Sex differences and the influence of social factors in a Chilean urban psychiatric hospital population

Abstract

Background:

The epidemiological literature has reported differences by sex in the prevalence of psychiatric diagnoses. However, we know little about how other socio-demographic factors participate in these differences.

Aim:

To identify the socio-demographic factors that correlate with prevalent psychiatric diagnoses in women and men in a Chilean urban psychiatric hospital population.

Method:

Socio-demographic information (age, educational level, marital status, family group and work status), psychiatric diagnoses and sex of the population were collected for 3,920 patients of a tertiary care hospital during a period of 8 years (2007–2014). The data were subjected to bivariate and multivariate analyses comparing the results by sex.

Results:

Among the most prevalent psychiatric diagnoses, those significantly correlated with sex were eating disorders and major depression (women) and schizophrenia (men). Socio-demographic factors behave differently in men and women regarding those diagnoses. Among the differences, working and being married correlated directly with the diagnosis of depression only among women. Living alone correlated directly with the diagnosis of schizophrenia among men, but correlated inversely among women.

Conclusion:

Dissimilar associations between sex, psychiatric diagnosis and socio-demographic factors found in this Latin American sample invite us to reflect on how social conditions crosscut the relation between sex and psychopathology and to include gender perspectives in psychiatric practices.

Keywords

Sex differences social factors psychiatric diagnosis major depression schizophrenia

Introduction

The relationship between psychopathology and gender¹ has been widely documented. As an example, the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association, 2013) includes a section devoted to the ‘gender-related diagnostic aspects’ of a large number of classifications. The manual also includes specifications in regard to sex among the diagnostic criteria in some cases.

The variable of sex² is present in most studies of psychiatric epidemiology, chiefly in those exploring the so-called risk factors that may lead to the emergence of a psychopathology. In the Latin American region, research coincides on the high prevalence of mood disorders and anxiety in women, in particular, diagnoses of major depression, bipolar affective disorder and generalized anxiety disorder. Among men, however, diagnoses involving alcohol consumption and abuse stand out (Kohn et al., 2005; Vicente, Rioseco, Saldivia, Kohn, & Torres, 2005). These regional prevalences apply equally to Chile, the country in which this investigation was carried out (Vicente et al., 2004; Vicente, Rioseco, Saldivia, Kohn, & Torres, 2002). Other psychopathologies, less common in the general population but significantly associated with sex, are personality disorders (borderline, dependent and histrionic), sexual dysfunctions and eating disorders, all common among women, and schizoid and antisocial personality, paraphilic disturbances, disruptive disorders, and impulse and conduct control among men (Hartung & Widiger, 1998; Kullgren, 1992; Pedersen & Simonsen, 2014; Torgersen, Kringlen, & Cramer, 2001)

Studies in this area have also explored the relationship between these prevalences and other socio-demographic variables, including the sex of the population. These are analyzed for their potential to predict the emergence of a psychopathology (Araya, Rojas, Fritsch, Acuna, & Lewis, 2001; Baumeister & Härter, 2007; Bunevicius et al., 2014; Bunting, Murphy, O’Neill, & Ferry, 2013; Cwikel, Zilber, Feinson, & Lerner, 2008; Girolamo et al., 2006; Jacobi et al., 2004; Vicente et al., 2004). However, epidemiologists have made few attempts to study the sex of the population as an independent variable in relation to other socio-demographic characteristics by examining possible socio-cultural differences between men and women with psychiatric diagnoses (e.g. Bijl, de Graaf, Ravelli, Smit, & Vollebergh, 2002; Gutiérrez-Lobos, Wölfl, Scherer, Anderer, & Schmidl-Mohl, 2000; Klose & Jacobi, 2004; Röder-Wanner, Oliver, & Priebe, 1997; Salokangas, Honkonen, Stengård, & Koivisto, 2001).

Using a register of psychiatric admissions in a public hospital in Santiago, Chile, this article describes and explores the different ways sex intersects with other socio-demographic variables in the most prevalent psychiatric diagnoses in this urban sample. Its interest lies in showing how some social conditions could affect men and women differently, especially as far as the possibility of receiving a psychiatric diagnosis is concerned.

Socio-demographic characteristics of Chile

Among the member countries of the Organisation for Economic Co-operation and Development (OECD), Chile ranks 34th among 38 countries on the ‘index for a better life’³ (OECD, 2017a). In terms of health, this same index highlights that Chile’s public spending in 2016 (US$1,200 per capita) is significantly lower than that of Western European countries (OECD, 2017b). According to estimates of the country’s Ministry of Health, between 2% and 3% of this budget go to mental health, a lower percentage than that of countries such as the United States (6%), Australia (9.6%), the United Kingdom (10 %), Sweden and New Zealand (11%) (Errázuriz, Valdés, Vöhringer, & Calvo, 2015).

According to data reported by the OECD, Chile has the seventh highest poverty rate, second only to Israel, Turkey, the United States, Mexico, Latvia and Japan, and has the second worst income distribution according to the Gini coefficient (OECD, 2017c). According to data from the World Bank, the per capita income of the country is calculated at US$13,792, while the minimum wage is US$420 - an insufficient income, considering that the average household expenditure in Chile is calculated at US$1,281, of which approximately US$206 goes to health (INE, 2013).

According to the latest information from the Chilean Institute of Statistics (Instituto Nacional de Estadística (INE)), the country’s population in 2017 is 50.47% female and 49.53% male, with a total of 17,909,754 people living in the country. Life expectancy for women is 81 years and for men is 76 years, with a 4% increase in the population over 64 years of age in the past two decades (Seremi de Salud, 2014). The marriage rate fell by 4.8% between 2002 and 2015, while age at marriage increased by 6 years on average in both men (30–36 years) and women (27–33 years) over this period (INE, 2015).

Santiago, the capital of Chile, has 40.5% of the population (7,036,762 inhabitants), of which 96.7% live in urban areas. Of the total population under the age of 30 years, 2.5% is without formal education and 16% is with complete higher education; 53.8% of its population is working, 10.1% is not working and 5.4% is unemployed (Seremi de Salud, 2014). Only 50.9% of women with primary education participate in the labor market compared to 87.2% of men with primary education. This gap decreases in the population with higher education, women obtaining a participation rate of 78.8% compared to 90.8% of men (ComunidadMujer, 2016).

Method

Population

The hospital, a public tertiary care provider dedicated to the treatment of high complexity pathologies, is the primary health center for nearly 17% of the population of Santiago, the capital of Chile. Its patients are referred from 46 health centers in the city (37 primary care and 9 secondary care), distributed in nine communes⁴ that are highly heterogeneous in their demographic characteristics.⁵ The majority of the hospital’s user population corresponds to that with the lowest resources in these communes, with an average monthly income of less than 250,000 Chilean pesos (about US$380), including people considered indigent or without any resources.

The data analyzed were extracted from an administrative database kept by the hospital’s psychiatric service and created in order to register and count admissions. The register includes personal information (ID number, place of residence), socio-demographic characterization (sex, age, educational level, work status, marital status and family circumstances) and institutional data (date of admission, center referred from and primary psychiatric diagnosis according to the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10)) of the user population.

This database corresponds to the administrative transcription of hospital admission forms. These forms were completed by nursing professionals responsible for the admission interviews, and the ICD-10 diagnosis corresponds to the diagnosis contained in the referral documents issued by the centers that comprise the health care network. At the time the research was conducted, the database contained 7,822 entries registered between 2007 and 2014.

Selection of sample

Cleansing and systematization of the database revealed the existence of a large amount of lost data. Given the administrative character of the database and the non-systematization of the missing data, it was not possible to replace the unreported data statistically. It was necessary, therefore, to carry out a sample selection to guarantee the validity and accuracy of the results. This selection was carried out according to the following criteria: (1) only those entries which contained complete information for the variables of sex and psychiatric diagnosis were considered as these were central for the study, and (2) cases without complete information on all the socio-demographic variables included (age, level of education, civil status, family group and work status) were discarded. As can be seen in Table 1, the excluded population presents more than 85% of lost data for each of the socio-demographic variables included in the study (except for age). Finally, the sample selected contained 3,920 entries.

Table 1.

Composition of the sample by sex.

Socio-demographic characteristics	Sample analyzed (3,920)						Sample excluded (3,902)
Sex	Women (n = 2,687)	Female Pop. (100%)	Men (n = 1,233)	Male Pop. (100%)	N	%	Women (n = 2,680)	Female Pop. (100%)	Men (n = 1,154)	Male Pop. (100%)	N	%
Age (years)
15–25	513	19.1%	327	26.5%	840	21.4	582	21.8%	303	26.6%	909	23.5
26–34	425	15.8%	191	15.5%	616	15.7	381	14.3%	175	15.3%	567	14.7
35–44	536	19.9%	236	19.1%	772	19.7	481	18.0%	232	20.3%	722	18.7
45–54	574	21.4%	213	17.3%	787	20.1	544	20.4%	187	16.4%	740	19.1
55–64	352	13.1%	164	13.3%	516	13.2	354	13.3%	141	12.4%	501	13.0
65–90	287	10.7%	102	8.3%	389	9.9	354	12.2%	103	9.0%	429	11.1
r = – .073**
Total (missing)							2,666 (14)	99.5% (0.5%)	1,141 (13)	98.9% (1.1%)	3,868 (34)	99.1 (0.9)
Educational level
Primary	589	21.9%	221	17.9%	810	20.7	68	23.1%	23	19.3%	91	21.9
Secondary	1,450	54.0%	708	57.4%	2,158	55.1	179	60.7%	71	59.7%	250	60.1
Higher	648	24.1%	304	24.7%	952	24.3	48	16.3%	25	21.0%	75	18.0
r = .031*
Total (missing)							295 (2,385)	11% (89%)	119(1,035)	10.3% (89.7%)	416 (3,486)	10.7 (89.3)
Marital status
Single	1,187	44.2%	742	60.2%	1,929	49.2	189	52.1%	106	65.8%	296	7.6
Cohabiting	169	6.3%	60	4.9%	229	5.8	15	4.1%	6	3.7%	22	0.6
Married	769	28.6%	290	23.5%	1,059	27.0	85	23.4%	32	19.9%	117	3.0
Separated	390	14.5%	117	9.5%	507	12.9	52	14.3%	14	8.7%	66	1.7
Widowed	172	6.4%	24	1.9%	196	5.0	22	6.1%	3	1.9%	25	0.6
r = .114**
Total (missing)							363 (2,317)	13.5% (86.5%)	161 (993)	14% (86%)	526 (3,376)	13.5 (86.5)
Family group
Origin	537	20.0%	434	35.2%	971	24.8	81	24.8%	54	36.7%	135	3.5
Nuclear	1,107	41.2%	331	26.8%	1,438	36.7	112	34.3%	36	24.5%	149	3.8
Extended	770	28.7%	282	22.9%	1,052	26.8	99	30.3%	38	25.9%	138	3.5
Living alone	97	3.6%	54	4.4%	308	7.9	15	4.6%	4	2.7%	19	0.5
Other	176	6.6%	132	10.7%	151	3.9	20	6.1%	15	10.2%	35	0.9
r = –.029
Total (missing)							327 (2,353)	12.2% (87.8%)	147 (998)	12.7% (87.3%)	476 (3,376)	12.2 (87.8
Work status
Working	1,087	40.5%	543	44.0%	1,630	41.6	112	32.8%	59	42.4%	173	35.9
Homemaker	752	28.0%	9	0.7%	761	19.4	100	29.3%	0	0.0%	100	20.7
Student	337	12.5%	212	17.2%	549	14.4	63	18.5%	27	19.4%	90	18.7
Unemployed	256	9.5%	232	18.8%	488	12.4	28	8.2%	25	18.0%	53	11.0
Not working	255	9.5%	237	19.2%	492	12.6	38	11.1%	28	20.1%	66	13.7
r = .158**
Total (missing)							341 (2,339)	12.7% (87.3%)	139 (1,015)	12% (88%)	482 (3,420)	13.7 (87.6)

Pop.: population.

Correlation is significant at a significance level of <.01; *Correlation is significant at a significance level of <.05.

Analysis

The data were subjected to univariate, bivariate and multivariate analyses with SPSS Statistics 17.0 software. Inter-group frequencies and percentages were calculated. The associations between categorical variables were calculated using the chi-square test and Pearson’s correlation coefficient (r) as a measure of the intensity of the relationship. Finally, using the technique of logistic regression, the variables were identified that correlated with the presence or absence of a particular psychopathological diagnosis, using a confidence level of 95% and 99% as a criterion.

The statistical analysis comprised three stages or phases associated with the study’s objectives. In the first phase, the sample was characterized by distinguishing the socio-demographic data of the mental health service user population by sex. In this phase, univariate and bivariate procedures were used to estimate frequencies and percentages segregated by sex.

In the second phase, the purpose was to establish the main diagnostic prevalences of the sample and to determine which of these showed relevant and statistically significant associations with the service user’s sex. In this phase, bivariate analysis was used to calculate the significance and intensity of statistical relationships between sex and prevalent psychiatric diagnoses.

In the last phase of the analysis, those psychiatric diagnoses that had a statistically significant association with sex (p < .01) and an association or effect equal or superior to r = .10 in intensity⁶ were selected. Logistic regression models were applied, segregated by sex (considering them as differentiated or independent samples) and adjusted for the socio-demographic variables of the study. The main associations found in the case of a particular psychiatric diagnosis were identified.

Results

First phase: socio-demographic composition of the sample

As can be seen in Table 1, women are a majority of the sample (68.5%). Service users’ age fluctuates between 15 and 90 years, averaging 41.4 years (16.7 SD), and a majority are over 35 years (62.9%). When age is broken down by sex, the largest percentage of women is between 45 and 54 years, although with an intra-group percentage difference of only 1.5 percentage points from the rank that follows it (35–44 years) and of 2.3 from the third prevalence (15–25 years). In men, the largest percentage is between 15 and 25 years, and there is a more heterogeneous intra-group difference, with 7.4 percentage points of difference from the rank that follows it (35–44 years) and of 9.2 from the third prevalence (45–54 years).

Regarding educational levels, for the most part the sample had reached secondary level (55.1%). Regarding work status, the largest percentage was working (41.6%). Despite this, most of the sample could be said to be economically inactive considering that 58.8% consisted of students, homemakers, unemployed and not working.⁷ Comparing work status by sex, the first majority of both men and women were employed, but the second majority differed by sex: not working in the case of men and homemakers in the case of women. Regarding the service users’ marital status, the largest number were single (49.2%) and the second largest number were married (27%). Comparing by sex, there was a greater concentration of single men (60.2%) than single women (44.2%).

The largest percentage of the service users (36.7%) shared their home with a spouse, partner or a child (nuclear family). Those living with relatives other than their nuclear family (i.e. in an extended family) made up the second largest percentage (26.8%). In third place (24.8%) were those living with a progenitor or carer (family of origin). Although without statistically significant differences (p > .05), the largest percentage of the women of the sample lived with their spouse, partner or a child (40.5%), while the men lived most commonly with one of their progenitors or carers (35.2%).

Second phase: main psychiatric diagnoses

In Table 2, the 10 most common psychiatric diagnoses in the sample are identified, accounting for 94.7% of all the diagnoses registered. In the female population, these diagnoses include 97.6% of the women, whereas of the male population, which is more heterogeneous, they include 88.2%.

Table 2.

Prevalence of psychiatric diagnoses by sex.

Psychiatric diagnoses	N	Prevalence
Psychiatric diagnoses	N	Total (N = 3,920)	Women (N = 2,687)	Men (N = 1,233)	F:M	r
F10. Mental and behavioral disorders due to use of alcohol	77	2.0%	1.2%	3.6%	1:1.4	.082*
F20. Schizophrenia	352	9.0%	4.9%	17.8%	1:1.6	.210**
F23. Acute and transient psychotic disorders	96	2.4%	2.4%	2.6%	2:1	.006
F31. Bipolar affective disorder	336	8.6%	9.4%	6.7%	3:1	−.045**
F32. Depressive episode	1,376	35.1%	38.6%	27.6%	3:1	−.107**
F33. Recurrent depressive disorder	196	5.0%	5.9%	3.1%	4.1:1	−.060**
F32–33. Major depression	1,572	40.1%	44.5%	30.7%	3.1:1	−.131**
F41. Other anxiety disorders	430	11.0%	11.2%	10.4%	2.3:1	−.013
F43. Reactions to severe stress and adjustment disorders	199	5.1%	5.0%	5.1%	2.1:1	.004
F50. Eating disorders	162	4.1%	5.7%	0.8%	15.2:1	−.113**
F60. Specific personality disorders	488	12.4%	13.3%	10.5%	2.7:1	−.039*

Correlation is significant at a significance level of <.01; *Correlation is significant at a significance level of <.05.

Depressive episode (F32) is the diagnosis with the greatest prevalence (35.1%), with a frequency among women three times that among men (3F:1M). Specific personality disorder (F60), the second most prevalent diagnosis (12.4%), has a similar distribution by sex (2.7F:1M). Other anxiety disorders (F41)⁸ is the third most prevalent diagnosis (11%) with a distribution of 2F:1M, although without statistical significance in terms of the sex of the population.

Diagnoses of Schizophrenia (F20), which are fourth in prevalence at 9.0% (1F:1.6M), and Diagnoses of Mental and behavioral disorders due to use of alcohol (F10), which are 10th in prevalence at 3.6% (1F:1.4M), are the only diagnoses among these 10 that are found mainly among men. This is significant considering that women make up 68.5% of the sample. Indeed, diagnoses of schizophrenia are the second most prevalent diagnosis among the male population (17.8%), while among women they occupy the eighth place (4.9%).

Bipolar affective disorder (F31) is the fifth most prevalent diagnosis at 8.6% (3F:1M), followed by Reactions to severe stress and adjustment disorders (F43) at 5.1% (2.1F:1M) and Recurrent depressive disorder (F33) at 5.0%. In this last case, the number of women diagnosed quadruples men (4F:1M). There is a considerable sexual difference in the diagnosis of Eating disorders (F50), which is eighth in prevalence (4.1%). This diagnosis is 15 times more frequent in women than in men (15F:1M). Ninth in prevalence is the diagnosis of Psychotic disorders (F23) at 2.4%, which is twice as prevalent in women as in men (2F:1M).

The diagnoses in which the association with the sex of the population was statistically most significant (p < .01) and with an intensity equal to or greater than r = .10 and which were most frequently found in women were Eating disorders (F50, r = –.113) and Depressive episode (F32, r = –.107). The diagnosis of Major depression, which corresponds to the diagnostic categories F32 and F33 in the latest edition of DSM (APA, 2013), showed a statistically significant relationship (p < .01) and intensity of r = –.131, possibly indicating a greater propensity of women for this diagnosis.

Regarding the most prevalent diagnoses among men, Schizophrenia (F20) presents statistically significant differences by sex (p < .01) and the strongest correlation within the sample (r = .21), suggesting that the men in the sample analyzed are more susceptible to the diagnosis of schizophrenia.

Third phase: socio-demographic factors correlated with psychiatric diagnoses associated with sex

In the analysis of the second phase of the study, the diagnoses significantly associated with the sex of the population (p < .01) and in which the association was most intense (r >.10) were Major depression (F32-33), Schizophrenia (F20) and Eating disorders (F50). In Phase 3, we describe the behavior of Major depression and Schizophrenia in relation to each of the socio-demographic variables studied when broken down by sex. It was not possible to carry out a comparative study in the case of F50 due to the small number of males with this diagnosis, which affects the multivariate analysis and prevents us from observing differences by sex in the socio-demographic factors studied.⁹

Schizophrenia (F20)

As described in the second phase, male service users were more likely to be diagnosed with schizophrenia; in fact, women were 72% less likely to receive this diagnosis than men (odds ratio (OR) = 0.28; 99% confidence interval (CI): 0.22, 0.37; Table 3).

Table 3.

Socio-demographic factors correlated with sex of diagnosis F20, Schizophrenia.

Socio-demographic characteristics		F20. Schizophrenia
		Analysis of the whole sample		Analysis of the female sample		Analysis of the male sample
		OR	(95% CI)	♀OR	(95% CI)	♂OR	(95% CI)
Sex	Female	0.28**	(0.22, 0.37)	–	–	–	–
Sex	Male	1.00	–	1.00		1.00
Age	Age (years)	1.01*	(1.00, 1.02)	1.02**	(1.01, 1.03)	1.01	(0.99, 1.02)
Family group	Origin	2.05**	(1.32, 3.18)	6.84**	(2.57, 18.24)	1.29	(0.77, 2.17)
	Nuclear	0.52*	(0.30, 0.88)	1.39	(0.50, 3.88)	0.35**	(0.17, 0.73)
	Extended	1.13	(0.71, 1.79)	2.43	(0.90, 6.53)	0.99	(0.57, 1.74)
	Others	1.57	(0.84, 2.91)	6.09**	(1.98, 18.79)	0.82	(0.36, 1.89)
	Living alone	1.00	–	1.00	–	1.00	–
Work status	Working	0.38**	(0.27, 0.53)	0.22**	(0.12, 0.40)	0.47**	(0.31, 0.71)
	Homemaker	1.05	(0.66, 1.65)	0.91	(0.52, 1.59)	0.00	–
	Student	0.24**	(0.15, 0.39)	0.25**	(0.11, 0.54)	0.22**	(0.12, 0.40)
	Unemployed	1.00	(0.70, 1.44)	1.05	(0.58, 1.92)	0.97	(0.62, 1.30)
	Not working	1.00		1.00		1.00
Educational level	Primary	0.90	(0.62, 1.31)	1.02	(0.58, 1.92)	0.80	(0.48, 1.31)
	Secondary	0.96	(0.72, 1.28)	1.02	(0.65, 1.63)	0.89	(0.62, 1.30)
	Higher	1.00		1.00		1.00
Marital status	Cohabiting	0.29**	(0.12, 0.69)	0.23*	(0.05, 1.00)	0.37	(0.12, 1.11)
	Married	0.28**	(0.18, 0.43)	0.32**	(0.17, 0.59)	0.26**	(0.14, 0.49)
	Separated	0.37**	(0.24, 0.57)	0.45**	(0.25, 0.81)	0.33**	(0.18, 0.61)
	Widowed	0.21**	(0.09, 0.52)	0.25**	(0.09, 0.67)	0.14	(0.02, 1.09)
	Single	1.00	–	1.00	–	1.00	–
Statistics	Constant	0.30	–	0.02	–	0.55	–
	–2log	1,941.20	–	902.5	–	1,007.50	–
	Cox and Snell	0.10	–	0.05	–	0.11	–
	Nagelkerke	0.23	–	0.17	–	0.19	–
	Hosmer and Lemeshow	0.55	–	0.95	–	0.91	–

OR: odds ratio; CI: confidence interval.

Correlation is significant at a significance level of <.01; *Correlation is significant at a significance level of <.05.

Among men, the analysis showed that there were less chances of finding this diagnosis among those who were married (OR = 0.26; 99% CI: 0.14, 0.49) or separated (OR = 0.33; 99% CI: 0.18, 0.61) in comparison with single men. Consistently, a diagnosis of schizophrenia is less probable in men living with their nuclear families (OR = 0.35; 99% CI: 0.17, 0.73) when compared to men living on their own. Men who were studying (OR = 0.22; 99% CI: 0.12, 0.40) or working (OR = 0.47; 99% CI: 0.31, 0.71) were less prone to receive the diagnosis than men who were not working. In synthesis, men who are single, live alone and are not working have a greater presence in the diagnosis of schizophrenia; by comparison, working men, students, those who are married or have been married and live with their nuclear families are found to be less diagnosed in this category.

A multivariate analysis of the female group shows that with advancing age, by years lived, the chances of presenting a diagnosis of schizophrenia increase (OR = 1.02; 99% CI: 1.01, 1.03), a situation not observed in the male population. In hierarchical order, starting with the strongest effect or association, women who live with their family of origin (OR = 6.84; 99% CI: 2.57, 18.2) or with other members outside the family (OR = 6.09; 99% CI: 1.98, 18.7) have a greater chance of being diagnosed with psychopathology compared to women who live alone. Regarding marital status, the diagnosis is less present among widows (OR = 0.25; 99% CI: 0.09, 0.67), married women (OR = 0.32; 99% CI: 0.17, 0.59), separated (OR = 0.45; 99% CI: 0.25, 0.81) and women who are cohabiting (OR = 0.23; 95% CI: 0.05, 1.00) when compared with single women. Also with relatively low chances of presenting the diagnosis are women who are studying (OR = 0.25; 99% CI: 0.11, 0.54) and working (OR = 0.22; 99% CI: 0.12, 0.40) when compared to women who are not working. In synthesis, in the female population, older women living with their family of origin, who are not working and are single are most likely to present the diagnosis of schizophrenia. Younger women who are working or studying, are married or were once married, and are living alone (unlike the men) are least likely to present this diagnosis.

Major depression F32–33

The multivariate analysis of diagnoses of Depressive episode and Recurrent depressive disorder (ICD-10), which are subsumed under the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) category of Major depression, shows that women (OR = 1.62; 99% CI: 1.38, 1.89; Table 4) are 62% more likely than men to receive this diagnosis and that in both sexes the chances increase with age (OR = 1.02; 99% CI: 1.01, 1.02).

Table 4.

Socio-demographic factors correlated with sex of diagnosis F32-33, Major depression.

Socio-demographic characteristics		F32-33. Major depression
		Analysis of the whole sample		Analysis of the female sample		Analysis of the male sample
		OR	(95% CI)	♀OR	(95% CI)	♂OR	(95% CI)
Sex	Female	1.62**	(1.38, 1.89)	–	–	–	–
Sex	Male	1.00	–	1.00	–	1.00	–
Age	Age (years)	1.02**	(1.01, 1.02)	1.02**	(1.01, 1.02)	1.02**	(1.01, 1.03)
Family group	Origin	0.75*	(0.56, 1.00)	0.78	(0.53, 1.13)	0.71	(0.45, 1.10)
	Nuclear	1.24	(0.95, 1.63)	1.29	(0.92, 1.81)	1.31	(0.82, 2.11)
	Extended	1.15	(0.87, 1.51)	1.29	(0.91, 1.83)	0.93	(0.58, 1.48)
	Other	0.95	(0.63, 1.43)	1.02	(0.61, 1.71)	0.84	(0.41, 1.69)
	Living alone	1.00	–	1.00	–	1.00	–
Work status	Working	1.39**	(1.11, 1.74)	1.46*	(1.09, 1.96)	1.30	(0.91, 1.86)
	Homemaker	0.99	(0.77, 1.27)	0.99	(0.73, 1.34)	0.46	(0.09, 2.45)
	Student	1.21	(0.88, 1.67)	1.19	(0.79, 1.78)	1.32	(0.77, 2.25)
	Unemployed	1.00	(0.76, 1.33)	1.01	(0.69, 1.46)	1.05	(0.69, 1.62)
	Not working	1.00	–	1.00	–	1.00	–
Educational level	Primary	1.09	(0.89, 1.33)	1.31*	(1.03, 1.66)	0.67*	(0.45, 1.00)
	Secondary	1.02	(0.87, 1.20)	1.05	(0.86, 1.27)	0.98	(0.73, 1.32)
	Higher	1.00		1.00	–	1.00	–
Marital status	Cohabiting	1.12	(0.83, 1.52)	1.20	(0.84, 1.70)	0.80	(0.42, 1.52)
	Married	1.32**	(1.09, 1.61)	1.36**	(1.08, 1.71)	1.19	(0.80, 1.78)
	Separated	1.33*	(1.07, 1.66)	1.32*	(1.03, 1.71)	1.31	(0.84, 2.05)
	Widowed	1.34	(0.95, 1.88)	1.23	–	2.41	(0.96, 6.09)
	Single	1.00	–	1.00	–	1.00	–
Statistics	Constant	0.16	–	0.23	–	0.2	–
	–2log	5,012.9	–	3,543.61	–	1,450.38	–
	Cox and Snell	0.06	–	0.05	–	0.05	–
	Nagelkerke	0.08	–	0.07	–	0.07	–
	Hosmer and Lemeshow	0.44	–	0.29	–	0.31	–

OR: odds ratio; CI: confidence interval.

Correlation is significant at a significance level of <.01; *Correlation is significant at a significance level of <.05.

In the men’s case, neither occupational activity nor marital status is significantly correlated with a diagnosis of major depression. Among women, however, both socio-demographic variables show a clear association with the diagnosis; working women (OR = 1.46; 95% CI: 1.09, 1.96) have a greater chance of being diagnosed compared to women who are not working, and women who are married (OR = 1.36; 99% CI: 1.08, 1.71) or separated (OR = 1.32; 95% CI: 1.03, 1.71) appear more diagnosed with major depression than single women.

The educational level of the population is also associated with this diagnosis differentially by sex. While women with primary education only (OR = 1.31; 95% CI: 1.03, 1.66) have a greater likelihood of diagnosis than women with higher education, men with primary education only (OR = 0.67; 95% CI: 0.45, 1.00) have less chance of receiving a diagnosis of this psychopathology than those with higher education.

In synthesis, among the female population, older women who are working, have less education and are married or separated have the greatest chance of being diagnosed with ‘Major depression’. Younger women who are not working, are more educated and are single have the least chance. On the male side, older men and – unlike the women – those with a higher educational level have the strongest chances of receiving this diagnosis compared to younger and less educated men.

Discussion

The greater presence of women in the hospital service studied highlights how being a woman carries with it a greater probability of receiving a psychiatric diagnosis, confirming what has been found widely in the relevant literature. Yet women are not found to be over-represented in every prevalent diagnostic category, which, moreover, does not always show a statistically significant association with the sex of the population. In general terms, the three diagnoses found to have the greatest prevalence in the sample are Depressive episode (F32), Specific personality disorders (F60) and Other anxiety disorders (F41); all of them are diagnoses that are preponderantly addressed to women.

The fact that Specific personality disorders (F60) are the second most prevalent diagnoses when Axes I and II diagnoses are considered at the same time shows their importance in the clinical scenario – with similar prevalences of those found in the relevant literature (Samuels, 2011; Samuels et al., 2002; Torgersen et al., 2001) – which would otherwise remain invisible.¹⁰ If we consider only Axis I diagnoses, the greatest prevalences are F32 and F41, which coincides with the literature, both national (Vicente et al., 2004; Vicente et al., 2002) and international (Baumeister & Härter, 2007; Bunting et al., 2013; Cwikel et al., 2008; Jacobi et al., 2004; Lejtzén, Sundquist, Sundquist, & Li, 2014; Serrano-Blanco et al., 2010; Skapinakis et al., 2013).

Unlike the findings of the bibliography in this area (Baumeister & Härter, 2007; Bijl et al., 2002; Bunting et al., 2013; Hernández, Orellana, Kimelman, Nuñez, & Ibáñez, 2005; Lejtzén et al., 2014; Serrano-Blanco et al., 2010; Vicente et al., 2004), the most prevalent diagnoses of anxiety in the sample analyzed (F41 and F43) were not significantly associated with the sex of the population, however, they were significantly associated with work status (p < .01), working people being more likely to receive these diagnoses.¹¹ The most prevalent diagnoses that did show an intense and statistically significant association with the sex of the population were Major Depression (F32-33) and Eating disorders (F50), in which women had a greater diagnostic propensity, and Schizophrenia (F20), in which men had a greater diagnostic propensity.¹²

Epidemiological research into affective disorders considers female sex to be a relevant risk factor, particularly for the diagnosis of major depression. In general terms, it identifies conjugal life as a protective factor against this and most other psychopathologies (Baumeister & Härter, 2007; Girolamo et al., 2006; Gutiérrez-Lobos et al., 2000; Husain, Gater, Tomenson, & Creed, 2004; Jacobi et al., 2004; Lejtzén et al., 2014; Mirza & Jenkins, 2004; Sachs-Ericsson & Ciarlo, 2000; Scott et al., 2010; Wang, 2004). This study, by contrast, shows that, for women, the chances of being diagnosed with major depression are significantly greater inside marriage. This finding – the result of crossing data on sex, marital status and a diagnosis of major depression – would remain invisible in the general analysis of the sample, the majority of which (49.2%) are single. A deeper study of the association is needed to explore the possible reasons linking this diagnosis specifically with married life in the case of women. If we consider that in this and other studies (Bebbington, 1998; Bebbington et al., 2003; Gutiérrez-Lobos, Scherer, Anderer, & Katsching, 2002; Jorm, 1987; Matud, Guerrero, & Matías, 2006; Mirowsky, 1996; Stansfeld et al., 2014) age is a statistically significant variable in the diagnosis of depression and that the majority age of the women in this sample (although with little intra-group heterogeneity) is between 45 and 54 years, while among men it is between 15 and 25 years, a possible explanation could be found in the interdependence of both variables, especially considering that the marriage rate in Chile has decreased among the young population. However, this would not explain why, within the male population, no civil status has a statistically significant association with the diagnosis of depression. Another possible explanation for this result could be found in macho violence (Ellsberg, Jansen, Heise, Watts, & García-Moreno, 2008; Hegarty, 2011; Krug, Dahlberg, Mercy, Zwi, & Lozano, 2003) considering that 31.9% of women in Chile have suffered intra-family violence (Adimark Gfk, 2013), as well as in the double working day that domestic work implies (Matud, Bethencourt, & Ibáñez, 2015).

In fact, the general analysis of the sample showed that unpaid domestic work traditionally carried out by women seems to be significantly associated with their possibility of receiving a psychiatric diagnosis. This variable is practically non-existent among the men in the study sample. Whether or not a causal factor, it is nevertheless a dimension of women’s daily life that is not just coincidentally related to a mental health diagnosis (Fatima & Parvez, 2016; Girolamo et al., 2006; Hayashi et al., 2016; Kim, Khang, Muntaner, Chun, & Cho, 2008; Noorbala, Bagheri Yazdi, Yasamy, & Mohammad, 2004; Patel, Araya, De Lima, Ludermir, & Todd, 1999; Pinho & Araújo, 2012; Serrano-Blanco et al., 2010). Even more so, if we consider that domestic work is a dimension that crosscuts the various employment states of women. The fact that 88.7% of mono-parental household heads in Chile are women gives this particular relevance (Ministerio de Planificación y Cooperación, 2011), especially considering that paid work is another variable significantly correlated with this diagnosis only in the case of the women, in a country with scanty labor regulations that guarantee labor dispute conciliation. However, new studies are required that explore the possible interaction of employment with the age variable in this diagnosis, a variable with statistical significance in both sexes. This is because, unlike the women, the age range with the greatest presence among men (26.5%) is between 15 and 25 years, precisely the segment with the highest unemployment rate in Santiago (Centro Micro Datos, 2016).

A result that is difficult to interpret is the differential relationship between educational level and a diagnosis of major depression, which is revealed only by breaking down by sex the logistic regression model for the most prevalent diagnoses. Although the men and women of the sample have similar levels of education, the men (unlike the women) show a direct correlation between educational level and their chances of receiving this diagnosis, an observation that contradicts the literature in this area (Araya et al., 2001; Heslin et al., 2016; Husain et al., 2004; Lejtzén et al., 2014; Mirza & Jenkins, 2004; Vicente et al., 2002). New studies and methods are needed to explore specifically this dimension segregated by sex in other contexts.

Although studies finding differences in the prevalence by sex of diagnoses of schizophrenia are scarce and inconclusive (Aleman, Kahn, & Selten, 2003; Hartung & Widiger, 1998; McGrath et al., 2004; Saha, Chant, Welham, & McGrath, 2005), the present findings suggest that men are significantly more prone to receive this diagnosis. This is a significant finding, above all considering that women made up the majority of the sample studied and could be a factor that explains the greater presence of men between the ages of 15 and 25 years, being a diagnosis of early onset principally among them (Sánchez, Téllez, & Jaramillo, 2012; Usall, 2003). This study shows that both men and women with a schizophrenia diagnosis tend to be single and economically inactive (Heslin et al., 2016; Marwaha & Johnson, 2004; Ramsay, Stewart, & Compton, 2012; Salokangas et al., 2001; Wheeler, 2007). The differences by sex appear in the cohabiting family group: men with this diagnosis tend to live alone, whereas the women tend to live with their family of origin. This could be interpreted as reflecting the socio-cultural convention of greater male independence – contrasting with the socio-cultural convention of female dependence – or, alternatively, the greater social isolation of men. This last interpretation could be explained by what the literature describes as a greater presence of negative symptoms in men (Mustafa, Bayatti, & Faruqui, 2013; Rietschel et al., 2015; Usall, 2003), something that should be explored by looking at how men and women experience stigmatization differently when faced with the diagnosis of schizophrenia (Boysen, Ebersole, Casner, & Coston, 2014), whose prevalence rate in the general population of Santiago is 1.02% (Araya et al., 2001). These results, moreover, invite further study of a possible incompatibility in the case of men between this diagnosis and social and family life, whereas in the case of women this is not observed.

Strengths and limitations

The databases obtained from mental health services provide important sources of epidemiological information for scientific and social investigation. The sample size and the time period covered by the database studied are, indeed, two of the main strengths of this study. However, use of such databases requires caution with statistical interpretations and reflections, considering their high ratio of lost values due to unstandardized data collection procedures, which could imply bias in the selection of the sample. Moreover, the sample studied corresponds to a specific population within the Chilean context, whose results cannot be extrapolated to the Chilean population with psychiatric diagnoses as a whole, for which new studies would be required that corroborate the findings in other hospital populations. It does, however, characterize a Latin American population that has not been usually considered in studies of diagnostic prevalence by sex, and this may be useful for comparing the results reported in other countries and whose data generally come from European or Western populations whose socio-demographic, and particularly economic, characteristics differ significantly from the population attended in the hospital studied. Both the tests of statistical significance and intensity as well as the logistic regression models used in this research are tools to identify and examine associations between the variables present in the sample. They cannot be considered sufficient instruments to explain social phenomena or establish causal relations between sex and any specific psychopathology.

Conclusion

Segregating the statistical models by sex made it possible to evaluate the socio-demographic variables that were significantly associated in men and women with chances of being diagnosed with a specific psychopathology, associations that would remain hidden in a general analysis. These results, which refer to a specific hospital population in Chile, open at least two possibilities for future use: the provision of greater specificity by sex to psychiatric diagnoses and their treatment (Johnson & Stewart, 2010; Wittchen, 2010) and the exploration of possible gender biases in how diagnoses are applied. The first possibility could have the undesired effect of converting feelings of malaise associated with social conditions and cultural norms into psychological problems specifically of women, something that should be considered with caution. The second possibility would need qualitative studies, ideally including an analysis of how mental health professionals do interpret this data. In order to apply these results for health care purposes and academic discussion, further insight into the subjective impact of socio-demographic factors among men and women in different contexts are also needed, something that should take into account the complex relations between psychiatry, some specific diagnoses and social inequalities based on sex and gender relations.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Attraction and Insertion of Advanced Human Capital Program (PAI) of the National Commission for Scientific and Technological Research (CONICYT) of the Government of Chile (Folio 82140022).

ORCID iD

Roselló Peñaloza:

Notes

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