Prevention and Early Intervention in Depression and Anxiety Disorders

CHAPTER 16
Prevention and Early Intervention in Depression and Anxiety Disorders


Irwin Nazareth1 and Tony Kendrick2


1 Department of Primary Care & Population Health, London, UK


2 Hull York Medical School, University of York, York, UK


Depression and anxiety disorders are common and costly


Reducing the prevalence of depression and anxiety disorders is a public health challenge for the twenty-first century. These mental health disorders affect as many as one in six people in the community. The 2007 Office for National Statistics (ONS) household survey of adult psychiatric morbidity in England found that 16% of working age adults had an anxiety or depressive disorder [1]. Of those, 4.4% were diagnosed with generalised anxiety disorder (GAD), 3% with post-traumatic stress disorder (PTSD), 2.3% with major depression, 1.4% with phobias, 1.1% obsessive–compulsive disorder (OCD) and 1.1% with panic disorder. The most common problem was mixed anxiety and depression, found in 9% of patients [1]. (The total adds to more than 16% because patients can have more than one disorder.)


The high prevalence and relapsing nature of depression and anxiety disorders means that they account for at least 35% of all disability and sick leave days due to mental health problems [2]. Even subclinical depression and anxiety states, which do not reach the threshold for psychiatric diagnostic classification, may have major impacts on quality of life [2, 3]. The commonest disorder, mixed anxiety and depression, causes 20% of days lost from work in Britain [3]. Although there are high indirect costs from the substantial burden of illness, direct treatment costs tend to be relatively low due to low recognition and low rates of treatment [2]. McCrone and colleagues estimated the likely costs of depression over 20 years from 2006 to 2026 to include £1.7–£3 billion in drugs, hospital care and social services, but the projected costs of employment loss and benefits for depression for the same period were up to £9.2 billion [4].


Course and prognosis: opportunities for prevention and early intervention


The onset of common mental disorders is usually in adolescence or early adulthood, and earlier onset disorders tend to have a worse prognosis. The estimated median age of onset for anxiety disorders was 11 years and for depression 30 years in the US National Comorbidity sample – half of all lifetime cases had started by 14 years and three-quarters by the age of 24 [5]. This means efforts to prevent the onset of depression and anxiety disorders need to start in adolescence if possible [6].


The longer the disorders continue before intervention, the worse the prognosis too, which is the rationale for intervention early in any specific episode. More than 50% of people following their first episode of major depression will go on to have at least one more episode and after the second and third episodes, the risk of relapse rises to 70% and 90% respectively [7]. So a past history of depression is an important predictor of future episodes, and interventions which increase resilience against future depression are important in secondary prevention.


Relapse and recurrence is common even among people whose disorders appear for the first time in old age, and elderly people often suffer subclinical symptoms for a period before the disorder becomes apparent, offering the possibility of early intervention to reduce the number of full-blown disorders [8].


Barriers to the presentation of symptoms and the early detection of disorders


Depression and anxiety disorders are even more common among patients attending general practices than they are in the community. The New Zealand Magpie Study of 2006 found a similar prevalence to the ONS study in the community, of 15%, but a higher prevalence, 21%, in primary care [9]. However only 38% of people with disorders in the ONS survey had asked their doctor for help [1], which is one reason why disorders are missed.


There are significant barriers to access to both primary care and secondary care for mental health problems, which particularly affect the elderly, and black and minority ethnic patients, especially those for whom English is not their first language [10]. The stigma attached to a label of depression or anxiety prevents many people from presenting their symptoms and asking for help overtly, especially older people brought up in a time when people were not encouraged to discuss their feelings for fear of appearing weak. Access to care is generally poorer among people of lower social class, those with sensory impairments or learning difficulties, and in particular sociodemographic groups including young men as well as older people.


Even when patients in primary care do present their symptoms of depression or anxiety they are often not diagnosed by their GPs. Kessler and colleagues screened general practice attenders to identify cases of anxiety and depression and followed them up over 3 years, looking whether and when they were diagnosed by their GPs. As many as 30% of the cases of anxiety and depression they found remained undetected by their GPs at 3 years follow-up, of which 14% were severe cases who were disabled by their problems [11].


As a consequence of their reluctance to present their symptoms, and the lack of GP recognition of many common mental health disorders, only a minority of patients get help in primary care. Only 24% of sufferers in the ONS survey were receiving treatment: 14% medication; 5% counselling or therapy; and 5% both [1].


General practitioners vary widely in their ability to recognise common mental health disorders, with some recognising virtually all the patients found to be depressed when independently interviewed by a psychiatrist, and others recognising very few [12]. According to Goldberg, 10 behaviours are associated with greater detection. These include factors such as making eye contact, asking open-ended questions, asking specifically about feelings, and asking about problems at home or at work [12]. Attempts to improve GP detection have met with mixed results [13–15], and interventions often fail to improve patient outcomes, despite changes in doctors’ consultation skills [16].


The fact that common mental disorders often go undiagnosed among primary care attenders has led to suggestions that clinicians should systematically screen for hidden disorders [17]. Screening is the presumptive identification of unrecognised disorders by the application of tests which can be applied rapidly. Screening tests attempt to distinguish in a population between a group of people who probably have a condition and the remainder who do not. However, screening is not without costs, in terms not only of the resources needed to mount a screening programme, but also in terms of missing some cases while falsely labelling some people who turn out not to have significant anxiety or depression on further assessment, because no screening test has 100% sensitivity and specificity [18].


Instead of screening, targeted case identification (case finding), which involves screening a smaller group of people known to be at higher risk based on the presence of particular risk factors, may be a more efficient way of improving the recognition of common mental health disorders in primary care. However, a key challenge in prevention and early intervention is to develop a clear understanding of the nature of risk factors for the development of disorders [19].


Predicting the onset of depression and anxiety disorders


Clinical prediction rules for cardiovascular diseases such as the Framingham, Dundee and the European risk scores are now widely used in primary care. These scores have over the last 10 years revolutionised the preventive management of coronary heart diseases and stroke and have allowed the development and delivery of a range of prevention approaches to cardiovascular diseases. Risk scores such as these have now been developed to predict future depression and anxiety disorders too.


We know a great deal about the risk factors for the common mental health disorders. Low socioeconomic status [20–22] and female sex [23] are most consistently identified. Socioeconomic risk factors include low income and financial strain [20], unemployment [20], work stress [23], social isolation [24], and poor housing [21]. Other factors such as family history of depression play a part [25].


It is important to distinguish between modifiable risk factors and factors that can only function as markers, such as female gender and a positive family history, which are not modifiable. Possible genetic markers have also been identified through research reporting a higher incidence of depression after stressful life events in people with one or two short alleles of the serotonin transporter gene [26]. However, a subsequent meta-analysis [27] found no association between the incidence of depression and the serotonin transporter gene, either alone or in combination with stressful life events.


Negative life events should still be considered a risk factor, regardless of genetic make-up. Additional risk factors identified in general practice populations are poor physical health, poor marital or other interpersonal relationships, a partner or spouse’s poor health, and problems with alcohol [28]. Poor social support, loneliness and physical disability are risks for older adults [29–31]. Unfortunately, effective strategies for the prediction of major depression are hindered by a lack of evidence about the combined effect of this large number of known risk factors.


Developing an algorithm to predict depression


The predictD algorithm for the onset of major depression in European general practice attenders was developed on people attending general practice in six European countries [32, 33]. The risk model was based on the approach used for creating risk indices for cardiovascular disease [34], which provide a percentage risk estimate over a given time period. The main outcome measure was DSM-IV major depression. Data were collected on 39 known risk factors in order to construct a risk model for onset of major depression using stepwise logistic regression. The model was then tested in an independent general practice attender population in Chile.


The predictD risk model contained 10 factors.



  • Age
  • Sex
  • Country
  • Educational level
  • History of depression
  • Family history of psychological difficulties
  • Physical health (subscale scores on the Short Form 12 generic health measure)
  • Mental health (subscale scores on the Short Form 12)
  • Unsupported difficulties in paid or unpaid work
  • Experiences of discrimination.

Half of the participants who developed major depression also met the criteria for an anxiety disorder. The algorithm demonstrated good discriminative power with a c-index [35] of 0.790 (95% CI 0.767, 0.813), which compares favourably with c-indices between 0.71 and 0.82 for a risk index for cardiovascular events developed in 12 European cohorts [36]. When predictD was then tested in a second population (in Chile) the c-index was 0.710 (95% CI 0.670, 0.749).


To put this more concretely, GP attenders in the upper quintile of risk (i.e. one in five of all attenders) on the PredictD algorithm in the United Kingdom are two and a half times more likely to develop major depression in the next 12 months than the average attenders [33].


This application of this algorithm for the early identification and prevention of people at risk for depression is now been evaluated in the United Kingdom and Spain in two separate randomised controlled trials.


Extending the algorithm to generalised anxiety and panic


As an extension to the work described above, a further evaluation of the anxiety symptoms was done on the same general practice attender cohort in order to construct a risk model for GAD and panic disorder (predictA) [37].


Similar factors contributed to the final algorithm.



  • Sex
  • Age
  • Country
  • Lifetime depression screen
  • Family history of psychological difficulties
  • Short Form 12 physical health subscale score
  • Short form 12 mental health subscale score
  • Unsupported difficulties in paid and/or unpaid work.

The discriminative power of this model was as good as predictD. The similarity between the two algorithms may be at least in part due to the close correlation (comorbidity) of depressive and anxiety disorders [38]. Recent calls have been made for not separating these disorders into separate chapters in the DSM/ICD psychiatric classification systems [39]. Moreover, others have suggested that a core psychopathology around neuroticism is common to both anxiety disorders and depression [40].An alternative explanation to comorbidity is the possibility that depressive and anxiety disorders are expressions of a broader latent pathological process [41–44]. In light of the findings for predictA [37], it would be prudent to consider the use of just one of these algorithms for both types of common mental disorders, as this would capture most of the people at risk of depression, GAD and panic disorders.


Improving the identification of disorders in primary care


Once people more at risk of depression have been identified, subsequent targeted case finding needs to be more systematic, to improve the levels of detection of disorders among primary care attenders.


The NICE guidelines on depression in adults (CG90) [45] and depression in people with chronic physical disorders (CG91) [46] recommend that GPs be alert for depression in patients with a past history of depression, and in patients with a chronic physical health problem, and ask them the two ‘Whooley’ questions to screen for depression.


Whooley and colleagues [47] found that two questions were particularly sensitive in identifying depression.



  • During the last month, have you often been bothered by feeling down, depressed or hopeless?
  • During the last month, have you often been bothered by having little interest or pleasure in doing things?

These questions will be familiar to GPs and practice nurses in the United Kingdom who have used them to screen patients with diabetes and coronary heart disease for depression annually, under the terms of the UK general practice quality and outcomes framework [48]. If a patient answers ‘yes’ to either question, the screen is positive.


If the person screens positive, further follow-up assessments should then be undertaken before reaching a diagnosis. A number of questionnaires have been identified as useful in the further assessment of possible depression. The NICE 2009 depression guideline recommends practitioners consider using measures such as the nine-item Patient Health Questionnaire [49], the depression subscale of the Hospital Anxiety and Depression Scale [50] or the Beck Depression Inventory, second edition [51, 52]. The rationale for using such instruments is that doctors’ global assessments of severity do not agree well with valid and reliable self-report measures of severity in terms of cut-off levels for case identification [53, 54], which can result in the overtreatment of mild cases and undertreatment of moderate to severe cases [15, 54].


For case finding in anxiety, the NICE guideline on the identification and pathways to care for common mental health disorders CG123 [55] recommends the two-item generalised anxiety disorder (GAD-2) questionnaire for detecting anxiety [56], which asks:


Over the last 2 weeks, how often have you been bothered by the following problems?



  • Feeling nervous, anxious or on edge?
  • Being unable to stop or control worrying?

The GAD-2 is scored as follows, according to the response: Not at all: 0, Several days: 1, More than half the days: 2, Nearly every day: 3. If a person scores 3 or more the practitioner should consider a possible anxiety disorder.


In the event of a positive response to the GAD-2 questionnaire, the common mental health disorders guideline [55] recommends considering asking a further five questions which together with the first two make up the GAD-7 questionnaire [57]:

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May 29, 2017 | Posted by in PSYCHIATRY | Comments Off on Prevention and Early Intervention in Depression and Anxiety Disorders

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