Stepped care for depression in primary care: What should be offered and how?
ABSTRACT Stepped-care approaches may offer a solution to delivering accessible, effective and efficient services for individuals with depression. In stepped care, all patients commence with a low-intensity, low-cost treatment. Treatment results are monitored systematically, and patients move to a higher-intensity treatment only if necessary. We deliver a stepped-care model targeting patients with depression. The first step consists of "watchful waiting", as half of all patients with a depressive episode recover spontaneously within 3 months. The second step, guided self-help, is the key element of the stepped-care model. Guided self-help, especially when offered through the internet, is effective and cost-efficient. The third step consists of brief face-to-face psychotherapy. Finally, in the fourth step, longer-term face-to-face psychotherapy and antidepressant medication might be considered. Patients are monitored by one person, a care manager, who is responsible for the decision to step up to the next treatment and for continuity of care. The different treatments within the stepped-care model are evidence-based. Data on cost-effectiveness of the full model are still scarce, but we recently demonstrated that the incidence of new cases of depression and anxiety could be halved by introducing stepped care. Effects of web-based guided self-help could be enhanced by incorporating them in a stepped-care model.
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ABSTRACT: The burden of rising health care expenditures has created a demand for information regarding the clinical and economic outcomes associated with complementary and alternative medicines. Meta-analyses of randomized controlled trials have found Hypericum perforatum preparations to be superior to placebo and similarly effective as standard antidepressants in the acute treatment of mild to moderate depression. A clear advantage over antidepressants has been demonstrated in terms of the reduced frequency of adverse effects and lower treatment withdrawal rates, low rates of side effects and good compliance, key variables affecting the cost-effectiveness of a given form of therapy. The most important risk associated with use is potential interactions with other drugs, but this may be mitigated by using extracts with low hyperforin content. As the indirect costs of depression are greater than five times direct treatment costs, given the rising cost of pharmaceutical antidepressants, the comparatively low cost of Hypericum perforatum extract makes it worthy of consideration in the economic evaluation of mild to moderate depression treatments.Australian and New Zealand Journal of Psychiatry 10/2010; 45(2):123-30. DOI:10.3109/00048674.2010.526094 · 3.77 Impact Factor
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ABSTRACT: Attitudes informing the use of technologies by the youth health workforce to improve young people's wellbeing: Understanding the nature of the "digital disconnect" Using a mixed-method approach, this study investigated: 1. the current role of technologies in improving young people's mental health; 2. the youth health workforce's attitudes towards the use of technologies to improve young people's mental health; and 3. how this workforce currently employs these technologies. Many participants believed that technologies play a considerable role in the lives of most young people and have the potential to influence mental health and wellbeing. Despite this potential, they are poorly understood and under-utilised in mental health promotion and the prevention, early intervention and treatment of mental ill-health. Participants commented that this situation could be improved if barriers to their use of technologies were overcome. Further research and investment is necessary to secure appropriate technological infrastructure in mental health services and in training staff to better understand young people's technology use and the range of technological strategies available to them. 15 Understanding the nature of the "digital disconnect" ver a quarter of all young Australians aged 16 to 24 experience a mental health difficulty in any one year (Australian Bureau of Statistics (ABS) 2010). Suicide rates remain high, with suicide one of the foremost causes of death in the 15-to 24-year-old age group (ABS 2011). Mental ill-health has enormous economic implications. It is estimated that in 2009 the direct costs of untreated mental illness in Australian young people totalled $10.6 billion (Access Economics 2009). Unless addressed, the effects of mental ill-health can persist over an individual's lifetime (Costello, Foley & Angold 2006) and lead to further occupational, economic and interpersonal difficulties. Presently, only 29% of young Australians with a mental health difficulty seek help when they need it (Burns et al. 2010; Slade et al. 2009) and timely and evidence-based treatments are only encountered by a small proportion of those young people who do receive care (Libby et al. 2007; Andrews et al. 2000). For many young Australians, information communication technologies are an integral part of their everyday lives, with over 95% of Australian young people using the internet (Ewing, Thomas & Schiessi 2008). Good evidence exists that technologies can be used effectively to improve mental health and wellbeing (Griffiths, Farrer & Christenssen 2010; Spek et al. 2007), especially among young people (Ryan, Schochet & Stallman 2010; Christensen & Hickie 2010; Burns et al. 2010). For those experiencing mental ill-health, the strategic use of technologies can help to overcome barriers to help-seeking such as physical access, confidentiality concerns and stigma (Gould et al. 2002). Acceptance of the use of technologies for improved mental health is high, as many young people have an affinity with mobile phones and the online environment (Iafusco, Ingenito & Prisco 2000). For those wishing to improve their overall wellbeing, technologies can assist in promoting social inclusion, access to material resources and freedom from discrimination and violence (Burns & Blanchard 2009; Burns et al. 2009). While positive results are seen from the use of self-directed eHealth interventions, there is some evidence that these are most effective if used as part of a stepped care model (Van Straten et al. 2010), with the support of a trained professional (Perini, Titov & Andrews 2009; Titov et al. 2009) or as an adjunct to face-to-face treatment (Hickie et al. 2010). Little is known about the attitudes of the youth health workforce towards the role that technologies play in young people's lives and their potential impact, both positive and negative, on mental health and wellbeing (Blanchard et al. 2008; Burns et al. 2009; Metcalf et al. 2008). Similarly, the way that members of the youth health workforce use these technologies in practice, or to access education and training, is not well understood. In light of this evidence on the usefulness of technologies, it is timely to investigate whether the youth health workforce has the capacity to support young people's engagement with technologies in ways that benefit their wellbeing. By the "youth health workforce"we mean those practitioners working in a range of roles across mental health promotion and the prevention, early intervention and treatment of mental ill-health, such as youth workers, social workers, psychologists, occupational therapists, mental health or psychiatric nurses, community development and arts practitioners, general practitioners and psychiatrists.
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ABSTRACT: A number of trials have examined the effects of self-guided psychological intervention, without any contact between the participants and a therapist or coach. The results and sizes of these trials have been mixed. This is the first quantitative meta-analysis, aimed at organizing and evaluating the literature, and estimating effect size. We conducted systematic literature searches in PubMed, PsycINFO and Embase up to January 2010, and identified additional studies through earlier meta-analyses, and the references of included studies. We identified seven randomized controlled trials that met our inclusion criteria, with a total of 1,362 respondents. The overall quality of the studies was high. A post-hoc power calculation showed that the studies had sufficient statistical power to detect an effect size of d = 0.19. The overall mean effect size indicating the difference between self-guided psychological treatment and control groups at post-test was d = 0.28 (p<0.001), which corresponds to a NNT of 6.41. At 4 to 12 months follow-up the effect size was d = 0.23. There was no indication for significant publication bias. We found evidence that self-guided psychological treatment has a small but significant effect on participants with increased levels of depressive symptomatology.PLoS ONE 06/2011; 6(6):e21274. DOI:10.1371/journal.pone.0021274 · 3.23 Impact Factor