Low-intensity CBT Skills and Interventions. Группа авторов
use of diagnoses in mental health disorders has been the subject of considerable discussion and controversy, not least because diagnostic systems are criticised by some as pathologising distress which might otherwise be considered part of the human condition (Kirschner, 2013). Researchers and clinicians have argued that mental health problems diagnosed by a symptom severity threshold being crossed, particular degree of dysfunction or chronicity of a problem would be better considered dimensionally, with symptoms occurring on one or more continua (Ayuso-Mateos et al., 2010). Specifically, for each group of symptoms used to diagnose mental health problems at present, someone might be considered to be at a certain point on a continuous dimension. This approach would be similar to common conceptualisations of personality where people score at various points on dimensions of extraversion, neuroticism, openness, etc. The pattern of points across various different dimensions might then demarcate the different mental health disorders being experienced.
Multidimensional diagnostic systems such as the Research Domain Criteria (RDoC; Insel et al., 2010) that classify mental disorders based on behavioural phenotypes and biomarkers therefore offer a different way to consider CMDs. By viewing mental disorders on continua, advocates of these systems suggest a more dynamic and flexible method of assessing mental disorders can be created (Insel et al., 2010). This approach may help address questions raised concerning the reliability and validity of diagnoses based on the current categorical diagnostic systems (e.g. Lieblich et al., 2015). Multidimensional approaches could help to resolve the issue of overlap between symptoms of different CMDs – for example, the experience of excessive worry or panic attacks common across many CMDs or the degree of co-morbidity between CMDs (Hirschfeld, 2001; Kessler et al., 2005). Furthermore, they may also help elucidate mechanisms of change and better explain why individuals with the same diagnosis that are given the same treatment can have substantially different treatment outcomes (Insel et al., 2010).
Probable Diagnosis Determining a Problem Statement
To facilitate reaching a probable diagnosis, a probable diagnosis decision support tool adapted from the IAPT Manual (NCCMH, 2018) can be helpful.
Figure 3.1 Probable diagnosis decision support tool (adapted from NCCMH, 2018)
LICBTs are reminded to use the questionnaire measures predominantly as a means to inform discussions in supervision regarding probable diagnoses and appropriate intensity of treatment, and for those diagnoses for which there are NICE evidence-based LICBT treatments, the same measures can be used throughout treatment to monitor progress.
The case example below can be used to simulate the decision-making processes for arriving at a probable diagnosis.
Clinical Example
Case Example: Sallyanne
Sallyanne is a 52-year-old woman who has three adult children. Her youngest child moved out of the family home after falling pregnant with her first child a year ago. She now lives alone after becoming separated from her husband six months ago. Sallyanne says that she has come for an assessment because she has been feeling incredibly stressed, nervous and tense over the last year. She describes recently feeling increasingly low as she struggles to sleep properly since her husband left, scoring 15 on the PHQ-9. She describes lying awake for hours at night not able to stop herself thinking and worrying about all sorts of things. She also describes herself as not being very good with change. She spent most of the first 15 minutes of the assessment talking about her daughter and new grandson. Sallyanne is very concerned that her daughter does not have the financial wherewithal to support her grandson, and she is concerned with the life he may have. She talks about political concerns, changes to the education system and the struggles local schools are having, the cost of university education and the difficulty young adults have with finding stable work, all of which make her worry for her grandson's future, her daughter's future and the future of generations to come. Sallyanne talks freely about her childhood, which she describes as an unhappy time and reports having been mistreated by her parents, fearing them, being unsure of what they would say or how they would treat her day to day. However, she does not have any unwanted or intrusive thoughts or nightmares about events that occurred.
Based on the case study, the probable diagnosis, treatment decision and justifications are shown in Table 3.1.
Table 3.1
Improving Diagnosis Rates
Diagnosis is important for determining appropriate, evidence-based treatments in LICBT services, but in 2018–19 across all IAPT services, only 63.5 per cent of service users who had at least one treatment session had received a probable diagnosis, and the range across all services nationally was between 14 and 99 per cent (NHS Digital, 2019). This suggests that for just over a third of IAPT service users across the country and nearly all of the service users in some services, the clinical problem may not have been adequately identified, which may have resulted in inappropriate treatment being delivered (Clark et al., 2018; Gyani et al., 2011).
Table 3.2
The proportion of patients with a recorded probable diagnosis has been found to be associated with the proportion of patients achieving good treatment outcomes – for example, reliable recovery and reliable improvement (Clark et al., 2018). A number of IAPT services have conducted service evaluations to understand the problems associated with a lack of appropriate use of probable diagnosis and clarify ways in which they might improve the use of diagnoses in their services. Such evaluations have shown that most diagnoses are under-identified by LICBTs when comparing rates of probable CMDs to those determined by an established diagnostic screening tool, particularly in relation to PTSD and OCD (Cernis et al., 2016). However, one probable diagnosis may be over-reported by LICBT practitioners. That is MADD, which was determined to be the probable diagnosis none of the time when the Psychiatric Diagnostic Screening Questionnaire (PDSQ) was used (Cernis et al., 2016), however it is quite often used in IAPT services (Table 3.2). To improve the use of probable diagnoses, some Step 2 services have offered training and consultation about diagnoses to their staff. Others have sent staff reminders when no probable diagnosis has been assigned and a patient has had at least two treatment sessions. This has helped increase the use of diagnoses and also increase the use of the PHQ9 and GAD in relation to MADD (Cernis et al., 2016). The services that have taken action have also had increased rates of good therapy outcomes for their patients (Pimm et al., 2016).
Summary
Making accurate diagnoses and recording them are central to effective interventions by LICBT practitioners. They inform problem statements, goals and treatment plans developed collaboratively with patients, help determine which type of intervention might best be provided and determine the appropriate way to monitor progress throughout treatment. It is therefore important that an assessment is undertaken to ascertain which probable diagnosis best describes the presenting difficulty for all people presenting to LICBT services. To support LICBT practitioners we have suggested the use of a simple screening tool with questions which can be considered for every patient seen by an LICBT practitioner. To use it fully, LICBT practitioners will need adequate training in recognising the common mental disorder diagnoses seen regularly in their services, in monitoring symptoms of these disorders and in communicating outcomes from such monitoring sensitively and usefully to their patients. Enabling LICBT practitioners to effectively determine probable diagnoses is vital in ensuring the effective delivery of appropriate evidence-based interventions and the best possible outcomes for patients.
Assessing Your Understanding
Declarative