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The biomedical model for depression is an approach that has been adopted by psychiatrists that proposes that there is a physical cause for the disorder, such as a particular gene or a chemical imbalance in the brain. This universal belief has caused an increase in the use of psychiatric medications over the past three decades and due to this, mental illnesses are often regarded as brain diseases that can be treated with drugs (Deacon, 2013).  Anti-depressant prescriptions in Britain have doubled in the past ten years, with doctors issuing 61 million prescriptions in 2015 alone (Meikle, 2015). However, it has more recently been suggested that there is no evidential basis for the role of these alleged biological linkages and that the mass of supposed evidence is inconsistent (Cromby et al, 2013). Thus, it is important that we explore the individual aspects of the biomedical model, including genetic factors and serotonin levels and evaluate the evidence for them. We then need to understand how each of them has become so widespread through influences such as ignorance of heterogeneity of symptom profiles and inconsistent findings, research funding and publication bias, all of which will be discussed in this paper.

To begin, the biomedical model proclaims that there is an association between a particular gene and depression. Taking this perspective suggests that depression is universal, not relative to each individual; everyone who has depression carries the same gene and thus should have the same symptoms. Kishi et al (2013) conducted a meta-analysis investigating the association between the serotonin 1A receptor gene and major depressive disorder (MDD). A significant association was found between two gene variations of the receptor gene and MDD. Consequently, it was concluded that the serotonin 1A receptor might play a role in depression. However, we are not able to determine cause and effect between the gene and MDD from the findings of this study, as they only suggest an association. So, it may be possible that a combination of factors needs to be investigated when looking at the causes of depression, including potential environmental and social influences, for example, childhood trauma or poverty, that may encourage the depression-related gene to be expressed. The biomedical model's claim of a genetic link to depression may not be sufficient because overall, despite all efforts, no singular gene has been shown to significantly increase the risk of MDD (Lohoff, 2011).

Contrastingly, through additional research it has become evident to many psychologists that depression is not a consistent syndrome as there is a heterogeneity of symptom profiles between individuals. Hence, it is argued that it is unlikely that there is a single gene related to depression because the profiles are so diverse and therefore the definition of the condition is too vague. This causes issues when using diagnostic tools, for example, the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) which has been heavily influenced by the biomedical model, states that an individual must be experiencing five or more of the listed depressive symptoms. The International Classification of Diseases (ICD-10), also largely based on biomedical ideas, states that an individual must have at least two of the listed symptoms for depression. Some examples of the listed symptoms are loss of interest in normally pleasurable activities, loss of appetite and loss of libido. But, the extensive variety of symptom profiles means that there are many combinations of symptoms that can occur and thus many different types of depression. There can also be sub-types of each individual symptom, which make these measures even more inappropriate. To illustrate, one individual may experience insomnia and loss of appetite, and another may experience hypersomnia and an increase in appetite. These symptom profiles are completely opposite, yet they would be classified as the same condition. How can we fit them all under one label when any two people with depression can be so drastically different?

This notion has been supported by McKercher et al's (2013) series of interviews that explored whether those who are physically active differ in their depression symptom profiles from those who are physically inactive. It was found that physically active men were less likely to suffer with insomnia, fatigue and suicidality than inactive men and that physically active women were less likely to suffer with hypersomnia, irrational guilt, vacillating thoughts and suicidality than inactive women. A naturalistic treatment study also found considerable heterogeneity in symptom profiles among older adults with depression and that factors like stress and social support were predictive of these differences (Hybels et al, 2011). These findings indicate that depression does not present itself in the same way in every individual and that external factors can have an influence on how it is expressed. The biomedical model does not take this into account.

Further, co-morbidity rates among different mental illnesses are too high, for instance it is common for someone to suffer with depression and anxiety simultaneously, again suggesting an unlikeliness that there is one specific gene that causes depression. There is research to support this by Dorly et al (2000) who demonstrated that 47.5% of those with MDD also met criteria for anxiety disorders and 26.2% of those with anxiety met criteria for MDD. These high co-morbidity rates between depression and other mental illnesses suggest that there is not a precise genetic variation that causes depression but that there are other processes that may cause someone to have a mental illness. To give an example, a person who is diagnosed with social anxiety may struggle to leave their house and talk to other people, and as a consequence they might get depressed because they do not have any friends or enough social support. This individual is an example of someone who has developed depression as a result of an experience completely unrelated to biology, undermining the genetic assumptions of the biomedical model.

Another important aspect of the biomedical model is the monoamine hypothesis which states that depression is a result of abnormalities of neurotransmitter levels. For instance, someone with depression may have serotonin deficiency due to lowered neurotransmitter levels or less serotonin receptors than normal (Cromby et al, 2013). Asberg et al (1976) found a significant relationship between the concentration of 5-hydroxyindolacetic acid (5-HIAA) in 68 individuals (determining serotonin levels in the body) and the severity of the depression. However, what Asberg did not stress was that the results did not show a clear difference between a depression group and a control group. In both groups, about 50% of people had regular levels of 5-HIAA, 25% had low levels and 25% had high levels. If serotonin played an important part in depression, shouldn't those with depression and those without show a significant difference in their 5-HIAA levels? It seems that this well-known and frequently cited study is not only out-dated, but also fails to address the most important point of all: there was no difference in serotonin levels between those with and without depression.

In spite of this limitation, there is still supporting evidence for psychiatric drug treatment that involves increasing serotonin levels. Burke, Gergel and Bose (2002) investigated the effect of a selective serotonin reuptake inhibitor (SSRI) in comparison to a placebo on depressive symptoms. Over an eight-week period, it was found that there was a significantly larger improvement in all measures of depression for those who had taken the SSRI compared to those who had taken the placebo. However, there have been criticisms of studies that are conducted in this way. The efficacy for antidepressants is not as strong as has been suggested in the past and that the small difference in improvement in depressive symptoms compared to the placebo may be due to observer effects (e.g. the psychiatrist assumes they will see an improvement and so records one) or patient expectancies (e.g. the patient assumes their mental health will improve and so it does). Also, the depressed individuals who are used to test the placebo effect are usually abruptly taken off medication and put onto the placebo, which in turn leaves them with withdrawal syndrome that is frequently misdiagnosed as relapse (Hengartner, 2017). As all the participants in the previously mentioned study were ‘outpatients with an ongoing major depressive episode', it is likely that this could have been the case.

Moreover, it is probable that the pharmaceutical industry has an influence on the research that is published supporting the biomedical model's assumptions. A substantial amount of research funding into distress comes from pharmaceutical companies and only a very small amount comes from other sources that are less likely to be biased (Cromby et al, 2013). It is thought by many that pharmaceutical industry-sponsored child and adolescent antidepressant trials should not be considered as informative about the efficacy of the drugs, and that in comparison, trials funded by the National Institute of Mental Health are more appropriate (Walkup, 2017). Pharmaceutical companies also spend more on marketing and promotion of antidepressants than on research and development, promoting their products through television advertisements particularly in America. They also utilise a clever use of vocabulary such as ‘antidepressant' which gives the perception of depression being a biomedical illness, thus encouraging patients to take them. This forces individuals, without their knowledge, to undergo the ‘conversion from patient to consumer' (Applbaum, 2006).

As well as this, it has been suggested that there is a publication bias in research into antidepressants. Some researchers have been known to publish clinical trials and their outcomes selectively which in turn has influenced the public perception on the drugs' effectiveness. Turner et al (2008) found that out of 74 studies registered by the Food and Drug Administration, 22 were not published due to negative results and 11 were published in a way that suggested they were positive although they showed negative results. A meta-analysis of anti-depressant reboxetine trials found that published data overestimated its benefit by up to 115% and underestimated its harm (Eyding et al, 2010). The implications of missing out results in publications can be exceedingly harmful because it encourages people to take the drug even though the negative side effects are usually severely underestimated and the positive mental health improvements are severely overestimated.

On the whole, it is clear that there are countless major issues with the biomedical medical of depression and they have had extreme negative impacts on the treatment mentally ill people receive today. There is an abundant amount of evidence-based analysis that counteracts its assumptions which needs to be taken into account. Psychiatrists are unable to establish a direct causal relationship between a singular biological component and depression because there are too many variations of the illness and it is too common that they happen concurrently, so other influences have to be important. It is also evident that the pharmaceutical industry's intentions have too heavily influenced research into the efficacy of anti-depressants and that more research needs to be conducted that is funded from other areas of business.

An extension of knowledge of causality in mental distress will enable a better understanding of mental health around the world. This will facilitate a decrease in the stigma and social distancing felt by those diagnosed with depression, as they will no longer be victims of their biology. It will also encourage mental health services to take the attention away from medication and to invest more in therapeutic interventions such as cognitive behavioural therapy or art therapy, that tackle not only the symptoms of depression but the social and environmental causes too. Talking, listening, empathising. That is what mental health services have been lacking. After many years of forcing a biomedical standpoint, it is now most useful to take an interactionist approach, combining medication and therapy, to finally combat the terrifying epidemic that is depression.

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