Developmental relationships between socio-economic disadvantage and mental health across the first 30 years of life

View author details View Less
  • 1 Royal Children’s Hospital and University of Melbourne, , Australia
  • | 2 University of Melbourne and Royal Children’s Hospital, , Australia
  • | 3 Deakin University, University of Melbourne and Royal Children’s Hospital, , Australia
  • | 4 University of Melbourne, , Australia
  • | 5 University of Melbourne and Royal Children’s Hospital, , Australia
  • | 6 Deakin University and Royal Children’s Hospital, , Australia
Full Access
Get eTOC alerts
Rights and permissions Cite this article

Understanding of how socio-economic disadvantage experienced over the life course relates to mental health outcomes in young adulthood has been limited by a lack of long-term, prospective studies. Here we address this limitation by drawing on data from a large Australian population cohort study that has followed the development of more than 2,000 Australians (and their families) from infancy to young adulthood since 1983. Associations were examined between prospective assessments of socio-economic position (SEP) from 4–8 months to 27–28 years and mental health problems (depression, anxiety, stress) and competence (civic engagement, emotional maturity, secure intimate relationship) at 27–28 years. The odds of being socio-economically disadvantaged in young adulthood were elevated eight- to tenfold in those who had experienced disadvantage in the family of origin, compared with those who had not (OR 8.1, 95% CI 4.5–14.5 to 10.1, 95% CI 5.2–19.5). Only concurrent SEP was associated with young adult mental health problems, and this effect was limited to anxiety symptoms (OR 2.0, 95% CI 1.1–3.9). In contrast, SEP had more pervasive impacts on young adult competence, particularly in the civic domain where effects were evident even from early infancy (OR 0.46, 95% CI 0.26–0.81). Findings suggest that one potentially important mechanism through which disadvantage compromises mental health is through limiting the development and consolidation of key psychosocial competencies needed for health and well-being in adulthood.

Abstract

Understanding of how socio-economic disadvantage experienced over the life course relates to mental health outcomes in young adulthood has been limited by a lack of long-term, prospective studies. Here we address this limitation by drawing on data from a large Australian population cohort study that has followed the development of more than 2,000 Australians (and their families) from infancy to young adulthood since 1983. Associations were examined between prospective assessments of socio-economic position (SEP) from 4–8 months to 27–28 years and mental health problems (depression, anxiety, stress) and competence (civic engagement, emotional maturity, secure intimate relationship) at 27–28 years. The odds of being socio-economically disadvantaged in young adulthood were elevated eight- to tenfold in those who had experienced disadvantage in the family of origin, compared with those who had not (OR 8.1, 95% CI 4.5–14.5 to 10.1, 95% CI 5.2–19.5). Only concurrent SEP was associated with young adult mental health problems, and this effect was limited to anxiety symptoms (OR 2.0, 95% CI 1.1–3.9). In contrast, SEP had more pervasive impacts on young adult competence, particularly in the civic domain where effects were evident even from early infancy (OR 0.46, 95% CI 0.26–0.81). Findings suggest that one potentially important mechanism through which disadvantage compromises mental health is through limiting the development and consolidation of key psychosocial competencies needed for health and well-being in adulthood.

Key messages

  • Socio-economic disadvantage can adversely impact on mental health.

  • Addressing socio-economic disparities is critical to improving population mental health.

  • Aspects of good mental health in young adulthood were related to socio-economic circumstances as far back as infancy.

  • Greater attention is needed to promoting positive aspects of mental health for young people from disadvantaged backgrounds.

Socio-economically disadvantaged adults have higher rates of psychological disorder (Silva et al, 2016), and lower levels of psychosocial assets like optimism and life satisfaction (Boehm et al, 2015). Reducing these socio-economic disparities is critical to strengthening population mental health (Silva et al, 2016). To inform comprehensive policy responses, granular evidence is needed on how exposure to disadvantage over time impacts on the development of both mental health difficulties and positive aspects of mental health (Kvalsvig et al, 2014; Goldfeld et al, 2019). To date, however, the capacity to examine the relationship between disadvantage from childhood to adulthood and mental health outcomes, conceptualised as both difficulties and competence, has been limited by a lack of available data. We address this gap by capitalising on data from one of Australia’s longest-running studies of emotional development, the Australian Temperament Project (Vassallo and Sanson, 2013).

Mental health difficulties and competence

Mental health problems have increased globally over the past decade and now account for 32.4% of years lived with disability and 13.0% of disability-adjusted life years (Vigo et al, 2016; Patel et al, 2018). The costs of these problems are substantial: in addition to the wide-ranging emotional, social and economic consequences for individuals and families, it has been estimated that, in the period 2010–30, mental health problems will generate a loss of US$16 trillion to the global economy due to increased healthcare costs and reduced labour participation (Bloom et al, 2011). Internalising difficulties such as depression and anxiety are among the most costly (Trautmann et al, 2016), with depression expected to be the leading cause of disease burden globally by 2030 (World Health Assembly, 2012).

While the need to address this growing burden of mental disorder is pressing, there is increasing recognition of the need to also invest effort in the promotion of positive aspects of mental health (Keyes et al, 2010). Competence is one conceptualisation of positive psychosocial adaptation that refers to success in meeting the developmental tasks expected of individuals of a given age in their cultural and historical context (Masten and Curtis, 2000; Kvalsvig et al, 2014). For young adults in Western industrialised countries, this includes developmental tasks such as taking on citizenship responsibilities, being able to regulate emotions, and developing healthy intimate relationships (Gresham and Elliott, 1990; Smart and Sanson, 2003; Hawkins et al, 2011; O’Connor et al, 2016). Competence in these domains is valued by both individuals and society, and supports young adults to lead meaningful lives that contribute to economic productivity and civil society (O’Connor et al, 2016; OECD, 2018).

Socio-economic disparities in mental health

A population approach to improving outcomes across these aspects of young adult mental health is endorsed in countries such as the US (DHHS, 1999) and Australia (NMHC, 2014). While mental health programmes and services aimed at individuals remain essential, downstream treatment services are costly and unlikely to improve population mental health on their own (Jorm, 2014). A population approach emphasises the need to focus on interventions and reforms that impact the institutions, social systems and public policies that fundamentally drive the health of populations (Lantz, 2018), shifting the focus of mental health intervention towards these upstream factors and their capacity to prevent adverse health outcomes from arising (WHO, 2002). Action on upstream determinants that improve the conditions of everyday life provide opportunities for not only improving population mental health, but also for reducing disparities in mental health for marginalised groups in society (Allen et al, 2014).

One such upstream factor is the adverse conditions associated with socio-economic disadvantage (Allen et al, 2014; Shim et al, 2014). A substantial body of literature suggests that disadvantaged populations have higher rates of psychological disorder (Silva et al, 2016). Early indications suggest that positive aspects of mental health, too, are likely to be compromised by exposure to socio-economic hardship (Duncan et al, 2010; O’Connor et al, 2016). Socio-economic disadvantage entails reduced access to material and psychosocial resources that minimise risk for, and increase capacity to meet, mental health challenges. For example, access to adequate healthcare, parenting support and general resources important to responding to life challenges are all reduced in socio-economically disadvantaged environments (Stansfeld et al, 2011).

Importantly, from a life course perspective, the detrimental impact of disadvantage on mental health would be expected to occur not in a single instance, but to unfold over time (Braveman and Barclay, 2009). Exposure to disadvantage is not an isolated event, and patterns of socio-economic advantage and disadvantage can shift over the life course (Goldfeld et al, 2018c). In the early years of childhood, socio-economic disadvantage is largely shaped by the family environment and parental resources such as education, occupational prestige and income (Blakemore et al, 2009). As young people transition to adulthood, they establish their own socio-economic context, shaped by their own educational and occupational achievements. However, effects of familial disadvantage may persist. For example, young Australian adults who grew up in families receiving social assistance are twice as likely to receive social assistance themselves (Cobb-Clark et al, 2017).

The timing of exposure to disadvantage over development is likely to be important, too (Braveman and Barclay, 2009). The ‘sensitive periods’ hypothesis emphasises the salience of early adversity in the family context for health outcomes (Pudrovska and Anikputa, 2014; Dunn et al, 2018). In support of this hypothesis, early life disadvantage has been found to predict adult outcomes like cynical hostility, hopelessness (Harper et al, 2002) and depressive symptoms (Luo and Waite, 2005). An alternative theory, however, is that the circumstances established by young adults themselves are more directly relevant to mental health outcomes (Dunn et al, 2018). That is, early childhood adversity affects adult health outcomes indirectly by shaping young people’s own socio-economic position (SEP), which in turn affects their adult mental health (Dunn et al, 2018). More recent exposure has been found to exert a stronger effect than earlier exposure in some studies, including for outcomes such as emotional and behavioural problems (Dunn et al, 2018), depression (Shanahan et al, 2011) and hopelessness (Harper et al, 2002).

Chronicity of exposure to disadvantage may be even more detrimental. Some populations are exposed to persistent poverty over time, while other populations have more social mobility, or intermittent exposures (Lee, 2011). The concept of cumulative risk suggests that exposure to sustained and prolonged socio-economic adversity is particularly detrimental, and can have a cumulative health impact that is greater than the sum of its parts (Evans et al, 2013). Studies that employ approaches such as trajectory modelling to distinguish pathways of exposure over time support the notion that prolonged, chronic exposure to disadvantage can carry potentially cumulative sequela for mental health, as well as other important health and social outcomes over the life course (Dunn et al, 2018).

The current study

Addressing socio-economic disparities in mental health is critical to improving population mental health (Allen et al, 2014; Shim et al, 2014). Doing so requires evidence on when and how to intervene across the life course, beginning from the earliest periods of life (Allen et al, 2014; Goldfeld et al, 2019). To date, however, empirical findings have been inconsistent as to how the timing of exposure to disadvantage across childhood and adolescence relates to mental health in young adulthood, and the capacity to look across both mental health problems and competence has been limited by a lack of available data. Here we aim to address this gap using rare data available in one of Australia’s longest-running population cohort studies that has followed the health and development of over 2,000 young Australians (and their families) from infancy to young adulthood (Vassallo and Sanson, 2013). Prospective assessments of SEP were captured from 4–8 months to 27–28 years, and mental health problems (depression, anxiety, stress) and competence (civic engagement, emotional maturity, secure intimate relationship) at 27–28 years. We first characterise the continuity of SEP from infancy to 27–28 years in this sample, and then examine: (1) associations between disadvantage exposure at each age and mental health outcomes in young adulthood, to explore potential sensitive period and recency effects, and (2) associations between trajectories of SEP exposure over development and mental health outcomes, to inform the role of cumulative risk.

Methods

Study sample

The Australian Temperament Project (ATP) is a large-scale prospective longitudinal study of psychosocial development. The initial sample comprised 2,443 infants (aged 4–8 months) and their parents from both rural and urban areas of the state of Victoria, Australia, recruited from Maternal and Child Health Centres in 1983. The sample was representative of the Victorian population at the time on indicators of SEP (parent occupation and education). Data were collected on multiple aspects of child development as well as family, school and community characteristics. Details of methods and procedures are provided elsewhere (Vassallo and Sanson, 2013). Briefly, families have been surveyed by mail every one to two years until participants reached 19 years of age, and every four years thereafter, with a total of 16 waves of surveys completed to date (Vassallo and Sanson, 2013).

Here we draw on seven waves of data collection, selected to provide coverage of key developmental periods: infancy (4–8 months), early childhood (5–6 years), middle childhood (9–10 years), early adolescence (13–14 years), late adolescence (17–18 years), early adulthood (23–24 years) and young adulthood (27–28 years). The outcome wave (27–28 years) was selected because it contained a comprehensive measurement of both mental health problems and competence. The analysis sample included N = 1,144 participants who had outcome data at 27–28 years and/or had data for the majority of analysed waves (at least five). More participants from lower SEP families were lost to the study by 27–28 years; the original sample included 19.8% of participants in the lowest SEP quartile, compared to 14.2% of the participating sample at 27–28 years (Hawkins et al, 2011). While loss to follow-up may introduce bias when examining adverse exposures in childhood, this potential can be minimised by using appropriate statistical approaches like multiple imputation as employed herein (Doidge et al, 2017).

Measures

Exposure: socio-economic position from infancy to adulthood

SEP in the family of origin was defined using parent-report data of both mothers’ and fathers’ circumstances from infancy (4–8 months) to late adolescence (17–18 years). Young adult participants reported on their own SEP in early adulthood (23–24 years) and young adulthood (27–28 years). SEP at each wave was measured as a composite of highest education and occupational level (Blakemore et al, 2009). Participants reported their current occupation, categorised according to the criteria developed by the Australian Bureau of Statistics, and highest educational level achieved, which was rated on an eight-point scale, ranging from elementary schooling to postgraduate degree.

To derive standardised scores, values for each parent’s or young adult’s education and occupation variables were converted to z-scores. An unweighted mean score was created by averaging the standardised scores on education and occupation (and, for family of origin, for both parents); this was then restandardised to have a mean of 0 and a standard deviation of 1. This was subsequently categorised into quintiles reflecting ‘low/disadvantaged’ (scores below the 20th percentile); ‘low-medium’ (20th–40th percentile); ‘medium’ (40th–60th percentile), ‘medium-high’ (60th–80th percentile); and ‘high’ (scores above the 80th percentile). As well as lower relative position in the social hierarchy, this translates to substantial differences in resources in absolute terms, both of which contribute to inequities (Adler and Tan, 2017). In this sample, for example, the majority (64%) of those with relatively low SEP at infancy had a mother with ninth- or tenth-grade secondary schooling, whereas the majority (51%) of mothers in the highest SEP category had a tertiary degree.

Outcomes: mental health at 27–28 years

Mental health was measured according to self-reported indicators of difficulties and competence at 27–28 years (Table 1). Indicators of mental health problems included depression, anxiety and stress (Lovibond and Lovibond, 1996). Indicators of competence included key developmental tasks of the young adult period that are valued by individuals and society and were captured in the data, including: civic engagement, emotional maturity and secure intimate relationships (Gresham and Elliott, 1990; Smart and Sanson, 2003; Hawkins et al, 2011; O’Connor et al, 2016). We dichotomised each of these indicators for interpretability, to capture relatively low or high levels of the outcomes of interest (Evans et al, 2013). Established cut-points were used to dichotomise each subscale of the 21-item Depression, Anxiety and Stress Scale (DASS), which indicate that an individual is experiencing considerable emotional symptoms (Lovibond and Lovibond, 1996). All indicators of competence used a cut-point of the top 20th percentile, in the absence of established cut-points, to identify young adults showing a relatively high level of that outcome compared to same-age peers. This cut-point was crude but had high face validity, corresponding to a skill level that exceeded the majority of the sample.

Table 1:

Self-reported measures of mental health problems and competence at 27–28 years of age

ConstructMeasureExample itemScoringCodingα
Mental health problems
DepressionDepression subscale of the 21-item Depression and Anxiety Stress Scale (DASS) (7 items) (Lovibond and Lovibond, 1996)I couldn’t seem to experience any positive feelingSelf-reported feelings over the past month, using a 4-point Likert scale including responses from 0 ‘did not apply’ to 3 ‘applied very much / most of the time’Summed scores were multiplied by two and scores of 14 or more were coded as ‘moderate or severe symptoms’, based on established cut-points (Lovibond and Lovibond, 1996)0.91
AnxietyAnxiety subscale of the DASS (7 items) (Lovibond and Lovibond, 1996)I was aware of dryness of my mouthAs for depressionAs for depression, with summed scores of 10 or more coded as ‘moderate or severe symptoms’0.80
StressStress subscale of the DASS (7 items) (Lovibond and Lovibond, 1996)I found it hard to wind downAs for depressionAs for depression, with summed scores of 19 or more were coded as ‘moderate or severe symptoms’0.83
Competence
Civic engagementThe adapted 16-item civic engagement scale (Stone and Hughes, 2002)I have joined with people to resolve a local or neighbourhood problemSelf-reported participation in 16 civic activities in the past 12 months, using a 5-point scale of 0, 1–2 times, 3–4 times, or 5–10 times, or 11+ timesThe average score was derived and the top 20% of scores were coded as ‘high engagement’ and the bottom 80% as ‘low engagement’0.69
Emotional maturityThe 12-item emotional maturity scale (Gresham and Elliott, 1990; Smart and Sanson, 2003; Hawkins et al, 2011)I show my concern for others when they experience difficultiesSelf-reported perceptions of emotional maturity across three domains (responsibility, self-control and empathy), using a 5-point Linkert scale including responses from 1 ‘never’ to 5 ‘always’As for civic engagement0.85
Secure intimate relationshipThe 6-item relationship quality scale (Braiker and Kelley, 1979)I have a sense of ‘belonging’ with my partnerSelf-reported feelings of closeness with partner/spouse if he/she was in a romantic relationship, using a 4-point Likert scale including responses from 1 ‘always’ to 4 ‘rarely/never’As for civic engagement. Those not in a relationship were coded as 0/low quality0.85

Potential confounders

Following VanderWeele’s (2019) recommendations for confounder selection, we posited potential confounders that were considered not to be mediators and, based on current knowledge, considered to be either a cause of the exposure or of the outcome, or of both, but not an instrumental variable (that is, a cause of the exposure that has no relation to the outcome except through the exposure) (VanderWeele, 2019). This included early life behaviour problems (parent-reported composite of colic, sleep problems and excessive crying in infancy), maternal age at birth (dichotomised at below or above 23 years following Goldfeld et al, 2018a), parents’ country of birth (English speaking countries / non-English speaking countries), and child’s sex (female/male), all measured at 4–8 months.

Statistical analysis

First, we examined the mental health characteristics of the sample, describing the proportion of participants with high levels of mental health problems (depression, anxiety, stress) and competence (civic engagement, emotional maturity and secure intimate relationship) at 27–28 years.

Socio-economic position from childhood to young adulthood

To characterise the course of socio-economic disadvantage over time in this sample, we used logistic regression analyses to examine SEP in the family of origin as a predictor of socio-economic disadvantage at 27–28 years. These models estimate the odds of being disadvantaged in young adulthood (SEP scores in the lowest quintile) if they had low, low-medium or medium-high SEP at each wave in childhood and adolescence, as compared to having the highest level of SEP in the prior wave. Estimates were adjusted for early behaviour problems, maternal age at birth, parents’ country of birth and child’s sex.

We also took a person-centred approach and described trajectories of SEP over time. Group-based trajectory modelling (Nagin, 1999) was used with the continuous, standardised SEP scores from infancy to 27–28 years to identify groups with similar patterns of socio-economic resources over time. The Bayesian information criterion (BIC; least negative value) and posterior probability of membership to a trajectory (above 0.7) were used to select the preferred model (Nagin, 1999; Nagin and Odgers, 2010).

Socio-economic position and young adult mental health

We next examined associations between exposure to socio-economic disadvantage over development and mental health outcomes. Logistic regression analyses were used to examine SEP at each age, and trajectories of SEP, as predictors of mental health difficulties and competence at 27–28 years. Estimates were adjusted for the already-mentioned potential confounders. Disadvantage exposure at each age was examined in a separate model without adjusting for subsequent SEP, because subsequent SEP was assumed to be on the causal pathway to mental health status (Schisterman et al, 2009).

Missing data

Missing data in the analysis sample ranged from 0% to 19.5%, with an average of 9.9% across study variables (Supplementary Table 1). Multiple imputation by chained equations was used to handle missing values (Rubin, 1987) and provide less biased estimates than complete case approaches (Doidge et al, 2017). Fifty imputed data sets were created. The imputation model included all study variables, as well as infant temperament at 4–8 months and mental health problems in the two waves prior to 27–28 years to help predict missing values. Results were combined using Rubin’s rules (Rubin, 1987). Results from imputed data are reported throughout. Analyses were conducted using Stata SE version 15.1 for Windows (© 1985–2013 Stata Corp LP, revision 30 October 2013).

Results

Mental health in young adulthood

In this sample, around one in ten young adults had elevated levels of mental health symptomatology: depression (15.1%), anxiety (13.8%), and stress (9.3%; Supplementary Table 2). Reflecting our cut-points and the variable distributions, the proportion of participants with high levels of mental health competence ranged from 28.3% (secure intimate relationship) to 18.6% (high civic engagement).

Socio-economic position from childhood to young adulthood

Logistic regression analyses showed that the odds of being socio-economically disadvantaged in young adulthood were elevated eight- to tenfold in those who had experienced disadvantage in the family of origin, compared with those who had not (OR 8.1, 95% CI 4.5–14.5 to 10.1, 95% CI 5.2–19.5; Table 2).

Table 2:

Logistic regression analyses examining the effect of socio-economic position in the family of origin (infancy to 18 years) on young adulthood socio-economic disadvantage at 27–28 years

PredictorsOdds ratio95% CI
LowerUpper
Infancy (4–8 months)
 Low8.264.6214.77
 Low-medium6.213.5110.98
 Medium4.412.517.74
 Medium-high2.341.304.20
Early childhood (5–6 years)
 Low8.064.5014.46
 Low-medium5.723.2610.05
 Medium3.411.926.05
 Medium-high1.070.542.11
Middle childhood (9–10 years)
 Low10.065.1919.50
 Low-medium9.775.1518.53
 Medium4.592.388.84
 Medium-high2.251.104.61
Early adolescence (13–14 years)
 Low9.775.2218.29
 Low-medium5.723.1010.58
 Medium3.751.977.11
 Medium-high1.530.743.15
Late adolescence (17–18 years)
 Low9.124.8317.25
 Low-medium5.562.9610.44
 Medium3.711.957.04
 Medium-high1.830.903.74

Note: CI=Confidence Interval; N = 1,144.

Estimates are adjusted for early life behaviour problems, maternal age at birth, parents’ country of birth and child’s sex. Socio-economic position (SEP) from infancy to late adolescence was categorised into quintiles of ‘low’ (scores below the 20th percentile); ‘low-medium’ (20th–40th percentile); ‘medium’ (40th–60th percentile), ‘medium-high’ (60th–80th percentile); and ‘high’ (scores above the 80th percentile), which is the reference group to which others are compared. SEP in young adulthood was similarly categorised into quintiles, and subsequently dichotomised with the lowest quintile taken to reflect socio-economic disadvantage.

Trajectories of disadvantage were identified that summarised groups of individuals with similar patterns of SEP over time. The preferred model had the lowest BIC value and average posterior probability values above 0.8. Four SEP trajectories were identified (Figure 1): 32.7% of participants had consistently low SEP from 0–1 to 27–28 years, with this trajectory group labelled ‘steady low SEP’; approximately one fifth (21.4%) of participants had consistently high SEP over time, with this pathway labelled ‘steady high SEP’; more than one quarter (26.2%) of participants experienced an ‘increasing SEP’ pattern as they established their own SEP in young adulthood, while 19.7% had a ‘decreasing SEP’ (became more disadvantaged) as they moved into adulthood.

The scatter graph with four fitted lines illustrates the socioeconomic positions (z-scores) that ranges from negative 1 to 1.5 in increments of 0.5 unit and age in years ranges from 0 to 30 in increments of 10 units. The horizontal line from (0, -1) to (28, -1) represent the persistent low position (32.7%). The decreasing curve from (0, 0.2) to (28, -0.3) represents decreasing position (19.7%). The decreasing dashed curve from (0, 1.4) to (28, 0.5) represents the steady high position (21.4%). Another decreasing dashed curve from (0, 1.4) to (28, 0.5) represents the increasing position (26.2%). All values are estimated.
Figure 1:

Trajectories of socio-economic position from 0–1 to 27–28 years of age

Citation: Longitudinal and Life Course Studies 2022; 10.1332/175795921X16459587898770

Note: SEP = socio-economic position.

Socio-economic position and young adult mental health outcomes

Mental health problems

At each age, the proportion of young adults reporting depression and stress was similar for those who were disadvantaged as compared to not disadvantaged (Figure 2). Consistent with these descriptive findings, adjusted logistic regressions showed little evidence of an association with socio-economic disadvantage in the family of origin, or of trajectories capturing pathways of exposure over time, with depression or stress at 27–28 years (Figure 3).

The dot plot categorizes the own adult SEP into young and early adulthood, the family of origin SEP into early to late adolescence, early to middle childhood and infancy, and SEP trajectory into infancy to adulthood. The problems are displayed as depression, anxiety, stress, civic, emotional maturity, and secure intimate relationship. Depression and stress problems are similar for advantaged and disadvantaged young adults. Anxiety is more among disadvantaged adults. Civic engagement, emotional maturity, and secure relationship are higher amongst advantaged adults.
Figure 2:

Proportion of young adults with mental health problems and competence according to socio-economic position at each age (%, 95% CI)

Citation: Longitudinal and Life Course Studies 2022; 10.1332/175795921X16459587898770

Note: SEP = socio-economic position; Civic = Civic engagement; Emot mat = emotional maturity; Secure rel = secure intimate relationship.
The dot plot categorizes the own adult SEP into young and early adulthood, the family of origin SEP into early to late adolescence, early to middle childhood and infancy, and SEP trajectory into infancy to adulthood. The problems are displayed as depression, anxiety, and stress. Depression and stress problems have little relationship with the socio-economic position of the family of origin and SEP trajectory. The anxiety symptoms for the 28 years old who were early and young adulthood disadvantaged are nearly twice of the rest.
Figure 3:

Odds of having mental health problems at 27–28 years of age according to socio-economic position (socio-economic disadvantage) at each age, adjusted for early life behaviour problems, maternal age at birth, parents’ country of birth and child’s sex

Citation: Longitudinal and Life Course Studies 2022; 10.1332/175795921X16459587898770

Note: SEP = socio-economic position. The reference group is high socio-economic disadvantage or steady high socio-economic disadvantage trajectory. Estimates show odds ratios with 95% confidence intervals.

For anxiety, however, a substantially higher proportion of those who were disadvantaged in adulthood had clinically significant symptoms, as compared to those who were socio-economically advantaged (9.3 percentage points higher in early adulthood and 7.8 higher in young adulthood; Figure 2). Logistic regression models showed that those who were disadvantaged in early and young adulthood had a twofold increase in the odds of clinically meaningful anxiety symptoms at 27–28 years, as compared to those with the highest SEP (early adulthood: OR 2.41, 95% CI 1.20–4.88; young adulthood: OR 2.01, 95% CI 1.05–3.85; Figure 3).

Competence

At each age, for each step of increasing advantage, a higher proportion of young adults had high levels of civic engagement (Figure 2). For example, 14.7% (95% CI 8.9–20) of those who were most disadvantaged in infancy reported high levels of civic engagement as young adults, compared to 26% of those who were most advantaged (95% CI 21–32). A similar difference was observed for emotional maturity and secure intimate relationships for the participants’ own SEP in adulthood. The proportion of young adults with high levels of competence also differed according to trajectory group membership. Those on a steady high trajectory (26% 95% CI 21–31) reported higher levels of civic engagement. Those on an increasing pathway reported the highest levels of emotional maturity (23%, 95% CI 18–28) and security in their intimate relationship (33%, 95% CI 27–39).

Adjusted logistic regression models showed that across all time points in both the family of origin and adulthood, and for the steady low trajectory group, those who were socio-economically disadvantaged had lower odds of being civically engaged in young adulthood, as compared to their high SEP peers (OR 0.37, 95% CI 0.23–0.59 to OR 0.46, 95% CI 0.26–0.81; Figure 4). There was also some evidence that those socio-economically disadvantaged in adulthood (OR 0.51, 95% CI 0.30–0.85 to OR 0.77, 95% CI 0.49–1.20), and who were on a low or decreasing trajectory, had lower odds of emotional maturity and secure intimate relationships at 27–28 years (steady low trajectory and emotional maturity: OR 0.68, 95% CI 0.44–1.06; decreasing trajectory and secure relationships: OR 0.69, 95% CI 0.44–1.09).

The dot plot categorizes the own adult SEP into young and early adulthood, the family of origin SEP into early to late adolescence, early to middle childhood and infancy, and SEP trajectory into infancy to adulthood. These are displayed for civic engagement, emotional maturity, and secure intimate relationship. The adults who were earlier disadvantaged from the family of origin section and low steady trajectory section had a lower civic engagement. The adults who were earlier disadvantaged low and decreasing trajectory section had lower emotional maturity, and secure intimate relationships.
Figure 4:

Odds of competence at 27–28 years of age according to socio-economic position (socio-economic disadvantage) at different time points, and for different socio-economic trajectories, adjusted for early life behaviour problems, maternal age at birth, parents’ country of birth and child’s sex

Citation: Longitudinal and Life Course Studies 2022; 10.1332/175795921X16459587898770

Note: SEP = socio-economic position. The reference group is high socio-economic disadvantage or steady high socio-economic disadvantage trajectory. Estimates show odds ratios with 95% confidence intervals.

Discussion

Using multiwave prospective data collected over three decades in a large-scale Australian community-based study, we examined associations between SEP across childhood and adolescence and mental health outcomes in young adulthood, defined in terms of both problems and competence. Even in the context of universal healthcare and a relatively good social safety net, there were strong continuities observed between SEP in the family of origin and in young adulthood. Young adults’ own socio-economic disadvantage was more strongly related to mental health problems than family of origin socio-economic disadvantage, and was pronounced in the anxiety domain. The effect of disadvantage on competence in young adulthood appeared to be more pervasive than for mental health difficulties, particularly in the civic domain where associations were evident even from early infancy.

The strong continuities observed between SEP in the family of origin and young adulthood circumstances aligns with the well-documented potential for intergenerational transmission of disadvantage (Cobb-Clark et al, 2017). The extent of this continuity is notable given our focus on relative SEP rather than more extreme forms of material poverty, and in the context of the reasonably strong social welfare policies in Australia (AIHW, 2017). Due to reduced access to resources and opportunities in the family, school and community, disadvantaged children are less likely to gain the cognitive, emotional and physical capacities needed for optimal educational and employment outcomes when they transition to adulthood (Cheng et al, 2016).

Young adults’ own socio-economic disadvantage, but not disadvantage in the family of origin, was associated with anxiety symptoms at 27–28 years. This finding is consistent with the ‘pathways’ hypothesis, whereby disadvantage in adulthood is proposed to be more proximally and directly relevant for mental health. This aligns with some previous studies (Harper et al, 2002; Shanahan et al, 2011; Dunn et al, 2018), but contrasts with those that have found long-term associations between early SEP and mental health difficulties in adulthood (for example, Harper et al, 2002; Luo and Waite, 2005). The magnitude of these latter effects have typically been small (Stansfeld et al, 2011), particularly when early behaviour problems are accounted for (Elovainio et al, 2012), which may explain why they did not emerge here. It is also noteworthy that, in contrast to previous research (Silva et al, 2016), factors other than SEP appeared to play a greater role in the prediction of depression and stress during this developmental period, with little evidence found for associations with SEP at any time points. Why and how these associations differ between facets of internalising problems will require further investigation.

Our results further suggest that exposure to disadvantage over the life course has the capacity to compromise the development of positive aspects of mental health. For the civic engagement domain, this included exposure to disadvantage extending back to early childhood, more recently in adolescence, and when considering trajectories of exposure over time. This is consistent with theories highlighting the relevance of early sensitive periods, recent exposure and total load of exposure over time, which are not mutually exclusive hypotheses (Hallqvist et al, 2004). Children growing up in higher socio-economic status families are likely to have more opportunities for relationships and activities that support the development of skills and competence, and a greater capacity to take advantage of neighbourhood resources such as social cohesion (Flanagan and Levine, 2010). Parents’ ability to invest in their children’s development and parents’ emotional well-being may also mediate the effects of socio-economic status on children’s capacity to develop successfully (Yeung et al, 2002). In addition, there may be bidirectional effects operating over time whereby earlier manifestations of competence assist young people in moving out of disadvantage in adulthood (Hudson, 2005).

Across all aspects of mental health, the strength of associations that we observed was typically modest. This reflects that SEP is not deterministic and that there is substantial variation of outcomes within levels of SEP. SEP describes the context and circumstances for development, and, as already noted, its impact on development is likely to be mediated through a wide variety of more proximal factors like parenting, neighbourhood resources and adverse experiences (Goldfeld et al, 2018b). A key question for continued investigation is why some individuals have resilient outcomes despite being exposed to socio-economic adversity (Schoon and Parsons, 2002; Boyce et al, 2012).

Implications and future directions

The present study demonstrates that exposure to disadvantage is relevant to both mental health problems and competence. This is notable because research and policy tend to focus strongly on inequities in mental health problems, with little attention to psychosocial strengths or assets. Failure to attend to facets of competence is likely to underestimate the extent of socio-economic disparities and opportunities to intervene (Keyes, 2007; Kvalsvig et al, 2014).

An important avenue to addressing these disparities is targeting disadvantage itself, such as through social welfare (cash transfers, for instance) and education policies. Australia’s policies in these areas during the childhood period of this sample, although reasonably strong by international standards (AIHW, 2017), did not succeed in fully closing gaps in outcomes for disadvantaged children and young people. Further efforts to enhance educational attainment, access to health services, and income support for those in greatest need are particularly important pathways to directly addressing disadvantage and improving population health (Mechanic, 2002).

To reduce the impact on mental health for those who are exposed to disadvantage, universal early childhood and school-based mental health promotion programmes offer opportunities to engage large numbers of children, including those from disadvantaged backgrounds (Greenberg et al, 2017; Dodge, 2020). A number of intervention studies have demonstrated that social and emotional skills can be modified through school-based approaches (Durlak et al, 2011), with gains to mental health and well-being sustained into adulthood (Taylor et al, 2017). While such programmes have the potential to benefit all children, increased attention is needed to the potential to close socio-economic gaps in mental health outcomes (O’Connor et al, 2021).

In formulating such responses, our results reinforce the need for a long-term view contextualised within a life course perspective. Efforts targeting single time points in development are unlikely to be sufficient to eliminate disparities in mental health. Rather, these need to initiate from childhood and be sustained in adolescence, as well as respond to the specific opportunities and challenges posed during the transition to adulthood (Brooks-Gunn, 2003). Beyond this is the need to incorporate an intergenerational perspective, given the strong continuities we observed here between disadvantage in the family of origin and disadvantage in adulthood (Cheng et al, 2016).

Strengths and limitations

The present study’s strengths include the availability of multiwave, prospectively collected data from infancy to 27–28 years in a large-scale community sample of young adults. Future research is needed to explore whether results generalise beyond the Australian context, which is characterised by universal healthcare and a strong welfare system. While our findings generalise most directly to current young adults in Australia and similar contexts, they are also likely to hold relevance for younger generations, for whom socio-economic gradients in psychosocial outcomes do not appear to have changed substantially (Redmond et al, 2013).

Limitations should also be considered related to the major sources of bias in long-term cohort studies (Sterne et al, 2016), such as the potential for selection bias. On average the ATP lost contact with around 1% of the original sample each year of operation, similar to cohort studies worldwide, raising the potential for selection bias. Most attrition happened in the first three waves of data collection; the ATP was originally intended as a cross-sectional, state-representative survey, and some families were lost to follow-up or declined to take part when it transitioned to an ongoing longitudinal study. Attrition has been greater for those who are more disadvantaged, meaning that the variation in socio-economic circumstances in the sample may be less extensive than that observed in the full Australian population. It is noteworthy, however, that the levels of mental health problems in this sample were similar to Australian population averages (Crawford et al, 2011).

The potential for measurement bias should also be considered. Socio-economic disadvantage is multidimensional (Blakemore et al, 2009), and in future research it would be of value to explore other sources of power and prestige such as income, wealth/assets and geographic advantage (Goldfeld et al, 2018b), as well as other sources of marginalisation, such as ethnicity, language and disability (Gkiouleka et al, 2018). Mental health is a similarly complex and multifaceted construct, and future work would benefit from triangulating self-report with the perspectives of other informants. We dichotomised each of the mental health outcomes, to allow examination of associations between SEP and relatively high levels of each domain. In future research, examination of continuous mental health scores to understand associations across the full spectrum of symptom and competence levels would be of interest.

While we accounted for early behaviour problems at baseline to reduce potential confounding bias, we did not examine the shape and trajectory of mental health problems over childhood and adolescence. We can therefore only speculate on potential selection effects, whereby poor mental health can in turn further entrench socio-economic disadvantage, by limiting an individual’s capacity to engage in education and employment (Stansfeld et al, 2011), as well as by incurring treatment costs. By presenting a comprehensive set of associations between SEP over development and later mental health, we have laid the foundation for future research to examine mediation pathways and causal processes underpinning these associations.

Conclusions

Even in the context of universal healthcare and a relatively good social safety net, we found strong continuities between SEP in the family of origin and an individual’s own SEP in young adulthood. We further found a complex relationship between these experiences of socio-economic disadvantage over the life course and mental health outcomes in young adulthood. Long-term histories of disadvantage, extending back to infancy, were associated with the compromised development of aspects of competence. We further found that effects of disadvantage on mental health problems (specifically, anxiety) appear limited to young adulthood in this sample. Findings emphasise the importance of addressing the root causes of disadvantage and investing in approaches to enhance social equity, as well as targeted approaches to promoting mental health competence in children and young people growing up with social disadvantage.

Funding

This research was supported by the Victorian Government’s Operational Infrastructure Support Program. Dr O’Connor was supported by the Melbourne Children’s LifeCourse, funded by Royal Children’s Hospital Foundation grant #2018–984. Prof Goldfeld is supported by Australian National Health and Medical Research Council (NHMRC) Practitioner Fellowship 1155290. Prof Craig Olsson is supported by an NHMRC Investigator Grant (APP1175086).

Acknowledgements

The Australian Temperament Project study is located at The Royal Children’s Hospital Melbourne and is a collaboration between Deakin University, The University of Melbourne, The Australian Institute of Family Studies, The University of New South Wales, The University of Otago (New Zealand), and the Royal Children’s Hospital; further information available at https://www.melbournechildrens.com/atp/. We acknowledge all collaborators who have contributed to the ATP, especially Professors Ann Sanson, Margot Prior and Frank Oberklaid, Dr Diana Smart and data manager Dr Christopher Greenwood. We would also like to sincerely thank the participating families for their time and invaluable contribution to the study. The views expressed in this paper are those of the authors and may not reflect those of their organisational affiliations, nor of other collaborating individuals or organisations.

Data availability

Ethics approvals do not permit these potentially reidentifiable data to be made publicly available, but access can be requested at https://lifecourse.melbournechildrens.com/data-access/.

Ethics approval statement

The current institutional body responsible for ethical approval of the Australian Temperament Project is The Royal Children’s Hospital Human Research Ethics Committee.

Conflict of interest

The authors declare that there is no conflict of interest.

References

  • Adler, N.E. and Tan, J.J.X. (2017) Tackling the health gap: the role of psychosocial processes, International Journal of Epidemiology, 46(4): 132931. doi: 10.1093/ije/dyx167

    • Search Google Scholar
    • Export Citation
  • AIHW (Australian Institute of Health and Welfare) (2017) Australia’s Welfare 2017, https://www.aihw.gov.au/getmedia/088848dc-906d-4a8b-aa09-79df0f943984/aihw-aus-214-aw17.pdf.aspx?inline=true.

    • Search Google Scholar
    • Export Citation
  • Allen, J., Balfour, R., Bell, R. and Marmot, M. (2014) Social determinants of mental health, International Review of Psychiatry, 26(4): 392407. doi: 10.3109/09540261.2014.928270

    • Search Google Scholar
    • Export Citation
  • Blakemore, T., Strazdins, L. and Gibbings, J. (2009) Measuring family socioeconomic position, Australian Social Policy, 8: 12168, http://www.dss.gov.au/sites/default/files/documents/05_2012/asp-8.pdf.

    • Search Google Scholar
    • Export Citation
  • Bloom, D.E., Cafiero, E.T., Jané-Llopis, E., Abrahams-Gessel, S., Bloom, L.R., Fathima, S., Feigl, A.B., Gaziano, T., Mowafi, M., Pandya, A. et al. (2011) The Global Economic Burden of Non-communicable Diseases, Geneva: World Economic Forum, http://www3.weforum.org/docs/WEF_Harvard_HE_GlobalEconomicBurdenNonCommunicableDiseases_2011.pdf.

    • Search Google Scholar
    • Export Citation
  • Boehm, J.K., Chen, Y., Williams, D.R., Ryff, C. and Kubzansky, L.D. (2015) Unequally distributed psychological assets: are there social disparities in optimism, life satisfaction, and positive affect?, PLoS One, 10(2): art e0118066, doi: 10.1371/journal.pone.0118066.

    • Search Google Scholar
    • Export Citation
  • Boyce, W.T., Sokolowski, M.B. and Robinson, G.E. (2012). Toward a new biology of social adversity, Proceedings of the National Academy of Sciences, 109(S2): 171438. doi: 10.1073/pnas.1121264109

    • Search Google Scholar
    • Export Citation
  • Braiker, H.B. and Kelley, H.H. (1979) Conflict in the development of close relationships, in R.L. Burgess and T.L. Huston (eds) Social Exchange in Developing Relationships, New York: Academic Press, pp 13568.

    • Search Google Scholar
    • Export Citation
  • Braveman, P. and Barclay, C. (2009) Health disparities beginning in childhood: a Life-course perspective, Pediatrics, 124(S3): S163S175. doi: 10.1542/peds.2009-1100D

    • Search Google Scholar
    • Export Citation
  • Brooks-Gunn, J. (2003) Do you believe in magic? What we can expect from early childhood intervention programs, Society for Research in Child Development, 17(1): 314.

    • Search Google Scholar
    • Export Citation
  • Cheng, T.L., Johnson, S.B. and Goodman, E. (2016) Breaking the intergenerational cycle of disadvantage: the three generation approach, Pediatrics, 137(6): e20152467, doi: 10.1542/peds.2015-2467.

    • Search Google Scholar
    • Export Citation
  • Cobb-Clark, D.A., Dahmann, S., Salamanca, N. and Zhu, A. (2017) Intergenerational Disadvantage: Learning About Equal Opportunity from Social Assistance Receipt, IZA Discussion Paper No 11070, Bonn: Institute of Labor Economics, https://www.iza.org/publications/dp/11070/intergenerational-disadvantage-learning-about-equal-opportunity-from-social-assistance-receipt.

    • Search Google Scholar
    • Export Citation
  • Crawford, J., Cayley, C., Lovibond, P.F., Wilson, P.H. and Hartley, C. (2011) Percentile norms and accompanying interval estimates from an Australian general adult population sample for Self‐report mood scales (BAI, BDI, CRSD, CES‐D, DASS, DASS‐21, STAI‐X, STAI‐Y, SRDS, and SRAS), Australian Psychologist, 46(1): 314. doi: 10.1111/j.1742-9544.2010.00003.x

    • Search Google Scholar
    • Export Citation
  • DHHS (US Department of Health and Human Services) (1999) Mental Health: A Report of the Surgeon General, Bethesda, MD: National Institute of Mental Health, https://profiles.nlm.nih.gov/spotlight/nn/catalog/nlm:nlmuid-101584932X815-img.

    • Search Google Scholar
    • Export Citation
  • Dodge, K.A. (2020) Annual research review: universal and targeted strategies for assigning interventions to achieve population impact, Journal of Child Psychology and Psychiatry, 61(3): 25567. doi: 10.1111/jcpp.13141

    • Search Google Scholar
    • Export Citation
  • Doidge, J.C., Edwards, B., Higgins, D.J. and Segal, L. (2017) Adverse childhood experiences, Non-response and loss to Follow-up: findings from a prospective birth cohort and recommendations for addressing missing data, Longitudinal and Life Course Studies, 8(4): 382400. doi: 10.14301/llcs.v8i4.414

    • Search Google Scholar
    • Export Citation
  • Duncan, G.J., Ziol-Guest, K.M. and Kalil, A. (2010) Early‐childhood poverty and adult attainment, behavior, and health, Child Development, 81(1): 30625. doi: 10.1111/j.1467-8624.2009.01396.x

    • Search Google Scholar
    • Export Citation
  • Dunn, E.C., Soare, T.W., Raffeld, M.R., Busso, D.S., Crawford, K.M., Davis, K.A., Fisher, V.A., Slopen, N., Smith, A.D., Tiemeier, H. and Susser, E.S. (2018) What life course theoretical models best explain the relationship between exposure to childhood adversity and psychopathology symptoms: recency, accumulation, or sensitive periods?, Psychological Medicine, 48(15): 256272. doi: 10.1017/S0033291718000181

    • Search Google Scholar
    • Export Citation
  • Durlak, J.A., Weissberg, R.P., Dymnicki, A.B., Taylor, R.D. and Schellinger, K.B. (2011) The impact of enhancing students’ social and emotional learning: a Meta-analysis of School-based universal interventions, Child Development, 82(1): 40532. doi: 10.1111/j.1467-8624.2010.01564.x

    • Search Google Scholar
    • Export Citation
  • Elovainio, M., Pulkki-Råback, L., Jokela, M., Kivimäki, M., Hintsanen, M., Hintsa, T., Viikari, J., Raitakari, O.T. and Keltikangas-Järvinen, L. (2012) Socioeconomic status and the development of depressive symptoms from childhood to adulthood: a longitudinal analysis across 27 years of follow-up in the Young Finns study, Social Science & Medicine, 74(6): 9239.

    • Search Google Scholar
    • Export Citation
  • Evans, G., Li, D. and Whipple, S. (2013) Cumulative risk and child development, Psychological Bulletin, 139(6): 134296. doi: 10.1037/a0031808

    • Search Google Scholar
    • Export Citation
  • Flanagan, C. and Levine, P. (2010) Civic engagement and the transition to adulthood, The Future of Children, 20(1): 15979. doi: 10.1353/foc.0.0043

    • Search Google Scholar
    • Export Citation
  • Gkiouleka, A., Huijts, T., Beckfield, J. and Bambra, C. (2018) Understanding the micro and macro politics of health: inequalities, intersectionality & institutions – a research agenda, Social Science & Medicine, 200: 928.

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., D’Abaco, E., Bryson, H., Mensah, F. and Price, A.M.H. (2018a) Surveying social adversity in pregnancy: the antenatal risk burden experienced by Australian women, Journal of Paediatrics and Child Health, 54(7): 75460. doi: 10.1111/jpc.13860

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., O’Connor, M., Cloney, D., Gray, S., Redmond, G., Badland, H., Williams, K., Mensah, F., Woolfenden, S., Kvalsvig, A. and Kochanoff, A. (2018b) Understanding child disadvantage from a social determinants perspective, Journal of Epidemiology & Community Health, 72(3): 2239.

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., O’Connor, M., O’Connor, E., Chong, S., Badland, H., Woolfenden, S., Redmond, G., Williams, K., Azpitarte, F., Cloney, D. and Mensah, F. (2018c) More than a snapshot in time: pathways of disadvantage over childhood, International Journal of Epidemiology, 47(4): 130716. doi: 10.1093/ije/dyy086

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., Gray, S., Azpitarte, F., Cloney, D., Mensah, F., Redmond, G., Williams, K., Woolfenden, S. and O’Connor, M. (2019) Driving precision policy responses to child health and developmental inequities, Health Equity, 3(1): 48994. doi: 10.1089/heq.2019.0045

    • Search Google Scholar
    • Export Citation
  • Greenberg, M.T., Domitrovich, C.E., Weissberg, R.P. and Durlak, J.A. (2017) Social and emotional learning as a public health approach to education, The Future of Children, 27(1): 1332. doi: 10.1353/foc.2017.0001

    • Search Google Scholar
    • Export Citation
  • Gresham, F.M. and Elliott, S.N. (1990) Manual for the Social Skills Rating System, Circles Pines, MN: American Guidance Service.

  • Hallqvist, J., Lynch, J., Bartley, M., Lang, T. and Blane, D. (2004) Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program, Social Science & Medicine, 58(8): 155562.

    • Search Google Scholar
    • Export Citation
  • Harper, S., Lynch, J., Hsu, W.L., Everson, S.A., Hillemeier, M.M., Raghunathan, T.E., Salonen, J.T. and Kaplan, G.A. (2002) Life course socioeconomic conditions and adult psychosocial functioning, International Journal of Epidemiology, 31(2): 395403. doi: 10.1093/ije/31.2.395

    • Search Google Scholar
    • Export Citation
  • Hawkins, M., Letcher, P., Sanson, A., O’Connor, M., Toumbourou, J. and Olsson, C. (2011) Stability and change in positive development during the transition from adolescence to adulthood, Journal of Youth and Adolescence, 40(11): 143652. doi: 10.1007/s10964-011-9635-9

    • Search Google Scholar
    • Export Citation
  • Hudson, C.G. (2005) Socioeconomic status and mental illness: tests of the social causation and selection hypotheses, American Journal of Orthopsychiatry, 75(1): 318. doi: 10.1037/0002-9432.75.1.3

    • Search Google Scholar
    • Export Citation
  • Jorm, A.F. (2014) Why hasn’t the mental health of Australians improved? The need for a national prevention strategy, Australian & New Zealand Journal of Psychiatry, 48(9): 795801.

    • Search Google Scholar
    • Export Citation
  • Keyes, C.L.M. (2007) Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health, American Psychologist, 62(2): 95108. doi: 10.1037/0003-066X.62.2.95

    • Search Google Scholar
    • Export Citation
  • Keyes, C.L.M., Dhingra, S.S. and Simoes, E.J. (2010) Change in level of positive mental health as a predictor of future risk of mental illness, American Journal of Public Health, 100(12): 236671. doi: 10.2105/AJPH.2010.192245

    • Search Google Scholar
    • Export Citation
  • Kvalsvig, A., O’Connor, M., Redmond, G. and Goldfeld, S. (2014) The unknown citizen: epidemiological challenges in child mental health, Journal of Epidemiology and Community Health, 68(10): 10048. doi: 10.1136/jech-2013-203712

    • Search Google Scholar
    • Export Citation
  • Lantz, P.M. (2018) The medicalization of population health: who will stay upstream?, The Milbank Quarterly, 97: 14, https://www.milbank.org/quarterly/articles/the-medicalization-of-population-health-who-will-stay-upstream/.

    • Search Google Scholar
    • Export Citation
  • Lee, J.S. (2011) The effects of persistent poverty on children’s physical, Socio-emotional, and learning outcomes, Child Indicators Research, 4(4): 72547. doi: 10.1007/s12187-011-9120-8

    • Search Google Scholar
    • Export Citation
  • Lovibond, S.H. and Lovibond, P.F. (1996) Manual for the Depression Anxiety Stress Scales, Sydney: Psychology Foundation of Australia.

  • Luo, Y. and Waite, L.J. (2005) The impact of childhood and adult SES on physical, mental, and cognitive Well-being in later life, Journals of Gerontology Series B, 60(2): S93S101. doi: 10.1093/gerona/60.1.93

    • Search Google Scholar
    • Export Citation
  • Masten, A.S. and Curtis, W.J. (2000) Integrating competence and psychopathology: Pathways toward a comprehensive science of adaptation in development, Development and Psychopathology, 12(3): 52950. doi: 10.1017/S095457940000314X

    • Search Google Scholar
    • Export Citation
  • Mechanic, D. (2002) Disadvantage, inequality, and social policy, Health Affairs, 21(2): 4859. doi: 10.1377/hlthaff.21.2.48

  • Nagin, D. (1999) Analyzing developmental trajectories: a semiparametric, Group-based approach, Psychological Methods, 4(2): 13957. doi: 10.1037/1082-989X.4.2.139

    • Search Google Scholar
    • Export Citation
  • Nagin, D.S. and Odgers, C.L. (2010) Group-based trajectory modeling in clinical research, Annual Review of Clinical Psychology, 6: 10938. doi: 10.1146/annurev.clinpsy.121208.131413

    • Search Google Scholar
    • Export Citation
  • NMHC (National Mental Health Commission) (2014) The National Review of Mental Health Programmes and Services, Sydney: NMHC.

  • O’Connor, M., Sanson, A.V., Toumbourou, J.W., Norrish, J. and Olsson, C.A. (2016) Does positive mental health in adolescence longitudinally predict healthy transitions in young adulthood?, Journal of Happiness Studies, 18(1): 17798.

    • Search Google Scholar
    • Export Citation
  • O’Connor, M., Arnup, S.J., Mensah, F., Olsson, C., Goldfeld, S., Viner, R.M. and Hope, S. (2021) Natural history of mental health competence from childhood to adolescence, Journal of Epidemiology and Community Health, 76(2): 1339.

    • Search Google Scholar
    • Export Citation
  • OECD (Organisation for Economic Co-operation and Development) (2018) Preparing Our Youth for an Inclusive and Sustainable World: The OECD PISA Global Competence Framework, Paris: OECD.

    • Search Google Scholar
    • Export Citation
  • Patel, V., Saxena, S. et al. (2018) The Lancet Commission on global mental health and sustainable development, The Lancet, 392(10157): 155398. doi: 10.1016/S0140-6736(18)31612-X

    • Search Google Scholar
    • Export Citation
  • Pudrovska, T. and Anikputa, B. (2014) Early-life socioeconomic status and mortality in later life: an integration of four Life-course mechanisms, Journals of Gerontology Series B, 69(3): 45160. doi: 10.1093/geronb/gbt122

    • Search Google Scholar
    • Export Citation
  • Redmond, G., Katz, I., Smart, D. and Gubhaju, B. (2013) How has the relationship between parental education and child outcomes changed in Australia since the 1980s?, Australian Journal of Social Issues, 48(4): 395413. doi: 10.1002/j.1839-4655.2013.tb00290.x

    • Search Google Scholar
    • Export Citation
  • Rubin, D.B. (1987) Multiple Imputation for Nonresponse in Surveys, New York: Wiley.

  • Schisterman, E.F., Cole, S.R. and Platt, R.W. (2009) Overadjustment bias and unnecessary adjustment in epidemiologic studies, Epidemiology, 20(4): 48895. doi: 10.1097/EDE.0b013e3181a819a1

    • Search Google Scholar
    • Export Citation
  • Schoon, I. and Parsons, S. (2002) Competence in the face of adversity: the influence of early family environment and Long-term consequences, Children & Society, 16(4): 26072.

    • Search Google Scholar
    • Export Citation
  • Shanahan, L., Copeland, W.E., Costello, E.J. and Angold, A. (2011) Child-, adolescent-and young Adult-onset depressions: differential risk factors in development?, Psychological Medicine, 41(11): 226574. doi: 10.1017/S0033291711000675

    • Search Google Scholar
    • Export Citation
  • Shim, R., Koplan, C., Langheim, F.J., Manseau, M.W., Powers, R.A. and Compton, M.T. (2014) The social determinants of mental health: an overview and call to action, Psychiatric Annals, 44(1): 226. doi: 10.3928/00485713-20140108-04

    • Search Google Scholar
    • Export Citation
  • Silva, M., Loureiro, A. and Cardoso, G. (2016) Social determinants of mental health: a review of the evidence, European Journal of Psychiatry, 30(4): 25992.

    • Search Google Scholar
    • Export Citation
  • Smart, D. and Sanson, A. (2003) Social competence in young adulthood, its nature and antecedents, Family Matters, 64: 49.

  • Stansfeld, S.A., Clark, C., Rodgers, B., Caldwell, T. and Power, C. (2011) Repeated exposure to socioeconomic disadvantage and health selection as life course pathways to mid-life depressive and anxiety disorders, Social Psychiatry and Psychiatric Epidemiology, 46(7): 54958. doi: 10.1007/s00127-010-0221-3

    • Search Google Scholar
    • Export Citation
  • Sterne, J.A.C., Hernán, M.A. et al. (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions, BMJ, 355: art i4919, doi: 10.1136/bmj.i4919.

    • Search Google Scholar
    • Export Citation
  • Stone, W. and Hughes, J. (2002) Social Capital: Empirical Meaning and Measurement Validity, Research Paper No 27, Melbourne: Australian Institute of Family Studies, https://aifs.gov.au/publications/social-capital-empirical-meaning-and-measurement-validity.

    • Search Google Scholar
    • Export Citation
  • Taylor, R.D., Oberle, E., Durlak, J.A. and Weissberg, R.P. (2017) Promoting positive youth development through School-based social and emotional learning interventions: a meta-analysis of Follow-up effects, Child Development, 88(4): 115671. doi: 10.1111/cdev.12864

    • Search Google Scholar
    • Export Citation
  • Trautmann, S., Rehm, J. and Wittchen, H.U. (2016) The economic costs of mental disorders: do our societies react appropriately to the burden of mental disorders?, EMBO Reports, 17(9): 12459. doi: 10.15252/embr.201642951

    • Search Google Scholar
    • Export Citation
  • VanderWeele, T.J. (2019) Principles of confounder selection, European Journal of Epidemiology, 34(3): 21119. doi: 10.1007/s10654-019-00494-6

    • Search Google Scholar
    • Export Citation
  • Vassallo, S. and Sanson, A. (2013) The Australian Temperament Project: The First 30 Years, Melbourne: Australian Institute of Family Studies, https://dro.deakin.edu.au/eserv/DU:30054685/hawkins-theaustralian-2013.pdf.

    • Search Google Scholar
    • Export Citation
  • Vigo, D., Thornicroft, G. and Atun, R. (2016) Estimating the true global burden of mental illness, The Lancet Psychiatry, 3(2): 1718. doi: 10.1016/S2215-0366(15)00505-2

    • Search Google Scholar
    • Export Citation
  • WHO (World Health Organization) (2002) Prevention and Promotion in Mental Health, Geneva: WHO, https://www.who.int/mental_health/media/en/545.pdf.

    • Search Google Scholar
    • Export Citation
  • World Health Assembly (2012) Global Burden of Mental Disorders and the Need for a Comprehensive, Coordinated Response from Health and Social Sectors at the Country Level, report of the Secretariat of the 65th World Health Assembly (A65/10), Geneva: World Health Organization, https://apps.who.int/iris/handle/10665/78898.

    • Search Google Scholar
    • Export Citation
  • Yeung, W.J., Linver, M.R. and Brooks-Gunn, J. (2002) How money matters for young children’s development: parental investment and family processes, Child Development, 73(6): 186179. doi: 10.1111/1467-8624.t01-1-00511

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Trajectories of socio-economic position from 0–1 to 27–28 years of age

  • View in gallery

    Proportion of young adults with mental health problems and competence according to socio-economic position at each age (%, 95% CI)

  • View in gallery

    Odds of having mental health problems at 27–28 years of age according to socio-economic position (socio-economic disadvantage) at each age, adjusted for early life behaviour problems, maternal age at birth, parents’ country of birth and child’s sex

  • View in gallery

    Odds of competence at 27–28 years of age according to socio-economic position (socio-economic disadvantage) at different time points, and for different socio-economic trajectories, adjusted for early life behaviour problems, maternal age at birth, parents’ country of birth and child’s sex

  • Adler, N.E. and Tan, J.J.X. (2017) Tackling the health gap: the role of psychosocial processes, International Journal of Epidemiology, 46(4): 132931. doi: 10.1093/ije/dyx167

    • Search Google Scholar
    • Export Citation
  • AIHW (Australian Institute of Health and Welfare) (2017) Australia’s Welfare 2017, https://www.aihw.gov.au/getmedia/088848dc-906d-4a8b-aa09-79df0f943984/aihw-aus-214-aw17.pdf.aspx?inline=true.

    • Search Google Scholar
    • Export Citation
  • Allen, J., Balfour, R., Bell, R. and Marmot, M. (2014) Social determinants of mental health, International Review of Psychiatry, 26(4): 392407. doi: 10.3109/09540261.2014.928270

    • Search Google Scholar
    • Export Citation
  • Blakemore, T., Strazdins, L. and Gibbings, J. (2009) Measuring family socioeconomic position, Australian Social Policy, 8: 12168, http://www.dss.gov.au/sites/default/files/documents/05_2012/asp-8.pdf.

    • Search Google Scholar
    • Export Citation
  • Bloom, D.E., Cafiero, E.T., Jané-Llopis, E., Abrahams-Gessel, S., Bloom, L.R., Fathima, S., Feigl, A.B., Gaziano, T., Mowafi, M., Pandya, A. et al. (2011) The Global Economic Burden of Non-communicable Diseases, Geneva: World Economic Forum, http://www3.weforum.org/docs/WEF_Harvard_HE_GlobalEconomicBurdenNonCommunicableDiseases_2011.pdf.

    • Search Google Scholar
    • Export Citation
  • Boehm, J.K., Chen, Y., Williams, D.R., Ryff, C. and Kubzansky, L.D. (2015) Unequally distributed psychological assets: are there social disparities in optimism, life satisfaction, and positive affect?, PLoS One, 10(2): art e0118066, doi: 10.1371/journal.pone.0118066.

    • Search Google Scholar
    • Export Citation
  • Boyce, W.T., Sokolowski, M.B. and Robinson, G.E. (2012). Toward a new biology of social adversity, Proceedings of the National Academy of Sciences, 109(S2): 171438. doi: 10.1073/pnas.1121264109

    • Search Google Scholar
    • Export Citation
  • Braiker, H.B. and Kelley, H.H. (1979) Conflict in the development of close relationships, in R.L. Burgess and T.L. Huston (eds) Social Exchange in Developing Relationships, New York: Academic Press, pp 13568.

    • Search Google Scholar
    • Export Citation
  • Braveman, P. and Barclay, C. (2009) Health disparities beginning in childhood: a Life-course perspective, Pediatrics, 124(S3): S163S175. doi: 10.1542/peds.2009-1100D

    • Search Google Scholar
    • Export Citation
  • Brooks-Gunn, J. (2003) Do you believe in magic? What we can expect from early childhood intervention programs, Society for Research in Child Development, 17(1): 314.

    • Search Google Scholar
    • Export Citation
  • Cheng, T.L., Johnson, S.B. and Goodman, E. (2016) Breaking the intergenerational cycle of disadvantage: the three generation approach, Pediatrics, 137(6): e20152467, doi: 10.1542/peds.2015-2467.

    • Search Google Scholar
    • Export Citation
  • Cobb-Clark, D.A., Dahmann, S., Salamanca, N. and Zhu, A. (2017) Intergenerational Disadvantage: Learning About Equal Opportunity from Social Assistance Receipt, IZA Discussion Paper No 11070, Bonn: Institute of Labor Economics, https://www.iza.org/publications/dp/11070/intergenerational-disadvantage-learning-about-equal-opportunity-from-social-assistance-receipt.

    • Search Google Scholar
    • Export Citation
  • Crawford, J., Cayley, C., Lovibond, P.F., Wilson, P.H. and Hartley, C. (2011) Percentile norms and accompanying interval estimates from an Australian general adult population sample for Self‐report mood scales (BAI, BDI, CRSD, CES‐D, DASS, DASS‐21, STAI‐X, STAI‐Y, SRDS, and SRAS), Australian Psychologist, 46(1): 314. doi: 10.1111/j.1742-9544.2010.00003.x

    • Search Google Scholar
    • Export Citation
  • DHHS (US Department of Health and Human Services) (1999) Mental Health: A Report of the Surgeon General, Bethesda, MD: National Institute of Mental Health, https://profiles.nlm.nih.gov/spotlight/nn/catalog/nlm:nlmuid-101584932X815-img.

    • Search Google Scholar
    • Export Citation
  • Dodge, K.A. (2020) Annual research review: universal and targeted strategies for assigning interventions to achieve population impact, Journal of Child Psychology and Psychiatry, 61(3): 25567. doi: 10.1111/jcpp.13141

    • Search Google Scholar
    • Export Citation
  • Doidge, J.C., Edwards, B., Higgins, D.J. and Segal, L. (2017) Adverse childhood experiences, Non-response and loss to Follow-up: findings from a prospective birth cohort and recommendations for addressing missing data, Longitudinal and Life Course Studies, 8(4): 382400. doi: 10.14301/llcs.v8i4.414

    • Search Google Scholar
    • Export Citation
  • Duncan, G.J., Ziol-Guest, K.M. and Kalil, A. (2010) Early‐childhood poverty and adult attainment, behavior, and health, Child Development, 81(1): 30625. doi: 10.1111/j.1467-8624.2009.01396.x

    • Search Google Scholar
    • Export Citation
  • Dunn, E.C., Soare, T.W., Raffeld, M.R., Busso, D.S., Crawford, K.M., Davis, K.A., Fisher, V.A., Slopen, N., Smith, A.D., Tiemeier, H. and Susser, E.S. (2018) What life course theoretical models best explain the relationship between exposure to childhood adversity and psychopathology symptoms: recency, accumulation, or sensitive periods?, Psychological Medicine, 48(15): 256272. doi: 10.1017/S0033291718000181

    • Search Google Scholar
    • Export Citation
  • Durlak, J.A., Weissberg, R.P., Dymnicki, A.B., Taylor, R.D. and Schellinger, K.B. (2011) The impact of enhancing students’ social and emotional learning: a Meta-analysis of School-based universal interventions, Child Development, 82(1): 40532. doi: 10.1111/j.1467-8624.2010.01564.x

    • Search Google Scholar
    • Export Citation
  • Elovainio, M., Pulkki-Råback, L., Jokela, M., Kivimäki, M., Hintsanen, M., Hintsa, T., Viikari, J., Raitakari, O.T. and Keltikangas-Järvinen, L. (2012) Socioeconomic status and the development of depressive symptoms from childhood to adulthood: a longitudinal analysis across 27 years of follow-up in the Young Finns study, Social Science & Medicine, 74(6): 9239.

    • Search Google Scholar
    • Export Citation
  • Evans, G., Li, D. and Whipple, S. (2013) Cumulative risk and child development, Psychological Bulletin, 139(6): 134296. doi: 10.1037/a0031808

    • Search Google Scholar
    • Export Citation
  • Flanagan, C. and Levine, P. (2010) Civic engagement and the transition to adulthood, The Future of Children, 20(1): 15979. doi: 10.1353/foc.0.0043

    • Search Google Scholar
    • Export Citation
  • Gkiouleka, A., Huijts, T., Beckfield, J. and Bambra, C. (2018) Understanding the micro and macro politics of health: inequalities, intersectionality & institutions – a research agenda, Social Science & Medicine, 200: 928.

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., D’Abaco, E., Bryson, H., Mensah, F. and Price, A.M.H. (2018a) Surveying social adversity in pregnancy: the antenatal risk burden experienced by Australian women, Journal of Paediatrics and Child Health, 54(7): 75460. doi: 10.1111/jpc.13860

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., O’Connor, M., Cloney, D., Gray, S., Redmond, G., Badland, H., Williams, K., Mensah, F., Woolfenden, S., Kvalsvig, A. and Kochanoff, A. (2018b) Understanding child disadvantage from a social determinants perspective, Journal of Epidemiology & Community Health, 72(3): 2239.

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., O’Connor, M., O’Connor, E., Chong, S., Badland, H., Woolfenden, S., Redmond, G., Williams, K., Azpitarte, F., Cloney, D. and Mensah, F. (2018c) More than a snapshot in time: pathways of disadvantage over childhood, International Journal of Epidemiology, 47(4): 130716. doi: 10.1093/ije/dyy086

    • Search Google Scholar
    • Export Citation
  • Goldfeld, S., Gray, S., Azpitarte, F., Cloney, D., Mensah, F., Redmond, G., Williams, K., Woolfenden, S. and O’Connor, M. (2019) Driving precision policy responses to child health and developmental inequities, Health Equity, 3(1): 48994. doi: 10.1089/heq.2019.0045

    • Search Google Scholar
    • Export Citation
  • Greenberg, M.T., Domitrovich, C.E., Weissberg, R.P. and Durlak, J.A. (2017) Social and emotional learning as a public health approach to education, The Future of Children, 27(1): 1332. doi: 10.1353/foc.2017.0001

    • Search Google Scholar
    • Export Citation
  • Gresham, F.M. and Elliott, S.N. (1990) Manual for the Social Skills Rating System, Circles Pines, MN: American Guidance Service.

  • Hallqvist, J., Lynch, J., Bartley, M., Lang, T. and Blane, D. (2004) Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program, Social Science & Medicine, 58(8): 155562.

    • Search Google Scholar
    • Export Citation
  • Harper, S., Lynch, J., Hsu, W.L., Everson, S.A., Hillemeier, M.M., Raghunathan, T.E., Salonen, J.T. and Kaplan, G.A. (2002) Life course socioeconomic conditions and adult psychosocial functioning, International Journal of Epidemiology, 31(2): 395403. doi: 10.1093/ije/31.2.395

    • Search Google Scholar
    • Export Citation
  • Hawkins, M., Letcher, P., Sanson, A., O’Connor, M., Toumbourou, J. and Olsson, C. (2011) Stability and change in positive development during the transition from adolescence to adulthood, Journal of Youth and Adolescence, 40(11): 143652. doi: 10.1007/s10964-011-9635-9

    • Search Google Scholar
    • Export Citation
  • Hudson, C.G. (2005) Socioeconomic status and mental illness: tests of the social causation and selection hypotheses, American Journal of Orthopsychiatry, 75(1): 318. doi: 10.1037/0002-9432.75.1.3

    • Search Google Scholar
    • Export Citation
  • Jorm, A.F. (2014) Why hasn’t the mental health of Australians improved? The need for a national prevention strategy, Australian & New Zealand Journal of Psychiatry, 48(9): 795801.

    • Search Google Scholar
    • Export Citation
  • Keyes, C.L.M. (2007) Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health, American Psychologist, 62(2): 95108. doi: 10.1037/0003-066X.62.2.95

    • Search Google Scholar
    • Export Citation
  • Keyes, C.L.M., Dhingra, S.S. and Simoes, E.J. (2010) Change in level of positive mental health as a predictor of future risk of mental illness, American Journal of Public Health, 100(12): 236671. doi: 10.2105/AJPH.2010.192245

    • Search Google Scholar
    • Export Citation
  • Kvalsvig, A., O’Connor, M., Redmond, G. and Goldfeld, S. (2014) The unknown citizen: epidemiological challenges in child mental health, Journal of Epidemiology and Community Health, 68(10): 10048. doi: 10.1136/jech-2013-203712

    • Search Google Scholar
    • Export Citation
  • Lantz, P.M. (2018) The medicalization of population health: who will stay upstream?, The Milbank Quarterly, 97: 14, https://www.milbank.org/quarterly/articles/the-medicalization-of-population-health-who-will-stay-upstream/.

    • Search Google Scholar
    • Export Citation
  • Lee, J.S. (2011) The effects of persistent poverty on children’s physical, Socio-emotional, and learning outcomes, Child Indicators Research, 4(4): 72547. doi: 10.1007/s12187-011-9120-8

    • Search Google Scholar
    • Export Citation
  • Lovibond, S.H. and Lovibond, P.F. (1996) Manual for the Depression Anxiety Stress Scales, Sydney: Psychology Foundation of Australia.

  • Luo, Y. and Waite, L.J. (2005) The impact of childhood and adult SES on physical, mental, and cognitive Well-being in later life, Journals of Gerontology Series B, 60(2): S93S101. doi: 10.1093/gerona/60.1.93

    • Search Google Scholar
    • Export Citation
  • Masten, A.S. and Curtis, W.J. (2000) Integrating competence and psychopathology: Pathways toward a comprehensive science of adaptation in development, Development and Psychopathology, 12(3): 52950. doi: 10.1017/S095457940000314X

    • Search Google Scholar
    • Export Citation
  • Mechanic, D. (2002) Disadvantage, inequality, and social policy, Health Affairs, 21(2): 4859. doi: 10.1377/hlthaff.21.2.48

  • Nagin, D. (1999) Analyzing developmental trajectories: a semiparametric, Group-based approach, Psychological Methods, 4(2): 13957. doi: 10.1037/1082-989X.4.2.139

    • Search Google Scholar
    • Export Citation
  • Nagin, D.S. and Odgers, C.L. (2010) Group-based trajectory modeling in clinical research, Annual Review of Clinical Psychology, 6: 10938. doi: 10.1146/annurev.clinpsy.121208.131413

    • Search Google Scholar
    • Export Citation
  • NMHC (National Mental Health Commission) (2014) The National Review of Mental Health Programmes and Services, Sydney: NMHC.

  • O’Connor, M., Sanson, A.V., Toumbourou, J.W., Norrish, J. and Olsson, C.A. (2016) Does positive mental health in adolescence longitudinally predict healthy transitions in young adulthood?, Journal of Happiness Studies, 18(1): 17798.

    • Search Google Scholar
    • Export Citation
  • O’Connor, M., Arnup, S.J., Mensah, F., Olsson, C., Goldfeld, S., Viner, R.M. and Hope, S. (2021) Natural history of mental health competence from childhood to adolescence, Journal of Epidemiology and Community Health, 76(2): 1339.

    • Search Google Scholar
    • Export Citation
  • OECD (Organisation for Economic Co-operation and Development) (2018) Preparing Our Youth for an Inclusive and Sustainable World: The OECD PISA Global Competence Framework, Paris: OECD.

    • Search Google Scholar
    • Export Citation
  • Patel, V., Saxena, S. et al. (2018) The Lancet Commission on global mental health and sustainable development, The Lancet, 392(10157): 155398. doi: 10.1016/S0140-6736(18)31612-X

    • Search Google Scholar
    • Export Citation
  • Pudrovska, T. and Anikputa, B. (2014) Early-life socioeconomic status and mortality in later life: an integration of four Life-course mechanisms, Journals of Gerontology Series B, 69(3): 45160. doi: 10.1093/geronb/gbt122

    • Search Google Scholar
    • Export Citation
  • Redmond, G., Katz, I., Smart, D. and Gubhaju, B. (2013) How has the relationship between parental education and child outcomes changed in Australia since the 1980s?, Australian Journal of Social Issues, 48(4): 395413. doi: 10.1002/j.1839-4655.2013.tb00290.x

    • Search Google Scholar
    • Export Citation
  • Rubin, D.B. (1987) Multiple Imputation for Nonresponse in Surveys, New York: Wiley.

  • Schisterman, E.F., Cole, S.R. and Platt, R.W. (2009) Overadjustment bias and unnecessary adjustment in epidemiologic studies, Epidemiology, 20(4): 48895. doi: 10.1097/EDE.0b013e3181a819a1

    • Search Google Scholar
    • Export Citation
  • Schoon, I. and Parsons, S. (2002) Competence in the face of adversity: the influence of early family environment and Long-term consequences, Children & Society, 16(4): 26072.

    • Search Google Scholar
    • Export Citation
  • Shanahan, L., Copeland, W.E., Costello, E.J. and Angold, A. (2011) Child-, adolescent-and young Adult-onset depressions: differential risk factors in development?, Psychological Medicine, 41(11): 226574. doi: 10.1017/S0033291711000675

    • Search Google Scholar
    • Export Citation
  • Shim, R., Koplan, C., Langheim, F.J., Manseau, M.W., Powers, R.A. and Compton, M.T. (2014) The social determinants of mental health: an overview and call to action, Psychiatric Annals, 44(1): 226. doi: 10.3928/00485713-20140108-04

    • Search Google Scholar
    • Export Citation
  • Silva, M., Loureiro, A. and Cardoso, G. (2016) Social determinants of mental health: a review of the evidence, European Journal of Psychiatry, 30(4): 25992.

    • Search Google Scholar
    • Export Citation
  • Smart, D. and Sanson, A. (2003) Social competence in young adulthood, its nature and antecedents, Family Matters, 64: 49.

  • Stansfeld, S.A., Clark, C., Rodgers, B., Caldwell, T. and Power, C. (2011) Repeated exposure to socioeconomic disadvantage and health selection as life course pathways to mid-life depressive and anxiety disorders, Social Psychiatry and Psychiatric Epidemiology, 46(7): 54958. doi: 10.1007/s00127-010-0221-3

    • Search Google Scholar
    • Export Citation
  • Sterne, J.A.C., Hernán, M.A. et al. (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions, BMJ, 355: art i4919, doi: 10.1136/bmj.i4919.

    • Search Google Scholar
    • Export Citation
  • Stone, W. and Hughes, J. (2002) Social Capital: Empirical Meaning and Measurement Validity, Research Paper No 27, Melbourne: Australian Institute of Family Studies, https://aifs.gov.au/publications/social-capital-empirical-meaning-and-measurement-validity.

    • Search Google Scholar
    • Export Citation
  • Taylor, R.D., Oberle, E., Durlak, J.A. and Weissberg, R.P. (2017) Promoting positive youth development through School-based social and emotional learning interventions: a meta-analysis of Follow-up effects, Child Development, 88(4): 115671. doi: 10.1111/cdev.12864

    • Search Google Scholar
    • Export Citation
  • Trautmann, S., Rehm, J. and Wittchen, H.U. (2016) The economic costs of mental disorders: do our societies react appropriately to the burden of mental disorders?, EMBO Reports, 17(9): 12459. doi: 10.15252/embr.201642951

    • Search Google Scholar
    • Export Citation
  • VanderWeele, T.J. (2019) Principles of confounder selection, European Journal of Epidemiology, 34(3): 21119. doi: 10.1007/s10654-019-00494-6

    • Search Google Scholar
    • Export Citation
  • Vassallo, S. and Sanson, A. (2013) The Australian Temperament Project: The First 30 Years, Melbourne: Australian Institute of Family Studies, https://dro.deakin.edu.au/eserv/DU:30054685/hawkins-theaustralian-2013.pdf.

    • Search Google Scholar
    • Export Citation
  • Vigo, D., Thornicroft, G. and Atun, R. (2016) Estimating the true global burden of mental illness, The Lancet Psychiatry, 3(2): 1718. doi: 10.1016/S2215-0366(15)00505-2

    • Search Google Scholar
    • Export Citation
  • WHO (World Health Organization) (2002) Prevention and Promotion in Mental Health, Geneva: WHO, https://www.who.int/mental_health/media/en/545.pdf.

    • Search Google Scholar
    • Export Citation
  • World Health Assembly (2012) Global Burden of Mental Disorders and the Need for a Comprehensive, Coordinated Response from Health and Social Sectors at the Country Level, report of the Secretariat of the 65th World Health Assembly (A65/10), Geneva: World Health Organization, https://apps.who.int/iris/handle/10665/78898.

    • Search Google Scholar
    • Export Citation
  • Yeung, W.J., Linver, M.R. and Brooks-Gunn, J. (2002) How money matters for young children’s development: parental investment and family processes, Child Development, 73(6): 186179. doi: 10.1111/1467-8624.t01-1-00511

    • Search Google Scholar
    • Export Citation
  • 1 Royal Children’s Hospital and University of Melbourne, , Australia
  • | 2 University of Melbourne and Royal Children’s Hospital, , Australia
  • | 3 Deakin University, University of Melbourne and Royal Children’s Hospital, , Australia
  • | 4 University of Melbourne, , Australia
  • | 5 University of Melbourne and Royal Children’s Hospital, , Australia
  • | 6 Deakin University and Royal Children’s Hospital, , Australia

Content Metrics

May 2022 onwards Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 83 83 35
PDF Downloads 60 60 31

Altmetrics

Dimensions