Relationship between Sociodemographic Factors and Depression in Australian Population Aged 16–85 Years

Abstract

Globally, it is estimated that 5% of adults suffer from depressive disorder. The current study aimed to investigate the association of sociodemographic factors with depression from the Australian national survey data. The analysis utilized the 2007 Australian National Survey of Mental Health and Wellbeing data upon 8841 participants aged 16 to 85 years. The outcome measures were lifetime depression and depression symptoms in the 12 months prior to the survey. Analytical techniques included binary logistic regression technique. The prevalence of lifetime depression was 15.2% (18.2% in females vs. 11.5% in males, p < 0.001) and a 12-month depression was 6.1% (7.4% in females vs. 4.5% in males, p < 0.001). For every year increase of age, there was a 1% decrease in the odds of lifetime depression and a 2% decrease of 12-month depression. There was a 20% less chance for lifetime depression and 33% less chance for 12-month depression for the fifth quintile compared to the first quintile of household income. Level of education was not significant. Depression is more common in younger aged people in Australian population, and significantly higher among females, and in people with low socioeconomic status. Appropriate intervention programs need to be conducted among this specific group.

1. Introduction

Depressive disorder is one of the most common mental disorders. It is defined as extreme sadness or despair that lasts more than a day, interferes with daily life activities and can cause physical symptoms such as pain, weight loss or gain, disruption of sleeping patterns, or lack of energy [1]. Depression can be caused by life events and personal factors such as family history, personality, serious medical illness, drug and alcohol and socio-economic status [2]. It is estimated that 5% of adults globally suffer from this disorder [3]. In Australia it has been estimated that depression costs $11.8 billion in lost productivity per year along with $12.9 billion in annual welfare payments [4].

Sociodemographic factors, including age [5,6,7], gender [5,8,9,10], household income [5,7,11,12,13,14,15], and level of education [6,7,16,17] have been identified and studied as being important factors contributing to the rate of depression. However, the findings are somehow non-conclusive. A Canadian study [5] concluded that depression decreased with age, while a Norway study [6] and a Japanese study [7] reported the opposite, with depression increasing with age. The relationship between gender and depression has been studied extensively, with females found to have higher rates of depression than men [5,8,9,10,16]. The Global Burden of Disease Study 2019 [16] reported that women were 1.7 times more likely to have depression than men. Previous studies also reported that lower levels of education [5,6,17,18] and lower levels of income [5,7,11,17,18] were associated with increased rates of depression. A meta-analysis of more than 50 cross-national epidemiologic studies was conducted on the relationship between socioeconomic status and depression [12]. They found that individuals with low income were 1.81 times increased odds of depression compared with those in the higher income categories. Another study found that lower socioeconomic status was associated with an increased likelihood of mood, anxiety, and substance use disorders [18].

The prevalence of depression was reported to be as high as 10% for Australian Adults [19]. Overall, Australian studies are minimal, and the associations of sociodemographic factors with depression are non-conclusive. Previous studies mainly focused on population subsets, focusing on rural communities [20,21,22]. These studies reported no significant association between gender, but the prevalence of psychological distress was higher among younger adults. A study [20] conducted in the Greater Green Triangle area covering the south-east of South Australia, and south-west (Corangamite Shire) and north-west (Wimmera) of Victoria among people aged 25–74 years using the Hospital Anxiety and Depression Scale (HADS) [23]. The study reported 31.4% psychological distress in men and 31.2% in women, 35% in people aged 25–44 years and 21% in people aged 75–74. A combination of studies to produce mental health indices was compiled from a number of Australian mental health studies [21]. They reported depression using only 6 of the 10 questions of the Kessler 10-item questionnaire [24], which makes comparisons problematic with most other studies that use the ICD-10 [25] as a measure for depression. The study reported only the association of a higher household income with higher rates of depression among men. In another study [26] conducted more than two decades ago to investigate the association of socioeconomic status, the outcome measure was ‘affective disorders’ instead of depression. The current study aimed to report the relationship between sociodemographic factors and depression in the Australian population.

2. Materials and Methods

The analysis utilized the 2007 National Survey of Mental Health and Wellbeing (SMHWB). This survey was designed to provide lifetime prevalence estimates for mental disorders and was conducted by the Australian Bureau of Statistics [19]. The survey was an Australian national face-to-face household cross-sectional survey of 8841 (60% response rate) people aged 16–85 years who were usual residents of private dwellings in Australia and was carried out using the World Mental Health Survey Initiative version of the Composite International Diagnostic Interview (CIDI).

Dwellings included in the survey in each state and territory were selected at random using a stratified, multistage area sample. This sample included only private dwellings from the geographic areas covered by the survey. The sampling was allocated to states and territories roughly in proportion to their respective population size.

Respondents were asked about experiences of mental health conditions throughout their lifetime. In the survey, 12-month measures were derived based on lifetime diagnosis and the presence of symptoms of that disorder in the 12 months prior to the survey interview. Diagnoses were made according to International Classification of Diseases-10 (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) [27].

Responses for participants with depression were extracted from the survey data for:

• Gender—male or female (categorical).
• Age—the range was from 16–85 years (continuous). For the purposes of the descriptive statistics and prevalence rates, it was displayed in tabular format into 10-year groups.
• Level of education groups—each year level at school from “Year 8 or below” to “Year 12 or above” (categorical).
• Income—gross weekly equivalized income of household was originally in decile ranges from <$277 to >$1713 (categorical), however due to the number of groups, it was consolidated into quintiles. The “not stated” or “not known” income groups were excluded from prevalence rates or input into the statistical model.

Statistical Analyses

We reported descriptive statistics of the factors to gain introductory insights into the studied factors. Prevalence rates of the factors to lifetime depression and 12-month depression were calculated, along with the chi-square test for trend, to determine if there was any significant trend for each factor. Binary logistic regression was conducted on the factors as it can be used to predict a dichotomous outcome variable. Gender, level of education and household income were treated as categorical variables, with age as a continuous variable. For the logistic regression results, odds ratio estimates, along with a 95% confidence interval (CI) were calculated after adjustment of covariates. Interaction effects with gender on other factors were investigated to determine if there was any significant interaction for stratification analysis for gender. Statistical software SPSS (SPSS Inc., version 26. Armonk, NY, USA: IBM Corp.) was used for the analysis.

3. Results

3.1. Descriptive Analysis

Of 8841 participants, 4027 (45.5%) were males and 4814 (54.5%) females. Highest year of school completed was skewed towards the Year 12 (45.9%) and Year 10 (24.6%) groups. Household income was generally well distributed, with the biggest group being the first quintile (21.4%). Participant ages were well distributed, with a peak in the 56–65-year group (22.5%). Household income was generally well distributed, with the biggest group being the first quintile (21.4%) (

Table 1. Distribution of selected sociodemographic factors in the 2007 SMHWB.

	Number	Percent (%)
Gender
Female	4814	54.5
Male	4027	45.5
Age
16–25	1552	17.6
26–35	1389	15.7
36–45	1587	18.0
46–55	1255	14.2
56–65	1988	22.5
66–75	1070	12.1
Highest year of school completed
Year 12 and above	4061	45.9
Year 11	1014	11.5
Year 10	2178	24.6
Year 9	773	8.7
Year 8 or below	815	9.2
Household income
First quintile	1894	11.9
Second quintile	1537	9.1
Third quintile	1472	8.4
Fourth quintile	1424	8.3
Fifth quintile	1473	8.3
Not stated	76	0.9
Not known	965	10.9

).

3.2. Prevalence of Depression

The prevalence of both lifetime depression (18.2% vs. 11.5%, p < 0.001) and 12-month depression (7.4% vs. 4.5%, p < 0.001) was significantly higher in females than males. The highest prevalence for lifetime depression (21.4%) and 12-month depression (8.4%) was in the 46-to-55-year group, with a decline in older age groups. For level of education, although Year 11 had the highest prevalence for lifetime depression (17.7%) and 12-month depression (7.1%), the associations were not significant. A general decrease in the prevalence was in household income from the first quintile to the fifth quintile for lifetime depression (16.3% to 13.6%, p = 0.006) (

Table 2. Prevalence and Odds Ratio of lifetime and 12-month depression on selected sociodemographic factors in the 2007 SMHWB.

		Lifetime				12-Month
	Number at Risk	Prevalence N (%)	p-Value	OR (95% CI) *	OR (95% CI) +	Prevalence N (%)	p-Value	OR (95% CI) *	OR (95% CI) +
Total	8841	1341 (15.2)				537 (6.1)
Gender			p < 0.001				p < 0.001
Male	4027	463 (11.5)		1.0 (ref)	1.0 (ref)	183 (4.5)		1.0 (ref)	1.0 (ref)
Female	4814	878 (18.2)		1.72 (1.52, 1.94)	1.70 (1.50, 1.92)	354 (7.4)		1.67 (1.39, 2.00)	1.64 (1.37, 1.98)
Age, per year increase			p = 0.003	0.99 (0.99, 1.00)	0.99 (0.99, 1.00)		p < 0.001	0.99 (0.98, 1.00)	0.98 (0.98, 0.99)
16–25	1552	179 (11.5)				96 (6.2)
26–35	1389	251 (18.1)				108 (7.8)
36–45	1587	303 (19.1)				121 (7.6)
46–55	1255	269 (21.4)				105 (8.4)
56–65	1988	245 (12.3)				78 (3.9)
66–75	1070	94 (8.8)				29 (2.7)
Level of education			p = 0.005				p = 0.575
Year 12 and above	4061	638 (15.7		1.0 (ref)	1.0 (ref)	243 (6.0)		1.0 (ref)	1.0 (ref)
Year 11	1014	179 (17.7)		1.15 (0.96, 1.38)	1.15 (0.96, 1.38)	72 (7.1)		1.20 (0.89, 1.61)	1.19 (0.90, 1.57)
Year 10	2178	324 (14.9)		0.94 (0.81, 1.08)	0.97 (0.83, 1.13)	133 (6.1)		1.00 (0.79, 1.26)	1.15 (0.92, 1.43)
Year 9	773	95 (12.3)		0.75 (0.60, 0.95)	0.82 (0.64, 1.04)	46 (6.0)		1.01 (0.72, 1.43)	1.25 (0.89, 1.75)
Year 8 or below	815	105 (12.9)		0.79 (0.64, 0.99)	0.91 (0.71, 1.16)	43 (5.3)		0.90 (0.63, 1.27)	1.32 (0.81, 1.91)
Household income			p = 0.006				p = 0.875
First quintile	1894	309 (16.3)		1.0 (ref)	1.0 (ref)	130 (6.9)		1.0 (ref)	1.0 (ref)
Second quintile	1537	228 (14.8)		0.89 (0.74, 1.08)	0.89 (0.74, 1.07)	92 (6.0)		0.86 (0.66, 1.14)	0.85 (0.65, 1.12)
Third quintile	1472	237 (16.1		0.98 (0.82, 1.18)	0.96 (0.80, 1.16)	93 (6.3)		0.92 (0.70, 1.21)	0.88 (0.67, 1.17)
Fourth quintile	1424	216 (15.2)		0.92 (0.76, 1.11)	0.88 (0.74, 1.07)	86 (6.0)		0.87 (0.66, 1.16)	0.82 (0.62, 1.10)
Fifth quintile	1473	200 (13.6)		0.81 (0.67, 0.98)	0.80 (0.65, 0.96)	70 (4.8)		0.68 (0.50, 0.91)	0.67 (0.50, 0.91)

* Odds Ratio (95% confidence interval (CI) (unadjusted); + Odds Ratio (95% confidence interval (CI) adjusted for variables in the model.

).

3.3. Association of Sociodemographic Factors with Lifetime and 12-Month Depression:

The association of sociodemographic factors with lifetime depression and 12-month depression for total participants and by gender are shown in Table 2 and

Table 3. Association of sociodemographic factors with lifetime and 12-month depression in total participants and by Gender in the 2007 SMHWB.

Lifetime					12-Month
	Male		Female		Male		Female
	OR (95% CI) *	OR (95% CI) +	OR (95% CI) *	OR (95% CI) +	OR (95% CI) *	OR (95% CI) +	OR (95% CI) *	OR (95% CI) +
Age, per year increase	1.0 (0.94, 1.06)	1.0 (0.99, 1.00)	0.93 (0.88, 0.97)	0.99 (0.99, 1.00)	0.90 (0.82, 0.99)	0.99 (0.98, 1.00)	0.99 (0.98, 0.99)	0.98 (0.98, 0.99)
Level of Education
Year 12 and above	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)
Year 11	1.08 (0.77, 1.52	1.08 (0.80, 1.48)	1.18 (0.93, 1.50)	1.18 (0.94, 1.49)	1.29 (0.77, 2.15)	1.19 (0.75, 1.89)	1.15 (0.80, 1.65)	1.19 (0.85, 1.67)
Year 10	1.01 (0.78, 1.31)	1.01 (0.79, 1.28)	0.88 (0.73, 1.07)	0.96 (0.79, 1.16)	1.1 (0.74, 1.65)	1.13 (0.78, 1.65)	0.94 (0.71, 1.25)	1.16 (0.88, 1.54)
Year 9	0.82 (0.55, 1.23)	0.82 (0.54, 1.17)	0.76 (0.56, 1.03)	0.84 (0.62, 1.14)	1.14 (0.64, 2.02)	1.1 (0.62, 1.93)	0.97 (0.63, 1.50)	1.35 (0.89, 2.07)
Year 8 or below	0.88 (0.61, 1.29)	0.87 (0.60, 1.31)	0.79 (0.58, 1.06)	0.94 (0.69, 1.29)	0.9 (0.49, 1.64)	1.14 (0.62, 2.11)	0.93 (0.60, 1.43)	1.46 (0.92, 2.32)
Household income
First quintile	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)	1.0 (ref)
Second quintile	0.83 (0.60, 1.16)	0.83 (0.59, 1.15)	0.96 (0.76, 1.20)	0.92 (0.73, 1.16)	0.7 (0.42, 1.16)	0.69 (0.42, 1.14)	0.98 (0.70, 1.36)	0.94 (0.68, 1.32)
Third quintile	1.07 (0.78, 1.47)	1.04 (0.75, 1.42)	0.99 (0.79, 1.25)	0.93 (0.73, 1.17)	0.77 (0.47, 1.25)	0.73 (0.45, 1.20)	1.04 (0.75, 1.45)	0.97 (0.69, 1.36)
Fourth quintile	0.97 (0.70, 1.35)	0.93 (0.65, 1.29)	0.93 (0.73, 1.17)	0.86 (0.68, 1.09)	0.84 (0.52, 1.37)	0.78 (0.47, 1.27)	0.91 (0.65, 1.29)	0.85 (0.60, 1.21)
Fifth quintile	0.97 (0.71, 1.33)	0.93 (0.67, 1.28)	0.78 (0.61, 0.99)	0.72 (0.56, 0.93)	0.74 (0.45, 1.20)	0.7 (0.43, 1.15)	0.68 (0.47, 1.00)	0.65 (0.44, 0.96)

* Odds Ratio (95% confidence interval (CI) (unadjusted); + Odds Ratio (95% confidence interval (CI) adjusted for variables in the model.

, respectively. Gender was a significant variable for both lifetime depression and 12-month depression after adjustment for covariates. Females were 70% more likely to experience lifetime depression (AOR = 1.70, 95% CI 1.50, 1.92) and 64% more likely to experience 12-month depression (AOR = 1.64, 95% CI 1.37, 1.98) (Table 2). For every year increase of age from 16 to 85, there was a 1% decrease in the odds of lifetime depression (AOR = 0.99, 95% CI 0.99, 1.00) and a 2% decrease of 12-month depression (AOR = 0.98, 95% CI 0.98, 0.99). Age was found to be significant in females (AOR = 0.99, 95% CI 0.99, 1.00) for a lifetime depression and both males (AOR = 0.99, 95% CI 0.98, 1.00) and females (AOR = 0.98, 95% CI 0.98, 0.99) were significant for 12-month depression (Table 3). Household income was significant in the fifth quinine for lifetime depression and 12-month depression, especially for females; lifetime depression (AOR = 0.72, 95% CI 0.86, 0.93) and 12-month depression (AOR = 0.65, 95% CI 0.44, 0.96). Level of education was not significant for lifetime depression or 12-month depression.

4. Discussion

Studies of the associations of sociodemographic factors with depression, especially among the Australian population, are minimal, and the results are non-conclusive. The current study aimed to investigate the association of sociodemographic factors with depression from the 2007 National Survey of Mental Health and Wellbeing (SMHWB). In this study, we reported the prevalence and association of a number of sociodemographic factors with lifetime depression and 12-month depression. The current study shows that the prevalence of lifetime and 12-month depression was higher in females and in younger people. Household income was another significant predictor of depression, especially in females. The results for gender were consistent with previous studies that females had significantly higher rates of depression than males [5,8,9,10,16], and depression is more prevalent among young women than young men [28]. Higher rates of depression among females can be due to several reasons, including reproductive and environmental factors, behavioral factors and could be genetics, and response bias [16,29]. However, some studies conducted in Australia [20,21,22] did not find gender as a significant factor for depression. This may be due to some of these studies using a different diagnostic tool for depression such as HADS-D [21], and the creation of an index to measure depression [22]. This study had a greater power due to the number of participants due to it being a national study.

The association of older age with a lower prevalence of depression aligns with some studies [5] and contradicts some other studies [6,7] outside Australia. However, in some Australian studies [20,21,22], age was not a significant factor in depression. The increasing rate of depression among younger people may be due to several reasons, including the stigma toward people with depression decreasing [30]. Over time, an increase in reporting of mental health problems among younger people could be another reason [31]. Other behavioral factors and life events during early adulthood could be another factor behind the increased prevalence of depression among younger people [32]. Other potential factors related to age that are not included in this study can explain the decline in depression rates in older age. Future studies have the potential to better understand the association of the older generation with a lower prevalence of depression.

Level of education was found to be insignificant in this study, which is not in agreement with the international studies or the available Australian study [23]. No association could be attributed to the categories of education levels chosen in the current study. Other studies defined broader categories such as “primary school” to “college/university ≥4 years” [6], and “high school only” to “bachelor’s degree or above” [26]. In the current study, the level of education was categorized very narrowly, such as year nine, year ten and so on. We attempted to combine different categories; however, it was abandoned due to the difficulty of overlapping categories when combining the secondary and tertiary types. A significant association could be if this study followed similar categories, especially if no education could be an independent group.

Income was found to be significant in this study. The fifth quintile of income was associated with a lower prevalence of lifetime and 12-month depression. This finding was consistent with previous findings [12,13,22]. The association of higher income with a lower prevalence of depression could be explained by the mechanisms of social causation and social selection theories that have been posited in understanding the link between mental health and income [33]. Social causation sets that adversity, stress, and reduced capacity to cope related to low-income increase the risk of development of mental illness [34,35]. However, more factors are needed to explain the living conditions in addition to the household income. Broadly said, the first income quintile can be attributed to lower-income households, while the fifth quintile can be attributed to the highest-income families. More factors are needed to allow the interpretation of the association between living conditions and household income which is a limitation of the current study.

The strengths of this study include extensive and nationwide data. However, the major limitation is that the study was conducted more than a decade ago; therefore, the findings could vary with different association levels from any recent studies. Additionally, the results are not from a longitudinal study to report any causal relationship and some factors and their categorization were not comparable to some previous studies to understand the association levels better.

5. Conclusions

The study concludes that the prevalence of depression was significantly higher among females, lower in older aged people and lower among people with higher household income in the Australian population. Appropriate intervention programs need to be conducted among these specific groups. Findings from this proposed study provides a better understanding of the significance of level of education, gender, age and income on the prevalence rate of depression. Significance of these factors can be used to inform the state and federal health departments of Australia and input into diagnostic tools for depression.