Printer Friendly

Gender differences in mental health during the economic crisis.

It is important to point out that Spain has been one of the countries most severely affected by the current crisis. As the Spanish labour market collapsed at the end of 2007, creating conditions of economic hardship for many ordinary people, tax revenues, also fell sharply due to less consumption and collapsed investment, creating a budget deficit of 12% of gross domestic product (GDP) in 2009. Although Spain has low debt levels of 60% of GDP compared with the rest of the European Union (20 per cent point less than Germany) and there are feasible alternatives to cutting budgets (Weisbrot & Montecino, 2010), the International Monetary Fund and European Commission have called for what they describe as "far-reaching" and "absolutely necessary" reforms to reduce government spending to the level set out in the Maastritch criteria (3% of GDP deficit by 2013) (Bloomerg, 2010; Day, 2011). Evidence suggests that the adverse consequences of economic downturns, such as unemployment, debt, economic hardship, impoverishment, insecurity, poor quality of life and social disruption, have a detrimental effect on mental health (Wahlbeck & McDaid, 2012; World Health Organization [WHO], 2011).

Previous research has shown that earlier periods of economic recession appear to have greater impact on mental health of men compared to women (Wahlbeck & McDaid, 2012; WHO, 2011) but it has been suggested that the growing participation of women in the labor market may reduce this differential impact (Bambra, 2010; Minton, Pickett, & Dorling, 2012). Historically, empirical analyses have mainly focused on the effects of unemployment on mental health of males and females. The role that gender plays in the relationship between unemployment and mental health has shown controversial results even in meta-analytic studies (McKee Ryan, Song, Wanberg, & Kinicki, 2005; Paul & Moser, 2009). Strandh, Hammarstrom, Nilsson, Nordenmark, and Russell (2013) studied gender differences of the impact of unemployment in mental health in two countries with different contextual situations (Sweden and Ireland). Unemployment was more negatively related to mental health among men than among women in Ireland, whereas men and women were equally affected by unemployment in Sweden. The authors concluded that the context has a major influence on the relationship between unemployment, gender and mental health.

However, the psychological effects of economic downturns seem to be more complex than the effects of unemployment alone. The gender effects of all macroeconomic changes involved in an economic crisis have been scarcely studied. Katikireddi, Niedzwiedz, and Popham (2012) determined that the current recession impacts on mental health of men within two years of the onset of crisis, but they argued that these changes and their gender patterning could not be adequately accounted for by differences in employment status. In a recent study developed in a large sample of non-institutionalized Spanish general population, Bartoll, Palencia, Malmusi, Suhrcke, and Borrell (2014), using the 12-item version of the General Health Questionnaire, found an increase in the prevalence of poor mental health among men and a slight decrease among women between 2006-2007 and 2011-2012. AgudeloSuarez et al. (2013) also reported an increase of the prevalence of poor mental health in male migrant workers, especially among the unemployed, those with low salaries, and those reporting family burden. The effect of unemployment has been replicated by Aguilar-Palacio, Carrera-Lasfuentes, and Rabanaque (2015) who also found that young males with long-term unemployment were at higher risk of suffering a mental disorder than young male workers.

Our group published a study that shows an increase in the prevalence of major depression disorder, generalized anxiety disorder, panic, somatoform and alcohol-related disorders in Spanish primary care attendees during the current economic crisis, related to unemployment, mortgage repayment difficulties and evictions (Gili et al., 2013) .

The aim of the present paper was to analyze gender differences in specific mental disorders before and during the current economic crisis in primary health care units in Spain, and to study how unemployment may impact the relationship between mental health and gender. We consider gender differences are important in this topic for the purpose of determining the specific risk groups in primary care during the economic crisis and to design future public policies and tailored interventions related to mental health. Our hypothesis was that economic downturns may have a different effect in men and women.

Method

Participants

In the first survey, a nationwide sample of 2000 primary care general practitioners, proportionately distributed by regions within Spain's 17 autonomous communities was selected. A total of 1925 physicians (96.2%) agreed to participate. Each practitioner was asked to select four patients, randomized by day of week and timetable, so as to represent the consulting population. In case of refusals, the next patient was invited to participate. In the second survey, 1300 primary care physicians were included and a total of 1175 (90.3%) agreed to participate, and each one invited five randomly selected patients to participate. A total of 7,940 patients were surveyed between January 2006 and January 2007, with a further 5,876 patients between February 2010 and April 2011. Informed consent was obtained from all participants. Studies received approval from the Local Ethics Committee and complied with Declaration of Helsinki.

Instruments

Patient information was collected by the general practitioners using a Case Report Form. The Case Report Form included gender, age, marital status, educational level, living alone or accompanied, rural or urban residence, employment status and body mass index.

Primary Care Evaluation of Mental Disorders (PRIME-MD) is a clinician-administered diagnostic instrument that was developed and validated for use in primary care settings (Spitzer, 1994). This questionnaire assesses five groups of mental disorders: mood, anxiety, somatoform symptoms, alcohol-related and eating disorders. Baca et al. (1999) set the sensitivity of PRIME-MD Spanish validation at 81.4%, and specificity at 66.1%. Sentitivity and specificity values regarding the specific mental disorders for the original standardization of the instrument are the following: Mood Disorders: sensitivity at 97.3% and specificity at 82.8%; Anxiety Disorders: sensitivity at 75% and specificity at 82.9%; Somatoform Disorders: sensivity at 90.7% and specifity at 60%; Alcohol-related Disorders: sensivity at 90% and specifity at 97.3%. The Eating Disorders module could not be validated in the original instrument due to insuficient number of detected cases.

Procedure

This study is based on the data of two nationwide sample crosssectional surveys conducted in Spain in 2006-2007 (survey I) and 2010-2011 (survey II), before and during the economic crisis (Gili et al., 2013).

Data analysis

Descriptive statistical analyses were generated including means, standard deviations, and percentages of categorical baseline characteristics of the sample. Differences in baseline characteristics of participants as a function of gender were also assessed through Chi-square test, and independent sample t-test or Mann-Whitney test if parametric assumptions were not met. Those characteristics that were found to be statistically different were set as confounders in further analyses.

To determine whether the economic crisis had the same effect in men and women, a log-linear analysis was carried out for each mental disorder (presence or absence) with gender (man or woman) and year of assessment (2006-2007 and 2010-2011) as exposure factors, and sample characteristics that were found to be statistically different at baseline as confounders. In this regard, we used a factorial model because our main hypothesis was based on the potential significance of the interaction between Gender and Year, and any potential confounder was also included in the model. Although log-linear analysis does not differentiate between independent and dependent variables, the theoretical background of the study permits interpreting variables as either the exposure or outcome variables (Horwitz, White, & Howell-White, 1996). Log-linear analysis can also assess how prevalent one category is compared to another of the same factor; and how much each factor contributes to the presence or absence of a mental disorder. Our main hypothesis was based on the potential significance of the interaction between Gender and Year, also weight of Employment confounder was reported in simple effects analysis. Additionally, entropy and concentration coefficients were calculated. The entropy coefficient assesses how much the inclusion of the confounders in the model reduces the variation of the whole set of factors, and the concentration coefficient assesses how much dispersion the confounder set induces in the whole model (Menard, 2009). Data were analysed using IBM SPSS for Windows and The R Integration Package for IBM SPSS Statistics. Statistical significance was set at 5%.

Results

Sociodemographic data of the sample are presented in Table 1. The mean age of the participants was 48.3 years (SD= 15.07), ranging from 18 to 98 years. The members of the sample were predominantly married (58.6%), living with someone (80.8%), and employed (91.8%). In the years 2006-2007, there were significant differences between men and women in body mass index (t= 17.11, df= 7931, p<.001), with men showing higher body mass index than women; family status ([chi square] =103.42, df= 3, p<.001), with women being more frequently widowed than men; educational level ([chi square]=91.94, df= 3, p<.001), with men being more likely to have completed high school or graduated education; environment ([chi square]=6.84, df= 1, p=.009), with women living more frequently in urban environments than men; and a statistical trend in employment ([chi square]=3.17, df= 1, p=.076), with more women being unemployed. In the sample recruited in 2010-2011, there were significant differences for gender in body mass index (t=16.03, df= 5874, p<.001), again, with men scoring higher in this dimension than women; family status ([chi square]=70.47, df= 3, p<.001), widowed women were more frequent; educational level ([chi square]=57.21, df= 3, p<.001), with results in the same direction as the previous assessment; living accompanied ([chi square]=12.17, df= 1, p<.001), women were more likely to live accompanied than men; and employment ([chi square]=63.97, df= 1, p<.001), as registered during 2006-2007, there were more unemployed women than men.

Log linear analysis was carried out for each disorder with body mass index, family status, educational level, environment, living accompanied and employment set as confounders. Detailed results of log linear analysis are shown in Table 2. All cells had expected frequencies greater than 1, and at least 80% of cell frequencies were greater than 5, so log linear requisites were met. The likelihood-ratio analyzed through Pearson's chi-square was not significant and near zero in every outcome, so the frequencies predicted by the model are not significantly different from the observed frequencies. Entropy and concentration coefficients were below 5% except in Probable alcohol abuse/dependence, where the inclusion of the confounders reduced the variation accountable for exposure factors by 9.8%. Gender main effect analysis showed significant differences in every outcome variable. Year exposure factor was also significant for every disorder but Probable Alcohol Abuse/Dependence ([chi square]=2.72, df= 1, p=.099) and Bulimia Nervosa ([chi square]=2.26, df =1, p=.133). Interaction between Gender and Year was significant for Major Depressive disorder ([chi square]=21.39, df =1, p<.001), Generalized Anxiety disorder ([chi square]=4.75, df=1, p=.029), and Nonspecific Multi-somatoform Disorder ([chi square]=7.04, df=1, p=.008).

Analysis of simple effects of the interaction was carried out for Major Depressive disorder, Generalized Anxiety disorder and Non-specific Multi-somatoform disorder with body mass index, family status, educational level, living accompanied and employment set as confounders. Analysis of gender simple effect showed significant differences in both genders for the three disorders proposed with every p-value lower than 0.001. Between 2006 and 2010, the prevalence of Major Depressive disorder increased 155.7% in men and 104.9% in women; Generalized Anxiety disorder increased 98.3% in men and 71.3% in women; and Non-specific Multi-somatoform disorder increased 100.05% in men and 37% in women. Detailed data about the generalized log-odd ratios of these comparisons are shown in Table 3. Also is important to point out the effect of the employment confounder across comparisons: in Major Depressive disorder, the associated Generalized Odds Ratio (GOR) was 2.557 for men (p<.001) and 2.046 for women (p=.002); in Generalized Anxiety disorder, it was 2.153 (p<.001) for men and 1.546 for women (p<.001); and in Non-Specific Multi-somatoform disorder, it was 1.680 for men (p<.001) and 1.301 for women (p=.014). Detailed data about gender simple effect analysis through generalized odds ratios are shown in Table 4.

Discussion

The main finding of our study is that the financial crisis differently affects the mental health of men and women attendees at primary health care units in Spain. Our results suggest that the prevalence of mental disorders--except for Probable Alcohol Abuse/Dependence and Bulimia Nervosa--increased significantly during the current economic crisis in both genders, but the gain is higher in men than in women for Major Depressive disorder, Generalized Anxiety disorder, and Non-Specific Multi-somatoform disorder. When both genders are compared in 2010, the prevalence ratios tend to equalize as a result of a higher increase in men. As far as we know, this is the first analysis of the impact of the precarious economic situation on the prevalence of specific mental disorders of men and women before and during the current financial crisis.

The growing sense of instability, uncertainty and loss of perspective that an economic crisis causes may lead to a sense of personal vulnerability, risk of status loss and social decline resulting in loss of hope and faith in the future (Angermeyer, Matschinger, & Schomerus, 2013)' and growing rates of mental disorders. Our data suggest a relatively larger increase in the prevalence of Major Depressive Disorder, Generalized Anxiety Disorder and Somatization Disorder among men than among women during the 2006-2011 period. The findings of the current study are consistent with some previous research on the impact of the economic crisis on mental health. Agudelo-Suarez et al. (2013) assessed changes in mental health in a sample of migrant workers after the eruption of the economic crisis in Spain; specifically, they compared the prevalence of poor mental health between 2008-2011. Their results show that prevalence was higher in unemployed men, with low salaries, and with family burden. Increase in prevalence of mental health disorders was also observed in women but the change is not significant. Bartoll et al. (2014) also reported an increase in the prevalence of poor mental health among men attributed to employment status. Among women, they noted a slight improvement in mental health, driven by younger individuals, employed and non-breadwinners. Therefore, socioeconomic inequalities in mental health became more pronounced among men but not among women.

Our findings could be explained by different factors. First, the institutional process of familiarization of family policies in the southern European countries has led to a distinctive gender regime (with informal rules) in which females were considered caretakers in a traditional family role and a single-earner family was promoted (Moreno Minguez, 2005; Saraceno, 1995; Trifiletti, 1999). Masculine identity is intricately linked to having a job in Mediterranean and western societies and it is severely threatened by unemployment. Therefore, uncertainty and sense of vulnerability about future employment may have a higher impact on men's mental health than on women's. This explanation is related to the assumptions of the different roles and social positions of men and women. Gender roles differ across time and space. Female labor force participation in the Nordic countries has been very high for a long time; while the female labor force participation in countries such as Spain, Germany or Ireland has historically been lower. For this reason, in a situation where gender relations are characterized by a relative similarity of the roles of men and women, there may be no gender differences in the relationship between unemployment and mental health. In contrast, in a situation where there are substantial discrepancies of the roles, there may be a differential relationship between unemployment and mental health (Strandh et al., 2013). Assumption of traditional gender roles makes men more sensitive to the effect of socioeconomic changes and may contribute to increased prevalence rates of depression, anxiety and somatization rates during economic downturns.

Second, in recent decades, the economy of Spain has been based mostly on personal services, tourism and construction sector and they are exactly the areas most affected by the crisis (Instituto Nacional de Estadistica, 2014a). Besides, the higher impact among men could be explained by the negative effects of the financial crisis that were strong in the construction and services sectors (Agudelo-Suarez et al., 2013). In fact, in 2009, women held only 7.8% of all construction jobs, 18% of engineering positions and 25% of industrials jobs (Instituto Nacional de Estadistica, 2010; Kumar, 2010). Moreover, economic crisis had less impact on domestic and care services in private households in the informal economy (Agudelo-Suarez et al., 2013; Instituto Nacional de Estadistica, 2014b).

Finally, in our study, we found that in 2006-2007 (survey I), there were more unemployed women compared with men. However, in 2010-2011 (Survey II), male unemployment rose dramatically. These findings are consistent with published data from the different labor force surveys; in 2006-2007, women suffered more unemployment than men. In that year, female unemployment rate was 11.11%, while men's was 6.02% (Instituto Nacional de Estadistica, 2006) . The evolution of the labor market has been more favorable for women in 2010-2011. Since 2006, women's unemployment rate rose 9 points whereas men's rose 14, with both genders reaching unemployment rates around 20% (20.79 and 19.95%, respectively) (Instituto Nacional de Estadistica, 2011).

It is important to point out the role of unemployment as a confounder in the relation between loss of income and prevalence of specific mental disorders. Unemployment in men has the same increasing effect over depressive symptoms as financial crisis. This finding is in line with Riumallo-Herl, Basu, Stuckler, Courtin, and Avedano (2014), who report the relationship between job loss and increased prevalence of Major Depressive disorder, and how personal wealth might lessen the impact of unemployment over depressive symptoms. Our data also suggest that loss of job affects the prevalence of Generalized Anxiety disorder in men more than financial crisis. The explanation may lie in a combination of unemployment with reduction of personal wealth as a consequence of financial crisis. This combined factor may interact with social discrimination associated with unemployment (Angermeyer et al., 2013), and trigger a pathological worry-based lifestyle which may lead to Generalized Anxiety disorder. In essence, the loss of the traditional masculinity role of provider (Moller-Leimkuhler, 2003)' when combined with financial crisis and loss of income, may be a risk factor of Generalized Anxiety disorder in men.

There are some limitations in the present study that should be considered: First, the sample is not population-based. It only includes those who attended primary care, although healthcare in Spain is universal and free. Second, the conventional measures of unemployment do not capture those who shifted from full employment to being on "sick leave" or "temporarily unable to work". Finally, the season of interview was not the same: the first wave occurred between January 2006 and January 2007 but the second wave was performed between February 2010 and April 2011. Despite these limitations, our study has important strengths: It is the first one to investigate the prevalence of specific mental disorders among men and women before and during the current economic crisis in a southern European country. Another strength is that the sample is large in each period and provides the necessary statistical power to analyse differences between genders.

In conclusion, our study supports previous evidence that economic crises are more strongly associated with decreasing mental health in men than in women. Spain has been one of the countries most severely affected by the current crisis, and the population has suffered the consequences of important unemployment rate increases, austerity policies and severe cuts of wages. So, further investigation is needed, and a third wave in the following years may provide information about mediumand long-term consequences of economic crisis on mental health and their impact on men and women. Bearing in mind that gender modulates the effect of depressive symptoms in quality of life (Gili et al., 2014) future research should address this issue and how loss of income may affect disability in both genders. The current economic crisis presents an opportunity to reinforce policies that would not only mitigate the impact of the recession on deaths and injuries arising from suicide attempts and alcohol use disorders, but would also improve global health and reduce the economic burden presented by impaired mental health and alcohol use disorders in any economic cycle (Cabello, Diaz, & Arredondo, 2012; Wahlbeck & McDaid, 2012). Our study suggests the crucial role of primary care units in providing patients with the appropriate mental health counseling and treatment and preventing problems by detecting high-risk groups. Our findings could be an important issue to plan future public policies related to mental health in primary care and to design tailored interventions for specific risk groups.

Our research provides two important evidences about this topic. First, financial crisis differently affects the mental health of men and women in Spain between 2006-2007 and 2010-2011. In particular, the prevalence of mental disorders--except for Probable Alcohol Abuse/Dependence and Bulimia Nervosa--increased significantly during the current economic crisis in both genders, but the gain is higher in men than in women for Major Depressive Disorder, Generalized Anxiety Disorder and Non-specific Multisomatoform Disorder. When both genders are compared in 2010, the prevalence ratios tend to equalize as a result of a higher increase in men. Second, unemployment is considered a significant predictor for some particular disorders, specifically in men. These results were consistent with some previous research that assesses general mental health during similar period of time and concludes that poor mental health was more prevalent among men.

It is important to point that Spain has been one of the countries deeply affected by the current crisis and its consequences. For these reasons, future public policies related to mental health are necessary to design tailored interventions for specific risk groups in primary care during economic crisis.

doi: 10.7334/psicothema2015.288

References

Agudelo-Suarez, A. A., Ronda, E., Vazquez-Navarrete, M. L., Garcia, A. M., Martinez, J. M., & Benavides, F. G. (2013). Impact of economic crisis on mental health of migrant workers: What happened with migrants who came to Spain to work? International Journal of Public Health, 58(4), 627-631.

Aguilar-Palacio, I., Carrera-Lasfuentes, P, & Rabanaque, M. J. (2015). Youth unemployment and economic recession in Spain: Influence on health and lifestyles in young people (16-24 years old). International Journal of Public Health, 60(4), 427-435.

Angermeyer, M. C., Matschinger, H., & Schomerus, G. (2013). Public attitudes towards people with depression in times of uncertainty: Results from three population surveys in Germany. Social Psychiatry and Psychiatric Epidemiology, 48(9), 1513-1518.

Baca, E., Saiz, J., Aguera, L., Caballero, L., Fernandez-Liria, A., Ramos, J., Gil, A., Madrigal, M., & Porras, A. (1999). Validacion de la version espanola del PRIME-MD: un procedimiento para el diagnostico de trastornos mentales en atencion primaria [Validation of the Spanish Version from PRIME-MD: A method for mental health diagnosis in Primary Care. Actas Espanolas de Psiquiatria, 27(6), 375-383.

Bambra, C. (2010). Yesterday once more? Unemployment and health in the 21st century. Journal of Epidemiology and Community Health, 64(3), 213-215.

Bartoll, X., Palencia, L., Malmusi, D., Suhrcke, M., & Borrell, C. (2014). The evolution of mental health in Spain during the economic crisis. European Journal of Public Health, 24(3), 415-418.

Bloomberg (2010). Spain, Portugal deficit reductions "absolutely necessary", EUs Rehn says.

Cabello, H. R., Diaz, L. C., & Arredondo, A. (2012). The economic impact of mental health services and the need for cost reduction programs: Suggestions from middle-income countries. Acta Psychiatrica Scandinavica, 126(4), 298-299.

Day, P (2011). Spain underperformed euro zone for 1st time in 2010. Reuter's.

Instituto Nacional de Estadistica (2006). Encuesta de Poblacion Activa 2006 [Labour Force Survey 2006]. Retrieved from: http://www.ine.es/ daco/daco42/daco4211/epa0306.pdf

Instituto Nacional de Estadistica (2010). Hombres y mujeres en Espana en

2010 [Women and men in Spain in 2010]. Retrieved from: http://www. ine.es/ss/Satellite?param1=PYSDetalleGratuitas&c=INEPublicacion_ C&p=1254735110672&pagename=ProductosYServicios%2FPYSLay out&cid=1259924822888&L=1

Instituto Nacional de Estadistica (2011). Encuesta de Poblacion Activa 2011 [Labour Force Survey 2011]. Retriewed from: http://www.ine. es/daco/daco42/daco4211/epa0410.pdf

Instituto Nacional de Estadistica (2014a). Poblacion activa por genero y ocupacion en Espana entre 2005 y 2013 [Working population by gender and occupation in Spain between 2005 and 2013]. Retriewed from: http://www.ine.es/jaxiBD/menu.do?L=0&divi=EPA&his=3&type=db

Instituto Nacional de Estadistica (2014b). Encuesta de Poblacion Activa [Labour Force Survey]. Retriewed from: http://www.ine.es/jaxi/menu. do?type=pcaxis&path=/t22/e308_mnu&file=inebase&N=&L=0

Gili, M., Castro, A., Navarro, C., Molina, R., Magallon, R., Garcia-Toro, M., & Roca, M. (2014). Gender differences on functioning in depressive patients. Journal of Affective Disorders, 166, 292-296.

Gili, M., Roca, M., Basu, S., McKee, M., & Stuckler, D. (2013). The mental health risks of economic crisis in Spain: Evidence from primary care centres, 2006 and 2010. European Journal of Public Health, 23(1), 103-108.

Horwitz, A. V., White, H. R., & Howell-White, S. (1996). The Use of Multiple Outcomes in Stress Research: A Case Study of Gender Differences in Responses to Marital Dissolution. Journal of Health and Social Behavior, 37(3), 278-291.

Katikireddi, S. V., Niedzwiedz, C. L., & Popham, F. (2012). Trends in population mental health before and after the 2008 recession: A repeat cross-sectional analysis of the 1991-2010 Health Surveys of England. BMJ Open, 2(5).

Kumar A. (2010). Women in Engineering and Technology. London: Engineering UK.

McKee-Ryan, F., Song, Z., Wanberg, C. R., & Kinicki, A. J. (2005). Psychological and physical well-being during unemployment: A metaanalytic study. The Journal of Applied Psychology, 90(1), 53-76.

Menard, S. (2009). Logistic Regression: From Introductory to Advanced Concepts and Applications. Thousand Oaks: SAGE Publications.

Minton, J. W., Pickett, K. E., & Dorling, D. (2012). Health, employment, and economic change, 1973-2009: Repeated cross sectional study. BMJ (Clinical Research Ed.), 344(7856), e2316.

Moller-Leimkuhler, A. M. (2003). The gender gap in suicide and premature death or: Why are men so vulnerable? European Archives of Psychiatry and Clinical Neuroscience, 253(1), 1-8.

Moreno Minguez, A. (2005) Empleo de la mujer y familia en los regimenes de bienestar del sur de Europa en perspectiva comparada. Permanencia de modelo de varon sustentador [Employment of women and family welfare schemes in southern Europe in comparative perspective. Permanence male breadwinner model]. REIS, 112, 127-159.

Paul, K. I., & Moser, K. (2009). Unemployment impairs mental health: Meta-analyses. Journal of Vocational Behavior, 74(3), 264-282.

Riumallo-Herl, C., Basu, S., Stuckler, D., Courtin, E., & Avedano, M. (2014). Job loss, wealth and depression during the Great Recession in the USA and Europe. International Journal of Epidemiology, 1-10.

Saraceno, C. (1995). Familismo ambivalente y clientelismo categorico en el Estado del Bienestar italiano [Ambivalent categorical family and patronage in the Italian Welfare State]. In S. Sarasa & L. Moreno (Eds.), El Estado del Bienestar en la Europa del Sur [The welfare state in Southern Europe] (pp. 261-288). Madrid: CSIC.

Spitzer, R. L. (1994). Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. JAMA: The Journal of the American Medical Association, 272(22), 1749-1756.

Strandh, M., Hammarstrom, A., Nilsson, K., Nordenmark, M., & Russel, H. (2013). Unemployment, gender and mental health: The role of the gender regime. Sociology of Health & Illness, 35(5), 649-665.

Trifiletti, R. (1999). Southern European welfare regimes and the worsening position of women, Journal of European Social Policy, 9(1), 49-64.

Wahlbeck, K., & McDaid, D. (2012). Actions to alleviate the mental health impact of the economic crisis. World Psychiatry, 11(3), 139-145.

Weisbrot, M., & Montecino, J. (2010). Alternatives to Fiscal austerity in Spain. Washington DC: Center for Economic and Policy Research.

World Health Organization (2011). Impact of economic crises on mental health. Retriewed from: http://www.euro.who.int/_data/assets/pdf_ file/0008/134999/e94837.pdf?ua=1

Margalida Gili (1), Emilio Lopez-Navarro (1), Adoracion Castro (1), Clara Homar (1), Capilla Navarro (1), Mauro Garcia-Toro (1), Javier Garcia-Campayo (2) and Miquel Roca (1)

(1) University of Balearic Islands and (2) University of Zaragoza

Received: November 6, 2015 * Accepted: July 5, 2016

Corresponding author: Margalida Gili

Institut Universitari d'Investigacio en Ciencies de la Salut (IUNICS-IDISPA)

University of Balearic Islands

07122 Palma de Mallorca (Spain)

e-mail: mgili@uib.es
Table 1
Demographic characteristics of the sample

                                        2006

                               Total            Men
                               2006          (n=3036)

Age (mean, SD)             48.59 (15.47)   48.67 (15.67)

Body Mass Index
  (mean, SD)               25.61 (4.03)    26.58 (3.27)

Family Status (n, %)
Single                      1592 (20.1)     676 (22.27)
Married/Couple              4821 (60.8)    1933 (63.67)
Widow                       841 (10.6)      196 (6.46)
Separated/Divorced           678 (8.6)       231 (7.6)

Educational level (n, %)
Uncompleted/No studies      2184 (27.6)     669 (22.03)
Elementary School           1823 (23.0)     682 (22.46)
High School                 2271 (28.6)     960 (31.62)
Collegue/Graduated          1652 (20.8)     725 (23.89)

Lives (n, %)
Alone                       1378 (17.4)     529 (17.42)
Accompanied                 6555 (82.6)    2507 (82.57)

Home (n, %)
Rural                       2083 (26.3)     847 (27.9)
Urban                       5850 (73.7)     2189 (72.1)

Unemployed (n, %)
Yes                          435 (5.5)      184 (6.06)
No                          7499 (94.5)    2852 (93.94)

                                        2006

                               Women
                             (n=4897)              Statistics

Age (mean, SD)             48.54 (15.67)         t= .36 p=.715

Body Mass Index
  (mean, SD)                25.03 (4.9)         t= 17.11 p<.001

Family Status (n, %)
Single                      916 (18.7)     [chi square]=103.42 p<.001
Married/Couple             2888 (58.97)
Widow                       645 (13.17)
Separated/Divorced          447 (9.16)

Educational level (n, %)
Uncompleted/No studies     1515 (30.94)    [chi square]=91.94 p<.001
Elementary School          1141 (23.29)
High School                1311 (26.77)
Collegue/Graduated          927 (18.93)

Lives (n, %)
Alone                       849 (17.34)     [chi square]=0.01 p=.921
Accompanied                4048 (82.66)

Home (n, %)
Rural                      1236 (25.24)     [chi square]=6.84 p=.009
Urban                      3661 (74.76)

Unemployed (n, %)
Yes                         251 (5.11)      [chi square]=3.17 p<.001
No                         4647 (94.89)

                                        2010

                               Total            Men
                               2010          (n=2528)

Age (mean, SD)             48.20 (14.51)   48.25 (13.84)

Body Mass Index
  (mean, SD)               25.85 (4.05)    26.81 (3.56)

Family Status (n, %)
Single                      1206 (20.5)     577 (22.82)
Married/Couple              3280 (55.8)    1467 (58.04)
Widow                       614 (10.4)      176 (6.96)
Separated/Divorced          776 (13.2)      308 (12.18)

Educational level (n, %)
Uncompleted/No studies      1183 (20.1)     402 (15.9)
Elementary School           1732 (29.5)     739 (29.23)
High School                 1829 (31.1)     854 (33.78)
Collegue/Graduated          1132 (19.3)     533 (21.08)

Lives (n, %)
Alone                       1268 (21.6)     600 (23.73)
Accompanied                 4608 (78.4)    1928 (76.26)

Home (n, %)
Rural                       2615 (44.5)    1116 (44.14)
Urban                       3261 (55.5)    1412 (55.85)

Unemployed (n, %)
Yes                         699 (11.9)      399 (15.78)
No                          5177 (88.1)    2129 (84.22)

                                           2010

                               Women              Statistics
                             (n=3348)

Age (mean, SD)             48.16 (15.01)         t=.22 p=.824

Body Mass Index
  (mean, SD)               25.13 (4.26)         t= 16.03 p<.001

Family Status (n, %)
Single                      629 (18.79)    [chi square]=70.47 p<.001
Married/Couple             1813 (54.15)
Widow                       438 (13.08)
Separated/Divorced          468 (13.98)

Educational level (n, %)
Uncompleted/No studies      781 (23.33)    [chi square]=57.21 p<.001
Elementary School           993 (29.66)
High School                 975 (29.12)
Collegue/Graduated          599 (17.89)

Lives (n, %)
Alone                       668 (19.95)    [chi square]=12.17 p<.001
Accompanied                2680 (80.05)

Home (n, %)
Rural                      1499 (44.77)     [chi square]=0.23 p=.63
Urban                      1849 (55.23)

Unemployed (n, %)
Yes                         300 (8.96)     [chi square]=63.97 p<.001
No                         3048 (91.04)

Table 2
Clinical characteristics of the sample

                                          2006

                                  Total        Men
                                  2006       (n=3036)

Major Depressive Disorder
(n, %)
Yes                            2298 (28.9)     644
No                             5636 (71.1)     2392

Dysthymia (n, %)
Yes                            1161 (14.6)     315
No                             6773 (85.4)     2721

Panic Attack Disorder (n, %)
Yes                             770 (9.7)      206
No                             7164 (90.3)     2830

Generalized Anxiety
Disorder (n, %)
Yes                            929 (11.7)      281
No                             7005 (88.3)     2755

Probable Alcohol Abuse/
Dependence (n, %)
Yes                             710 (8.9)      551
No                             7224 (91.1)     2485

Bulimia Nervosa
(n, %)
Yes                             44 (0.6)        8
No                             7890 (99.4)     3028

Multisomatofm Disorder
(n, %)
Yes                            1111 (14.0)     295
No                             6823 (86.0)     2741

Non-Specific Multi-
somatoform Disorder (n, %)
Yes                            1175 (14.8)     365
No                             6765 (85.2)     2671

                                       2006

                                Women          Statistics
                               (n=4897)

Major Depressive Disorder
(n, %)
Yes                              1654     [chi square]=143.631
No                               3244            p<.001

Dysthymia (n, %)
Yes                              846      [chi square]=71.368
No                               4052            p<.001

Panic Attack Disorder (n, %)
Yes                              564      [chi square]=47.844
No                               4334            p<.001

Generalized Anxiety
Disorder (n, %)
Yes                              648       [chi square]=28.63
No                               4250            p<.001

Probable Alcohol Abuse/
Dependence (n, %)
Yes                              159      [chi square]=510.863
No                               4739            p<.001

Bulimia Nervosa
(n, %)
Yes                               36       [chi square]=7.555
No                               4862            p=.006

Multisomatofm Disorder
(n, %)
Yes                              816      [chi square]=75.030
No                               4082            p<.001

Non-Specific Multi-
somatoform Disorder (n, %)
Yes                              810      [chi square]=30.283
No                               4088            p<.001

                                      2010

                                  Total         Men
                                   2010       (n=2528)

Major Depressive Disorder
(n, %)
Yes                            2794 (47.5)      1082
No                             3082 (52.5)      1446

Dysthymia (n, %)
Yes                            1474 (25.1)      505
No                             4402 (74.9)      2023

Panic Attack Disorder (n, %)
Yes                             920 (15.7)      302
No                             4956 (84.3)      2226

Generalized Anxiety
Disorder (n, %)
Yes                            1155 (19.7)      452
No                             4721 (80.30)     2076

Probable Alcohol Abuse/
Dependence (n, %)
Yes                             520 (8.8)       412
No                             5356 (91.1)      2116

Bulimia Nervosa
(n, %)
Yes                              60 (1.0)        13
No                             5816 (99.0)      2515

Multisomatofm Disorder
(n, %)
Yes                            1390 (23.7)      442
No                             4486 (76.3)      2086

Non-Specific Multi-
somatoform Disorder (n, %)
Yes                            1256 (21.4)      546
No                             4620 (78.6)      1982

                                       2010

                                Women          Statistics
                               (n=3348)

Major Depressive Disorder
(n, %)
Yes                              1712      [chi square]=40.117
No                               1636            p<.001

Dysthymia (n, %)
Yes                              969       [chi square]=61.62
No                               2379            p<.001

Panic Attack Disorder (n, %)
Yes                              618       [chi square]=46.263
No                               2730            p<.001

Generalized Anxiety
Disorder (n, %)
Yes                              703       [chi square]=8.866
No                               2645            p=.003

Probable Alcohol Abuse/
Dependence (n, %)
Yes                              108      [chi square]=305.114
No                               3240            p<.001

Bulimia Nervosa
(n, %)
Yes                               47       [chi square]=11.278
No                               3301            p<.001

Multisomatofm Disorder
(n, %)
Yes                              948       [chi square]=93.568
No                               2400            p<.001

Non-Specific Multi-
somatoform Disorder (n, %)
Yes                              710        [chi square]=.131
No                               2638            p=.717

Table 3
Log linear analysis results

                                               Entropy   Concentration
                               [chi square]      (%)          (%)

Major Depression Disorder          0.63          3.8          4.9
Dysthymic Disorder                 0.82          2.8          2.7
Panic Attack Disorder              0.88           2           1.5
Generalized Anxiety Disorder       0.85          1.7          1.4
Probable Alcohol
  Abuse/Dependence                 1.06          9.8          4.9
Bulimia Nervosa                    1.01          2.4          0.2
Multi-somatoform Disorder          0.83          2.9          2.8
Non-Specific
  Multi-somatoform Disorder        0.82           1           0.9

                                   Gender             Year

                                Wald      p      Wald      p

Major Depression Disorder      89.54    <.001   210.27   <.001
Dysthymic Disorder             67.06    <.001   107.83   <.001
Panic Attack Disorder           40.5    <.001   66.56    <.001
Generalized Anxiety Disorder   20.01    <.001   80.32    <.001
Probable Alcohol
  Abuse/Dependence             266.96   <.001    2.72    .099
Bulimia Nervosa                 7.89    .005     2.26    .133
Multi-somatoform Disorder      68.84    <.001   93.64    <.001
Non-Specific
  Multi-somatoform Disorder      21     <.001   55.18    <.001

                                 Interaction

                               Wald      p

Major Depression Disorder      21.39   <.001
Dysthymic Disorder             3.43    .064
Panic Attack Disorder          2.64    .104
Generalized Anxiety Disorder   4.75    .029
Probable Alcohol
  Abuse/Dependence             0.24    .623
Bulimia Nervosa                0.13    .724
Multi-somatoform Disorder      0.38    .539
Non-Specific
  Multi-somatoform Disorder    7.04    .008

Log linear analysis results for factorial model with gender and
year of assessment as exposure factors. First row shows the
effect contrasted through Wald's test

Table 4
Gender simple effect analysis trough generalized odd ratios (GOR)

                                                         Non-specific
Level          Outcome       Major        Generalized       Multi-
                          Depressive        Anxiety       somatoform
                           Disorder        Disorder        disorder

Men              GOR         2.557           1.983           2.005
               IC 95%    2.262 - 2.890   1.680 - 2.341   1.725 - 2.331
               p value      <0.001          <0.001          <0.001

Unemployment     GOR         2.557           2.153           1.680
               p value      <0.001          <0.001          <0.001
                 GOR         2.049           1.713           1.370

Women          IC 95%    1.866 - 2.250   1.517 - 1.930   1.220 - 1.539
               p value      <0.001          <0.001          <0.001

Unemployment     GOR         2.046           1.546           1.301
               p value       0.002          <0.001           0.014

Only significant interactions found in the logit model have been
analyzed
COPYRIGHT 2016 Colegio Oficial De Psicologos Del Principado De Asturias
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Gili, Margalida; Lopez-Navarro, Emilio; Castro, Adoracion; Homar, Clara; Navarro, Capilla; Garcia-To
Publication:Psicothema
Date:Oct 1, 2016
Words:6124
Previous Article:A predictive study of antecedent variables of workaholism.
Next Article:Testing the alleged superiority of the indulgent parenting style among Spanish adolescents.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |