Abstract
Purpose
Rates of help-seeking for common mental health problems are lower for men, but less is known about patterns of engagement once they are in contact with services. Previous research has been limited in its ability to understand the intersection between service user characteristics and engagement. This study compared analytic approaches to investigate intersectional associations between sociodemographic and socioeconomic indicators and use of psychological treatment services by men.
Method
Data from 9,904 male service users attending two psychological treatment services in London were analysed. The association between ethnicity, sexual orientation, religious affiliation and employment status of service users and service use outcomes was explored using multinomial logistic regression and latent class analysis (LCA).
Results
Being from a minoritised ethnic background, of Muslim faith, being unemployed, and living in the most deprived neighbourhoods were associated with greater risk of not commencing or completing treatment. Seven classes were identified in LCA, with men predominately differentiated by self-reported ethnicity and religion. Compared with the ‘White British, non-religious’ class, the ‘Asian Muslim’ class and the ‘minoritised ethnic, non-religious’ class were at higher risk of disengagement, whilst the ‘Asian, other religion’ class were at higher risk of being referred elsewhere rather than completing initiated treatment.
Conclusions
There were significant inequalities in engagement by men associated with ethnicity, religion and socioeconomic status. Compared with the regression models, further nuance was apparent in LCA regarding the intersection of gender, religion and ethnicity. Identifying groups at greater risk of discontinuation of treatment could inform more personalised pathways through care.
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Introduction
The prevalence of specific mental health problems differs between men and women. For example, men are both more likely to be diagnosed with substance use disorders (SUD) and to complete suicide than women [1,2,3], and both the prevalence of common mental health disorders (CMD) (such as anxiety disorders and depression) and rate of treatment-seeking for mental health problems is considerably higher in women [3,4,5,6]. Some of these gender differences might be explained by a perceived incompatibility of expressing and seeking help for mental distress whilst conforming with masculine traits such as stoicism and self-reliance, meaning mental distress could be judged (by self or others) as a sign of inherent weakness [4, 7, 8]. However, studies investigating the link between conformity with masculine norms and help-seeking have typically been conducted with healthy, White, male students or general community members with self-reported diagnoses [7]. As such, within group differences of men accessing clinical services are not well understood, and the discourse on the role of masculinity has often been “victim-blaming” (i.e. the man’s stubbornness fuels their lack of service use) [9]. This may deflect attention away from the potential ways in which being a man intersects with other facets of social identity in both health promoting and health depleting ways [9]. Even less is known about men’s mental health service use once contact has been initiated, with the quantitative literature predominately focussing on measuring help-seeking, typically operationalised by whether professional help has been sought in the past year or during the person’s lifetime [10]. Few studies which do investigate ‘disengagement’, discriminate between those service users who make an independent decision to discontinue treatment compared to those that discontinue in agreement with their clinician, despite this being an important distinction [11]. As such, there is little knowledge of the pathway through care men experience once they have sought help. Qualitative studies suggest a tendency for men to: (i) seek help only after exhausting all other perceived avenues for support; and (ii) struggle to fully engage in the therapeutic process [12]. This indicates that gender shapes men’s experience of using services throughout the treatment pathway.
The literature on social determinants of mental health service use indicates that, in addition to gender, other service user characteristics such as being from a minoritised ethnic group, both being younger and older in age are associated with reduced levels of service use [13,14,15,16,17], whereas being Lesbian, Gay, Bisexual (LGB), attending religious services [18,19,20,21], and lower socioeconomic status (SES) [14, 22, 23] are associated with increased use. However, this literature treats social determinants as single factors which exert independent effects. Yet, the experience of being a gay Christian Black man, for example, may not be adequately captured by exploring the effects of sexual orientation, religious affiliation, ethnicity, and gender, independently and instead, it may be important to consider the intersectionality of these factors. Intersectionality draws attention to the multiple and mutually constitutive social positions occupied by people, seeking to uncover the corresponding levels of power or disadvantage conferred to the individual [24]. The theory posits that exploration of particular combinations of social positions exert influence that is distinct from the sum of those positions [25].
Few studies have sought to investigate men’s mental health service use from an intersectional perspective. However, those that have, reported intersectional influences which have led to a more nuanced understanding. For example Parent et al. 2018 analysed mental health help-seeking across three ethnic groups and found greater wealth (measured using an ‘income-poverty ratio’) was positively associated with help-seeking among White men, negatively associated with help-seeking among Black men and unrelated to help-seeking among Mexican American men [26]. This approach focussed on gender and ethnicity as groups of interest, however considering multiple sociodemographic and socioeconomic factors has the potential to further explore intersectionality in relation to men’s mental health service use.
To date, the intersectional perspective has more commonly been applied using qualitative methods. This may reflect (contested) perceptions of ill fit with quantitative epistemology and methodology [27, 28]. There is a need for more quantitative research to address intersectional questions to increase the scope of intersectional knowledge and advance thinking around which methods are most complementary. One potential quantitative method for exploring intersectionality is Latent Class Analysis (LCA) [29, 30], a data-driven clustering approach used to identify statistically distinct subgroups [31]. LCA aligns well with the intersectional approach because it allows for the simultaneous consideration of multiple interacting risk-factors which can be difficult to achieve using traditional multiple regression modelling. LCA has been used to identify groups at greater risk of developing CMD based upon individual social identity indicators (ethnicity, migrant status and multiple indicators of SES) [29] or identifying groups who are more or less likely to benefit from psychological therapy based upon demographic and self-reported symptom data [32], but at present not to identify groups with differential service use.
Identifying subgroups at risk of discontinuing from treatment might help reduce levels of unmet need among men and improve their outcomes. Both attending fewer treatment sessions and poor engagement with psychological interventions are associated with worse clinical outcomes and greater likelihood of needing additional care [33, 34], and there is mounting evidence that tailoring services to meet the needs of underserved groups can improve uptake and outcomes [35,36,37]. Discontinuation from treatment can occur in several ways, including the service user cancelling or failing to attend sessions, or the service not being deemed to be the most suitable to meet the service user’s needs. This might be a joint decision between the service user and treating clinician, or a clinician-led decision, or even one led by service managers. Discontinuation might include the service user being referred on to other services, but as this is likely to lead to continued service use it might be considered different from the two reasons above. Likewise, if a service user never attends sessions, or declines treatment with a service, it cannot be considered a form of discontinuation of treatment as no treatment was initiated. Addressing disparities among service users that discontinue care by disengaging, compared to the service being deemed not suitable for their needs may require different responses, so differentiating between the two may be of clinical value. Likewise, establishing whether discontinuation occurs prior, or subsequent to treatment commencing, may help inform service interventions to tackle the problem.
Given the novelty of exploring differential service use by men using LCA, this study compares traditional methods of regression modelling to LCA to explore associations. The aims of this study are to (1) explore the association between sociodemographic and socioeconomic indicators and use of psychological treatment services; (2) identify distinct subgroups of service users defined by sociodemographic and socioeconomic indicators and (3) to explore the associations between identified classes and service use.
Methods
Services and service users
Data for this study come from two community-based psychological treatment services in North East London. These Improving Access to Psychological Therapies (IAPT) services offer evidence-based psychological treatments for anxiety and depression. A key feature of IAPT is the central role of data collection for monitoring and evaluation purposes which has resulted in exceptionally high levels of data completeness [38]. As a national programme, activity levels and outcomes for all services are reported annually and can be compared. In 2017–2018, nationally 978,477 people attended an initial assessment appointment at an IAPT service in England [39]. Following which, the service was considered ‘unsuitable’ for service users in 28,733 (3%) cases so they did not proceed on to treatment, and 395,035 (40%) service users were recorded as ending after having had only one appointment. In that same year, 517,942 (53%) service users that were referred and assessed by IAPT services completed a course of treatment. Although not stated, the remaining 36,767 (4%) service users not accounted for in this breakdown may represent those with missing service use data. The reason for the end of an episode of care is defined within the IAPT programme by the treating clinician at the point of discharging a service user; if the service user has completed the scheduled or planned number of sessions they are determined to have completed treatment.
Service users self-identifying as male that had undergone an assessment and had been discharged from their episode of care (including those not entering treatment) with the two included IAPT services between October 2011 (when services were operational and data collection began) and February 2020, were included in the current study. Where service users had received multiple episodes of care, for example additional treatment episodes, only the first episode of care was included. Service users were excluded if there was no data on their reason for ending contact with the service following an initial assessment, as this is the outcome of interest for this study. The reason for missing outcome data was unclear, and comparison between those with and without outcome data indicated some differences between groups regarding ethnicity, sexuality, religion, and neighbourhood deprivation level (See Appendix A). A total of 9904 service users met inclusion criteria and were included in the analyses (see Appendix B for participant flow diagram).
Measures
Indicator variables
The indicator variables used in this study are derived from routinely available data collected at an initial assessment with the services for all service users. Some variables were recoded and combined due to limited responses in some categories. These included self-reported:
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Ethnicity: categorised into UK Census categories: “White”, “Black”, “Mixed”, “Asian”, and “Other”.
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Sexual orientation: using categories “heterosexual” and “lesbian, gay, bisexual (LGB)”.
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Religious affiliation: using “no stated religion”, “Christian”, “Muslim”, and “Other”.
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Employment status: categorised into “unemployed” or “not-unemployed”. The unemployed group was comprised of those indicating that they are “unemployed and seeking work”, “unable to work due to sickness or disability” and those “not actively seeking work”. The not-unemployed group comprised of those indicating that they are either “employed”, “students”, “homemakers”, “volunteers not seeking work” or “retired”.
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Neighbourhood deprivation: using the English Indices of Multiple Deprivation (IMD) [40] which measure relative deprivation across multiple domains (including income, employment, education and housing) of small areas in England. IMD scores were calculated for individual service users based on their residential postcode (grouped by Lower Layer Super Output Area (LSOA). For the purposes of this study IMD scores were collapsed into quintiles.
Outcomes
Two nominal categorical outcome variables were created using the reason recorded by the treating IAPT clinician for the end of each service user’s treatment episode. Following the initial assessment, service users were categorised into one of the following groups of “Assessment phase” outcomes: (i) ‘entered treatment’, (ii) ‘disengaged’, (iii) ‘service deemed unsuitable’. The ‘entered treatment’ group were those who went on to have at least 1 treatment session or were referred to another clinical service for treatment, as defined by the services [41]. Those who entered IAPT treatment were then categorised into the one of the following “Treatment phase” groups: (i) completed treatment, (ii) disengaged, (iii) referred elsewhere.
Covariates
Depression and anxiety symptom scores at baseline, measured on the Patient Health Questionnaire 9-item [42] and the Generalised Anxiety Disorder scale 7-items [43] respectively, as well as service user age were included as continuous covariates in regression analysis, as these variables have been associated with outcomes, including engagement, in previous analyses using similar data [44, 45]. Data on each service user’s ‘problem descriptor’, used by the services as a proxy for diagnosis, were included as categorical covariate within regression analyses and grouped in accordance with conventions set by previous publications using IAPT datasets [46, 47].
Statistical analysis
To answer the first aim, multinomial logistic regression models were constructed to explore associations between indicator variables and service use outcomes. The full sample (n = 9904) was used to test associations with the assessment phase outcomes. A smaller sample which included service users who entered treatment after the assessment phase (n = 6852) was used to test associations with the treatment phase outcomes. Given the relatively low levels of missing data, ‘missing’ was created as an additional value for categorical variables and included in the analyses so that service users with missing values were not subject to listwise deletion in the initial multinomial regression models [44]. For this analysis, the following models were estimated for both assessment and treatment phase outcomes, with each indicator variable: Model 1 tested the unadjusted association between each indicator variable and the outcomes; model 2 adjusted for age, problem descriptor and symptom severity; model 3 fully adjusted for all indicator variables and covariates.
To meet the second objective, LCA was conducted using ethnicity, sexual orientation, religious affiliation, and employment status as class indicators. Missing data on these indicator variables was not included as its own category and instead was managed using Full Information Maximum-Likelihood through the Expectation Maximisation (EM) algorithm [48]. Given that neighbourhood deprivation level represents an area, rather than individual level, variable it was not modelled in the LCA but was instead treated as a covariate alongside age in later regression models testing the association between the classes and service use outcomes. Selection on the optimum class solution was made in accordance with established criteria [32, 49,50,51] with full details provided in Appendix C. Once the final class solution was identified, multinomial logistic regression models were constructed to explore the associations between identified classes and service use outcomes. Model 1 included class (unadjusted) and model 2 was adjusted for age, problem descriptor, symptom severity and neighbourhood deprivation. Multinomial regression models were constructed in stata15 [52] with relative risk ratios (RRR) and 95% confidence intervals (95%CI) reported, and LCA was performed in Mplus V8 [53].
Results
Sample characteristics
Descriptive statistics of the sample are presented in Table 1. Nearly 41% of service users identified as belonging to a minoritised ethnicity group, 60% identified as religious, and 93% as heterosexual. Local area deprivation scores were high, compared to the national average. Following assessment, most service users started treatment (69%) with the remaining group either disengaging or the service was deemed unsuitable for them, in roughly equal numbers. Of the number of service users who entered treatment (n = 6852), 57% were considered to have completed their treatment episode, and 35% were considered to have disengaged from treatment. This figure is slightly higher than the national average of 53% for treatment completion amongst service users entering treatment in 2017–2018.
Service use patterns by social status indictors
The association between each indicator variable and assessment outcomes is presented in Table 2, and for treatment outcomes in Table 3.
Assessment phase
Across unadjusted and adjusted models, Asian and Black men were at a higher risk of disengaging following assessment compared to White men (Asian: RRR = 1.32 (95% CI) = 1.07–1.61), p = 0.008; Black: RRR = 1.32 (95% CI = 1.07–1.64), p = 0.010). LGB men were found to be at increased risk of disengaging than heterosexual men; however, this association was attenuated in the adjusted models (p = 0.107). Muslim men were more at risk of disengaging than non-religious men; however, this was also attenuated in the fully adjusted model. Christian men were at a lower risk of disengaging from treatment compared to the non-religious men in all models (RRR = 0.85 (95% CI = 0.72–1.00), p = 0.044). In contrast, no associations between socioeconomic indicators and disengagement were identified across models. Black men were at increased risk of being deemed unsuitable compared to White men (RRR = 1.26 (95% CI = 1.01–1.56), as were unemployed men compared to not-unemployed men (RRR = 1.74 (95% CI = 1.53–1.98), p < 0.001). No other associations were observed.
Treatment phase
Having commenced treatment, Black and Asian men were at higher risk of disengaging than completing treatment compared to the White men. However, this was attenuated for both groups in adjusted models. LGB men were at reduced risk of disengagement but only when controlling for age and symptom severity covariates. Muslim men were at increased risk of disengaging compared to non-religious men across all models (RRR = 1.31 (95% CI = 1.05–1.62), p = 0.015). Men from Other religious groups were at less risk of disengaging then the non-religious men, but this was attenuated in the fully adjusted model. Unemployed men were at an increased risk of disengaging from treatment (compared to not-unemployed) across all models (RRR = 1.29 (95% CI = 1.14–1.46), p < 0.001). Similarly, men living in the most deprived neighbourhoods were at greater risk of disengaging compared to those in the least deprived neighbourhood in all models (RRR = 1.84 (95% CI = 1.27–2.66), p = 0.001). Men living in neighbourhoods in second least deprived quintile were also at increased risk of disengaging from treatment (RRR = 1.47 (95% CI = 1.02–2.66), p = 0.04). Asian men were found to be at increased risk of being referred elsewhere than completing treatment compared to White men across all models (RRR = 1.46 (95% CI = 1.06–2.00), p = 0.019). No association was found between sexual orientation and being referred elsewhere. Men from the other religious groups were at increased risk of being referred elsewhere; however, this association was attenuated in the fully adjusted model. Unemployed men were at increased risk of being referred elsewhere compared to not-unemployed men across all models (RRR = 2.25 (95% CI = 1.84–2.75), p < 0.001). No association was found between neighbourhood deprivation level and being referred elsewhere.
Latent class analysis
Model fit statistics for the LCA and the model selection procedure are presented in Appendix C. A seven-class model was selected as the optimal class solution, with ethnicity and religion appearing to most distinguish between class identities. Descriptive labels were assigned to each class to summarise the prominent characteristics of each in relation to ethnicity and religion. Table 4 presents the seven identified classes.
Association between class and service use
The association between the classes identified in the LCA were then tested for association with the service use outcomes using multinomial regression analysis. The results are presented in Table 5 and described below.
Following assessment, men in the minoritised ethnic, non-religious group (class 2) and Asian, Muslim group (class 3) were at increased risk of disengaging from treatment rather than starting treatment compared to those in the White British, non-religious group (class 1) (Class 2: RRR = 1.29 (95% CI = 1.02;1.63), p = 0.031; Class 3: RRR = 1.41 (95% CI = 1.20;1.66), p < 0.001). In contrast, White British Christian men (class 4) were at lower risk of disengagement than starting treatment, compared to those in the White non-religious group; however, this was attenuated in the adjusted model. No associations were found between class groups and the service deemed unsuitable group.
Having commenced treatment, men in the Asian, Muslim group (class 3) were at increased risk of disengaging from treatment rather than completing treatment, compared to men in the White British, non-religious group (class 1) (RRR = 1.31 (95% CI = 1.12;1.53), p = 0.001). Men from the Asian, other religion group (class 5) were found to be at lower risk of disengaging than completing treatment, but this was attenuated in the adjusted model. Men from the Asian, other religion group (class 5) were at increased risk of being referred elsewhere rather than completing treatment compared to men from the White British, non-religious group (class 1) (RRR = 1.60 (95% CI = 1.17–2.19), p = 0.003).
Discussion
Drawing from a large diverse sample, this study compared approaches to understanding the intersectional impact of sociodemographic and socioeconomic factors associated with men’s mental health service use using two different statistical methods. Findings from the initial regression analyses reveal considerable disparities in relation to the mental health service use of men. Being from a minoritised ethnic group, being Muslim, being unemployed, and living in a deprived neighbourhood were associated with a discontinuation of service contact. These results are consistent with previous research findings which suggest that being from a minoritised ethnic group and being of lower socioeconomic status are associated with lower levels of service use [13,14,15,16,17]. However, the results also go further to reveal that religious group, a characteristic rarely explored, is an important sociodemographic characteristic for men’s mental health service use. That men self-identifying as Christian are less likely to disengage following assessment and those self-identifying as Muslim are more likely to disengage after commencing treatment suggests that disaggregating by religious group, rather than just religiosity, may be important where possible.
Seven subgroups of men were identified in the LCA, providing a more detailed picture of the intersectional impact of social determinants on service use. For example, where there was an association between identifying as Black or Asian and a higher risk of disengagement following assessment in the regression models, in the LCA analysis it was apparent that both Asian Muslim men and men who are both non-religious and from a minoritised ethnic group were more likely to disengage following assessment. However, little differences between classes with regard to employment status, which was an important predictor of outcomes in the initial regression models, might suggest that a potentially important source of disadvantage could be overlooked if the LCA had been the sole analytic model used.
Limitations
Data were drawn from two services in North East London, therefore inclusion of additional services would be needed for generalisability. It is possible that patterns of disparities will differ according to the composition of the local populations served and by organisational features of individual services, including the priority given to adapting practice to promote inclusivity. This study was limited to using variables derived from routinely collected data; there are several other unmeasured social demographic characteristics, such as migrant status and relationship status, that might be of value to explore in future work. Although one of the strengths of this study stems from the use of an ethnically diverse dataset, the need to ensure sufficient power to detect effects and to optimise the chances of model convergence meant that ethnicity categories with known heterogeneity remained, by necessity, broad. Similarly, additional measures of SES (e.g. housing status, educational attainment, and income levels) could have better reflected the complexity of the construct [54]. It is acknowledged that the unemployed and not-unemployed groups used here encompassed a considerable range of circumstances. The outcome data were based upon the reasons clinicians assigned for end of treatment episode; future research is needed which explores this topic from the service user perspective. Finally, 6% of the total sample did not have outcome data and were therefore excluded. Differences were observed between the excluded group and those included, such as a higher likelihood of being Asian, being Muslim and residing in more deprived neighbourhoods in the group with missing outcome data. Exploring the cause of this was beyond the scope of the current analysis but it may be of interest to services to investigate, potentially at the case-note level, why these data were not available. Using LCA it was not possible to provide a full insight into the intersectional relationships between the full range of social status variables used, with the class solutions offering little differentiation by sexual orientation or employment status. This may reflect higher levels of uncertainty in the data than anticipated or a challenge inherent to using power-based statistics to detect intersectional groups which, by their nature, will have smaller case numbers than the larger categories they are derived from. Given that only 3% of the sample identified as non-heterosexual, it is perhaps unsurprising that this group did not feature in the classes. Whilst the 7-class solution was the best fit using the available metrics and provided a more clinically relevant grouping of service users, it may be that LCA was not the optimum approach using these variables, especially given the low frequency of some categories. Whilst LCA has been used on similar samples and variables [29, 47], future research might explore alternative classification procedures such as CART or machine learning classification algorithms which might offer new ways of identifying sub-groups based on lower frequency differences, but this is yet to be demonstrated in the field.
Research and clinical implications
The results demonstrate disparities which vary by outcome, highlighting the importance of refining service use measures where possible. Failure to differentiate between clinician or service user led decision-making and stage along the treatment pathway may have led to certain subgroups at risk of discontinuing treatment remaining hidden. Furthermore, the action needed to reduce disengagement will be different from that required to better understand and rectify possible disparities in clinician-led decisions to discontinue treatment.
‘Clinician-led decisions’, reflect not only the judgement and experience of an individual therapist but the values and possibilities afforded by the organisation in which they are embedded. As such, action to reduce disparities is required at all levels. Rates of disengagement can be reduced for underserved groups such as men and minoritised ethnicity groups by adapting services to meet their specific needs [35,36,37, 55]. Recent clinical guidelines have been developed by IAPT services aiming to improve access and outcome equity for minority ethnic IAPT service users [56]. Suggestions to improve engagement levels of minoritized ethnic groups include increasing workforce diversity, ensuring adequate staff training and supervision in cross-cultural competence and offering culturally adapted and culturally responsive therapies [56]. Culturally adapted care has also been shown to improve outcomes for minoritised ethnicity groups, particularly where adaptations also occur at the organisational level, for example, improved access via community outreach or providing locations deemed more appropriate by target groups [57]. The results of the current study justify the expansion of current clinical guidelines focussed upon minoritised ethnicity groups to include consideration of the needs of men, particularly Muslim and socio-economically disadvantaged men. However, the intersectional nature of the results complicates the nature of this task; caution is needed to ensure that in organising clinical guidance according to different facets of social identity there is not an inadvertent, homogenising of other important social characteristics that may exist within the group. Greater risk of disengagement for some subgroups may stem from the lived experience of occupying multiple stigmatised identities, which may be inadequately captured if facets of social identity are treated independently. It is important therefore that the current momentum towards developing guidance for culturally adapted services is balanced against an equal emphasis of the continued need to assess and work flexibly with each client’s uniquely differing values and needs [58].
The disparities found in disengagement levels may also be reduced were clinicians to invest extra time discussing, with the most ‘at-risk’ service users, the benefits of staying the course of treatment. Normalising the challenges that can be encountered when attending therapy and providing space for service users to voice any concerns they have could enable joint problem solving between clinician and service user to tackle perceived barriers to engagement.
It was found that certain groups of men were more likely to be perceived by clinicians as having needs better met elsewhere. This constitutes a less explored source of disparity which, based upon the results here, warrants further attention. Unfortunately, whilst service suitability is routinely discussed and clinical reasoning is detailed in individual records, it is not coded in a format conducive to inclusion in large scale quantitative analysis. In future, services may want to consider including a coded measure to better understand such disparities. There are several possible explanations. It may be that, despite controlling for diagnosis and symptom severity levels, residual confounding due to unmeasured aspects of the clinical presentations (e.g. chronicity of illness or substance use comorbidity) could explain the effect or some proportion of it. It is plausible that being unemployed may in this context be a proxy marker of increased complexity of need. It may also be that diagnostic labels, which exclude service users from IAPT treatment, such as bipolar disorder or depression with psychotic symptoms, are being applied to men from minoritised ethnicity backgrounds at greater rates, reflective of a broader trend described in the literature of higher incidence of psychosis and bipolar disorder found in minoritised ethnicity groups [59, 60]. However, given that service users who, following the initial assessment, were referred to another clinical service for treatment were not included in the ‘service deemed unsuitable’ group, these hypotheses only apply to the ‘referred elsewhere’ group. More relevant to the ‘service deemed unsuitable’ group may be some form of associated systematic bias leading to discontinuation for this reason, such as requiring sessions in the evenings or at weekends, which may not have been possible. Finally, it may be that certain groups of service users may be perceived as more ‘difficult to engage’, perhaps due to cultural variations in the expression of distress less familiar to the clinician or due to inhibited ability to express distress due the intersection of socialised notions of masculinity with other aspects of their identity. More research is needed which investigates the reasons behind the disparities in the outcomes found here, ideally by focussing on service users who declined treatment altogether.
Availability of data and material
This dataset contains personal National Health Service information and is not made routinely available.
Code availability
Mplus code is available upon request to the corresponding author.
References
Bilsker D, White J (2011) The silent epidemic of male suicide. B C Med J 53(10):529–534
McHugh RK, Votaw VR, Sugarman DE, Greenfield SF (2018) Sex and gender differences in substance use disorders. Clin Psychol Rev 66:12–23. https://doi.org/10.1016/j.cpr.2017.10.012
Steel Z et al (2014) The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol 43(2):476–493. https://doi.org/10.1093/ije/dyu038
Addis ME, Mahalik JR (2003) Men, masculinity, and the contexts of help seeking. Am Psychol 58(1):5–14. https://doi.org/10.1037/0003-066X.58.1.5
Galdas PM, Cheater F, Marshall P (2005) Men and health help-seeking behaviour: literature review. J Adv Nurs 49(6):616–623. https://doi.org/10.1111/j.1365-2648.2004.03331.x
Andrews G, Issakidis C, Carter G (2001) Shortfall in mental health service utilisation. Br J Psychiatry 179:417–425. https://doi.org/10.1192/bjp.179.5.417
Seidler ZE, Dawes AJ, Rice SM, Oliffe JL, Dhillon HM (2016) The role of masculinity in men’s help-seeking for depression: a systematic review. Clin Psychol Rev 49(September):106–118. https://doi.org/10.1016/j.cpr.2016.09.002
Möller-Leimkühler AM (2003) The gender gap in suicide and premature death or: why are men so vulnerable? Eur Arch Psychiatry Clin Neurosci 253(1):1–8. https://doi.org/10.1007/s00406-003-0397-6
Affleck W, Carmichael V, Whitley R (2018) Men’s mental health: social determinants and implications for services. Can J Psychiatry 63(9):581–589. https://doi.org/10.1177/0706743718762388
Dixon-Woods M et al (2006) Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol 6:1–13. https://doi.org/10.1186/1471-2288-6-35
O’Brien A, Fahmy R, Singh SP (2009) Disengagement from mental health services: a literature review. Soc Psychiatry Psychiatr Epidemiol 44(7):558–568. https://doi.org/10.1007/s00127-008-0476-0
Johnson JL, Oliffe JL, Kelly MT, Galdas P, Ogrodniczuk JS (2012) Men’s discourses of help-seeking in the context of depression. Sociol Heal Illn 34(3):345–361. https://doi.org/10.1111/j.1467-9566.2011.01372.x
Alonso J et al (2007) Population level of unmet need for mental healthcare in Europe. Br J Psychiatry 190:299–306. https://doi.org/10.1192/bjp.bp.106.022004
Bebbington PE et al (2000) Unequal access and unmet need: Neurotic disorders and the use of primary care services. Psychol Med 30(6):1359–1367. https://doi.org/10.1017/S0033291799002950
Cooper C et al (2013) Ethnic inequalities in the use of health services for common mental disorders in England. Soc Psychiatry Psychiatr Epidemiol 48(5):685–692. https://doi.org/10.1007/s00127-012-0565-y
Cooper C et al (2010) The treatment of common mental disorders across age groups: results from the 2007 adult psychiatric morbidity survey. J Affect Disord 127(1–3):96–101. https://doi.org/10.1016/j.jad.2010.04.020
Raleigh VS et al (2007) Ethnic variations in the experiences of mental health service users in England. Results of a national patient survey programme. Br J Psychiatry 191:304–312. https://doi.org/10.1192/bjp.bp.106.032417
Cochran SD, Sullivan JG, Mays VM (2003) Prevalence of mental disorders, psychological distress, and mental health services use among lesbian, gay, and bisexual adults in the United States researchers have shown that some forms of mental disorders, particularly. J Consult Clin Psychol 71(1):53–61
Grella CE, Greenwell L, Mays VM, Cochran SD (2009) Influence of gender, sexual orientation, and need on treatment utilization for substance use and mental disorders: findings from the California Quality of Life Survey. BMC Psychiatry 9:1–10. https://doi.org/10.1186/1471-244X-9-52
AbdAleati NS, Mohd Zaharim N, Mydin YO (2016) Religiousness and mental health: systematic review study. J Relig Health 55(6):1929–1937. https://doi.org/10.1007/s10943-014-9896-1
Harris KM, Edlund MJ, Larson SL (2006) Religious involvement and the use of mental health care. Health Serv Res 41(2):395–410. https://doi.org/10.1111/j.1475-6773.2006.00500.x
Ten Have M, Meertens V, Scheepers P, Te Grotenhuis M, Beekman A, Vollebergh W (2005) Demand for mental health care and changes in service use patterns in the Netherlands, 1979 to 1995. Psychiatr Serv 56(11):1409–1415. https://doi.org/10.1176/appi.ps.56.11.1409
Weich S, Nazareth I, Morgan L, King M (2007) Treatment of depression in primary care: Socio-economic status, clinical need and receipt of treatment. Br J Psychiatry 191:164–169. https://doi.org/10.1192/bjp.bp.106.032219
Crenshaw K (1991) Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Rev 43(6):1241. https://doi.org/10.2307/1229039
Bowleg L (2008) When Black + lesbian + woman ≠ Black lesbian woman: the methodological challenges of qualitative and quantitative intersectionality research. Sex Roles 59(5–6):312–325. https://doi.org/10.1007/s11199-008-9400-z
Parent MC, Hammer JH, Bradstreet TC, Schwartz EN, Jobe T (2018) Men’s mental health help-seeking behaviors: an intersectional analysis. Am J Mens Health 12(1):64–73. https://doi.org/10.1177/1557988315625776
Else-Quest NM, Hyde JS (2016) Intersectionality in quantitative psychological research: II. Methods and techniques. Psychol Women Q 40(3):319–336. https://doi.org/10.1177/0361684316647953
Else-Quest NM, Hyde JS (2015) Intersectionality in quantitative psychological research: I. Theoretical and epistemological issues. Psychol Women Q 40(2):155–170. https://doi.org/10.1177/0361684316629797
Goodwin L, Gazard B, Aschan L, MacCrimmon S, Hotopf M, Hatch SL (2018) Taking an intersectional approach to define latent classes of socioeconomic status, ethnicity and migration status for psychiatric epidemiological research. Epidemiol Psychiatr Sci 27(6):589–600. https://doi.org/10.1017/S2045796017000142
Garnett BR, Masyn KE, Austin SB, Miller M, Williams DR, Viswanath K (2014) The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis. J Youth Adolesc 43(8):1225–1239. https://doi.org/10.1007/s10964-013-0073-8
Finch WH, Bronk KC (2011) Conducting confirmatory latent class analysis using Mplus. Struct Equ Model 18(1):132–151. https://doi.org/10.1080/10705511.2011.532732
Saunders R, Buckman JEJ, Pilling S (2020) Latent variable mixture modelling and individual treatment prediction. Behav Res Ther 124:103505. https://doi.org/10.1016/j.brat.2019.103505
Clark DM, Canvin L, Green J, Layard R, Pilling S, Janecka M (2018) Transparency about the outcomes of mental health services (IAPT approach): an analysis of public data. Lancet 391(10121):679–686. https://doi.org/10.1016/S0140-6736(17)32133-5
Dahlsgaard KK, Beck AT, Brown GK (1998) Inadequate response to therapy as a predictor of suicide. Suicide Life Threat Behav 28(2):197–204. https://doi.org/10.1111/j.1943-278X.1998.tb00639.x
Benish SG, Quintana S, Wampold BE (2011) Culturally adapted psychotherapy and the legitimacy of myth: a direct-comparison meta-analysis. J Couns Psychol 58(3):279–289. https://doi.org/10.1037/a0023626
Bhui KS et al (2015) Interventions to improve therapeutic communications between Black and minority ethnic patients and professionals in psychiatric services: systematic review. Br J Psychiatry 207(2):95–103. https://doi.org/10.1192/bjp.bp.114.158899
Sagar-Ouriaghli I, Godfrey E, Bridge L, Meade L, Brown JSL (2019) Improving mental health service utilization among men: a systematic review and synthesis of behavior change techniques within interventions targeting help-seeking. Am J Mens Health. https://doi.org/10.1177/1557988319857009
Clark DM (2018) Realizing the mass public benefit of evidence-based psychological therapies: the IAPT program. Annu Rev Clin Psychol 14:159–183. https://doi.org/10.1146/annurev-clinpsy-050817-084833
Moller NP, Ryan G, Rollings J, Barkham M (2019) Correction to: the 2018 uk nhs digital annual report on the improving access to psychological therapies programme: a brief commentary (BMC Psychiatry (2019) 19 (252) DOI: 10.1186/s12888-019-2235-z). BMC Psychiatry 19(1):1–5. https://doi.org/10.1186/s12888-019-2277-2
Ministry of Housing Communities and Local Government (2019) National Statistics English Indices of Deprivation 2019,” Minist. Housing, Communities Local Gov 2019. [Online]. Available: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019
Maguire N (2012) Psychological therapies. Med 40(12):668–671. https://doi.org/10.1016/j.mpmed.2012.09.003
Kroenke K, Spitzer RL, Williams JBW (2001) The PHQ-9 46202: 606–613
Spitzer RL, Kroenke K, Williams JBW, Löwe B (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166(10):1092–1097. https://doi.org/10.1001/archinte.166.10.1092
Saunders R et al (2021) Older adults respond better to psychological therapy than working-age adults: evidence from a large sample of mental health service attendees. J Affect Disord 294(June):85–93. https://doi.org/10.1016/j.jad.2021.06.084
Buckman JEJ, Stott J, Main N et al (2021) Understanding the psychological therapy treatment outcomes for young adults who are not in education, employment, or training (NEET), moderators of outcomes, and what might be done to improve them. Psychol Med. https://doi.org/10.1017/S0033291721004773
Buckman JEJ et al (2018) The impact of alcohol use on drop-out and psychological treatment outcomes in improving access to psychological therapies services: an audit. Behav Cogn Psychother 46(5):513–527. https://doi.org/10.1017/S1352465817000819
Saunders R, Buckman JEJ, Cape J, Fearon P, Leibowitz J, Pilling S (2019) Trajectories of depression and anxiety symptom change during psychological therapy. J Affect Disord 249:327–335. https://doi.org/10.1016/j.jad.2019.02.043
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Nylund KL, Asparouhov T, Muthén BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model 14(4):535–569. https://doi.org/10.1080/10705510701575396
Saunders R, Cape J, Fearon P, Pilling S (2016) Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. J Affect Disord 197:107–115. https://doi.org/10.1016/j.jad.2016.03.011
Spinhoven P, Batelaan N, Rhebergen D, van Balkom A, Schoevers R, Penninx BW (2016) Prediction of 6-yr symptom course trajectories of anxiety disorders by diagnostic, clinical and psychological variables. J Anxiety Disord 44:92–101. https://doi.org/10.1016/j.janxdis.2016.10.011
S. Corporation (2009) Mata reference manual (11)
Muthén L, Muthén B (2007) Mplus user’s guide (version 7). Los Angeles Author. [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Mplus+user+guide#8
Buckman JEJ, Saunders R, Stott J et al (2022) Socioeconomic indicators of treatment prognosis for adults with depression. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2022.0100
Spendelow JS (2015) Cognitive–behavioral treatment of depression in men: tailoring treatment and directions for future research. Am J Mens Health 9(2):94–102. https://doi.org/10.1177/1557988314529790
Beck A, Naz S, Brooks M, Jankowska M (2019) Improving access to psychological therapies (IAPT). Black, Asian and Minority Ethnic Service User Positive Practice Guide 2019. Br Assoc Behav Cogn Psychother: 52. [Online]. Available: https://www.babcp.com/files/IAPT-BAME-PPG-2019.pdf
Laura-Louise Arundell PB, Pilling S (2021) The effectiveness of adapted psychological interventions for people from Black, Asian and minority ethnic, or migrant groups: a systematic review. Clin Psychol Rev 88:12063. https://doi.org/10.1016/j.cpr.2021.102063
Dinos S (2015) Culturally adapted mental healthcare: evidence, problems and recommendations. BJPsych Bull 39(4):153–155. https://doi.org/10.1192/pb.bp.115.050872
Lloyd T et al (2005) Incidence of bipolar affective disorder in three UK cities: results from the ÆSOP study. Br J Psychiatry 186:126–131. https://doi.org/10.1192/bjp.186.2.126
Fearon P et al (2006) Incidence of schizophrenia and other psychoses in ethnic minority groups: results from the MRC AESOP Study. Psychol Med 36(11):1541–1550. https://doi.org/10.1017/S0033291706008774
Funding
JEJ Buckman is supported by the Wellcome Trust (Grant Code 201292/Z/16/Z) and the Royal College of Psychiatrists. S Pilling, is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. None of these funders had any role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
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This study was conducted as part of the North and Central East London IAPT Service Improvement and Research Network (NCEL IAPT SIRN). Permission was granted by the network for the data to be used for this analysis and data were hosted in accordance with locally agreed procedures. No further data on patients other than that already routinely collected by the services was used for this project and NHS ethical approval was not required for this study (confirmed by the Health Research Authority July 2020, Reference Number 81/81).
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All patients are informed at initial contact with the services that their anonymised data may be used for service improvement and research purposes and that this may include publication, and they are able to opt-out at this point. The identities of participants were unknown to the research team.
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Smyth, N., Buckman, J.E.J., Naqvi, S.A. et al. Understanding differences in mental health service use by men: an intersectional analysis of routine data. Soc Psychiatry Psychiatr Epidemiol 57, 2065–2077 (2022). https://doi.org/10.1007/s00127-022-02256-4
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DOI: https://doi.org/10.1007/s00127-022-02256-4