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Abstract

Influence from bias is unavoidable in clinical decision-making, and mental health assessment seems particularly vulnerable. Individuals with intellectual disabilities have increased risk of developing co-occurring mental disorder. Due to the inherent difficulties associated with intellectual disabilities, assessment of mental health in this population often relies on a different set of strategies, and it is unclear how these may affect risk of bias. In this theoretical paper, we apply recent conceptualisations of bias in clinical decision-making to the specific challenges and strategies in mental health assessment in intellectual disabilities. We suggest that clinical decision-making in these assessments is particularly vulnerable to bias, including sources of bias present in mental health assessment in the general population, as well as potential sources of bias which may be specific to assessments in this population. It follows that to manage potential bias, triangulating information from multi-informant, multi-method, interdisciplinary assessment strategies is likely to be necessary.

Keywords

assessment bias diagnosis intellectual disabilities mental health

Introduction

Intellectual disabilities (American Psychiatric Association, 2013), also referred to as disorders of intellectual development (World Health Organization, 2018), are diagnosed when the intellectual development of an individual diverges from typical development to such a degree that their IQ is more than two standard deviations below the population average (IQ < 70; American Psychiatric Association, 2013; World Health Organization, 2018). Other diagnostic criteria include difficulties in adaptive functioning and presence of atypical intellectual development before 18 years of age. Diagnoses of intellectual disabilities are specified as mild (IQ 50-69), moderate (IQ 35-49), severe (IQ 20-34), and profound (IQ <20; American Psychiatric Association, 2013; World Health Organization, 1992, 2018). In other words, these diagnoses encompass all individuals at the natural lower end of the normal distribution for intelligence (Bertelli et al., 2018; Burack et al., 2021).

Intellectual disabilities are a heterogeneous set of conditions with a magnitude of different causes (Bertelli et al., 2018; Burack et al., 2021; Siegel et al., 2020; Totsika et al., 2022), and the exact cut-off of IQ < 70 may be considered somewhat arbitrary (Burack et al., 2021). Prevalence estimates indicate that 0.7-3% of the population meets the criteria for intellectual disabilities (Maulik et al., 2011; Totsika et al., 2022; Westerinen et al., 2007) with a higher prevalence for mild and moderate intellectual disabilities than for severe or profound (Thurm et al., 2019; Leonard et al., 2003). Intelligence and adaptive behaviour involve numerous different components and subcomponents that may vary in their relationships to each other, and there is a high degree of diversity and variability across different domains also within each level of intellectual disability (Burack et al., 2021). When the numerous aetiologies are taken into account, intellectual disabilities are associated with a “virtually infinite number of manifestations and combinations of phenotypes” (Burack et al., 2021, p. 13.6).

Individuals with intellectual disabilities are particularly susceptible to developing mental health problems (Deb et al., 2022; Einfeld et al., 2011; Mazza et al., 2020; McMahon and Hatton, 2021; Siegel et al., 2020; Totsika et al., 2022), but mental health assessment in this population may be challenging. These individuals may have difficulties verbally communicating about their symptoms and experiences (Bertelli and Moss, 2022; Dagnan and Lindsay, 2012; Deb et al., 2022; Havercamp et al., 2022; Man and Kangas, 2020; Morrison and Gillig, 2012; Siegel et al., 2020; Totsika et al., 2022), mental health problems may manifest in uncommon or atypical ways (Bertelli and Moss, 2022; Dagnan and Lindsay, 2012; Helverschou et al., 2011; Siegel et al., 2020), including as “challenging” behaviours, such as aggression and self-injurious behaviours (Bertelli and Moss, 2022; Deb et al., 2022; Emerson, 2001; Mevissen et al., 2016; Painter et al., 2018; Peña-Salazar et al., 2022; Rittmannsberger et al., 2020), and there is a lack of adapted assessment tools (Dagnan and Lindsay, 2012; Deb et al., 2022; Halvorsen et al., 2022a, 2022b; Havercamp et al., 2022; Helvershou et al., 2020; Rush et al., 2004; Totsika et al., 2022).

Autism spectrum disorder (ASD), a neurodevelopmental condition involving difficulties in social interaction and communication, as well as repetitive/restricted behaviours (American Psychiatric Association, 2013; Lai et al., 2014), have been found to co-occur in 18-28 % of individuals with intellectual disabilities (Bryson et al., 2008; Tonnsen et al., 2016). However, ASD may be underdiagnosed in this population due to difficulties establishing this diagnosis in individuals with severe/profound intellectual disabilities and limited verbal language skills (Saemundsen et al., 2010). Challenges in assessment of mental disorder seem to be further increased for individuals with intellectual disabilities who are also autistic (Bakken et al., 2016; Dalhaug et al., 2022; Halvorsen et al., 2022b; Helverschou et al., 2011; Maddox et al., 2020; Peña-Salazar et al., 2022; Totsika et al., 2022; Underwood et al., 2011, 2015). In particular, atypical symptom expressions may be more common (Bakken et al., 2016; Helverschou et al., 2011) and these individuals may communicate symptoms and experiences in ways that are more challenging for mental health professionals to interpret and understand (e.g. Kildahl et al., 2020b).

Due to these challenges in assessment, the strategies employed in mental health assessment in individuals with intellectual disabilities necessarily diverge from typical strategies in mental health assessment, particularly for individuals with limited verbal language skills. This includes reliance on proxy informants (Bertelli and Moss, 2022; Dagnan and Lindsay, 2012; Deb et al., 2022; Emerson et al., 2013; Helverschou et al., 2020; Man and Kangas, 2020; Rush et al., 2004; Siegel et al., 2020), direct clinical observation (Dagnan and Lindsay, 2012; Deb et al., 2022; Morrison and Gillig, 2012; Rush et al., 2004; Valdovinos et al., 2019), and use of assessment tools with unclear psychometric properties (Dagnan and Lindsay, 2012; Halvorsen et al., 2022a, 2022b; Helverschou et al., 2020). Obtaining a comprehensive medical and developmental history is considered a critical part of these assessments (Bertelli and Moss, 2022; Dalhaug et al., 2022; Deb et al., 2022; Helverschou et al., 2020; Man and Kangas, 2020; Morrison and Gillig, 2012; Rush et al., 2004), but neither medical charts and records nor retrospective reports by caregivers are necessarily reliable or sufficiently informative (Dagnan and Lindsay, 2012; Fusar-Poli et al., 2017). Furthermore, in contrast to patients in the general population, individuals with intellectual disabilities may not be able to verbally add to or correct information obtained from clinical records, nor provide their own accounts of symptom development and treatment history.

Because the diagnostic criteria used in the general population may not be sufficiently sensitive in individuals with intellectual disabilities, adaptations of these criteria have been published. The Diagnostic Manual – Intellectual Disabilities, currently in its second instalment (DM-ID 2; Fletcher et al., 2017) contains adaptations of criteria from the DSM-5. The Diagnostic Criteria for Psychiatric Disorders for Use with Adults with Learning Disabilities/Mental Retardation (DC-LD; The Royal College of Psychiatrists, 2001) similarly provides operationalised criteria for the ICD-10. These adaptations and operationalisations seem to have clinical utility (e.g. Fletcher et al., 2009; Sawyer, 2018), but they have been developed by experts and not validated through research.

While previous literature (e.g. Dagnan and Lindsay, 2012; Man and Kangas, 2020; Siegel et al., 2020) has acknowledged that mental health assessment in intellectual disabilities may be influenced by extraneous factors, whether and how the specific challenges in these assessments may affect risk of bias has been sparsely discussed. Adequate diagnostic formulations are prerequisites for providing appropriate treatment and care for individuals with intellectual disabilities and co-occurring mental health conditions. Thus, awareness of the ways in which bias may affect diagnostic decision-making, as well as strategies to recognise and manage it, is important to ensure the quality of, and equitable access to, mental health services for individuals with intellectual disabilities. The current, theoretical paper aims to discuss the specific challenges and strategies involved in mental health assessment in intellectual disabilities in light of recent conceptualisations of bias in clinical decision-making.

Bias in clinical decision-making

Conceptualising bias

According to Kahneman et al. (2021), bias and noise are the components of error in human judgement. While noise results in random error, bias influences judgement by systematically skewing it. Influence from bias has been described as unavoidable in clinical decision-making (Bate et al., 2012; Croskerry, 2002; Harding, 2004; Kahneman et al., 2021; Magnavita and Lilienfeld, 2016; Norman and Eva, 2010; Wieringa et al., 2018), and evidence suggests that bias may influence clinical decisions on multiple levels, including diagnosis, treatment, and levels of care (Crosskerry, 2002; FitzGerald and Hurst, 2017; Harding, 2004; Magnavita and Lilienfeld, 2016). Due to the inherent ambiguities of mental conditions, decision-making in mental health care is particularly vulnerable (Kahneman et al., 2021; Snowden, 2003).

Bias may operate on multiple levels; in individual practitioners, practice networks or programs, or at the community level (Snowden, 2003). Traditional conceptualisations of bias in clinical decision-making and evidence-based health care seem to be based on a philosophical understanding of what constitutes truth and fact referred to as the ideal limit theorem (Wieringa et al., 2018). The ideal limit theorem presupposes that truth and facts are “out there” and just need to be identified. Thus, bias is understood as something negative that gets in the way of facts, something that needs to be minimised or removed (Wieringa et al., 2018). An underlying assumption seems to be that the truth is unambiguous and the right decision or course of action will be evident once all bias is removed.

However, it has been argued that an understanding of bias based on the ideal limit theorem is insufficient for understanding decision-making in clinical practice. As argued by Wieringa et al. (2018), transferring this understanding of truth and its associated understanding of bias from the study of groups to the single-case scenario is highly problematic. Even if research has identified how patients displaying a certain set of symptoms profit from being prescribed a specific medication in 80% of cases, a single patient cannot be prescribed antibiotics in 100 parallel scenarios (Wieringa et al., 2018). There is only a single scenario. Because it is impossible to predict the future, the practitioner cannot know beforehand whether the patient belongs to the group that will profit or the one that will not. Approaches based on the ideal limit theorem produce knowledge concerning the tendencies of groups, not specific knowledge concerning individuals within these groups. Thus, the idea that removing all bias will somehow reveal the right decision is likely to be too simplistic in clinical practice (Wieringa et al., 2018; Engebretsen et al., 2016).

Building on the work of philosopher Isabelle Stengers (2002, 1997), Wieringa et al. (2018) suggest a reconceptualisation of bias in clinical decision-making. Stengers claims that all science is inherently biased because all knowledge and truth is built on interests. According to this view, science and interests are not opposites, as facts are in the fundamental sense built on interests. Something becomes a fact only when it attracts interest, and facts are never absolute, but result from an interest or bias towards a certain line of questioning. Bias is thus defined in terms of a value-driven perspective, i.e. as in influencing what inquiries are viewed as worthwhile. In other words, bias is inherent to all kinds of science and knowledge production and therefore unavoidable.

This view of bias is not considered incompatible with the ideal limit theorem, and rather than arguing that the ideal limit theorem is obsolete or unnecessary, Wieringa et al. (2018) describe it as insufficient to understand and manage bias in single-case scenarios. Moreover, operationalising bias merely as a source of error is inconsistent with the literature on cognitive bias in decision-making (Bate et al., 2012; Kahneman, 2011; Kahneman et al., 2021; Norman and Eva, 2010; Wieringa et al., 2018). Heuristics and biases are seen as products of mental short-cuts, i.e. information processing strategies that are usually effective in dealing with large amounts of information, uncertainty and ambiguity (Bate et al., 2012; Crosskerry, 2002; Kahneman, 2011, 2003; Norman and Eva, 2010). They may be effective, they may fail, but these short-cuts are not bad in and of themselves (Norman and Eva, 2010). In other words, bias may be seen as having productive and non-productive aspects (Wieringa et al., 2018; see also Kahneman, 2011, 2003).

Consequences of bias

López (1989) has described ethnic/racial bias as resulting in two major kinds of distortion to diagnostic decision-making: overpathologising and minimisation. Overpathologising occurs when non-pathological behaviour of minority individuals is interpreted as a sign of mental disorder. Minimisation is the opposite, when practitioners overlook genuine signs of mental disorder. Thus, influence from bias may result in treatable mental disorders being overlooked in clinical decision-making, or misdiagnoses of mental disorder where none is present due to, for example, misinterpretation of minority characteristics.

Diagnostic overshadowing (Reiss et al., 1982; see also Bertelli and Moss, 2022; Helverschou et al., 2011; Jopp and Keys, 2001; Levitan and Reiss, 1983; Mason and Scior, 2004; Reiss and Szyszko, 1983; Siegel et al., 2020; Totsika et al., 2022) has been described as a common pitfall in mental health assessment in intellectual disabilities, where symptoms of mental disorder are misattributed to the intellectual disability or a co-occurring ASD, resulting in such minimisation (Jopp and Keys, 2001; Levitan and Reiss, 1983; Mason and Scior, 2004; Reiss and Szyszko, 1983). While bias seems likely to be involved (Mason and Scior, 2004), the specific mechanisms in diagnostic overshadowing remain unclear (Jopp and Keys, 2001). Other authors have warned against overdiagnosing mental disorder in individuals with intellectual disabilities (Hurley, 1996), including misattributing aspects of intellectual disabilities or ASD to a co-occurring mental disorder (see also Bakken and Høidal, 2014; Dalhaug et al., 2022; Palucka et al., 2008). Furthermore, Siegel et al. (2020) has warned against overpathologising by misattributing developmentally appropriate behaviour to a co-occurring mental disorder, which may occur if the behavioural expectations for the individual are made according to their chronological rather than their developmental age.

Managing bias

Clinical decision-making is a complex, situated endeavour that takes place within a particular social context and usually depends on a multitude of types of knowledge in dealing with non-frequent events (Bate et al., 2012; Croskerry, 2002; Engebretsen et al., 2016; Norman and Eva, 2010; Wieringa et al., 2018). In line with the conceptualisation of bias by Wieringa et al. (2018), applying concepts of bias as something that needs to be recognised and managed is likely to be more helpful than attempts to remove it. This has the potential to guide practitioners in the process of “making explicit the premises and values” on which their reasoning is built (Greenhalgh and Russell, 2006, p.41), which may aid them in differentiating valid inferences from less valid ones.

General sources of bias

Cognitive bias in mental health professionals

Cognitive biases have been described to affect clinical decision-making (Bate et al., 2012; Croskerry, 2002; Kahneman et al., 2021; Norman and Eva, 2010; Wieringa et al., 2018), including the conjunction fallacy (specific conditions are considered more probable than general ones), search satisficing (when one diagnosis has been identified, assessment is discontinued), anchoring (undue emphasis placed on salient features), and several others (see Bate et al, 2012; Croskerry, 2002; Kahneman, 2011). There is little reason to assume that practitioners working with individuals with intellectual disabilities are less susceptible to these biases than other practitioners.

Moreover, mental disorder symptom manifestations are often more ambiguous, and various mental health problems more challenging to differentiate, in individuals with intellectual disabilities compared to the general population (Dagnan and Lindsey, 2012; Fletcher et al., 2017). Due to this increased ambiguity, clinical judgement in mental health assessment in individuals with intellectual disabilities may be particularly vulnerable to these general, cognitive biases. This vulnerability is likely to vary with the degree of intellectual disability, as the ambiguity of symptom presentations is further increased in individuals with severe intellectual disabilities and/or limited verbal language skills, compared to those with mild or moderate intellectual disabilities (Deb et al., 2022; Fletcher et al., 2017). Presence of co-occurring conditions such as epilepsy, sensory impairment, and ASD may further increase challenges in interpreting and differentiating behavioural symptom manifestations (Bakken et al., 2016; Deb et al., 2022; Fletcher et al., 2017; Helverschou et al., 2011; Kildahl et al., 2019b; Morrison and Gillig, 2012; Siegel et al., 2020), thereby further increasing the potential influence of cognitive bias.

Bias arising from patient characteristics

Gender bias has been shown to affect clinical decision-making in implicit and explicit ways (FitzGerald and Hurst, 2017; López, 1989; Marcelin et al., 2019), as has racial/ethnic bias (FitzGerald and Hurst, 2017; López, 1989; Marcelin et al., 2019; Snowden, 2003), bias from cultural/sexual minority status (Duke, 2011; FitzGerald and Hurst, 2017; López, 1989; Marcelin et al., 2019), age (FitzGerald and Hurst, 2017; López, 1989), socioeconomic status (López, 1989), disability status (Bate et al., 2012), and other characteristics (FitzGerald and Hurst, 2017). There is little reason to assume that mental health professionals working with individuals with intellectual disabilities are less affected by these potential biases than other professionals. Furthermore, the relationship between gender and neurodevelopmental conditions is complex (Polyak et al., 2015), as are the relationships between these conditions and race/ethnicity (Hellerud and Bakken, 2019; Morinaga et al., 2021; Roman-Urrestarazu et al., 2021; Schwartz et al., 2022; Scior et al., 2013; Tromans et al., 2021), aging (Howlin and Magiati, 2017; Hwang et al., 2020; Moss et al., 2019; Perkins and Moran, 2010), and sexuality (Duke, 2011; Sommarö et al., 2020).

Having intellectual disabilities involves a disability status and thereby a minority group affiliation in and of itself (Barnes, 2016; Grue, 2011; Scior and Werner, 2016). Studies have described individuals with intellectual disabilities facing discrimination and other barriers to accessing health care (Ali et al., 2013; Pelleboer-Gunnink et al., 2017; Whittle et al., 2018), indicating that someone having an intellectual disability may elicit biased responses from health care professionals. Furthermore, patients belonging to other minorities may be able to seek out health professionals belonging to the same minority. Due to the very nature of their disability, however, individuals with intellectual disabilities are unlikely to find a medical doctor, psychiatrist or psychologist sharing their minority status and the experiences associated with having this disability status. Advocates and activists working for improved services for individuals with intellectual disabilities are often family members or professionals, meaning individuals with intellectual disabilities themselves are rarely able to contribute directly with their own views on service development and adaptation, setting this group apart from other minorities. Thus, lack of representation on several levels of mental health services may contribute to the otherness or separateness experienced by people with intellectual disabilities in mental health services differing from that experienced by other minorities, in quality as well as severity.

Moreover, individuals with intellectual disabilities belonging to cultural and/or sexual minorities may be viewed as belonging to double or triple minorities, potentially affecting clinical decision-making at multiple levels and in multiple ways (FitzGerald and Hurst, 2017; López, 1989). While it is unclear whether and how these different minority statuses may interact in mental health assessments, individuals with intellectual disabilities belonging to ethnic or racial minorities have two co-occurring minority statuses to which genuine symptoms of mental disorder may be misattributed (Jopp and Keys, 2001; López, 1989; Reiss et al., 1982). It is therefore possible that individuals with intellectual disabilities belonging to racial/ethnic minorities are at even greater risk of experiencing minimisation of symptom reports.

Potential bias from various assessment strategies

Numerous papers and book chapters have described the strategies used for mental health assessment in individuals with intellectual disabilities (e.g. Bakken et al., 2016; Bertelli and Moss, 2022; Dagnan and Lindsay, 2012; Deb et al., 2022; Helverschou et al., 2011, 2020; Man and Kangas, 2020; Morrison and Gillig, 2012; Siegel et al., 2020; Underwood et al., 2011, 2015). While multi-method strategies are often emphasised, the risk and role of potential bias in use of each of these alternative strategies has been sparsely discussed, and it is unclear how potential bias may be managed when these strategies are combined.

Self-report

Potential bias in self-report includes the so-called social desirability bias (King and Bruner, 2000), involving individuals answering questions or surveys portraying themselves as what they perceive to be more socially desirable than what others may consider them. Also malingering, where symptoms are portrayed as more severe than others would report them to be, often in an effort to access care or treatment, has been reported to occur in mental health assessments (Rogers and Bender, 2018). However, it is currently unclear whether and how these biases may affect self-report in individuals with intellectual disabilities.

Individuals with intellectual disabilities may have difficulties understanding the materials and instruments used in mental health assessments (Deb et al., 2022; Prosser and Bromley, 2012; Siegel et al., 2020). This may include risk of recency (tendency to choose the last alternative) and acquiescence biases (a tendency to respond in a confirmatory manner; Havercamp et al., 2022), as well as systematically skewing information about conditions described in more abstract language.

Moreover, self-report by people with intellectual disabilities may be interpreted differently by practitioners, in particular if these individuals are also autistic. Lim et al. (2022) describe how the differing nonverbal communication strategies of autistic people may lead them to be perceived as more deceptive and less credible than non-autistic people. Recent research emphasises that communication difficulties occurring between autistic and non-autistic people are bidirectional (Davis and Crompton, 2021), i.e. not only may autistic individuals struggle with self-report but practitioners may struggle with interpreting and understanding such self-report by autistic people with intellectual disabilities. This may involve a risk of overlooking symptoms of mental disorder (e.g. Kildahl et al., 2020b).

Direct, clinical observation

Individuals with intellectual disabilities are a heterogeneous population (Bertelli et al., 2018; Burack et al., 2021), and similar heterogeneity has been described for ASD (Lai et al., 2014). As described, mental health assessments in these populations may involve an inherent risk of misattribution of potential symptoms to the underlying condition(s). Due to these heterogeneities, however, even experienced practitioners may be at risk of such misattributions unless the specific individual’s developmental history is taken into account (Deb et al., 2022; Siegel et al., 2020).

In a case report describing a patient with mild intellectual disability, Kildahl et al. (2019a) describe “a risk that behaviours that are not a part of the specific individual’s ASD presentation, but have developed later as a response to insufficient care conditions, are interpreted as relating to the ASD only because they may present as ASD symptoms in other individuals” (p. 62-63; see also Dagnan and Lindsay, 2012; Helverschou et al., 2020, 2011; Kildahl et al., 2020b). Similarly, Deb et al. (2022) state that it is “of paramount importance to have a baseline of mental state to compare with any possible emergent psychiatric symptoms” (p. 12). Clinical observation that is not interpreted and understood in context of other available information about the specific individual's general development, symptom trajectories and potential contextual influences on behaviour, is likely to be of limited value, as well as being vulnerable to the influence of bias. This also applies to judgements based on single observations in a particular context, as patients may have conditions in which symptoms fluctuate over time, e.g. bipolar disorder (Rysstad et al., 2022; Valdovinos et al., 2019), or complex compound conditions that are challenging for practitioners to disentangle (Deb et al., 2022; Kildahl et al., 2019b).

Proxy ratings

Reliance on proxy ratings, i.e. professional caregivers or family members reporting on symptoms and difficulties in place of self-report (Andresen et al., 2001; Dagnan and Lindsay, 2012; Helverschou et al., 2020; Siegel et al., 2020), increases with decreasing verbal proficiency of the patient (Siegel et al., 2020). Thus, assessment in individuals with severe/profound intellectual disabilities and/or limited verbal language skills usually relies more heavily on proxy ratings than assessments in individuals with mild intellectual disabilities.

It has been suggested that use of proxy ratings is problematic (e.g. Andresen et al., 2001; Dagnan and Lindsay, 2012), but knowledge is sparse with regard to whether and how use of proxy ratings may influence risk of bias. Comparing self-report with proxy report, one recent study found proxy reports likely to underestimate the stress experienced by adults with intellectual disabilities (Scott and Havercamp, 2018), while Andresen et al. (2001) found proxy informants to overestimate impairment and underestimate pain and quality of life. Similarly, autistic children’s self-report of mental health symptoms may differ from parental report (Kalvin et al., 2020; Santore et al., 2020), highlighting that proxy reports are inferential (Postorino et al., 2017; Stancliffe, 1995) and therefore not interchangeable with self-report.

Rating scales focusing on observable behaviour

Assessment tools used in this population often involve proxy informants rating the frequencies and severities of observable behaviours (Halvorsen et al., 2022a; Helverschou et al., 2020; Siegel et al., 2020). However, scoring on these scales may be influenced by informants’ prior knowledge and expectations, relationship with the proband, as well as the specific contexts in which they have observed the individual (Andresen et al., 2001; Havdahl et al., 2017; Havercamp et al., 2022; Helverschou et al., 2020; Kildahl et al., 2017; Kildahl and Jørstad, 2022; Man and Kangas, 2020; Perkins, 2007). For instance, if the informant has only observed the patient in specific contexts and the “challenging” behaviour is context dependent, this informant’s scoring unlikely to be an adequate description of the individual’s difficulties across time and context. Also, informants may not have access to information about the individual’s history and previous experiences, including trauma, which may be important for understanding their behaviour (Dalhaug et al., 2022; Kildahl et al., 2020a, 2020b; Man and Kangas, 2020; Mevissen et al., 2016; Truesdale et al., 2019).

The different mental disorders vary according to the degree that their core symptoms result in distinct, observable behavioural expressions. For instance, the core symptoms of trauma disorders are subjective/experiential, and their behavioural expressions may overlap with behavioural manifestations of symptoms associated with anxiety, depression and “challenging” behaviour (Kildahl et al., 2020b; McNally et al., 2021; Mevissen et al., 2016; Rittmannsberger et al., 2020). Thus, reliance on proxy reports may systematically skew symptom reports towards externally observable symptoms of mental disorder at the expense of internalising or intra-psychic symptoms (Dalhaug et al., 2022; Perkins, 2007), suggesting that reliance on proxy informants may bias what mental disorder diagnoses are made. For example, if a person with severe intellectual disability is displaying signs of altered arousal and fear, it is likely to be less challenging for practitioners to make an anxiety disorder diagnosis rather than a trauma-related one (e.g. Kildahl et al., 2021), as the latter would require more extensive inferences concerning subjective experience. Finally, because ASDs may share surface symptom overlaps with mental disorders, risk of bias may be increased when using assessment tools not especially adapted or developed for people with intellectual disabilities and co-occurring ASD (Bakken and Høidal, 2014; Halvorsen et al., 2022b; Helverschou et al., 2011, 2020; Underwood et al., 2011, 2015).

Power asymmetries

The relationships between individuals with intellectual disabilities and caregivers usually involve considerable power asymmetries (Brown, 1999; Delmar, 2012; Wolkorte et al., 2019), including asymmetries of definitional power (Nunkoosing and Haydon-Laurelut, 2011). Caregivers have the power to decide what is considered problematic, occasionally using this power to place the problem within the individual concerned, discounting contextual factors (Nunkoosing and Haydon-Laurelut, 2011). When individuals with intellectual disabilities themselves are interviewed about what they see as eliciting “challenging” behaviours, they have been found to emphasise staff behaviour (Wolkorte et al., 2019). Thus, there may be a considerable discrepancy in the understanding of “challenging” behaviours between caregivers and individuals with intellectual disabilities themselves, and this discrepancy is likely to be more difficult for practitioners to detect in assessments relying more heavily on proxy ratings.

The potential bias associated with power asymmetries is particularly evident in cases where individuals with intellectual disabilities are victims of violence or abuse at the hands of their caregivers or family members (Brown, 1999), experiences for which individuals with intellectual disabilities are at increased risk (Dion et al., 2018; McDonnell et al., 2019). Moreover, individuals depending on others in their daily lives may be at risk of experiencing harmful care practices or negligence in services (Daveney et al., 2019; Kildahl et al., 2020a; Kildahl and Jørstad, 2022; Mevissen et al., 2016; Strand et al., 2004). These events may not be reported (Spivakovsky, 2018), or they may be reported in ways in which contextual (caregiver-related and systemic) factors are minimised (Björne et al., 2021), in spite of the traumatic potential of such events for people with intellectual disabilities (Daveney et al., 2019; Kildahl et al., 2020a; Kildahl and Jørstad, 2022).

Caregivers and the individual undergoing assessment may have differing interests in the assessment that do not necessarily overlap. In addition to wanting to help the person in question, professional caregivers may have excessive workloads and therefore have an interest in reducing their own stress and discomfort (Brown, 1999; McBrien and Candy, 2012; Ryan et al., 2021). These co-occurring interests may not always be commensurable. Caregivers may also have difficulties coping with normal levels of discomfort or distress in individuals with intellectual disabilities, leading to what Delmar (2012) refers to as excess of care, which may bias proxy ratings in a sort of malingering by proxy. Thus, proxy ratings may be affected by the same kinds of bias as self-report.

The practitioner as an outsider

In the general population, mental health assessment is primarily conducted in interviews and interaction between the practitioner and the patient. In individuals with intellectual disabilities, assessments frequently require the practitioner to interact with the patient, professional caregivers, family members, and service provider organisations (Dagnan and Lindsay, 2012; McBrien and Candy, 2012). Thus, practitioners are entering and navigating as outsiders in an established social context and hierarchy that involves a complex, pre-existing set of social relations (Ineland et al., 2018; Ryan et al., 2021). This is likely to affect assessments at multiple levels, including instances where informants ascribe intentions or characteristics to other informants, adding further complexity and potential sources of bias to the use of proxy ratings.

The mental health professional’s role in this social context is usually construed as an expert outsider, which may involve power asymmetries with caregivers and family members that practitioners need to be aware of to be able to manage bias potentially arising from these social systems. The mere presence of a mental health professional may influence symptom reports, downplaying other aspects of an individual’s challenges, due to the availability heuristic (Kahneman, 2011). Finally, while bias may occur due to complex interactions between multiple patient characteristics and characteristics of the health professionals (FitzGerald and Hurst, 2017), it is likely that also family members and professional caregivers in these assessments may elicit bias in practitioners in similar ways (e.g. racial/ethnic bias).

Implications: Multimethod assessments and triangulation

Multimethod interdisciplinary assessments

The strategies used in these assessments are all vulnerable to the influence of bias. However, because the mechanisms of bias are likely to differ for the various assessment strategies, concurrent use of multiple strategies in each assessment may aid practitioners in making “explicit the premises and values” (Greenhalgh and Russell, 2006, p. 41) upon which they are basing their judgement. To achieve this, practitioners need to be aware of the potential sources of bias associated with each assessment strategy, in addition to the mechanisms in cognitive bias, and potential bias activated by patient characteristics (Croskerry, 2002; Kahneman et al., 2021; Magnavita and Lilienfeld, 2016). Because the latter may in part be practitioner-related (FitzGerald and Hurst, 2017; Ivers et al., 2021; Mason and Scior, 2004), interdisciplinary assessment teams may further contribute to elucidating possible influence of bias.

As an example, contextual explanations of “challenging” behaviours emphasised by people with intellectual disabilities themselves (Wolkorte et al., 2019) may be under-appreciated by professional caregivers (Nunkoosing and Haydon-Laurelut, 2011). Facilitating recognition of this potential bias may be achieved by combining proxy report with self-report by patients, direct observation, and/or supplementing with further proxy reports in which diverging opinions among caregivers are explicitly sought. Similarly, risk of overlooking conditions primarily manifesting as subjective/experiential phenomena, including pain, may be reduced by obtaining developmental histories and exploring the developmental trajectories of symptoms (Dagnan and Lindsay, 2012; Dalhaug et al., 2022; Deb et al., 2022; Helverschou et al., 2011; Rasmussen et al., 2020), as well as routinely exploring trauma histories of these individuals (Kildahl et al., 2020b) and conducting thorough medical examinations as part of any mental health assessment in this population to rule out pain or somatic illness (Ali et al., 2013; Andersen et al., 2001).

However, when relying on multiple sources of information, it may be unclear to practitioners how to weight, integrate and interpret findings from these different sources. In particular, information from different informants or sources diverging, or practitioners from different disciplines interpreting behaviours in different ways, may present considerable challenges.

Triangulation and integration: Managing convergence and divergence

Methodologically, individual mental health assessments in people with intellectual disabilities bear close resemblance to case studies (Howard, 1983; Yin, 2014), in that they apply multiple methodologies and sources of data to elucidate and understand a specific object or phenomenon (i.e. the mental health status of a specific individual). According to the pragmatist perspective, different methodologies unearth or produce knowledge regarding different aspects of the phenomenon under study (Feilzer, 2010; Howard, 1983). In other words, data concerning the mental health of a specific individual gathered from different sources produce knowledge regarding different aspects of their mental health status and functioning.

The process of integration of knowledge produced using different methodologies is referred to as triangulation (Green and Thorogood, 2018; Moran-Ellis et al., 2006, Yin, 2014). Because the different methodologies have different limitations and potential sources of bias that should be taken into account, and because they produce knowledge concerning different aspects of the patient’s difficulties, their findings may not be used for confirmatory or dis-confirmatory purposes (Small, 2011). However, if the interpretations and understandings from different assessment strategies converge, this may increase the confidence in the validity of the individual findings from each approach (Slife and Gantt, 1999). Divergence may indicate that caution is required, while simultaneously providing opportunities for further interpretation and hypothesising.

For example, a recent case report by Kildahl and Jørstad (2022) described a case involving an autistic man with severe intellectual disability and post-traumatic stress disorder, in which the scores on behaviour rating scales differed considerably between the patient’s family and his professional caregivers. This divergence was used as a starting point for further exploration and hypothesising, resulting in the final understanding that the patient’s symptoms were attenuated when he was with his family because visits occurred in an environment with fewer trauma reminders, a context which was perceived as more predictable and safe by the patient than what was possible to achieve outside of the family’s visits. Thus, further exploration of diverging findings provided a more nuanced understanding of the patient’s difficulties than what would have been possible had they been interpreted solely as dis-confirmatory or conflicting.

Divergence may also be a result of differences in interpretation and understanding of the same behaviour in the same context, due to differing relationships with the proband, and how well the proxy informant knows the person (Perkins, 2007). To aid in exploration of such divergence, integration of information from different sources may occur in more dynamic ways. For instance, practitioners may alternate between observing the patient and interviewing family members and professional caregivers, attempting not only to gain access to informants’ understanding of the patient, but also attempting to understand how informants arrived at this understanding and what aspects of behaviour they are emphasising in their inferences. This process has previously been described as helpful, including in highly complex cases (Kildahl et al., 2021; Kildahl et al., 2019b; see also Man and Kangas, 2020).

In conclusion, exploration of divergence may focus on establishing whether it occurs due to observations being made in different contexts (which may involve presence of different stressors or symptom triggers), whether it occurs due to differing relationships with the proband, and whether it occurs due to difference in interpretation and understanding of the individual’s behaviour by proxy informants. Divergence between reports and findings may also be a result of the limitations of currently available assessment tools or and methodologies. It is also likely to be helpful to develop and explore diverging hypotheses in these assessments, as previous assessments and observations by both mental health practitioners and informants may be influenced by bias and contribute to diagnostic overshadowing.

Reflective assessment practices

In order to recognise and reduce the influence of bias, practitioners need to be attentive to the limitations of their own knowledge and understanding of each specific patient at any time (Croskerry, 2002; Harding, 2004; Magnavita and Lilienfeld, 2016). Because these assessments require integration of information from different resources, this attentiveness is equally needed during data collection as well as during integration of findings, requiring practitioners to avoid continual judgement throughout these processes. Generating and exploring alternative hypotheses/explanations is likely to be helpful in maintaining and tolerating the uncertainty of not knowing (Croskerry, 2002; Magnavita and Lilienfeld, 2016). Practitioners need to be aware of the potential influence of cognitive bias (Croskerry, 2002; Harding, 2004; Kahneman, 2011), and how assessments in individuals with intellectual disabilities may be particular vulnerable to these. Finally, they need to be aware of their own characteristics, their ideas and attitudes concerning mental health in intellectual disabilities, and how these may implicitly affect their interactions with the patient and the complex social systems in which they live.

The use of guidelines (e.g. Fletcher et al., 2017; Siegel et al., 2020) is an important tool for reducing bias, as well as noise, thereby improving the accuracy of clinical judgement (Kahneman et al., 2021). Due to the vulnerability to bias, diagnostic guidelines for mental disorder in individuals with intellectual disabilities should include multiple strategies and assessment tools. This is particularly critical in individuals with severe or profound intellectual disabilities where symptom expressions may be more ambiguous and reliance on proxy informants more extensive. However, as pointed out by Wieringa et al. (2018), guidelines are unlikely to be sufficient. Managing racial/ethnic bias, for instance, is likely to require active reflection and deliberate practice by the practitioners (Ivers et al., 2021). Due to the specific challenges involved, clinical experience/expertise specific to individuals with intellectual disabilities is likely to be necessary to recognise and manage bias arising from these assessment strategies (see Magnavita and Lilienfeld, 2016; Malterud, 2001). However, the practitioner needs to be aware of the limitations of their own experience, as even extensive personal experience amounts to little more than anecdotal evidence (Magnavita and Lilienfeld, 2016; Ruscio, 2012).

Because bias is a result of a complex interaction between patient-related, practitioner-related, and context-related characteristics, as well as characteristics of specific instruments and symptom groups, the relative contributions by different potential sources of bias will be difficult to disentangle in each individual case. Moreover, because context and patient-related characteristics influence risk of bias, the relative contributions from different potential sources of bias and interactions between them are likely to vary for assessments concerning different individuals and in different contexts. Thus, group level data concerning the relative contributions from different potential sources of bias may not be helpful in individual scenarios (see Wieringa et al., 2018). In other words, applying concepts of bias as something that needs to be recognised and managed is likely to be more helpful than attempts to quantify or remove it (Wieringa et al., 2018), suggesting that reflective assessment practices are an essential part of mental health assessment in individuals with intellectual disabilities.

Bias in evaluation of treatment and intervention

Knowledge concerning treatment of mental disorder in individuals with intellectual disabilities is limited (e.g. Siegel et al., 2020), requiring practitioners to adapt treatment approaches for the general population, often without a solid basis in research-based knowledge. It has been suggested that evaluation of treatment effects in individuals with intellectual disabilities may be just as challenging as diagnostic decision-making, requiring use of similar strategies (Rysstad et al., 2022). Thus, evaluation of treatment effects may be just as vulnerable to bias as diagnostic decision-making in individuals with intellectual disabilities, if not even more due to the potential presence of confirmation bias (Kahneman, 2011; see also Palucka et al., 2008) in practitioners having made the diagnosis in the first place. This indicates that similar, multimethod approaches as those described for assessment are likely necessary to adequately determine effects of intervention and treatment for mental disorder in this population.

Conclusions

Mental health assessment in individuals with intellectual disabilities seems particularly vulnerable to cognitive bias, as well as being vulnerable to an additional set of potential biases due to the use of different assessment strategies. Furthermore, people with intellectual disabilities are a minority group that may elicit biased responses from professionals, and these individuals’ experiences of the mental health system may differ from that of people with other types of disabilities. Also, these assessments are likely to be just as vulnerable to gender, racial/ethnic, and other types of minority-related bias.

Assessments being influenced by bias may result in either overpathologising or minimising of mental health issues, i.e. diagnostic overshadowing. The combination of vulnerabilities to bias indicates that single-strategy approaches to mental health assessment in individuals with intellectual disabilities are not recommended, not because the conclusions they reach are necessarily invalid but because it is impossible to recognise whether or how these have been affected by bias. This equally applies to research on these topics, including diagnostic and treatment studies.

Deliberate and reflective assessment practices emphasising triangulation of multi-method, interdisciplinary, multi-informant approaches are likely to be necessary to facilitate practitioners’ ability to recognise and manage potential bias in these assessments. Facilitating self-report by people with intellectual disabilities is critical, as report from proxy informants are based on inferential data concerning subjective phenomena and are therefore not interchangeable with self-report.

Footnotes

Acknowledgements

The first author thanks his colleagues at the Regional Section Mental Health, Intellectual Disabilities/Autism, for their comments on a draft of this manuscript. Parts of the manuscript are based on the first author’s doctoral dissertation, for which the second and third authors were supervisors.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Arvid Nikolai Kildahl

References

Ali

Scior

Ratti

Strydom

King

Hassiotis

(2013) Discrimination and other barriers to accessing health care: Perspectives of patients with mild and moderate intellectual disability and their carers. PloS one 8(8): e70855. DOI: 10.1371/journal.pone.0070855

American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders 5th ed. Arlington, VA: American Psychiatric Publishing.

Andresen

Vahle

Lollar

(2001) Proxy reliability: Health-related quality of life (HRQoL) measures for people with disability. Quality of Life Research 10: 609-619. DOI: 10.1023/A:1013187903591

Bakken

Helverschou

Høidal

Martinsen

(2016) Mental illness with intellectual disabilities and autism spectrum disorders. In: Hemmings

Bouras

(eds) Psychiatric and behavioural disorders in intellectual and developmental disabilities (3rd edition). Cambridge, UK: Cambridge University Press, pp. 119-128.

Bakken

Høidal

(2014) Asperger syndrome or schizophrenia, or both? Case identification of 12 adults in a specialized psychiatric inpatient unit. International Journal of Developmental Disabilities 60(4): 215-225. DOI: 10.1179/2047387713Y.0000000022

Barnes

(2016) The minority body: A theory of disability. Oxford, UK: Oxford University Press.

Bate

Hutchinson

Underhill

Maskrey

(2012) How clinical decisions are made. British Journal of Clinical Pharmacology 74(4): 614-620. DOI: 10.1111/j.1365-2125.2012.04366.x

Bertelli

Cooper

Salvador-Carulla

(2018) Intelligence and specific cognitive functions in intellectual disability: Implications for assessment and classification. Current Opinion in Psychiatry 31(2): 88–95. DOI: 10.1097/YCO.0000000000000387

Bertelli

Moss

(2022) Diagnostic issues. In: Bertelli

Deb

Munir

Hassiotis

Salvador-Carulla

(eds) Textbook of psychiatry for intellectual disability and autism spectrum disorder. Cham, Switzerland: Springer Nature Switzerland, pp.107-122.

10.

Björne

Deveau

Nylander

(2021) Passing laws is not enough to change staff practice: The case of legally mandated “incident” reporting in Sweden. Journal of Intellectual & Developmental Disability 46(2): 186-196. DOI: 10.3109/13668250.2021.1873751

11.

Brown

(1999) Abuse of people with learning disabilities. Layers of concern and analysis. In: Stanley

Manthorpe

Penhale

(eds) Institutional abuse. Perspectives across the lifespan. London, UK: Routledge, pp.89-109.

12.

Bryson

Bradley

Thompson

Wainwright

(2008) Prevalence of autism among adolescents with intellectual disabilities. The Canadian Journal of Psychiatry 53(7): 449-459. DOI: 10.1177/070674370805300710

13.

Burack

Evans

Russo

Napoleon

Goldman

Iarocci

(2021) Developmental perspectives on the study of persons with intellectual disability. Annual Review of Clinical Psychology 17: 339-363. DOI: 10.1146/annurev-clinpsy-081219-090532

14.

Croskerry

(2002) Achieving quality in clinical decision making: Cognitive strategies and detection of bias. Academic Emergency Medicine 9(11): 1184-1204. DOI: 10.1197/aemj.9.11.1184

15.

Dagnan

Lindsay

(2012). People with intellectual disabilities and mental ill-health. In: Emerson

Hatton

Dickson

Gone

Caine

Bromley

(eds) Clinical psychology and people with intellectual disabilities (2nd edition). Chichester, UK: John Wiley & Sons, pp. 313-383.

16.

Dalhaug

Storvik

Kildahl

(2022) Undiagnosed psychotic disorder in autistic individuals with intellectual disabilities and suspected obsessive-compulsive disorder: An explorative, clinical study. Journal of Mental Health Research in Intellectual Disabilities. Epub ahead of print 31 August 2022. DOI: 10.1080/19315864.2022.2117880

17.

Daveney

Hassiotis

Katona

Matcham

Sen

(2019) Ascertainment and prevalence of post-traumatic stress disorder (PTSD) in people with intellectual disabilities. Journal of Mental Health Research in Intellectual Disabilities 12(3-4): 211-233. DOI: 10.1080/19315864.2019.1637979

18.

Davis

Crompton

(2021) What do new findings about social interaction in autistic adults mean for neurodevelopmental research? Perspectives on Psychological Science 16(3): 649-653. DOI: 10.1177/1745691620958010

19.

Deb

Perera

Krysta

Ozer

Bertelli

Novell

Wieland

Sappok

(2022) The European guideline on the assessment and diagnosis of psychiatric disorders in adults with intellectual disabilities. The European Journal of Psychiatry 36(1): 11-25. DOI: 10.1016/j.ejpsy.2021.10.002

20.

Delmar

(2012) The excesses of care: A matter of understanding the asymmetry of power. Nursing Philosophy 13(4): 236-243. DOI: 10.1111/j.1466-769X.2012.00537.x

21.

Dion

Paquette

Tremblay

Collin-Vézina

Chabot

(2018) Child maltreatment among children with intellectual disability in the Canadian Incidence Study. American Journal on Intellectual and Developmental Disabilities 123(2): 176-188. DOI: 10.1352/1944-7558-123.2.176

22.

Duke

(2011) Lesbian, gay, bisexual, and transgender youth with disabilities: A meta-synthesis. Journal of LGBT Youth 8(1): 1-52. DOI: 10.1080/19361653.2011.519181

23.

Einfeld

Ellis

Emerson

(2011) Comorbidity of intellectual disability and mental disorder in children and adolescents: A systematic review. Journal of Intellectual & Developmental Disability 36(2): 137-143. DOI: 10.1080/13668250.2011.572548

24.

Emerson

(2001) Challenging behaviour: Analysis and intervention in people with severe intellectual disabilities. Cambridge, UK: Cambridge University Press.

25.

Emerson

Felce

Stancliffe

(2013) Issues concerning self-report data and population-based data sets involving people with intellectual disabilities. Intellectual and Developmental Disabilities 51(5): 333-348. DOI: 10.1352/1934-9556-51.5.333

26.

Engebretsen

Heggen

Wieringa

Greenhalgh

(2016) Uncertainty and objectivity in clinical decision making: A clinical case in emergency medicine. Medicine, Health Care and Philosophy 19(4): 595-603. DOI: 10.1007/s11019-016-9714-5

27.

Feilzer

(2010) Doing mixed methods research pragmatically: Implications for the rediscovery of pragmatism as a research paradigm. Journal of Mixed Methods Research 4(1): 6-16. DOI: 10.1177/1558689809349691

28.

FitzGerald

Hurst

(2017) Implicit bias in healthcare professionals: a systematic review. BMC medical ethics 18(1): 1-18. DOI: 10.1186/s12910-017-0179-8

29.

Fletcher

Barnhill

Cooper

(2017) Diagnostic manual – Intellectual disability (DM-ID 2): Textbook of diagnosis of mental disorders in persons with intellectual disability (2nd ed.). Kingston, NY: NADD.

30.

Fletcher

Havercamp

Ruedrich

Benson

Barnhill

Stavrakaki

(2009) Clinical usefulness of the diagnostic manual-intellectual disability for mental disorders in persons with intellectual disability: Results from a brief field survey. The Journal of Clinical Psychiatry 70(7): 967-974.

31.

Fusar-Poli

Brondino

Rocchetti

Panisi

Provenzani

Damiani

Politi

(2017) Diagnosing ASD in adults without ID: accuracy of the ADOS-2 and the ADI-R. Journal of Autism and Developmental Disorders 47(11): 3370-3379. DOI: 10.1007/s10803-017-3258-2

32.

Green

Thorogood

(2018) Qualitative methods for health research (4th edition). London, UK: Sage.

33.

Greenhalgh

Russell

(2006) Reframing evidence synthesis as rhetorical action in the policy making drama. Healthcare Policy 1(2): 34-42.

34.

Grue

(2011) Discourse analysis and disability: Some topics and issues. Discourse & Society 22(5): 532-546. DOI: 10.1177/0957926511405572

35.

Halvorsen

Helverschou

Axelsdottir

Brøndbo

Martinussen

(2022a) General measurement tools for assessing mental health problems among children and adolescents with an intellectual disability: A systematic review. Journal of Autism and Developmental Disorders. Epub ahead of print 12 January 2022. DOI: 10.1007/s10803-021-05419-5

36.

Halvorsen

Kildahl

Helverschou

(2022b) Measuring comorbid psychopathology. In: Matson

Sturmey

(eds) Handbook of autism and pervasive developmental disorder. Assessment, diagnosis, and treatment. Cham, Switzerland: Springer, pp. 429-447. DOI: 10.1007/978-3-030-88538-0_18

37.

Harding

(2004) Psychiatric disability and clinical decision making: The impact of judgment error and bias. Clinical Psychology Review 24(6): 707-729. DOI: 10.1016/j.cpr.2004.06.003

38.

Havdahl

Bishop

Surén

Øyen

Lord

Pickles

von Tetzchner

Schjølberg

Gunnes

Hornig

Lipkin

Susser

Bresnahan

Magnus

Stenberg

Reichborn-Kjennerud

Stoltenberg

(2017) The influence of parental concern on the utility of autism diagnostic instruments. Autism Research 10(10): 1672-1686. DOI: 10.1002/aur.1817

39.

Havercamp

Barnhill

Bonardi

Chapman

Cobranchi

Fletcher

Rabidoux

Seely

Tassé

Nisonger Center RRTC on Health and Function (2022) Straight from the horse's mouth: Increasing self-report in mental health assessment in individuals with intellectual disability. Journal of Applied Research in Intellectual Disabilities 35(2): 471-479. DOI: 10.1111/jar.12952

40.

Hellerud

JMA

Bakken

(2019) Family and caregivers’ experience of mental illness in migrants with intellectual disability – reflections on practice. Advances in Mental Health and Intellectual Disabilities 13(2): 76-88. DOI: 10.1108/AMHID-06-2018-0029

41.

Helverschou

Bakken

Martinsen

(2011) Psychiatric disorders in people with autism spectrum disorders: Phenomenology and recognition. In: Matson

Sturmey

(eds) International handbook of autism and pervasive developmental disorders. New York, NY: Springer, pp. 53-74. DOI: 10.1007/978-1-4419-8065-6_5

42.

Helverschou

Kildahl

Bakken

(2020) Checklists and structured interviews. In: Matson

(ed) Handbook of dual diagnosis. Assessment and treatment in persons with intellectual disorders. Cham, Switzerland: Springer, pp. 167-193. DOI: 10.1007/978-3-030-46835-4_11

43.

Howard

(1983) Toward methodological pluralism. Journal of Counseling Psychology 30(1): 19–21. DOI: 10.1037/0022-0167.30.1.19

44.

Howlin

Magiati

(2017) Autism spectrum disorder: Outcomes in adulthood. Current Opinion in Psychiatry 30(2): 69-76. DOI: 10.1097/YCO.0000000000000308

45.

Hurley

(1996) Psychiatric disorders in children and adolescents with mental retardation and developmental disabilities. Current Opinion in Pediatrics 8(4): 361-365.

46.

Hwang

Foley

Trollor

(2020) Aging well on the autism spectrum: An examination of the dominant model of successful aging. Journal of Autism and Developmental Disorders 50(7): 2326-2335. DOI: 10.1007/s10803-018-3596-8

47.

Ineland

Molin

Sauer

(2018) Handling plurality and dealing with difficult work experiences. A comparative study of human service professionals’ work with individuals with intellectual disabilities. Journal of Policy and Practice in Intellectual Disabilities 15(1): 36-42. DOI: 10.1111/jppi.12227

48.

Ivers

Johnson

Rogers

(2021) The association between implicit racial bias and mindfulness in mental health practitioners. Journal of Counseling & Development 99(1): 11-23. DOI: 10.1002/jcad.12350

49.

Jopp

Keys

(2001) Diagnostic overshadowing reviewed and reconsidered. American Journal on Mental Retardation 106(5): 416-433. DOI: 10.1352/0895-8017(2001)106<0416:DORAR>2.0.CO;2

50.

Kahneman

(2003) A perspective on judgment and choice. Mapping bounded reality. American Psychologist 58(9): 697-720. DOI: 10.1037/0003-066X.58.9.697

51.

Kahneman

(2011) Thinking, fast and slow. London, UK: Penguin Books.

52.

Kahneman

Sibony

Sunstein

(2021) Noise. A flaw in human judgement. London, UK: William Collins.

53.

Kalvin

Marsh

Ibrahim

Gladstone

Woodward

Grantz

Ventola

Sukhodolsky

(2020) Discrepancies between parent and child ratings of anxiety in children with autism spectrum disorder. Autism Research 13(1): 93-103. DOI: 10.1002/aur.2220

54.

Kildahl

Bakken

Holm

Helverschou

(2017) Assessment of psychosis in ASD/ID: a case study. Advances in Mental Health and Intellectual Disabilities 11(1): 17-23. DOI: 10.1108/AMHID-11-2016-0036

55.

Kildahl

Bakken

Matre

EAW

Hellerud

JMA

Engebretsen

Helverschou

(2021) Case study: Identification of anxiety and subsequent intervention in an adolescent male with autism, severe intellectual disability and self-injurious behaviour. International Journal of Developmental Disabilities 67(5): 327-338. DOI: 10.1080/20473869.2020.1850160

56.

Kildahl

Engebretsen

Helverschou

(2019a) Attachment disorder in autism spectrum disorder and intellectual disability. Advances in Mental Health and Intellectual Disabilities 13(2): 57-66. DOI: 10.1108/AMHID-09-2018-0039

57.

Kildahl

Engebretsen

Horndalsveen

Hellerud

JMA

Wiik

Aasen

Helverschou

(2019b) Psychiatric assessment in congenital blindness, ASD and ID: Experience from two clinical cases. Advances in Mental Health and Intellectual Disabilities 13(5): 194-203. DOI: 10.1108/AMHID-03-2019-0007

58.

Kildahl

Helverschou

Bakken

Oddli

(2020a) “If we do not look for it, we do not see it”: Clinicians' experiences and understanding of identifying post-traumatic stress disorder in adults with autism and intellectual disability. Journal of Applied Research in Intellectual Disabilities 33(5): 1119-1132. DOI: 10.1111/jar.12734

59.

Kildahl

Helverschou

Oddli

(2020b) Clinicians’ retrospective perceptions of failure to detect sexual abuse in a young man with autism and mild intellectual disability. Journal of Intellectual & Developmental Disability 45(2): 194-202. DOI: 10.3109/13668250.2019.1680821

60.

Kildahl

Jørstad

(2022) Post-traumatic stress disorder symptom manifestations in an autistic man with severe intellectual disability following coercion and scalding. Journal of Intellectual & Developmental Disability 47(2): 190-194. DOI: 10.3109/13668250.2021.1995930

61.

King

Bruner

(2000) Social desirability bias: A neglected aspect of validity testing. Psychology & Marketing 17(2): 79-103. DOI: 10.1002/(SICI)1520-6793(200002)17:2<C79::AID-MAR2>3.0.CO;2-0

62.

Lai

M-C

Lombardo

Baron-Cohen

(2014) Autism. The Lancet 383(9920): 896-910. DOI: 10.1016/S0140-6736(13)61539-1

63.

Leonard

Petterson

Bower

Sanders

(2003) Prevalence of intellectual disability in Western Australia. Paediatric and Perinatal Epidemiology 17(1): 58–67. doi: 10.1046/j.1365-3016.2003.00469.x

64.

Levitan

Reiss

(1983) Generality of diagnostic overshadowing across disciplines. Applied Research in Mental Retardation 4(1): 59-64. DOI: 10.1016/S0270-3092(83)80018-6

65.

Lim

Young

Brewer

(2022) Autistic adults may be erroneously perceived as deceptive and lacking credibility. Journal of Autism and Developmental Disorders 52(2): 490-507. DOI: 10.1007/s10803-021-04963-4

66.

López

(1989) Patient variable biases in clinical judgment: Conceptual overview and methodological considerations. Psychological Bulletin 106(2): 184–203. DOI: 10.1037/0033-2909.106.2.184

67.

Maddox

Crabbe

Beidas

Brookman-Frazee

Cannuscio

Miller

Nicolaidis

Mandell

(2020) “I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults. Autism 24(4): 919-930. DOI: 10.1177/1362361319882227

68.

Magnavita

Lilienfeld

(2016) Clinical expertise and decision making: An overview of bias in clinical practice. In: Magnavita

(ed) Clinical decision making in mental health practice. Washington, DC: American Psychological Association, pp. 23–60. DOI: 10.1037/14711-002

69.

Malterud

(2001) The art and science of clinical knowledge: Evidence beyond measures and numbers. The Lancet 358(9279): 397–400. DOI: 10.1016/S0140-6736(01)05548-9

70.

Man

Kangas

(2020) Best practice principles when working with individuals with intellectual disability and comorbid mental health concerns. Qualitative Health Research 30(4): 560-571. DOI: 10.1177/1049732319858326

71.

Marcelin

Siraj

Victor

Kotadia

Maldonado

(2019) The impact of unconscious bias in healthcare: How to recognize and mitigate it. The Journal of Infectious Diseases 220(Supplement_2): S62-S73. DOI: 10.1093/infdis/jiz214

72.

Mason

Scior

(2004) ‘Diagnostic overshadowing’ amongst clinicians working with people with intellectual disabilities in the UK. Journal of Applied Research in Intellectual Disabilities 17(2): 85-90. DOI: 10.1111/j.1360-2322.2004.00184.x

73.

Maulik

Mascarenhas

Mathers

Dua

Saxena

(2011) Prevalence of intellectual disability: A meta-analysis of population-based studies. Research in Developmental Disabilities 32(2): 419–436. doi: 10.1016/j.ridd.2010.12.018

74.

Mazza

Rossetti

Crespi

Clerici

(2020) Prevalence of co-occurring psychiatric disorders in adults and adolescents with intellectual disability: A systematic review and meta-analysis. Journal of Applied Research in Intellectual Disabilities 33(2): 126-138. DOI: 10.1111/jar.12654

75.

McBrien

Candy

(2012) Working with organisations or: Why won’t they follow my advice? In: Emerson

Hatton

Dickson

Gone

Caine

Bromley

(eds) Clinical psychology and people with intellectual disabilities (2nd edition). Chichester, UK: John Wiley & Sons, pp. 161-180.

76.

McDonnell

Boan

Bradley

Seay

Charles

Carpenter

(2019) Child maltreatment in autism spectrum disorder and intellectual disability: Results from a population-based sample. Journal of Child Psychology and Psychiatry 60(5): 576-584. DOI: 10.1111/jcpp.12993

77.

McMahon

Hatton

(2021) A comparison of the prevalence of health problems among adults with and without intellectual disability: A total administrative population study. Journal of Applied Research in Intellectual Disabilities 34(1): 316-325. DOI: 10.1111/jar.12785

78.

McNally

Taggart

Shevlin

(2021) Trauma experiences of people with an intellectual disability and their implications: A scoping review. Journal of Applied Research in Intellectual Disabilities 34(4): 927-949. DOI: 10.1111/jar.12872

79.

Mevissen

Didden

de Jongh

(2016) Assessment and treatment of PTSD in people with intellectual disability. In: Martin

Preedy

Patel

(eds) Comprehensive guide to post-traumatic stress disorder. Cham, Switzerland: Springer, pp. 281–299. DOI: 10.1007/978-3-319-08613-2_95-2

80.

Moran-Ellis

Alexander

Cronin

Dickinson

Fielding

Sleney

Thomas

(2006) Triangulation and integration: Processes, claims and implications. Qualitative Research 6(1): 45-59. DOI: 10.1177/1468794106058870

81.

Morinaga

Rai

Hollander

Petros

Dalman

Magnusson

(2021) Migration or ethnic minority status and risk of autism spectrum disorders and intellectual disability: Systematic review. European Journal of Public Health 31(2): 304-312. DOI: 10.1093/eurpub/ckaa108

82.

Morrison

Gillig

(2012) Psychiatric assessment. In: Gentile

Gillig

(eds) Psychiatry of intellectual disabilities: A practical manual. Chichester, UK: John Wiley & Sons, pp. 14-25.

83.

Moss

Eirinaki

Savage

Howlin

(2019) Growing older with autism – The experiences of adult siblings of individuals with autism. Research in Autism Spectrum Disorders 63: 42-51. DOI: 10.1016/j.rasd.2018.10.005

84.

Norman

Eva

(2010) Diagnostic error and clinical reasoning. Medical Education 44(1): 94-100. DOI: 10.1111/j.1365-2923.2009.03507.x

85.

Nunkoosing

Haydon-Laurelut

(2011) Intellectual disabilities, challenging behaviour and referral texts: A critical discourse analysis. Disability & Society 26(4): 405-417. DOI: 10.1080/09687599.2011.567791

86.

Painter

Hastings

Ingham

Trevithick

Roy

(2018) Associations between mental health problems and challenging behavior in adults with intellectual disabilities: A test of the behavioral equivalents hypothesis. Journal of Mental Health Research in Intellectual Disabilities 11(2): 157-172. DOI: 10.1080/19315864.2018.1431747

87.

Palucka

Bradley

Lunsky

(2008) A case of unrecognized intellectual disability and autism misdiagnosed as schizophrenia: Are there lessons to be learned? Mental Health Aspects of Intellectual Disabilities 11(2): 55-60.

88.

Pelleboer-Gunnink

Van Oorsouw

WMWJ

Van Weeghel

Embregts

PJCM

(2017) Mainstream health professionals' stigmatising attitudes towards people with intellectual disabilities: A systematic review. Journal of Intellectual Disability Research 61(5): 411-434. DOI: 10.1111/jir.12353

89.

Peña-Salazar

Arrufat

López

JMS

Fontanet

Roura-Poch

Gil-Girbau

Carbonell-Ducastella

Serrano-Blanco

(2022) Intellectual disability, autism spectrum disorders, psychiatric comorbities and their relationship with challenging behavior. Journal of Mental Health Research in Intellectual Disabilities 15(2): 77-94. DOI: 10.1080/19315864.2022.2029641

90.

Perkins

(2007) Self- and proxy reports across three populations: Older adults, persons with Alzheimer’s disease, and persons with intellectual disabilities. Journal of Policy and Practice in Intellectual Disabilities 4(1): 1-10. DOI: 10.1111/j.1741-1130.2006.00092.x

91.

Perkins

Moran

(2010) Aging adults with intellectual disabilities. JAMA: The Journal of the American Medical Association 304(1): 91–92. DOI: 10.1001/jama.2010.906

92.

Polyak

Rosenfeld

Girirajan

(2015) An assessment of sex bias in neurodevelopmental disorders. Genome Medicine 7(1): 1-11. DOI: 10.1186/s13073-015-0216-5

93.

Postorino

Kerns

Vivanti

Bradshaw

Siracusano

Mazzone

(2017) Anxiety disorders and obsessive-compulsive disorder in individuals with autism spectrum disorder. Current Psychiatry Reports 19(12): 1-8. DOI: 10.1007/s11920-017-0846-y

94.

Prosser

Bromley

(2012) Interviewing people with intellectual disabilities. In: Emerson

Hatton

Dickson

Gone

Caine

Bromley

(eds) Clinical psychology and people with intellectual disabilities (2nd edition). Chichester, UK: John Wiley & Sons, pp. 107-120.

95.

Rasmussen

Nordgaard

Parnas

(2020) Schizophrenia-spectrum psychopathology in obsessive-compulsive disorder: An empirical study. European Archives of Psychiatry and Clinical Neuroscience 270(8): 993-1002. DOI: 10.1007/s00406-019-01022-z

96.

Reiss

Levitan

Szyszko

(1982) Emotional disturbance and mental retardation: Diagnostic overshadowing. American Journal of Mental Deficiency 86(6): 567–574.

97.

Reiss

Szyszko

(1983) Diagnostic overshadowing and professional experience with mentally retarded persons. American Journal of Mental Deficiency 87(4): 396-402.

98.

Rittmannsberger

Yanagida

Weber

Lueger-Schuster

(2020) The association between challenging behaviour and symptoms of post-traumatic stress disorder in people with intellectual disabilities: A Bayesian mediation analysis approach. Journal of Intellectual Disability Research 64(7): 538-550. DOI: 10.1111/jir.12733

99.

Rogers

Bender

(2018) Clinical assessment of malingering and deception (4th ed.). New York, NY: The Guilford Press.

100.

Roman-Urrestarazu

van Kessel

Allison

Matthews

Brayne

Baron-Cohen

(2021) Association of race/ethnicity and social disadvantage with autism prevalence in 7 million school children in England. JAMA Pediatrics 175(6): e210054. DOI: 10.1001/jamapediatrics.2021.0054

101.

Ruscio

(2012) The clinician as subject: Practitioners are prone to the same judgment errors as everyone else. In: Lilienfeld

O’Donohue

(eds) The great ideas of clinical science: 17 principles that every mental health professional should understand. New York, NY: Routledge, pp. 59-78.

102.

Rush

Bowman

Eidman

Toole

Mortenson

(2004) Assessing psychopathology in individuals with developmental disabilities. Behavior Modification 28(5): 621-637. DOI: 10.1177/0145445503259830

103.

Ryan

Bergin

Wells

(2021) Work-related stress and well-being of direct care workers in intellectual disability services: A scoping review of the literature. International Journal of Developmental Disabilities 67(1): 1-22. DOI: 10.1080/20473869.2019.1582907

104.

Rysstad

Kildahl

Skavhaug

Dønnum

Helverschou

(2022) Case study: Organizing outpatient pharmacological treatment of bipolar disorder in autism, intellectual disability and Phelan-McDermid syndrome (22q13. 3 deletion syndrome). International Journal of Developmental Disabilities, 68(3): 378-387. DOI: 10.1080/20473869.2020.1756113

105.

Saemundsen

Juliusson

Hjaltested

Gunnarsdottir

Halldorsdottir

Hreidarsson

Magnusson

(2010) Prevalence of autism in an urban population of adults with severe intellectual disabilities–a preliminary study. Journal of Intellectual Disability Research 54(8): 727-735. DOI: 10.1111/j.1365-2788.2010.01300.x

106.

Santore

Gerber

Gioia

Bianchi

Talledo

Peris

Lerner

(2020) Felt but not seen: Observed restricted repetitive behaviors are associated with self-report - but not parent-report - obsessive-compulsive disorder symptoms in youth with autism spectrum disorder. Autism 24(4): 983-994. DOI: 10.1177/1362361320909177

107.

Sawyer

(2018) DM-ID 2 is a very useful tool. Journal of Mental Health Research in Intellectual Disabilities 11(1): 107-109. DOI: 10.1080/19315864.2017.1399180

108.

Schwartz

Rogers

Russinova

(2022) Predictors of community-based employment for young adults with intellectual disability and co-occurring mental health conditions. Disability and Rehabilitation 44(9): 1609-1618. DOI: 10.1080/09638288.2021.1907457

109.

Scior

Addai-Davis

Kenyon

Sheridan

(2013) Stigma, public awareness about intellectual disability and attitudes to inclusion among different ethnic groups. Journal of Intellectual Disability Research 57(11): 1014-1026. DOI: 10.1111/j.1365-2788.2012.01597.x

110.

Scior

Werner

(2016) Intellectual disability and stigma. Basingstoke, UK: Palgrave Macmillan.

111.

Scott

Havercamp

(2018) Comparisons of self and proxy report on health-related factors in people with intellectual disability. Journal of Applied Research in Intellectual Disabilities 31(5): 927-936. DOI: 10.1111/jar.12452

112.

Siegel

McGuire

Veenstra-VanderWeele

Stratigos

King

American Academy of Child and Adolescent Psychiatry (AACAP) Committee on Quality Issues (CQI) Bellonci

Hayek

Keable

Rockhill

Bukstein

Walter

(2020) Practice parameter for the assessment and treatment of psychiatric disorders in children and adolescents with intellectual disability (intellectual developmental disorder). Journal of the American Academy of Child & Adolescent Psychiatry 59(4): 468–496. doi: 10.1016/j.jaac.2019.11.018

113.

Slife

Gantt

(1999) Methodological pluralism: A framework for psychotherapy research. Journal of Clinical Psychology 55(12): 1453-1465. DOI: 10.1002/(SICI)1097-4679(199912)55:12<1453::AID-JCLP4>3.0.CO;2-C174

114.

Small

(2011) How to conduct a mixed methods study: Recent trends in a rapidly growing literature. Annual Review of Sociology 37(1): 57-86. DOI: 10.1146/annurev.soc.012809.102657

115.

Snowden

(2003) Bias in mental health assessment and intervention: Theory and evidence. American Journal of Public Health 93(2): 239-243. DOI: 10.2105/AJPH.93.2.239

116.

Sommarö

Andersson

Skagerström

(2020) A deviation too many? Healthcare professionals’ knowledge and attitudes concerning patients with intellectual disability disrupting norms regarding sexual orientation and/or gender identity. Journal of Applied Research in Intellectual Disabilities 33(6): 1199-1209. DOI: 10.1111/jar.12739

117.

Spivakovsky

(2018) The impossibilities of “bearing witness” to the violence of coercive interventions in the disability sector. In: Spivakovsky

Seear

Carter

(eds) Critical perspectives on coercive interventions: Law, medicine and society. New York, NY: Routledge, pp. 97-113.

118.

Stancliffe

(1995) Assessing opportunities for choice-making: A comparison of self- and staff reports. American Journal on Mental Retardation 99(4): 418–429.

119.

Stengers

(1997) Power and invention: Situating science. Minneapolis: University of Minnesota Press.

120.

Stengers

(2002) Sciences et pouvoirs. La democratie face a la technoscience. Paris, France: La Decouverte/Poche.

121.

Strand

Benzein

Saveman

(2004) Violence in the care of adult persons with intellectual disabilities. Journal of Clinical Nursing 13(4): 506-514. DOI: 10.1046/j.1365-2702.2003.00848.x

122.

The Royal College of Psychiatrists (2001) DC-LD [Diagnostic Criteria for Use with Adults with Learning Disabilities/Mental Retardation]. London, UK: Gaskell.

123.

Thurm

Farmer

Salzman

Lord

Bishop

(2019) State of the field: Differentiating intellectual disability from autism spectrum disorder. Frontiers in Psychiatry 10: 526. doi: 10.3389/fpsyt.2019.00526

124.

Tonnsen

Boan

Bradley

Charles

Cohen

Carpenter

(2016) Prevalence of autism spectrum disorders among children with intellectual disability. American Journal on Intellectual and Developmental Disabilities 121(6): 487-500. DOI: 10.1352/1944-7558-121.6.487

125.

Totsika

Liew

Absoud

Adnams

Emerson

(2022) Mental health problems in children with intellectual disability. The Lancet Child & Adolescent Health 6(6): 432-444. DOI: 10.1016/S2352-4642(22)00067-0

126.

Tromans

Chester

Gemegah

Roberts

Morgan

Yao

Brugha

(2021) Autism identification across ethnic groups: A narrative review. Advances in Autism 7(3): 241-255. DOI: 10.1108/AIA-03-2020-0017

127.

Truesdale

Brown

Taggart

Bradley

Paterson

Sirisena

Walley

Karatzias

(2019) Trauma-informed care: A qualitative study exploring the views and experiences of professionals in specialist health services for adults with intellectual disabilities. Journal of Applied Research in Intellectual Disabilities 32(6): 1437-1445. DOI: 10.1111/jar.12634

128.

Underwood

McCarthy

Chaplin

Bertelli

(2015) Assessment and diagnosis of psychiatric disorder in adults with autism spectrum disorder. Advances in Mental Health and Intellectual Disabilities 9(5): 222-229. DOI: 10.1108/AMHID-05-2015-0025

129.

Underwood

McCarthy

Tsakanikos

(2011) Assessment of comorbid psychopathology. In: Matson

Sturmey

(eds) International handbook of autism and pervasive developmental disorders. New York, NY: Springer, pp. 287-293.

130.

Valdovinos

Seibert

Piersma

Wilkinson

Griffith

Hillring

Boyle

(2019) Characterizing mood states in individuals diagnosed with bipolar disorder and intellectual disability. Journal of Mental Health Research in Intellectual Disabilities 12(1-2): 26-44. DOI: 10.1080/19315864.2018.1561770

131.

Westerinen

Kaski

Virta

Almqvist

Iivanainen

(2007) Prevalence of intellectual disability: A comprehensive study based on national registers. Journal of Intellectual Disability Research 51(9): 715–725. doi: 10.1111/j.1365-2788.2007.00970.x

132.

Whittle

Fisher

Reppermund

Lenroot

Trollor

(2018) Barriers and enablers to accessing mental health services for people with intellectual disability: A scoping review. Journal of Mental Health Research in Intellectual Disabilities 11(1): 69-102. DOI: 10.1080/19315864.2017.1408724

133.

Wieringa

Engebretsen

Heggen

Greenhalgh

(2018) Rethinking bias and truth in evidence-based health care. Journal of Evaluation in Clinical Practice 24(5): 930-938. DOI: 10.1111/jep.13010

134.

Wolkorte

van Houwelingen

Kroezen

(2019) Challenging behaviours: Views and preferences of people with intellectual disabilities. Journal of Applied Research in Intellectual Disabilities 32(6): 1421-1427. DOI: 10.1111/jar.12631

135.

World Health Organization (1992) International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) .

136.

World Health Organization (2018) International classification of diseases for mortality and morbidity statistics, 11th Revision (ICD-11). Retrieved from https://icd.who.int/browse11/l-m/en

137.

Yin

(2014) Case study research. Design and methods (5th ed.). London, UK: Sage.

Bias in assessment of co-occurring mental disorder in individuals with intellectual disabilities: Theoretical perspectives and implications for clinical practice

Abstract

Keywords

Introduction

Bias in clinical decision-making

Conceptualising bias

Consequences of bias

Managing bias

General sources of bias

Cognitive bias in mental health professionals

Bias arising from patient characteristics

Potential bias from various assessment strategies

Self-report

Direct, clinical observation

Proxy ratings

Rating scales focusing on observable behaviour

Power asymmetries

The practitioner as an outsider

Implications: Multimethod assessments and triangulation

Multimethod interdisciplinary assessments

Triangulation and integration: Managing convergence and divergence

Reflective assessment practices

Bias in evaluation of treatment and intervention

Conclusions

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

References