Personalised medicine, disease prevention, and the inverse care law: more harm than benefit?

Managing the moral expansion of medicine

Science and technology have vastly expanded the realm of medicine. The numbers of and knowledge about diseases has greatly increased, and we can help more people in many more ways than ever before. At the same time, the extensive expansion has also augmented harms, professional responsibility, and ethical concerns. While these challenges have been studied from a wide range of perspectives, the problems prevail. This article adds value to previous analyses by identifying how the moral imperative of medicine has expanded in three ways: (1) from targeting experienced phenomena, such as pain and suffering, to non-experienced phenomena (paraclinical signs and indicators); (2) from addressing present pain to potential future suffering; and (3) from reducing negative wellbeing (pain and suffering) to promoting positive wellbeing. These expansions create and aggravate problems in medicine: medicalization, overdiagnosis, overtreatment, risk aversion, stigmatization, and healthism. Moreover, they threaten to infringe ethical principles, to distract attention and responsibility from other competent agents and institutions, to enhance the power and responsibility of professionals, and to change the professional-beneficiary relationship. In order to find ways to manage the moral expansion of medicine, four traditional ways of setting limits are analyzed and dismissed. However, basic asymmetries in ethics suggest that it is more justified to address people’s negative wellbeing (pain and suffering) than their positive wellbeing. Moreover, differences in epistemology, indicate that it is less uncertain to address present pain and suffering than future wellbeing and happiness. Based on these insights the article concludes that the moral imperative of medicine has a gradient from pain and suffering to wellbeing and happiness, and from the present to the future. Hence, in general present pain and suffering have normative priority over future positive wellbeing.

Prediction model for the onset risk of impaired fasting glucose: a 10-year longitudinal retrospective cohort health check-up study

BACKGROUND

Impaired fasting glucose (IFG) is a prediabetic condition. Considering that the clinical symptoms of IFG are inconspicuous, these tend to be easily ignored by individuals, leading to conversion to diabetes mellitus (DM). In this study, we established a prediction model for the onset risk of IFG in the Chongqing health check-up population to provide a reference for prevention in a health check-up cohort.

METHODS

We conducted a retrospective longitudinal cohort study in Chongqing, China from January 2009 to December 2019. The qualified subjects were more than 20 years old and had more than two health check-ups. After following the inclusion and exclusion criteria, the cohort population was randomly divided into a training set and a test set at a ratio of 7:3. We first selected the predictor variables through the univariate generalized estimation equation (GEE), and then the training set was used to establish the IFG risk model based on multivariate GEE. Finally, the sensitivity, specificity, and receiver operating characteristic curves were used to verify the performance of the model.

RESULTS

A total of 4,926 subjects were included in this study, with an average of 3.87 check-up records, including 2,634 males and 2,292 females. There were 442 IFG cases during the follow-up period, including 286 men and 156 women. The incidence density was 26.88/1000 person-years for men and 18.53/1000 person-years for women (P<0.001). The predictor variables of our prediction model include male (relative risk (RR) =1.422, 95 % confidence interval (CI): 0.923-2.193, P=0.3849), age (RR=1.030, 95 %CI: 1.016-1.044, P<0.0001), waist circumference (RR=1.005, 95 %CI: 0.999-1.012, P=0.0975), systolic blood pressure (RR=1.004, 95 %CI: 0.993-1.016, P=0.4712), diastolic blood pressure (RR=1.023, 95 %CI: 1.005-1.041, P=0.0106), obesity (RR=1.797, 95 %CI: 1.126-2.867, P=0.0140), triglycerides (RR=1.107, 95 %CI: 0.943-1.299, P=0.2127), high-density lipoprotein cholesterol (RR=0.992, 95 %CI: 0.476-2.063, P=0.9818), low-density lipoprotein cholesterol (RR=1.793, 95 %CI: 1.085-2.963, P=0.0228), blood urea (RR=1.142, 95 %CI: 1.022-1.276, P=0.0192), serum uric acid (RR=1.004, 95 %CI: 1.002-1.005, P=0.0003), total cholesterol (RR=0.674, 95 %CI: 0.403-1.128, P=0.1331), and serum creatinine levels (RR=0.960, 95 %CI: 0.945-0.976, P<0.0001). The area under the receiver operating characteristic curve (AUC) in the training set was 0.740 (95 %CI: 0.712-0.768), and the AUC in the test set was 0.751 (95 %CI: 0.714-0.817).

CONCLUSIONS

The prediction model for the onset risk of IFG had good predictive ability in the health check-up cohort.

Risk prediction model of gestational diabetes mellitus based on nomogram in a Chinese population cohort study

To build a risk prediction model of gestational diabetes mellitus using nomogram to provide a simple-to-use clinical basis for the early prediction of gestational diabetes mellitus (GDM). This study is a prospective cohort study including 1385 pregnant women. (1) It is showed that the risk of GDM in women aged ≥ 35 years was 5.5 times higher than that in women aged < 25 years (95% CI: 1.27–23.73, p < 0.05). In the first trimester, the risk of GDM in women with abnormal triglyceride who were in their first trimester was 2.1 times higher than that of lipid normal women (95% CI: 1.12–3.91, p < 0.05). The area under the ROC curve of the nomogram of was 0.728 (95% CI: 0.683–0.772), the sensitivity and specificity of the model were 0.716 and 0.652, respectively. This study provides a simple and economic nomogram for the early prediction of GDM risk in the first trimester, and it has certain accuracy.

Are there socio-economic inequalities in utilization of predictive biomarker tests and biological and precision therapies for cancer? A systematic review and meta-analysis

BACKGROUND

Novel biological and precision therapies and their associated predictive biomarker tests offer opportunities for increased tumor response, reduced adverse effects, and improved survival. This systematic review determined if there are socio-economic inequalities in utilization of predictive biomarker tests and/or biological and precision cancer therapies.

METHODS

MEDLINE, Embase, Scopus, CINAHL, Web of Science, PubMed, and PsycINFO were searched for peer-reviewed studies, published in English between January 1998 and December 2019. Observational studies reporting utilization data for predictive biomarker tests and/or cancer biological and precision therapies by a measure of socio-economic status (SES) were eligible. Data was extracted from eligible studies. A modified ISPOR checklist for retrospective database studies was used to assess study quality. Meta-analyses were undertaken using a random-effects model, with sub-group analyses by cancer site and drug class. Unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) were computed for each study. Pooled utilization ORs for low versus high socio-economic groups were calculated for test and therapy receipt.

RESULTS

Among 10,722 citations screened, 62 papers (58 studies; 8 test utilization studies, 37 therapy utilization studies, 3 studies on testing and therapy, 10 studies without denominator populations or which only reported mean socio-economic status) met the inclusion criteria. Studies reported on 7 cancers, 5 predictive biomarkers tests, and 11 biological and precision therapies. Thirty-eight studies (including 1,036,125 patients) were eligible for inclusion in meta-analyses. Low socio-economic status was associated with modestly lower predictive biomarker test utilization (OR 0.86, 95% CI 0.71-1.05; 10 studies) and significantly lower biological and precision therapy utilization (OR 0.83, 95% CI 0.75-0.91; 30 studies). Associations with therapy utilization were stronger in lung cancer (OR 0.71, 95% CI 0.51-1.00; 6 studies), than breast cancer (OR 0.93, 95% CI 0.78-1.10; 8 studies). The mean study quality score was 6.9/10.

CONCLUSIONS

These novel results indicate that there are socio-economic inequalities in predictive biomarker tests and biological and precision therapy utilization. This requires further investigation to prevent differences in outcomes due to inequalities in treatment with biological and precision therapies.

The Limits of Empowerment: How to Reframe the Role of mHealth Tools in the Healthcare Ecosystem

This article highlights the limitations of the tendency to frame health- and wellbeing-related digital tools (mHealth technologies) as empowering devices, especially as they play an increasingly important role in the National Health Service (NHS) in the UK. It argues that mHealth technologies should instead be framed as digital companions. This shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as well as the risk of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency. The article concludes by stressing that reframing the narrative cannot be the only means for avoiding harm caused to the NHS as a healthcare system by the introduction of mHealth tools. Future discussion will be needed on the overarching role of responsible design.

The commercialization of the biomedical sciences: (mis)understanding bias

The growing commercialization of scientific research has raised important concerns about industry bias. According to some evidence, so-called industry bias can affect the integrity of the science as well as the direction of the research agenda. I argue that conceptualizing industry's influence in scientific research in terms of bias is unhelpful. Insofar as industry sponsorship negatively affects the integrity of the research, it does so through biasing mechanisms that can affect any research independently of the source of funding. Talk about industry bias thus offers no insight into the particular epistemic shortcomings at stake. If the concern is with the negative effects that industry funding can have on the research agenda, conceptualizing this influence as bias obscures the ways in which such impact is problematic and limits our ability to offer solutions that can successfully address the concerns raised by the growing role of private funding in science.

Plastic diagnostics: The remaking of disease and evidence in personalized medicine

Politically authorized reports on personalized and precision medicine stress an urgent need for finer-grained disease categories and faster taxonomic revision, through integration of genomic and phenotypic data. Developing a data-driven taxonomy is, however, not as simple as it sounds. It is often assumed that an integrated data infrastructure is relatively easy to implement in countries that already have highly centralized and digitalized health care systems. Our analysis of initiatives associated with the Danish National Genome Center, recently launched to bring Denmark to the forefront of personalized medicine, tells a different story. Through a “meta-taxonomy” of taxonomic revisions, we discuss what a genomics-based disease taxonomy entails, epistemically as well as organizationally. Whereas policy reports promote a vision of seamless data integration and standardization, we highlight how the envisioned strategy imposes significant changes on the organization of health care systems. Our analysis shows how persistent tensions in medicine between variation and standardization, and between change and continuity, remain obstacles for the production as well as the evaluation of genomics-based taxonomies of difference. We identify inherent conflicts between the ideal of dynamic revision and existing regulatory functions of disease categories in, for example, the organization and management of health care systems. Moreover, we raise concerns about shifts in the regulatory regime of evidence standards, where clinical care increasingly becomes a vehicle for biomedical research.

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Personalized medicine has led to calls for speedy revisions of disease taxonomies.

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A “meta-taxonomy” of disease taxonomic revisions is presented and discussed.

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Fine-graining disease categories has regulatory implications for healthcare systems.

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A Danish case illustrates difficulties in aligning multiple functions of disease codes.

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We argue that the political call for speed should be treated with caution.

Equity and Value in 'Precision Medicine'

Precision medicine carries huge potential in the treatment of many diseases, particularly those with high-penetrance monogenic underpinnings. However, precision medicine through genomic technologies also has ethical implications. We will define allocative, personal, and technical value ('triple value') in healthcare and how this relates to equity. Equity is here taken to be implicit in the concept of triple value in countries that have publicly funded healthcare systems. It will be argued that precision medicine risks concentrating resources to those that already experience greater access to healthcare and power in society, nationally as well as globally. Healthcare payers, clinicians, and patients must all be involved in optimising the potential of precision medicine, without reducing equity. Throughout, the discussion will refer to the NHS RightCare Programme, which is a national initiative aiming to improve value and equity in the context of NHS England.

A definition and ethical evaluation of overdiagnosis

Overdiagnosis is an emerging problem in health policy and practice: we address its definition and ethical implications. We argue that the definition of overdiagnosis should be expressed at the level of populations. Consider a condition prevalent in a population, customarily labelled with diagnosis A. We propose that overdiagnosis is occurring in respect of that condition in that population when (1) the condition is being identified and labelled with diagnosis A in that population (consequent interventions may also be offered); (2) this identification and labelling would be accepted as correct in a relevant professional community; but (3) the resulting label and/or intervention carries an unfavourable balance between benefits and harms. We identify challenges in determining and weighting relevant harms, then propose three central ethical considerations in overdiagnosis: the extent of harm done, whether harm is avoidable and whether the primary goal of the actor/s concerned is to benefit themselves or the patient, citizen or society. This distinguishes predatory (avoidable, self-benefiting), misdirected (avoidable, other-benefiting) and tragic (unavoidable, other-benefiting) overdiagnosis; the degree of harm moderates the justifiability of each type. We end with four normative challenges: (1) methods for adjudicating between professional standards and identifying relevant harms and benefits should be procedurally just; (2) individuals, organisations and states are differently responsible for addressing overdiagnosis; (3) overdiagnosis is a matter for distributive justice: the burdens of both overdiagnosis and its prevention could fall on the least-well-off; and (4) communicating about overdiagnosis risks harming those unaware that they may have been overdiagnosed. These challenges will need to be addressed as the field develops.

The commercialization of university-based research: Balancing risks and benefits

BACKGROUND

The increasing push to commercialize university research has emerged as a significant science policy challenge. While the socio-economic benefits of increased and rapid research commercialization are often emphasized in policy statements and discussions, there is less mention or discussion of potential risks. In this paper, we highlight such potential risks and call for a more balanced assessment of the commercialization ethos and trends.

DISCUSSION

There is growing evidence that the pressure to commercialize is directly or indirectly associated with adverse impacts on the research environment, science hype, premature implementation or translation of research results, loss of public trust in the university research enterprise, research policy conflicts and confusion, and damage to the long-term contributions of university research. The growing emphasis on commercialization of university research may be exerting unfounded pressure on researchers and misrepresenting scientific research realities, prospects and outcomes. While more research is needed to verify the potential risks outlined in this paper, policy discussions should, at a minimum, acknowledge them.

Mastering variation: variance components and personalised medicine

Various sources of variation in observed response in clinical trials and clinical practice are considered, and ways in which the corresponding components of variation might be estimated are discussed. Although the issues have been generally well‐covered in the statistical literature, they seem to be poorly understood in the medical literature and even the statistical literature occasionally shows some confusion. To increase understanding and communication, some simple graphical approaches to illustrating issues are proposed. It is also suggested that reducing variation in medical practice might make as big a contribution to improving health outcome as personalising its delivery according to the patient. It is concluded that the common belief that there is a strong personal element in response to treatment is not based on sound statistical evidence. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The Rotterdam Study: 2016 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.