to be partially accountable for this lack of consistency and as a response the WHO launched a large-scale epidemiological study to minimize this bias. The World Mental Health (WMH) Survey Initiative is a series of population surveys carried out in 28 countries with identical methodology (Kessler & Ustun, 2008). Its results still revealed remarkable interregional differences in the prevalence of major depression, which ranged from 1.1% in China to 10.4% in Brazil for the last 12 months (Bromet et al., 2011). These differences may be the mixed result of genetic susceptibility, cultural differences and methodological difficulties when applying the same measurements tools to different populations.

One of the studies included in the WHO Mental Health project was the European Study of the Epidemiology of Mental Disorders (ESEMeD) study, a large-scale nationally representative population survey carried out in 2001–2002 using a sample from five Spanish provinces. This study estimated that 10.6% of the Spanish population had suffered from MDD at some point in their lives, while a 12-month prevalence was estimated at 4.0%, one of the lowest rates found in Europe. Other Spanish community-based studies, which used regional samples, reported a 12-month prevalence of 6% (Navarro-Mateu et al., 2015). And a point prevalence ranging from 1.5% (Calvó-Perxas, Garre-Olmo, & Vilalta-Franch, 2015) to 1.8% (Ayuso-Mateos, 2001).

Despite the valuable information provided, none of the above-mentioned studies included Andalusian population in this sample. The southernmost region of the Iberian Peninsula, Andalusia is the most populated autonomous community in Spain and its historical and social circumstances to some extent set it apart from the rest of the country. Moreover, there are no epidemiological studies that explore the prevalence of depression in Spain after the onset of the economic recession of 2008, which struck this country heavily. Granada, an Andalusian province with a population of nearly one million people, was one of the most severely affected, reaching an unemployment rate of 37.4% in 2012 (Instituto Nacional de Estádistica (INE), 2018a, 2018b).

This is potentially relevant considering that several studies show that unemployment increases the risk for depression (Bijlsma, Tarkiainen, Myrskylä, & Martikainen, 2017; McGee & Thompson, 2015; Wege, Angerer, & Li, 2017). Moreover, there’s evidence suggesting an increase in the overall prevalence of mental disorders as a result of the recent economic crisis (Medel-Herrero & Gomez-Beneyto, 2017).

In addition, clinical subtypes of MDD, which are considered here, have been largely overlooked in epidemiological research, with no community-based studies currently available in Spain on this topic. Three subtypes of MDD appear to be especially important in the clinical practice: melancholic depression, characterized by its endogenous phenotype; depression with psychotic features, which may be mood-congruent or mood-incongruent, and double depression, defined by the co-occurrence of acute major depressive episodes and dysthymia (Sadock & Sadock, 2009).

Updated evidence on the epidemiology of this disorder, able to reflect the recent changes in our society, is needed to design strategies for its prevention and treatment.

In this report, we present data from the GranadΣp study regarding the prevalence and correlates of MDD and subtypes in the Andalusian province of Granada. The GranadΣp is a pilot study that forms part of the Plan Integral de Salud Mental en Andalucía epidemiological mental health (PISMA-ep) study, which uses a sample from all eight provinces of Andalusia. The PISMA-ep study is one of the largest epidemiological mental health surveys ever carried out in Spain and the first of its kind in Andalusia.

Methods

Study context and design

The GranadΣp is a cross-sectional study based on a community-dwelling adult population living in the province of Granada, Southern Spain. This study aims to provide data on the prevalence and correlates of psychiatric disorders in this area.

The GranadΣp study was approved by the Research Ethic Committee of the University of Granada. A more detailed description of its methodology can be found elsewhere (Cervilla et al., 2016).

Sample

The sample size necessary to calculate a prevalence of 2% with a ± 0.8% accuracy at a 95% confidence interval (CI) was estimated at 1,176. Participants were selected from a multistage clustered sample, using different standard stratification levels. We considered city size, dividing the municipalities in three categories: urban (over 10,000 inhabitants), intermediate (between 2,001 and 10,000 inhabitants) and rural (up to 2,000 inhabitants. A simple random method was used to select a number of municipalities for each size, as well as sections and street routes within each town. One in every 4 consecutive homes in the selected street routes was visited.

The inclusion criteria for respondents were being between 18 and 80 years old and having resided in Granada for at least a year. Exclusion criteria included not being able to complete the interview due to illness, not being a fluent Spanish speaker, having dementia or mental retardation and being institutionalized.

Out of 1,176 individuals approached, 810 community-based adults living in the province of Granada agreed to take part in the study, amounting for a response rate of 69%.

Measures

The Spanish version of the Mini International Neuropsychiatric Interview (MINI) (Bobes García, 2006, 1998) was our main diagnostic tool. The MINI is a brief diagnostic structured interview that generates Axis I DSM-IV (American Psychiatric Association, 2000) and ICD-10 (World Health Organization, 2009) compatible diagnoses for 16 mental disorders, including MDD. This interview consists of a screening section for each diagnostic, leading to a further set of questions in case the detection was positive (Lecrubier et al., 1997). We used this tool to explore the presence of MDD and MDD with melancholic symptoms. We used the Subscale of Psychotic Symptoms (SCID-I/P) to assess the presence of psychotic symptoms in patients with MDD, which allowed us to establish the diagnostic subtype of MDD with psychotic symptoms. The presence of double depression was determined using the dysthymia module of the MINI.

Methodological research documented satisfactory psychometric properties for the MINI interview, with good rates of validity and reliability when used on a community-based population (Kadri et al., 2005; Otsubo et al., 2005; Rossi et al., 2004).

The outcome variable in our study was a DSM-IV/ICD-10 compatible MDD diagnosis, which was generated from the MINI interview. Comorbidity of MDD with other psychiatric diagnoses was also detected through the MINI. However, to better assess the existence of psychotic disorders and symptoms, we used the SCID-I/P as a double control tool (First, Spitzer, Gibbon, & Williams, 2002).

Presence of personality disorders was assessed using the Standardized Assessment of Personality-Abbreviated Scale (SAPAS). In addition, two personality traits, neuroticism and impulsivity were assessed using the corresponding sections of the Zuckerman–Kuhlman Personality Questionnaire.

The Global Assessment of Functioning (GAF), Screen for Cognitive Impairment in Psychiatry (SCIP) and 12-Item Short Form Health Survey (SF-12) scales were used to explore global functioning, cognition and physical health status, respectively, with lower scores indicating poorer performance in these areas.

Childhood maltreatment was assessed through the abbreviated Childhood Trauma Questionnaire. We divided those who have suffered from childhood maltreatment into three groups: the emotional group, which included participants who had suffered from psychological maltreatment only; the physical maltreatment group, including those with a history of physical mistreatment in childhood, with or without psychological mistreatment; and the sexual abuse group, associated or not with any of the other subtypes of maltreatment. Life threatening events (LTE) were explored using a validated reference list of 12 categories of events that had occurred in the past 6 months.

Smoking was assessed with the Fagerström Test for Nicotine Dependence, while alcohol dependence was explored with the cut-annoyed-guilty-eye (CAGE) questionnaire for alcoholism. Cannabis use was also explored through self-report.

Respondents were also administered a standard battery of sociodemographic variables, including sex, age, marital status, employment and educational level. The data collected were matched with the information available in the general census records.

Procedure

Face-to-face interviews were conducted between October 2011 and September 2012 by professionally trained psychologists and took place either in the participant’s local primary health care center or in their homes. All interviewers attended a 1 week training course imparted by the researchers.

Statistical analysis

Current (2 weeks) prevalence of MDD was calculated using the STATA-13 software package. Prevalence was weighted by urban/rural status. Association between MDD and the potential correlates was verified through chi-square tests. A random-effects binary logistic regression was performed to obtain the most parsimonious multivariate model for MDD. Bivariate associations are also presented, with 95% confidence intervals. Statistical significance was evaluated using two-sided tests with p < .05 level of significance.

Results

Sample characteristics

Our sample is composed of 810 respondents, 48% of which were male. Mean age was 47.2 years. A further description of the sample is presented in Table 1.

Table 1.

Characteristics of the sample.

		n (Total = 810)	%
Gender	Male	389	48.0
Gender	Female	421	52.0
Age (years)	18–24	53	65.4
	25–40	264	32.6
	41–64	368	45.4
	>65	144	17.8
Marital Status	Married/coupled	585	72.2
	Single	138	17.1
	Separated/divorced	51	6.4
	Widowed	35	4.3
Employment status	Full-time student	52	64.2
	Employed	351	43.3
	Unemployed	177	21.9
	Retired	110	13.6
	Home care	92	11.4
	Disabled	27	33.3
Level of education	Primary or less	170	21.0
Level of education	Secondary or higher	640	79.0
Urbanicity	Urban	487	60.2
	Intermediate	241	29.8
	Rural	81	10.0

Prevalence of MDD

Current (2 weeks) prevalence of MDD was 5.2% (95% CI 3.7–6.7). As for the subtypes of MDD, estimates of melancholic, psychotic and double forms of depression were 2.8%, 1.1% and 1%, respectively. For all forms of depression, higher rates were found among women. Prevalence of MDD shows an upward trend from the youngest cohort to the 41–64 age group and a decline from 65 years onward. No age pattern could be found for the subtypes of MDD.

Full results on prevalence are shown in Table 2.

Table 2.

Current (2 weeks) prevalence (%) of MDD and its subtypes by age and sex.

	MDD(95% CI)	Melancholic MDD(95% CI)	Psychotic MDD(95% CI)	Double depression(95% CI)
Sex
Male	2.8 (1.2–4.5)	1.5 (0.3–2.8)	0.0 (0.0–0.0)	0.3 (0.0–0.8)
Female	7.1 (4.7–9.6)	7.1 (2.2–5.9)	2.1 (2.2–5.9)	1.7 (0.4–2.9)
Age (years)
18–24	1.9 (0.0–5.5)	5.7 (0.0–11.9)	1.9 (0.0–5.5)	0.0 (0.0–0.0)
25–40	3.4 (1.2–5.6)	1.9 (0.2–3.5)	1.9 (0.2–3.5)	1.5 (0.0–3.0)
41–64	7.4 (4.7–10.2)	3.4 (1.5–5.4)	0.6 (0.0–1.4)	0.9 (0.0–1.8)
>65	4.2 (0.9–7.5)	2.1 (0.0–4.4)	0.7 (0.0–2.1)	0.7 (0.0–2.1)
Total	5.2 (3.7–6.7)	2.8 (1.7–4.0)	1.1 (0.4–1.7)	1 (0.3–1.6)

MDD = major depression disorder; CI = Confidence interval.

Bivariate associations between MDD and covariates

Bivariate analysis showed that being female was associated with an increased risk of MDD (odds ratio (OR) = 2.4), as was having a family history of mental illness (OR = 3.4). Higher odds were also found for those who had suffered childhood sexual abuse, but no significant association was found for other types of childhood maltreatment. Employment status was a factor significantly related to MDD in the unemployed, homemaker and disabled groups. Marital status and educational level failed to show a significant association with MDD.

Respondents with MDD showed significantly lower scores in the GAF, SF-12 and SCIP scales, indicating a decreased overall functioning, greater cognitive impairment and poorer physical health. MDD was also associated with a higher score on the SAPAS personality disorder scale, a higher degree of neuroticism and impulsivity, a greater number of LTE and an increased risk of suicide.

Tables 3 and 4 show the crude ORs and differences of means for the association between MDD and the variables studied.

Table 3.

Bivariate associations for current (2 weeks) MDD: Odds ratios.

		OR	95% CI
Gender	Male	1.0
Gender	Female	2.4*	(1.2–4.8)
Marital status	Married/coupled	1.0
Marital status	Uncoupled	1.3	(0.7–2.5)
Occupation	Employed	1.0
	Unemployed	2.9*	(1.3–6.7)
	Retired	2.0	(0.7–1.5)
	Homemaker	3.2*	(1.2–8.5)
	Disabled	4.3*	(1.1–16.5)
	Full-time student	0.3	(0.0–5.4)
Level of education	Primary or less	0.7	(0.4–1.5)
Level of education	Secondary or higher	1.0
Family history of mental illness	Yes	3.4*	(1.8–6.3)
Family history of mental illness	No	1.0
Childhood trauma	Any	1.5	(0.7–3.3)
	Sexual	19.1*	(2.6–139.3)
	Physical	0.4	(0.1–2.8)
	Emotional	1.6	(0.6–4.1)
	None	1.0
Psychiatric comorbidities	Cannabis use	3.2*	(1.5–6.7)
	Nicotine dependence	1.4	(0.5–3.6)
	Any drug addiction	18.6*	(5.9–58.3)
	Any anxiety disorder	15.7*	(7.8–31.6)
	Any psychosis	25.9*	(9.4–71.3)

MDD = major depression disorder; CI = confidence interval; OR = OR = odds ratio.

Statistically significant at 0.05 level, two-sided test.

Table 4.

Bivariate associations for current (2 weeks) MDD: Difference of means.

	Control group		MDD
	Mean	SD	Mean	SD	df	T value	p
Functionality	92.1	10.9	62.4	9.1	47.1	20.14	< 0.001
Health status	50.5	8.2	43.1	8.8	807	5.24	< 0.001
Neuroticism	1.4	1.7	3.0	1.9	149.5	−8.08	< 0.001
Impulsivity	−1.2	1.1	−0.9	1.3	145.3	−2.31	0.022
Screening of personality disorder	3.0	1.4	4.1	1.5	148.3	−5.82	< 0.001
Estimated IQ	112.0	19.1	104.9	18.1	159.0	0.82	0.420
Suicide risk	0.1	0.5	1.0	1.0	807	−10.43	< 0.001
Alcohol dependence	0.2	0.6	0.1	0.3	807	1.15	0.249
Nicotine dependence	1.3	2.3	1.8	2.8	145.3	0.07	0.039
Number of LTE	1.6	1.5	2.1	1.7	807	−2.33	0.02
Cognitive impairment	97.2	30.7	83.5	25.3	806	3.33	0.002

MDD = major depression disorder; SD = standard deviation; df = degrees of freedom; IQ = intellectual quotient; LTE = life threatening events.

Multivariate regression model for MDD

Multivariate factor regression analysis revealed six factors independently associated with MDD: family history of mental illness, degree of neuroticism, number of LTE, health status, cognitive impairment and cannabis use. Although we initially included sex and age in our model, we decided to take both out as they did not associate significantly with MDD. The original association between female sex and MDD was accounted for by neuroticism and, to a lesser extent, physical health.

Table 5 shows the multivariate regression model for MDD.

Table 5.

Multivariate association model for current (2 weeks) MDD.

	OR	95% CI	SD	p
Family history	2.70	1.35–5.34	0.941	0.005
Neuroticism	1.52	1.26–1.84	0.148	0.000
Life threatening events	1.28	1.02–1.60	0.146	0.030
Health status	0.94	0.91–0.97	0.017	0.001
Cognitive impairment	0.99	0.97–1.00	0.006	0.024
Cannabis use	2.95	1.11–7.81	1.465	0.029

MDD = major depression disorder; CI = confidence interval; OR = odds ratio; SD = standard deviation.

Discussion

Our study showed that MDD is a highly prevalent condition in the Granada population, affecting just over 1 in every 20 people at the time data were collected. This figure is higher than those found in most of the previous community-based mental health surveys carried out in Spain, especially considering the short reference period of 2 weeks used in our study (Ayuso-Mateos, 2001; Calvó-Perxas et al., 2015; Gabilondo et al., 2010; Urbina-Torija, Flores-Mayor, García-Salazar, Torres-Buisán, & Torrubias-Fernández, 2007).

Although methodological differences preclude a direct comparison of results, our findings suggest a higher than expected prevalence of MDD in the province of Granada. One of the factors that may contribute to this result is that our study was carried out amid the financial crisis that began in 2008. Spain was one of the countries more seriously affected by the recession, with the region of Andalusia reaching some of the highest unemployment rates in Europe. Several studies suggest that the economic crisis has had a negative impact on both physical and mental health in the Spanish population, and a recent study carried out in Andalusia reported a sharp increase in suicide rates after the onset of the crisis, associated with unemployment (Bartoll, Palència, Malmusi, Suhrcke, & Borrell, 2013; Córdoba-Doña, San Sebastián, Escolar-Pujolar, Martínez-Faure, & Gustafsson, 2015; Lopez-Bernal, Gasparrini, Artundo, & McKee, 2013; Miret et al., 2014). However, a casual association cannot be established and no comparison over time can be made due the absence of previous direct data on the prevalence of MDD in this region. Moreover, Granada was already one of the poorest provinces in Spain long before the economic crisis and continues to be so today, which may account for its probable higher rate of depression. Finally, an overestimation of MDD caused by the assessment tool used cannot be ruled out.

Female gender is one of the most consistent factors associated with depression, having shown a significant association with MDD in several studies, both in Western and non-Western countries (Kessler, 2003; Liu et al., 2015; Markkula et al., 2015). In the WMH Survey Initiative, women were reported to have an increased risk for MDD in 15 of the 18 countries explored (Bromet et al., 2011). The reason for this association is not yet entirely understood. Some of the factors that have been proposed are a different expression of the serotonin transporter polymorphisms, gender-related hormonal profiles, differences in the cellular immunity response to stress and cultural differences between sexes (Chang, Chang, Fang, Chang, & Huang, 2017; Fagniart, Reynaert, Jacques, Lepièce, & Zdanowicz, 2016; Piccinelli & Wilkinson, 2000). In our study, we too found that being female increased the risk for MDD. However, this association disappeared in the multivariate model, as a confounding effect was found between female sex and neuroticism, and, to a lesser extent, between female sex and physical health status. Thus, higher rates of depression among women may be mediated by the higher degree of neuroticism found in the female sex, which in turn may be the result of a complex set of environmental and biological factors.

In addition to neuroticism, impulsivity and the general risk of having a personality disorder measured by the SAPAS score were also associated with MDD. This effect may be produced by means of social interaction. A positive interaction enables the creation of strong relationships and the development of an appropriate support network. Furthermore, personality can influence coping strategies and the ability to overcome adversity. This relationship between personality and depression has been previously noted in literature (Hakulinen et al., 2015; Roohafza et al., 2016).

Comorbidity with other mental health disorders frequently occurs in MDD, especially with anxiety disorders. Patients with MDD may develop other mental disorders over the course of the illness, in the same way that a depressive episode may appear in people suffering from other conditions (Talkovsky, Green, Osegueda, & Norton, 2017). Also, different mental disorders may have some pathogenic mechanisms in common (Mathew, Pettit, Lewinsohn, Seeley, & Roberts, 2011; McTeague et al., 2017).

Sexual childhood abuse was associated with MDD, although this association disappeared when adjusting for number of LTEs, as both variables may be collinear with each other. Furthermore, authors have described a synergistic interaction between childhood abuse and LTE (Power et al., 2013). In addition, the risk conferred by childhood abuse may also be enhanced by genetic vulnerability (Gutiérrez et al., 2015). One of the ways childhood abuse could predispose to MDD is through the theory of learned helplessness, which states that children repeatedly subjected to traumatic experiences, against which they find themselves defenseless, may lose their capacity to cope with hardship in their adulthood (Vollmayr & Gass, 2013).

In respect of physical health, several studies have found an association between depression and both general health status and specific medical conditions. Some authors claim this association is the result of the traumatic experience and the dysfunction derived from the illness, while others point to biological factors such as the modification of inflammatory pathways, which would help explain the two-way relationship often found between depression and organic disease (Carney, Freedland, Miller, & Jaffe, 2002; Felger & Lotrich, 2013; Miller, Maletic, & Raison, 2009).

While no association was found for the estimated intellectual quotient (IQ) and the educational level, cognitive performance was independently associated with MDD. There is evidence suggesting depression and cognitive impairment share some pathological pathways that may account for this relationship (Allison & Ditor, 2014; Gonda et al., 2015).

Cannabis has been frequently linked to psychosis, but its association with depression is not as well studied and the direction of causality is not completely clear. However, cannabis use has been described as a risk factor for developing depression in a sufficient number of studies, while there is less evidence supporting the opposite (Degenhardt, 2002; Lev-Ran et al., 2013; Smolkina et al., 2017). Mechanisms by which cannabis could lead to depression include neurochemical alterations of monoamines, impaired cognitive performance and psychosocial failure (Marmorstein & Iacono, 2011; Nader & Sanchez, 2017; Van de Giessen et al., 2016).

These findings should be considered in light of the study limitations. The cross-sectional design does not allow for causal inferences and the associations need to be confirmed in longitudinal studies. In addition, methodological differences make it difficult to compare our results with those of previous studies carried out in Spain and Europe, while the absence of similar studies in Andalusia precludes a comparison within the region. Some limitations concerning the diagnostic tool should also be noted. The MINI does not allow to record the length of the depressive episodes, nor the lifetime prevalence of depression. Finally, patients with a mental illness may be more reluctant to participate, which may result in a selection bias.

Conclusion

MDD is a highly prevalent mental disorder in the Granada population. Family history of mental illness, cognitive impairment, poor health status, neuroticism, LTEs and cannabis use were the main correlates of depression. The confounding effect between female sex and neuroticism may help explain the higher prevalence of depression found among women.

The GranadΣp study provides important insights on the epidemiology of MDD in Southern Spain and we expect our results will help policy makers to design strategies for the prevention and management of the condition. This study will be expanded as part of the PISMA-ep project to include the remaining seven provinces of the region of Andalusia.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a grant from the Consejería de Economía, Innovación y Ciencia de la Junta de Andalucía (10-CTS-6682).

ORCID iD

Jorge Cervilla

References

Allison

Ditor

(2014). The common inflammatory etiology of depression and cognitive impairment: A therapeutic target. Journal of Neuroinflammation, 11, 1–12.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychiatric Press.

Ayuso-Mateos

(2001). Depressive disorders in Europe: Prevalence figures from the ODIN study. The British Journal of Psychiatry, 179, 308–316.

Bartoll

Palència

Malmusi

Suhrcke

Borrell

(2013). The evolution of mental health in Spain during the economic crisis. European Journal of Public Health, 24, 415–418.

Bijlsma

Tarkiainen

Myrskylä

Martikainen

(2017). Unemployment and subsequent depression: A mediation analysis using the parametric G-formula. Social Science & Medicine, 194, 142–150.

Bobes García

. (2006). Banco de instrumentos básicos para la práctica de la psiquiatría clínica. Barcelona, Spain: Ars Medica.

Bobes

. (1998). A Spanish validation study of the mini international neuropsychiatric interview. European Psychiatry, 13, 198–199. doi:10.1016/S0924-9338(99)80240-5

Bourdon

Rae

Locke

Narrow

Regier

(1992). Estimating the prevalence of mental disorders in US adults from the epidemiologic catchment area survey. Public Health Reports, 107, 663–668.

Bromet

Andrade

Hwang

Sampson

Alonso

de Girolamo

... Kessler

R. C.

(2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine, 9, 90.

10.

Bunting

Murphy

O’Neil

Ferry

(2013). Prevalence and treatment of 12-month DSM-IV disorders in the Northern Ireland study of health and stress. Social Psychiatry and Psychiatric Epidemiology, 48, 81–93.

11.

Calvó-Perxas

Garre-Olmo

Vilalta-Franch

(2015). Prevalence and sociodemographic correlates of depressive and bipolar disorders in Catalonia (Spain) using DSM-5 criteria. Journal of Affective Disorders, 184, 97–103.

12.

Carney

Freedland

Miller

Jaffe

(2002). Depression as a risk factor for cardiac mortality and morbidity. Journal of Psychosomatic Research, 53, 897–902.

13.

Cervilla

Ruiz

Rodríguez-Barranco

Rivera

Ibáñez-Casas

Molina

... Gutiérreza

(2016). Protocol and methodology of the epidemiological mental health study in Andalusia: PISMA-ep. Revista De Psiquiatría Y Salud Mental, 9, 185–194.

14.

Chang

Fang

Chang

Huang

(2017). Gender-specific association between serotonin transporter polymorphisms (5-HTTLPR and rs25531) and neuroticism, anxiety and depression in well-defined healthy Han Chinese. Journal of Affective Disorders, 207, 422–428.

15.

Cho

M. J.

Seong

S. J.

Park

J. E.

Chung

I. W.

Lee

Y. M.

Bae

(2015). Prevalence and Correlates of DSM-IV mental disorders in South Korean adults: The Korean epidemiologic catchment area study 2011. Psychiatry Investigation, 12, 164–170.

16.

Córdoba-Doña

San Sebastián

Escolar-Pujolar

Martínez-Faure

Gustafsson

(2014). Economic crisis and suicidal behaviour: The role of unemployment, sex and age in Andalusia, Southern Spain. International Journal for Equity in Health, 13, 55.

17.

Degenhardt

(2002). The link between cannabis use and psychosis: Furthering the debate. Psychological Medicine, 33, 3–6.

18.

Egede

(2007). Major depression in individuals with chronic medical disorders: Prevalence, correlates and association with health resource utilization, lost productivity and functional disability. General Hospital Psychiatry, 29, 409–416.

19.

Fagniart

Reynaert

Jacques

Lepièce

Zdanowicz

(2016). Depression, gender and cellular immunity: Influence of gender and severity of depression on the cellular immunity. Psychiatria Danubina, 28, 179–182.

20.

Felger

Lotrich

(2013). Inflammatory cytokines in depression: Neurobiological mechanisms and therapeutic implications. Neuroscience, 246, 199–229.

21.

First

M. B.

Spitzer

R. L.

Gibbon

Williams

J. B. W.

(2002). Structured clinical interview for DSM-IV-TR axis I disorders, research version, patient edition (SCID-I/P). New York, NY: Biometrics Research, New York State Psychiatric Institute.

22.

Gabilondo

Rojas-Farreras

Vilagut

Haro

Fernández

Pinto-Meza

Alonso

(2010). Epidemiology of major depressive episode in a southern European country: Results from the ESEMeD-Spain project. Journal of Affective Disorders, 120, 76–85.

23.

Goldney

R. D.

Eckert

K. A.

Hawthorne

Taylor

A. W.

(2010). Changes in the prevalence of major depression in an Australian community sample between 1998 and 2008. Australian New Zealand Journal of Psychiatry, 44, 901–910.

24.

Gonda

Pompili

Serafini

Carvalho

Rihmer

Dome

(2015). The role of cognitive dysfunction in the symptoms and remission from depression. Annals of General Psychiatry, 14(1), 27. doi: 10.1186/s12991-015-0068-9

25.

Graaf

Have

Gool

Dorsselaer

(2012). Prevalence of mental disorders and trends from 1996 to 2009: Results from the Netherlands mental health survey and incidence study-2. Social Psychiatry and Psychiatric Epidemiology, 47, 203–213.

26.

Gutiérrez

Bellón

Rivera

Molina

King

Marston

... Cervilla

(2015). The risk for major depression conferred by childhood maltreatment is multiplied by BDNF and SERT genetic vulnerability: A replication study. Journal of Psychiatry & Neuroscience, 40, 187–196.

27.

Hakulinen

Elovainio

Pulkki-Råback

Virtanen

Kivimäki

Jokela

(2015). Personality and depressive symptoms: Individual participant meta-analysis of 10 cohort studies. Depression and Anxiety, 32, 461–470.

28.

Instituto Nacional de Estádistica (INE). (2018a). Cifras oficiales de población resultantes de la revisión del Padrón municipal a 1 de enero [Official population figures from the municipal registry on january 1st]. Retrieved from http://www.ine.es/jaxiT3/Datos.htm?t=2852

29.

Instituto Nacional de Estádistica (INE) (2018b). Tasas de actividad. Paro Y Empleo Por Provincia [Work activity Rates. Unemployment and employment by province]. Retrieved from http://www.ine.es/jaxiT3/Datos.htm?t=3996

30.

Jonsson

Bebbington

(1994). What price depression? The cost of depression and the cost- effectiveness of pharmacological treatment. The British Journal of Psychiatry, 164, 665–673.

31.

Kadri

Agoub

Gnaoui

Alami

Hergueta

Moussaoui

(2005). Moroccan colloquial Arabic version of the mini international neuropsychiatric interview (MINI): Qualitative and quantitative validation. European Psychiatry, 20, 193–195.

32.

Kessler

Ustun

T. B.

(2008). The WHO world mental health surveys: Global perspectives of mental health surveys. New York, NY: Cambridge University Press.

33.

Kessler

(2003). Epidemiology of women and depression. Journal of Affective Disorders, 74, 5–13.

34.

Kessler

(2012). The costs of depression. Psychiatric Clinics of North America, 35, 1–14.

35.

Kessler

McGonagle

K. A.

Zhao

Nelson

C. B.

Hughes

Eshleman

... Kendler

. (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Archives of General Psychiatry, 51, 8–19.

36.

Kiejna

Piotrowski

Adamowski

Moskalewicz

Wciórka

Stokwiszewsk

(2015). The prevalence of common mental disorders in the population of adult poles by sex and age structure–An EZOP Poland study. Psychiatria Polshka, 49, 15–27.

37.

Lecrubier

Sheehan

Weiller

Amorim

Bonora

Harnett Sheehan

... Dunbar

G. C.

(1997). The mini international neuropsychiatric interview (MINI): A short diagnostic structured interview: Reliability and validity according to the CIDI. European Psychiatry, 12, 224–231.

38.

Lev-Ran

Roerecke

Le Foll

George

McKenzie

Rehm

(2013). The association between cannabis use and depression: A systematic review and meta-analysis of longitudinal studies. Psychological Medicine, 44, 797–810.

39.

Liu

Yan

Guo

H. L.

Tang

Y. L.

Rakofsky

J. J.

(2015). Prevalence of major depressive disorder and socio-demographic correlates: Results of a representative household epidemiological survey in Beijing, China. Journal of Affective Disorders, 129, 74–81.

40.

Lopez-Bernal

Gasparrini

Artundo

McKee

(2013). The effect of the late 2000s financial crisis on suicides in Spain: An interrupted time-series analysis. European Journal of Public Health, 23, 732–736.

41.

Markkula

Suvisaari

Saarni

Pirkola

Peña

Saarni

(2015). Prevalence and correlates of major depressive disorder and dysthymia in an eleven-year follow-up – Results from the Finnish health 2011 survey. Journal of Affective Disorder, 179, 73–80.

42.

Marmorstein

Iacono

(2011). Explaining associations between cannabis use disorders in adolescence and later major depression: A test of the psychosocial failure model. Addictive Behaviors, 36, 773–776.

43.

Mathew

Pettit

Lewinsohn

Seeley

Roberts

(2011). Co-morbidity between major depressive disorder and anxiety disorders: Shared etiology or direct causation? Psychological Medicine, 41, 2023–2034.

44.

McGee

Thompson

(2015). Unemployment and depression among emerging adults in 12 states, behavioral risk factor surveillance system, 2010. Preventing Chronic Disease, 12, E38. doi: 10.5888/pcd12.140451

45.

McTeague

Huemer

Carreon

Jiang

Eickhoff

Etkin

(2017). Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. American Journal of Psychiatry, 174, 676–685.

46.

Medel-Herrero

Gomez-Beneyto

(2017). Impacto de la crisis económica del 2008 en el número de jóvenes hospitalizados por patología psiquiátrica. Revista De Psiquiatría Y Salud Mental. EPub ahead of Print 22 November 2017. doi: 10.1016/j.rpsm.2017.10.002

47.

Miller

Maletic

Raison

(2009). Inflammation and its discontents: The role of cytokines in the pathophysiology of major depression. Biological Psychiatry, 65, 732–741.

48.

Miret

Caballero

F. F.

Huerta-Ramírez

Moneta

Olaya

Chatterji

... Ayuso-Mateos

J. L.

(2014). Factors associated with suicidal ideation and attempts in Spain for different age groups: Prevalence before and after the onset of the economic crisis. Journal of Affective Disorders, 163, 1–9.

49.

Mohammadi

M. R.

Davidian

Noorbala

A. A.

Malekafzali

Naghavi

H. R.

Pouretemad

H. R.

(2005). An epidemiological survey of psychiatric disorders in Iran. Clinical Practice and Epidemiology in Mental Health, 1, 16.

50.

Nader

Sanchez

(2017). Effects of regular cannabis use on neurocognition, brain structure, and function: A systematic review of findings in adults. The American Journal of Drug and Alcohol Abuse 44, 4–18.

51.

Navarro-Mateu

Tormo

Salmerón

Vilagut

Navarro

Ruíz-Merino

... Alonso

(2015). Prevalence of mental disorders in the South-East of Spain, one of the European regions most affected by the economic crisis: The cross-sectional PEGASUS-Murcia project. PLoS ONE, 10, e0137293.

52.

Otsubo

Tanaka

Koda

Shinoda

Sano

Tanaka

(2005). Reliability and validity of the Japanese version of the mini-international neuropsychiatric interview. Psychiatry Clinical Neuroscience, 59:, 517–526.

53.

Piccinelli

Wilkinson

(2000). Gender differences in depression: Critical review. The British Journal of Psychiatry, 177, 486–492.

54.

Power

Lecky-Thompson

Fisher

Cohen-Woods

Hosang

Uher

... McGuffin

(2013). The interaction between child maltreatment, adult stressful life events and the 5-HTTLPR in major depression. Journal of Psychiatric Research, 47, 1032–1035.

55.

Roohafza

Feizi

Afshar

Mazaheri

Behnamfar

Hassanzadeh-Keshteli

Adibi

(2016). Path analysis of relationship among personality, perceived stress, coping, social support, and psychological outcomes. World Journal of Psychiatry, 6, 248–256.

56.

Rossi

Alberio

Porta

Sandri

Tansella

Amaddeo

(2004). The reliability of mini-international neuropsychiatric interview–Italian version. Journal of Clinical Psychopharmacology, 24, 561–563.

57.

Sadock

B. J.

Sadock

V. A.

(2009). Mood disorders. In Sadock

B. J.

Sadock

V. A.

(Eds.), Kaplan and Sadock’s synopsis of psychiatry (pp. 527–578). Philadelphia, PA: Lippincott Williams & Wilkins.

58.

Smolkina

Morley

Rijsdijk

Agrawal

Bergin

Nelson

... Lynskey

M. T.

(2017). Cannabis and depression: A twin model approach to co-morbidity. Behavior Genetics, 47, 394–404.

59.

Talkovsky

Green

Osegueda

Norton

(2017). Secondary depression in transdiagnostic group cognitive behavioral therapy among individuals diagnosed with anxiety disorders. Journal of Anxiety Disorders, 46, 56–64.

60.

Urbina-Torija

J. R.

Flores-Mayor

J. M.

García-Salazar

M. P.

Torres-Buisán

Torrubias-Fernández

R. M.

(2007). Síntomas depresivos en personas mayores. Prevalencia y factores asociados. Gaceta Sanitaria, 21, 37–42.

61.

Van de Giessen

Weinstein

Cassidy

Haney

Dong

Ghazzaoui

... Abi-Dargham

(2016). Deficits in striatal dopamine release in cannabis dependence. Molecular Psychiatry, 22, 68–75.

62.

Vollmayr

Gass

(2013). Learned helplessness: Unique features and translational value of a cognitive depression model. Cell and Tissue Research, 354, 171–178.

63.

Wege

Angerer

(2017). Effects of lifetime unemployment experience and job insecurity on two-year risk of physician-diagnosed incident depression in the German working population. International Journal of Environmental Research and Public Health, 14, 904.

64.

World Health Organization. (2009). The ICD-10 classification of mental and behavioural disorders. Geneva, Switzerland: World Health Organization.