The association between resilience resources, contextual factors and mental health status: a national population-based study

Mental health status is shaped through the cumulative effect of risk and protective factors which can affect the individual’s ability to adapt to new situations and develop healthy coping mechanisms. A complex interplay of individual and societal factors can lead to variation in mental health status across the population. Common mental disorders are expected to become the most common diseases by 2030 [1], with recent studies suggesting that one in five people in the world will experience a mental disorder in their lifetime [2]. This highlights the need to focus research on better understanding risk and protective factors of mental health outcomes and ways of supporting those who are at risk of experiencing poor mental health.

Resilience has been previously described as the ability to bounce back from negative life events and function normally, using self-regulation and cognitive coping skills when faced with stressful situations to reduce the deleterious effects on the individual and maintain their wellbeing [3]. Recent definitions have expanded the scope of resilience as a socioecological construct separating it from psychological resilience (or ego-resilience) and highlighting the interplay between individual processes and contextual factors [4]. The need for capacity to negotiate intra- and interpersonal resources to achieve “high” resilience in the face of adversity is further reflected by the re-conceptualisation of resilience as a construct that is shaped by individual attributes, personal relationships and a sense of belonging [5].

Resilience has been linked with demographic characteristics such as increasing age and female gender, with women also being more at risk of poor mental health outcomes [6]. The “cost of caring” hypothesis suggests that women are at an increased risk of mental ill health due to experiencing chronic stress linked to adverse life events [7]. Although individual factors such as resilience may be closely associated with mental health, interpersonal and contextual factors such as the neighbourhood environment might moderate the link between individual characteristics and mental wellbeing [8]. Socio-economic status, both at household and community levels, has been associated with perceived social capital and cohesion, which can significantly affect physical and mental health [9, 10]. Additionally, low levels of social cohesion have been linked to unhealthy behaviours such as alcohol use [11] and poor self-rated health [12]. Providing informal care for a family member or friend, despite having been previously used as an index of resilience [13], can also be a contributing factor to poorer mental health, especially for younger individuals [14].

Across different settings and samples, intrinsic characteristics such as cognitive coping styles have been shown to moderate the potential effect of adverse experiences acting as protective factors to mental wellbeing [15, 16], while physical health and engagement to physical activities or exercising have been psychological resilience thus better physical and mental health especially among older adults [17, 18]. Beyond the individual and household-level factors, a range of wider social predictors including official records and resident perceptions on the characteristics of the living environment including neighbourhood crime and violence have been linked with poor mental health both through direct and indirect pathways such as victimisation or development of maladaptive coping strategies [19‐21]. This association is more pronounced within areas with low levels of social cohesion [22] where perceived lack of safety might lead to loneliness and reduced wellbeing [23]. However, a number of previous studies have examined risk factors in isolation and within small selective sub-populations (for example with partners/spouses or older people), limiting their generalisability to other settings [24, 25] and causing issues in the implementation of complex models [26]. There are also questions regarding the epidemiological validity of existing evidence as this largely depends on studies using self-reported assessment of mental health in the form of survey data [9, 10], postal questionnaires for cohort samples [25] or Census returns for population-based samples [27]. This limitation could be overcome by including different types of assessment tools, such as measures based on diagnostic criteria and healthcare records, which are not limited by report bias and could potentially help explain the current lack of consensus on population mental health assessment.

Though the interrelation of individual and contextual factors has been explored previously, knowledge of the specific psychosocial pathways leading to poor mental health remains limited. There is a need to understand both the independent effects of individual and contextual factors, as well as their cumulative effect on population mental health. Therefore, the use of population-based data which provide a wide range of socio-demographic characteristics and other risk factors closely linked to mental health status could provide deeper understanding of the underlying mechanisms which may lead to mental ill health.

The final imputed dataset included 27,869 HWW participants with information in at least one of the key variables of interest. The imputed study cohort (see Table 1) included individuals across the age-range, with a higher proportion of middle-aged and older individuals (55–64 years: 18.9%). A greater proportion of our cohort was female (70.6%), of White background (98.2%) and with a high-level occupational status (higher occupations: 46.9%). The distribution of socio-demographic characteristics is consistent with other population surveys [28], reflecting the proportion of the population who are more likely to seek help from healthcare services. One in five (26%) were carers, of which most were providing only 1–9 h of care per week (18.3%). In addition, 10.2% of the cohort reported having to give up their work to care for someone. A large proportion of the cohort resided in urban settings (61.9%), while in terms of area deprivation our sample was broadly spread across all levels of deprivation, but with a trend towards fewer participants from most deprived areas (least deprived: 24%; intermediate: 21.4%; most deprived: 14.2%).

Although HWW participants were in general likely to report healthy lifestyles, 35.5% drank above alcohol guideline levels and 41.4% were physically inactive, while a smaller proportion were eating unhealthily and smoking (17.0% and 12.6% respectively). Over one in four self-reported poor mental health through the MHI-5 questionnaire (27.8%), while that proportion was lower for the WEMWBS scale (21%) and mental health treatment according to healthcare records (15%).

After adjustment, across all three mental health measures (Fig. 1), there was evidence of an association between both physical inactivity (MHI-5: adjusted Odds Ratio (OR_adj) = 1.51 95%CI = 1.37–1.66; WEMWBS: OR_adj = 1.34 95%CI = 1.17–1.53; EHR: OR_adj = 1.56 95%CI = 1.22–1.99) and unhealthy eating (MHI-5: OR_adj = 1.89 95%CI = 1.66–2.15; WEMWBS: OR_adj = 1.71 95%CI = 1.45–2.01; EHR: OR_adj = 1.43 95%CI = 1.13–1.81) with poor mental health. However, there was only evidence (at a 5% significance level) of an association between smoking and poor mental health for the self-report mental health measures (MHI-5: OR_adj = 1.79 95%CI = 1.55–2.06; WEMWBS: OR_adj = 1.50 95%CI = 1.21–1.87) versus EHR (EHR: OR_adj = 1.31 95%CI = 0.91–1.90), while there was insufficient evidence at the 5% level of an association between drinking above guideline levels and poor mental health status.

In terms of resilience, higher levels of resilience were associated with better mental health across all measures, though the link was more prominent with self-report measures (MHI-5: OR_adj = 0.92 95%CI = 0.91–0.93; WEMWBS: OR_adj = 0.87 95%CI = 0.86–0.88; EHR: OR_adj = 0.96 95%CI = 0.95–0.97).

In these adjusted models, there was a reverse association between age and mental health status when comparing measures, with self-report being more likely among young people while receipt of treatment was more strongly linked with older age (See Supplementary Table 2). Furthermore, the association between female gender and poor mental health status was more prominent when using treatment as the dependent variable, compared to using the MHI-5. There was no statistically significant association between ethnicity and mental health status.

The associations between contextual factors and population mental health are shown in Fig. 1. Individuals with low level occupations were more likely to report poor mental health across self-report measures (MHI-5: OR_adj = 1.45 95%CI = 1.26–1.67; WEMWBS: OR_adj = 1.59 95%CI = 1.29–1.96). There was only evidence (at a 5% significance level) of an association between being a student or unemployed (MHI-5: OR_adj = 1.83 95%CI = 1.50–2.24; WEMWBS: OR_adj = 1.48 95%CI = 1.13–1.98) or a carer (MHI-5: OR_adj = 1.30 95%CI = 1.18–1.43; WEMWBS: OR_adj = 1.38 95%CI = 1.20–1.58) and poor self-reported mental health.

The potential effect of caring on employment status was further demonstrated with those giving up work to provide care being more likely to experience mental ill health across all three measures. Low social cohesion (MHI-5: OR_adj = 1.14 95%CI = 1.07–1.21; WEMWBS: OR_adj = 1.44 95%CI = 1.34–1.54) and low neighbourhood attraction (MHI-5: OR_adj = 1.17 95%CI = 1.10–1.25; WEMWBS: OR_adj = 1.17 95%CI = 1.10–1.25; EHR: OR_adj = 1.10 95%CI = 1.01–1.20) were also associated with poorer mental health, highlighting the close relationship between neighbourhood cohesion and mental health. However, when looking into other area level characteristics, there was evidence (at a 5% significance level) of an association between area level deprivation (Most deprived: MHI-5: OR_adj = 1.34 95%CI = 1.16–1.56; WEMWBS: OR_adj = 1.34 95%CI = 1.11–1.63; EHR: OR_adj = 1.24 95%CI = 1.03–1.49) and mental health as assessed by both self-report measures and EHR, though that was not the case for area of settlement.

Following the observation that individual and contextual factors differed in terms of their association with mental health across our population, it was further observed that across the three mental health measures, even after including caring (See Fig. 2) in the contextual factors, individual factors were able to explain almost double the amount of variance.

It has been reported that carers experience poorer physical and mental health due to challenges relating to their daily caring responsibilities. However, there is evidence that they also experience high satisfaction from caregiving, even in cases of high care demands, a disparity that has been attributed to intra- and interpersonal resources including resilience and social support [43]. Given that carers have been shown to differ from the general population, we further explored how these individual factors were linked to caring status by comparing carers with their non-carer peers using multivariate logistic regression analysis.

Table 2 shows that there was a gradient effect of age on caring status, with increasing age associated with increased likelihood of being a carer (35–44 years: OR_adj = 1.34 95%CI = 1.12–1.59; 75 + years: OR_adj = 2.36 95%CI = 1.79–2.98; Reference group: 16–24 years). Women were more likely to be carers (OR_adj = 1.40 95%CI = 1.27–1.54), while a reverse effect was observed for ethnicity, with those of Non-White background being less likely to report being carers (OR_adj = 0.70 95%CI = 0.50–0.97). Although carers have previously been shown to present with better health status [23], in the current study they were more likely to smoke (OR_adj = 1.27 95%CI = 1.09–1.47) and eat unhealthily (OR_adj = 1.16 95%CI = 1.03–1.29). Finally, there was insufficient evidence at the 5% level of an association between resilience and caring status (low resilience: OR_adj = 1.00 95%CI = 0.99–1.01).

Following the observation that the effect of resilience within carers differs from that across the population, we replicated the analysis using the cluster of individual factors and the resilience sub-scales, which include individual, relational and contextual resilience resources (Table 3). There was evidence (at a 5% significance level) of an association between individual characteristics and mental health across all measures (MHI-5: OR_adj = 0.83 95%CI = 0.81–0.85; WEMWBS: OR_adj = 0.77 95%CI = 0.75–0.79; EHR: OR_adj = 0.92 95%CI = 0.89–0.95), indicating a close correlation between individual resilience and mental health. Furthermore, the measure exploring personal relationships was only associated with self-report measures (MHI-5: OR_adj = 0.95 95%CI = 0.92–0.99; WEMWBS: OR_adj = 0.92 95%CI = 0.90–0.95), while there was only evidence (at a 5% significance level) of an association between the contextual measures and mental health as assessed by WEMWBS (OR_adj = 0.93 95%CI = 0.91–0.96). It was also noted that the assessment of resilience as a binary construct vs a single continuous scale might be masking the amount of variance explained (Nested Nagelkerke R²) by individual factors across all mental health measures (e.g. MHI-5: binary R²: 3.6%; continuous R²: 7.3%; sub-scales derived R²: 9% and sub-constructs derived R²: 9.3%).

This population-based study characterised putative contextual and individual risk factors associated with poor mental health and investigated their contribution to mental health status in a national population survey in Wales. Individual factors, including resilience and health behaviours, such as physical activity, were associated with the likelihood of experiencing poor mental health as assessed by both self-report measures and electronic healthcare records. Contextual factors, including neighbourhood cohesion and household and area level factors were also associated with poor mental health, though the associations were more prominent when using the self-report measures rather than mental health treatment. When both individual and contextual factors were included in the model, Individual factors were more closely associated (at a 5% significance level) with poor mental health and explained a greater amount of variance in relation to mental health across all measures, even after accounting for the role of caring responsibilities, which has been previously linked to mental ill health, as a contextual risk factor.

Across both individual and contextual factors, self-report measures captured population mental health better, showing a stronger association between each factor and mental health status. Resilience has been previously highlighted as a key factor closely linked to mental health across the population [6] and within carers [44] who tend to be more at risk of poor mental health when compared to their non-carer peers. In this context, resilience has been shown to relate to the way individuals respond to stress, adapt to arising problems and maintain their mental wellbeing [45]. When compared to contextual factors and particularly social cohesion and neighbourhood attraction which are two key aspects of neighbourhood cohesion [10], resilience showed a stronger association. This could suggest that neighbourhood conditions, such as deprivation and cohesion, might mediate the link between individual factors and poor mental health rather than increase the risk of mental ill health across the population, though a reverse association between mental health and neighbourhood perceptions has been proposed [46]. Interestingly, the strength of the association differed depending on the way the resilience construct was used. When multiple aspects of it were taken into consideration separately, resilience, especially individual resilience resources, was strongly linked to poor mental health suggesting that there is a complex interplay between a variety of personal skills, coping mechanisms and resilience. Social skills have been previously identified as moderators between stress and poor mental health during childhood [47], when personal resilience skills are developed shaping protective factors against mental ill health across the life span. This variation across sub-constructs is in line with previous studies proposing that resilience is a high-order concept with underlying components that might get swamped when it is used as a single-measure construct [48]. Relational and contextual factors can influence resilience (and vice versa), whilst resilience resources within individuals seem to be closely linked to the individual’s capacity to cope with stressful situations. This further highlights the need for a socioecological approach to resilience, which acknowledges the moderating role individual resilience can have between contextual factors and positive psychological outcomes [4]. In this cross-sectional study, it is also important to consider the potential bi-directional effect of this association. Whilst individuals with poor resilience resources might be more likely to experience poor mental health, individuals with poor mental health may experience maladaptive coping strategies and have reduced access to both intra- and interpersonal resources.

Unhealthy lifestyle behaviours have been linked to both poorer physical and mental health [49]. In the current study we observed that physical inactivity and unhealthy eating habits were strongly associated with poorer mental health, a finding that is in accordance with other population cohorts [50]. The role of smoking behaviours should not be neglected as there was evidence (at a 5% significance level) of an association with self-report measures of poor mental health and there was a trend towards a similar association in the EHR data. The observed variation between types of measures could relate to social desirability bias with respondents being reluctant to report unhealthy behaviours such as substance use in the context of a study focusing on population health and wellbeing [51]. This could also explain the difference in the direction of effect size observed in relation to unhealthy drinking, despite the lack of a significant association, with unhealthy drinking showing a protective link with mental health according to self-report measures but not mental health treatment.

In this study, the association between individual factors and mental ill health was more prominent than that of contextual factors, with both scales, MHI-5 and WEMWBS, capturing the association of interest more effectively than EHR. This finding could relate to the sensitivity and specificity of the three measures, as the two self-report questionnaires could be biased due to respondent or recall bias. These self-report scales have previously been used in community and healthcare settings to assess symptoms of depression and anxiety [52] and mental wellbeing [53]. Mental health treatment could capture more severe cases of mental ill health and could therefore exclude a proportion of the population who are experiencing poor mental health and have not yet taken the step to seek help from a healthcare professional or receive an appropriate diagnosis. The variation between measures could potentially be explained by the role of stigma in relation to help-seeking behaviours which has been identified across population sub-groups including ethnic minorities and individuals residing in rural areas [27].

The associations observed between demographic and lifestyle behaviours and individual and contextual psychosocial factors were also observed in the subset of carers. Caring status was associated with unhealthy behaviours, with carers being more likely to smoke and eat unhealthily, suggesting that they might not be looking after themselves due to factors related to their caring responsibilities [54]. This finding which contrasts with the suggestion that carers are intrinsically healthier than their non-carer peers, is supported by research demonstrating that only intense caring (providing care for more than 20 h per week), is linked to poor health status among carers [23] but could also be linked to the disproportionate number of older carers in our population which might bias the underlying age variation. In contrast, no significant variation was observed in terms of drinking above guidelines and physical inactivity, which could be attributed to either no differences between carers and non-carers in terms of drinking habits and physical activity or social desirability, resulting in a reluctance to report unhealthy behaviours that have been significantly discouraged through public health campaigns. Interestingly, the lack of association between caring and resilience is a finding that is contrary to what has been previously found in the relevant literature but could relate to the fact that the majority of studies on resilience of carers focus on carers of older adults [43], who tend to present with advanced illness, such as dementia, or be at the end of life [55]. Carers in our cohort might differ in terms of the type of health condition experienced by the person they care for, and it may be informative to collect further data to explore this.

This population-based study found that individual factors are more intrinsically linked to mental health than contextual factors. It also highlighted resilience, when examined as a complex set of skills and resources, to be more closely associated with mental ill health than neighbourhood or social cohesion. These findings contribute to greater understanding of the interplay between individual and contextual factors by examining their independent effects along with their potentially cumulative effect on mental health. Future research and clinical practice could benefit from considering the complexities of resilience as a socio-ecological construct, especially when focusing on population subgroups who are more at risk of experiencing poor mental health (such as carers), as well as accounting for the potential role of existing underlying mechanisms which interact with extrinsic factors (such as neighbourhood environment) leading to different pathways of mental ill health across the population.