E-Book, Englisch, 169 Seiten
De Leo / Poštuvan / Postuvan Resources for Suicide Prevention
1. Auflage 2017
ISBN: 978-1-61334-454-5
Verlag: Hogrefe Publishing
Format: EPUB
Kopierschutz: 6 - ePub Watermark
Bridging Research and Practice
E-Book, Englisch, 169 Seiten
ISBN: 978-1-61334-454-5
Verlag: Hogrefe Publishing
Format: EPUB
Kopierschutz: 6 - ePub Watermark
Keep up-to-date with recent research and practice in suicide
This book is based on the TRIPLE i in Suicidology international conferences, which are organised annually by the Slovene Centre for Suicide Research in memory of the late Prof. Andrej Marušic with the aim of promoting intuition, imagination, and innovation in the research and prevention of suicide and suicidal behaviour. The carefully selected chapters provide food for thought to practitioners, researchers, students, and all those who come into contact with the tragedy of suicide, with the hope of stimulating new ideas and interventions in the difficult fight against suicidal behaviours.
In four parts, the internationally renowned team of authors summarise the achievements of suicidology so far (both in quantitative and qualitative research), present effective interventions in suicide prevention (including for youths and older people) and knowledge gained in bereavement and postvention studies (such as in different cultures and those bereaved by suicide), and highlight future directions for suicide research and prevention.
The volume is thus a useful resource for all those interested in keeping up-to-date with recent research and practice in suicide.
Zielgruppe
Clinical psychologists, psychiatrists, psychotherapists, and
counselors, as well as students.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
|3|Chapter 1
Translating Research Into Practice
The Quantitative Perspective Enrique Baca-García1,2,3,4 and Victoria de Leon-Martínez1 1IIS Fundacion Jimenez Diaz, Madrid, Spain 2New York State Psychiatric Institute, New York, NY, USA 3Columbia University, New York, NY, USA 4CIBERSAM, Madrid, Spain Why are some people healthy and others not? This seems to be a simple question. The answer, however, is complex and has to do not only with disease and illness, but also with who we are, where we live and work, and the social and economic policies of our government, all of which play a role in determining our health. (Institute of Medicine, 2003) Introduction
Suicide prevention is a global healthcare priority (Hunt et al., 2006). In 2002, it was estimated that one person committed suicide every 40 s (World Health Organization [WHO], 2002). In recent years, the number of suicides has increased, making suicide the third leading cause of death worldwide in people 15–44 years old (Holmes, Crane, Fennell, & Williams, 2007). In addition to the human cost, suicidal behaviour (suicide attempts and suicide completion) creates a considerable economic burden. The annual cost of suicidal behaviour has been estimated to be US $33 billion in the United States alone (Coreil, Bryant, & Henderson, 2001). Despite these |4|negative figures, the research effort for understanding, treating, and preventing this type of behaviour is far from proportionate. Concepts of Suicidal Behaviour
Suicide is a permanent solution to a temporary problem. Edwin Schneidman, MD (Founder of Suicidology) As set forth by Robins and Guze (1970), suicidal behaviour meets the criteria for diagnostic validity. Suicidal behaviour is (1) clinically well-described (Mann et al., 2005), (2) research has identified post mortem and in vivo laboratory markers (Robins & Guze, 1970), (3) it can be subjected to a strict differential diagnosis (Posner, Oquendo, Gould, Stanley, & Davies, 2007), (4) follow-up studies confirm its presence at higher rates in those with a previous diagnosis (Oquendo, Currier, & Mann, 2006), and (5) it is familial (Brent et al., 2002). This behaviour is enormously complex. Rates of suicide differ across regions worldwide (see Figure 1.1) due to geographic/climate differences, sociocultural, and economic differences and differences in the availability of lethal methods. In addition, differences may be produced by differing accuracies of registries (Rihmer, Belsö, & Kiss, 2002). Figure 1.1. World map of age-standardised suicide rates for 2012. (Reprinted by permission from WHO, 2014) Interestingly, the scientific data on suicide are not focused on areas of high suicidal prevalence. Most of the scientific and clinical research generated focuses on |5|the populations of Western countries. Unfortunately, the highest rates of suicide are not found in these areas but in Baltic countries as well as in Russia. Furthermore, high rates of suicide in females correspond to Eastern countries, such as India and China. Despite this trend in the non-Western world, this phenomenon is not solely exclusive to Eastern countries. Within the United States, the highest rates of suicide are found in the western states while academic and scientific research institutions are concentrated on the two coasts. Models of Suicidal Behaviour
There are several conceptual models of suicide. Perhaps the most complete model with clinical and preventive value is the stress-diathesis model of suicide (Figure 1.2). Figure 1.2. Stress-diathesis model of suicide. Select biological vulnerabilities or risk factors integrate with life stressors resulting in an individual’s predisposition to suicidal behaviors. These factors coupled with increasing stress can lead to the crossing of a threshold resulting in a progression of suicidal behaviors escalating to a suicide attempt or completed suicide. Preventing Suicide
To combat the issue of suicide, several countries have adopted national prevention strategies. For example, the National Suicide Prevention Strategy for Eng|6|land, 2002, endeavoured to reduce the rate of suicide nationwide by 20%. This programme used the following methods to meet this goal: reducing the availability and lethality of suicide methods, reducing risks in key high-risk groups, promoting mental well-being in the wider population, improving reporting of suicidal behaviour in the media, promoting research on suicide and suicide prevention, and improving monitoring of progress toward the Saving Lives: Our Healthier Nation target to reduce suicide. Following 10 years of application, the programme successfully achieved its objective of decreasing suicide rates by 20% in England. In contrast, fluctuations in suicidal behaviour have been identified in the United States, which are reflective of oscillating suicide rates between 10.0 per 100,000 to 19.0 per 100,000 over the past 100 years. The natural fluctuation of suicide rates has been estimated to be 50%. In contrast to national strategies, regional and local strategies have also been applied. Local alternatives with specific catchment areas (Hampton, 2010) have had better results in comparison with their national counterparts. These strategies at the local and community levels focus on improving the treatment of mood disorders; redesigning depression care delivery based on safety; focusing on patient preference, need, and value; reducing waiting times and delays; avoiding money wasting; and providing equal care to all patients. Research Experience Preventing Suicide
In recent years, the authors of this chapter have progressively been involved in several research areas on suicidal behaviour, which cover a wide range of focuses: from epidemiology to genetics as well as psychopathological and clinical topics. In fact, the exploration of the genetic underpinnings of psychiatric disorders has been one of the endeavours of the authors since the very beginning. Over the past 12 years, several association studies of functional polymorphisms have been carried out. Currently, the capacity for greater patient recruitment in our gene bank has been developed, thus enabling the obtaining of samples from more than 4,000 patients. The authors are projecting network studies to replicate their results. We have also pioneered the use of data mining in suicidology (Oquendo, Baca-Garcia et al., 2012; Baca-Garcia et al., 2006). Data mining is an eclectic discipline, also known as machine learning, that involves the process of extracting patterns from data. With this process, we are able to analyse massive amounts of information, especially problems with high multidimensionality (large amount of variables) and unknown probability distribution. Data mining is highly useful in situations with the following conditions: large quantities of data, noisy incomplete data, imprecise data, complex data structures, and when conventional statistical analyses are not possible. Several different types of techniques can be employed when |7|using data mining, such as Bayesian networks, regression analyses, neural networks, clustering, genetic algorithms, decision trees, and support vector machines. This computational approach enables an exploration of data to identify patterns and structures not suspected to be a priori and thus lead to the generation of new hypotheses. This is critical, especially in areas with huge datasets for which hypothesis testing and/or traditional analytic strategies have led to disappointing results, such as in genetics and brain imaging studies. In Figure 1.3, the impact of data-mining methodologies in different medical specialties can be compared. At the moment, there are few studies using data mining in the field of psychiatry, while specialties that employ large and multidimensional datasets, such as genetics or biomedicine, are familiar with data mining and have experience using its methods. Figure...
