Gender differences in mental health during the economic crisis.
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.
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.
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.
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).
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%.
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.
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.
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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)
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
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|Author:||Gili, Margalida; Lopez-Navarro, Emilio; Castro, Adoracion; Homar, Clara; Navarro, Capilla; Garcia-To|
|Date:||Oct 1, 2016|
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