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Exploring the Benefits of Playing Online Games for Mental Health

In recent years, online games have gained immense popularity across all age groups. While some may argue that spending time playing games online is a waste of time, research suggests that there are actually numerous mental health benefits associated with engaging in this activity. From improving cognitive abilities to reducing stress and anxiety, let’s explore the positive effects of playing online games on mental well-being.

Enhancing Cognitive Abilities

Online games often require players to think critically, solve problems, and make quick decisions. These activities can significantly enhance cognitive abilities such as memory, attention span, and problem-solving skills. For instance, strategy-based games like chess or puzzle-solving games like Sudoku can help sharpen analytical thinking and logical reasoning.

Furthermore, many online games involve complex narratives that require players to follow the storyline, remember important details, and make connections between different elements. This kind of mental exercise can improve memory retention and overall cognitive functioning.

Stress Relief and Relaxation

In today’s fast-paced world, stress has become a common issue affecting individuals of all ages. Engaging in online games can provide a much-needed escape from daily life stressors and offer a sense of relaxation. When playing online games, people often get absorbed in the virtual world, temporarily forgetting their worries and responsibilities.

Moreover, participating in challenging game levels or competing against other players can release endorphins – natural mood-boosting chemicals in the brain – leading to a feeling of happiness and contentment. This positive emotional state helps alleviate stress levels and promotes overall well-being.

Social Interaction and Connection

Contrary to popular belief that online gaming isolates individuals from real-life social interactions, many multiplayer online games actually foster social connections among players. Online gaming communities provide platforms for individuals with common interests to engage in teamwork or compete against each other while simultaneously building relationships.

Through voice chats or messaging systems within these gaming platforms, players can communicate and collaborate with others, forming friendships and even virtual communities. This social interaction can have a positive impact on mental health by reducing feelings of loneliness and promoting a sense of belonging.

Cognitive Distraction and Anxiety Reduction

For individuals dealing with anxiety or other mental health conditions, online games can serve as a cognitive distraction tool. Engaging in an immersive gaming experience diverts attention away from anxious thoughts or intrusive worries, allowing the mind to focus on the game’s challenges instead.

Additionally, online games that incorporate relaxation techniques such as soothing music or calming visuals can help induce a state of relaxation and tranquility. By redirecting attention towards these calming elements, players can experience temporary relief from anxiety symptoms.

In conclusion, playing online games can have significant mental health benefits. From enhancing cognitive abilities to providing stress relief and promoting social interaction, the positive effects of engaging in this activity should not be overlooked. However, it is important to maintain a healthy balance between gaming and other aspects of life to ensure overall well-being. So go ahead, enjoy your favorite online game guilt-free knowing that it’s not just entertainment but also beneficial for your mental health.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Anna Cigarini 1 , 2 ,
Julián Vicens ORCID: orcid.org/0000-0003-0643-0469 1 , 2 , 3 ,
Jordi Duch ORCID: orcid.org/0000-0003-2639-6333 3 , 4 ,
Angel Sánchez 5 , 6 , 7 , 8 &
Josep Perelló ORCID: orcid.org/0000-0001-8533-6539 1 , 2

Scientific Reports volume 8 , Article number: 3794 ( 2018 ) Cite this article

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Public health

An Author Correction to this article was published on 26 September 2018

This article has been updated

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ‘care in the community’ framework.

Introduction

Approximately one fifth of the world population will suffer some mental disorder (MD) at some point in their lives, such as anxiety or depression 1 . The direct economic costs of MD, including care and indirect effects, is estimated to reach $6 trillion in 2030, which is more than cancer, diabetes, and respiratory diseases combined 2 . As part of a global effort to scale up services and bring down costs, reliance is increasingly made upon informal social networks 3 . A holistic approach to mental health promotion and care provision is then necessary, and emphasis is placed on the idea of individuals-in-community: individuals with MD are defined not just alone but in relationship to others 4 . Such a paradigm shift implies superseding the traditional physician-patient dyad to include caregivers, relatives, social workers, and the community as a whole, recognizing their crucial role in the recovery process.

A key aspect in the definition and aetiology of MD has to do with social behavior 5 : behavioral symptoms, or consequences at the behavioral level, characterize most MD. For instance, autism, social phobia, or personality disorders are determined by the presence of impairments in social interaction. Other disorders result in significant difficulties in the social domain, such as depression or psychotic disorders. Further, conditions that are intrinsically behavioral (as for eating disorders or substance abuse) seem to be exacerbated by the influence of social peers. A large body of research has therefore looked at the neural basis of social decision-making among individuals with MD to identify objective biomarkers that may prove useful for its diagnosis, therapy evaluation, and understanding 6 , 7 , 8 . However, such a methodology does not well fit into the individuals-in-community paradigm. We argue that an agent-based approach which draws upon experimental game theory might prove insightful and ecologically valid for the study of behavior in a given social environment.

Within the mental health literature, the use of game theory as a way to understand the multi-faceted dimensions of behavior has received already quite some attention 9 , 10 . Most research addressed the issue of behavioral differences between individuals with MD and healthy populations 6 , 7 , 11 , 12 , 13 , 14 , 15 , 16 . These works, that point to cognitive and affective processing impairments 6 , 16 , 17 , further support the idea that MDs are associated with significant and pervasive difficulties in social cognition and altered decision-making at various levels. Yet, despite these studies are of very much interest, they are primarly concerned with dyadic interactions among people with specific MDs. That is, they lack insights into the complexity of individual behaviors of MD within a specific social context.

Here we adopt a novel community perspective. Our objective is twofold: First, we aim to develop a thorough taxonomy of the behavioral traits of role groups within the collective. We thus account for both the heterogeneity of actors, and for multiple types of social interactions. We strongly believe that to predict and understand behavior is necessary to consider the relationship context in which individuals are embedded. Therefore diversity of roles, motivations or capabilities, must be taken into account. Also, real life social interactions occur in different forms; sometimes people must work together, some others they have to coordinate or anti-coordinate their behaviors, yet in other situations they find themselves in more or less disadvantaged positions. It is therefore of crucial importance to encompass a comprehensive range of strategic situations if we are to appreciate behavior. That is, traits such as trust, altruism, or reciprocity, along with the person’s own expectations, all play a role in the process of decision making in social contexts. This calls for an experimental approach in which participants face several strategic settings. Our second objective is to provide quantitative accounts of social capital within the mental health community, bringing the notion of social capital into the forefront of mental health care. Far from being universally defined, its core contention is that social networks are a valuable asset, providing a basis for social cohesion and cooperation towards a common goal 18 (which is, in our case, mental care provision). It thus encompasses those norms and forces that shape social interactions, serving as the glue that holds society together 19 .

For these purposes, we have designed an experimental setup that probes into the complexity of the interdependencies at play within the mental health ecosystem. Accordingly, our experiments take place in a socialized, lab-in-the-field setting 20 , in order to be as close as possible to the dynamic and unique nature of real-life social interactions. The design of our socialized setup is based on a participatory process and citizen science practices 20 which counted on the collaboration of all stakeholders of the mental health ecosystem. By combining all these ingredients, we have developed a framework that, as will be shown below, allows to capture some difficult-to-observe aspects of behavior and social capital within mental health ecosystems as a way to understand how communities contribute to care and resocialization.

A full description of the games we implemented can be found in the Methods section below, but for clarity we briefly describe here the games we used. We had participants play two dyadic games, namely the Trust game, in which they had to lend money to another player who then obtains a return, and has the option to send some money back to the lender; players played in both roles. They also played the well known Prisoner’s Dilemma, in which they had to choose to cooperate or to try to benefit from the other’s cooperation. Finally, they played a collective risk dilemma, in which the whole group had to reach a common goal to avert a catastrophe that most likely would wipe out their money. Participants belonging to the mental health ecosystem played with each other in group of six players. However, they could by no means guess with whom they were actually playing.

We begin the presentation of our results from the dyadic games of our suite of strategic interactions. Aggregate behavioral measures point to systematic deviations from self-interested predictions which are in line with previous literature on experimental game play 21 . In the Prisoner’s Dilemma (PD), the average cooperation rate across all individuals is c = 0.61 ± 0.03 (standard error of the mean), which is notoriously well above the Nash equilibrium prediction of c = 0. Participants behavior in the PD is also significantly associated with their estimates about the likely cooperation of the partner ( ${\chi }^{2}=32.48$ , p = 1.2 · 10 8 ), with 44% of all participants expecting the partner to cooperate, and thus cooperating themselves. This points to the crucial role of positive expectations on cooperative behavior 22 . Further, participants trust and reciprocate positive amounts in the Trust Game (5.79 ± 0.15 monetary units (MU) and 41.3 ± 1.37% of the amount available to return, respectively), again departing largely from Nash equilibrium conjectures of 0 MU transferred. The results also suggest that in considering the mental health community in its whole, thus accounting for the diversity of actors and roles, the global picture does not substantially differ from society at large.

Sectorial and dyadic behavior

As we stated above, our main interest is to delve into the behavior of the different actors who make up the mental health ecosystem Fig. 1 summarizes the results for the five groups of individuals concerned. The heatmap yields several insights that are worth commenting upon.

Heatmap of behavioural traits’ average and deviation of the mean across games. Collectivity refers to the ratio of contribution in the Collective-Risk Social Dilemma. Cooperation and Optimism refers to the ratio of cooperation and expected cooperation, respectively, in Prisoner’s Dilemma. Trust and Reciprocity refers to the ratio of capital trusted and reciprocated in Trust Game. The left part shows the ratio of individuals without mental conditions: caregivers (professionals and relatives with caregiving tasks) and non-caregivers (relatives without caregiving tasks, friends and others). The right part shows the actions of individuals with mental conditions. Therefore, the number in each cell indicates the ratio of social preferences per subjects in each social dilemma and the color scale shows the deviation of the mean measured in SD units.

In one-shot dyadic interactions some marked differences in the frequency of cooperative behaviors (PD) arise within the collective formed by affected with MD, caregivers, non-caregivers (Kruskal-Wallis rank sum test, $H=6.04,df=2,p=0.0488$ ). Further pairwise comparisons (see Supplementary Table S1 ) show that participants with anxiety and caregivers are more likely to opt for the cooperative strategy compared to participants with bipolar disorder, psychosis or other members of the collective. Participants with anxiety are also the ones with the most positive expectations about the partner’s behavior compared to all but caregivers (see Supplementary Table S2 ). Also, relatives, friends and other members with no MD defect more than caregivers (Mann-Whitney U test, $U=1352,p=0.02839$ ), being relatives remarkably less cooperative than the rest of the collective c = 0.33 ± 0.16. This suggests that cooperation among members of the mental health ecosystem is contextually based, depending on the role that actors play in the recovery process. It also varies across diagnostics, revealing a marked cooperativeness and optimism of individuals with anxiety disorders.

On the other hand, in sequential dyadic interactions (TG) all participants trust more than half of their endowment, being the distribution of initial transfers similar across groups. No variation is indeed found in trust levels between participants with MD, caregivers and non caregivers (Kruskal-Wallis rank sum test, $H=2.75,df=2,p=0.25$ ). Yet, at the time of reciprocating the partner’s behavior, participants with anxiety and depression return the least (37.5 ± 3.3%). The difference is significant if compared to return transfers of participants with psychosis or other diagnostics (see Supplementary Table S4 ).

Group interaction

Our experimental setup has proven extremely informative in its most novel section, namely the analysis of group interactions framed within the Collective Risk Dilemma (CRD), with no prior result within the mental health literature. In global terms, the average amount contributed to the public good (22.6 MUs) is much more than the fair contribution of 20 MUs, where by fair we understand sharing equally the total amount needed for the threshold (120 MUs) among all six participants. Here it is important to keep in mind that participants were told that all money contributed would go to reforestation projects, so it is not irrational to keep contributing beyond the threshold as many of our subjects did. The key result in the CRD is that large, significant differences (t-test, $t=2.85,df=242,p=0.0047$ ) are found between participants with and without mental disorders. The former contribute with 22.95 ± 0.63 MUs compared to 20.34 ± 0.68 MUs from the latter, and therefore it appears that when repeated interaction and sustained teamwork (CRD) are required, people with MD contribute much more to the common goal (See Supplementary Section 1.6.2).

Contribution dynamics vary according to group composition in terms of number of participants with mental disorder conditions and other actors involved in the recovery process. All groups successfully reach the target collecting on average 135.64 ± 1.75 MUs (see Supplementary Section 1.6.1). Similarly to other public good experiments, contributions decrease over time 23 . While in the first round participants contribute around 56.3% of the allowed contribution per round (2.2 ± 0.07 MUs, where the social optimum is 2 MU), contributions drop when the endgame effect sets in. A Spearman’s rank-order correlation of contributions over rounds corroborates this negative time trend ( $\rho =-0.757,p < 0.05$ ). Both patients and actors involved in the recovery process reduce their contributions by the end of the game. However, in almost all rounds, participants with a mental condition contribute more than caregivers and non caregivers, for whom motivations to contribute decline steadily (see Fig. 2 ).

(a) Individual contribution over rounds. Evolution of contributions (mean and standard error of the mean) during the game between participants with mental disorder conditions, caregivers and non-caregivers. We can see that all groups behave similarly and in an identical way to a previous experiment run outside the mental health ecosystem 40 . (b) Average individual contribution per round. Average contribution and standard error of the mean in the mental health ecosystem. There are significant differences between participant with MD and the rest of actors, caregivers (t-test, $t=2.107,df=155,p < 0.0294$ ) and non-caregivers (t-test, $t=2.499,df=48,p=0.01588$ ). Distribution of choices by participants with MD ( c ), caregivers (d) and non-caregivers ( e ). The most of participants with MD (43.6%) selected the maximum contribution (4), while the caregivers (46.5%) and non-caregivers (48.9%) mostly selected the fair contribution (2).

In terms of the group composition, groups where individuals with MD conditions constitute half or the majority of the group (n = 36) do much better in sustaining cooperation compared to groups where firsthand affected are the minority (n = 9). It is here worth to mention that participants may see who the rest of the members are but ignore who is exactly making the choice in the game (see Methods for further details). As Fig. 3b shows, while average individual contributions are similar in the last periods (rounds 6–10 t-test, t = 0.19, p = 0.85), groups with half or more individuals with MD contribute significantly more at the beginning of the game (rounds 1–5 t-test, t = 2.79, p = 0.0054). Hence, the presence of three or more individuals with a mental condition in the group has a positive and stabilizing effect on average individual contributions. Likewise, in games with a low proportion of participants affected with MD the group achieved the goal, on average, later than in games with more than 50% of participants affected with MD (see Fig. 3a ).

(a) Average round of achievement. Round (mean and standard error of the mean) in which the group of six achieved the target. (b) Aggregated contributions per group composition. Contributions (mean and standard error of the mean) in the first and last five-rounds per number of individuals with MD in a group. There are significant differences (t-test p < 0.01) in contributions in the first part of the game. (c ) Contributions per group of six. Total group contributions by number of individuals with mental conditions in the group. (d ) Gini index of final payoff within groups. Level of inequality in final payoff based on the number of individuals with MD in each group.

If we then break down the analysis by group type, we find that group members contribute and benefit differently from cooperation (see Fig. 3c ). Indeed, final payoffs within groups are far from being equally distributed (see Fig. 3d ), with the highest inequality found in the group where the number of patients equals the number of actors involved in the recovery process (Gini coefficient = 0.289). We thus see clearly that the cost of collective action is mainly supported by individuals with a mental disorder. Given that they contribute the most within all groups, lower investments are needed for other members of the collective to reach the common target. Yet, in 4/6 and 5/6 groups caregivers reduce average individual contributions while non-caregivers pay more than their fair share. In 1/6 and 2/6 groups, on the other hand, caregivers are the ones who compensate the unfair contributions of other members. These last groups are the ones that ensure the lowest inequality in final payoffs. Therefore, while our results are unambiguous about the larger readiness for collective action among people with MD, we cannot claim nothing about the rest of the collective.

Let us now turn to the discussion of the above results and their implications (see Table 1 for a summary of the key findings). As a first general remark, through our lab-in-the field experiment we found that an ecosystem approach to mental health care brings with it a quite complex scenario with several interesting insights. To begin with, participants with anxiety symptoms display a markedly different behavior compared to other diagnostics: they are more likely to opt for the cooperative strategy compared to individuals with bipolar disorder or depression, and return significantly less than participants with psychosis or other disorders. Since the current study is the first to investigate social decision-making within a heterogeneous population of individuals diagnosed with MD, a comparison with previous research is only possible referring to studies focusing on specific clinical and quite homogeneous populations. Several experiments have demonstrated deficits in cooperative behavior among individuals with anxiety or depression when playing iterated versions of the PD 11 , 17 , 24 , 25 , but results about altruism (Ultimatum Game) and trust are inconsistent between studies 6 , 7 , 11 , 12 , 17 , 26 . Individuals with major depressive disorders (which include anxiety and depressive symptoms) have also been found to systematically differ when their emotional responses to fairness are compared 6 , 17 , showing higher levels of negative feelings when faced with unfair treatments. One of the hypothesis advanced to explain the systematic behavioral differences of individuals with anxiety relates to a potentiated sensitivity to negative stimuli as well as a tendency to treat neutral or ambiguous stimuli as negative or as less positive 6 , 12 , 17 , 27 . This hypothesis might find support in our results as for the low returns in the Trust Game, despite displaying relatively high trust in the partner’s behavior and very high expectations. Indeed, participants with depressive or anxiety symptoms in our experiment significantly over-punish trustee transfers, but the low returns are independent of the amount received. This seems to imply that participants with mood disorders respond negatively to their partner behavior, as if they interpret their partner’s choice in a negative sense. Alternatively, fairness considerations may be playing a role: low returns of participants with mood disorders might therefore be due to different fairness perceptions 6 , 12 , 17 , which result in a bias towards negative reactions rather than positive rewarding.

Deficits in economic game play have also been documented for individuals with bipolar disorder. Studies report low and decreasing trust levels over sequential interactions, skeptical beliefs about the partner’s behavior and a tendency to break cooperative interactions 28 , 29 . Again, this is partly supported by our results. Negative expectations of participants with bipolar disorder indeed agree with a low frequency of cooperative choices, little amounts of money sent to trustees, and low contributions to collective action. In line with King-Casas et al . results 29 , while individuals with depression trust in the cooperativeness of other people, those with bipolar personality disorders do not. Cognitive dysfunctions (insula response) might possibly reflect an atypical social norm in this group 29 . Consequently, defection by partners might not violate the social expectations of individuals with BPD. In contrast, in our experiment, participants with bipolar disorder return the most within the group of individuals with a mental disorder. That is, they report a strong willingness to positively respond to a norm of trust as to signal their partner trustworthiness. Therefore, conditioned on the previous action of the partner, it seems that individuals with BPD are willing to show cooperative behavior. Considering now individuals with high levels of psychopathy, they have been found to make less fair offers, accept less fair offers, and show very high levels of defection 15 , 16 , 30 . Major explanations for such behavior point to deficits in emotion regulations (amygdala dysfunctions), which would lead to lack of anxiety, empathy, and guilt, coupled with exaggerated levels of anger and frustration 30 and to the absence of prepotent biases toward minimizing the distress of others 16 . In this case, our experiments do not confirm those previous results: Indeed, participants with psychosis are the ones who trust, contribute the most to the public good, and are willing to take costly actions to reciprocate their partner’s behavior. It could be possible that, as psychopathic disorders are in fact a large group of different ones, behavioral differences among subgroups may lead to this discrepancy. In connection with these results, it is interesting to note that recent results on a large population of patients with paranoia suggest that distrust is not the best explanation for reduced cooperation and alternative explanations incorporating self-interest might be more relevant 31 , 32 . This calls for further research into this particular family of MD to clarify whether or not the behavioral characterization applies to all or to a subclass of them.

However, pointing to deficits in social cognition can only account for a partial explanation of individual behavior, and does not contribute to community care narratives. The fact that nothing in this direction has been reported before also reinforces the need to adopt a more holistic view on the interdependencies at play within the mental health collective. Indeed, if statistically relevant differences in cooperative behavior are found across diagnostics, they also depend on the role that actors play in the recovery process. That is, caregivers display exceedingly large degrees of cooperativeness and optimism in one-shot interactions. Caregivers can be thus considered the strong ties of the mental health ecosystem, of particular value when one seeks emotional support. With the de-institutionalization of health systems, caregivers have indeed become key players in care provision. Taking into account their behavior and expectations is therefore of particular interest to extend the support tailored to their needs. These actions should improve the effectiveness of their role by guiding them 33 . Yet, relatives who do not strictly contribute to caregiving practices turn out to be the weak links. It is thus likely that interventions designed to increase their participation in the community might help improve the recovery process.

Also, members of the mental health ecosystem do not equally contribute and benefit from collective action. Rather, systematic behavioral differences arise as the number of social interactions increase, i.e., when teamwork is required for the collective to benefit as a whole. This suggests that considering repeated games may prove extremely insightful for the purpose of the research. Indeed, our experiments show that individuals with MD are the ones who contribute the most to the public good: they make larger efforts towards reaching the collective goal, thus playing a leading role for the functioning of the ecosystem. As a consequence, groups with half or more participants with MD do better in sustaining cooperation in the first rounds, which implies that a community care setting might prove successful for capability building. Yet, large proportions of individuals with MD in a group result in higher inequalities in final gains, which reach the maximum when the number of individuals with MD equals the number of caregivers or relatives. This means that community care perspectives might also take account of group composition to deal with potential inequalities arising from differential capabilities. In summary, we have explored the behavior of all individuals and role groups who make up the mental health ecosystem through an extensive suite of games that simulate strategic social situations. Overall, the results point to the availability of large social capital in the mental health community that can make a difference in the welfare and recovery process of firsthand affected, and suggest that the community-centered approach to mental care may turn out to be very beneficial. Indeed, the behavior of individuals with MD can be better explained by examining not only their cognitive abilities, but also the web of relationships in which they are embedded. Yet, that web of relationships presents opportunities and imposes constraints.

Though we depicted some behavioral differences in dyadic interactions, most importantly we found that individuals with MD show a remarkably larger disposition towards sustaining cooperation within groups. The larger readiness of individuals with MD to contribute to the collective action problem can thus be seen as a way to claim their place in the community. By having participants unaware of their partner’s identity, we could indeed measure participants decisions based solely on the value they placed on the group’s welfare, independently of its composition or other factors. Yet, the fact that participants with MD contribute the most implies for other members of the group lower investments to reach the common target. This, on the other hand, unveils the vulnerability of individuals with a diagnosis of MD. Repeated or periodic and more situated experiments with digital platforms 34 , in the future, can surely provide further valuable insights into the effect of participants prior knowledge of and relation with the partner on their behavior. We are indeed sure that our experimental setup can prove helpful in complementing the diagnostic process of physicians and health professionals and even to evaluate care service providers. On the other hand, other possible application of this approach arises in the realm of behavior change interventions 35 , that should focus on the aspects that are more specific of every disorder.

In conclusion, the results reinforce the idea of community social capital as a key approach to the recovery process based on an ecosystem paradigm (see also the recent results in ref. 36 about the role and impact of family and community social capital on MD in children and adolescents). Also, if on the one hand the fact that the results of our dyadic games are in general agreement with previous studies validates our procedure; on the other hand it supports the validity and contributions of neuroeconomics and experimental approaches to the study of MD. Finally, given that our work has been carried out in a fully socialized context, this approach can be applied to any similar’ ‘care in the community’ initiative. The adoption of our setup could lead to the identification of core groups that can boost and sustain cooperation within a given community. It can also help in discriminating among different communities in order to identify best practices and optimize resource allocation 37 .

All participants were fully informed about the purpose, methods and intended uses of the research. No participant could approach any experimental station without having signed a written informed consent. The use of pseudonyms ensured the anonymity of participants’ identity, in agreement with the Spanish Law for Personal Data Protection. No association was ever made between the participants’ real names and the results. The whole procedure was approved by the Ethics Committee of Universitat de Barcelona. All methods were performed in accordance with the relevant guidelines and regulations.

Experimental design

As indicated in the main text, the dialogue with the main stakeholders of the mental health ecosystem was at the centre of the project. Around 20 representatives including members of the Catalonia Federation of Mental Health (Federació Salut Mental Catalunya), firsthand affected, relatives, caregivers, and other professionals related to both the health and social sector, informed and validated the whole research through focus groups and further discussions, leading to the largest experiment of this kind ever carried out. Citizen science principles guided the whole experimental design process in order to raise concerns grounded in the daily life of mental health professionals and service users, and to increase public awareness. The experimental dilemmas being proposed served both to advance in knowledge on the social dynamics at play within ‘care in the community’ settings and as a self-reflection experience for all participants. The experimental design process developed in four main phases: (i) identification of the behavioral traits perceived as of fundamental importance within the community, (ii) operationalization of those same behavioral traits thorugh game theoretical paradigms and literature reviews, (iii) definition of the socio-demographic information relevant for the analysis, and (iv) a beta testing of the digital interface (including contents, time duration, and language used). The locations where the experiments took place were accorded with the Catalonia Federation of Mental Health in an attempt to explore the functioning of some communities of interest for inclusive and effective policy making. The Federation provided a fundamental support throughout the whole experiments’ implementation, serving as a crucial intermediaire between the scientists and different mental health collectives. It also provided valuable insights to better interpret the data obtained.

Participants and procedure

To our knowledge, experimental work on this issue has been conducted only recently and on specific collectives of orders of magnitude smaller. A total of 270 individuals participated in the experiments, that were run over 45 sessions between October 2016 and March 2017. The experiments were carried out in Girona (n = 60), Lleida (n = 120), Sabadell (n = 48) and Valls (n = 42). Participants were either diagnosed with a mental condition (n = 169) or members of the broader mental health ecosystem (n = 101), including professionals of the health and social sector (n = 52), formal and informal caregivers (n = 17), relatives (n = 9), friends (n = 4) and other members of the collective (n = 19). Individuals with a mental condition had to self-assess their diagnosis selecting one from a spectrum of options agreed upon with representatives of the mental health ecosystem during the co-design phases of the experiment. Those participants who had received more than one diagnosis had to select the one they considered to be the most relevant. Overall, they had received a diagnosis of psychosis (n = 63), depression (n = 33), anxiety (n = 31), bipolar disorder (n = 17) or other unspecified diagnosis (n = 25). They ranged in age from 21 to 77 years old (these are weighted values since for ethical and privacy reasons participants were only asked to choose among different age ranges) with 47.2 years on average. Further, 55.6% were men and 44.4% were women. Yet, actors involved in the recovery process were predominantly women (76.2%), and up to 21.8% of them was over 60 years old (see Supplementary Section 1.1). Participants were told that they would play against each others a set of games meant to explore human decision-making processes. They played in random groups of six players through a web interface specifically developed for the research. They were informed that they had to make a decision under different conditions and against different opponents in every round. Every game represented an interactive situation requiring the participants to make a decision, the result of which depended also on the opponent’s behavior. To incentivize the participation, they would earn a voucher worth their final score (the experimental settings and instructions, can be found in the Supplementary Section 1.2 and 1.3 respectively). First, participants participated in a Collective Risk Dilemma 23 against five opponents. Briefly, the game is a public goods game with threshold: If the participants’ total contribution after 10 rounds is lower than a given threshold, they loose all the money they kept with a probability of 90%. Otherwise, they are told that the money collected in the common fund are spent in reforesting land plots in Catalonia, where the experimental sessions took place, and each participants earns the money left in the personal account. After completing the task, participants played one round of the Trust Game 38 in both roles: as trustors and as trustees. They played against different partners in each role. Finally, they played one round of a Prisoner Dilemma 39 with (unincentivized) belief elicitation about their counterpart’s behavior prior to playing. Before starting the games, participants had to complete a brief survey covering some key dimensions of their sociodemographic background. The assignment of players’ partners in the dyadic games was completely random and every action was made with a different partner. The average (standard error of the mean) time for completing the three experiments (CRD, PD and TG and tutorials) is around 12 minutes, 705.86 ± 17.93 s. At the end of each session, participants received a gift card worth their earnings. The average individual earning is 46.84 ± 0.77 MUs equivalent to a 4.04 ± 0.077 EUR voucher. The behavioral patterns that emerged do not reveal significant variation across the different experiments, which may suggest that our results are robust to generalizations (see Supplementary Section 1.7).

Statistical analysis

Results were analyzed at two levels: first, we tested for behavioral differences between the whole group of individuals with mental condition compared to members of the mental health ecosystem; we then checked for systematic behavioral variation across diagnostics and role played in the recovery process. In one shot, two-person dyadic interactions we performed Mann-Whitney-U tests for independent groups to compare the distributions of cooperative choices (PD), and initial and back transfers (TG), between individuals with and without a mental condition. We then checked for marginal differences within groups using Kruskal-Wallis tests, and post-hoc comparisons were run with Mann-Whitney-U tests adjusting for p-values with the Holm-Bonferroni method. Welch’s two-tailed t-tests were performed to check for differences in average contributions (CRD) between participants with and without a MD, controlling for unequal variances and sample sizes. Finally, ANOVA and further Tukey HSD post-hoc comparisons served to check for differences in average contributions over round across diagnostics and members of the mental health community.

Accession codes

Data is available in an structured way at Zenodo public repository with DOI 10.5281/zenodo.1175627.

Change history

26 september 2018.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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Acknowledgements

We thank the community of patients, caregivers and families working within the Federació de Salut Mental Catalunya (Catalonia Mental Health Federation) for the enthusiasm and for their invaluable help in the design and realization of the experiments. We are also especially thankful to I Bonhoure for the necessary logistics to make the experiments possible, to F Español for contributing in the first steps in the experimental design, to M Poll for always giving us the institutional support from inside the Federation, to both E Ferrer and F Muñoz for building the bridge between us and the mental health ecosystem and to X Trabado for encouraging us to run this research. This work was partially supported by Federació de Salut Mental Catalunya; by MINEICO (Spain), Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) through grants FIS2013-47532-C3-1-P (JD), FIS2016-78904-C3-1-P (JD), FIS2013-47532-C3-2-P (JP), FIS2016-78904-C3-2-P (JP, AC); by Generalitat de Catalunya (Spain) through Complexity Lab Barcelona (contract no. 2014 SGR 608, JP) and through Secretaria d’Universitats i Recerca (contract no. 2013 DI 49, JD, JV); and by the EU through FET Open Project IBSEN (contract no. 662725, AS) and FET-Proactive Project DOLFINS (contract no. 640772, AS).

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Anna Cigarini, Julián Vicens & Josep Perelló

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J.D., A.S., and J.P. conceived the original idea for the experiment; J.V. and J.D. prepared the software for the final experimental setup; A.C. and J.V. analyzed the data; and all authors carried out the experiments, discussed the analysis results, and wrote the paper.

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Cigarini, A., Vicens, J., Duch, J. et al. Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action. Sci Rep 8 , 3794 (2018). https://doi.org/10.1038/s41598-018-21900-1

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DOI : https://doi.org/10.1038/s41598-018-21900-1

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v.43(4); 2023 Apr
PMC10111570

Original quantitative research - Access to mental health support, unmet need and preferences among adolescents during the first year of the COVID-19 pandemic

Lauren r. gorfinkel.

1 Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

Gaelen Snell

Mari del casal.

2 Centre for Applied Research in Mental Health and Addictions, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada

Kimberly Schonert-Reichl

3 Department of Educational and Counselling Psychology, and Special Education, Faculty of Education, University of British Columbia, Vancouver, British Columbia, Canada

Martin Guhn

4 School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada

Hasina Samji

Introduction:.

The COVID-19 pandemic has had widespread effects on adolescent mental health. However, little is known about support-seeking, unmet need and preferences for mental health care among adolescents.

The Youth Development Instrument (YDI) is a school-administered survey of adolescents (N=1928, mean age=17.1, SD=0.3) across British Columbia, Canada. In this cohort, we assessed the characteristics of accessed mental health supports, prevalence of unmet need and preferences for in-person versus internet-based services.

Overall, 40% of adolescents obtained support for mental health, while 41% experienced unmet need. The most commonly accessed supports were family doctors or pediatricians (23.1%) and adults at school (20.6%). The most preferred mode of mental health care was in-person counselling (72.4%), followed by chat-based services (15.0%), phone call (8.1%) and video call (4.4%). The adjusted prevalence of accessing support was elevated among adolescents with anxiety (adjusted prevalence ratio [aPR]=1.29, 95%CI:1.10–1.51), those who used alcohol (1.14, 1.01–1.29), gender minorities (1.28, 1.03–1.58) and sexual minorities (1.28, 1.03–1.45). The adjusted prevalence of unmet need was elevated among adolescents with depression (1.90, 1.67–2.18), those with anxiety (1.78, 1.56–2.03), females (1.43, 1.31–1.58), gender minorities (1.45, 1.23–1.70) and sexual minorities (1.15, 1.07–1.23).

Conclusion:

Adolescents of gender or sexual minority status and those with anxiety were more likely than others to have discussed mental health concerns and also to have reported unmet need. The most common sources of support were primary health care providers and adults at school, while the most and least preferred modes of support were in-person and video call services, respectively.

Among adolescents, during the COVID-19 pandemic, the most commonly accessed mental health supports were primary care providers and adults at school.
The most preferred mode of mental health care was in-person counselling, while the least preferred mode was video calling.
There was particular unmet need for mental health support among adolescents with depression, those with anxiety, gender minorities and sexual minorities.
Future interventions should target these underserved groups, emphasizing the role of primary care, school-based interventions and inperson service options.

Introduction

Adolescent mental health and substance use have been areas of substantial concern during the COVID-19 pandemic, 1 as there have been documented increases in major depressive disorder, generalized anxiety, drinking and cannabis use since April 2020. 2 , 3 During the pandemic, a substantial proportion of adolescents began using substances primarily in solitude, 3 and it is estimated that nearly half of teens experienced an increase in depressive or anxious symptoms. 4

To date, a large body of literature has examined the characteristics associated with poor adolescent mental health during this period, 5 identifying both risk and protective factors. 1 , 6 Some studies have further examined access to mental health services during the pandemic through administrative records, including pediatric emergency department visits 7 , 8 and specialist mental health referrals. 9 , 10 These studies provide key information regarding mental health service provision during the pandemic, as they are not limited by response bias or participant recall. However, there is limited evidence surrounding access to less emergent or specialist forms of mental health support, such as family doctors or teachers.

Few studies have examined adolescents in the general population rather than solely adolescent patients included in administrative records. 11 This is an important gap, as administrative records do not allow for assessment of unmet need, since they include only adolescents who have already accessed care. Administrative records also do not allow for calculation of the prevalence of support-seeking in a general adolescent sample, since they are limited to patient participants and to particular forms of mental health support.

There is also limited evidence around adolescent perceptions of virtual mental health care, which rapidly increased during the pandemic. 12 , 13 Despite virtual care becoming more accessible, it is unknown how adolescents feel about online services, or which forms of virtual support they most prefer. One online survey found that a majority of young people with internalizing disorders would be willing to consider virtual services; 4 however, this study included young adults up to 29 years of age and did not measure actual access to support. Thus, despite the pandemic revealing vulnerabilities in youth mental health, there remain clear gaps in our understanding of adolescent support-seeking, unmet need and preferences regarding mental health services.

We therefore sought to assess the prevalence of accessing mental health support by adolescents in British Columbia during the first year of the COVID-19 pandemic, as well as characteristics associated with support-seeking and unmet mental health care needs. We further sought to identify the main sources of mental health support and the preferred delivery format for mental health services by adolescents during the first year of the COVID-19 pandemic. Using this information, we aimed to comment on the optimal setting, mode of delivery and target populations for adolescent mental health interventions during and after the pandemic period.

The context: school responses to COVID-19 in British Columbia

Unlike many other Canadian provinces and US states, British Columbia maintained mostly in-person learning throughout the winter and spring of 2021, 14 allowing for in-person survey delivery. However, like elsewhere across the globe, adolescent extracurriculars, recreational activities and social gatherings were prohibited. In June 2020, the provincial government invested CAD 5 million towards the expansion of mental health resources, 15 the majority of which went to developing e-mental health interventions. 13 , 15 , 16 However, it is unknown how adolescents felt about this care model, or how well digital services served mental health needs.

Ethics approval

This study was approved by the UBC Behavioural Research Ethics Board.

Setting and participants

The Youth Development Instrument (YDI) is an in-school, computerized survey of secondary school students, conducted in partnership with select school districts in British Columbia (BC), the Human Early Learning Partnership at the University of British Columbia (HELP-UBC) and clinical, community, youth and government advisors. The YDI measures well-being and development in a general population sample of students enrolled at BC secondary schools. In this setting, schools maintained mostly in-person learning throughout the winter and spring of 2021, allowing for in-person survey delivery. From February to June 2021, high schools within selected school districts invited Grade 11 students to participate in the study during a designated delivery day or week. Due to small class sizes, one independent school additionally surveyed students in Grades 10 and 12 (n=50). Overall, the mean age was 17.1 years (standard deviation=0.3), with 99.9% of the sample falling between the ages of 16 and 18 years.

Data for the YDI were collected from February to June 2021 and included all eligible students in six participating school districts and one independent high school. Two months prior to survey delivery, participating schools were provided with an administration guide to maximize implementation consistency. At least one month prior to survey delivery, passive consent letters were distributed to student parents and guardians with the aim of limiting systemic selection bias. Students additionally provided assent after reviewing complete information about the study and having the opportunity to ask follow-up questions.

Most participants completed the survey online using school or personal devices during class hours. Four schools additionally emailed a link to a virtual survey for students who were absent during in-school delivery (n=32). The survey was administered using the University of British Columbia Survey Tool hosted by Qualtrics, and took an average of 45 minutes to complete. All data were stored with Population Data BC, a multi-university data and education repository for individual-level data with robust privacy, confidentiality and security protocols.

Access to mental health support was assessed using the stem question “In the past 6 months, did you see or talk to anyone from the following places about any concerns you may have had about your mental health?” Response options were “Family doctor or pediatrician’s office,” “Walk-in clinic,” “Urgent care clinic, hospital or emergency room,” “Agency that provides mental health care or addiction services for children or adolescents,” “A psychiatrist, a psychologist, a social worker or some other type of counsellor” and “Teacher, adult or counsellor at school.” Students with a positive response to one or more of these options were categorized as having accessed a mental health support in the last six months.

Unmet mental health care need was assessed using the question “In the past 6 months, was there ever a time when you felt you might need professional help for mental health concerns (i.e. problems with emotions, attention, behaviours or use of drugs or alcohol) but you did not seek help?” Students who responded “Yes” were categorized has having unmet mental health care needs.

Additionally, students were asked to rank their preferred delivery format for receiving mental health care, including in-person, phone call, Internet (including website and online chat/text) and video call.

Mental health and substance use indicators

Depression and anxiety were measured using validated screening tools used frequently in population surveys and clinical settings: the Patient Health Questionnaire 8 (PHQ-8) and the Generalized Anxiety Disorder Questionnaire 2 (GAD-2). Respondents who scored ≥10 on the PHQ-8 screened positive for depression, while those who scored ≥3 on the GAD-2 screened positive for anxiety. 17 , 18 Alcohol and cannabis use were measured using the questions “How many times in the last 4 weeks have you drunk alcohol (wine, liquor, beer, coolers)?” and “How many times in the last 4 weeks have you smoked cannabis (marijuana)?” Potential responses included “Never done this,” “Not in the last 4 weeks,” “One or a few times,” “Weekly,” “Most days,” and “Almost every day.” Consistent with prior studies, 8 , 19 any alcohol or cannabis use was defined using a cut-off of “One or a few times,” while near-daily alcohol or cannabis use was defined using a cut-off of “Most days.” 19 , 20

Demographic characteristics

Demographic characteristics included gender, ethnicity, sexual orientation and socioeconomic status. Gender was identified by asking participants “How do you describe your gender?”, with response options “Boy” (reference), “Girl” and “In another way.” Ethnicity was identified by asking participants to choose from a list of options consistent with those listed in the Canadian census. We used responses to this question to define three broad ethnicity categories: White (reference), Asian and Other. These categories were chosen because they were the largest ethnicity categories in our sample, as well as in British Columbia as a whole. 21 An a priori decision to use three categories was made in light of sample size and methodological considerations around the need to preserve degrees of freedom in statistical analyses.

Sexual orientation was identified using the question stem “Do you identify as:” with response options “Heterosexual/straight,” “Gay/lesbian,” “Bisexual/pansexual,” “Asexual,” “Queer,” “Questioning/unsure” and “If none of the above (or if you would like to choose multiple categories), please specify: __.” Due to sample size considerations, sexual orientation was dichotomized to heterosexual and sexual minorities.

Socioeconomic status was measured using the revised six-item Family Affluence Scale, a continuous scale that has been validated in numerous youth samples. 22 , 23

Statistical analysis

As students were surveyed within their given high schools, the data in the current study were clustered by school. To account for this clustering, data were analyzed using a modified Poisson regression, as described by Zou et al. 24 This model specifies a log link function with a Poisson distribution and robust standard errors in order to approximate the risk ratio when outcome prevalence exceeds 20%, as is the case in the current study. As our study was cross-sectional, the use of modified Poisson regression allowed for the calculation of adjusted prevalence ratios, which describe the relative adjusted prevalence of an outcome in one exposure group versus another.

We tested the crude and adjusted associations of mental health conditions, substance use and demographic characteristics with (1) accessing any mental health care support and (2) reporting any unmet mental health care need during the COVID-19 pandemic. Mental health conditions included depression and anxiety, while substance use included past-month drinking and cannabis use. Demographic characteristics included gender (male, female, other), sexual orientation (heterosexual, sexual minority), ethnicity (White, Asian, other), and family affluence (continuous Family Affluence Scale score). Adjusted models controlled for all other mental health and demographic characteristics. Adjusted estimates were therefore derived from one of two models: one examining all predictors and having accessed mental health support, and one examining all predictors and self-identified unmet need. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, US) and considered two-tailed differences of p <0.05 statistically significant.

Of the 3795 adolescents invited to participate in the study, 2350 completed the survey (response rate=61.9%). After removing outliers (i.e. participants with average response times under 2 seconds, n=44) and those with missing exposure or outcome data (n=378), the final sample included 1928 students (82.0% of all respondents) from 31 unique high school programs. Overall, included participants had a higher proportion of girls (47.5% vs. 37.3% among excluded participants) and higher scores on the Family Affluence Scale (3rd quartile=38.6% vs. 31.8%, 4th quartile 17.2% vs. 13.5%). There were no other significant differences between included and excluded participants (data available upon request from the authors). Sample characteristics are presented in Table 1 .

What was the prevalence of mental health conditions and substance use among adolescents during the COVID-19 pandemic?

Overall, 39.7% of the sample screened positively for depression, 43.7% screened positively for generalized anxiety, 26.8% reported using alcohol in the past month, 2.0% reported using alcohol near daily in the past month, 16.8% reported using cannabis in the past month and 7.3% reported using cannabis near daily in the past month. Among adolescents who drank in the past month, 7.4% did so on a near-daily basis, while among adolescents who smoked cannabis, 43.3% did so on a near-daily basis. Compared to males, depression and anxiety were more common among females (depression: 50.8% vs. 27.0%; anxiety: 60.3% vs. 25.8%) and gender minority students (depression: 79.6% vs. 27.0%; anxiety: 79.6% vs. 25.8%). Depression and anxiety were also more common among students who reported belonging to a sexual minority group compared to heterosexual students (depression: 65.1% vs. 32.6%; anxiety: 68.4% vs. 36.8%). A detailed breakdown of mental health and substance use by demographic characteristics is presented in Table 2 .

What was the prevalence of accessing support and unmet mental health care need among adolescents? Which mode of mental health support was most preferred?

Of the sample, 40.3% accessed a mental health support in the past six months, while 59.7% did not. Similarly, 40.8% experienced unmet need for mental health care, while 59.2% did not. Mental health support was accessed by approximately half of those who screened positive for depression, anxiety, past-month drinking or past-month cannabis use. Unmet need was reported by nearly 70% of adolescents with depression or anxiety, 55% of adolescents with past-month drinking and 60% of adolescents with past-month cannabis use. Overall, the most commonly accessed supports were family doctors or pediatricians (23.1%); teachers, adults or counsellors at school (20.6%); and mental health professionals (14.9%). The most preferred mode of mental health care was in-person counselling (72.4%), followed by Internet (including website and online chat/text; 15.0%), phone call (8.1%) and video call (4.4%). A detailed breakdown of mental health support access and unmet need by mental health status is presented in Table 3 .

Which adolescent characteristics were associated with accessing support?

In adjusted analyses, anxiety was associated with a 29% increase in the prevalence of accessing mental health support (adjusted prevalence ratio [aPR]=1.29, 95% CI:1.11–1.51), while alcohol use was associated with a 14% increase in the prevalence of accessing mental health support (1.14, 1.01–1.29). Neither depression (1.11, 0.96–1.28) nor cannabis use (1.12, 0.96–1.31) was significantly associated with accessing mental health support ( Table 4 ).

With respect to demographic characteristics, gender minority status was associated with a 28% increase in the prevalence of accessing mental health support compared to males (aPR=1.28, 95% CI: 1.03–1.59), while sexual minority status was associated with a 23% increase in accessing support compared to heterosexuals (1.23, 1.04–1.45). The adjusted prevalence of accessing mental health support was not significantly elevated among females compared to males (1.17, 1.00–1.38), minority ethnicities (Asian vs. White: 0.92,0.78–1.08; Other vs. White: 1.02, 0.90–1.16) or adolescents with lower family affluence (0.99, 0.96–1.01; Table 4 ).

Which adolescent characteristics were associated with unmet mental health care needs?

In adjusted analyses, depression was associated with a 90% increase in the prevalence of reporting unmet need (aPR=1.91, 95% CI: 1.67–2.18), while anxiety was associated with a 78% increase in the prevalence of reporting unmet need (1.78, 1.55–2.03). Neither alcohol use (1.11, 0.98–1.25) nor cannabis use (1.09, 0.97–1.22) was significantly associated with unmet need for mental health care ( Table 4 ).

With respect to demographic characteristics, the prevalence of unmet need for mental health support was 43% higher among females compared to males (aPR=1.43, 95% CI: 1.30–1.58), 45% higher among gender minority adolescents compared to males (1.45, 1.23–1.70) and 15% higher among sexual minorities compared to heterosexual students (1.15, 1.08–1.23). Compared to White students, Asian students had a 33% lower prevalence of unmet need (0.67, 0.60–0.76). The prevalence of unmet need was not significantly different for other minority ethnicities (0.98, 0.92–1.04) or adolescents with lower family affluence (0.99, 0.97–1.01; Table 4 ).

This study examined the association of varying mental health and demographic characteristics with (1) accessing mental health support, and (2) experiencing unmet need for mental health care among adolescents in BC, Canada, during the COVID-19 pandemic.

In addition to examining access and unmet need, we descriptively examined which forms of mental health support were most commonly used, and which modes of mental health care were most preferable to adolescents. We highlight five major findings:

1. Symptoms of depression and anxiety were extremely common, with approximately 40% of adolescents screening positive for each of these conditions. High-frequency cannabis use was also common, with 43% of adolescents who used cannabis in the past month reporting smoking on a near-daily basis.

2. The most commonly accessed mental health supports were family doctors or pediatricians, followed by adults at school, highlighting the importance of primary care and school-based interventions in addressing adolescent mental health.

3. The most preferred mode of mental health care delivery was in-person (72%), while the least preferred mode was video call (4%), suggesting that video call–based counselling services may not be the ideal e-health intervention for addressing adolescent needs.

4. Adjusting for covariates, the rate of accessing mental health support was significantly elevated among adolescents with anxiety, past-month drinking, gender minority status and nonheterosexual orientation.

5. Adjusting for covariates, the rate of unmet mental health need was significantly elevated among adolescents with depression, anxiety, female gender, nonbinary gender and nonheterosexual orientation, suggesting that these groups are in need of more targeted mental health intervention during the pandemic.

Overall, our results suggest a number of important recommendations for developing mental health interventions aimed at adolescents during and after the COVID-19 pandemic.

First, in order to reach the broadest range of youth, this study suggests that interventions should focus on primary care and school settings. Consistent with prior literature, 25 - 27 we found family physicians, pediatricians, teachers and school counsellors to be the most common sources of mental health support for adolescents. It is important that primary care providers be aware of this important role in adolescents’ lives, and screen for mental health problems accordingly. Our finding that adults at school were common sources of mental health support may have been, in part, due to the relative inaccessibility of professional counselling services and the continuation of in-person classes in British Columbia during the pandemic. As stated above, British Columbia maintained mostly in-person learning throughout the winter and spring of 2021, 14 potentially mitigating some of the social isolation due to online-only learning for many secondary school students. 28 Nevertheless, rates of mental health problems were high in this sample, with nearly half of adolescents screening positively for anxiety or depression. Like primary care physicians, teachers and school counsellors remain an extremely important source of support for adolescents. Maintaining the availability of this support should be prioritized during and after the pandemic.

Second, our study provides recommendations regarding the mode of mental health care delivery best suited to adolescent populations. After in-person care, the most popular forms of service delivery were chat-based communication (including website, online chat and text) and phone calls. The least preferred form of service delivery was video call, a finding that was consistent across adolescents with varying mental health concerns. Ironically, at the start of the pandemic in British Columbia, video call–based services saw some of the largest growth in funding and accessibility for adolescents. 13 , 15 , 16 This study suggests that text- and phone-based services may appeal to a wider range of this population.

Third, our results help provide recommendations for targeting and better customizing youth mental health interventions. Accessing mental health support was associated with anxiety, minority gender status and nonheterosexual orientation. However, experiencing unmet need for services was correlated with depression, anxiety, female gender, gender minority status and nonheterosexual orientation. These groups are consistent with those in other studies who suffered the greatest mental health consequences due to the COVID-19 pandemic. 4 , 5 , 29 - 31 This study further suggests that many of these adolescents are not accessing desired care, particularly females and those with depression, who were more likely than others to report unmet need but not more likely to access care.

Strengths and limitations

To our knowledge, this is one of the only studies to examine primary support-seeking by a general population sample of adolescents during the pandemic, or to assess self-identified unmet need for services.

Still, this study has several limitations. First, although all eligible students in participating secondary schools were included in the sampling frame, our initial sample of schools was drawn on the basis of convenience and included primarily adolescents aged 17. Therefore, this sample was not representative of the entire BC adolescent population. Additionally, it is possible that there was some response bias in favour of females and adolescents with higher family affluence scores, as these groups were less likely to have missing data in our sample. Still, the effect size of these differences was small, and there were no significant differences by ethnicity, sexuality, depression status, anxiety status, past-month alcohol use or past-month cannabis use.

Second, students were asked to recall whether they had accessed support or felt unmet need in the past six months, creating the potential for recall bias. It is possible that adolescents with symptoms of depression or anxiety were more likely to remember speaking to an adult about their problems. Some students may have underestimated their past-month substance use frequency, and we may not have accurately captured students who did not recognize their need for mental health support.

Third, while mood disorders were measured over the prior two weeks, support access and unmet need were measured over the prior six months. Students who received successful care, or recovered from mental health problems without receipt of care, were categorized as screening negatively for mental health problems in our dataset. Future studies should use longitudinal data to compare the relative success of different mental health supports.

Fourth, some students may have misreported information due to social desirability bias or fear of repercussions. To mitigate this, students were assured that their data would not be shared with their peers, parents or schools and were informed of safeguards used to ensure the privacy and security of their responses.

Fifth, since the study setting had only minor school closures during the COVID-19 pandemic, results may not be generalizable to adolescents who completed school from home throughout the pandemic. However, if in-person schooling is believed to be protective against poor mental health outcomes, our results may be interpreted as underestimating unmet need.

During the COVID-19 pandemic, adolescents with depression, anxiety, female gender, gender minority status and nonheterosexual orientation were at particular risk of experiencing mental health problems and unmet need. Future interventions should aim to expand outreach to these underserved groups, emphasizing the role of primary care, school-based interventions and in-person services. Virtual interventions should offer phone- and chat-based services, which were consistently more preferable to adolescents than video calling.

Acknowledgements

The Youth Development Instrument (YDI) study is supported by funding provided to Dr. Hasina Samji from the British Columbia Centre for Disease Control and Simon Fraser University.

Conflicts of interest

The authors have no conflicts of interest relevant to this article to disclose.

Authors’ contributions and statement

Study design and conceptualization by LG; data acquisition and study implementation by GS, MC, JW and HS; data analysis, preparation and interpretation by LG, GS and DL; manuscript drafting and revising by LG, GS, DL, MC, JW, MG, KSR and HS.

The content and views expressed in this article are those of the authors and do not necessarily reflect those of the Government of Canada.

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A quantitative approach to the intersectional study of mental health inequalities during the COVID-19 pandemic in UK young adults

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Published: 24 January 2023

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Darío Moreno-Agostino 1 , 2 ,
Charlotte Woodhead 2 , 3 ,
George B. Ploubidis 1 , 2 na1 &
Jayati Das-Munshi 2 , 3 , 4 na1

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Mental health inequalities across social identities/positions during the COVID-19 pandemic have been mostly reported independently from each other or in a limited way (e.g., at the intersection between age and sex or gender). We aim to provide an inclusive socio-demographic mapping of different mental health measures in the population using quantitative methods that are consistent with an intersectional perspective.

Data included 8,588 participants from two British cohorts (born in 1990 and 2000–2002, respectively), collected in February/March 2021 (during the third UK nationwide lockdown). Measures of anxiety and depressive symptomatology, loneliness, and life satisfaction were analysed using Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) models.

We found evidence of large mental health inequalities across intersectional strata. Large proportions of those inequalities were accounted for by the additive effects of the variables used to define the intersections, with some of the largest gaps associated with sexual orientation (with sexual minority groups showing substantially worse outcomes). Additional inequalities were found by cohort/generation, birth sex, racial/ethnic groups, and socioeconomic position. Intersectional effects were observed mostly in intersections defined by combinations of privileged and marginalised social identities/positions (e.g., lower-than-expected life satisfaction in South Asian men in their thirties from a sexual minority and a disadvantaged childhood social class).

We found substantial inequalities largely cutting across intersectional strata defined by multiple co-constituting social identities/positions. The large gaps found by sexual orientation extend the existing evidence that sexual minority groups were disproportionately affected by the pandemic. Study implications and limitations are discussed.

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Introduction

The quantitative study of health inequalities has often been inadequately underpinned by social theory [ 1 ]. Quantitative studies have frequently focused on examining inequalities in relation to broad social categories such as gender, race/ethnicity, and socioeconomic position (SEP), with the social forces driving these inequalities [ 2 , 3 , 4 , 5 ] often being under-acknowledged. This can contribute to the perpetuation of deficit-based or damage-centred perspectives which locate the “problem” of inequality within the group(s) being examined rather than the underlying structures and processes [ 6 , 7 ], which serve as the up-stream, fundamental causes of such inequalities [ 5 ]. Similarly, the complexity of personal experience, in that people occupy more than one social identity/position which can include a mix of advantaged and disadvantaged identities/positions that are dynamic and context-dependent [ 8 , 9 , 10 ], gets frequently under-recognised.

Intersectionality theory [ 11 ] supports a move away from some of these issues by highlighting that social identities and positions are “interdependent and mutually constitutive rather than independent and uni-dimensional” [ 12 ]. It acknowledges that, due to interlocking systems of oppression, the experiences of a person living at a particular intersection (e.g., Black woman) cannot be understood by independently looking at the experiences associated to each of the identities and positions that define it (in the same example, the experiences associated with being Black and a woman).

Although intersectional research poses challenges for both qualitative and quantitative methodological approaches [ 12 ], it has relied mostly on qualitative methods. Quantitative approaches to intersectionality have been criticised for their potential to unintentionally reinforce the idea that the observed inequalities may be natural or intractable [ 13 , 14 ] and “blunt [the] critical edge and transformative aims” of intersectionality [ 3 ] by simply describing those inequalities. Intercategorical approaches to intersectional complexity [ 15 ], where analytical categories (e.g., based on gender) are used to explore inequalities, and the focus on identifying significant differences across such categories (a focus that has been named “intersectionality as a testable hypothesis”) [ 11 ], have also been criticised. By focusing on the differences between groups, these approaches may dismiss the differences within those groups, unintentionally reinforcing the idea that they are homogeneous [ 11 ]. Furthermore, the use of the most privileged categories (e.g., White, male) as the reference can implicitly maintain the idea of dominant groups being the standard to which the rest of categories should be compared [ 16 ]. This can also result in a lack of evidence on intersections defined by combinations of privileged and marginalised identities and positions, which is essential to understand and address health inequalities [ 17 ].

Nonetheless, quantitative approaches provide unique opportunities to accurately document population health inequalities [ 14 ]. First, many of the above-mentioned critiques are not inherent to quantitative methods [ 12 , 18 ]. Categories can be provisionally adopted to explore inequalities across intersections [ 15 ] and acknowledged as proxies for the interlocking systems of oppression [ 14 , 17 ]. Furthermore, aspects such as SEP reflect material conditions rather than social constructions. In addition, novel quantitative approaches [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] can help overcome some of the critiques. Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) models [ 17 , 23 ] constitute a paradigmatic example. Unlike more traditional quantitative intercategorical approaches (e.g., fixed-effects regression models with interaction terms), MAIHDA models open the way to provide evidence at intersections that would otherwise be overlooked [ 18 , 21 ]. Moreover, they provide estimates of the variability/heterogeneity within those intersections and the proportion of variability that is attributable to differences between them [known as Variance Partition Coefficient (VPC) or Intra-Class Correlation (ICC)]. Such estimates can be interpreted as a measure of the “discriminatory accuracy” of the categories provisionally adopted to define the intersections, and can be relevant to inform public policy, because targeting interventions at specific intersections when very little of the variability is attributable to differences between intersections (i.e., when discriminatory accuracy is low) may lead to ineffective interventions [ 23 ].

MAIHDA models focus on the difference between the expected levels at particular intersections, operationalised as the sum of the effects of each of the categories that define them (i.e., the “sum of the parts” or the additive effects), and the observed levels at those intersections. Such “excess” or residual effect represents what is above and beyond the “sum of the parts”, what is unique to that particular intersection: the “intersectional effect”. Intersectional effects represent the impact of experiences of marginalisation and/or privilege due to interlocking systems of oppression in the outcomes under study [ 25 ]. The distinction between intersectional “experiences” and “effects” is crucial: failure to find significant intersectional effects does not preclude the existence of different experiences lived by different intersections [ 21 , 25 ]. Hence, MAIHDA models provide the opportunity to study intersectional complexity from one angle, which can then be complemented by qualitative, experiential, and other quantitative approaches for a more complete understanding [ 18 , 26 ]. This angle is descriptive in the sense that it does not engage in the statistical analysis of causal processes driving the inequalities described [ 13 ]. However, by explicitly engaging with social theory, they can situate those inequalities in the context of the underpinning social processes causing them, thus “maintain(ing) the critical and transformative edge of intersectionality” [ 1 ].

An applied example: mental health inequalities during the COVID-19 pandemic in the UK

The onset of the COVID-19 pandemic has had unequal implications for different groups within the population [ 27 , 28 ]. Evidence suggests disproportional mental health effects among disadvantaged population groups including adolescents and young adults, women, racialised and ethnically minoritised groups, sexual and gender minority groups, and those in more disadvantaged SEP [ 29 ]. UK-based research replicates these findings in outcomes such as anxiety and depressive symptomatology, psychological distress, loneliness, and life satisfaction [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. In most cases, however, mental health inequalities by different social identities and positions have been reported independently from each other. Hence, the mutual co-constitution of those broader social categories has been left unacknowledged (or has been acknowledged in a very limited way, such as at the intersection between age and sex or gender) [ 12 , 18 ].

Using MAIHDA models, this study aims to provide evidence within the UK on mental health across multiple intersectional positions defined by categories closely tied to social power such as age, sex, race/ethnicity, sexual orientation, and SEP. This will first provide a “socio-demographic mapping” of the levels of different mental health measures within the population [ 14 ], which in turn will support the development of hypotheses for further research and suggest avenues for public health resource allocation.

This study focused on the most recent assessment of two British cohorts: Next Steps (NS) [ 44 ] and Millennium Cohort Study (MCS) [ 45 ], with participants born in 1990 and 2000–2002, respectively. This assessment took place in February/March 2021, during the third nationwide lockdown [ 46 ], as part of the third wave of the ‘COVID-19 Survey’ [ 47 ]. Both cohorts implemented oversampling methods to ensure representation from marginalised populations [ 44 , 45 ]. We focused on participants who were alive and still residing in the UK during the third wave of the COVID-19 Survey (February/March 2021). Due to the use of web and telephone interviews, the largest response rates within the target population were achieved in this wave of the COVID-19 Survey: 26.4% (NS) and 23.0% (MCS). Overall, 8588 participants (4167 from NS, 4421 from MCS) were included. All participants provided informed consent. Further details on the sample and procedure are available elsewhere [ 47 ].

Measures of anxiety symptomatology, depressive symptomatology, loneliness, and life satisfaction were collected using the same assessment tools across the two cohorts. Anxiety and depressive symptomatology were measured using the 2-item versions of the Generalised Anxiety Disorder (GAD-2) [ 48 ] and Patient Health Questionnaire (PHQ-2) [ 49 ]. These questionnaires enquire about how frequently the respondent has been bothered by core experiences of anxiety or depression, respectively, with scores ranging from 0 (lowest anxiety/depression) to 6 (highest anxiety/depression). Loneliness was measured with the University of California Los Angeles 3-item loneliness scale (UCLA-3) [ 50 ], which enquires about the extent to which the respondent has felt lack of companionship, left out, or isolated from others, and with scores ranging from 3 (lowest loneliness) to 9 (highest loneliness). Life satisfaction was measured with the Office for National Statistics (ONS) single question [ 51 ], with scores ranging from 0 (lowest life satisfaction) to 10 (highest life satisfaction).

Indicators/proxies for social identities/positions

Cohort/generation was assigned from the cohort of provenance. NS participants were in their early 30s at the time of the interview, whereas MCS participants were in their late teens/early 20s.

Information on birth sex as a binary variable (female or male) was obtained from the parents in the earlier waves.

Information on race/ethnicity corresponded to the most recent self-designated racial/ethnic group, complemented by the parents’ report wherever the former was not available. Responses were obtained following the ONS criteria [ 52 ] and, due to the small number of participants in some of the individual groups, grouped into White (including all White groups), Mixed (including all Mixed groups), South Asian (including Indian, Pakistani, and Bangladeshi groups), Black (including Black African, Black Caribbean, and Black British groups), and Other (including all ethnicities not included in the previous groups).

Self-reported information on sexual orientation was obtained from participants. Due to the small number of cases in some of the minority categories, we grouped participants into heterosexual versus sexual minority (including bisexual, gay/lesbian, and other) for analyses.

The residential Index of Multiple Deprivation (IMD) was used as an indicator of the current household SEP. A binary variable indicating whether the person lived in an area above (less deprived) or below (more deprived) the within-country median IMD rank was derived (the methodology used to generate IMDs varies across UK countries [ 53 ]). Self-reported information on housing tenure, collected during the COVID-19 Survey and grouped into Owners (including part owners) and Not owners, was used as an alternative indicator of the current household SEP. Finally, harmonised data on parental social class at age 11/14 years were used as an indicator of the household SEP during childhood [ 54 ], grouped into Non-manual/advantaged (including Professional, Managerial and Technical, and Skilled non-manual groups) and Manual/disadvantaged (including Skilled manual, Partly skilled, and Unskilled). Residential IMD was prioritised as SEP indicator due to the smaller number of missing data.

Intersectional strata were first generated not including socioeconomic indicators, resulting in 2 (cohorts/generations) * 2 (birth sex) * 5 (ethnicity groups) * 2 (sexual orientation) = 40 intersectional strata (stratification 40). Strata including indicators of SEP were then generated using either residential IMD rank (stratification 80a), current housing tenure (stratification 80b), or harmonised childhood social class (stratification 80c), resulting in up to 80 strata reflecting the intersection with different aspects of the SEP.

Statistical analysis

We used MAIHDA models [ 17 , 23 ] to obtain estimates of the residual/intersectional effects (i.e., what is beyond what would be expected based on the fixed/main/additive effects, conceptually similar to interaction effects) and predicted effects (including both the expected and residual/intersectional effects) at the different intersectional strata in each outcome. We first estimated intercepts-only (or “null” [ 17 , 25 ]) models with no predictors to obtain estimates of the degree of clustering or correlation within the strata (or, analogously, the proportion of the variance explained by differences across strata) ( VPC intercepts-only ). Then, main models were estimated including the variables adopted to define the intersectional strata as predictors. The fixed effects of each of those predictors (cohort/generation, birth sex, racial/ethnic group, sexual orientation, and the appropriate SEP indicator depending on the stratification used) represent the main/additive effects of the specific category across all intersections (non-intersectional effects). The VPC from the main models (VPC main ) returns information on the degree of clustering or correlation within intersectional strata (or, analogously, the proportion of the variance explained by differences across strata) after accounting for the fixed (or main, or additive) effects of each of the variables used to define these (the “sum of the parts”) [ 17 ]. The percentage of between-strata variance accounted for by the inclusion of those main/additive effects, or Proportional Change in Variance (PCV), was obtained as

Models were estimated using the four above-mentioned stratifications (40, 80a, 80b, and 80c). Following the procedure and code laid out by Dr Claire Evans [ 21 ], models were first estimated using Bayesian Markov Chain Monte Carlo (MCMC) procedures [ 55 ] with diffuse priors, initialisation values obtained from analogous models estimated with quasi-likelihood methods, and 50,000 iterations with a burn-in period of 5000 iterations and thinning every 50 iterations. Stratum-specific residual values (the intersectional effects [ 25 ]) and predicted values (including both the stratum-specific residuals and the fixed effects of each of the social identities/positions defining the stratum) were obtained from the main models, and 95% credible intervals (CI) were constructed using the 2.5 and 97.5 percentiles of those values across the MCMC iterations.

Initial checks (Supplementary Appendix S1) suggested that survey non-response was introducing bias. Based on these results, Bayesian MCMC MAIHDA models may be adequate to provide a socio-demographic mapping of the mental health levels among the survey respondents. Weighted analyses to account for the survey design and non-response are not yet implemented in Bayesian MCMC MAIHDA models. We estimated an identical set of models with maximum-likelihood (ML) estimation using weights to account for survey design and non-response, thus increasing the generalisability of the results beyond the survey respondents to each survey’s target population. One caveat is that ML estimation does not provide confidence intervals for the stratum-specific residuals (the intersectional effects).

Fixed-effects multiple regression models including the interaction across all the variables adopted to define the intersections were estimated for comparison purposes. Details on the rationale for these additional analyses are available in Supplementary Appendix S2.

MCMC MAIHDA models were estimated in MLwiN version 3.01 [ 56 ], using the runmlwin function [ 57 ] in Stata/MP 17.0 [ 58 ]. ML MAIHDA models and multivariable regression models were estimated in Stata/MP 17.0.

Most participants across both cohorts were female, White, and heterosexual (Supplementary Table S1). Sample sizes varied across models due to different missingness in the outcomes and SEP indicators. When accounting for the SEP indicators, some strata corresponding to intersections with racial/ethnic and sexual minority groups had no observations (Supplementary Table S2). There was a large variability in the number of observations by stratum, ranging from 1 to 1669, and the percentage of strata with 20 or more observations ranged from 45.0% to 62.5% (Supplementary Table S3).

As shown in Table 1 , the degree of clustering into the intersectional strata (or, analogously, the proportion of variance explained by differences across strata) before including the fixed effects of the variables used to define them (the VPC intercepts-only ) was generally larger for anxiety and depressive symptomatology than for loneliness and life satisfaction. This suggests that the discriminatory accuracy of the variables defining the intersections was generally larger for anxiety and depressive symptomatology. The discriminatory accuracy varied across outcomes when using different SEP indicators, being largest for anxiety and depressive symptomatology when using IMD rank, housing tenure for loneliness, and childhood social class for life satisfaction. PCVs under MCMC (unweighted) were large in all cases (> 90.0%), indicating that the main/additive effects accounted for most of the variability between clusters. PCVs were generally smaller under ML (weighted) due to larger proportions of residual variance between strata (VPC main ), suggesting larger intersectional effects.

Results from the MCMC (unweighted) models using 40 intersectional strata evidenced large inequalities across strata in the predicted values of all outcomes (Supplementary Figure S1). Although most differences were accounted for by the main/additive effects of the variables defining the strata, and all intersectional effects overlapped with zero (no effect), some strata had higher- or lower-than-expected levels (Supplementary Figure S2). Results from the ML (weighted) models (Supplementary Figures S3-S4) were similar, with most of the differences across strata being accounted for by the main effects as indicated by the high PCVs (Table 1 ).

Figure 1 and Fig. 2 provide a ‘socio-demographic mapping’ of the predicted levels in the different mental health outcomes using residential IMD rank as SEP indicator according to the MCMC (unweighted) estimation, evidencing large inequalities across intersectional strata. Fixed (main/additive) and random effects from these MCMC models are included in Supplementary Table S4. Most of the privileged categories (male, heterosexual, socioeconomically advantaged) showed better outcome levels, with large and consistent gaps across sexes, sexual orientations, and cohorts/generations (participants in their 30s showed better results than those a decade younger across all outcomes). Inequalities by IMD rank were comparatively smaller. Black participants generally showed the lowest levels of anxiety and depressive symptomatology and loneliness. This was not the case for life satisfaction, where Black and White participants showed fairly similar results across intersections with other variables, and the lowest levels were observed among those in the “Other” ethnicity group, which were also among the intersections showing the worst mental health outcomes. Using different SEP indicators (Supplementary Figures S5–S6) led to very similar results, although gaps by SEP were typically larger when using housing tenure as indicator. The divide by sexual orientation was consistent across all outcomes, accounting for some of the largest gaps in all outcomes.

Anxiety and depressive symptomatology predicted values of each intersectional stratum using residential Index of Multiple Deprivation (IMD) rank as the indicator of socioeconomic position. Markov Chain Monte Carlo (MCMC) estimation, unweighted results. M male, F female. White includes all White groups; South Asian includes Bangladeshi, Indian, and Pakistani groups; Black includes Black African, Black Caribbean, and Black British groups; Other includes all other ethnic group not included in the other categories

Loneliness and life satisfaction predicted values of each intersectional stratum using residential Index of Multiple Deprivation (IMD) rank as the indicator of socioeconomic position. Markov Chain Monte Carlo (MCMC) estimation, unweighted results. M male, F female. White includes all White groups; South Asian includes Bangladeshi, Indian, and Pakistani groups; Black includes Black African, Black Caribbean, and Black British groups; Other includes all other ethnic group not included in the other categories

The ‘socio-demographic mapping’ of the predicted values at each intersection was more heterogeneous when accounting for survey design and non-response (Supplementary Figures S7-S8). The fixed/main/additive effects from these models (Supplementary Table S5) were, however, largely similar to those from the unweighted models, and sexual orientation was again associated with most of the largest gaps across all stratifications and outcomes. Most differences in fixed effects between weighted and unweighted approaches were found by racial/ethnic group. Being in the “Other” ethnicity group was associated with worse levels in anxiety, whereas those in the Mixed ethnicity group showed worse loneliness and life satisfaction outcomes.

Figure 3 and Fig. 4 show the residual values (intersectional effects) of each intersectional stratum using residential IMD as SEP indicator according to the MCMC (unweighted) estimation (similar plots using the alternative SEP indicators are available in Supplementary Figures S9–S12). All intersectional effects’ CIs overlapped with or were very close to zero (no effect). The only significant intersectional effect corresponded to the loneliness levels of the stratum including White heterosexual males in their 30s owning/part owning a house, which were M residual = − 0.19 (95% CI − 0.39, − 0.005) lower-than-expected. Some strata at the intersection between privileged and marginalised social identities/positions tended to have worse-than-expected (e.g., South Asian heterosexual males in their 30s living in less deprived areas) or better-than-expected (e.g., South Asian heterosexual males in their teens/20s living in more deprived areas) outcomes.

Anxiety and depressive symptomatology residual values (intersectional effects) and 95% credible intervals of each intersectional stratum using residential Index of Multiple Deprivation (IMD) rank as the indicator of socioeconomic position. Markov Chain Monte Carlo (MCMC) estimation, unweighted results. Strata defined by generation/cohort (first digit: 1 Next Steps/1990, 2 Millennium Cohort Study/2000–2002), birth sex (second digit: 0 Male, 1 Female), ethnicity (third digit: 1 White, 2 Mixed, 3 South Asian, 4 Black, 5 Other), sexual orientation (fourth digit: 0 Heterosexual, 1 Sexual minority), residential IMD rank (fifth digit: 0 More deprived, 1 Less deprived)

Loneliness and life satisfaction residual values (intersectional effects) and 95% credible intervals of each intersectional stratum using residential Index of Multiple Deprivation (IMD) rank as the indicator of socioeconomic position. Markov Chain Monte Carlo (MCMC) estimation, unweighted results. Strata defined by generation/cohort (first digit: 1 Next Steps/1990, 2 Millennium Cohort Study/2000–2002), birth sex (second digit: 0 Male, 1 Female), ethnicity (third digit: 1 White, 2 Mixed, 3 South Asian, 4 Black, 5 Other), sexual orientation (fourth digit: 0 Heterosexual, 1 Sexual minority), residential IMD rank (fifth digit: 0 More deprived, 1 Less deprived)

Larger residual values (intersectional effects) at some intersections were found in the weighted analyses (ML estimation, Supplementary Figures S13–18). Most of the largest intersectional effects corresponded to strata at the intersection of privileged and marginalised social identities/positions. For instance, the largest worse-than-expected levels were found for anxiety among South Asian heterosexual men in their thirties living in less deprived areas ( M residual = 0.52); for depression among heterosexual men in their 30s from the “Other” ethnicity group living in more deprived areas ( M residual = 1.10) (Supplementary Figure S13); for loneliness among South Asian heterosexual women in their teens/20s owning a house ( M residual = 1.05) (Supplementary Figure S16); and for life satisfaction among South Asian men in their thirties from a sexual minority and a disadvantaged social class at childhood ( M residual = − 1.52) (Supplementary Figure S18).

Comparison with fixed-effects multiple regression approach

Results from the fixed-effects multiple regression approach are included in Supplementary Tables S6-S9. Several interaction terms were statistically significant. In line with the differences across the MCMC unweighted and ML weighted MAIHDA models, the unweighted and weighted regression models’ results varied in some cases, with some interaction terms becoming statistically significant after accounting for the survey and non-response weights, often involving sexual and ethnic minorities. Many of the significant interaction terms found under both approaches were based on very few (down to two) observations and using a specific intersection (White heterosexual males in their 30s in a disadvantaged SEP) as reference.

We aimed to provide a “socio-demographic mapping” [ 14 ] of the mental health inequalities within the UK population during the COVID-19 pandemic from an intersectional perspective, using MAIHDA models. We documented levels of anxiety, depression, loneliness, and life satisfaction across multiple intersecting social identities/positions tied to social power and explored whether there were intersectional effects observable above and beyond the effects associated with any identity/position in isolation. In our first approach, similar to previous MAIHDA applications [ 21 , 25 , 59 , 60 ], we found that, among the study participants, most of the differences across intersectional strata were accounted for by the additive effects of the social identities/positions used to define those intersections. Our second approach aimed to account for the biasing effect of differential non-response to make the results generalisable beyond the study participants. Using this approach, we found even larger inequalities across strata and different-than-expected outcome levels in some intersectional strata, defined in most cases by combinations of privileged and marginalised social identities/positions. Both approaches evidenced the existence of large inequalities in all outcomes. Some of the largest inequalities were observed by sexual orientation, followed by birth sex and cohort/generation, with sexual minorities, females, and younger people (in their teens/20s) showing worse levels. These findings exemplify the multifaceted way in which mental (ill) health inequalities are socially patterned [ 5 ].

From a methodological standpoint, our study showcases some of the desirable features of MAIHDA models to the quantitative analysis of inequalities from an intersectional perspective. All intersections (multiply advantaged and disadvantaged, as well as all combinations in between) were included and voiced in the “socio-demographic mapping” [ 14 ], which prevented reinforcing the idea of reference categories as the “standard” [ 16 , 17 ]. Combinations of privileged and marginalised identities were among those with the largest positive and negative intersectional effects in the two MAIHDA modelling strategies used, highlighting how inequalities are not limited to groups with multiply advantaged or disadvantaged positions, and that they may also be contextually contingent [ 8 , 9 ]. Importantly, the lack of evidence of significant or large intersectional effects, regardless of the quantitative approach used, does not rule out the existence of different intersectional lived experiences [ 25 ], as they may not necessarily reflect upon differences in the outcomes under study. Using MAIHDA models also helped us to further embrace intersectional complexity by acknowledging the existence of heterogeneity not only between but also within intersections [ 11 ]. Discriminatory accuracy levels were similar or larger than those found in most applications of MAIHDA (where VPC intercepts-only or ICC tend to be < 0.05 [ 22 ]), and generally larger for anxiety and depressive symptomatology than for loneliness and life satisfaction. These varied across stratifications using different SEP indicators, suggesting that the experiences attached to these SEP indicators may have different impacts across different outcomes.

From a substantive standpoint, our study covers a gap in the knowledge about population mental health inequalities during the pandemic from an intersectional perspective [ 29 ], and particularly among young adults who, according to previous evidence [ 35 , 37 , 38 , 41 , 42 ], have been most adversely affected by the pandemic. Women, young adults, and those in more disadvantaged socioeconomic positions had worse mental health at the time of data collection (February/March 2021, during the third UK nationwide lockdown). These results exemplify the structural, up-stream, fundamental causes (e.g., sexism, classism, heteronormativity) of inequality, leading to differential exposures to experiences such as discrimination and stigma [ 2 , 5 ]. The mental health inequalities by sexual orientation observed in our study are a grim example of this, extending recent evidence from earlier data collection time points in MCS [ 61 , 62 , 63 ] and showing that these inequalities are large and, in most cases, cut across different mental health outcomes, cohorts/generations, sexes, racial/ethnic groups, and socioeconomic levels. Inequalities by sexual orientation may be explained by the differential exposure to experiences such as reduced peer support availability and increased exposure to discrimination or familial rejection (e.g., increased time spent in family contexts that may have been unsupportive), as well as poorer pre-pandemic health and mental health [ 64 , 65 , 66 , 67 ]. Although disproportionate COVID-19 infection and mortality rates in minoritised racial/ethnic groups have been documented [ 68 ], we did not find consistent evidence of mental health inequalities by racial/ethnic groups. The weighted results suggested that some racial/ethnic groups (particularly the Mixed and “Other” ethnicity groups) had worse levels in multiple outcomes. This goes in line with previous research suggesting larger distress levels during the pandemic in the UK general adult population using similar groups [ 42 ], and adds to the mixed evidence on loneliness, where coarser ethnicity/racial groups (White vs non-White) have been used [ 30 , 69 ]. Estimates of the additive/main effects associated with different racial/ethnic groups were the most variable across the two MAIHDA modelling approaches used (unweighted vs weighted), suggesting a larger bias of non-response in these estimates.

Limitations and future directions

This is, to our knowledge, the first study to document population inequalities in different mental health outcomes during the pandemic using MAIHDA models, with the already mentioned advantages of doing so relative to other more traditional approaches. These results must be interpreted considering several limitations. Despite the diversity in the cohorts, the number of participants from racially/ethnically minoritised and sexual minority groups was small. This had multiple implications for our study. We had to group some of the least frequent categories (e.g., sexual minorities and ethnic groups), lumping together people with different experiences, perspectives, histories, cultures, and complexity in relation to experiences of marginalisation and oppression, thus obscuring (and increasing) the sources of heterogeneity within intersections. Even after grouping those categories, some of the intersections had none or very few observations, which prevented us from mapping the missing intersections and likely limited our ability to detect intersectional effects at some of these intersections which may be at risk. The small sample size at some intersections may also explain some of the differences across the MAIHDA models and the multiple regression fixed-effects models: MAIHDA models introduce a correction (shrinkage) to adjust the estimates of intersections by their precision (based on their size) [ 22 ]. This has been documented to result in smaller number of statistically significant intersectional effects compared to fixed-effects approaches [ 21 , 25 ], which do not include this correction thus potentially resulting in significant interaction effects based on very few observations. Surveys designed to ensure sufficient sizes at all intersections to be studied are needed to overcome these limitations [ 12 , 18 ].

Second, the small number of indicators in our outcome measures contributed towards measurement error, thus artificially increasing their heterogeneity. This also prevented us from exploring the equivalence of the measures across the intersections under study. Future research using longer versions of these and other instruments may result in more reliable/accurate outcome measurements, while also enabling testing measurement equivalence using suitable methods [ 70 ].

Third, due to differential non-response across groups [ 47 ], the results from the MCMC analyses, which permit assessing the statistical significance of the intersectional effects, may only be generalisable to the study participants. Since weighted analyses have not yet been implemented for MCMC MAIHDA models, we tried to overcome this limitation by re-estimating the MAIHDA models with ML using survey and non-response weights, at the cost of not obtaining confidence intervals for the intersectional effects. Both approaches resulted in remarkably similar main/additive effects, but both discriminatory accuracy and intersectional effects were generally larger in the weighted results. Aside the obvious need for implementation of weighted analysis in standard MAIHDA models, boostrapping conditioned on clusters defined by intersections may be a potential solution to obtain confidence intervals for the intersectional effects when using weighted ML, but methodological work beyond the scope of this paper, including formal simulations, is needed to test this approach.

Fourth, the “socio-demographic mapping” provided is only applicable to the social identities/positions under study: we were, for instance, unable to examine mental health of transgender and gender diverse groups despite evidence suggesting they were also disproportionately adversely affected by the pandemic [ 71 , 72 ].

Finally, the cross-sectional design provides a snapshot of the inequalities at one time-point, coinciding with a lockdown period. This may not be generalisable to other pandemic periods, as longitudinal UK-based evidence shows that levels of different mental health measures changed over the pandemic course [ 30 , 31 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Future studies may cover this gap by extending the MAIHDA modelling approach to longitudinal designs.

Conclusions

We have illustrated how quantitative methods can be used to study population intersectional mental health inequalities. Our study evidences large mental health inequalities across (and within) intersectional strata in the population. Large proportions of these inequalities can be accounted for by the main/additive effects of the variables used to define those intersections (cohort/generation, birth sex, racial/ethnic group, sexual orientation, and SEP), with particularly large inequalities by sexual orientation across all studied outcomes. Our analyses also suggest that some of those inequalities were not strictly equivalent across all intersections and support the notion (and the importance of acknowledging) that inequalities are not limited to groups with multiply advantaged or disadvantaged identities/positions. The large gaps found by sexual orientation support and extend existing evidence that sexual minority groups were disproportionately affected by the pandemic. Interventions to provide support, along with further research aimed at understanding intersectional experiences of discrimination across different racial/ethnic groups and socioeconomic levels, are crucial.

Data availability

Deidentified data and documentation on Next Steps [SN 2000030] and Millennium Cohort Study [SN 2000031] are available from the UK Data Service: https://ukdataservice.ac.uk/ .

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Acknowledgements

We would like to thank all individuals who participated in the two cohort studies for so generously giving up their time over so many years, and all the study team members for their tremendous efforts in collecting and managing the data. We would also like to thank Dr Clare R. Evans for her input, for providing very helpful code to run the analyses used in this study, and for her inspiring work; and Dr Annie Irvine, Dr Rochelle A. Burgess, Dr Dörte Bemme, Dr Dominique Behague, and the anonymous reviewers for their feedback and recommendations to improve the manuscript.

This paper represents independent research part supported by the ESRC Centre for Society and Mental Health at King’s College London [ES/S012567/1]. DM, CW, GBP, and JD are part supported by the ESRC Centre for Society and Mental Health at King's College London [ES/S012567/1]. JD is also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London and the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the ESRC, NIHR, the Department of Health and Social Care, or King’s College London. Next Steps and the Millennium Cohort Study are supported by the Centre for Longitudinal Studies, Resource Centre 2015-20 grant [ES/M001660/1] and a host of other co-funders. The COVID-19 data collections were funded by the UKRI grant Understanding the economic, social and health impacts of COVID-19 using lifetime data: evidence from 5 nationally representative UK cohorts [ES/V012789/1].

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George B. Ploubidis and Jayati Das-Munshi are joint senior authors.

Authors and Affiliations

Centre for Longitudinal Studies, UCL Social Research Institute, University College London, 55-59 Gordon Square, London, WC1H 0NU, UK

Darío Moreno-Agostino & George B. Ploubidis

ESRC Centre for Society and Mental Health, King’s College London, Melbourne House, 44-46 Aldwych, London, WC2B 4LL, UK

Darío Moreno-Agostino, Charlotte Woodhead, George B. Ploubidis & Jayati Das-Munshi

Department of Psychological Medicine, King’s College London, Institute of Psychiatry, Psychology & Neuroscience, 16 De Crespigny Park, London, SE5 8AF, UK

Charlotte Woodhead & Jayati Das-Munshi

South London and Maudsley NHS Trust, London, UK

Jayati Das-Munshi

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Contributions

D.M. conceived the study and carried out the analyses. D.M. and C.W. prepared the first draft. G.B.P. and J.D. supervised the project and provided critical feedback. All authors reviewed and contributed to the final manuscript.

Corresponding author

Correspondence to Darío Moreno-Agostino .

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Moreno-Agostino, D., Woodhead, C., Ploubidis, G.B. et al. A quantitative approach to the intersectional study of mental health inequalities during the COVID-19 pandemic in UK young adults. Soc Psychiatry Psychiatr Epidemiol (2023). https://doi.org/10.1007/s00127-023-02424-0

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Received : 13 September 2022

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Published : 24 January 2023

DOI : https://doi.org/10.1007/s00127-023-02424-0

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Multilevel modelling
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Multilevel analysis of individual heterogeneity and discriminatory accuracy
Intercategorical complexity

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