Abstract
The development of personalised prevention has been followed by rising concerns over over-medicalisation, overdiagnosis and overtreatment. Indeed, it has been said to lead to the discovery of a multitude of small abnormalities of low significance and low predictive validity. Complex diseases are now detected in their earliest stages, even though they might never have negatively impacted a person’s quality of life or lifespan. In such cases, the harm caused to these persons cannot be overlooked. This chapter aims to explore the mechanisms through which personalised prevention could perhaps be used to de-escalate these issues. First, its development takes place in a context where issues of over-medicalisation, overdiagnosis and overtreatment are already prevalent in preventive medicine, meaning that personalisation might potentially be used to reduce the number of people to whom screening and preventive measures are proposed. Secondly, the process of risk stratification encouraged by personalisation offers a more nuanced understanding of risk – from very low to very high – thus enabling de-escalation measures for newly diagnosed people. Lastly, the more radical impact that personalisation and risk stratification could have on nosologies themselves might encourage the implementation of preventive measures that ensure that identified at-risk individuals have access to needed forms of public support. Woven into the development of this chapter is the notion that personalised prevention can bring forth more complex conceptualisations of people’s vulnerability to disease.
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Notes
- 1.
Primary prevention encompasses all interventions before health problems might even start to occur. Secondary prevention designates measures of early detection and interventions, in the very first stages of an illness and before the onset of severe signs and symptoms. Tertiary prevention, on the other hand, covers the management of illness post diagnosis in order to slow down or stop its progression, or potential complications – as such, it is integrated within medical care rather than public health.
- 2.
Multiscale modeling of risks, here, refers to attempts (in systems medicine, for example) to predict the problematic behaviour of diverse biological and/or other systems through their interactions at multiple scales. It can therefore be used in attempts to solve problems which have important features at multiple scales of time and/or space (e.g., tissues and organs, as well as molecular biomarkers).
- 3.
While the predictive validity of some markers has been established – for example, between 55 and 65% of women who present the BRCA1 genetic mutation will develop breast cancer by the age of 70 years, and 45% of women who inherit the BRCA2 mutation will develop breast cancer by 70 years (Ramaswami et al. 2018) – there are a great many cases where predictive validity remains much too low to be of clinical use – in psychiatry, for example, “so far no biomarkers [...] or other risk markers are available to create profiles to enhance prediction and therapeutic selection in psychiatry. Stratified or personalized interventions remain aspirational, yet potentially within reach” (McGorry et al. 2014).
- 4.
The PSA test is a blood test used primarily to screen for prostate cancer. The test measures the amount of prostate-specific antigen (PSA) in the blood.
- 5.
Referring to Boorse’s distinction between disease, meaning the malfunctioning of biological systems, and illness, referring to the deterioration of one’s well-being (Boorse 1975).
- 6.
Meaning that there is evidence of rapid progression.
- 7.
E.g., Binet stage A, Rai stages 0-II.
- 8.
The CLL-IPI uses a weighted grading of five independent prognostic factors: TP53 deletion and/or mutation (collectively called TP53 dysfunction), immunoglobulin heavy chain variable (IGHV) mutational status, serum β2-microglobulin, clinical stage, and age (Hallek 2019): “One very important value of the CLL-IPI also lies in the fact that it identifies – more accurately than the clinical staging – CLL patients without need of therapy. Patients with a low risk CLL-IPI (0–1) and asymptomatic disease do not require treatment” (Ibid., 2019).
- 9.
Positive symptoms, in psychiatry, are understood as symptoms which bring about new phenomena, such as hallucinations, delusions, etc., while negative symptoms point to the loss of capacities, like apathy, speech difficulties or social and emotional withdrawal.
- 10.
The prevalence of schizophrenia, for example, is generally said to be around 1% in the general population; but “in early studies [...], the risk of developing a psychotic disorder increased from the expected rate of approximately 10% in family high-risk groups to approximately 30% to 50% in clinical high-risk samples followed for one to two years” (Addington and Heinssen 2012). Ethical debates surrounding the benefits/risks ratio of pre-emptive psychiatry have been fierce, balancing the possibility of over-medicalisation , overdiagnosis, overtreatment , stigmatisation, and injustice against that of being able to prevent the development of a particularly harmful disorder.
- 11.
The prodrome of a disease encompasses early signs or symptoms before more diagnostically specific signs and symptoms develop.
- 12.
Prevention programmes, in principle, can follow three different approaches – universal, selective and/or indicated. Each reflects segments of the population as well as levels of risk. A universal prevention targets the general population unspecifically as a whole; a selective prevention approach aims at a segment of the population which is clinically healthy, but at a high risk for the disease (e.g., because of a genetic liability); indicated prevention targets persons who already show clinical signs and are possibly in a prodromal state.
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Tinland, J. (2022). Personalised Prevention: Increasing or Decreasing Over-Medicalisation , Overdiagnosis and Overtreatment ?. In: Beneduce, C., Bertolaso, M. (eds) Personalized Medicine in the Making. Human Perspectives in Health Sciences and Technology, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-74804-3_5
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