Qual Life Res DOI 10.1007/s11136-015-0946-2

Longitudinal trajectory patterns of social support: correlates and associated mental health in an Australian national cohort of young women Libby Holden • Annette J. Dobson • Robert S. Ware Richard Hockey • Christina Lee

•

Accepted: 17 February 2015 Ó Springer International Publishing Switzerland 2015

Abstract Purpose Although social support is a significant contributor to health and well-being, little is known about patterns of perceived social support over time in young adulthood. It is also unclear which personal characteristics are associated with different patterns, and whether there is an association between social support and mental health over time. We explore these issues in a large national cohort of young women. Methods We used six waves of longitudinal data spanning 16 years, from 10,369 women from the Australian Longitudinal Study on Women’s Health, initially aged 18–23. We used group-based trajectory modelling to identify patterns of social support across Surveys 2–6; multinomial logistic regression to identify socio-demographic and health-behaviour predictors at Survey 1 and correlates at Survey 6 for each trajectory group; and generalised linear mixed modelling to estimate mean levels of mental health over the trajectory period for each group, adjusted for confounders. Results Four distinct trajectory groups of social support were identified: ‘High’ (58.5 %), ‘Decreasing’ (20.6 %),

Electronic supplementary material The online version of this article (doi:10.1007/s11136-015-0946-2) contains supplementary material, which is available to authorized users. L. Holden
C. Lee School of Psychology, University of Queensland, Brisbane, QLD, Australia L. Holden (&)
A. J. Dobson
R. S. Ware
R. Hockey School of Public Health, University of Queensland, Room 322, Public Health Building, Herston Rd, Herston, Brisbane, QLD 4006, Australia e-mail: [email protected]

‘Low’ (9.3 %), and ‘Increasing’ (11.6 %). Poor health and living outside metropolitan areas at both Surveys 1 and 6 were characteristics of women in all trajectory groups other than the ‘High’ group, as were early motherhood and being un-partnered at age 34–39. Other characteristics were specific to one or two trajectory groups. Patterns of mental health over time were consistent with patterns of social support. Conclusion Longitudinal trajectory patterns of social support are associated with mental health, health behaviours, and demographic factors even in early adulthood. Keywords Social support
Trajectories
Mental health
Predictors
Longitudinal
Early adulthood

Introduction It is well-established that social support is a major contributor to health and well-being [1, 2], and that events and psychosocial resources during early adulthood can affect the trajectory of physical and mental health throughout adult life [3–5]. However, few longitudinal studies have explored trajectories of perceived social support over early adulthood [4, 6]. One used linear growth models, stratified by gender, to show that increasing social support was associated with decreasing depressive symptoms [6]. Another combined measures of self-esteem, self-efficacy, and social support—theoretically quite different constructs—as a composite measure of well-being, and constructed trajectory groups, using data at three time points from ages 18 to 26 [4], to show that more positive well-being trajectories were associated with success in developmental tasks such as obtaining employment and forming couple relationships [4]. In this paper, we examine trajectories of perceived

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social support over 16 years of early adulthood. We examine their relationships with socio-demographic and health-related variables, including mental health, among Australian women, providing a more comprehensive exploration of predictors and correlates of social support over time [2, 6–16]. Higher levels of social support are known to be associated with higher level of education [17], higher social class [18], and employment [18]. Several large-scale studies have shown that higher social support is linked with lower mortality [19], better disease outcomes [20], and lower rates of smoking and alcohol abuse [21]. A recent meta-analysis [22] showed that social networks change with age and as a consequence of major life events. Early adulthood involves major and interconnected contextual and social role changes [5, 23], and it is also a period when the prevalence of poor mental health is highest among women [24]. Life events and decisions during this transition period can fundamentally influence whole of life trajectories [3–5]. Therefore, a greater understanding of the correlates of patterns of social support in early adulthood may support the prevention or early identification of potential threats to physical and mental health in later life. Associations between social support and mental health have been established in large community samples of adults, adolescents and young adults, both cross-sectionally [7–10] and longitudinally [6, 11–13]. Several longitudinal studies have found social support to predict subsequent mental health [6, 12, 14–16]. However, the positive association between social support and mental health varies by gender, socioeconomic status, and life stage [2]. Social support functions in somewhat different ways for women and for men [2, 25, 26], with stronger associations between social support and mental health among women [2], and thus gender-specific analysis is appropriate. Social support is associated with mental health [6, 11–13], and evidence suggests that this relationship is not uniform across gender or stage of life [2], but little is known about patterns of social support over time or their association with mental health over time, particularly in the crucial early adulthood period. This study uses 16 years of longitudinal self-report data from a large national cohort of young Australian women to answer the following research questions: (1) can repeated measures of perceived social support over time in early adulthood among young women be summarised meaningfully into trajectory groups? If so, (2) which socio-demographic and health-behaviour characteristics differ among these groups, and (3) is there an association between changing social support and mental health over time, after adjusting for potential confounders?

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Methods The Australian Longitudinal Study on Women’s Health (ALSWH) The ALSWH initially involved three age cohorts of women, who were aged 18–23 years (1973–1978 cohort), 45–50 years (1946–1951 cohort), and 70–75 years (1921–1926 cohort) when first surveyed in 1996. Women were selected from the Australian national health insurance database (Medicare), which includes all citizens and permanent residents, with over-sampling of women from rural and remote areas. Further details on these cohorts and the recruitment methods are described elsewhere [27]. Participants This analysis focuses on six waves of data from 10,369 women in the 1973–1978 cohort, surveyed in 1996, 2000, 2003, 2006, 2009, and 2012. A total of 14,247 women completed Survey 1, which did not include measures of social support. At subsequent surveys, all women were invited to participate, even if they had not returned the preceding survey, unless they had actively withdrawn from the project or were known to have died. Women were included in the present analyses if they completed questions on social support for two or more of Surveys 2–6 (N = 10,369). Comparison with Australian census data for 1996 indicated that the initial study sample was broadly demographically representative of their age group, but were slightly more likely to be born in Australia and to have post-school qualifications [28]. The ALSWH has ethical clearance from the Universities of Newcastle and Queensland, and informed consent is obtained at every survey. Measures Social support was measured using a six-item version of the MOS Social Support Scale, modified from the 19-item measure [29], and validated for use in the ALSWH [30]. The questions relate to support available in a range of circumstances, such as needing help if confined to bed, or someone to talk to about problems. Response options ranged from: none of the time = 1 to all of the time = 5. Scores were summed to produce a variable with a potential range of 6–30, which correlates well with the original 19-item total score (r = 0.97) [30]. Mental health was measured with the SF-36 Mental Health Index (MHI-5), which gives a score ranging from 0 to 100, with higher scores indicating better mental health

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[31, 32]. A validated cut-off point of B52 was applied to categorise women as experiencing low mental health [33]. Demographic variables Area of residence was derived from the respondent’s home address and categorised using standard Australian classifications of rurality. The following variables were derived from questions asked at each survey unless otherwise indicated. Language spoken at home was asked at Survey 1 only. Education level was used at Survey 6, but not at Survey 1, because many respondents were still completing their education at that time. Ability to manage on available income was used as a proxy for financial circumstances. Relationship status was categorised as partnered if the women were married or cohabiting, and not partnered if they were never married, separated, divorced, or widowed. Work/study data were derived from a series of questions about time use. Motherhood was categorised as ‘yes’ if the respondents had ever had a live birth, and otherwise ‘no’. The variables motherhood and relationship status were strongly associated and led to unstable estimates; consequently, these variables were combined into a single variable with four categories. In this sample, the polychoric correlation was 0.62 (asymptotic standard error: 0.02) at Survey 1 and 0.72 (ASE: 0.01) at Survey 6. Health behaviour, health status, and health service use Alcohol consumption was measured with standard questions on frequency and quantity [35]. Smoking status was categorised as current, never, and ex-smokers. Body Mass Index (BMI) was calculated using self-reported height and weight [36]. Physical activity was measured using items on frequency and intensity of exercise [37] (this variable was available for Surveys 2–6 only). General health tertiles were based on cut-points from the SF-36 General Health subscale (scale range of 0–100) at Survey 1, for the analysis sample only. Any previous depression was based on an accumulative variable which captured, at each survey, any prior or current self-reported episode of doctor-diagnosed depression. Statistical analyses All analyses were undertaken using SAS 9.4. To compare those women included and excluded from the analyses, median social support scores were compared using a Wilcoxon rank sum test because these data were left skewed, with ceiling effects. Mental health scores were compared using a Student’s t test. To describe the women included in these analyses, demographic and health-related variables at Survey 1 and Survey 6 were compared, using logistic regression.

Group-based trajectory modelling (GBTM) across Surveys 2–6 was used to identify patterns in social support status over time [38]. GBTM is a form of latent class growth modelling, identifying clusters of individuals following the same or similar trajectories [39]. A censored normal model was used because it is suited to continuous data with ceiling effects. The number of trajectory groups and the polynomial order, which determines the shape of each trajectory, was selected based on several criteria: the optimal number of groups was assessed with Bayesian information criterion (BIC), explanatory power (knowledge of the data), a minimum sample size in each group ([7 %), close correspondence between the estimated probability of group membership and the proportion assigned to each group, and an average posterior probability of group membership of [0.7 [39, 40]. Supplementary Table S1 provides more detailed information on model selection. Sensitivity analyses were performed to check the stability of the trajectory patterns using (a) only those who completed all of Surveys 2–6 when social support data were available and (b) using split-halves of the sample that completed two or more surveys. Separate cross-sectional multinomial logistic regression models were used to identify Survey 1 predictors, and Survey 6 correlates, of social support trajectory group membership. Only variables statistically significantly associated with social support trajectory groups in univariate models were included. In addition, we describe the proportion of mothers at each survey for each trajectory group and the proportion partnered, with 95 % confidence intervals (95 % CI). Finally, mean mental health scores from Surveys 2–6 for each social support trajectory group were plotted using generalised linear mixed modelling, with a random intercept to account for individual-level variability over time and an unstructured covariance matrix with adjustment for time-varying covariates. To identify covariates that were significantly associated with mental health, we used Student’s t test for dichotomous variables and analysis of variance for nominal variables. To identify variables significantly associated with social support, we used Wilcoxon rank sum tests. Variables were considered potential confounders and therefore included in models if they were significantly associated with both social support and mental health at Survey 2, when social support data were first available. These variables and survey number were included as fixed effects.

Results Women were included in this study if they completed two or more of Surveys 2–6 and had no missing data on social

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higher odds of being overweight or obese and lower odds of experiencing poor mental health.

Australian Longitudinal Study on Women’s Health (ALSWH) 1973-78 cohort N = 14,247

Participated in any of Surveys 2-6 when social support data available N = 11,922

Sample eligible for study N=10,369

Analysis at Survey 2 N= 8,087

Analysis at Survey 6 N= 7,886

Completed Survey 1 but no subsequent survey N=2,325

Did not complete 2 or more surveys with social support data available at those surveys, therefore not eligible for study N = 1,553

Did not respond to ALSWH at Survey 2 so not included in analyses of Survey 2 data N = 2,282

Did not respond to ALSWH at Survey 6 so not included in analyses of Survey 6 data N = 2,483

Fig. 1 Participant flow chart of those included and excluded in analyses

support at those surveys (N = 10,369; Fig. 1). Comparisons of women included and excluded from the main analyses were undertaken using data from all eligible women who completed Survey 2 (N = 8087). Median social support score at Survey 2 was not significantly different among those included (25, 95 % CI 25–26) and excluded (25, 95 % CI 24–25); p = 0.08. However, mean mental health (MHI-5) score at Survey 2 was significantly higher among those included than those excluded, with a significant difference of 2.4 points (95 % CI 1.2–3.6, p = 0.0008). Comparison of characteristics of study participants at Survey 1 (age 18–23 years) and Survey 6 (age 34–39 years) Table 1 shows differences in the demographic and healthrelated profile of this cohort between Surveys 1 (1996) and 6 (2012). As would be expected over this life stage, by Survey 6, women were significantly more likely to have post-secondary education, to be partnered, have children, and be in the paid workforce and out of study. Other variables also showed expected changes over time: for example, by Survey 6, compared to Survey 1, women had

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Social support trajectory groups Figure 2 depicts the best-fitting trajectory patterns of social support across Survey 2 to Survey 6 (2000–2012). The model had four groups, with quadratic patterns used to describe the shapes of the ‘Low’ and ‘High’ trajectory groups and cubic patterns (indicating greater betweensurvey variation) for the ‘Decreasing’ and ‘Increasing’ groups. The average posterior probabilities of correct assignment for each trajectory group; that is, the average of individuals’ probabilities of being assigned to a trajectory pattern that is a good fit for their pattern of social support, were: ‘High’ = 0.91, ‘Decreasing’ = 0.78, ‘Low’ = 0.88, ‘Increasing’ = 0.75. Over half the women (6070; 58.5 %) were in the ‘High’ trajectory group characterised by high social support scores at all surveys. Approximately one-fifth (2133; 20.6 %) were in the ‘Decreasing’ social support trajectory group. The ‘Low’ group (962; 9.3 %) had low social support scores at all surveys, and the ‘Increasing’ group (1204; 11.6 %) started with scores similar to the ‘Low’ group but their scores increased over time until levelling out between Surveys 5 and 6 at a score close to the ‘High’ group. Sensitivity analyses using split-halves of the sample used in these analyses, and using only those that had completed all of Surveys 2–6, found the same social support trajectory patterns with consistent proportions in each trajectory group. Characteristics at Survey 1 (age 18–23 years) predicting social support trajectory group membership Survey 1 characteristics associated with social support trajectory patterns spanning Surveys 2–6 are reported in Table 2. After adjustment for all other statistically significant variables, the following characteristics were associated with higher odds of membership in all trajectory groups compared to the reference group (‘High’ social support): poor mental health, financial difficulty, poor general health, early motherhood, and living outside metropolitan areas. In addition, those in the ‘Low’ and ‘Increasing’ groups had higher odds of not working or studying, and those in the ‘Low’ group had higher odds of being non-drinkers or rarely drinking. Becoming a mother early, by Survey 1, either with or without being in a relationship, was significantly associated with all trajectory groups other than the reference group of ‘High’ social support.

Qual Life Res Table 1 Characteristics of women in the analyses sample at Survey 1 (N = 10,369) and Survey 6 (N = 7886), with odds ratios (OR) and 95 % CI of characteristics at Survey 6 compared to Survey 1 Survey 1 (1996) N (%)

Survey 6 (2012) N (%)

OR (95 % CI)

Major cities

5327 (51)

4284 (57)

1.00

Inner regional Outer regional/remote/very remote

3184 (31) 1851 (18)

2044 (27) 1128 (15)

0.80 (0.75–0.86) 0.76 (0.70–0.82)

English

9571 (93)

–

Not English

685 (7)

–

University

1326 (13)

4224 (54)

1.00

Trade/Technical College

1841 (18)

2135 (28)

0.36 (0.33–0.40)

School only

7148 (69)

1399 (18)

0.06 (0.06–0.07)

No difficulty

5242 (51)

4377 (56)

1.00

Difficult sometimes

3352 (32)

2407 (31)

0.86 (0.81–0.92)

Impossible/very difficult

1743 (17)

1020 (13)

0.70 (0.64–0.77)

Area of residence

Language spoken at homea

Education

Ability to manage on income

Relationship Not partnered Partnered Motherhood

8075 (78)

1658 (21)

2248 (22)

6148 (79)

1.00 13.3 (12.4–14.3)

No

9453 (93)

2026 (26)

Yes

745 (7)

5820 (74)

1.00

Mother and partnered

512 (5)

5165 (66)

1.00

Mother and not partnered

232 (2)

618 (8)

0.26 (0.22–0.32)

Not mother and not partnered

7728 (76)

1036 (13)

0.01 (0.01–0.02)

Not mother and partnered

1684 (17)

978 (13)

0.06 (0.05–0.07)

Work only

4082 (40)

5202 (67)

1.00

Work and study

1426 (14)

1140 (15)

0.63 (0.57–0.68)

Study only

3552 (35)

211 (3)

0.05 (0.04–0.05)

Neither

1156 (11)

1220 (16)

0.83 (0.76–0.91)

36.45 (33.3–39.9)

Motherhood and relationship

Work/study

Alcohol consumption Low risk drinker

5457 (53)

4557 (58)

1.00

Non-drinker Rarely drinks

796 (8) 3480 (34)

910 (12) 1999 (26)

1.37 (1.24–1.52) 0.69 (0.64–0.74)

Risky/very risky drinker

536 (5)

357 (5)

0.80 (0.69–0.92)

Never smoked

5471 (55)

3914 (44)

1.00

Ex-smoker

1507 (15)

4655 (52)

4.32 (4.02–4.64)

Current smoker

2981 (30)

300 (3)

0.14 (0.12–0.16)

Healthy

6356 (69)

3728 (49)

1.00

Underweight

859 (9)

187 (2)

0.37 (0.32–0.44)

Smoking status

Body Mass Index

Overweight

1421 (15)

1970 (26)

2.36 (2.18–2.56)

Obese

569 (6)

1722 (23)

5.16 (4.66–5.72)

–

1940 (26)

Physical activityb High

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Qual Life Res Table 1 continued Survey 1 (1996) N (%)

Survey 6 (2012) N (%)

Moderate

–

1596 (21)

Low

–

2835 (38)

Nil

–

1106 (15)

3606 (35)

3297 (42)

OR (95 % CI)

General healthc High tertile (C82)

1.00

Moderate tertile ([65 \ 82)

2988 (29)

2469 (31)

0.61 (0.57–0.65)

Low tertile (B65)

3740 (36)

2074 (26)

0.90 (0.84–0.97)

High

8263 (80)

6786 (86)

1.00

Low

2088 (20)

1073 (14)

0.63 (0.58–0.68)

Mental health category

a

Not collected at Survey 6;

b

not collected at Survey 1; c tertiles based on Survey 1 data; bold indicates significant at p \ 0.05

Social Support Trajectories with 95% Confidence Intervals Low

Decreasing

Increasing

High

28

Social Support (Range 6 -30)

26 24 22 20 18 16 14 12 2

3

4

5

6

Trajectory group membership was strongly associated with the combination of motherhood and relationship status at Survey 6. For both these variables, separately and when combined, the pattern of association with social support trajectory group membership was very different at Survey 1 and Survey 6. The proportion of women who were already mothers by Survey 1 was highest in the ‘Low’ trajectory group and lowest in the ‘High’ group; this pattern gradually changed over time and was reversed at Survey 6 (Fig. 3). An even stronger reversal was observed when considering those partnered or not partnered at each survey (Fig. 4), where the percentage partnered at each survey closely mirrors the changing patterns of social support over time seen in the trajectory group patterns.

Survey

Fig. 2 Social support trajectory groups for Surveys 2–6, with 95 % CI

Characteristics at Survey 6 (age 34–39 years) associated with social support trajectory group membership The following Survey 6 characteristics were significantly associated with higher odds of membership in all other trajectory groups, compared to the ‘High’ social support group: living outside metropolitan areas, experiencing financial difficulty, experiencing poor general health and poor mental health, and being un-partnered, either with or without children (see Table 3). Those in the ‘Low’ and ‘Increasing’ groups had higher odds of being non-drinkers or rarely drinking, and of being ex-smokers. The ‘Decreasing’ group had higher odds of still studying at Survey 6 and higher odds of risky drinking. The ‘Low’ group also had higher odds of risky drinking and of combining both work and study.

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Mental health changes over time Figure 5 shows mean mental health over the trajectory period (Surveys 2–6) for each social support trajectory group, after adjustment for those time-varying covariates that were significantly associated with both social support and mental health at Survey 2. Mental health was consistently high for the ‘High’ group, consistently low for the ‘Low’ group, consistently moderate and stable for the ‘Decreasing’ group, and increased over time for the ‘Increasing’ group.

Discussion Trajectory patterns of social support Using longitudinal self-report data from a large nationally representative sample of young Australian women, we identified four distinct trajectory patterns of social support over a 12-year period, from age 22–27 to age 34–39. Over

Qual Life Res Table 2 Adjusted multinomial logistic regression of Survey 1 characteristics associated with social support trajectories from Surveys 2 to 6; adjusting for all listed covariates Low support N = 870 (9.1 %)

Decreasing support N = 1962 (20.6 %)

Increasing support N = 1103 (11.6 %)

OR

95 % CI

OR

95 % CI

OR

95 % CI

Area of residence Major cities

1.00

Inner regional

1.55

1.30–1.85

1.19

1.00 1.06–135

1.32

1.00 1.14–1.67

Outer regional/remote/very remote

2.22

1.82–2.70

1.33

1.15–1.54

1.59

1.33–1.90

Ability to manage on income No difficulty

1.00

Difficult sometimes

1.63

1.36–1.95

1.15

1.00 1.02–1.29

1.22

1.00 1.05–1.42

Impossible/very difficult

2.53

2.06–3.11

1.39

1.19–1.62

1.32

1.09–1.16

Motherhood and relationship Mother and partnered

1.00

Mother and not partnered

1.35

0.83–2.19

1.10

0.71–1.73

1.32

0.84–2.09

Not mother and not partnered

0.58

0.42–0.80

0.71

0.54–0.93

0.55

0.41–0.75

Not mother and partnered

0.52

0.36–0.73

0.78

0.58–1.04

0.53

0.38–0.73

Work/study Work only

1.00

1.00

1.00

1.00

1.00

Work and study

0.75

0.58–0.97

0.72

0.61–0.85

0.96

0.78–1.19

Study only

0.80

0.66–0.96

0.82

0.73–0.94

0.96

0.81–1.13

Neither

1.43

1.13–1.82

1.11

0.91–1.34

1.68

1.34–2.11

Alcohol consumption Low risk drinker

1.00

Non-drinker

2.02

1.56–2.62

1.13

1.00 0.92–1.38

0.94

1.00 0.72–1.23

Rarely drinks

1.27

1.07–1.51

1.08

0.96–1.21

1.07

0.92–1.24

Risky/very risky drinker

1.06

0.76–1.46

0.95

0.75–1.21

0.88

0.65–1.20

Smoking status Never smoked

1.00

Ex-smoker

1.35

1.10–1.67

0.96

1.00 0.82–1.13

1.05

1.00 0.86–1.27

Current smoker

1.04

0.87–1.26

1.10

0.97–1.24

1.07

0.91–1.25

1.08–1.41

1.30

1.10–1.54

1.22–1.59

1.48

1.25–1.75

1.20–1.57

1.69

General health High tertile ([82) Moderate tertile ([65 B 82)

1.00 1.51

1.22–1.86

1.00 1.23

Low tertile (B65)

2.21

1.81–2.69

1.39

2.12–2.97

1.37

1.00

Mental health category High

1.00

Low

2.51

1.00

1.00 1.44–1.99

Reference = high social support, N = 5580 (58.6 %) Language spoken at home was not significant in univariate analysis. BMI was excluded due to large amounts of missing data

half the young women had consistently high social support, with around one-fifth showing a decreasing trajectory, and the remaining women had either increasing, or consistently low, levels of social support. Characteristics associated with these trajectory groups were identified from two separate surveys: one administered 4 years earlier than the surveys used to calculate the trajectories (aged 18–23) and one at the end of the trajectory period (aged 34–39).

Characteristics associated with social support trajectory patterns The ‘Low’ social support trajectory group was significantly different to the consistently high group in several ways suggestive of general disadvantage and poor health at both time points. These findings are generally consistent with other evidence that low social support is associated with

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Qual Life Res Table 3 Adjusted multinomial logistic regression of Survey 6 characteristics associated with social support trajectories from Surveys 2–6; adjusting for all listed covariates Low support N = 548 (8.1 %)

Decreasing support N = 1405 (20.7 %)

Increasing support N = 726 (10.7 %)

OR

OR

OR

95 % CI

95 % CI

95 % CI

Area of residence Major cities

1.00

Inner regional

1.44

1.14–1.82

1.00 1.33

1.15–1.55

1.00 1.21

1.00–1.47

Outer regional/remote/very remote

2.37

1.82–3.09

1.45

1.20–1.75

1.40

1.11–1.76

Education University

1.00

Trade/Technical College

1.34

1.06–1.70

1.00 1.16

0.99–1.35

1.00 1.14

0.94–1.38

School only

1.60

1.23–2.09

1.08

0.90–1.30

1.11

0.88–1.40

Ability to manage on income No difficulty

1.00

Difficult sometimes

1.57

1.24–1.97

1.00 1.24

1.07–1.44

1.00 1.19

0.99–1.44

Impossible/very difficult

2.22

1.67–2.97

1.67

1.36–2.04

1.68

1.30–2.17

1.00 6.02

4.44–8.17

1.00 3.34

2.64–4.22

1.00 1.83

1.31–2.57

10.91

8.43–14.1

3.96

3.28–4.79

2.50

1.92–3.26

1.29

0.89–1.86

0.84

0.67–1.05

2.15

1.72–2.68

Motherhood and Relationship Mother and partnered Mother and not partnered Not mother and not partnered Not mother and partnered Work/study Work only

1.00

1.00

1.00

Work and study

1.49

1.15–1.93

1.20

1.00–1.44

0.99

0.79–1.26

Study only

1.35

0.78–2.34

1.53

1.05–2.22

0.75

0.41–1.36

Neither

1.05

0.78–1.40

0.98

0.81–1.28

1.01

0.80–1.28

Alcohol consumption Low risk drinker

1.00

1.00

1.00

Non-drinker

1.73

1.26–2.36

1.10

0.89–1.36

1.41

Rarely drinks

1.79

1.42–2.24

1.11

0.95–1.29

1.24

1.02–1.50

Risky/very risky drinker

1.77

1.17–2.70

1.37

1.02–1.86

1.16

0.78–1.73

0.86–1.13

1.23

1.04–1.46

0.54–1.08

0.73

0.46–1.17

1.10–1.81

Smoking status Never smoked Ex-smoker

1.00 1.41

1.14–1.74

1.00 0.99

Current smoker

0.91

0.57–1.44

0.76

1.00

Body Mass Index Healthy

1.00

Underweight

0.92

0.48–1.76

1.00 1.19

0.80–1.77

1.00 1.07

0.63–1.82

Overweight

1.09

0.85–1.40

0.92

0.78–1.07

1.05

0.87–1.29

Obese

1.09

0.85–1.40

0.86

0.72–1.02

1.10

0.89–1.36

Physical activity High

1.00

Moderate

1.08

0.80–1.46

1.08

1.00 0.89–1.30

1.15

1.00 0.90–1.46

Low

1.17

0.90–1.52

1.12

0.94–1.32

1.25

1.01–1.54

Nil

0.97

0.70–1.34

1.03

0.83–1.27

1.15

0.897–1.51

General health High tertile ([82)

1.00

Moderate tertile ([65 B 82)

1.68

1.28–2.20

1.42

1.22–1.66

1.29

1.06–1.56

Low tertile (B65)

3.29

2.52–4.31

2.10

1.77–2.50

1.70

1.37–2.20

123

1.00

1.00

Qual Life Res Table 3 continued Low support N = 548 (8.1 %)

Decreasing support N = 1405 (20.7 %)

Increasing support N = 726 (10.7 %)

OR

95 % CI

OR

95 % CI

OR

2.16–3.51

1.83

1.51–2.21

1.29

95 % CI

Mental health category High

1.00

Low

2.75

1.00

1.00 1.00–1.66

Reference = high social support, N = 4094 (60.5 %)

Fig. 3 Percentage of women who were mothers at each survey in each social support trajectory group, with 95 % CI

90 80 70

Percentage

60 50 40 30 20 10 0

Fig. 4 Percentage of women who were partnered at each survey in each social support trajectory group, with 95 % CI

100

90

80

70

Percentage

60

50

40

30

20

10

0

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Qual Life Res

Adjusted Mean Mental Health Scores 80

for each Social Support Trajectory at each of Surveys 2-6

75

Adjusted Mean 70 Mental Health Score Range 65 0-100

60

55

Social Support Trajectory Groups

Fig. 5 Adjusted mean mental health score, with 95 % CI, in each social support trajectory group; adjusting for time-varying covariates: survey, area of residence, education, ability to manage on available income, relationship status, work/study, alcohol use, smoking status, general health, and any previous doctor-diagnosed depression

poor health, unhealthy lifestyles, and social disadvantage [14, 41]. Among the ‘Decreasing’ group, social support at Survey 1, although lower than the ‘High’ group, was much higher than among the other two trajectory groups. Nevertheless, these women showed several characteristics that were more consistent with the ‘Low’ and ‘Increasing’ groups. Although the odds of having these characteristics were not as strong as either the ‘Low’ or ‘Increasing’ trajectory groups, both with low social support at Survey 1, those with ‘Decreasing’ social support had characteristics at Survey 1 that might predict their deteriorating social support over time. This study shows deteriorating outcomes for this group, including poorer health behaviours, mental health, and financial circumstances. This was the only trajectory group that showed almost no increase in mental health scores over time. Poor mental health is most prevalent in adolescence and very early adulthood (16–24 years), and decreases over time [24]. Therefore, the absence of improvement in mental health among this group highlights the importance of targeting social support in health promotion interventions to improve mental health outcomes. The ‘Increasing’ social support group is of interest for a different reason. They started with social support levels

123

consistent with those in the ongoing ‘Low’ group and were characterised by much the same set of disadvantages. However, their social support improved over time; and at Survey 6, their odds of poorer mental health, financial difficulty, and neither working nor studying had declined. This study cannot clearly identify factors that are associated with the increase in social support among these women, although relationship status may be important. These results suggest that initial disadvantage need not lead inevitably to continuing problems throughout early adulthood. This group is also noteworthy for showing a much greater increase over time in mean mental health scores than other groups. This marked improvement again highlights the interconnectedness of mental health and social support among young women. These results clearly indicate that characteristics such as poor general and mental health, early motherhood, living in rural areas, and experiencing financial difficulty are associated with less than optimal social support trajectories in subsequent years. These findings can assist policy makers, clinicians, and health promotion workers to identify those at greater risk of low social support. However, the ‘Increasing’ group in particular suggests that some women are able to mobilise social support despite disadvantage; further research focusing on such women might identify personal or environmental characteristics associated with this capacity. Such research could potentially provide valuable information of use in the development of social interventions. Poor physical health was identified in this study as both a predictor and a subsequent correlate of all trajectories other than consistently high social support. These findings are consistent with other researches demonstrating that social support contributes to physical health and that physical health improves the ability to achieve and maintain social connectedness [42], and demonstrates that these patterns hold in a large national sample of young adult women. Low socio-economic status, whether defined on the basis of income [43], education [17], or parent’s occupation [15], is consistently linked to low social support. It has also been argued that social support may be the mediating factor in the association between socio-economic factors and health outcomes [15]. These findings add strength to this hypothesis, which is particularly relevant to interventions designed to improve lifespan trajectories of health and well-being. Relationship status has also been linked to social support in previous research [19, 44], but little work has been conducted in early adulthood. In line with an understanding that it is appropriate that sources of social support vary across life stages [19, 44], we showed that relationship status at age 18–23, when most Australians

Qual Life Res

are single, was not a significant predictor of social support trajectories. However, there was a very strong association by the end of the trajectory period, when the women were at an age when marriage is normative in Australia [45]. We also found differing patterns in the percentage of women partnered at each survey for each trajectory. Early motherhood (that is, becoming a mother before Survey 1, aged 18–23) predicted less advantageous trajectories of social support. This is consistent with research suggesting that there is a socially appropriate time for motherhood and that motherhood outside this period is associated with poorer mental health outcomes [46]. The association between social support and alcohol changed between Surveys 1 and 6. At Survey 1, the only significant association was that the ‘Low’ support group were more likely to be non-drinkers or rarely drink. But by Survey 6, we found a U-shaped association between alcohol consumption and social support, similar to that found in the association between alcohol and mental health [47, 48]. Low or decreasing social support was associated with nondrinking or rarely drinking on one end of the spectrum and with risky drinking on the other. This finding is consistent with other research [49]. Living outside of metropolitan areas was consistently associated with poorer social support trajectories in this study. This is not a factor that has generally been considered in other research on social support, perhaps because rural living is less of a disadvantage in other countries. In Australia, rural living can mean social isolation, limited access to health and social services, and extreme physical isolation, and this finding may be unique to these particular social and geographic conditions. Social support trajectories and associated mental health over time The strong relationships between social support and mental health found in this study are consistent with other researches conducted in early adulthood [6, 15, 16]. However, this study extends on this work, by using social support trajectories and related mental health patterns over 12 years, and adds to existing evidence that a lack of social support predisposes people to poor mental health, even among young women [6, 12, 14, 16]. This study has several strengths, including longitudinal data spanning 16 years from a large and representative sample of young women in early adulthood. However, limitations such as the absence of information on social support prior to Survey 2 and prior to the commencement of the study also need to be acknowledged.

Conclusion This study demonstrates that distinct trajectories of social support are identifiable in a generally healthy young adult population. It shows that self-report measures of physical health, mental health, and socio-economic status differ among these groups. These findings in a young adult population are particularly important, because of the likelihood that poor social support at this life stage may be associated with a continuing negative pattern of increasing poor mental health and poor social connectedness that may have more severe health consequences with increasing age. Therefore, policies and health promotion interventions that attempt to mitigate risk factors for poor social support will promote the social integration of young people and enhance their future health and well-being.

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