biopsychosocial model dissertation

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Article Contents

Neurodiversity at work, introduction: an emerging paradigm, the biopsychosocial history of neurominorities, occupational considerations of neurodiversity, implications of the neurodiversity phenomenon for medical practitioners, conclusions, data availability statement, acknowledgements.

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Neurodiversity at work: a biopsychosocial model and the impact on working adults

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Nancy Doyle, Neurodiversity at work: a biopsychosocial model and the impact on working adults, British Medical Bulletin , Volume 135, Issue 1, September 2020, Pages 108–125, https://doi.org/10.1093/bmb/ldaa021

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The term neurodiversity is defined and discussed from the perspectives of neuroscience, psychology and campaigners with lived experience, illustrating the development of aetiological theories for included neurodevelopmental disorders. The emerging discourse is discussed with relevance to adults, social inclusion, occupational performance and the legislative obligations of organizations.

Literature is reviewed from medicine, psychiatry, psychology, sociology and popular press. No new data are presented in this article.

There is consensus regarding some neurodevelopmental conditions being classed as neurominorities, with a ‘spiky profile’ of executive functions difficulties juxtaposed against neurocognitive strengths as a defining characteristic.

The developing nomenclature is debated and the application of disability status versus naturally occurring difference. Diagnosis and legal protections vary geographically, resulting in heretofore unclear guidance for practitioners and employers.

The evolutionary critique of the medical model, recognizing and updating clinical approaches considering the emerging consensus and paradigmatic shift.

It is recommended that research addresses more functional, occupational concerns and includes the experiences of stakeholders in research development, moving away from diagnosis and deficit towards multi-disciplinary collaboration within a biopsychosocial model.

Neurodiversity has become a popular concept at work and is increasingly popular within the business press, following the promotion of targeted inclusion programs from famous companies such as SAP, Virgin and Microsoft 1 as well as the worldwide docuseries ‘Employable Me’/The Employables’. 2 However, from an academic point of view, neurodiversity is not yet well captured, suffers from poor and conflicting definitions, confusing, overlapping symptomatology and little guidance on practical support at work. This introductory article provides an overview of the history of neurodiversity in order to contextualize a description of occupational presentation, which has wide reaching impact on the social determinants of health. Bringing together threads from disparate research fields including medicine, psychology, sociology, education, management science and vocational rehabilitation, a multi-disciplinary, biopsychosocial summary of the current picture is presented. A key issue for clinicians is understanding how to respond to the emerging dynamics when delivering everyday consultation and treatment for individuals. Practice guidance is provided, as well as advice on referring individuals to available workplace support. Avenues for future, multi-disciplinary research are recommended.

Defining neurodiversity

The term ‘Neurodiversity’ was originally developed by stakeholders influenced by the social model of disability. 3–5 It was based on ‘Biodiversity’, a term primarily devised for political ends: to advocate for conservation of all species, since a high level of biodiversity is considered desirable and necessary for a thriving ecosystem. Neurodiversity advocates adapted this principle to argue that society would benefit from recognizing and developing the strengths of autism or dyslexia (for example) instead of pathologizing their weaknesses. 6 , 7 Within the discipline of psychology, though, weaknesses have historically been the focus of research and practice. Analysis of cognitive strengths is only used to differentiate between general learning disabilities and specific-learning disabilities. A definition has emerged for psychologists and educators which positions neurodiversity ‘within-individuals’ as opposed to ‘between-individuals’. 8 To elucidate: the psychological definition refers to the diversity within an individual’s cognitive ability, wherein there are large, statistically-significant disparities between peaks and troughs of the profile (known as a ‘spiky profile’, see Fig. 1 ). 8 , 9 A ‘neurotypical’ is thus someone whose cognitive scores fall within one or two standard deviations of each other, forming a relatively ‘flat’ profile, 8 be those scores average, above or below. Neurotypical is numerically distinct from those whose abilities and skills cross two or more standard deviations within the normal distribution.

A ‘Spiky Profile’ showing example IQ scores.

Figure 1 is adapted from the British Psychological Society report on Psychology at Work, 10 page 44, and depicts scores from the Wechsler Adult Intelligence Scale, 11 which provides clear guidance on the level of difference between strengths and weaknesses that is typical or of clinical significance. Scores are used to support a diagnosis of dyslexia, Developmental Coordination Disorder (DCD, previously referred to as ‘dyspraxia’, see Table 2 ) and Attention Deficit Hyperactivity Disorder (ADHD) 8 , 12 , 13 and to understand the cognitive ability of an employee following injury or illness. 14

To refer to individuals, the terms ‘neurodivergent’ ‘neurodifferent’ and ‘neurodiverse’ are in current use both academically and for self-identification; this is a matter of stakeholder debate. 15–17 In recognition of the lack of consensus regarding which term is more appropriate, all may be referred to interchangeably, asking individuals how they prefer to identify. Spiky-profile conditions have historically been grouped under umbrella terms such as hidden impairments, specific-learning disabilities and neurodevelopmental disorders. Autistic autism researchers have critiqued the widespread use of deficit highlighting in nomenclature and advise a wide scale revision of language. 18 A new umbrella term is proposed herein for included conditions that is neutral and statistically accurate: ‘neurominorities’.

Stakeholder activism

Judy Singer, the Australian sociologist, is widely credited with producing the first research-based publication of the term in 1999, following her thesis in which she synthesized her first-person experience in the middle of three generations of women ‘somewhere’ on the autistic spectrum, with critical disability studies. 19 In the decades since publication, the term and its philosophy of difference, not deficiency, has been appropriated for a wider range of conditions, diagnoses and identities and formed a grass roots movement. 17 , 20 This paper focuses on the main four neurominorities of ADHD, autism, DCD and dyslexia for ease of comparison, literature review and recommendation. It is acknowledged that this will overlook some conditions and complexities within the emerging paradigm, yet many of the principles can be applied in a wider context.

The Neurodiversity movement holds ambitions of equal rights for members, appreciation of the diversity of human cognition and political power to break down structures of exclusion. 6 Nowhere has this argument been more compelling to date than the innovative technology, finance and defence industries, where programs to deliberately hire neurominority employees are becoming more frequent as talent strategies, rather than social responsibility projects. 1 , 15 , 21–25 Neurominority employees may bring talents, yet, in line with the spiky profile, there may also be difficulties. Understanding the discourse is essential for understanding neurodiversity at work because it affects how we must fluidly move between medical and social models to support individuals and employers.

Ontological controversy

The disparity between the language and assumptions of psychological and medical ‘experts’ and the lived experience of ‘stakeholders’ has led to dissent and conflict both between and within these groups. 6 , 17 , 26 For example, some argue that autism is inherently disabling irrespective of context 27 ; some propose that differentiating between autistic people who are ‘high’ or ‘low’ functioning is fundamentally discriminatory, 28 whereas others propose their identity to be a superpower. 29 In practice, we will encounter individuals from all these polarized perspectives, some who feel strongly about their position, and will assert their right to be treated accordingly. However, we should not presume that those who do not self-advocate articulately to be without preference; agency in accessing professional services is compromised by layers of intersectional exclusion including race, gender, class and sexual orientation, as well as verbal skills themselves. In research, there are increasingly calls to include stakeholder voice (‘nothing about us without us’ 5 ) and to focus on matters of importance to those with lived experience, such as workplace adjustment interventions, outcomes and inclusion best practice, as opposed to diagnosis and deficits. 30–32 Educopsychological and psychiatric debates include comorbidity and the difficulty distinguishing between the conditions, 33–37 as well as controversy around the ‘correct’ approach for differential diagnosis, given that the main conditions are diagnoses of exclusion, in which there are no objective, clear measures of assessment. 38–40 A clear way forward for medical research and clinical practice is thus lacking, and it is important to understand the confusing influence of a disconnected discourse upon occupational health guidance. Employers may look to experts for advice and find the advice incongruous with the popular business narrative. Employees may be hired as part of a talent program yet present with stress and anxiety. Practitioners are thus advised to enquire with patients directly for feedback and to approach any treatment sensitively, balancing the influence of intervention protocol controversy: for example, Applied Behavioural Analysis is considered abusive and traumatic by many autistic people. 41

Psychological adoption

The umbrella term of neurodiversity began to replace ‘Specific Learning Difficulties’ for some educational psychologists in the late 2000s 20 , 42 , 43 and became common within occupational psychology in the 2010s. 13 In psychological literature, ADHD, autism, DCD and dyslexia are the conditions most frequently referred to under this banner, though others have also included mental health conditions such as depression and anxiety, 6 , 9 , 20 general learning disability 20 as well as Tourette Syndrome, dyscalculia, dysgraphia and acquired brain injury, 9 depending upon whether the within or between definition is applied. Distinction has been made between conditions that are applied and developmental, clinical and developmental, acquired and transient or acquired and chronic 9 ; Table 1 depicts a taxonomy of neurominorities adapted and updated from the British Psychological Society’s Psychology at Work report. 10 (Please refer to the full chapter for more details).

A taxonomy of neurominorities

The major themes within the medical history of the main developmental conditions are presented in the next section. Neurocognitive, psychosocial and legal commonalities are noted, within which occupational advice must be contextualized. A timeline of the various theories is depicted in Table 2 . Note that all start with a pathologization of socially referenced behaviour or skill, followed by varying hypothetical causal nature/nurture theories. All include reference to ‘Executive Functions’ by the 21st century, defined as ‘goal-oriented self-regulation—including planning, organisation, response inhibition and behavioural sequencing’. 44

Timeline of neurominorities

An evolutionary critique of the psychomedical model

Given the extent of overlap between the conditions, the under-diagnosis of females who instead present with anxiety, depression or eating disorders, 45 , 46 and the estimated prevalence of each condition, a reasonable estimate of all neurominorities within the population is around 15–20%, i.e. a significant minority. Research supports a genetic component to most conditions 47 which, when considered with combined prevalence rates, suggests an evolutionary critique of the medical model: if neurodivergence is essentially disablement, why do we keep replicating the gene pool? The less extensive, yet persistent, body of work indicating specialist strengths within neurodiversity, 9 , 20 , 48–52 supports the hypothesis that the evolutionary purpose of divergence is ‘specialist thinking skills’ to balance ‘generalist’ thinking skills (as per the ‘spiky profile’). The evolutionary perspective is congruent with the Neurodiversity movement and essential to understanding the occupational talent management perspective that is currently in vogue.

The psychomedical histories outlined in Table 2 speak to the evolutionary critique for two reasons. Firstly, they demonstrate the consistency of the ‘specific’ rather than ‘general’ nature of impairment (the spiky profile) across all four conditions over time, irrespective of the changing nature of causal theories. The conditions are named and identified according to their most prominent deficits, which are themselves contextualized within our normative educational social history. Dyslexia is discovered around the same time as literacy becomes mainstream through education; ADHD becomes more prevalent with the increasing sedentary lifestyles from the industrial revolution; autism increases in line with modern frequency of social communication and sensory stimulation and DCD as our day-to-day need for motor control of complex tools and machinery becomes embedded. The evolutionary critique of neurodevelopmental disorders is that their perceived pathology is related to what we consider normal in modern times, as opposed to what is normal development within the human species. 3 , 7 , 53–55 Secondly of interest from the timeline in Table 2 is the final column, wherein we see that, despite consistent observation of similar neurobiological differences, we lack a single unifying theory for any condition.

Towards a biopsychosocial model

Most humans are average in all functional skills and intellectual assessment, some excel at all, some struggle in all and some have a spiky profile, excelling/average/struggling. The spiky profile may well emerge as the definitive expression of neurominority, within which there are symptom clusters that we currently call autism, ADHD, dyslexia and DCD; some primary research supports this notion. 33 , 56 In the future, these may shift according to our educational and occupational norms such as social demands, sedentary lifestyles, literacy dependency and automation of gadgets. To elucidate, although there are clear biological markers for those with a spiky profile 33 , 55 which lead to observable, measurable psychological differences, there is nothing innately disabling about those differences when we consider a traditional, tribe-based community of humans. Within the biopsychosocial model of neurodiversity, understanding work-related intervention and treatment becomes more about adjusting the fit between the person and their environment 58 than about treating a disorder. Critical review of the extant biopsychosocial research supports the social model proposition that the individual is not disabled, but the environment is disabling.

The legal status of neurodiversity

Since the early 21st century, most nations have adopted disability legislation congruent with the United Nations Statute 59 on the rights of Persons with Disabilities. Such legislation refers to the need for organizations to make ‘accommodations’, to use US terminology, 60 referred to in the UK as ‘adjustments’, 61 such that individuals with disabilities can learn, work and be included in society. The term accommodation is adopted for the remainder of this paper to denote the general process of compromise and flexibility; adjustment is used to refer to the implementation of equipment, services or changes to requirement, though in practice the two are used interchangeably dependent on geographical location. Note that there is no compulsion for individuals to change to fit in, no mention of treatment for individuals. Legally compliant intervention is at the organizational level, including the requirement for businesses, services and educators to work towards ‘Universal Design’, in which there is flexibility of environment, communication and tools to accommodate the widest possible range of human experience. 62 Disability status is predicated not on diagnosis of condition, but on the assessment of functional impairment, the extent to which the individual is inhibited and excluded. One could have a diagnosis of diabetes, spinal injury, psychiatric disorder and be disabled or not, depending on the impact on ‘normal day-to-day functioning’ that persists over a minimum period, for example 12 months. 61 The context may or may not disable the individual (‘disabled people’) as opposed to the disability automatically assigned to the person by nature of their diagnosis (person with a disability). From a legal perspective then, any form of neurominority may qualify for protections requiring accommodation depending on what is currently normal and how that interacts with an individual’s cluster of functional difficulties.

Neurodiversity should not be used as a synonym for disability, hence the adoption of the neurominority term herein. Many neurominority employees find themselves in need of disability accommodation at work. 63 , 64 Irrespective of legal protection, social and occupational exclusion are endemic for neurominorities. Studies vary in the percentages quoted, yet there is persistent evidence of disproportional representation within prison populations, 64–67 long-term unemployment 32 , 68–70 and failure to achieve career potential. 70 , 71 Exclusion rates point to an economic, social and moral imperative to improve outcome-based research, from which we can advise practitioners and individuals on which adjustments improve inclusion, within a biopsychosocial model.

To summarize the context before moving to presentation and accommodation: an occupational narrative has developed around the ‘diamond in the rough’, 72 in which neurominority employees resemble thwarted geniuses, who would be able to succeed given the right support, environment or tools. The extent to which this narrative is plausible for individuals is not well captured by academic research, which is biased towards reductive neuropsychology in search of ‘bits that are broken’ 3 , 73 as opposed to more functional, contextual performance. A reductive, medical paradigm of research is incongruent with the legal status of neurominorities as protected conditions in most developed countries, to which organizations must adjust. Given this, in the following section, a neutral pragmatic summary of knowledge regarding individual-level symptoms and occupational difficulties is attempted. The effectiveness of adjustments and issues related to systematic inclusion at the organizational level is discussed.

Occupational symptomatology

At the functional level, there are similarities between neurominorities in terms of presentation. As alluded to in Table 2 , executive functions are a common psychological complaint, resulting in difficulties with short-term and working memory, attention regulation, planning, prioritizing, organization and time management. Self-regulation of work performance is required in many modern employment contexts and therefore these issues present as the most disabling for individuals. 74 There is also commonality among strengths, many related to higher order cognitive functioning reliant on comprehension and creativity. 75   Table 3 , adapted again from the British Psychological Society’s 2017 report, 10 describes reported strengths and weaknesses associated with the four main neurominorities. The comparatively fewer references regarding strengths may reflect a research bias as opposed to an accurate representation of lived experience; it certainly is incongruent with the ‘talent’ narrative that is becoming dominant in workplaces.

Work-related difficulties and strengths attributed to neurominorities

Accommodations

The aim of occupational accommodations for neurominorities is to access the strengths of the spiky profile and palliate the struggles. The most frequently deployed adjustments 31 , 76–78 fall into the categories listed in Table 4 . Note that additional time to complete work is not mentioned; this adjustment is common in education but not at work, because it is not reasonable to pay someone the same money to produce less work. In exams, the validity of extra time is because we are measuring long-term memory or analytic skill via the medium of literacy alone, when verbal, visual and/or spatial skills may be more relevant in the workplace (for example multiple choice quizzes to assess medical knowledge). When assessment methods are more matched to the eventual job performance (for example observation of physical examination skills using role play patients) extra time becomes less important. This principle applies across education, recruitment and employment but is poorly understood by lay people or those without an understanding of cognitive functions and the antecedent components of job performance.

Typical adjustments for neurominorities

Adjustment effectiveness

There are very few studies evaluating the effectiveness of adjustments in the workplace and this is an urgent research need. 79 Rice and Brooks 30 stated the following in the conclusion of their adult dyslexia interventions review (p. 12): ‘good practice in this field rests almost entirely on professional judgment and common sense, rather than on evidence from evaluation studies’ . Over-reliance on heuristical guidance must be addressed by the research communities, and requires collaboration within applied psychological sub-disciplines, occupational therapy, occupational medicine and human resources departments. The limited evidence that does exist broadly supports the implementation of adjustments 64 , 74 , 77 , 80 , 81 but, without sophistication, we are unable to speak to quality control, return on investment or predict which type will work for different individuals/roles. Intensive personalized employment support (IPS) such as that provided to autistic people with multiple needs and people with moderate mental health conditions have found that the benefits only outweigh the costs when wider community measures such as housing and health costs are factored in. 82–85 However, employment-based neurominority adjustments typically cost <£1200 per person, 86 which is cheaper than the cost of re-recruiting 87 and significantly cheaper than IPS. More broadly, cross-sectional research has indicated biases in cost perceptions of disability adjustment, with objective records of expenditure less than presumed. 88 Although an appropriate evidence-base builds, practitioners must be guided by individual presentation, compromise and collaboration with employers, bearing in mind the biopsychosocial model and the strongly held opinions of individuals. Professionals can support the ambitions of the Neurodiversity movement in the workplace but must also maintain a cautious approach to prognosis of workplace performance improvements and job retention until we have more longitudinal data.

Accessing adjustments

In the UK, the Access to Work program funded by the Department of Work and Pensions provides a free assessment to over 30 000 disabled people per annum, including ~6000 neurominority employees or those with mental health needs. 86 The program acts as triage and signposting, enabling individual employees to self-refer and acquire an assessment of workplace need, following which a report is produced recommending adjustment as per the types in Table 4 . Access to Work is widely respected in the UK and valued by users 89 but there is a clear short fall in resourcing, considering the number of eligible individuals likely to be working in the UK. There are few programs of its kind internationally. The same role is more typically provided informally or privately by Occupational Health, Human Resources or Employee Assistance Programs from an organizational point of view, acting on behalf of the company rather than the individual. Professionals providing these services are less likely to possess the specialist skills in working with executive functions deficit, and are liable to misdiagnose/mistreat executive dysregulation as stress, anxiety or wilful lack of motivation, particularly with women, black people and ethnic minorities. 46 , 90 , 91– 94 Clinicians are advised to check for specialist knowledge on referral rather than assume a mental health generalist will have required expertise. Adjustments tend to be provided as a compliance activity per individual, with few businesses looking systemically at Universal Design 62 , 95 for neurominorities as would be recommended in the United Nations Convention on disability. 59 Access to accommodations is thus predicated on individual disclosure, typically occurring following a conflict or episode of poor performance. Individuals are reluctant to voluntarily disclose in advance 79 , 96 , 97 as they fear discrimination (with some justification 98 ) and therefore the aims of the disability legislation programs worldwide are not yet having the intended effect on inclusion. 99–101

In this final section, the potential ways in which medical practice can embrace the developments of the Neurodiversity movement and support individuals within a biopsychosocial model are explored.

Reactional stress and hidden neurominority

Physicians are likely to be interacting with neurominority individuals who are in work, unemployed, incarcerated or requiring health care. Neurominorities can be misdiagnosed as mental health issues due to symptoms overlapping with bipolar disorder, anxiety, depression and/or eating disorder. 45 , 46 , 102 A presenting mental health need may be a direct consequence of unsupported neurominority; an individual who is frustrated, excluded and unable to reach potential will naturally feel anxious or depressed. We need to improve recognition of cognitive symptoms (as opposed to mood) in frontline medical and nursing services to ensure accurate signposting. There is a potential for a vicious cycle in which treatment for mood and stress will only mask an underlying cognitive deficit or difference leading to ‘revolving door’ patterns of health care access. Where possible, physicians should feel comfortable to ask about the possibility of a neurominority as an explanation for ongoing distress and underachievement and refer to a specialist psychiatrist or psychologist for confirmation. It must also be noted that those experiencing precarious employment and unemployment will be experiencing adverse impacts on health, stress and well-being more generally, 103 that those with neurominorities are more likely to be under-employed and that access to diagnosis is compromised by intersectional influences of race, gender, sexual orientation and socioeconomic background.

Following diagnosis

Once a condition or conditions have been identified, an individual may feel vindicated, and experience catharsis. Psychology practitioners report their clients’ mental shift following correct diagnosis at the identity level and warn that, done badly, it can lead to disempowerment. 12 However, done well, understanding one’s strengths and weaknesses can lead to breaking down barriers and removing self-reproach. A late diagnosis adult client reported ‘now that I understand my dyspraxia and that I literally have less processing power than most people, I don’t give myself such a hard time. I take things slowly, at my own pace, which reduces my anxiety and actually makes me do things more accurately’. Physicians can recommend approaching employers for accommodation advice, recommending Access to Work if in the UK and human resources departments globally. A benefit of increased, general Neurodiversity awareness has been capacity building within businesses to manage and respond to requests for adjustments. Although not every employer has an established process, and there are still incidences of discrimination, guidance from respected professional bodies in human resources is clear about legally-compliant activities and how to access them. 104 , 105

Accommodations in providing medical treatment

Differences in sensory perception have been reported as a hallmark of neurominority internal experience, 102–108 which may affect pain management, sleep patterns and increase routine-change difficulties during in-patient care. Pamphlets explaining treatment, obtaining consent, confirming after care may not be read or absorbed by those struggling with literacy or attention. Difficulties in independently maintaining organizational routines might affect self-management of medication protocols. Neurominority patients may therefore respond differently to treatment, with increased anxiety or confusion. A patient who appears unwilling to take responsibility for health care may be forgetful not defiant, even if they seem verbally competent, which could result in prejudicial treatment from clinicians. A significant risk is that a neurominority individual may experience a ‘meltdown’, defined as ‘an intense response to overwhelming situations’. 109 For those with sensory sensitivity, overwhelm can be caused by pain, bright hospital lights, background noise, smells and continual changes in personnel. Someone experiencing a meltdown may scream, shout, swear and become physically aggressive to avoid being touched (as opposed to physically violent with intent to harm). Medical staff in all contexts need to be aware that any unnecessary physical contact or verbal persuasion in this circumstance may exacerbate distress rather than palliate and that an appropriate response is to provide calm, quiet decompression space wherever possible.

Treatment for mental ill health, insomnia and stress-related illness must be reviewed in terms of the reactive nature of distress in the context of continual sensory overload and/or exclusion. A common theme in discrimination for neurominorities is to be told to ‘try harder’ or ‘yes, but we all feel like that sometimes’, leading to self-doubt and increased exposure to harm. Validation of the biological nature of distress from clinicians can be liberating, but it does not follow that alleviating distress must always result from individual treatment. In this context, care must be taken to alleviate acute symptoms, but encourage long-term adaptation of the individual’s environment, developing self-awareness and agency over home situations and work. Psychosocial support referral should accompany pharmacological treatment; a multi-disciplinary approach is recommended. At work for example, occupational physicians specifically can advocate for neurominority inclusion with employers, by liaising with human resources and occupational psychologists to systemically improve environmental compatibility in building, schedule and work-station design accommodating assistive technology, sensory overwhelm, the need to move and reducing social anxiety.

Research avenues

The dearth of research about the occupational implications of neurodiversity is less of a gap, and more of a ‘blind spot’. 110 Medical research could support the development of evidence-based practice in employment by incorporating long-term outcomes into treatment and diagnostic studies. Including dependent variables such as formal education, salary, hours of employment, job satisfaction and career attainment will provide occupational psychologists with much-needed data on inclusion and adjustment effectiveness. Collaborations with multi-disciplinary occupational health teams are required to improve our understanding of what works, for whom and when. A Realist methodology 111 within a ‘Pragmatic Paradigm’ 112 can embrace the biopsychosocial model and enable us to provide better advice to individuals and employers. There is an opportunity to make a real difference to the lives of many and improve health outcomes through social inclusion using a neurodiversity focus.

From within an emerging paradigm, clinicians and researchers must appreciate the shift in discourse regarding neurodiversity from an active, vocal stakeholder group and embrace new avenues for study and practice that address practical concerns regarding education, training, work and inclusion. This article has provided an overview of the neurodiversity employment picture; namely high percentages of exclusion juxtaposed against a narrative of talent and hope. Understanding the importance of nomenclature, sensory sensitivity and the lasting psychological effects of intersectional social exclusion is key for physicians wanting to interact confidently and positively with neurominorities. The proposed biopsychosocial model allows us to provide therapeutic intervention (medical model) and recommend structural accommodation (legislative obligation) without pathologization (social model). In other words, we can deal pragmatically with the individuals who approach us and strive for the best outcomes, given their profile and environment. It is acknowledged that, by focusing on the main four developmental neurominorities to the exclusion of others, some nuance is unexplored, though the main principles herein can be applied more broadly. Summarized above are adjustments representing current best practice, though the need to assess and evaluate these beyond description and cross-sectional studies is highlighted. The neurodiversity phenomenon is coming of age and will begin to translate into public policy and education as well as employment. Medical and social scientists are uniquely placed to support an ambitious inclusion agenda through rigorous evaluative research partnerships.

A new umbrella term is proposed herein for included conditions that is neutral, statistically accurate and has support from communities with lived experience: ‘neurominorities’. 156 , 157

No new data were generated or quantitatively analysed in support of this review.

The author thanks her academic supervisor, Prof. Almuth McDowall and Judy Singer for their dedication to collaboration, constructive critique and dialectic exchange.

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• attention-deficit/hyperactivity disorder
• autistic disorder
• neuroscience
• principles of law and justice
• biopsychosocial model
• executive functioning
• adult attention deficit hyperactivity disorder
• neurodevelopmental disorders

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• Volume 11, Issue 8
• Biopsychosocial implications of living with multiple sclerosis: a qualitative study using interpretative phenomenological analysis
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• http://orcid.org/0000-0001-6546-6168 Seada A Kassie 1 , 2 ,
• Jannat Alia 3 ,
• http://orcid.org/0000-0001-5813-6400 Lynda Hyland 2
• 1 Shared Clinical Services , American Center for Psychiatry and Neurology , Abu Dhabi , UAE
• 2 Psychology , Middlesex University Dubai , Dubai , UAE
• 3 Department of Neurology , American Center for Psychiatry and Neurology , Abu Dhabi , UAE
• Correspondence to Seada A Kassie; s.kassie{at}mdx.ac.ae

Background Multiple sclerosis (MS) is estimated to affect 2.8 million people worldwide, with increasing prevalence in all world regions (Walton et al ). While there is no cure for MS, medication and lifestyle modifications can slow disease progression and enhance patients’ quality of life. The biopsychosocial model of health recognises important interactions among biological, psychological and social factors in illness, including those relating to illness management, which contribute to the experience of those diagnosed with MS.

Objective This qualitative, idiographic study aimed to explore the lived experiences of patients in the United Arab Emirates (UAE) diagnosed with MS.

Methods Semistructured interviews were conducted with a purposive sample of eight patients with MS ranging in age from 25 to 56 years. All participants were residing in the UAE at the time of data collection. Interpretative phenomenological analysis was used to analyse the data.

Results Three superordinate themes were identified from patients’ candid accounts of their lives with MS, highlighting issues of illness management, acceptance and gratitude, and adaptive coping. These themes broadly illustrate biological, psychological and social aspects of patients’ MS experiences.

Conclusion The study emphasised the importance of adopting the biopsychosocial model to treat and manage MS. Additionally, it highlights the need for routine assessment and early, multidimensional approach with multidisciplinary team efforts to improve patients’ quality of life.

• multiple sclerosis
• qualitative research
• health services administration & management

Data availability statement

Data are available on reasonable request. Data will not be shared, but reasonable requests may be directed to the corresponding author.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2021-049041

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Strengths and limitations of this study

This qualitative study highlights the experiences of people living with multiple sclerosis in the United Arab Emirates, which has received comparatively less research attention than in ‘Western’ countries.

The idiographic approach permitted in-depth exploration of the participants’ lived experiences, absent in previous quantitative accounts of the same topic in the United Arab Emirates.

Given the qualitative nature of this study, generalisability of findings was not a focus of this research.

The various personal and disease characteristics of participants may have introduced unforeseen heterogeneity into the sample.

Introduction

A chronic demyelinating disease of the central nervous system, multiple sclerosis (MS) is estimated to affect 2.8 million people worldwide, with increasing prevalence in all world regions. 1 Although the exact cause of MS is still unknown, susceptibility to the disease is influenced by genetic, immunological and environmental factors. 2–6 MS occurs more frequently in areas farther from the equator, including Canada, Denmark, Australia and New Zealand. 7 Although there is currently no cure for MS, treatments can help modify or slow disease progression, treat clinical exacerbations and generally improve overall health and physiological function. 4 Lifestyle factors such as smoking and obesity are known to increase disability progression. 8 9 In combination with disease-modifying medications, rehabilitation strategies can manage MS symptoms. Such rehabilitation programmes focus on treating physiological and cognitive deficits, designed to improve and maintain one’s ability to perform daily life activities. 10 Alongside biomedical treatment, studies increasingly highlight the need for a multidimensional approach with multidisciplinary team efforts to treat MS due to its complexity, unpredictability and the invisibility of some of its symptoms. 11–13

The biopsychosocial (BPS) model, an approach to illness diagnosis and management, is guided by a multidimensional view of an illness’s biomedical, psychological and social features. 14 15 In clear contrast with the biomedical model, the BPS model is both a philosophy and a medical paradigm that proposes the need for humanising and empowering patients through the use of empathy and compassion in medical practice. 15 The model encourages attention to the complexities of interactions among physiological, psychological and social aspects of an illness in determining appropriate and effective responses to primary and secondary symptoms. Examples of psychosocial factors include mood, personality, behaviour, coping, social support, family relationships and socioeconomic factors. These factors contribute to the overall experience of patients diagnosed with MS. Various aspects of life with MS have been examined through a BPS lens, including pain, 16 17 fatigue, 18 19 resilience 20 21 and quality of life. 12 While the disease is consistently associated with reduced quality of life, studies over several decades show that disease severity alone does not fully explain this. 12 22 23 Such arguments are consistent with the BPS model of health, which considers the simultaneous implication of biological, psychological, and social aspects to fully understand and appreciate the patient’s subjective well-being, quality of life and overall functioning. 12

Negative experiences of people living with MS have been identified across various BPS domains. These include self-enforced social distancing as a means of protection, 24 25 the impact of fatigue on communication, 26 the disease’s impact on perceived dignity 27 and personal relationships. 28 Irvine et al 29 discussed how identity is redefined following MS diagnosis, and how initial functional challenges can ameliorate over time, with adjustment to the condition. Similarly, Strickland et al 30 presented a phenomenological account of how people living with MS reflect on the transition from their prediagnosis to postdiagnosis selves and learn to live with their illness. The impact on one’s sense of self might not be inevitable 31 32 ; it may depend on the extent to which MS symptoms impact the daily roles that were integral to the person’s prediagnosis life. 32 Interestingly, some positives have been shown to emerge from diagnosis with chronic illness, including ‘post-traumatic growth’. 33 In MS, this includes diverse outcomes such as increased compassion and mindfulness, improvements in family relations and lifestyle, 34 health gains and increased spirituality. 35 Multiple studies have also examined quality of life, 12 22 23 meaning-making 31 and growth 33 in individuals living with MS and other chronic illnesses.

In the United Arab Emirates (UAE) and the wider Middle East and North Africa (MENA) region, quantitative studies focusing on illness management and patient care services highlight the lack of a multidimensional approach using multidisciplinary teams in treating neurological disorders and common comorbidities. 36 37 Unlike the western world, the MENA region is still limited in providing holistic care to patients with chronic illnesses. 38–44 The current study aimed to qualitatively explore patients’ individual accounts and subjective experiences of living with MS in the UAE. By doing so, the objective was to solidify the need for a multidimensional approach to providing care in the UAE and the wider MENA region, which may enhance patients’ quality of life. 36 37 In this study, the term multidimensional refers to the provision of patient care using the BPS model of health. By looking at person-specific factors and the concept of health in more depth than merely viewing it as the absence of illness, an insight into how people navigate their lives with this chronic illness may be gained. Through a phenomenological approach, the study explored factors that may play a substantial role in determining the quality of life and overall adjustment of patients living with MS in the UAE.

This study used a qualitative, idiographic approach to explore participants’ experiences of living with MS. A phenomenological approach informed all aspects of the study, from conceptualisation to data collection, analysis and presentation of findings. Through this window into the subjective, lived experiences of participants, it is possible to identify new meanings, which can inform how these experiences are understood by health professionals. 45

Sampling and participants

Researchers recruited a small, homogeneous, purposively selected sample as per the requirements of interpretative phenomenological analysis (IPA). 46 The study included eight participants diagnosed with MS (International Classification of Diseases (ICD-10: G35) using the McDonald diagnostic criteria. 47 They ranged in age from 25 to 56 years and were representative of patients living with the same type of condition in the same geographic location. In addition to a clinical diagnosis of MS, the inclusion criteria were that participants were over 18 years and did not have other diagnosed neurological disorders. Demographic and clinical details of patients are given in table 1 .

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Demographic and clinical characteristics of participants

Data collection

All participants provided informed consent. One-to-one, semistructured interviews were conducted with participants at their preferred location. Semistructured interviews are compatible with the chosen analysis, 48 allowing the participants and the researcher to explore new, potentially unexpected discussion topics. Interviews, lasting approximately 30 min, were audiorecorded and were carried out by SAK, a clinical research professional with background in psychology. The interviews consisted of nine questions, allowing for prompts. The interview guide is provided in figure 1 .

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Interview questions and prompts.

Data analysis

The idiographic approach of IPA 46 49 was used to analyse the data. This iterative process involved familiarisation with data, identification of descriptive, linguistic, and conceptual codes, and the development of emergent themes and superordinate themes per case. These steps were repeated for each participant. Once completed, a master table of superordinate themes was identified for the group. Researchers adopted a data-driven analytical approach and findings were viewed in light of the BPS model of health. Doing so allowed the researchers to explore, in-depth, the lived experiences of participants. 50 LH, a chartered psychologist, conducted data analysis in Microsoft Word. The findings were subsequently audited by SAK. The coconstruction of meaning and the double hermeneutic of IPA (wherein the researchers make sense of how the participants reflect on their own experiences) was intrinsic to this study. Both researchers engaged in critical reflexivity to examine how their own experiences and expectations might impact the analytical process to ensure that the participants’ voices were heard. Additionally, rigour of this study was enhanced in consultation with the Consolidated Criteria for Reporting Qualitative Research checklist. 51

Patient and public involvement

Patients and the public were not involved in the design or conduct of this study.

Respondents spoke candidly about their experiences of living with MS, discussing issues ranging from symptom management to perceptions of quality of life, and the types of support they received. Three superordinate themes were identified—‘Acceptance and management’, ‘Counting one’s blessings’ and ‘Living differently’.

Acceptance and management

Participants varied greatly in their self-reported illness management. The heterogeneity of experiences is unsurprising, given that MS does not have a single, simple trajectory. Symptoms experienced by some (eg, fatigue, balance issues, numbness), were absent for others. Several patients engaged in a range of positive health behaviours, and all but one reportedly took disease-modifying therapies (DMTs); they attributed physical and psychological improvements to their adherence to treatment and a change in their overall attitude towards their diagnoses. However, others reported non-adherence with central components of their treatment regimen, including not taking their medication as prescribed. One participant struggled with MS to an extreme extent, across physical, psychological and social domains, whereas several others displayed adjustment and acceptance. Their varying experiences of illness management are explored in this section.

Participants who had high expectations of their illness management were self-critical when they believed they did not reach this ideal. This was particularly evident among those who actively researched their condition, potential treatment options, and who made lifestyle changes. Several participants reported positive outcomes from attending physiotherapy or from self-directed exercise. Lokesh’s case illustrates this; discussing his engagement in physical activity as providing therapeutic benefits, he criticises his consistency:

Lokesh: I should be doing more exercise. I do yoga, I do meditation. That helps in flexibility. Interviewer: You’ve seen results? Lokesh: I see results when I don’t do it for a while, I could feel that I’m much worse. Interviewer: I see. How long have you been doing this? Lokesh: Since the beginning.

Even if self-critical, Lokesh’s MS management, which began at the time of diagnosis, demonstrates an active coping style. Unlike some other participants, his early focus on treatment and his beliefs about its effectiveness may have helped Lokesh come to terms with his diagnosis. Throughout his interview, he projects an image of someone mindful of the illness’s demands, but who strives towards the best possible life with MS.

This drive towards health is shown by others, for example, Imad. The year following his diagnosis was characterised by anxiety about death. However, the health behaviours he initiated brought about a positive sense of self: ‘I went like freakishly healthy for about six months… I stopped smoking, I stopped drinking.’ His use of the term ‘freakish’ alludes to his belief that he went too far, although he suggested that this is a typical reaction after MS diagnosis. While Imad notes that he does not exercise as much as in his early postdiagnosis days, he continues to engage in positive health behaviours.

I try to run three times a week. The only one I do consistently is my long run on Friday morning because it’s Friday morning. I wake up, my wife and my kid are still passed out, and I go for my run. I think that’s the best thing.

Recognising the physical and psychological well-being benefits of lifestyle change, Imad advocates: ‘Nutrition, exercise is key. Exercise is key, I’m telling you. Anyone who has MS, get off your ass and work out.’

Even when benefits were evident, engaging in positive health behaviours such as diet or physiotherapy was a challenge for some. Aliyah admitted giving up on physiotherapy, something that previously worked for her. Indeed, she believed that physiotherapy outperformed medication in treating her balance issue, stating ‘it was REALLY good.’ Although she stated that she had no free time after starting her postgraduate degree, she also admitted intentional non-adherence to the recommended treatment.

Aliyah: I never miss the pills. But with the injections, I used to (laughs). Interviewer: You used to forget, okay. Aliyah: Not forget. I used to intentionally miss it. I hated it… I used an auto-injector, and every time I took the injection I used to be scared, even though I did it so many times. I hated it… Interviewer: Have any of your doctors recommended a special diet to you? Aliyah: Yeah, I actually, I've actually seen one of the dieticians here and she recommends a specific diet, but I have not, uh, I haven't like, uh… done it. You know why? Because I hate it when I have to think about it.

Aliyah did not share her diagnosis with work colleagues, not wanting small mistakes she might make at work to be ‘blamed on MS.’ She further said that she hates it when people ‘blame MS for everything’, suggesting that this may be something she has encountered in other realms of her life. She believes that ignoring MS is a positive strategy in her illness management, stating ‘what I found good about myself is that I completely ignore that I have MS, like I completely ignore it, I think this helps.’ This type of avoidant coping was evident to varying degrees among four of the eight interviewees. By choosing to not think about MS or adopt lifestyle changes recommended by healthcare providers, they believed they retained a greater semblance of ‘normal’, old selves.

Rashid’s experience was markedly different; he neither coped actively nor was he able to ignore the physical and psychological toll MS had on his life. By his admission, he struggled to manage his MS:

It almost feels like I'm helpless. I’m having a vision problem. I used to drive cars, but now I can't. The balance and the depression is like a big… it’s a big deal. And I’m just trying to live. The problems affect me in a bad way… I don’t know what’s wrong, is it MS, or… Even when I feel happy it just feels like seconds.

With marked deterioration in his health, including major depressive disorder and loss of sight and balance, he shared his perception of obstacles that impacted his MS management. He no longer took DMT, citing the cost as prohibitive. However, he also alluded to side effects as a reason for stopping both MS therapy—‘… it was almost like giving me a heart attack, so it was not working’—and treatment for his clinical depression—‘…I was like killing myself slowly.’

At the time of his interview, Rashid’s only regular interactions were with his sole supporter, his mother—‘She’s my life actually. She’s my motivation, you know, to be alive.’ It was evident that Rashid had lost hope. Indeed, his story was characterised by loss—the loss of a young man’s social life, truncated education opportunities, and an ever-shrinking lifeworld. Describing himself as peaceful and quiet, but also as helpless and without friends, there was resignation in his narrative.

Contrasting the concept of loss, other accounts demonstrated growth after diagnosis. Acceptance and management may be initially challenging, but can improve over time. Anna shared encouraging feedback she received.

[The support group leader told me] ‘I've really seen a change in you this past couple of years, seeing you blossom. Taking charge of, managing your MS, vs just it being something that you were up against.” So, I can, I can say now, that I am doing a better job at managing it. But it’s not perfect, you know, it’s really hard.

The support group leader’s words acknowledged Anna’s efforts and reassured her that she was on the right path. There was a sense that, for Anna, management was a process rather than an attainable, final state. She recognised that setbacks would happen, but that these would not defeat her. Anna’s change was not merely in the shift from life before MS to life after MS, but rather in her incremental MS management improvements over time.

Counting one’s blessings

Participants’ narratives touched on their outlook on life with MS and the resultant changes in their interpersonal relationships. While all noted the impact of MS on their psychological well-being and relationships—new roles were undoubtedly negotiated with family and friends—the changes wrought by MS were not always viewed negatively.

Six participants viewed themselves as lucky in several ways and reported gratitude for ‘blessings’ in their lives. Primarily, they appreciated support from family, friends, work colleagues, healthcare providers and others living with MS. Participants juxtaposed their experiences of those who support them with those who do not. Immediate family members (parents, children, siblings, spouses) were often credited with being chief sources of support. In Batool’s case, her husband is the central figure in her support network.

My husband—he understands what I’m going through. Not fully, but he tries his best. He already let’s say (took) off some of my responsibilities, uh, and whenever he feels like I need rest he will say to me ‘just you go, I will do it.’ So, he’s really my main supporter.

She later elaborates on the redistribution of household responsibilities:

I have four kids, now I don’t do everything for them, okay, previously I was doing A to Z for them. Now I try to keep them independent as well, and align my husband with the activities that they have. It’s a responsibility that we both have.

This form of instrumental support allows Batool more time to rest and take care of her health. Identifying their ‘shared’ responsibility, she suggests that she and her husband are partners in their relationship and the management of her illness. Moreover, she appears to see an additional benefit in relinquishing some of her childcare responsibilities; her children have become more self-reliant.

It was apparent that family was not always a source of support. Killian, one of the younger people interviewed in this study recalled his father’s immediate reaction to his diagnosis as ‘you’re going to f***ing die, you’re going to die.’ The detail with which Killian recalled his father’s words demonstrates the impact they had. There was an acute absence of support in Killian’s family home in the 6 years following his diagnosis, which resulted in his development of non-familial networks of support, comprising colleagues and friends. Killian referred to himself as ‘lucky’ twelve times during his interview; reasons included his early diagnosis, the availability of effective medication, having a supportive work environment, and maintaining a positive attitude towards life with MS.

I'm lucky, because, you know stuff like, you're working, there're a lot of people with this ailment who are a lot worse. That in itself gives me some hope. Regardless of how anxious, what anxiety I have, I’m very lucky. That my treatment so far has been so good even through very tough times since my ailment, my diagnosis. So, I think I’m just thankful that I’ve been diagnosed when I was young, that’s it. I think I’m just lucky.

Killian’s maintenance of a positive outlook since his diagnosis was not easy for him. He refers to his innate tendency to view the glass as half empty and acknowledges difficulties in maintaining an optimistic outlook. Viewing himself as fortunate relative to others with the same condition perhaps offsets the negativity and lack of hope he encountered at the time of his diagnosis.

One participant, Maya, reported being thankful for the illness itself. She regarded her diagnosis as a turning point in her husband’s relationship with God. A silver lining to her MS journey, she credited her illness with resolving his fractured faith.

I’m so happy, I’m actually so, so, so happy that I got MS. My husband has a ‘faith issue’, let’s put it that way, and, because of MS, he got so much faith. Look, my husband loves me so much, and I love him so much. He was so devastated and broken to pieces, and he realized ‘She got a disease that has no cure! That nobody can do anything about.’ I couldn’t walk. I literally couldn’t walk. He had to carry me up and down the stairs… he was so broken apart and he realized the only one who could fix it is God! ‘I better get back to him! I better get back on track.’ I’m so happy, you have no idea!

This acknowledgement of her husband’s devastation at her diagnosis is jarring when viewed alongside Maya’s happiness, particularly in the context of their loving relationship. However, ascribing a positive dimension to the MS diagnosis may have allowed Maya to cope, see a ‘bigger picture’, and make meaning from her new identity.

Living differently

The contrast between participants’ lives ‘before’ and ‘after’, connected only by a liminal period of symptoms, was shared by five participants; they reported their lives had significantly changed since the diagnosis. Most viewed their illness and the resultant lifestyle changes as a burden. For some, the demands of symptoms, treatment regimen, and the pressure to conform to healthy but restrictive lifestyles, were incompatible with the daily responsibilities that predated diagnosis.

Batool explained how the demands placed on her did not change after she was diagnosed with MS. She struggled to reconcile the two ‘Batools’.

I don’t want people to compare me with my previous life, when I didn’t have MS. I did lots of changes in my life to accommodate myself with the disease. I just need people to understand that this is a new Batool, okay, that’s been trying to develop herself. But do not compare me please with the previous Batool that had no MS in her life.

This dichotomised self and Batool’s literal identification as a ‘new’ person with a previous life illustrates the extent of the change caused by MS. While she explicitly recognises this change, it is evident that she believes others do not. She suggested that continuous demands placed on her by others stem from a lack of awareness about MS, an illness she refers to as ‘a monster in (my) head.’ Despite pressure to be her old self, and live up to her old responsibilities, Batool listens to her body and the physical implications of MS such as fatigue:

I feel like my body is calling, I have to, you have to get shut down, to go to sleep. Because I need energy, so, people might not understand, like if, especially you have family commitment, and, uh, like you are escaping from this responsibility, but for me, I try to ignore it.

Although invisible to others, symptoms of MS, especially fatigue, impacted day-to-day activities. Like Batool, Anna regarded MS as resulting in an ‘enforced slow-down’.

Cognitively it’s had a huge impact on my memory and my ability to focus. On a bad day I’ll get really terrible aphasia. Like, speaking as good as I am right now is a very good day for me (laughs), so, which is difficult when you’re trying to do something like class, you know, or talk to other people and things of that nature… On a bad fatigue day, I may spend all day napping, crawled up with my cats, and I always really feel guilty because I'm not able to do any housework or anything like that. But it’s also something that I've come to accept, that every now and then I'm just going to have a bad day like it’s just going to happen.

Previously minor activities were celebrated: ‘Hey, I have the energy to go out and meet with my friends. That’s fantastic, that means a lot to me.’ Although Anna reports feeling guilty because of her inability to manage some responsibilities, she shows a pragmatic acceptance of what she can and cannot do.

Diagnosed shortly after completing high school, Aliyah’s entire adult life has been impacted by MS. She shared that she did not have the opportunity to experience early adulthood in the same way as her peers. For example, she did not learn to drive, partially due to fears regarding balance, her most prevalent MS symptom. Despite asserting that she can still do the things she used to, Aliyah acknowledges a need to ‘think’ about her activities: ‘Before I wouldn’t think about it I would just take the stairs, now I have to think about it, like okay, how is my balance now, how is everything now? I have to, like, think about it.’ Apart from curtailing the independence associated with young adulthood, the absence of this rite of passage differentiates her from her peer group (those without MS) and others diagnosed with MS at a later age.

The experiences of participants illustrated above contrast with those of Killian, Lokesh and Imad, who acknowledge the challenges posed by MS, but refuse to be defined by their diagnosis. Killian reportedly experienced MS symptoms throughout his early life and was habituated to what he termed as the ‘fuzzy’ or ‘sticky’ sensation of MS. He did not indicate that the diagnosis changed his life course. Having lived with symptoms for several years before his diagnosis, he said ‘I don’t consider (MS) a disability, I don’t consider that an anomaly, I know how to go about my normal life. I stayed on a job, I went to university with it, it was normal.’ While he identifies difficulties in life with this condition, he did not believe that MS should define his identity. He advises that others “… don’t let the MS consume you. It’s, it’s partially life. You have to manage it but it shouldn’t be the centre of your life.’

Lokesh also references the concept of normality, although a ‘new normal’, while acknowledging that his life has changed since the diagnosis:

Obviously you can’t do much, as much as you’d like to, in terms of exercise, in terms of walking, in terms of interacting with people. It’s not that I withdraw socially, but I avoid too much interaction. I get tired. I feel like I need time for myself.

Even though the physical implications of MS were apparent in his case (he had impaired gait and balance issues), he claimed ‘I don’t feel different. I behave and think normally. It’s just that physically you might not look the same.’ Lokesh says that his physical appearance as someone with an evident disability does not bother him, ‘I don’t really care about what people think.’

Inner strength helps Imad, who views himself as someone who actively confronts life’s challenges. A self-professed optimist and extrovert, his attitude helped him deal with the shock of the diagnosis and manage the condition in the longer term.

I think I have a character of, you know, get things done. Get knocked down, get back up, do it again. I’m an entrepreneur, you know I’ve started my own business. It’s not easy, it’s not my first business, so, you know it’s a challenge that you have to take. You have two options, right? Either you’re going to sit there and cry in the corner, or you’re going to go do something about it, so I get up and do something about it.

Participants’ subjective experiences highlight variability in life with MS. Most, irrespective of the myriad symptoms, were practical in their outlook, recognising limitations but getting on with their lives. The psychological, physical and social impacts of MS were evident in narratives around change or, conversely, determined allegiance to prediagnosis identities.

This study explored the subjective experiences of patients living with MS in the UAE. An interpretative phenomenological approach 49 was used to analyse participants’ transcribed interviews from which three superordinate themes emerged. These are ‘Acceptance and management’, ‘Counting one’s blessings’ and ‘Living differently’. The themes exhibited the participants’ varied, subjective experiences of living with MS, but they also highlighted commonalities in physiological, psychological and social factors that characterise life with this chronic illness.

Under the theme of ‘acceptance and management’, participants touched on coming to terms with their diagnosis and dealing with the changes in their day-to-day lives due to physiological limitations caused by MS. 29–32 Some achieved benefits from attending physiotherapy sessions to alleviate the physical symptoms, such as difficulties in balance and walking. All but one participant were on DMTs at the time of data collection, with some taking additional measures (eg, setting phone reminders for medication and implementing a strict exercise schedule) to adhere to their treatment. The theme ‘counting one’s blessings’ showcases how MS affected participants in both family and social circles. The impact of having (or not having) family or social support was palpably seen in almost all participants’ accounts, and is similarly evident in previous accounts within Western contexts. 33 34 Those who reported family and social support viewed this as indispensable to their management of MS. Identity redefinition and self-prioritisation were noticeable in the theme ‘living differently’, wherein participants, to varying degrees, came to terms with their new postdiagnosis realities. In some cases, participants had to carve out time and space for themselves, previously dedicated to other priorities. Personal attributes helped in this transition to a new life. Traits such as self-efficacy, self-acceptance and adaptive coping seemed to counteract negative illness perceptions and, in some cases, non-adherence to treatment plans. This was also evident in previous studies wherein self-efficacy and locus of control were determinants of health status, adherence to treatment, disease management, adjustment and health-related quality of life. 52–56 Strober 12 found that quality of life was positively associated with adaptive coping, perceived social support, self-efficacy and locus of control, and negatively associated with primary and secondary symptoms. Psychosocial adjustment to MS takes time; it is influenced by the self and by others, and it can impact the uptake of rehabilitative opportunities. 29 57 58 While the three identified themes are discussed separately, the biological, psychological and social elements in the lives’ of the participants were found to interact and often even overlap. The close and often complex interaction of biological, psychological and social factors in the participants’ journey is indicative of the need for a multidimensional approach involving multidisciplinary team efforts to support patients with MS.

The BPS model proposes the simultaneous consideration of biological, psychological and social aspects of illness to fully understand and appreciate the patient’s subjective well-being, quality of life and overall functioning. 14 15 As reported in previous studies, 12 17 18 a multidimensional approach with multidisciplinary team efforts is optimal to alleviating symptoms experienced by patients with MS, both primary and secondary. As evident among the current sample, fatigue was a prevalent, primary MS symptom. 59 60 Fatigue can be debilitating; although it can interfere with life in professional and personal spheres, participants report that this symptom, similar to other invisible symptoms of MS, is not always recognised by others. 13 Indeed, according to Ayache and Chalah, 61 fatigue is frequently overlooked in clinical practice despite its high prevalence. While aware of the symptoms, clinicians may not fully appreciate the extent of its impact on patients. 62 63 Secondary or comorbid symptoms of MS include psychosocial factors such as depression, 64 anxiety, 64 unemployment due to physical dysfunction 65 or cognitive impairment, 66 67 reduced quality of life 68–72 and reduced subjective well-being. 12 Wollin et al 70 found that depression, anxiety, self-efficacy, social support and stress explain 40% of the variance in quality of life when disease severity and duration were accounted for. Depression in MS is estimated at 30%, with anxiety around 22.1%. 64 The current sample included three people with a clinical diagnosis of major depressive disorder and four with general anxiety disorder. Implications of psychiatric comorbidities include limited opportunities for employment, 65 signalling a clear need to optimise patient care (eg, through provision of comprehensive psychological support). Additionally, patients with MS with cognitive impairment may face difficulty in finding/maintaining employment and engaging in social activities. 66 67 71 72 In particular, two participants’ accounts of cognitive and physical impairment showed a stark impact on their social and occupational functioning. Some participants in the current sample expressed how much they value support from family and friends; often, a lack of MS awareness on the part of family members caused challenges for them. Particularly in the UAE and the wider MENA region, it is pertinent to increase public awareness through education/awareness campaigns and set up social support groups in healthcare facilities to reduce misconceptions about MS 36 39 and to indirectly facilitate illness management. 73 These findings, along with previous quantitative studies focusing on common primary and secondary MS symptoms affecting the general and health-related quality of life, emphasise the importance of a holistic approach to treating and managing MS. 36 37 These and other similar studies highlight the importance of routine assessment and early, effective intervention in MS using the BPS model.

Limitations

The study is not without limitations. Although the study team aimed for sample homogeneity, it became evident throughout the interview process that there were clear differences among the subjects in terms of both disease characteristics and individual circumstances that may have introduced unforeseen heterogeneity to the sample. These differences include MS types, length of time since diagnosis, family dynamics, access to care and treatment for comorbid conditions. For example, clinically diagnosed psychiatric and cognitive comorbidities were present in six out of the eight participants, including general anxiety disorders, major depressive disorder, adjustment disorder, mild cognitive deficit and mild memory deficit. Some participants were already receiving pharmacological and psychological treatment for the said comorbidities. As a result, we propose careful interpretation of the results keeping such differences within context. Nevertheless, the heterogeneity observed in the sample is also representative of the characteristics of MS as a chronic illness; no two individuals are the same, even when carrying the same diagnosis.

Future research directions

This study focused on a small sample of UAE-based people living with MS. In light of how social support enhanced the participants’ illness management, further research attention should be directed towards perceived social stigma and invisible symptoms of MS, such as fatigue, pain, sleep disturbances and cognitive impairment. While fatigue was the most commonly reported symptom in the current study, pain is reported by approximately 65%–92% of patients. 74–77 Given that chronic pain has been shown to impact self-identity and increase the likelihood of self-isolation, 76 it may negatively affect psychological well-being and MS management. Sleep disturbances are prevalent among those with MS, 77 as is cognitive impairment. 67 67 The hidden nature of these symptoms can result in family members, friends and employers showing limited understanding of MS patients’ need to ‘live differently’, postdiagnosis. 13 73 Future research may explore potential bidirectional relationships between psychosocial factors and such invisible symptoms, and the resultant impact on coping and quality of life.

Implications and conclusion

This qualitative study offers a unique insight into patients' lived experiences with MS and a deeper understanding of the need for a multidimensional approach to managing this chronic illness. Practitioners working with patients who have MS may consider the transferability of these findings. Within the clinical setting, a multidisciplinary team approach (from initial diagnosis to long-term management) is optimal to address primary and secondary symptoms experienced by patients with MS. Clinical care providers could enhance existing provision to accommodate the multidimensional needs of patients with MS by enhancing patient care services, particularly psychological/psychiatric treatments and supports. Through such advancements, it is hoped that health outcomes and quality of life for patients living with MS can be significantly improved.

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Not required.

Ethics approval

The study was reviewed and approved by the Institutional Review Board of the American Centre for Psychiatry and Neurology, UAE (reference number: ACPN-IRB-PN-0030).

Acknowledgments

The authors would like to acknowledge and thank Dr. Alia Ammar, Clinical Neuropsychologist at the American Centre for Psychiatry and Neurology for facilitating the interview process by giving the research team access to the MS support group.

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Contributors All authors have contributed to the study and the preparation of this manuscript following the ICMJE authorship criteria. SAK contributed to designing the study, preparing and submitting documents to the IRB and securing approval, collecting and transcribing data, auditing the analysis and writing up the manuscript. LH contributed to designing the study, analysing data and writing up the manuscript. JA contributed to contacting the patients for the interviews, arranging the interviews and critically reviewing the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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The biopsychosocial model in mental health

Affiliation.

• 1 Department of Psychological Medicine, University of Otago-Christchurch, Christchurch, New Zealand.
• PMID: 32735174
• DOI: 10.1177/0004867420944464

Publication types

• Introductory Journal Article
• Mental Disorders / psychology*
• Mental Health*
• Models, Biopsychosocial*
• Models, Psychological*

The Biopsychosocial Model of Health and Disease pp 1–43 Cite as

The Biopsychosocial Model 40 Years On

• Derek Bolton 3 &
• Grant Gillett 4  
• Open Access
• First Online: 29 March 2019

56k Accesses

6 Citations

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The first chapter outlines George Engel’s proposal of a new biopsychosocial model for medicine and healthcare in papers 40 years ago and reviews its current status. The model is popular and much invoked in clinical and health education settings and has claim to be the overarching framework for contemporary healthcare. On the other hand, the model has been increasingly criticised for being vague, useless, and even incoherent—clinically, scientifically and philosophically. The combination of these two points signifies something of a crisis in the conceptual foundations of medicine and healthcare. We outline some of the emerging evidence implicating psychosocial as well as biological factors in health and disease, and propose the following solution to the vagueness problem: that the scientific and clinical content of the model relates to specific conditions and stages of conditions, so that there is, for example, a biopsychosocial model of cardiovascular disease, diabetes or depression. Much the same point applies to the narrower biomedical model. However this raises the question: what is the point of having a general model? Our response is that it is needed to theorise biopsychosocial interactions in health and disease. In the light of historical prejudices against psychosocial causation deriving from physicalist reductionism and dualism, recognised by Engel and current commentators on the biopsychosocial model, this is a non-trivial task that occupies subsequent chapters.

• Biomedical model
• Biopsychosocial model
• Philosophy of medicine
• Medical models

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1.1 Doing Well—But with Underlying Problems

1.1.1 engel’s proposed improvement on the biomedical model.

In his classic paper published in 1977 George Engel proposed a new model for medicine, the biopsychosocial model, contrasted with the existing biomedical model [ 1 ]. While recognising the great advances in biomedicine, Engel argued that nevertheless the biomedical model was limited, and insufficient for many aspects of medical science and healthcare. These limitations were extensive, comprising failure to take account of the following: the person who has the illness, the person’s experience of, account of and attitude towards the illness ; whether the person or others in fact regard the condition as an illness; care of the patient as a person; for some conditions such as schizophrenia and diabetes, the effect of conditions of living on onset, presentation and course; and finally, the healthcare system itself also cannot be conceptualised solely in biomedical terms but rather involves social factors such as professionalisation ([ 1 ], pp. 131–135). Engel argued that a broadening of the biomedical approach, a new biopsychosocial model, was needed to take account of all these factors ‘contributing to both illness and patienthood’ ([ 1 ], p. 133).

1.1.2 The Presumed ‘Overarching Framework’

In his review of a recent book on the biopsychosocial model by Nassir Ghaemi [ 2 ], in The American Journal of Psychiatry , Kenneth Kendler starts with the sentence: “This book is about a very important topic—the overarching conceptual framework of our field of psychiatry” ([ 3 ], p. 999).

Whether the biopsychosocial model has this status for the rest of medicine is less clear, given the prominence of biomedicine and its biomedical model. Nevertheless, ‘the rest of medicine’ is not one thing, and the various medical specialities differ in their relative involvement with the biological, the psychological and the social. Primary care, also known as general practice or family medicine, is well-known to be much involved in psychological and social factors, and another clear example is public health. The relevant contrast here is with biomedicine, but biomedicine is not itself a medical speciality, but a particular kind of biological science-based medicine that can be applied across medical specialities, in some more than in others. Although Engel starts his paper referring to the ‘medical model’, he soon switches to ‘biomedical model’ and this is the term he uses for the contrast with his new proposed ‘biopsychosocial model’. In short it is not only psychiatry but also all the other non-biomedical aspects of medicine and its specialities that apparently require the broader biopsychosocial model.

We will review some of the health science suggesting the need for a biopsychosocial model in the next section, but first let us consider some current major trends in health, disease and healthcare that point to the same conclusion.

Engel was primarily concerned with psychosocial aspects of managing illness within hospitals, complementing the biomedical approach in hospital care. The example he discussed in detail in his 1980 ‘clinical applications’ paper was of myocardial infarction [ 4 ]. However, it has become clear in the intervening decades that managing illnesses in hospital is a particular and expensive way of providing healthcare. Illness severe enough to require hospital admission has high burden of suffering and disability, and high costs of hospital care, including biomedical investigations and treatments. It would be better all round to prevent illness altogether, or to detect and manage it earlier to prevent worsening, and also better to provide community and social care where possible to avoid or shorten hospital admissions. Implementing this last strategy involves practical psychological and social factors, such as availability of social supports or social care. The first two strategies, primary and secondary prevention, interact with psychosocial factors such as lifestyle, social capital and health literacy.

At the same time the importance of many of the areas of neglect that Engel conveniently listed under one heading—as shortcomings of the biomedical model—have been ratcheted up by diverse trends including socio-cultural changes, economics and globalisation. The voice of the service user has gained strength from civil rights and general emancipatory social changes; rising costs of healthcare in economically developed countries have focussed minds on containing costs by service reorganisations of diverse kinds; health has become globalised in many ways, such as improving health services in economically developing countries, or in the need for international policy to manage epidemics that can now spread more rapidly worldwide.

Other trends since Engel wrote that have also broadened the focus to include more than the biomedical model have to do with changing patterns of population health. Among the greatest achievements of biomedicine have been the identification, treatment and control of infectious diseases. However, and connected, the current burden of ill health in the population now includes many conditions that are not infectious diseases and which have no available complete cure—the so-called non-infectious diseases (NCDs), sometimes also called long-term conditions (LTCs)—such as cardiovascular disease, diabetes, recurrent depression and schizophrenia. In addition, as people live longer, for many reasons including biomedical advances, the proportion of the elderly increases, especially in the absence of immigration, and care of the elderly in hospital accounts for a high proportion of healthcare costs. In short, what biomedicine is good at no longer solves a large part of the population health burden and costs, and can contribute to rising costs by keeping us alive longer (thank you at a personal level) but at great expense—to someone, especially the younger generations. What is needed to theorise all these developments is much more complicated than biomedicine or the biomedical model were ever designed for. As well as biomedicine, what is needed is a complex mix of social science , politics, economics, environmental and social epidemiology and psychology —and no doubt more scientific specialities under development.

A further development in the decades since Engel ’s papers that has added overwhelming weight to the case for a model that can encompass biological, psychological and social factors has been accelerating research on the causes of illness , the basis for primary prevention. The recent research, to be reviewed briefly in the next section, makes two things clear: first, that for many diseases, causes or risks are present from very early on, and second, that for many these causes or risks are combinations of biological, psychological and social. Prospective epidemiological studies suggest that risks for many major illnesses, physical and mental, start early in development, many in childhood, and that risks include social factors such as poverty and other forms of social exclusion , some specific family level factors such as neglect and abuse, and life - style factors such as exercise and diet. Findings on what have come to be called ‘social determinants of health ’ were summarised and publicised for example by Michael Marmot in his 2010 Strategic Review of Health Inequalities in England [ 5 ]. At the same time, but proceeding largely separately, there have been rapid advances in genetics . Over the past few decades many physical and mental health conditions have been found to have a genetic risk—and genetic risk starts from conception, and interacts with non-genetic factors including but not limited to psychosocial factors of the sort identified in the social epidemiological literature. In short, these sciences combined have produced a whole new dimension of the claim of the biopsychosocial model that conditions of living—as well as biological factors—may affect the onset, presentation and course of an illness .

For all these various kinds of reasons, since Engel wrote his papers some 40 years ago, the biopsychosocial model has become the orthodox overarching model for health, disease and healthcare. It is much cited and taught in healthcare trainings of all sorts and in workshops and ward rounds the world over. In simple terms it recommends to healthcare to take into account all three aspects, the biological, the psychological and the social. It is particularly useful in psychology and social work healthcare professions, and in medical practice that has to deal with the psychological and the social as much as the biomedical, primary care (family medicine) being the clearest example [ 6 ], and in-hospital medical training that emphasises the importance of a comprehensive management plan. In all these contexts the biopsychosocial model easily wins, facilitating identification and integration of different aspects of care aimed at different aspects of the patient’s life, disease and management. To illustrate further good fit with much current practice, the biopsychosocial model obviously aligns with the rationale of multidisciplinary teams, and with the increasing recognition of the value of the service user’s views in providing good and effective healthcare.

Given the prominent status and use of the biopsychosocial model, it is clearly of great importance that the model is clear and robust. At this point, however, there is a very large problem, because there have been increasing charges in the medical literature that in fact the biopsychosocial model—popular and accommodating as it may be—is far from being clear and robust, but is in fact deeply flawed.

1.1.3 But Lacks Content, Validity and Coherence

Engel’s biopsychosocial model has long been criticised for having various kinds of limitation, along with suggestions for improvements (e.g. [ 7 , 8 , 9 ]). Increasingly, however, there have been more radical criticisms. Such radical criticisms are of two main types: first, that the model lacks specific content , is too general and vague ; and second, that it lacks scientific validity and philosophical coherence. Given the popularity of the biopsychosocial model and its presumed status as overarching framework for medicine and healthcare, such radical criticisms signal significant underlying theory problems.

The first broad heading of criticism is well argued by Nassir Ghaemi , a psychiatrist at Tufts, in his 2010 book with the telling title: ‘ The Rise and Fall of the Biopsychosocial Model ’ [ 2 ]. Ghaemi argues that the model is vague, too general, tells us nothing specific of value, hence is inefficient and sometimes distracting; it ‘gives mental health professionals permission to do everything but no specific guidance to do anything’ ([ 2 ], p. 82). The way Ghaemi tells the story, the biopsychosocial model arose in the context of competing general views about illness , favouring one or other of the social, the psychological/psychoanalytic and the biological. These general views—one might call them ideologies without criticism—were views of the whole domain of illness , offering general accounts, discriminating not much between kinds of case to which they applied and kinds of case to which they did not. Ghaemi interprets the biopsychosocial model as an elegant—if problematic and ultimately unviable—solution to these ideological conflicts: the unseemly turf wars could be ended, a truce could be declared, if all the participants won, if they were not really in opposition at all, but were in fact all true general accounts of illness and healthcare in all their aspects. The problem whether the cause of illness, and hence in theory its prevention and treatment, is biological, psychological or social is solved, because the answer is ‘all three’ ([ 10 ], p. 3; [ 2 ], ch. 6).

It has to be said that this line of thought is not apparent in Engel’s main papers [ 1 , 4 ]. Ghaemi does however quote a characterisation of the biopsychosocial model from another of Engel’s papers consistent with presumed generality: ‘all three levels, biological, psychological, and social, must be taken into account in every health care task’ ([ 11 ], p. 164; [ 10 ], p. 3). This claim Ghaemi understands as meaning that the three levels ‘are all, more or less equally, relevant, in all cases, at all times’ ([ 10 ], p. 3). In these quotes one can see the point of the allegations that the biopsychosocial model is a slogan, too vague to be of any use. And moreover, when pinned down, more than likely just wrong, counter-evidenced exactly by the successes of biomedicine, in which biological factors alone adequately explain diseases and treatments, such as bacterial infections and anti-biotics cures. Effective biomedicine is an anomaly for any general claim to the effect that ‘everything is biopsychosocial’, an obvious point that warrants repetition (e.g. [ 2 , 12 ]).

So, the charge is that the biopsychosocial model is vague without specific content. If, on the other hand, the model is firmed up to a very general proposition about the general relevance of all three kinds of factors, it is likely to be just false, exactly because of biomedicine. Faced with this obvious enough fact, a possible move is retreat to vagueness, but at the cost of content, as highlighted increasingly by critical commentary.

As mentioned above when illustrating the current important status or aspirations of the biopsychosocial model, Kenneth Kendler opens his review of Nassir Ghaemi ’s book with the statement that its topic is very important, the overarching conceptual framework of psychiatry ([ 3 ], p. 999). In his review Kendler goes on to quote Ghaemi’s negative conclusion, ‘The BPS model has never been a scientific model or even a philosophically coherent model. It was a slogan…’ ([ 2 ], p. 213), and comments: ‘While the reader may think this a little harsh…, I think he is substantially correct in this assessment’ ([ 3 ], p. 999). On the other hand, Kendler ends his review with a reminder of the importance of the biopsychosocial model as a teaching tool in family medicine, concluding: ‘While I agree with Ghaemi that the Biopsychosocial model has been a failure as a scientific paradigm, it probably continues to serve a useful clinical and teaching function in psychiatry and medicine’ ([ 3 ], p. 999). Kendler correctly identifies the major tension here: the biopsychosocial model is a useful tool for clinical and teaching functions, but apparently lacks scientific validity and philosophical coherence.

But then probably all cannot be problem free on the teaching front either. Here is Chris McManus , Professor of Psychology and Medical Education at University College London, reviewing an earlier edited book on biopsychosocial medicine in The Lancet ([ 13 ], p. 2169):

Biopsychosocial medicine’s challenge is to transcend the vague, aspirational inclusivity of its name, and to create a model that truly merits being called a model, and is properly explanatory and predictive … Arm-waving and the inclusion of everything ultimately says and does little of practical consequence.

Ghaemi , Kendler and McManus all basically agree in their negative assessments of the biopsychosocial model.

Given the popularity of the biopsychosocial model, its use in teaching and the clinic, its presumed status as the overarching framework for psychiatry and perhaps for medicine generally, such authoritative negative assessment signals significant problems at the conceptual foundations.

We believe that these two kinds of charge put to the biopsychosocial model, querying its content, validity and coherence, are cogent, but can be met. What they signal is not the end of the model—witness the fact that it persists, for good reasons already indicated—but the need to rethink and reinvigorate it. The answer to the content problem, we suggest, is that the content lies in scientific and clinical specifics , not generalities. This is proposed in the next section, beginning with a brief review of the emerging basic and clinical science supporting the biopsychosocial model. This response to the content problem, however, immediately raises the question: if the content of the biopsychosocial model lies in specifics, what is the point of the general model? We suggest that this question relates to core scientific theory, at the place where it merges into philosophy, and is therefore here that the problem of scientific validity and philosophical coherence is to be addressed. We define this problem in Sect.  1.3 , and address it in detail through subsequent chapters.

1.2 Locating the Content of the Biopsychosocial Model

1.2.1 emerging evidence of psychosocial causation.

Just as the biomedical model is of interest because of the substantial and well-established evidence base of biomedicine, so the biopsychosocial model warrants attention insofar as there is evidence of psychological and social as well as biological factors in health and disease. There has been an accumulation of such evidence in recent decades, and before moving the main theoretical argument forwards, we pause to review some of it.

This review carries a health warning! It is uncritical and unsystematic; we have usually not distinguished strength of evidence of the studies cited below (uncontrolled to randomised controlled and replicated), nor commented on other aspects of methodological strengths (such as sampling strategies and sample size), nor on conflicting and uncertain results, nor have we employed a systematic literature search strategy. Many of the papers cited are reviews, more or less systematic. The purpose here is only to orientate the unfamiliar reader to wide range of research that has supported on-going interest in the interplay of biological, psychological and social factors in health and disease and hence the biopsychosocial model.

Over the past few decades the picture that has emerged for causes of disease onset, especially for the non-communicable diseases, also known as the LTCs, is one of complex, multifactorial causation, involving many risk factors of relatively small effect, affecting multiple outcomes. The recent research on social factors as causes or risks for poor health—the so-called ‘social determinants of health ’—is probably the most well-known, new face validation of the need for a broad biopsychosocial model. Among the most influential social epidemiological research programmes are the Whitehall Studies of British civil servants, led by Michael Marmot [ 14 , 15 , 16 ]. These longitudinal cohort studies found robust correlations between variance in incidence for a wide range of health conditions—coronary heart disease, premature mortality, some cancers, lung disease, gastrointestinal disease, depression, suicide, sickness absence, back pain and general feelings of ill-health—and civil service grade. The social gradient in health —the correlation between indices of social status and health outcomes—is now well-established; much is now known about the social determinants of health [ 17 , 18 ], and something like the biopsychosocial model has to be invoked in order to comprehend it. As typically for epidemiology, most findings on the social gradient in health come from association studies only, retrospective or prospective. Establishing causation is more complex, using such as controlled cohort studies, natural experiments or animal models.

Other large research programmes have investigated associations between adverse psychosocial exposure in childhood and later health outcomes. A landmark programme is the Adverse Childhood Experiences Study (ACE Study) in the United States, carried out by Kaiser Permanente and the Centers for Disease Control and Prevention. The ACE study has demonstrated associations between adverse childhood experiences, such as physical and emotional neglect and abuse, and a large range of physical as well as mental health outcomes (e.g. [ 19 ]).

Lifestyle factors, comprising behaviours and associated beliefs, attitudes and values, have been increasingly implicated as risks, or conversely as protective factors, for a wide range of physical health conditions [ 14 , 18 ]. For example risk factors for some cancers and cardiovascular disease include such as smoking, alcohol use, diet, exercise and chronic stress . Lifestyle factors can be covered under the same heading as social factors, or separately. Either way, lifestyle factors interact strongly with social context, reflecting Engel’s insight that the person is essentially within a social context: diet for example, depends to some extent on choice, but also on what is available and affordable; stress —to be considered in Chapter 4 —depends on individual characteristics but also on task demands and available resources.

Lifestyle and psychological factors can be distinguished: the former are behavioural, while the latter, such as beliefs, attitudes and values, are mental. At the same time they are closely linked. One reason is that psychological factors motivate lifestyle, but there is also a general linkage between our psychology and our behaviour, namely, that we respond to reality at it appears to us, at any given time, to be. We pick this up as a theoretical point in more detail later, in Chapter 3 (Sect.  3.1 , heading “ Mind Is Embodied ”). In the present context it appears in evidence suggesting that it is not objectively measured social status but social status as perceived , so-called ‘subjective social status’ that accounts for more of the variance in health outcomes (see e.g. [ 20 , 21 ]). This interesting finding becomes part of the complex jigsaw puzzle of biopsychosocial aetiology.

Over the same past few decades that evidence for psychosocial factors in health and disease has been accumulating, so also has evidence of genetic effects. For some health conditions such as Huntington’s chorea, and some cancers, there are massive genetic effects, but for the majority of health conditions, the proportion of population variance attributable to genetic influence is much less than 100%, the picture being rather of relatively small effects of multiple genes , with the remaining variance attributable to non-genetic, environmental factors. Combining these broad kinds of research programmes presents a biological-psychological-social and-environmental picture, and new epigenetics is likely to help explain how the various kinds of factor interact. These issues are taken up in Chapter 3 , Sect. 3.4 .

Post-onset course of disease raises different causal questions: what are the processes determining course, for example, progression, stability, fluctuation or recovery? Treatment effects are a special case, assessed using a range of designs including randomised controlled trials. There has been accumulating evidence from randomised controlled treatment trials since the late 1970s of treatment effects of psychosocial interventions on some mental health conditions. Among the first was a randomised controlled trial of cognitive behaviour therapy for depression published by Beck et al. [ 22 ] showing effectiveness, but further, the same effectiveness as for anti-depressant medication. In effect this trial showed that a psychological intervention could achieve the same result as a biomedical intervention, and it paved the way for accelerating developments of tested psychological treatments for a wide range of mental health conditions and the translation of these into national health service provisions. There are complications, as always, for example, as to the extent to which psychological therapy outperforms pill placebo control, but the principle that some psychotherapies help some mental health conditions has been established (e.g. [ 23 ]).

The position is different with physical illnesses . Put strongly, there is a glaring gap in the evidence for the biopsychosocial picture as a whole, namely, absence of persuasive evidence of psychosocial treatment effects on the course of major physical illnesses. There is no clinical trial that finds effects of psychological therapy on physical illnesses such as, say, diabetes, cancers, cholera or advanced cardiovascular disease. We just wish to make the point that no psychotherapy or any other kind of psychosocial intervention turns around such disease processes once established, and this is a major apparent fact that needs to be taken into account in discussing the relative merits of the biomedical model and the broader biopsychosocial model. This is linked to the fact that for the many conditions that are managed biomedically in acute hospitals, successfully in some cases, there need be no special interest in the broader biopsychosocial model, and any advocate of the broader model has to accommodate the fact that whatever other significant roles they may have, psychosocial factors apparently make no difference to the course or treatment of major physical illnesses .

That said—and we intend it to be a big that —there is emerging evidence that psychosocial factors may be implicated in the prognosis of some among the very large range of medical conditions. For example: breast cancer (e.g. [ 24 ]), atopic disease, generally [ 25 ], including for asthma [ 26 ]; HIV [ 27 , 28 , 29 ] and musculoskeletal disorders (e.g. [ 30 ]). In addition, psychosocial factors have been implicated in outcomes of surgical procedures, for example, chronic pain [ 31 ]; lumbar and spinal surgery [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ]; liver transplant (e.g. [ 39 ]) and coronary artery bypass (e.g. [ 40 , 41 , 42 ]). In addition, there is evidence for psychosocial factors in wound healing [ 43 , 44 ], and extent of fatigue after traumatic brain injury [ 45 ]. Psychosocial factors have also been implicated in responses to other interventions for medical conditions, such as inpatient rehabilitation for stroke patients (e.g. [ 46 ]), and effects of hospitalisation on older patients (e.g. [ 47 ]).

Reference to psychosocial factors affecting course of medical and post-surgical conditions is not intended to be read as either conclusive or general. Many studies on this general topic are of associations only, and there are many mixed results. Hence the subtitle of this section, ‘emerging evidence’, and the explicit qualification of specificity to particular conditions and stages. Further, absence of reports of psychosocial effects on medical conditions, while it may suggest simply that the research has not yet been done, may also indicate that results have been negative and unpublished, and further back in the clinical research sequence, that clinicians have not seen evidence warranting case study research reports, progressing to cohort studies, and so on. This takes us back to the point made first, that some major medical conditions, such as the primary dysfunction in diabetes, or advanced cancers, or advanced cardiovascular disease, appear to be influenced exclusively by biological factors, impenetrable to psychosocial processes and interventions, and in some cases also unresponsive to biological interventions.

An old-fashioned way of making this point is to say that the mind cannot control biological processes such as abnormal cell growth. In the old dualist framework, however, the mind couldn’t really control anything material, not cell growth, but not arms and legs either, so the discriminating point got lost in the metaphysics. In the new post-dualist scientific framework, to be outlined in Chapter 3 , the ‘mind’ is not immaterial, not causally impotent, but more a matter of the central nervous system regulating some internal systems as well as the behaviour of the whole in the environment, and in these terms there are researchable differences between what the central nervous system can control and what it cannot. Extent of control may be modifiable, subject to individual differences, training and practice, but we know now that even at its best the central nervous system is not an omnipotent controller: there are places and processes that CNS signalling pathways do not reach, for example, cell growth, linked to the fact that the cells are very basic, similar in humans as in yeast; nor does the brain control the journey and final resting place of an embolus, and a long list of other biological processes and outcomes, benign or catastrophic. And this list can be contrasted with a list of biological processes and pathways that can or might have CNS involvement, as suggested by studies cited above. These issues and options only open up, however, in a new post-dualist metaphysics and biopsychological scientific paradigm, which are large themes to be addressed through the book. For now, we return to review the findings on biopsychosocial factors.

The next point to note is that, even for those physical health conditions that are unaffected by psychosocial factors, generally or at specific stages, still such factors may be relevant to clinically significant aspects of disease progression and management. These are factors such as access to treatment, participation in the recommended treatment regime, associated pain, psychological/mental health complications and health-related quality of life . Some details and literature as follows:

Access to healthcare is an obvious heading, covering diverse factors such as public health screening to ensure timely detection, health literacy, availability, accessibility and affordability of care, and quality of care—all factors heavily dependent on personal, class and state economics, associated therefore with the social gradient in health [ 5 , 48 , 49 and e.g. 50 ].

Acceptability of/participation in the recommended treatment regime. Psychosocial factors are associated with medication non-adherence, for example, following acute coronary syndrome [ 51 ], in haemodialysis patients [ 52 ], in youth with newly diagnosed epilepsy [ 53 ]. One systematic review of study of psychosocial factors predicting non-adherence to preventative maintenance medication therapy produced a negative result and call for more research [ 54 ].

Psychosocial factors in pain. Pain as an important phenomenon and concept spanning the biopsychosocial and will be considered further in Chapter 4 . Clinical studies implicating psychosocial factors include: in chronic pain [ 55 , 56 ] and in pain associated with specific conditions/sites, such as multiple sclerosis [ 57 ]; musculoskeletal pain [ 58 , 59 ]; low back pain [ 60 , 61 ]; spinal pain [ 62 ]; chronic prostatitis/chronic pelvic pain syndrome in men [ 63 ]; osteoarthritis [ 64 ]; cancer-related pain [ 65 ] and pain after breast cancer surgery [ 66 ].

Psychological/mental health complications of medical conditions . This is an increasingly recognised issue, with implications for quality of life (on which more below), social impairments and costs, in primary care [ 67 ], in LTCs [ 68 ] and in oncology [ 69 , 70 ]. Accumulating clinical experience and research has led to a new UK NHS policy directive requiring psychological therapy services to be integrated into physical healthcare pathways [ 71 ].

Quality of life . There is a substantial literature on psychosocial factors and health-related quality of life in medical conditions, for example, in patients with haematological cancer [ 72 ]; children with myelomeningocele [ 73 ]; colorectal cancer survivors [ 74 , 75 ]; myocardial infarction [ 76 ]; after hip fracture in the elderly [ 77 ]; newly diagnosed coronary artery disease patients [ 78 ]; adults with epilepsy [ 79 ], and after surgery [ 80 ]; and youth-onset diabetes myelitis [ 81 ].

Accumulating health data of the sort indicated above implicating psychosocial as well as biomedical factors, taken together, cover a large proportion of population health and health service provision in clinics and hospital beds. In other words, they are massively important, looked at in terms of population health, individual suffering, or economic costs; they are not a side-issue compared with conditions or stages of conditions that involve biological factors alone.

The psychosocial data have accumulated over the past few decades and have vindicated Engel’s proposal of a new model for medicine and healthcare. Engel was ahead of the game, and the popularity of his model is explained at least partly by the fact that it appeared as a ready-made framework for accommodating the emerging evidence of psychological and social causal factors in determining health and disease.

In these terms its clear that we need a biopsychosocial model of the sort that Engel anticipated, but one that can meet the criticisms reviewed previously that the model, at least as we currently invoke it, has serious problems including lack of content and incoherence. We propose in the next section a solution to the content problem, based, as would be expected, on emerging findings implicating psychosocial as well as biological factors of the sort outlined above. As to the coherence problem, this will involve theorising the categories of ‘biological’, ‘psychological’ and ‘social’ in such a way that they can interact in health and disease. This theorising will occupy the rest of the book. One strand was already mentioned earlier in this section: the old dualism between mind and body is replaced by a partial and to some extent negotiable interaction between the central nervous system and other biological systems. This theory-shift will be taken up in Chapter 3 , along with the proposal that the primary concept of the psychological is embodied agency , with implications for health, drawn out further in Chapter 4 : a person’s psychological health depends on the development of a viable enough sense of agency , while conversely, if agency is seriously compromised, such as in conditions of chronic stress , their mental health is liable to suffer, and so also, via complex biopsychosocial pathways, is their physical health.

1.2.2 The Scientific and Clinical Content Is in the Specifics

Let us pick up the line of argument in this chapter. The biopsychosocial model is much invoked, with claim to be the overarching framework for psychiatry and other branches of medicine such as primary care, perhaps for medicine generally. It has however been severely criticised, for being vague, without scientific or clinical content. Here is our suggested remedy: the scientific content and clinical utility of the biopsychosocial model is not to be found in general statements, but rather is specific to particular health conditions, and, further, specific to particular stages of particular health conditions . We provided above a brief, non-systematic, non-critical review of some of the emerging evidence of involvement of psychological and social as well as biological factors. All the evidence refers to particular health conditions or classes of conditions, and particular stages: risks for onset, post-onset course, including under treatment, adjustment and quality of life .

At the time Engel wrote there was not much evidence of causes of diseases and treatment effects, with important exceptions in the case of some major infectious diseases. But especially, compared with now, relatively little was known, though much was speculated, about the role of psychosocial factors in health and disease. Since then, in the intervening decades, there have been massive new research programmes, not only in biomedicine, but in clinical psychology , neuroscience , social epidemiology and genetics , and in treatment trials, pharmacological and psychological. Much more is now known about the causes of diseases and about possible disease mechanisms, with associated technologies for prevention, early detection and treatment. This broad evidence base has led in turn to treatment guidelines for specific conditions, to the whole apparatus of evidence-based clinical care, to be used alongside a thorough assessment of the individual case. Much of the science and clinical management is now psychological and social as well as biological. Given this situation as it is now, the scientific and clinical content of the biopsychosocial model is in the specifics, not in a ‘general model’. Much the same, by the way, can be said of biomedicine and its associated biomedical model: medicine, whether biomedical or biopsychosocial, deals with complex, specific systems.

The proposal that the content problem is resolved by focussing on specifics not generality also helps explain how the problem arises. In brief, it is because the specifics are too many and too complex, that some shorthand, vague gesturing, is sometimes useful. The basic and clinical sciences of the past few decades invoke very many kinds of factors in their models: biological factors—biological systems, including neural systems and genetic mechanisms—but also psychological factors—such as temperament, personality, lifestyle, adjustment, quality of life —and also social determinants of health and disease—variants on social inclusion or exclusion—together with the implication that all these things interact over time, in the course of life and the illness , in complicated and barely understood ways. So, on occasions when the question arises, for example in clinical consultation or healthcare education systems: ‘and what are the factors involved in this or that disease, or individual presentation?’—the quick answer would be: ‘it’s all biopsychosocial’, or ‘it’s as the biopsychosocial model says’. The full answer is much longer, in the systemic reviews of the epidemiological and clinical sciences, treatment trials and clinical guidelines—but this full story does not fit in a ward round or clinical consultation; it more makes up years long healthcare educational training programmes. As workable compromise, the brief throwaway—‘it’s all biopsychosocial’ could be expanded into something more informative along these lines: ‘In this condition there are possibly (or probably) biological, psychological and social factors involved, in some stages, some of which have been identified, with more or less confidence, combining together in such-and-such ways, though interactive causal pathways are bound to be complex and (typically) not yet well understood—the details of what is known and hypothesised about the condition to date is in the literature/is among the topics in one of your teaching modules’.

Such an answer, and the science it refers to, is about a particular health condition, such as diabetes, or depression. In this sense there are multiple specific biopsychosocial models: a model for diabetes, depression, cardiovascular disease, schizophrenia; and so forth. Further, much depends on what stage or what aspect of a particular condition we have in mind, whether pre-onset aetiological risks for onset, or post-onset course, involving many issues including maintaining factors, treatment responses, complications, psychological adjustment and factors affecting quality of life . The factors involved in these various stages and aspects typically differ within any particular condition, and especially they differ in the relative involvement of biological, psychological and social. For example, social epidemiological studies suggest that social factors as well as biological are implicated in the aetiology of a wide range of health conditions, such as cardiovascular disease and depression, while treatment might not be so, as in surgical intervention for advanced cardiovascular disease, or pharmacological therapy for depression. This latter is typically best combined with psychological therapy, which might also be indicated to aid adjustment and recovery of quality of life following cardiovascular surgery. In short, there is need for much discrimination between what conditions we are talking about, what stages of conditions and questions of interest in each. This is the specificity and complexity of diseases and therefore of the science and its models.

We stress here that we mean no implication that particular diagnostic categories are valid once and for all, or optimal in terms of explanation or prediction. Rather, they simply represent the current consensus state of clinical practice and clinical science and are liable to revision, to subtyping or supra-typing, or to replacement altogether. The proposal is that biopsychosocial medicine, like biomedicine, is applied to specific health conditions, in terms of which the science at any one time is conducted; but identification and classification of these conditions are subject to change.

In brief, our proposal is that, while the biopsychosocial model can sometimes appear as vague hand-waving, absent any scientific or clinical content, this is because we are looking for content in the wrong place, in the general model, rather than in the epidemiological and clinical science literatures about particular conditions. This proposal, if accepted, solves the content problem.

On the other hand, that said, such a solution immediately raises a still more radical problem for the biopsychosocial model: if it’s all about specifics, what is the point of having a ‘general model’?!

1.2.3 So What’s the Point of a ‘General Model’?

Engel wrote about the biopsychosocial model in a way that suggested it had scientific content and clinical utility. His 1980 paper [ 4 ] was on clinical applications of the biopsychosocial model, the main example being myocardial infarction, consistent with the reasonable expectation that the model specified biopsychosocial causal pathways in particular conditions and hence could guide clinical practice. However, the position regarding what is known in the science has radically changed in the intervening decades, and now, as argued in the preceding section, the ‘general model’ is probably now not the place to look for causal pathways, clinical applications and treatment guidance, which are rather to be found in the health science literatures.

One possibility in the circumstances, as the evidence accumulates, is that the general model might summarise the evidence for all the health conditions, along something like the following lines: “Psychological and social factors as well as biological factors (each of these being of many different kinds) are relevant to all health conditions and all healthcare, though they vary in their relative contributions, depending on the condition and the stage of the condition, between 0-100%, or mostly between, say, 20-80% – summing to something like 100%”.

However, while such a general proposition might be true, give or take some percentage points, it clearly has no or not much content, or use, in for example shaping guidance about prevention or clinical management. It is certainly less informative and useful than the full picture for a specific health condition. It is true that a general statement of the model such as the above can serve to remind us and our students to keep one’s mind open to the range of biopsychosocial factors, but the treatment guidelines and the science behind them already now say this, if applicable, and there is limited gain from repeating the fact—vaguely. Used in this way, the model runs the risk of being, minimally, a bucket to throw research findings into, convenient for hand-waving purposes. As for basic scientists and clinical trialists, they investigate the causes, mechanisms and treatment of cardiovascular disease, depression, and so forth; with definitely or probably not much need or time for a ‘general model’.

So what is the point of a general model? Perhaps as a theory of health and disease. But the line of thought we are pursuing is exactly that health and disease are not one thing, or two things, but each many things, depending which system within us is functioning well or poorly. Even so, the general picture still matters when the whole of health is in question, for example in estimating and projecting population health, planning and prioritising health services and research funding, on treatment, primary or secondary prevention, planning syllabuses for health education, or modelling linkages between health outcomes and outcomes in other sectors such as education, productivity or national happiness. Clinicians, patients and researchers may well be concerned with specific conditions, but for many other purposes views of the whole are required. The concept of biomedicine arose in the recognition that many effective health technologies had in common that they relied on biological factors only, notwithstanding complex biopsychosocial presentations. Such a concept then drives further lines of enquiry, investigating biological factors in other conditions. An analogous point applies to the biopsychosocial model. A related point is a need for a framework to organise accumulating research findings, to recognise emerging patterns, to identify what is known, with more or less certainty, and what is not known. This applies to specific conditions such as cardiovascular disease, or addictions, but it also applies across health conditions as a whole.

There are many purposes for a general model and accordingly many ways of constructing such a thing. We focus here on the general biopsychosocial model as a core philosophical and scientific theory of health, disease and healthcare, which defines the foundational theoretical constructs—the ontology of the biological, the psychological and the social—and especially the causal relations within and between these domains .

While the details of the relative roles of biological, psychological and social factors in specific health conditions, at particular stages, are matters for the health sciences, the general, or core, biopsychosocial model is more of an exercise in the philosophy of science—in this case, philosophy of biology , philosophy of mind and social theory, but especially as applied to health and disease. These philosophies are especially relevant in the present case, because there is massive historical baggage, carried in the long history of physicalism , dualism and reductionism , that makes biopsychosocial ontology and causation deeply problematic. This whole problem area needs rethinking and reconceptualising in the light of current scientific paradigms and philosophical theory.

1.3 The General Model: Biopsychosocial Ontology and Interactions

1.3.1 defining the problem.

Engel was well aware of the philosophical problems involved in the shift from the biomedical model to the biopsychosocial. This is how he characterises the biomedical model ([ 1 ], p. 130):

The biomedical model embraces both reductionism , the philosophic view that complex phenomena are ultimately derived from a single primary principle, and mind-body dualism , the doctrine that separates the mental from the somatic. Hence the reductionist primary principle is physicalistic; that is, it assumes that the language of chemistry and physics will ultimately suffice to explain biological phenomena.

The biomedical model so understood, as based on these philosophical views, is antithetical to any extension to a biopsychosocial model, and conversely, if the biopsychosocial model is to be viable, it has to overcome the challenges they pose. This is well recognised by thoughtful commentators on the biopsychosocial model, including those, quoted previously, who criticise the model for its hand-waving tendencies. Here is Chris McManus in his review for The Lancet cited previously ([ 13 ], p. 2169):

The challenges for the Biopsychosocial Model involve reductionism , dualism , mechanism, methodology, and causality. The psychological and the sociological are ineluctably phenomena of the mind, and the reductionist challenge is how to integrate the mental with the cellular, molecular, and genetic levels at which biomedicine now works.

Ken Kendler in his review quoted earlier, goes on to identify the philosophical issues relevant to the biopsychosocial model and the work that needs to be done ([ 3 ], p. 999):

[These are] the issues that the Biopsychosocial model at least seemed to be addressing—how to integrate the diverse etiologic factors that contribute to psychiatric illness and how to conceptualize rigorously multidimensional approaches to treatment. [There is] a range of exciting recent developments in the philosophy of science on approaches to complex biological systems, which are quite relevant to these issues… [which] examine scientific approaches to complex, nonlinear living systems and explore various models of explanatory pluralism, from DNA to mind and culture….

The importance of understanding causal interactions between kinds of factors is also highlighted by Dan Blazer in his review of Nassir Ghaemi’s book [ 82 ] (p. 362):

[There are] emerging efforts across all of medicine to integrate biological, psychological, and social factors in the exploration of the causes and outcomes of both physical and psychiatric illnesses …. These efforts are not eclectic but transdisciplinary, efforts which are leading to a much better understanding of how biological, psychological, and social factors interact through time.

Both Kendler and Blazer identify the current challenge of constructing a coherent view of causation in health and disease that can encompass biological, psychological and social factors. Kendler refers to recent philosophical developments and Blazer to emerging efforts in health sciences, both implying a historical dimension and that something new needs to happen and is happening, at a conceptual level as well as a scientific level.

Engel’s characterisation of the biomedical model, a reasonable one in the 1970s, had it supposing that only the biological exists, or is alone causal in health and disease, and it exists as physics and chemistry, with the same principles or laws of causation. The ontology was flat and reductionist: nothing new grew out of the basic physics and chemistry, and any other domain with aspirations to be causal had to be ultimately reduced back to the basics. To construct an alternative to this set of assumptions it is necessary to envisage ontology and causal relations other than, and in some metaphorical sense ‘above’, those in physics and chemistry. Engel proposed systems theory for this purpose, and as we shall consider in later chapters, we think this is fundamentally the right way to go.

A systems theory approach in fact already underlies the solution to the content problem we proposed in the previous section. We proposed in Sect.  1.2 , heading “ The Scientific and Clinical Content Is in the Specifics ”, that the content is to be found in the science and clinical guidelines on specific health conditions. This is the indicated move because specific systems are distinctive, with their own distinctive functions, operating principles and vulnerabilities to dysfunction, which therefore have to be modelled separately. Healthcare science along with other systems sciences, essentially deals in specifics. This has always applied to biomedicine, which deals with particular biological systems. It also applies in psychology , which deals with particular psychological systems, such as motivation and fear, and in clinical psychological theory—for example, cognitive behaviour therapy has specific models for such as depression, obsessive-compulsive disorder and panic disorder.

The question arises then: what is the core theory linking together the various applications to specific systems? For biomedicine, in the way that Engel characterised it in the 1970s, the core theory was that biology is physics and chemistry, and biological causation is physico-chemical causation. This has changed; it is no longer true of current biomedicine; this is the topic of the next chapter. The core theory underpinning cognitive behavioural therapy, as stated by its founders Aaron Beck and colleagues [ 22 ] (p. 3) is startlingly brief, that cognitions cause affect and behaviour. However, even this brief statement of the core model does crucial work: it highlights the working assumption that intervening with cognition is the way to modify troubling emotions and behaviour, and it links together the various types of cognitive behaviour models for diverse conditions. Even in the absence of explicit theory of causation, there can be evidence of causal connection from well-designed treatment trials, but also, in this particular case there is a long and respectable history of the cognitive theory of the emotions and the philosophy of practical reason that provides conceptual familiarity for working purposes.

The contrast here is with the biopsychosocial core model: there is no long and respectable history of philosophy and science theorising causal interactions between the biological, the psychological and the social. To the contrary, the history since the beginnings of modern science in the seventeenth century consists of assumptions and arguments that psychological and social causation are impossible or even incomprehensible, that there is no distinctive biological causation either, over and above physics and chemistry. The historical background is entirely hostile to the whole idea of biopsychosocial causal pathways, and there is therefore a need for an explicit theory as to what the new idea is. It is this, we propose, that is the purpose of the general biopsychosocial model; in short, to theorise biopsychosocial causal interactions.

We review some main relevant historical background below, under the heading “ Prejudicial Theory: Physicalism, Reductionism, Dualism ”. First, in the next section, we consider how the search for biopsychosocial theory is not only of interest to reworking a model proposed some 40 years ago, but has arisen in the health sciences themselves.

1.3.2 Biopsychosocial Data in Search of Theory

The emerging evidence of psychosocial causation in health and disease of the sort briefly outlined in Sect.  1.2 , comes from studies using empirical methodologies that have been developed and applied substantially since Engel wrote his papers on the biopsychosocial model. Prior to these new research methods, there was little or no demonstrated evidence of psychological and social causes of physical health conditions. Their effects were not as plain—as massive—as those identified by biomedicine, as for example effects on incidence of cholera of drinking contaminated water from a particular pump, or recovery following treatment by antibiotics. In the absence of a significant body of evidence of a causative or curative role of psychological and social factors in particular diseases, claims as to their importance were bound to have an uncertain status: were such claims meant to be general, to apply to all conditions, meant to be obvious, or based on prejudice or expert consensus—or specific to particular conditions? In the absence of much evidence, the appearance of ideology was inevitable—and this is one of the key points behind Ghaemi’s critique of Engel’s biopsychosocial model [ 2 ], considered previously (Sect. 1.1 ). However, the amount of evidence and most importantly the type of evidence bearing on these issues has changed radically in the 40 years since Engel proposed the model. We refer to use of novel statistical methodologies and associated study designs that are sensitive to multiple factors, relatively small, partial causal influences, usually called risk factors, contributing in some way to a complex nexus of causation associated with a particular outcome of interest. The development of these new methodologies was based on nineteenth-century conceptual work on the scientific demonstration of causation, and early twentieth-century work in the theory of statistical inference.

Much of the intellectual work clarifying the scientific methodology required for the determination of causes was done by J. S. Mill in his A System of Logic [ 83 ]. Hume [ 84 ] had seen that causality is linked to generality, that the statement ‘A causes B’ implies that events of type A are always followed by events of type B. This implies also that knowledge of causes enables prediction, that the next A will be B. Mill saw, however, that in practice what is observed on any one occasion is not simply an event of type A being followed by an event of type B, but this conjunction in a complex of circumstances, C. To establish a causal link between A and B the possible confounding effects of C have to be determined. This involves observing the effects of C without A, on the one hand, and A without C on the other. These principles, elucidated by Mill as the ‘methods of agreement and difference’, underlie our modern idea of controlled experimentation.

Robert Koch’s pioneering work in microbiology in the closing decades of the nineteenth century made four postulates as methodology to determine the causal relationship between a microbe and a disease, applied to the aetiology of cholera and tuberculosis [ 85 , 86 ]. Koch’s postulates tapped similar principles to Mill’s , including assumptions of generality and isolation of the suspected active causal ingredient—‘isolation’ here requiring cutting edge technology of the time. Interestingly Koch himself recognised that there was a problem with the generality requirement, which takes us on to the next main point.

Hume , Mill and Koch supposed that causality is general—applies to ‘all’. However, in practice in the lifesciences, medicine, psychology and the social sciences we rarely find universal generalisations, but rather partial ones, of the form: A is followed by B in a certain proportion of observed cases. One function of a universal generalisation is to license the simple inductive inference: the next observed A will be followed by B. In the absence of a universal generalisation, the problem is to determine the probability of the next A being followed by B, given that the proportion in the sample so far observed. This is the problem for the theory of statistical inference, developed in the first decades of the twentieth century.

The theory of statistical inference is a necessary condition of being able to detect reliable small correlations between two factors, between say amount of daily exercise and cardiovascular function at a later time. The implications of correlations being small—much less than 1 and not much above 0—is that other factors are at work, signalling the need for investigation of multiple factors associated with the particular outcome of interest. Investigation requires a group study in which each factor is each measured and their association or correlation with the outcome computed. Analysis of variance, ANOVA, is one class of statistics that can be used for such purposes: there is an outcome of interest, the so-called dependent variable, and several independent variables, hypothesised to effect it. For example, the dependent variable may be onset of cardiovascular disease by 40 years, the independent variables are individual characteristics such as weight, diet, smoking, exercise, multiple deprivation index, family history as assumed proxy for genetic vulnerability, and the results of the ANOVA will quantify the amounts of variance in outcome and hence risk attributable to these several factors, alone or in combination. Other classes of statistical analyses can be used, more or less closely related, depending for example on the nature of the variables (e.g. categorical or continuous) and on study design (e.g. cross-sectional or longitudinal). Use of such methods has become pervasive in the human sciences in the past few decades, reflecting the fact that the phenomena are complex with multiple causes; instances when a single variable completely explains a phenomenon (accounts for all or most of the variance) are rare.

Naturalistic studies of populations in the first instance establish correlations only, and further investigation is needed to establish causation, using or approximating to experimental methods of the sort elaborated by Mill and Koch. Experimental designs for establishing causation typically involve at least two groups, assumed to be identical in relevant respects—either known or suspected to affect the outcome of interest—except for one factor, the factor of interest. Differences of outcome between the two groups are then attributable to the factor of interest in accordance with Mill’s method of difference. The factor of interest is often a treatment—an ‘intervention’. Confidence in the assumption that the two groups are otherwise identical in relevant respects is critical in these methodologies, and there are many methods of ‘matching’ groups to achieve this. The philosophical justification for regarding controlled designs as the appropriate methodology for establishing causation such as treatment effects has been argued elsewhere [ 87 ]. The gold standard for maximising this confidence—the true experimental design—is taken to be randomisation, with sufficiently large numbers, such that possible confounding causal factors can be reasonably assumed to be distributed equally between the groups. Quasi-experimental designs, such as matching cohorts, can also be used, though the confidence that unknown confounders are equally matched is less. There are also ‘natural experiments’ (see e.g. [ 88 ]), and sometimes the background base rates absent the putative cause are safely assumed.

If we establish that a universal correlation is causal, the finding can be expressed as A causes B. Typically in the life and human sciences, correlation between factors is partial—variation in A accounts for only part of the variance in outcome B—in which case the correlation can be expressed as: A raises probability of B, in some specified degree depending on the size of the correlation. If B is a harmful outcome, such as a poor health outcome, this is often expressed: A raises risk of B, in some specified degree.

Population studies of risk factors for the onset of disease cannot use randomisation designs, plainly for ethical reasons, and are generally limited to more or less refined quasi-experimental methodology. Experimentation is left to animal studies. Treatment studies of the effect of an intervention on the course of a disease once onset can use randomisation designs—again subject to ethical constraints.

The new study designs and analytical methodologies showed effects—typically small—of psychological and social factors. The same methodology of course can show the importance of biological factors of small effect, such as genetic and epigenetic effects.

Relevant to our main theme, however, we can note that while these new study designs and statistical methodologies are well theorised, as is the determination of causes by experimental and related methods, they provide in themselves no theory of the factors indexed by the variables and no theory of causal mechanisms linking them. They can provide evidence of biopsychosocial causal connections, but no theory about them. This absence of theory is important because of the historical background of dualism and physicalist reductionism , noted at the beginning of this section (under the heading “ Defining the Problem ”), that would exclude any distinctive forms of biological (as opposed to physico-chemical), psychological and social causation . We review some main points of this historical background next.

1.3.3 Prejudicial Theory: Physicalism , Reductionism , Dualism

Engel’s characterisation of the biomedical model—quoted at the beginning of this section, uses a few key technical terms: reductionism , physicalism and physicalist reductionism (Engel uses ‘physicalistic’). These terms refer to complex and controversial concepts with long histories, and we will use working characterisations as follows:

Physicalism is the view that everything that exists is physical. This is an ontological statement—about what there is. It has often been combined with the corresponding statement about causation: that all causation is physical, covered by physical laws. On the assumption that chemistry is basically physics, physicalism can be expressed in terms of physics + chemistry. The contemporary philosophical literature on physicalism is substantial (for recent review see e.g. [ 89 ]). Working around physicalism is necessary to establish a biopsychosocial model and is addressed in more detail in the next chapter.

Reductionism has various meanings. In one of the senses used by Engel in his characterisation of the biomedical model, quoted at the beginning of this section, it is a scientific claim that complex phenomena have a main cause of a particular type. In the medical context, reductionism in this sense would claim that there is a main cause of one or other kind: biological (e.g. an infection or lesion), or psychological (e.g. unconscious conflicts, or maladaptive cognitive style), or social (e.g. social exclusion ; labelling). There is also a philosophical or metaphysical doctrine of reductionism , deriving from physicalism , as follows:

Physicalist reductionism follows from the strong version of physicalism which has ontology and causation as all a matter of physics. It is a strict consequence for other sciences, such as chemistry, biology , psychology and social science : either they are true causal sciences, in which case they must ultimately reducible to the concepts and laws of physics; or, otherwise, they are pseudo-sciences, or at least, ‘sciences’ that do not deal with causation. Physicalist reductionism so understood is a philosophical or metaphysical doctrine in the sense that it is known or alleged a priori; it is not based on scientific research, but rather prejudges what there is to be discovered. Physicalist reductionism along with its roots in physicalism is taken up in the next chapter.

Physicalism has a long history, its roots lying in what historians of science refer to as the ‘mechanisation of the world picture’ in the seventeenth century [ 90 , 91 , 92 ]. This involved defining the primary qualities of nature in mathematical terms, as mass, extension and motion, covered by the few universal laws of Newtonian mechanics. The mechanisation of nature created mind–body dualism , because the thing that never did seem to be physical was immediate experience: sense-perceptions, thinking, pain and the like. Physical objects including the human body have the primary qualities, while the mind was something else, immaterial and unlocated. Physicalism and dualism are twins, one born straight after the other, combative from the start, each refuting the other, the one supported by the great edifice of modern mechanics, the other known immediately by experience, battling ever since.

It is impossible to overstate the massive influence of modern physics and its accompanying philosophy of nature on the subsequent development of western science through the eighteenth and nineteenth centuries. As sciences developed, studying apparently distinctive domains and processes, the dominant physicalism applied its stringent reductionist test: either the new aspiring science was valid as causal science, in which case it should be reducible to physics, or, it was not reducible to physics, in which case it was pseudo-science, or at best, a ‘science’ studying non-causes. The chemistry that emerged in the nineteenth century passed the test and joined physics. As to biology , psychology and social science , on the other hand, physicalist reductionism aided by dualism caused disunity and more or less havoc—some key points in brief as follows, to be picked up in later chapters:

Biology as we now understand it developed in the nineteenth century, drawing from previous roots in medicine, natural history and botany (see e.g. Ernst Mayr’s seminal work on the history and philosophy of biology, [ 93 ]). This large, complex field, comprising many subfields, with distinctive domains, questions and methods, had an ambiguous relation with physicalism and reductionism . In some areas of biology , especially in medicine, physiology and new subspecialities such as microbiology—there was the possibility of reduction of biological phenomena as chemistry. A key development was Lavoisier’s work on the relation between combustion and respiration, initiating the scientific research programme that became biochemistry. However, for other parts of the broad and diverse field of biology, reducing the phenomena of life to chemistry was not such a clear option. This applied especially to developmental embryology and evolutionary biology , which aimed to understand the formation of individual organisms and whole species, and which used explanatory concepts more akin to older, Aristotelian concepts such as form and function. Such alternative concepts, contrasted with physics and chemistry, will appear in later chapters as we develop biopsychosocial theory. Biology could embrace physicalist reductionism , or ignore it, or argue against it head on. This third option was the doctrine of ‘vitalism ’, which posited a biological life force in addition to mechanical, or more broadly physico-chemical, forces. Vitalism is in this sense a direct response to the mechanisation of the world picture in modern science, a point made by Bechtel and Richardson [ 94 ] (p. 1051):

Vitalism is best understood… in the context of the emergence of modern science during the sixteenth and seventeenth centuries. Mechanistic explanations of natural phenomena were extended to biological systems by Descartes and his successors. Descartes maintained that animals, and the human body, are ‘automata’, mechanical devices differing from artificial devices only in their degree of complexity. Vitalism developed as a contrast to this mechanistic view.

As to psychology , this new science inherited the Cartesian dualist assumptions: immaterial mind evident immediately in consciousness, and the mechanical body. Psychology struggled with the oddness of mind as its subject matter for several decades, then shifted to the other option, compatible with physicalism and reductionism , aligning psychology with physics and chemistry. This was behaviourism, and here is Watson [ 95 ] (p. 158) summarising the new approach:

Psychology , as the behaviorist views it, is a purely objective, experimental branch of natural science which needs introspection as little as do the sciences of chemistry and physics. It is granted that the behaviour of animals can be investigated without appeal to consciousness… This suggested elimination of states of consciousness as proper objects of investigation in themselves will remove the barrier from psychology which exists between it and the other sciences. The findings of psychology become the functional correlates of structure and lend themselves to explanation in physico-chemical terms.

The social sciences , on the other hand, as they emerged through the nineteenth century never were going to lend themselves to comprehension in physico-chemical terms. This would be desperate business. Their subject-matter was, briefly stated, forms and processes of social organisation, which looked a very long way from physics and chemistry, further away than even psychology . As to principles of social causation , perhaps there were universal laws governing change, but equally, social systems and events appeared as specific, even unique. In short, the ontology of the natural sciences was no use to the emerging social sciences , and their methodology was of limited or questionable use. Accordingly alternative approaches developed, drawing from philosophical traditions other than physicalism , emphasising understanding and meaning, ‘hermeneutics’, rather than causal explanation of nature. Here is Anthony Giddens on this point [ 96 ] (pp. viii–ix):

The tradition of the Geisteswissenschaften, or the ‘hermeneutic’ tradition, stretches back well before Dilthey, and from the middle of the eighteenth century onwards was intertwined with, but also partly set off from, the broader stream of Idealistic philosophy. Those associated with the hermeneutic viewpoint insisted upon the differentiation of the sciences of nature from the study of man. While we can ‘explain’ natural occurrences in terms of the application of causal laws, human conduct is intrinsically meaningful, and has to be ‘interpreted’ or ‘understood’ in a way which has no counterpart in nature. Such an emphasis linked closely with a stress upon the centrality of history in the study of human conduct, in economic action as in other areas, because the cultural values that lend meanings to human life, it was held, are created by specific processes of social development.

To sum up, physicalist reductionism had a massive influence on the development of the biological, psychological and social sciences . It prioritised physics, subsequently physics and chemistry, as the benchmark of empirical science and causal explanation. Parts of biology measured up, as biochemistry, evolutionary biology didn’t; psychology struggled; and the social sciences were so far off the mark that new views of science including alternatives to causal explanation were needed.

Against this background, deeply entrenched theory, antithetical to any distinctive forms of biological (as opposed to physico-chemical), psychological and social causation , Engel’s proposal of the biopsychosocial model was audacious. It was, however, prescient, because in the intervening decades the empirical evidence has built up, as outlined in Sect.  1.2 , under the heading “ Emerging Evidence of Psychosocial Causation ”. A main virtue of the empirical, empiricist methodology of Hume and Mill , outlined in Sect.  1.3 , under the heading “ Biopsychosocial Data in Search of Theory ”, is that it can accumulate evidence of causal connections, driving the science forwards, unhindered by theoretical prejudice. The scientific methodology for determining associations and causal connections between one or more factors and a health outcome in indifferent to the nature of the factor variables involved, in particular it has no interest in whether they are called ‘biological’, ‘psychological’ or ‘social’; the methodology has no interest in ontological matters at all—it cares only that the variables are measurable. Equally the empirical and statistical methodology has not much or nothing to say about causal mechanisms . Free of the historical theoretical baggage, it has been able to study relations between biological, psychological and social factors and health outcomes of interest, the upshot of which has been accumulation of evidence that psychological and social factors are at least associated with some health outcomes, physical and mental, and with some evidence of causal impact. Such free creativity is typical of empirical science. On the other hand, the downside is that we have apparently established biopsychosocial ontology and causal interactions, but so far untheorised, and—still feeling the effects of physicalist reductionism in the last few centuries of science—with perplexity and incredulity that such a thing is possible.

1.3.4 Theorising Biopsychosocial Interactions—Not Parallel Worlds

The proposal of biopsychosocial ontology and causal relations—under the weight of philosophical and scientific prejudice according to which psychological and social causation are impossible, even incomprehensible, and there is no distinctive biological causation either, over and above physics and chemistry—is audacious and the task of making theoretical sense of it is non-trivial.

Engel’s biopsychosocial model is a very suitable heading for examining these issues. His papers certainly identified many of them, probably all that were apparent at the time he wrote them. However, Engel’s model is only a heading for the major task of elucidating theory that can comprehend the paradigms and findings of the health sciences of the past few decades that invoke the full range of and interactions between biological, psychological and social factors in health and disease.

We propose to start with biology and especially its relation to physics and chemistry. It is the assumption that biology is no more than physics and chemistry that locks in the physicalist philosophy that the laws of physics and chemistry are the only causal laws. While that philosophical position remains in play, without viable alternative, it is difficult to make out any distinctive psychological or social causation and especially difficult to theorise biopsychosocial interactions. There is simply too much historical conceptual baggage in the way, variations of dualism and the disunity of the sciences.

We will be considering theory changes that have accelerated in the decades since Engel wrote. Up to the 1970s, just about everybody supposed that biology (as least as physiology) was reducible to physics and chemistry, but psychology and social sciences hardly, and so much the worse for them. In the 1970s, however, the reducibility of biology to physics became questionable, with recognition that all the ‘special sciences’, apart from physics/chemistry, had distinctive concepts and apparently causal explanations. However, exactly what the other sciences are sciences of, and what becomes of physicalism , dualism and reductionism , and especially how the various sciences are meant to relate to one another— all remained unclear and contested. Jerry Fodor’s 1974 paper [ 97 ] had the full title ‘Special Sciences (Or: The Disunity of Science as a Working Hypothesis)’. Fodor’s 1997 [ 98 ] update was equally informatively titled, as ‘Special Sciences: Still Autonomous After All These Years’, concluding ‘The world, it seems, runs in parallel, at many levels of description. You may find that perplexing…’

This parallel world view—or perhaps it should be parallel worlds plural—in which it is supposed that as well as the physico-chemical world, there is also a biological world (unless that is the same as the physico-chemical world), and a psychological world, and the social world—is certainly perplexing. It does not get much less perplexing if ‘parallel world(s)’ is replaced by ‘many (parallel) levels of description’. Such a view however is exactly what is intellectually arrived at when forced to acknowledge, when no longer able to deny, that the biological, psychological and social sciences are now established as valid sciences including causal determinations, in some reasonable sense of ‘causal’, such as: can predict; when no longer able to deny this, while at the same time continuing to assume that the physico-chemical world is closed to anything other than physico-chemical causation.

This parallel worlds/levels of description approach can be applied in the health sciences, leading to the idea that psychological and social models of health and disease, as well as the biomedical, can somehow all be valid, but at different levels of description. As indicated previously in Sect.  1.1 , Nassir Ghaemi argued that the biopsychosocial model has been used exactly to resolve turf wars between these various disciplines, by allowing them all to claim validity at the same time, the upshot being irredeemable vagueness and incoherence. We noted however that this thought is not prominent in Engel’s papers, which philosophically relies rather on systems theory in which there is interaction between domains.

Philosophically, the parallel world(s) move, historically inevitable as it probably was, is not really coherent; what is needed rather is a more liberal view of worldly ontology and causation that can encompass not only physics and chemistry but also biological, psychological and social processes and principles of change. In any case, so far as the current sciences are concerned, and especially the health sciences, the idea of parallel causal explanations is unhelpful; rather, what is needed is theory of multifactorial interactive causation. Specifically, data of the sort reviewed in Sect.  1.2 under the heading “ Emerging Evidence of Psychosocial Causation ”, suggesting biopsychosocial involvement in health and disease, need to be theorised in terms of biopsychosocial interactions. The quotes from Chris McManus , Ken Kendler and Dan Blazer considered at the beginning of this section, when setting up the task of the general biopsychosocial model, all refer to the need to integrate biological, psychological and social factors. Another aspect of the same point is that the various kinds of factors are found in the science to account for different proportions of the variance in health outcomes, with relative proportions of the three varying between health conditions and stages of condition. From the point of view of the science, a sentence along such lines as: ‘biological, psychological and social factors (always) each severally account for 100% of the variance – at different levels of description’—is completely incomprehensible.

1.3.5 Finding the Right Metaphor: Evolution and Development

It is not straightforward to find the right metaphor for the relation between the biological, the psychological and the social. The most common is in terms of hierarchical levels, but it suffers from reductionist connotations that lower levels are more basic, more causal, than higher ones. Alternatively, as a transitionary move away from reductionism , appraised in the previous section, it can be interpreted as different levels of ontology and/or description running in parallel, but this makes interactions mysterious. Systemic approaches that envisage interactions are the key, major improvement, but still the metaphors struggle. One, used by Engel in his 1980 paper [ 4 ], is ‘nested squares’ of systemic inter-activity, from the within-body biological, outwards to self-organised activity in the external environment, including interactions with immediate conspecifics, through to complex patterns of social organisation and regulation. This ‘nested’ domains metaphor is not up to much either, however, insofar as it lends itself to the implicit though odd presumption that the inner domain is sorted out first, then the next grows around it, then the next around that; in effect to the idea, absurd once spelt out, that our internal biology comes first, then activity in the outside world, then activity with conspecifics. This sequencing beginning with ‘first’ makes no sense temporally or systemically. Internal biology, functioning in the environment, including with other biological beings, cannot be separated from one another, conceptually or temporally.

What is missing from and obscured by these two-dimensional picture metaphors of levels and nested domains is the temporal, evolutionary and developmental, parameter . Everything is present in the original, primitive, prototypic forms . A cell is an individual unit, separate from but essentially interacting with the environment, extracting and expending energy, including interaction with other biological entities such as viruses. Parent sea birds catch fish and put it in the mouths of developmentally immature offspring, promoting the biologically necessary energetic reactions by bringing the chemicals into close enough proximity, acting like a catalyst—unless the fish is taken away first by a bigger bird of the same or different species. All these biological-environmental-individual-within-and-between-species-interactive processes are involved from the start in the simple forms , which become ever more complex. In short, no static metaphor, whether in terms of levels or nested systems, capable of being drawn on a page, does justice to the new systems sciences, which essentially invoke dynamical interaction in present time, on the basis of co-evolution through deep time.

1.3.6 Developing the General Model

Evolution and development involve increasing complexity of forms , and our argument will be that these forms bring with them new causal properties. Another way of expressing this is to say that what comes into being are increasingly complex systems, and that these systems have new and distinctive causal properties. There is in particular a quantum leap at the boundary between inanimate and biological material in which new forms or systems appear that manage the physics and chemistry of the matter, specifically energy exchanges governed by physico-chemical equations. This is the argument of Chapter 2 , Sect.  2.1 . The biological/biomedical sciences in the last half-century have done all the work to undo the restrictive assumption that biology is only physics and chemistry and to construct instead new deep theory involving another kind of ontology , turning on dynamical forms , and causation as regulation and control. The way out of physicalist reductionism starts here—exactly at the place where physics and chemistry become biology. This is the argument of Chapter 2 , Sect.  2.2 .

The evolution of life forms ends up with human psychological and social phenomena. This ‘ends up with’, as currently understood in the science, is not a matter of logic or scientific law, but is entirely contingent—accidental. In this sense, biopsychosocial systems theory is unlike some traditional philosophical systems, which start with axioms and deduce the rest, or which elucidate natural law that covers everything. So when we move from defining key features of biology , in Chapter 2 , to defining key features of psychological and hence social phenomena in Chapter 3 , there is a gap, evident at the start in Sect.  3.1 , one which cannot be filled in by logic or natural law, but only by contingent facts of evolution , development and change.

Human psychological and social phenomena have lives of their own—multiple distinctive modes of operation, turning on systemic concepts and principles already evident in biology, such as form , organisation , ends , communication , rules and regulations . In the evolution and development of new forms or systems, it can be said that they all share—from the start, and remaining in—the same ‘ontological space/time’. This is a good way of capturing the fact that they can bump into one another and affect one another, that they causally interact, as opposed to being in parallel universes. This is to say, the ontological point is at the same time essentially a point about causal interaction. We propose defining key features of psychosocial phenomena and causation in the first sections of Chapter 3 , Sects. 3.1 – 3.4 , consistent with the key features of biology proposed in Chapter 2 . With the whole biopsychosocial system in view, we return in Sect.  3.4 , to the general theory of biopsychological systems, interwoven ontology and causal theory. We address the vexed issues of top-down causes, vexed from the point of view of physicalist reductionism : psychological effects on biological processes, and social effects on our biology and psychology . However, by this stage in the argument—and in the current science we intend to be tracking—the prejudicial concepts and assumptions of physicalist reductionism are nowhere to be seen. Rather, in the new approach, there are coherent core concepts and principles of causation by regulatory control , which are found already in biology , and which can elucidate in a relatively straightforward way the logic of what is traditionally regarded as top-down processing in biological, psychological and social domains. In brief, control mechanisms employ agents at the lower level, compliant with any laws that may apply at that level, but also acting as messengers from higher levels, defined by networks of relations at those higher levels.

The detailed arguments elucidating the general theory of biopsychosocial interactions are developed through the next two chapters. The fourth chapter expands on relevance to health and disease. In fact, however, the whole theory is at its core, from the start, a theory of health and disease. This is because the theory is fundamentally normative, in terms of concepts such as functioning well or badly, being well or unwell. The contrast here with physicalist reductionism is striking: the old theory makes a point of excluding any hint of normativity, with no interest in any difference between life and death or anything else related.

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Bolton, D., Gillett, G. (2019). The Biopsychosocial Model 40 Years On. In: The Biopsychosocial Model of Health and Disease. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-11899-0_1

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Introducing the Biopsychosocial Model for good medicine and good doctors

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Introducing the Biopsychosocial approach as the model for good medicine and good doctors.

Development of a new model

Until recent decades the traditional approach towards health and disease has been the medical or biological model where a person’s ill- health was exclusively treated by medical means. At the time this seemed satisfactory but recent research in psychology and the social sciences has challenged this approach and sought to develop a new more extensive model of health that can be applied in clinical practice.

In spite of the traditional dominance of the biomedical model, the time seems right for expanding the model to the Biopsychosocial Model as the social and psychological influences of today’s health problems do not fit the narrow framework of the biomedical model. There has been much discontentment with the medical model to the extent that Engel (1977) suggested that it had acquired the authority and tradition of dogma. It was this same author who devised the Biopsychosocial Model and stated in his landmark paper that,

“We are now faced with the necessity and the challenge to broaden the approach to disease to include the psychosocial without sacrificing the enormous advantages of the biomedical approach”.

The Biopsychosocial Model of health and illness as proposed by Engel (1977) implies that behaviours, thoughts and feelings may influence a physical state. He disputed the long-held assumption that only the biological factors of health and disease are worthy of study and practice. He argued that psychological and social factors influence biological functioning and play a role in health and illness also. This is a more realistic model in light of the role lifestyles play in a society having entered the new millennium. This new theoretical model therefore has been developed in an attempt to improve on the disease approach and narrow view with respect to health and illness held by the medical model so that psychological and social factors of the individual can also be considered.

The Biopsychosocial Model is a very important step in medical care as it broadens the scope with which health and illness can be examined in clinical practice. Considering this model leads to the patient being interviewed as a person with an individual lifestyle and not simply as a patient with a disease which has deviated them from normal functioning. Thus the clinician will have many avenues to explore before they make their diagnosis and hopefully they will be able to provide preventative information to the patient about how they may adjust their lifestyle in order to have a better quality of life. This model can be used in medical schools to train doctors in the art of good communication, understanding and compassion.

The Biopsychosocial Model in Clinical Practice

One of the primary criticisms of new theoretical models is that they may lack scientifically proved evidence that they can work. This would be an unjust criticism of the Biopsychosocial Model however as there has been substantial and extensive research into how this model can interface with modern health problems and exert a considerable improvement. Many of the modern illnesses such as heart disease and cancer have been found to have psychological and social components to their aetiology. For example it has been estimated that 30% of cancers are associated with tobacco use and that diet accounts for some incidence of digestive tract cancers (Doll and Peto, 1981). Psychological factors such as self-esteem and perceived control have been identified as potential markers to help increase health- promoting behaviours like exercise and reduction of over-consumption. Also since it is known that individual’s susceptibility to coronary heart disease is increased by factors such as hypertension, smoking, high cholesterol and type A personality traits, then interventions can be designed to seek change in a person’s lifestyle.

Possibly the most general biopsychosocial illness is that caused by excess stress, that term used to describe situations in which individuals are faced with environmental or other demands which exceed their immediate ability to cope (Lazarus and Folkman, 1984). Very often these situations produce adverse psychological and physiological changes and sometimes they are associated with a disease outcome. With the Biopsychosocial model, stress can be examined from each of these perspectives. Firstly, biological factors like high blood pressure levels, muscle tension and an individual’s decreased resistance to disease as a result of immuno- suppression could be sources of investigation. Psychological factors like increasing risk behaviours (smoking, large alcohol intake), coping mechanisms and predisposition to anxiety could be examined. In fact a study conducted on stress and burnout in psychiatric nurses showed that the biggest factor in causation of burnout as measured on the Maslach Burnout Inventory (MBI) was not job-demands but high trait anxiety levels (Mc Inerney,1999).

Different interventions for modifying risk behaviours and so incidence of disease can be carried out on an individual or small group basis using stress- management or relaxation techniques. However, it has been found that it is very difficult for individuals to give up risky behaviours and adopt more healthy lifestyles. It is therefore necessary to alter the cognitions (beliefs, perceptions and attributes) that patients have about their health and illness which play a role in determining their behaviour. Cognitive-behavioural therapy, once exclusively used in the domain of clinical psychology has proved successful in dealing with illnesses that would previously have been viewed as requiring medical intervention e.g. cardiovascular disease.

Social factors like loneliness, lack of participation in social activities like exercising, the effects of unemployment and the effects of working in an environment where long and unsociable working hours are the norm are examples of where interventions may be implemented. Good research will need to be continued to identify the health risks associated with different behaviours and social conditions. This data should then be brought to the attention of the Government to bring about changes at a political, economic and social level so we may seek to eliminate conditions like poverty, unemployment and loneliness. Since many high-risk behaviours are often associated with these adverse social conditions, it may only be after changes occur at a political level that the vicious cycle of social circumstances affecting psychological and medical circumstances will be broken. At an individual level, families can exert a range of either positive or negative influences on the health status and psychosocial adjustment of patients. Family support can reduce the stressful impact of illness, assist in the development of coping mechanisms by the patient and encourage compliance with medical regimens (Flor& Turk,1985) Biopsychosocial interventions may be achieved in clinical practice by introducing these psychological or social interventions at the primary care level. For example, GPs and hospital doctors should be able to apply some psychological techniques themselves to intervene in the patients lifestyle to avoid them needing medical treatment given that there is an increased amount of training in these areas being offered in medical schools. It is no longer sufficient that doctors feel that they only deal with broken bones; they must also seek to mend the mind.

There are far-reaching implications of this model to the training of good doctors and for good medical practice. An interesting area where this model is being used is in the psychiatric hospital where a multi- disciplinary team consisting of a consultant psychiatrist, junior doctor, psychologist, social worker and psychiatric nurse consider the patients problem firstly as a whole and then divide their resources. This has a very effective result as all the needs of the patient are met and the team is aware of the needs of the patient to improve their quality of life.

Sadly, it appears that there is a scarcity of psychological or social intervention in Irish general hospitals. Hospital doctors should have clear guidelines about what health professional they should contact if they believe that a patient may benefit from psychological or social intervention. Also junior doctors, having benefited from exposure to behavioural science should be able to apply some psychological techniques themselves to intervene in the patients lifestyle to reduce the likelihood of them needing medical treatment for conditions like coronary heart disease.

World Health Organisation (WHO) Challenge

“Health is a positive concept emphasising social and personal resources, as well as physical capabilities.” WHO,1986.

As a basis of meeting the WHO challenge proposed by this statement, medical professionals will need to be familiar with the research identifying the health risks associated with different behavioural and social conditions and not just the biological illness itself. Therefore it is no longer sufficient for clinicians to state that treatment is successful in terms of its effect on a specific biological illness but it is now also necessary to know whether the treatment gives significant improvement in the way in which a person lives.

The model inherently places a lot of emphasis on the individuals control over their body and health and this may be difficult and confusing for the chronically ill or those who battle in vain with weight, smoking or drinking habits. However this is where the doctor uses his or her expertise and experience in knowing how to approach the sensitive issues of a patient’s daily life.

Doll,R., Peto,R. (1981) The causes of cancer. New York: Oxford University Press.

Engel,G. (1977) The need for a new medical model: a challenge for biomedical science. Science, 196:126-9.

Flor, H., Turk, D.C. (1985) Chronic Illness in an adult family member: Pain as a prototype.

Lazarus,R.S., Folkman,S. (1984) Stress, appraisal and coping. New York: Springer-Verlag.

Competing interests: No competing interests

• Research article
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A comprehensive multivariate model of biopsychosocial factors associated with opioid misuse and use disorder in a 2017–2018 United States national survey

• Francisco A. Montiel Ishino   ORCID: orcid.org/0000-0002-2837-726X 1 , 3 ,
• Philip R. McNab   ORCID: orcid.org/0000-0003-0169-2814 2 ,
• Tamika Gilreath   ORCID: orcid.org/0000-0001-9545-9153 3 ,
• Bonita Salmeron 1 &
• Faustine Williams   ORCID: orcid.org/0000-0002-7960-2463 1  

BMC Public Health volume  20 , Article number:  1740 ( 2020 ) Cite this article

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Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors.

A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older ( N  = 85,580; weighted N  = 248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives).

The comprehensive model found that criminality (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI] = 1.98–3.37, p  < 0.001), self-reported health (i.e., excellent compared to fair/poor [AOR = 3.71, 95% CI = 2.19–6.29, p  < 0.001], good [AOR = 3.43, 95% CI = 2.20–5.34, p < 0.001], and very good [AOR = 2.75, 95% CI = 1.90–3.98, p  < 0.001]), no private health insurance (AOR = 2.12, 95% CI = 1.55–2.89, p  < 0.001), serious psychological distress (AOR = 2.12, 95% CI = 1.55–2.89, p  < 0.001), suicidality (AOR = 1.58, 95% CI = 1.17–2.14, p  = 0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR = 3.01, 95% CI = 2.30–3.93, p  < 0.001), alcohol (AOR = 1.40, 95% CI = 1.02–1.92, p  = 0.038), marijuana (AOR = 2.24, 95% CI = 1.40–3.58, p  = 0.001), cocaine (AOR = 3.92, 95% CI = 2.14–7.17, p  < 0.001), methamphetamine (AOR = 3.32, 95% CI = 1.96–5.64, p  < 0.001), tranquilizers (AOR = 16.72, 95% CI = 9.75–28.65, p  < 0.001), and stimulants (AOR = 2.45, 95% CI = 1.03–5.87, p  = 0.044).

Conclusions

Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics.

Peer Review reports

Estimates indicate that up to 29% of persons misuse prescription pain relievers for chronic pain, [ 1 ] and between 8 to 12% develop a use disorder [ 2 , 3 ]. The United States (US) Department of Health and Human Services declared the opioid crisis a public health emergency in 2017, although the first wave of the epidemic emerged in the 1990s [ 3 ]. Opioid related deaths increased 345% between 2001 to 2016 [ 4 ]. Subsequently, between July 2016 and September 2017 deaths due to illicit opioid overdose increased by 30%, leading to an emergency declaration in 45 states [ 4 ].

Projections indicate that if current prevention and intervention strategies do not change by 2025, the rate of misuse and overdose death will rise by 61% [ 5 ]. In response to the epidemic, multiple federal, state, and local agencies have implemented various strategies to address the opioid crisis. Increasing the availability of naloxone—a medication that reverses the effects of an overdose—is projected to reduce opioid-related deaths by approximately 4% according to the most recent projections [ 6 ]. Other interventions like reduced prescribing for pain patients and excess opioid management can increase life years and quality-adjusted life years, but overdose deaths would increase among those with opioid dependence due to a move from prescription opioids to heroin [ 6 ]. Overall, supply-side prevention strategies are estimated to have minimal impact, preventing only 3.0 to 5.3% of overdose deaths [ 6 ].

As current interventions are inadequately addressing the multidimensional and far-reaching nature of the opioid epidemic [ 5 , 6 ], some scholars have suggested developing more tailored approaches to reach specific, underrepresented populations [ 7 ]. Non-Hispanic whites, for instance, have become the primary focus for multiple prevention programs and strategies as they have been found to misuse opioid at greater rates [ 8 , 9 , 10 ]. However, multiple racial/ethnic groups have been found to be at differential risk, as well as differentially affected by opioid misuse [ 8 , 9 , 10 ].

Opioid misuse and/or use disorder are also linked to other risk factors besides race and ethnicity. Scholl et al. [ 9 ] found that younger age was a significant predictor of misuse. The current opioid misuse and/or use disorder literature has also found that race/ethnicity and age become less predictive of misuse when they are considered in the context of other biopsychosocial factors such as sex and gender. For instance, Nicholson and Vincent [ 11 ] observed that Black women with lower socioeconomic status had an increased the probability of misuse, while older age, higher educational attainment, and rural residence were associated with a lower probability [ 11 , 12 ].

Other biopsychosocial factors like criminality and sexual identity, although understudied, have been associated with misuse and/or use disorder [ 13 , 14 ]. For instance, Pierce et al. [ 13 ] found that individuals testing positive for opioid use had higher rates of criminality—though the relationship was strongest for less serious crimes. Sexual minorities, such as those identifying as gay/lesbian or bisexual, have also been reported to be at risk of opioid misuse [ 14 , 15 , 16 ]. For instance, Duncan et al. [ 14 ] found that, compared to heterosexuals, those identifying as bisexual or gay/lesbian were at 78 and 115% increased odds of misuse, respectively.

General health and access to healthcare have a role in opioid misuse and/or use disorder, but most research has focused on hospitalized subpopulations and physical pain [ 1 , 17 , 18 ], which will not be covered here. The general health and access to healthcare relationship, however, is less clear among noninstitutionalized populations. One particular aspect of healthcare access in the form of health insurance is believed to have a role in opioid misuse. Some studies argue that health insurance companies may facilitate opioid misuse [ 19 ], whereas others have observed that an increase in health insurance coverage was linked to a reduction in opioid-related deaths [ 20 ]. Mental health is another facet of health for which there is an unclear relationship with opioid misuse and/or use disorder, as specific disorders may influence the association differently. Nevertheless, opioid misuse and/or use disorder has been found to be associated with severe mental illness like depression and anxiety [ 21 , 22 ], as well as suicidality [ 22 , 23 , 24 ].

Concurrent substance use such as nicotine and tobacco dependence [ 25 , 26 ], alcohol [ 27 ], sedatives [ 28 ], methamphetamines [ 29 ], tranquilizers [ 30 , 31 , 32 ], other analgesics [ 33 ], and marijuana [ 34 ] have been positively associated with opioid misuse and use disorder [ 34 , 35 ]. Marijuana’s association may be context dependent, as it has a mixed relationship with opioid use, misuse, and use disorder [ 36 ]. Polysubstance abuse must be critically assessed in context of opioid use as multiple associations may exist due to the varied effects of synergizing the opioid high. A better understanding of how polysubstance abuse occurs in context of multiple social and environmental factors is critical [ 28 , 29 , 37 ].

We hypothesized that sociodemographic factors, while crucial to the comprehensive risk model, would not be critical predictors, when they were included with socioecological and health factors, or with other substance dependence or abuse. The purpose of this study was to fill in a critical gap in the literature to improve population-level prevention strategies by identifying the most salient predictors of opioid misuse and/or use disorder.

While epidemiologic studies have examined the relationship of various risk factors on opioid misuse and use disorder among non-institutionalized populations, comprehensive models are relatively absent. To address the opioid epidemic, we need to identify the risk factors associated with the etiology of misuse to intervene at multiple levels, tailor interventions for specific populations, and prevent the distal events of use disorder like overdose. In response to this need, we comprehensively examined the relationship of opioid misuse and/or use disorder and biopsychosocial characteristics using four domains: (1) sociodemographic factors; (2) socioecological factors (e.g., criminality); (3) health factors (e.g., self-reported general health; mental health, suicidality; access to health services); and (4) other substance dependence or abuse. We took this approach to determine the most salient risk factors for opioid misuse and/or use disorder in a representative, noninstitutionalized US adult sample.

We used multivariate logistic regression on the combined 2017 [ 38 ] and 2018 [ 39 ] National Survey on Drug Use and Health (NSDUH) to examine the relationship of biopsychosocial characteristics and opioid misuse and/or use disorder. Opioid misuse was characterized as heroin use and/or prescription pain reliever misuse in the past year based on NSDUH definitions [ 40 ]. Individuals taking prescribed pain relievers may develop a tolerance to pain relief that can lead to taking the prescription at higher doses and/or more frequently than prescribed, which would constitute misuse [ 40 ]. Furthermore, heroin was included with misuse as any opioid creates the same adverse effects as prescription pain relievers, which in turn may develop into opioid use disorder [ 40 ]. Use disorder was characterized by heroin use disorder, prescription pain reliever use disorder, or heroin and prescription pain reliever use disorder, as they may not be mutually exclusive in the NSDUH [ 40 ]. Biopsychosocial characteristics, as well as sociodemographic and other substance dependence or abuse were tested independently in unadjusted models. Adjusted models were then built using a block entry method to test biopsychosocial characteristics on opioid misuse and/or misuse disorder in the following order: (Model 1) sociodemographic indicators; (Model 2) socioecological indicator; (Model 3) health indicators; and (Model 4) other substance dependence or abuse. All variables were retained as controls and covariates in subsequent models. We accounted for the complex survey design of the NSDUH by the strata and clusters provided, as well as adjusting the analytical weights to account for two years. All analyses were conducted with Stata 16 (StataCorp LLC, College Station, TX). The study received exemption from the Institutional Review Board, as no human participants were involved in this research. The analysis was not pre-registered, and the results should be considered exploratory.

Sociodemographic variables and factors

Five age categories were used: (1) 18 to 25; (2) 26 to 34; (3) 35 to 49; (4) 50 to 64; and (5) 65 and older. The binary category of male and female was used for sex/gender. Race/ethnicity was divided into seven categories: (1) non-Hispanic white; (2) non-Hispanic Black/African American; (3) non-Hispanic Native American/Alaska Native; (4) non-Hispanic Native Hawaiian/other Pacific Islander; (5) non-Hispanic Asian; (6) non-Hispanic more than one race; and (7) Hispanic. Sexual identity had three categories: (1) heterosexual; (2) gay/lesbian; and (3) bisexual. Place of residence was based on 2009 Core-Based Statistical Areas (CBSAs) defined by the Office of Management and Budget [ 41 ]: (1) CBSA with 1 million or more persons; (2) CBSA with fewer than 1 million persons; and (3) segment not in a CBSA. Total family income was divided into four categories: (1) less than $20,000; (2) $20,000 to $49,999; (3) $50,000 to $74,999; and (4) $75,000 or more. Employment status was divided into five categories: (1) full−/part-time job; (2) unemployed; (3) retired; (4) disabled; and (5) other which included keeping house full time and in school/training. Educational attainment was divided into four categories: (1) less than high school; (2) high school graduate; (3) some college/associate’s degree; and (4) college graduate.

Socioecological factors

The criminality variable was based on if the participant had been arrested and booked for breaking the law, excluding minor traffic violations. Booked was defined as being taken into custody and processed by the legal system, even if the participant was later released.

Health factors

Health factors included overall perceived health, having access to private health insurance, and mental health indicators. Overall self-reported health was categorized as (1) excellent, (2) very good, (3) good, and (4) fair/poor. The private health insurance category was based on if respondent had obtained it through (1) employment by paying premiums to an insurance company; (2) the Health Insurance Marketplace; or (3) a health maintenance organization (HMO), fee-for-service plans, or single-service plans. The mental health indicators were severe psychological distress and suicidality. A severe psychological distress indicator within the past year was based on responses from past-month Kessler-6 (K6) items and the worst month in the past-year K6 items. K6 items are from a screening instrument for nonspecific psychological distress developed by Furukawa, Kessler, Slade, and Andrews, [ 42 ] and Kessler et al. [ 43 ] Suicidality was assessed if at any time in the past year a participant had seriously thought about trying to commit suicide.

Substance misuse, dependence, and/or abuse factors

The outcome of opioid misuse and/or use disorder was defined as misuse and/or dependence or abuse of prescription pain relievers and/or heroin use in the past year. Opioid use disorder was classified using the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria for dependence or abuse criteria based on heroin use disorder, prescription pain reliever use disorder, or heroin and prescription pain reliever use disorder in the past year based on NSDUH methodology and terminology [See https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHMethodsSummDefs2018/NSDUHMethodsSummDefs2018.pdf ]. While opioid substance use disorder was classified under the DSM-V, the NSDUH used the DSM-IV criteria of dependence or abuse, as such we opted to use the DSM-V terminology [ 15 , 18 ]. Nicotine dependence in the past month was assessed using Nicotine Dependence Syndrome Scale scores and the Fagerstrom Test of Nicotine Dependence scale in the past month. Alcohol dependence and abuse in the last year was also ascertained. Dependence and abuse in the past year were also determined for marijuana, cocaine, hallucinogens, inhalants, methamphetamine, tranquilizers, stimulants (i.e., independent of methamphetamine), and sedatives [ 44 ].

Statistical analysis

We performed descriptive analyses to detail the characteristics of NSDUH sample participants. We checked the data for normality of the residuals, homoscedasticity, multicollinearity, outliers and influence. After the data were found to be adequate for the logistic regression model, four weighted multivariate models were built using Stata survey procedure. All models were weighted and accounted for clustering and stratification of the complex survey design. All findings are reported in odds ratios (ORs) or adjusted odds ratios (AORs) using a 95% confidence interval (CI) and p -value for significance criteria.

Sample characteristics

The sample consisted of 85,580 individuals (weighted N  = 248,008,986) over the age of 18. Male and female participants were represented about equally—48% male (weighted N  = 119,711,438) and 52% female (weighted N  = 119,711,438). The majority of the weighted sample was non-Hispanic white (63.6%), resided in a high population density CBSA (54.1%), identified as heterosexual (94.8%), had a family income of $75,000 or more (38.9%), were college graduates (32.1%), were employed (62.7%), had no history of arrest and booking (83.4%), were in very good health (36.1%), had private health insurance (66.6%), had no serious psychological distress in past year (88.6%), and displayed no suicidality (95.7%). See Table  1 for a detailed breakdown of the sample’s characteristics.

Of the sample, 865 individuals (weighted N  = 1,976,471) reported opioid misuse. Other substances that the sample had dependence on or abused were nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, hallucinogens, and sedatives. See Table  2 for a complete report of the sample’s substance dependence and abuse profile.

Logistic regression

Independent unadjusted models.

All sociodemographic and biopsychosocial characteristics, as well as other substance dependence or abuse were tested independently in unadjusted models to examine the relationship of each characteristic on opioid misuse. All characteristics tested with exception of residence at some level were found to be a significant factor predictive of opioid misuse. See Table  3 for all associations.

Adjusted multivariate logistic regression models

Model 1 found that sociodemographic factors such as age, sex/gender, race/ethnicity, sexual identity, educational attainment, family income, and employment status were positively associated with opioid misuse. In Model 2, we added the socioecological factor of past criminality, which was positively associated with opioid misuse, while controlling for sociodemographic factors. In Model 3, health factors such as overall reported health, serious psychological distress in past year, suicidality in the past year, and not having private health insurance were added (while controlling for sociodemographic and socioecological factors) and were positively associated with opioid misuse. In Model 4, other substance dependence and abuse were added to the model, which was controlled for sociodemographic, socioecological, and health factors. Model 4 was selected for interpretation.

Comprehensive model of opioid misuse

Compared to no prior history, having past criminality was associated with significantly increased odds of opioid misuse (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI]: 1.98–3.37, p  < 0.001). Overall self-reported health status was associated with opioid misuse when individuals reported fair/poor (AOR = 3.71, 95% CI:2.19–6.29, p  < 0.001), good (AOR = 3.43, 95% CI: 2.20–5.34, p  < 0.001), and very good health (AOR = 2.75, 95% CI: 1.90–3.98, p  < 0.001) compared to those that reported excellent health. Among individuals with no private health insurance, there was 2.12 increased adjusted odds (95% CI: 1.55–2.89, p  < 0.001) of opioid misuse compared to participants with health insurance. Similarly, participants who experienced past serious psychological distress or suicidality had 3.05 adjusted odds (95% CI: 2.20–4.23, p  < 0.001) and 1.58 odds (95% CI: 1.17–2.14, p  = 0.004) of opioid misuse, respectively, when compared to those with no history. Participants exhibiting substance dependence or abuse, with the notable exception of inhalants and sedatives, were positively associated with increased adjusted odds of opioid misuse compared to those with no substance dependence or abuse (nicotine: AOR = 3.01, 95% CI: 2.30–3.93, p  < 0.001; alcohol: AOR = 1.40, 95% CI: 1.02–1.92, p  = 0.038; marijuana: AOR = 2.24, 95% CI: 1.40–3.58, p  = 0.001; cocaine: AOR = 3.92, 95% CI: 2.14–7.17 p  < 0.001; methamphetamine: AOR = 3.32, 95% CI: 1.96–5.64 p  < 0.001; tranquilizers: AOR = 16.7, 95% CI: 9.75–28.7, p  < 0.001; stimulants: AOR = 2.45, 95% CI: 1.03–5.87, p  = 0.044). See Table  4 for more detail.

Opioid misuse and use disorder prevention strategies and programs must focus on multiple associated risk factors in the context of the person and their environment to ameliorate the ongoing epidemic. As epidemics do not occur in a vacuum, we accounted for the biopsychosocial characteristics associated with opioid misuse and/or use disorder. Sociodemographic, socioecological, and health factors, as well as other substance dependence or abuse were found to be independently significant for opioid misuse and/or use disorder. However, we found in our comprehensive model that socioecological indicators like criminality, health status factors including serious psychological distress and suicidality, and private health insurance were significant risk characteristics, as well as nicotine, alcohol, marijuana, cocaine, methamphetamine, tranquilizer, and stimulant dependence or abuse.

In our comprehensive biopsychosocial model we observed that sociodemographic factors functioned as controls rather than predictors for opioid misuse and/or use disorder. While other studies have focused on sociodemographic factors for describing risk in opioid misuse and overdose death [ 8 , 9 , 37 , 45 , 46 ], our model further revealed the significance of accounting for socioecological and health related risk factors in context of opioid misuse and/or use disorder. Our findings were similar to a study by Mojtabai, Amin-Esmaeili, Nejat, and Olfson [ 47 ] that found prescribed-opioid misuse was associated with criminality, mental health distress, and other substance abuse or dependence. Similarly, a study by Grigsby and Howard [ 34 ] discovered that prescription opioid and polysubstance users had the greatest probability of past-year criminality and mental health distress.

The relationship of opioid misuse and/or use disorder, mental health distress, and socioecological factors like criminality are complex, and may be co-occurring. To understand this risk process we can look to a study by Prince [ 22 ], which found that individuals with opioid misuse disorder who had a severe mental illness were at an increased risk of criminality and suicidality. The risk increased for those using only heroin, both heroin and prescription opioids, and all other substances, in that order [ 22 ]. Moreover, we found that common mental health disorders such as major depression, dysthymia, generalized anxiety disorder, or panic disorder in the general population predicted a 96% increase in prescribed opioid use [ 48 ]. While the relationship between criminality, mental health, and substance use is notable for developing tailored interventions, an overemphasis on this link may also perpetuate harmful stigma and mask important underlying factors. For example, adverse childhood experiences may contribute to all three: criminality, mental health disorders, and opioid misuse and use disorder [ 49 , 50 , 51 ].

Of note from our findings was that race/ethnicity in the presence of other socioecological and health factors related to polysubstance use may not be strongly associated with polysubstance dependence/abuse and opioid misuse and/or use disorder [ 52 ]. For instance, we found non-Hispanic Whites, American Indian/Alaska Natives, and non-Hispanic multiracial individuals were a significant group until polysubstance dependence/abuse was accounted for in the comprehensive model, but it may be explainable by other contextual factors [ 53 , 54 ]. Whites, for example, are often prescribed more opioids compared to their Non-Hispanic Black counterparts, regardless of genuine clinical need [ 53 ]. Furthermore, other possibilities to consider between and within racial/ethnic groups are access to illicit drugs for purchase and use of drugs by friends and family members, as well as adverse childhood experiences or trauma [ 51 , 55 , 56 , 57 ].

Other substance dependence or abuse has been associated with opioid misuse based on various risk factors [ 11 , 25 , 30 , 45 , 58 ]. In our study, we found that nicotine [ 25 , 26 ], alcohol [ 25 , 27 ], cocaine [ 58 ], methamphetamine [ 29 ], tranquilizers [ 31 , 32 , 59 ], other illicit stimulants [ 15 ], and marijuana [ 25 ] have a positive relationship with opioid misuse and use disorder. The stimulant effect from nicotine, cocaine, methamphetamine, and other illicit stimulants may mitigate the depressive effects of opioids and may increase the “high” effect [ 29 ]. Substances such as tranquilizers have been reported to be used to heighten, maintain, and extend the effect of the “high” [ 31 , 32 , 33 ], which may explain the elevated odds ratio of 16.7 when compared to all other substance dependence or abuse. Further research would be necessary to capture this context. Tranquilizer dependence and abuse is also of particular note, as most opioid overdose reported in the US involved some type of tranquilizer—for example, benzodiazepines [ 60 , 61 ].

Our study also revealed an increased association of opioid misuse and/or use disorder with marijuana compared to non-marijuana users. This relationship, however, has been found to have mixed associations in previous studies. In the cases of marijuana dependence or abuse there is a positive relationship with opioid misuse [ 34 ]. A more recent review found that medical marijuana use may decrease the probability of opioid use [ 36 ]. Campbell et al. [ 36 ] further revealed that medical cannabis laws may slow the increase of opioid overdose deaths in states with medical cannabis laws compared to states with none. Alcohol has been another substance with mixed associations for opioid misuse and use disorder. For instance, Fernandez et al. [ 27 ] reported that alcohol dependence or abuse was not associated with opioid misuse. We found, however, in our comprehensive adjusted model that alcohol dependence or abuse was associated with a higher probability for opioid misuse, in line with the findings of Witkiewitz et al. [ 62 ]. Overall, prevention strategies and prevention programs must focus on both the combined use of legal and illicit substances.

Our study used a comprehensive approach to understand how multiple biopsychosocial characteristics relate, in context, to opioid misuse and/or use disorder. Since the current opioid crisis is not unlike prior substance use disorder crises of the past, our goal was to provide data that can be used to inform primary, secondary, and tertiary prevention efforts along the continuum from opioid misuse to use disorder—with attention to particular groups and contextual factors. By identifying risk factors within our model, we were able to contextually examine biopsychosocial characteristics to inform future research and prevention strategies to intervene upon opioid use disorder and related distal outcomes for noninstitutionalized US adults. Tailored interventions could be effective for individuals reentering society from incarceration, experiencing unemployment, suffering from psychological distress, and/or using public health insurance [ 63 ]. Examples include reentry programs, jobs placement programs, and integrated mental health and substance abuse treatment [ 64 , 65 , 66 , 67 ]. Nonetheless, opioid use and misuse disorder may occur alongside use of other substances, and both the determinants and effects of concurrent use must be addressed by interventions [ 5 ]. Our hope is that our results do not perpetuate stigma but rather encourage the development of effective interventions for specific populations.

Lastly, our study using a biopsychosocial model elucidated that the opioid epidemic is not an epidemic as much a syndemic. The opioid syndemic involves multiple interacting social, health, and psychological factors with comorbid substance co-use that synergizes the negative effects of opioid misuse and/or use disorder [ 68 , 69 ]. Future interventions will need to acknowledge the opioid syndemic as multiple dynamic and complex factors and health outcomes that come as a result not only from misuse and/or use disorder, but policies and environmental contexts. As such, future studies will have to use complex models to move beyond one-dimensional outcomes to understand the contextual issues of opioid misuse and/or use disorder and improve not only overdose outcomes but person-level quality of life.

Limitations

To our knowledge, this is the first US population-level study to comprehensively address risk profiles of opioid misuse using the latest national survey data available. Like most surveys of this kind, there are limitations to the NSDUH. The most prominent limitation is the use of self-reported data. These data are subject to the individual participant’s bias, truthfulness, recollection, and knowledge. Second, although the data are nationally representative, the survey is cross-sectional, and it excludes some subsets of the population. The NSDUH only targets noninstitutionalized US citizens, so active-duty military members and institutionalized groups (e.g., prisoners, hospital patients, treatment center patients, and nursing home members) are excluded. Thus, if substance use differs between US noninstitutionalized and institutionalized groups by more than 3%, data may be problematic for the total US population [ 44 ]. A particularly notable limitation of the NSDUH is that it does not include information regarding chronic pain. This omission necessarily narrowed our analysis and inhibited our ability to create a truly comprehensive model. Another issue that may have introduced bias is participant knowledge or lack thereof concerning opioids and other substances [ 70 ]. Moreover, heroin is a less commonly used opioid and there are issues in accounting for the true prevalence of this substance use [ 70 , 71 ]. Finally, the opioid misuse data do not fully account for synthetic opioids like fentanyl.

This study provides the most recent and comprehensive risk assessment of possible biopsychosocial characteristics indicative of opioid misuse. Findings provide the population-level risk factors to improve risk assessments and to tailor future interventions to stem and ameliorate the opioid epidemic. For instance, at-risk individuals had a history of criminality, serious psychological distress, suicidality, no private health insurance, and substance dependence or abuse. Individuals, however, are not variables representative of risk factors on an outcome to opioid misuse and/or use disorder. At a population-level analysis, we must acknowledge that results of a variable-centered approach such as this work only represent findings based on a population average. More specialized approaches, such as person-centered ones, are necessary to study specific at-risk groups and opioid misuse and/or use disorder [ 72 ]. Thus, these findings serve as a population-level risk profile using the most recent US nationally representative data to inform epidemiological trends and possible large-scale interventions.

Availability of data and materials

All National Survey on Drug Use and Health datasets analyzed during the current study are available in the Substance Abuse & Mental Health Data Archive (SAMHDA) database repository, https://www.datafiles.samhsa.gov/study-series/national-survey-drug-use-and-health-nsduh-nid13517 , of which public access is open.

Abbreviations

Adjusted odds ratio

Core-based statistical areas

United States

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Acknowledgements

We would like to thank Claire Rowan for her valuable feedback, time, and support—all of which enhanced this work.

The effort of Drs. Francisco A. Montiel Ishino and Faustine Williams, and Ms. Bonita Salmeron was supported by the Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily reflect the views of the National Institutes of Health. Use of these data does not imply the National Institutes of Health agrees or disagrees with any presentations, analyses, interpretations, or conclusions herein, nor was it involved in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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FAMI and TG conceived the study. FAMI designed the study. FAMI acquired, cleaned, managed, and analyzed the data under supervision of TG. All authors interpreted the results. FAMI and BS drafted the manuscript, supervised by TG and FW. PM, TG, and FW substantially modified and approved the submitted version of the manuscripts. All authors read and approved the final version of the manuscript. The content is solely the responsibility of the authors and does not necessarily reflect the views of the National Institutes of Health.

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Montiel Ishino, F.A., McNab, P.R., Gilreath, T. et al. A comprehensive multivariate model of biopsychosocial factors associated with opioid misuse and use disorder in a 2017–2018 United States national survey. BMC Public Health 20 , 1740 (2020). https://doi.org/10.1186/s12889-020-09856-2

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Understanding the Biopsychosocial Model of Health and Wellness

A holistic approach to well-being

Dr. Amy Marschall is an autistic clinical psychologist with ADHD, working with children and adolescents who also identify with these neurotypes among others. She is certified in TF-CBT and telemental health.

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

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• The Three Aspects of the Biopsychosocial Model

How the Biopsychosocial Model Impacts Mental Health

• Criticism of the Model

How Healthcare Professionals Use the Biopsychosocial Model

How clients and patients can use the biopsychosocial model.

The biopsychosocial model is an approach to understanding mental and physical health through a multi-systems lens, understanding the influence of biology, psychology, and social environment. Dr. George Engel and Dr. John Romano developed this model in the 1970s, but the concept of this has existed in medicine for centuries.

A biopsychosocial approach to healthcare understands that these systems overlap and interact to impact each individual’s well-being and risk for illness, and understanding these systems can lead to more effective treatment. It also recognizes the importance of patient self-awareness , relationships with providers in the healthcare system, and individual life context.

Dr. Akeem Marsh, MD , physician and author of Not Just Bad Kids , described the biopsychosocial model as “at its core, centering around social determinants of mental health in connection with the ‘standard’ biomedical and psychological models. One of the more common ways in which it is represented when using the model is through the four ‘Ps’ of case formulation: predisposing, precipitating, perpetuating, and protective factors.”

Learn more about how providers can use the biopsychosocial model to offer holistic care and how clients and patients can benefit from this approach.

What Are the Three Aspects of the Biopsychosocial Model?

When understanding an individual’s physical and mental health through the biopsychosocial model, we consider physiological factors such as genetics and illness pathology (biological); thoughts, emotions, and behavior (psychological); and socioeconomic components, social support, and culture (social). How do each of these components inform the model as a whole?

“Biology” refers to our genetics , physical health, and the functioning of our organ systems. Our physical well-being impacts our mental health for multiple reasons. First, our brain is an organ and can become unwell just like any other organ. Second, physical health conditions can wear on mental health. For example, chronic pain can lead to symptoms of depression.

Additionally, just like we can have genetic predisposition to a physical disability, mental health has genetic roots as well. According to Dr. Marsh, “Genetics are the most basic level by which mental health is influenced, and on some level has an impact for everyone.” In other words, “Whatever the phenotypical expression, genetics does play a role to some degree.” The expression is in turn influenced by the environment.

Psychological

Mental health is health, and one’s psychological well-being impacts both mental and physical health. Unhealthy and maladaptive moods, thoughts, and behaviors can all be symptoms of mental health conditions, and in turn can contribute to our overall health. Mental health and behavior can be cyclical; for example, an individual who self-isolates as a symptom of depression may experience increased depressive symptoms as a result of isolation.

Routine physical activity is known to promote positive mental wellness, while inadequate or excessive physical activity can contribute to different types of mental health struggles.

Addressing these symptoms is key in improving mental health.

Dr. Marsh shares the impact of external factors on health: “The expression [of genetics] is in turn influenced by environment.” Changes in one’s environment can impact mental health, both positively and negatively. In the previous example of depression and isolation , individuals who have appropriate social support experience fewer mental health issues compared to those without this support.

An individual who is struggling with their mental health might need social support and environmental changes just as much as they need therapy or medication intervention for their symptoms.

Traditionally, healthcare has focused primarily on the medical and biological side of the patient’s needs, and mental health care has focused on the psychological side. While it makes logical sense to address manifesting symptoms, a holistic approach to care that aims to address the social as well as the psychological and biological contributions to illness can be more health-promoting.

Sometimes, for instance, addressing an underlying social need or environmental stressor can improve mental health more effectively than other psychological or biological treatments. This may allow for less-invasive treatments and interventions, and it can improve the individual’s well-being in a way that non-holistic models overlook.

Criticism of the Biopsychosocial Model

Although many providers support a holistic approach to care and implement the biopsychosocial model in practice, like any model it has limitations. Dr. Marsh notes that there are concerns about its evidence backing: “Some people believe that [the biopsychosocial model] is not scientific, as in it has not quite met the ‘gold standard’ of being validated through multiple randomized trials, as it is a uniquely challenging study prospect.” How can researchers study controlled variables in a model that requires holistic care that takes individual needs into account?

At the same time, the model has many strengths and can benefit patients in the healthcare and mental health systems: “It has been researched extensively and shown positive results when applied in different ways,” Dr. Marsh said.

Mental health professionals who utilize the biopsychosocial model in practice include extensive medical history, family history, genetics, and social factors in assessments in addition to psychological information.

Additionally, they use this information to ensure that all of the client’s needs are met , as many medical issues can manifest with mental health symptoms. Therapy services to treat, for example, depression caused by an under-functioning thyroid is unlikely to be effective.

When adopted appropriately, health professionals conceptualize patients that they work with in a broad context that attempts to understand and see patients as a whole person—complex human being with nuance, so much more than just a cluster of symptoms or diagnosis.

This model lets providers see the whole person beyond their presenting symptoms.

While the biopsychosocial model has its place in the healthcare and mental healthcare systems, individuals might also implement tenants of this model in their own lives. This means being aware of how environmental factors impact their mental and physical health, as well as how their genetics and medical history in turn influence behaviors, thoughts, and emotions.

It can help individuals better understand themselves as complex, whole beings as well. “I believe that [the biopsychosocial model] could enhance their self-awareness and understanding of themselves, along with broadening their personal sense of what issues or challenges may be going on with them," says Dr. Marsh.

Engel GL. The need for a new medical model: a challenge for biomedicine .  Science . 1977;196(4286):129-136. doi:10.1126/science.847460

Soltani S, Kopala-Sibley DC, Noel M. The co-occurrence of pediatric chronic pain and depression: a narrative review and conceptualization of mutual maintenance .  The Clinical Journal of Pain . 2019;35(7):633-643. doi:10.1097/AJP.0000000000000723

Alsubaie MM, Stain HJ, Webster LAD, Wadman R. The role of sources of social support on depression and quality of life for university students .  International Journal of Adolescence and Youth . 2019;24(4):484-496. doi:10.1080/02673843.2019.1568887

By Amy Marschall, PsyD Dr. Amy Marschall is an autistic clinical psychologist with ADHD, working with children and adolescents who also identify with these neurotypes among others. She is certified in TF-CBT and telemental health.

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• Brain Behav
• v.11(5); 2021 May

How biopsychosocial depressive risk shapes behavioral and neural responses to social evaluation in adolescence

Jason stretton.

1 Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge UK

Nicholas D Walsh

2 School of Psychology, Faculty of Social Sciences, University of East Anglia, Norwich UK

3 Division of Humanities and Social Sciences, California Institute of Technology, Pasadena CA, USA

Susanne Schweizer

4 Division of Psychology and Language Sciences, University College London, London UK

Anne‐Laura van Harmelen

5 Developmental Psychiatry Section, Department of Psychiatry, University of Cambridge, Cambridge UK

Michael Lombardo

6 Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia Cyprus

Ian Goodyer

Tim dalgleish.

7 Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge UK

Associated Data

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Introduction

Understanding the emotional responsivity style and neurocognitive profiles of depression‐related processes in at‐risk youth may be helpful in revealing those most likely to develop affective disorders. However, the multiplicity of biopsychosocial risk factors makes it difficult to disentangle unique and combined effects at a neurobiological level.

In a population‐derived sample of 56 older adolescents (aged 17–20), we adopted partial least squares regression and correlation models to explore the relationships between multivariate biopsychosocial risks for later depression, emotional response style, and fMRI activity, to rejecting and inclusive social feedback.

Behaviorally, higher depressive risk was associated with both reduced negative affect following negative social feedback and reduced positive affect following positive social feedback. In response to both cues of rejection and inclusion, we observed a general neural pattern of increased cingulate, temporal, and striatal activity in the brain. Secondly, in response to rejection only , we observed a pattern of activity in ostensibly executive control‐ and emotion regulation‐related brain regions encompassing fronto‐parietal brain networks including the angular gyrus.

The results suggest that risk for depression is associated with a pervasive emotional insensitivity in the face of positive and negative social feedback.

To understand the emotional responsivity style and neurocognitive profiles of depression‐related processes in at‐risk youth may be helpful in predicting those most likely to develop affective disorders. We explored the relationships between multivariate biopsychosocial risks for later depression, emotional response style and fMRI activity, to rejecting and inclusive social feedback. Data indicate risk for depression in adolescence is associated with a pervasive emotional insensitivity in the face of positive and negative social feedback.

1. INTRODUCTION

With an estimated 300 million people suffering from depression, major depressive disorder (MDD) is the leading cause of disability globally (WHO, 2017 ). Many cases of MDD onset prior to or during adolescence (Kessler et al.,  2005 ), and while there is a growing body of evidence surrounding the epidemiology of childhood and adolescent depression, it is still difficult to predict who will go on to develop MDD (Hankin,  2015 ). Research into etiological mechanisms needs to investigate precursors of MDD within the context of wider socioemotional development. In this study, we explored the relationship between biopsychosocial risk for depression and affective and neural responses to positive and negative social evaluation.

1.1. Emotional responsivity in depression

MDD is primarily characterized as a disorder of mood, indexed by alterations in the style of emotional responding to positive and negative stimuli (Rottenberg et al.,  2005 ). There are several competing accounts as to how the depressed mood state impacts emotional responding. Negative potentiation views propose that those with MDD show increased negative reactions to negative stimuli, with negligible differences in positive reactivity, relative to nondepressed peers (Golin et al.,  1977 ). Positive attenuation views, in contrast, propose reduced positive reactivity to positive stimuli in those with MDD, with little or no differences in negative reactivity (Allen et al.,  1999 ), though as this view focuses primarily on reactivity to positive stimuli, positive attenuation is compatible with negative potentiation (i.e., individuals with MDD could exhibit both patterns simultaneously) (Bylsma et al.,  2008 ). Finally, the emotion context insensitivity (ECI) hypothesis (Rottenberg et al.,  2005 ) proposes reduced emotional reactivity to both positive and negative stimuli, in line with evolutionary accounts of depression as a functional state that fosters motivational disengagement from the environment (Allen & Badcock,  2003 ; Beck & Bredemeier,  2016 ; Gilbert & Allan,  1998 ; Nesse,  2000 ). Meta‐analysis of studies evaluating emotional reactivity in MDD suggests that the ECI account is most consistent with the data, with clinically depressed individuals exhibiting reductions in both positive and negative affect relative to nondepressed peers (Bylsma et al.,  2008 ). Furthermore, formerly depressed individuals who are currently not experiencing a depressive episode have been shown to exhibit an ECI style of emotional responding relative to never‐depressed controls, consistent with the notion that ECI may reflect a trait‐like depressive disposition (Iacono et al.,  1984 ). Consistent with this, in such remitted samples, degree of ECI has been shown to predict later depressive relapse (Lethbridge & Allen,  2008 ). This raises the important question as to whether those who have never experienced depression but who are deemed at risk of later MDD onset would exhibit systematic differences in the way that they respond to emotional provocation and whether these differences would be consistent with an ECI analysis.

1.2. Depression risk

There are multiple pathways to depression, with risk factors spanning the entire biopsychosocial spectrum including: (a) childhood adversity (CA)—comprising diverse environmental factors, including but not limited to physical abuse, sexual abuse, emotional maltreatment, low socioeconomic status, parental psychopathology, negative life events, and family discord. CA is a robust predictor of later psychopathology including depression (Costello et al.,  2005 ; Spinhoven et al.,  2010 ; van Harmelen et al.,  2016 ); (b) predisposing biological factors including increased cortisol reactivity (Bruce et al.,  2009 ; Power et al.,  2012 ); (c) clinical predictors including presence of subclinical levels of depressive symptomatology, a history of previous psychiatric difficulties (Kim‐Cohen et al.,  2003 ), frequency of mild daily stressors (Monroe & Harkness,  2005 ), and psychological factors such as high neuroticism (Clark et al.,  1994 ) and low self‐esteem (Orth et al.,  2008 ). This constellation of risk across the lifespan has been framed within a triple vulnerability model (Barlow,  2000 ) which proposes three strata of risk—general biological and subsequent early environmental (predominantly CA) vulnerabilities that then provide a diathesis context for later stressors such as negative life events, social isolation, and other sources of distress that are disorder‐specific and in this case lead to the onset of depression (Brown & Naragon‐Gainey,  2013 ).

This extensive constellation of identified biopsychosocial risk factors comprises elements that are arguably highly interrelated, making it difficult to disentangle unique and combined effects at a biological level (Rutter,  2012 ). The current literature also invariably focuses on one or a small number of risk factors. Consequently, there is a need for multivariate approaches encompassing multiple risk factors to better inform the relationship between depression risk and the socioemotional and neurocognitive profiles of ecologically valid emotional responding. In the present study, we adopted a multivariate approach to risk, including a range of key biological, social, and psychiatric variables that had been evaluated as part of a longitudinal cohort study of adolescent emotional development (Goodyer et al.,  2010 ). We used a social evaluation paradigm (Dalgleish et al.,  2017 ) to examine the relationship between biopsychosocial risk for depression, and emotional responses to positive and negative information, in this case rejecting and inclusive social feedback which we reasoned would have particular emotional potency for an adolescent sample (Blakemore,  2008 ).

Investigating the association between risk for depression and emotional responses to social evaluation is critical as it is a period of rapidly changing social environments, and the need to belong is strong and important to fulfil (Patrick et al.,  2007 ). Cues of social acceptance and social rejection provide critical information about the adolescent's degree of inclusivity at any given social moment (Baumeister & Leary,  1995 ). A social evaluation paradigm therefore represents a compelling context within which to examine patterns of emotional responsivity in this age group. Prior studies show blunted positive affect is evident in children at high risk for developing depression due to parental psychopathology (Weissman et al.,  1987 ) and lower levels of positive emotion in nonclinical adolescent samples can predict depressive symptoms a year later (Lonigan et al.,  2003 ). Furthermore, adolescents at high risk for developing depression and currently depressed adolescents display similarly decreased positive affect compared with low‐risk adolescents (Dietz et al.,  2008 ). Based on this, and the aforementioned meta‐analytic evidence in depressed adults (Bylsma et al.,  2008 ), our behavioral hypothesis was adolescents with higher levels of multivariate risk for later depression would exhibit ECI in their emotional responses, with reduced positive and negative reactivity to cues of social inclusion and rejection, respectively.

1.3. Depression risk and neural responsivity to psychosocial stress and reward

As well as the relationship between multivariate depression risk and behavioral indices of emotional reactivity in response to social rejection and inclusion, we additionally wanted to elucidate patterns of neural activity. Specifically, we planned to elucidate latent brain‐behavior relationships using a multivariate partial least squares (PLS) correlation approach (Krishnan et al.,  2011 ) to investigate the latent structure of the biopsychosocial factor(s) associated with risk and their relationship with neural activity during the social evaluation task measured using functional magnetic resonance imaging (fMRI).

Differential patterns of neural processing of stress and reward are well‐established in the MDD literature (see (Pizzagalli,  2014 ) for review), indicating that the ventral and dorsal striatum may play a pivotal role. The processing of monetary reward (Pizzagalli et al.,  2009 ) and socially appetitive stimuli (Elliott et al.,  1998 ; Epstein et al.,  2006 ) in MDD has been consistently associated with blunted activation of the ventral and dorsal striatum thought to reflect dysfunction in coding the motivational significance of rewards and deficiencies in positive‐reinforcement learning, respectively (Pizzagalli,  2014 ).. This is in line with event‐related potential (ERP) data which consistently shows diminished activity to the processing of motivationally salient stimuli and to the receipt of reward, suggesting depression is associated with emotional disengagement and deficits in reward processing (Proudfit et al., 2015 ).

In addition, there is evidence for disrupted processing of psychosocial stress and reward in samples defined by risk factors for depression and for psychopathology more generally. In the context of CA psychosocial stress in the form of social rejection has been associated with increased dorsomedial prefrontal cortex (PFC) activation in young adults with a history of childhood emotional maltreatment (van Harmelen et al.,  2014 ) and with reduced connectivity and activation of the dorsal anterior cingulate cortex (dACC) and dorsolateral PFC in children exposed to neglect, physical abuse and domestic violence (Puetz et al.,  2014 ). Additionally, increased subgenual PFC activity during social rejection has been shown to be predictive of depressive symptomatology one year after assessment (Masten et al.,  2011 ). Only one preliminary study has investigated the psychosocial reward of social acceptance, showing that high‐risk youth, defined as those with a parental history of depression, exhibited reduced responses to acceptance in the caudate, insula, and ACC and increased activity in fronto‐temporal regions relative to low‐risk controls (Olino et al.,  2015 ). This is in line with monetary reward tasks, which have generally shown a reduced striatal response to reward anticipation and feedback in those who have experienced early adversity (Goff et al.,  2013 ; Hanson et al.,  2016 ; Mehta et al.,  2010 ). Taken together, these results suggest the striatum may be a potential neural substrate for the interaction between stress, reward, risk, and MDD (Pizzagalli,  2014 ). Our social evaluation paradigm provides context for both psychosocial reward, through social inclusion, and psychosocial stress, through social rejection and is thus well suited to investigate this further.

There are a number of possible ways in which patterns of neural activity across these implicated circuits associated with positive and negative social feedback may relate to the different theories articulated above concerning emotional responsivity in depression and in those at risk. First, attenuated emotional responsiveness may be reflected in differential activity in emotion regulation and executive control brain regions in the context of either just positive (the positive attenuation model) or both positive and negative (the ECI model) social feedback, reflecting enhanced top‐down control over emotional responsiveness within these neural circuits. Secondly, there may be an analogous shift in the influence of bottom‐up processes whereby activity in reward‐related brain regions might be altered in response to positive evaluations, relative to neutral evaluations (positive attenuation models). Finally, activity in limbic brain regions predominantly associated with negative emotionality, such as the amygdala, may also be affected (negative reactivity and ECI models) in response to negative evaluations relative to neutral evaluations. Examination of patterns of neural activity using FMRI during the social evaluation task will enable us to investigate these different possibilities.

2. MATERIALS AND METHODS

2.1. participants.

Participants [ N  = 56; Mean ( SD ) age = 18 (0.7), range 17–20 years; 31 females, see Table  1 ] were a subset from the ROOTS study (Total N  = 1,143), a population‐derived longitudinal investigation of adolescent emotional development (Goodyer et al.,  2010 ). Inclusion criteria for this neuroimaging sub‐study were as follows: normal or corrected‐to‐normal vision; English speaking; and of Northern European descent (to facilitate genetic allele comparisons for different components of the study). Exclusion criteria were as follows: any history of neurological trauma resulting in loss of consciousness; current psychotropic medication use; current neurological disorder; current DSM‐IV Axis 1 disorder; presence of metal in body; specific learning disability, and IQ < 85 on the Weschler Abbreviated Scale of Intelligence (WASI). The selection and recruitment process sought to recruit a subsample with a broad range of depressive risk and is described in more detail in (Walsh et al.,  2012 ) (see also Figure  S1 ).

Sample demographics and social evaluation ratings

Means and standard deviations () are shown for age, positive affective response ratings, and negative affective response ratings. ACORN; A Classification of Residential Neighborhoods; CA, childhood adversity; SES; Socioeconomic status.

Importantly, participants recruited to this neuroimaging sub‐study showed no significant selection bias compared with the total ROOTS sample in terms of gender ratio or socioeconomic status as assessed using the UK ACORN (A Classification Of Residential Neighborhoods) geodemographic measure (Morgan & Chinn,  1983 ) ( http://www.caci.co.uk ). Participants in the present study did have lower levels of self‐reported depressive symptoms at the time of scanning relative to the overall ROOTS sample (measured age 17), due to the exclusion of those ROOTS members with current psychiatric disorders from the present study (see below).

2.2. Ethical considerations

The study was carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines and approved by the Cambridgeshire Research Ethics Committee. All participants provided written informed consent. If participants were under 18 years old, informed written consent was gathered from a parent and/or legal guardian.

2.3. Biopsychosocial risk variables

The biopsychosocial risk variables for depression assessed in the current study were collected longitudinally at the following time‐points: Time 1 aged 14; Time 2 aged 16; Time 3 aged 17. Assessed variables included: retrospectively reported childhood adversities (CA; Time 1); previous participant psychiatric history (Times 1 and 3); parental psychiatric history (Time 1); family discord as measured by the Family Assessment Device (FAD; Time 1); recent negative life events assessed at age 14 (RNLE14; Time 1) and again at age 17 (RNLE17; Time 3); morning cortisol levels (Time 1); and current depressive symptomatology (on the Beck Depression Inventory; BDI; Time 3). Details about each variable are included in the Data S1 , Figure  S2 , and Table  S2 . See Table  S3 for a correlation matrix of the variables.

2.4. Social evaluation task

The social evaluation task was administered after Time 3. A full task description can be found in the Data S1 and in (Dalgleish et al.,  2017 ). The task is designed to elicit affect based on peer‐mediated social feedback based on personally salient information. Briefly, each participant is subject to 36 judgements—evaluations by 6 judges on 6 social attributes—based on a pre‐recorded video of the participant discussing their lifetime aspirations. The six social attributes comprise social competence, motivation, self‐confidence, personal strength, social attractiveness, and emotional sensitivity. Judgements are delivered as part of a “Big Brother” style voting assessment of the participant, relative to three peers. The task is delivered within the MRI scanner. Each of the 36 judgement epochs begins with an 8‐s slide showing which judge would be judging which attribute (e.g., David will now be judging you on social attractiveness ). This is followed by an 8‐s fixation period, and then an 8‐s result (feedback) slide, showing whether each participant has been judged to be the best (positive feedback), middle (neutral feedback), or worst (negative feedback) on that particular attribute relative to their peers. Following each result slide, participants complete a 10‐s VAS affective response rating (ranging from 0 (extremely negative)—11 (extremely positive)) to index how they feel following the feedback. The peers and judges were in fact fictional and the 36 trials were rigged so as to provide 12 trials of “best” feedback (Positive trials), 12 trials of “middle” feedback (Neutral trials), and 12 trials of “worst” feedback (Negative trials) for each participant. Attribute and judge order were counterbalanced across participants. At the end of the 36 feedback trials, an overall judgement from each judge was presented to the participant, detailing whether they had made it through to the next round. Of these six final judgements, 5 were “worst” and one was “middle” resulting in the subject being told they had been voted out. This was done to maintain task credibility, and these ratings were set aside from the analysis.

2.5. Behavioral analysis

PLS regression is a dimension reduction approach that is coupled with a regression model. PLS works well for data with relatively small sample sizes and a large number of parameters (Wold et al.,  1984 ). The algorithm reduces the number of parameters using a technique similar to principal components analysis to extract a set of components that describes maximum correlation between the predictors and response variable(s). Components were evaluated for significance based on the percentage of variance explained in both the predictor variables and the response variable and were retained if they explained more than 10% (equivalent to small effect size (Cohen,  1992 )) of the variance in both variable sets. If a component was retained, the factor loadings were then used to determine the importance of each variable to the component, measured as correlation coefficients ±0.4. Low factor loadings indicate relatively low importance to the projection of the latent variable but still contribute to the overall pattern of the latent factor. First, all psychiatric risk variables (CA, RNLE14, RNLE17, previous psychiatric history, parental psychiatric history, FAD score, BDI score, cortisol) were entered into separate PLS regression models to predict the mean affective response ratings across the Positive and Negative (each minus Neutral) trials of the social evaluation task, obtained during the fMRI session. All analyses were carried out using SPSS v22.

2.6. Image acquisition and preprocessing

MRI scanning was conducted at the Medical Research Council Cognition and Brain Sciences Unit on a 3‐Tesla Trio Tim Magnetic Resonance Imaging scanner (Siemens, Germany) by using a 32‐channel head coil gradient set. Whole‐brain data were acquired with echoplanar T2*‐weighted imaging (EPI), sensitive to BOLD signal contrast (48 sagittal slices, 3 mm‐thickness; TR = 2,000 ms; TE = 30 ms; flip angle = 78°; FOV 192 mm; voxel size: 3 × 3 × 3 mm). To provide for equilibration effects the first 5 volumes were discarded. T1‐weighted structural images were acquired at a resolution of 1x1x1 mm.

SPM8 software ( www.fil.ion.ucl.ac.uk/spm/ ) was used for fMRI data analysis. The EPI images were sinc interpolated in time for correction of slice timing differences and realignment to the first scan by rigid body transformations to correct for head movements. The mean EPI for each subject was inspected after realignment to ensure there were none with signal dropout. Field maps were estimated from the phase difference between the images acquired at the short and long TE and unwrapped, employing the FieldMap toolbox. Field map and EPI imaging parameters were used to establish voxel displacements in the EPI image. Application of the inverse displacement to the EPI images served the correction of distortions. Utilizing linear and nonlinear transformations, and smoothing with a Gaussian kernel of full‐width‐half‐maximum (FWHM) 8‐mm, EPI, and structural images were coregistered and normalized to the T1 standard template in Montreal Neurological Institute (MNI) space (International Consortium for Brain Mapping). Global changes were removed by proportional scaling, and high‐pass temporal filtering with a cutoff of 128 s was used to remove low‐frequency drifts in signal.

2.7. Imaging analysis

Briefly, after preprocessing statistical analysis was performed using the general linear model. Analysis was carried out to establish each participant's voxel‐wise activation during the feedback and rating trials. Activated voxels in each experimental context were identified using an epoch‐related statistical model representing each of the three feedback trials and subsequent affect ratings, convolved with a canonical haemodynamic response function and mean‐corrected. Six head‐motion parameters defined by the realignment were added to the model as regressors of no interest. Multiple linear regression was then applied to generate parameter estimates for each regressor at every voxel. At the first level, the following feedback contrasts (based on activations to the result slides) were generated; “positive feedback” minus “neutral feedback” to isolate social acceptance/inclusion; “negative feedback” minus “neutral feedback” to isolate social rejection/exclusion.

2.8. Multivariate associations between biopsychosocial risk and fMRI activations

To identify neural systems correlated with a latent variable (LV) for biopsychosocial psychiatric risk, measured by our combination of CA, RNLE14, RNLE17, previous psychiatric history, parental psychiatric history, FAD score, BDI score, and morning cortisol, we applied the multivariate statistical technique of PLS correlation, using PLSGUI ( http://www.rotman‐baycrest.on.ca/pls/ ). The goal of this “Behavioral PLS” is to take 2 multivariate matrices (one for behavioral variables and the other for brain variables) and find the combination of LVs from the brain and behavioral matrices that express the largest amount of common information (i.e., largest covariance) (Krishnan et al.,  2011 ). This has been applied in studies of obsessive‐compulsive disorder, autism, and psychotic disorder (Dean et al.,  2013 ; Ecker et al.,  2012 ; Menzies et al.,  2007 ) among others. In our case, these analyses identify the set of brain voxels most robustly correlated with the LV pattern underlying biopsychosocial risk measures in adolescents responding to social evaluation. A permutation test (10,000 permutations) evaluated the significance of identified LVs, and 10,000 bootstrap resamples were used to assess the reliability of voxels with the strongest contribution to the pattern. For visualization of the most reliable voxels contributing to the patterns, we used a bootstrap ratio of (−) 3 and a cluster extent threshold of 250 voxels. The bootstrap ratio can be viewed/interpreted as a pseudo Z ‐statistic, since it is the ratio of a voxel's “salience” (i.e., a latent variable linear combination of the original variables) divided by the standard error estimated from bootstrapping (Krishnan et al.,  2011 ). This bootstrap ratio allows us to infer which voxels were most important and reliable in terms of their contribution to the overall pattern identified by PLS.

Using this approach, we investigated whether our combination of adverse biopsychosocial variables was associated with activation patterns in each of the feedback contrasts. Guided by the whole‐sample conjunction analysis results reported previously (Dalgleish et al.,  2017 ) and our hypothesis of a common pattern of heightened risk being associated with emotional attenuation for both Positive and Negative social feedback in the behavioral data, we initially ran a “two‐condition PLS” (Positive > Neutral and Negative > Neutral) to assess whether any latent brain‐behavior pairs explained a similar degree of variance across both feedback conditions. We then ran separate PLS analyses on the Positive > Neutral and Negative > Neutral contrasts in order to test for any context‐specific effects of positive and negative evaluation alone.

2.9. Analysis excluding participants with a psychiatric history

Although prior mental health diagnosis is a risk factor for future psychopathology, in the context of depression vulnerability and to clarify the relevance of our findings to those who had never previously met criteria for a diagnosis, it was important to test that any resulting brain‐behavior pairs were not specific to prior mental health difficulties. Thus, we ran an additional sensitivity analysis by conducting the same PLS analyses on the subsample of participants who had no previous psychiatric history of any kind ( n  = 38).

2.10. Univariate associations within the fMRI data

We also performed follow‐up univariate analyses in SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). These allow us to test the univariate contribution of each psychosocial risk variable to each feedback contrast in order to increase confidence in the contribution of any one single variable to patterns of neural activity. A series of 1‐sample t tests were run on each feedback contrast with the following risk variables as covariates: CA, RNLE14, RNLE17, previous psychiatric history, parental psychiatric history, FAD score, BDI score, and morning cortisol level. We performed a whole‐brain analysis, and images were assessed for cluster‐wise significance using a cluster‐defining threshold of p  < .001 uncorrected; the .05 FWE‐corrected critical cluster size was 350 voxels ( https://doi.org/10.5281/zenodo.1689891 ). In order to additionally test the contribution of each single variable to the patterns of activity across both positive and negative evaluation, we entered the Positive > Neutral and Negative > Neutral feedback contrasts into a series of repeated measures ANOVAs including the same covariates listed above.

3.1. Behavioral data

We have previously reported the main behavioral and neural effects across the whole sample on the Social Evaluation Task (Dalgleish et al.,  2017 ) and the present focus is on the relationship between task performance and biopsychosocial risk for depression. To summarize these prior findings, as expected participants overall rated Negative social feedback as more upsetting than Neutral feedback ( t  = 12.6, df  = 55, p  < .001) and Positive feedback as less upsetting than Neutral ( t  = 13.5, df  = 55, p  < .001) (see Table  1 ).

Turning to the relationships involving biopsychosocial risk, our PLS regression model of the behavioral data identified one optimal risk component that predicted affective response ratings to Negative (minus Neutral) feedback trials. This component loaded most strongly on CA (0.55), BDI scores (0.44), and FAD scores (0.57) (see Table  S4 for full component loadings). The component explained a small‐medium amount of variance (Cohen,  1992 ) in the predictor risk variables ( R 2  = .16) and a medium amount of variance in the affective response ratings to these negative trials ( R 2  = .23). A Pearson correlation confirmed that this risk component was associated with lower levels of participant‐rated negative affect ( r  = .48, p  < .001; n.b. greater negative affect was indexed with increasing negative integers as per the subtraction formula to derive the rating score, hence the positive correlation). The predominantly positive loadings (6 of the 8 risk variables), together with an overall association with lower negative affect, are therefore in line with the ECI hypothesis of higher depression risk being associated with blunted emotional responding to negative feedback (Rottenberg et al.,  2005 ), and counter to negative potentiation models relating risk to augmented responding (Golin et al.,  1977 ; Rottenberg et al.,  2005 ).

In predicting affective response ratings to Positive (minus Neutral) feedback, PLS again identified one risk component. This component loaded most strongly on CA (−0.44) and FAD scores (−0.65), as with the Negative feedback contrast, as well as morning cortisol (−0.41), and explained a small‐medium amount of variance in the predictor risk variables ( R 2  = .17) and a small‐medium amount of variance in the affective response ratings to these Positive trials ( R 2  = .14). A Pearson correlation confirmed the risk component was associated with positivity ( r  = .37, p  = .004). The negative loadings of the PLS component on all 8 variables, together with an overall positive association with positive affect, are again consistent with the ECI hypothesis, but also with the positive attenuation view (Allen et al.,  1999 ), of higher depression risk being associated with blunted emotional response to positive feedback.

Overall, the behavioral PLS regression results indicate that those with higher biopsychosocial risk profiles for depression derive both reduced negative and reduced positive affect from relevant socially evaluative feedback, relative to lower risk participants, in line with predictions based on the ECI hypothesis (Rottenberg et al.,  2005 ).

3.2. FMRI results

In our previous paper (Dalgleish et al.,  2017 ), across all participants we reported greater activation in the bilateral dACC and left AI when participants received negative compared with neutral social feedback, consistent with the wider social rejection literature (see (Eisenberger,  2012 ) for review) suggesting that this dACC‐AI matrix is implicated in the processing of “social pain”. However, we also found that these same regions were activated (along with the ventromedial prefrontal cortex (vmPFC) and ventral striatum bilaterally) when receiving positive (relative to neutral) social feedback. A conjunction analysis revealed that these activations in the dACC and AI were significantly present across both contrasts indicating a shared neural involvement in the processing of social rejection and inclusion information in these regions. Here, we wanted to examine the relationship between our multivariate depressive risk factors and neural activation to Negative and Positive (relative to Neutral) social feedback, both when considered together within one analysis in line these earlier results (Dalgleish et al.,  2017 ), as well when considered separately.

3.3. Multivariate PLS correlation activation

In line with this, to assess the collective contribution of our set of biopsychosocial risk variables on the relevant activation maps, multivariate PLS correlation was first conducted across both Positive (minus Neutral) and Negative (minus Neutral) conditions of the Social Evaluation Task to assess any shared behavioral contribution, and thereafter on each of the two feedback contrasts separately.

3.3.1. Shared behavioral contributions to Negative > Neutral AND Positive > Neutral feedback

A 2‐condition PLS model of the relationship between our collective risk variables and brain activity in response to both Negative > Neutral AND Positive > Neutral feedback conditions revealed one significant latent brain‐behavior pair, accounting for 30% of the variance ( d  = 124.2, permutation p  = .003). Figure  1b shows the PLS behavioral saliences (transformed into correlations for ease of interpretation). Saliences are similar to the loadings in principal component analysis (PCA). The error bars show the 95% confidence intervals estimated from bootstrapping. This “correlation overview” graph shows that for the significant LV pair, there were stable correlations (i.e., confidence intervals not including zero) between the “brain scores” (i.e., the dot‐product of the brain LV saliences and the individual's imaging data, giving an overall summary of the brain data for each individual) (McIntosh and Lobaugh 2004 ) and presence of CA, in both the Positive AND Negative feedback conditions. For the remaining risk variables, the individual correlations were less robust as the confidence intervals included zero (in at least one of the two conditions), although it is important to note that these variables of course still contribute to the overall pattern of the brain‐behavior LV. The brain regions where this pattern was most reliably identified included a pattern of activation in the ventral striatum, posterior cingulate cortex, middle cingulate cortex, middle temporal gyrus and superior temporal gyrus (Figure  1a and Table  2 ). The data suggest a shared contribution of the presence of CA specifically (and of heightened risk generally) to patterns of increased activation in these regions across both Positive and Negative (minus Neutral) social feedback. Follow‐up univariate analysis revealed no significant activations.

(a) Activated brain regions (R = Right. L = Left) and (b) behavioral correlations, with biopsychosocial risk variables from the PLS model examining neural responses to the Negative > Neutral AND Positive > Neutral feedback contrasts. BDI, Beck depression inventory; CA, childhood adversity; FAD, family assessment device; Par, parental; PHx, psychiatric history; RNLE, recent negative life events

Activated brain regions associated with the Negative > Neutral AND Positive > Neutral feedback contrasts in the PLS model including the set of biopsychosocial risk variables

3.3.2. Behavioral contribution to Negative > Neutral feedback

We next compiled a PLS model of the relationship between our collective risk variables and brain activity for the Negative > Neutral contrast considered alone. One significant latent brain–behavior pair (LV) was identified, which accounted for 33.4% of the covariance between activation in this contrast and our set of biopsychosocial risk variables ( d  = 97.1, permutation p  = .037). Figure  2b shows the PLS behavioral saliences were robustly associated with three of the risk variables: parental psychopathology ( r  = .34), current BDI scores ( r  = −.26), and cortisol ( r  = .25). Parental psychopathology and cortisol showed positive associations, while BDI showed a negative association. The brain regions where this pattern was most reliably identified comprise the superior frontal gyrus, middle frontal gyrus, and posterior superior temporal lobe extending into the inferior parietal lobe and angular gyrus (see Figure  2a and Table  3 ). There were no significant active voxels for any of the univariate regression analyses.

(a) Activated brain regions (R = Right. L = Left) and (b) behavioral correlations, with biopsychosocial risk variables from the PLS model examining neural responses to the Negative > Neutral feedback contrast. BDI, Becks depression inventory; CA, childhood adversity; FAD, family assessment device; Par, parental; PHx, psychiatric history; RNLE, recent negative life events

Activated brain regions associated with the Negative > Neutral Contrast in the PLS model including the set of biopsychosocial risk variables

3.3.3. Behavioral contribution to Positive > Neutral feedback

Our PLS model revealed no significant brain‐behavior LV pair(s) for Positive > Neutral feedback trials considered alone, nor were there any significant univariate regression effects.

3.4. Sensitivity analysis: multivariate PLS activations excluding individuals with any psychiatric history

We subsequently ran the same three PLS analyses (negative and positive feedback together, negative alone, positive alone) as above on participants with no prior psychiatric history ( n  = 38) in order to test the sensitivity of the brain‐behavior relationships to prior mental illness (though it is worth reiterating that psychiatric history did not robustly contribute to the initial analysis, see Figures  1b and ​ and2b 2b for correlational overviews). Across all three analyses, a similar pattern of results was observed to those reported above (see Figures  S3 and S4 and Tables  S5 and S6 ). For the shared condition PLS, in line with our initial analysis only 1 LV pair was significant. This was robustly positively associated with CA across both conditions and involved the same brain regions (Figure  S3 and Table  S5 ). The Negative > Neutral feedback, in line with our initial analysis, indicated a robust negative association with BDI and a positive association with parental psychiatric history but now with the addition of a positive association with CA, and neurally encompassed the same brain regions as before (Figure  S4 and Table  S6 ). There was again no significant LV pair for the Positive > Neutral Feedback condition alone.

These results indicate that the findings including the whole sample were not significantly skewed by the inclusion of a minority of participants with a previous psychiatric history.

4. DISCUSSION

We investigated the relationship between a cluster of theoretically derived and empirically validated biopsychosocial depression risk variables and behavior and neural activations in a social evaluation task in a longitudinal population‐derived sample of late adolescents (Dalgleish et al.,  2017 ). As hypothesized, and in line with the emotion context insensitivity (ECI) hypothesis of blunted emotional reactivity applied to the domain of depressive risk, the behavioral data revealed that risk was associated with both reduced negative affect following negative social feedback and reduced positive affect following positive social feedback. The behavioral results support the notion that adolescents characterized as higher in biopsychosocial risk of depression (and of psychopathology generally) display a similar profile of emotional reactivity to adults with MDD—specifically, reduced reactivity to both positive and negative social feedback—in line with the ECI hypothesis (Bylsma et al.,  2008 ; Rottenberg et al.,  2005 ). Importantly, this was the case even in those adolescents with no prior psychiatric history, suggesting that this blunted emotional reactivity style may predate the onset of any psychopathology, although of course the current data can only speak to risk variables. ECI is grounded in evolutionary theories of depression (Allen & Badcock,  2003 ; Beck & Bredemeier,  2016 ; Gilbert & Allan,  1998 ; Nesse,  2000 ), whereby it is proposed that dampened emotional reactivity is one component of a systemic disengagement from the environment to minimize continued activity which may be wasteful or dangerous in adverse situations (Bylsma et al.,  2008 ).

In a previous study assessing cognitive reappraisal of emotion in the same sample of adolescents, we showed those with a history of CA (relative to those without) had an enhanced capacity to downregulate both positive and negative affect (Schweizer et al.,  2016 ). We interpreted this as the CA environment serving as a practice ground to hone explicit emotion regulation skills. In the current study, a higher risk of depression predicted lower positive and negative affect following social feedback, in the absence of any emotion regulation instructions. The lack of uniqueness associated to any single variable reinforces our view that depressive risk is not related to any one single variable but is formed via the interplay between a constellation of biological, psychological, and social factors. The current results therefore extend these previous findings and support a more general notion of emotion attenuation associated with biopsychosocial risk.

In terms of neural activity, we identified latent brain‐behavior relationships associated with high biopsychosocial risk. First, in response to both cues of rejection (negative feedback) and inclusion (positive feedback), we observed a general pattern of increased cingulate, temporal, and striatal activity. Secondly, in response to rejection only , we observed a pattern of activity in ostensibly executive control‐ and emotion regulation‐related brain regions encompassing fronto‐parietal brain networks including the angular gyrus.

Research on brain reward‐region responsivity in association with biopsychosocial risk for depression has been mixed. Reward processing in those who have experienced early adversity has been accompanied by reduced activation of the ventral striatum in a number of studies (Goff et al.,  2013 ; Hanson et al.,  2016 ; Mehta et al.,  2010 ), and this has been interpreted as an adaptive avoidant response during approach‐avoidance conflict situations (Teicher & Samson,  2016 ) conferring long‐term risk.. In contrast, and in line with the present findings of augmented activity in reward‐related brain networks as a function of risk, Dennison and colleagues ( 2016 ) reported increased striatal response, specifically in the left nucleus accumbens and putamen, while passively viewing positive relative to neutral social stimuli in a group of maltreated older adolescents relative to a control group with no history of maltreatment. Further, in a longitudinal community‐based study of adolescent girls, low parental warmth—a risk factor for subsequent MDD (Hipwell et al.,  2008 )—measured at age 11 was associated with increased striatal activity during reward anticipation measured at age 16 (Casement et al.,  2014 ). Finally, the posterior cingulate and striatum show increases and decreases in response to up‐ and downregulation of socially driven emotions during neuroeconomic strategy games (Grecucci et al.,  2013 ). The increased activation of these regions in response social evaluation in our study could therefore represent the downstream effects of the enhanced emotion regulation capabilities of the sample (Schweizer et al.,  2016 ), potentially reflecting a putative resilience mechanism to social evaluation.

Our findings of increased activity in fronto‐parietal regions commonly associated with cognitive reappraisal of emotion (Buhle et al.,  2014 ) and high‐order executive control (Burgess et al.,  2007 ; Koechlin & Hyafil,  2007 ), in response to social rejection are in‐keeping with an ECI analysis whereby brain regions associated with cognitive control and emotion regulation are recruited to dampen emotion responses.

It is important to consider the relationship between our findings and notions of stress inoculation and resilience (Rutter,  2012 ). Evolutionary theorists (Allen & Badcock,  2003 ; Beck & Bredemeier,  2016 ; Gilbert & Allan,  1998 ; Nesse,  2000 ) argue that depressed mood , including the pervasive emotional insensitivity that we find here, is in fact an adaptive or resilient response to risks of social exclusion, illness, or threats to valued resources. Depressed mood serves to withdraw the beleaguered individual from potentially disadvantageous social disputes and shifts the focus toward repair and resource conservation. It is only when this systemic response becomes entrenched or chronic that clinical depression occurs. This suggests that those biopsychosocial factors that confer a greater risk for clinical depression will also likely confer a greater risk for pervasive depressed mood, including emotion context insensitivity, as a putative resilient response. In this context, then, risk and resilience are two sides of the same coin because depressed mood—an adaptive or resilient response—also places the individual at risk of clinical depression—a maladaptive response—if that mood state becomes entrenched. This interpretation is in line with the negative correlation we observed between depressive symptom severity (BDI) and the neural response pattern to negative social evaluation. Moreover, this complexity is supported by the results of our sensitivity analysis, which excluded participants with a prior mental health difficulty but revealed largely unchanged latent brain‐behaviour pairs relative to the whole sample. This sensitivity analysis subsample has navigated the period of mid‐adolescence associated with the greatest risk of onset of depression (and other disorders; (Spinhoven et al.,  2010 ) without experiencing a psychiatric episode. For them, it therefore makes sense to characterise the relationship between elevated risk on our suite of biopsychosocial variables and emotion insensitivity as a putative marker of resilience.

It is important to note that, while a strength of the present study is the depth and extent of the assessment of depressive risk, there are nevertheless other depressive risk variables that may well contribute to the pattern of results we report here, which were not collected as part of the ROOTS protocol. Further studies would be welcome to assess the validity of our findings in similar population‐based cohorts, and to test the specificity of the brain‐behavior pairs with other neurocognitive profiles aside from psychosocial stress, such as in emotional regulation or cognitive flexibility paradigms. In addition, it should also be noted that we have a relatively small sample size ( N  = 56) to assess depressive risk across multiple parameters. This is however, why we opted to use the PLS method which has been validated for use in data such as this (Wold et al.,  1984 ). Furthermore, the robustness of our neuroimaging analysis with 10,000 permutation tests and 10,00 bootstraps lend confidence to the validity of the brain‐behaviour relationships we observed.

In conclusion, this is the first study to our knowledge that has investigated the relationship between multivariate depressive risk and emotional response style, and latent brain‐behavior relationships of neurocognitive activation patterns during a social evaluation task. We provide tentative evidence to support the ECI hypothesis of emotional reactivity in adolescents at high risk of depression. The study is strengthened by the use of a population‐derived sample; however, in the absence of follow‐up data, we are unable to make firm inferences about the relationship of the current variables and later psychopathology.

AUTHOR CONTRIBUTIONS

TD, IG, DM, and NW devised the study. NW and SS collected the data. JS, ML, and ALvH analyzed the data. JS and TD wrote the paper.

FUNDING INFORMATION

All authors report no biomedical financial interests or potential conflict of interests.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2005 .

Supporting information

Acknowledgments.

The authors gratefully thank colleagues at the Department of Psychiatry, University of Cambridge and the MRC Cognition and Brain Sciences Unit, Cambridge for help during this work. This work was supported by grants from Friends of Peterhouse Medical Fund Cambridge (RG 51114), the Wellcome Trust (RG 074296), and the UK Medical Research Council (MC US A060 0019).

Stretton J, Walsh N D, Mobbs D, et al. How biopsychosocial depressive risk shapes behavioral and neural responses to social evaluation in adolescence . Brain Behav . 2021; 11 :e02005. 10.1002/brb3.2005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Jason Stretton and Nicholas D Walsh are contributed equally to the work presented in this manuscript and should be regarded as Joint First Authors.

DATA AVAILABILITY STATEMENT

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