Equity and Value in 'Precision Medicine'

Feasibility of precision smoking treatment in a low-income community setting: results of a pilot randomized controlled trial in The Southern Community Cohort Study

BACKGROUND

The feasibility of precision smoking treatment in socioeconomically disadvantaged communities has not been studied.

METHODS

Participants in the Southern Community Cohort Study who smoked daily were invited to join a pilot randomized controlled trial of three smoking cessation interventions: guideline-based care (GBC), GBC plus nicotine metabolism-informed care (MIC), and GBC plus counseling guided by a polygenic risk score (PRS) for lung cancer. Feasibility was assessed by rates of study enrollment, engagement, and retention, targeting > 70% for each. Using logistic regression, we also assessed whether feasibility varied by age, sex, race, income, education, and attitudes toward precision smoking treatment.

RESULTS

Of 92 eligible individuals (79.3% Black; 68.2% with household income < $15,000), 67 (72.8%; 95% CI 63.0-80.9%) enrolled and were randomized. Of these, 58 (86.6%; 95% CI 76.4-92.8%) engaged with the intervention, and of these engaged participants, 43 (74.1%; 95% CI 61.6-83.7%) were retained at 6-month follow-up. Conditional on enrollment, older age was associated with lower engagement (OR 0.83, 95% CI 0.73-0.95, p = 0.008). Conditional on engagement, retention was significantly lower in the PRS arm than in the GBC arm (OR 0.18, 95% CI 0.03-1.00, p = 0.050). No other selection effects were observed.

CONCLUSIONS

Genetically informed precision smoking cessation interventions are feasible in socioeconomically disadvantaged communities, exhibiting high enrollment, engagement, and retention irrespective of race, sex, income, education, or attitudes toward precision smoking treatment. Future smoking cessation interventions in this population should take steps to engage older people and to sustain participation in interventions that include genetic risk counseling.

TRIAL REGISTRATION

ClinicalTrials.gov No. NCT03521141, Registered 27 April 2018, https://www.

CLINICALTRIALS

gov/study/NCT03521141.

Increased Variation in Esophageal Cancer Treatment and Geographic Healthcare Disparity in Michigan

BACKGROUND

Regional variation in complex healthcare is shown to negatively impact health outcomes. We sought to characterize geographic variance in esophageal cancer operation in Michigan.

STUDY DESIGN

Data for patients with locoregional esophageal cancer from the Michigan Cancer Surveillance Program from 2000 to 2013 was analyzed. We reviewed the incidence of esophageal cancer by county and region, and those with locoregional disease receiving an esophagectomy. Counties were aggregated into existing state-level "urban vs rural" designations, regions were aggregated using the Michigan Economic Recovery Council designations, and data was analyzed with ANOVA, F-test, and chi-square test.

RESULTS

Of the 8,664 patients with locoregional disease, 2,370 (27.4%) were treated with operation. Men were significantly more likely to receive esophagectomy than women (p < 0.001). Likewise, White, insured, and rural patients were more likely than non-White (p < 0.001), non-insured (p = 0.004), and urban patients (p < 0.001), respectively. There were 8 regions and 83 counties, with 61 considered rural and 22 urban. Region 1 (Detroit metro area, southeast) comprises the largest urban and suburban populations; with 4 major hospital systems it was considered the baseline standard for access to care. Regions 2 (west; p = 0.011), 3 (southwest; p = 0.024), 4 (east central; p = 0.012), 6 (northern Lower Peninsula; p = 0.008), and 8 (Upper Peninsula; p < 0.001) all had statistically significant greater variance in annual rates of operation compared with region 1. Region 8 had the largest variance and was the most rural and furthest from region 1. The variance in operation rate between urban and rural differed significantly (p = 0.005).

CONCLUSIONS

A significant increase in variation of care was found in rural vs urban counties, as well as in regions distant to larger hospital systems. Those of male sex, White race, rural residence, and those with health insurance were significantly more likely to receive operation.

Artificial intelligence and imaging: Opportunities in cardio-oncology

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.

Value-Based Healthcare Project Implementation in a Hierarchical Tertiary Hospital: Lessons Learned

An important innovation in healthcare is the value-based healthcare (VBHC) framework, a way to solve health services' sustainability problems and ensure continuous improvement of healthcare quality. The Quality and Safety Unit at the Hospital Universitario 12 de Octubre has been since May 2018 coordinating the implementation of several healthcare innovation projects within the paradigm of VBHC. Implementing innovations in a complex institution, such as a tertiary hospital, is a challenge; we present here the lessons learned in the last 4 years of work. We detail exclusively the aspects related to continuous improvement and value addition to the process. In summary, for any VBHC project implementation, we found that there are five main issues: (1) adequate data quality; (2) development of data recording and visualization tools; (3) minimizing healthcare professional's effort to record data; (4) centralize governance, coordination, and transparency policies; (5) managerial's implication and follow-up. We described six steps key to ensure a successful implementation which are the following: testing the feasibility and complexities of the entry process; establishing leadership and coordination of the project; developing patient-reported outcomes and experience measurements; developing and adapting the data recording and data analysis tools; piloting in one or more medical conditions and evaluating the results and project management. The implementation duration can vary depending on the complexity of the Medical Condition Clinical Process and Patient Pathways. However, we estimate that the implementing phase will last a minimum of 18 and a maximum of 24 months. During this period, the institution should be capable of designing and implementing the proposed innovations. The implementation costs vary as well depending on the complexity, ranging from 90,000 euros to 250,000 euros. Implementation problems included the resistance to change of institutions and professionals. To date, there are few successful, published implementations of value-based healthcare. Our quality of care and patient safety methodological approach to the implementation has provided a particular advantage.

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.

The promise of public health ethics for precision medicine: the case of newborn preventive genomic sequencing

Precision medicine aims to tailor medical treatment to match individual characteristics and to stratify individuals to concentrate benefits and avoid harm. It has recently been joined by precision public health-the application of precision medicine at population scale to decrease morbidity and optimise population health. Newborn preventive genomic sequencing (NPGS) provides a helpful case study to consider how we should approach ethical questions in precision public health. In this paper, I use NPGS as a case in point to argue that both precision medicine and precision public health need public health ethics. I make this argument in two parts. First, I claim that discussions of ethics in precision medicine and NPGS tend to focus on predominantly individualistic concepts from medical ethics such as autonomy and empowerment. This highlights some deficiencies, including overlooking that choice is subject to constraints and that an individual's place in the world might impact their capacity to 'be responsible'. Second, I make the case for using a public health ethics approach when considering ethics and NPGS, and thus precision public health more broadly. I discuss how precision public health needs to be construed as a collective enterprise and not just as an aggregation of individual interests. I also show how analysing collective values and interests through concepts such as solidarity can enrich ethical discussion of NPGS and highlight previously overlooked issues. With this approach, bioethics can contribute to more just and more appropriate applications of precision medicine and precision public health, including NPGS.

Investigating the knowledge of and public attitudes towards genetic testing within the Visegrad countries: a cross-sectional study

BACKGROUND

Previous studies have investigated various factors that can determine the attitudes of the citizens considering genetic testing. However, none of them investigated how these attitudes may differ between the Visegrad countries.

METHODS

In this cross-sectional study a questionnaire developed by Dutch researchers was translated and used in Hungary, Slovakia, Czechia and Poland. In each country 1000 adult citizens were asked on the topics of personal benefits regarding genetic tests, genetic determinism, and finally, the availability and usage of genetic testing. Multivariate robust regression model was created including several possible influencing factors (such as age, sex, education, marital status, religiousness, and having a genetic test within the nuclear family) to identify the possible differences between the four countries.

RESULTS

The Hungarian citizens had the most positive opinion on the personal benefits of genetic testing followed by the Czech, Slovak and Polish. All differences were significant in this regard. Considering genetic determinism, the Slovak citizens had a significantly firmer belief in this issue compared to the Hungarians. No other significant differences were observed in this domain. On the topic of the availability and use of genetic testing the Hungarian citizens had the most accepting opinion among the four countries, followed by the Czech citizens. In this domain the Polish and Slovak answers did not differ significantly from each other.

CONCLUSIONS

Significant differences were observed even when considering various confounding effects. As the underlying reasons for these discrepancies are unknown, future studies should investigate this enigma among the four countries.

Value-based genomic screening: exploring genomic screening for chronic diseases using triple value principles

BACKGROUND

Genomic screening has unique challenges which makes it difficult to easily implement on a wide scale. If the costs, benefits and tradeoffs of investing in genomic screening are not evaluated properly, there is a risk of wasting finite healthcare resources and also causing avoidable harm.

MAIN TEXT

If healthcare professionals - including policy makers, payers and providers - wish to incorporate genomic screening into healthcare while minimizing waste, maximizing benefits, and considering results that matter to patients, using the principles of triple value (allocative, technical, and personal value) could help them to evaluate tough decisions and tradeoffs. Allocative value focuses on the optimal distribution of limited healthcare resources to maximize the health benefits to the entire population while also accounting for all the costs of care delivery. Technical value ensures that for any given condition, the right intervention is chosen and delivered in the right way. Various methods (e.g. ACCE, HTA, and Wilson and Jungner screening criteria) exist that can help identify appropriate genomic applications. Personal value incorporates preference based informed decision making to ensure that patients are informed about the benefits and harms of the choices available to them and to ensure they make choices based on their values and preferences.

CONCLUSIONS

Using triple value principles can help healthcare professionals make reasoned and tough judgements about benefits and tradeoffs when they are exploring the role genomic screening for chronic diseases could play in improving the health of their patients and populations.

Strategic Opportunities for Leveraging Low-cost, High-impact Technological Innovations to Promote Cardiovascular Health in India

Accelerated epidemiological transition in India over the last 40 years has resulted in a dramatic increase in the burden of cardiovascular diseases and the related risk factors of diabetes and hypertension. This increase in disease burden has been accompanied by pervasive health disparities associated with low disease detection rates, inadequate awareness, poor use of evidence-based interventions, and low adherence rates among patients in rural regions in India and those with low socioeconomic status. Several research groups in India have developed innovative technologies and care-delivery models for screening, diagnosis, clinical management, remote-monitoring, self-management, and rehabilitation for a range of chronic conditions. These innovations can leverage advances in sensor technology, genomic tools, artificial intelligence, big-data analytics, and so on, for improving access to and delivering quality and affordable personalized medicine in primary care. In addition, several health technology start-ups are entering this booming market that is set to grow rapidly. Innovations outside biomedical space (eg, protection of traditional wisdom in diet, lifestyle, yoga) are equally important and are part of a comprehensive solution. Such low-cost, culturally tailored, robust innovations to promote health and reduce disparities require partnership among multi-sectors including academia, industry, civil society, and health systems operating in a conducive policy environment that fosters adequate public and private investments. In this article, we present the unique opportunity for India to use culturally tailored, low-cost, high-impact technological innovations and strategies to ameliorate the perennial challenges of social, policy, and environmental challenges including poverty, low educational attainment, culture, and other socioeconomic factors to promote cardiovascular health and advance health equity.

The 'molecularly unstratified' patient: a focus for moral, psycho-social and societal research

The biomedical paradigm of personalised precision medicine - identification of specific molecular targets for treatment of an individual patient - offers great potential for treatment of many diseases including cancer. This article provides a critical analysis of the promise, the hype and the pitfalls attending this approach. In particular, we focus on 'molecularly unstratified' patients - those who, for various reasons, are not eligible for a targeted therapy. For these patients, hope-laden therapeutic options are closed down, leaving them left out, and left behind, bobbing untidily about in the wake of technological and scientific 'advance'. This process creates a distinction between groups of patients on the basis of biomarkers and challenges our ability to provide equitable access to care for all patients. In broadening our consideration of these patients to include the research ecosystem that shapes their experience, we hypothesise that the combination of immense promise with significant complexity creates particular individual and organisational challenges for researchers. The novelty and complexity of their research consumes high levels of resource, possibly in parallel with undervaluing other 'low hanging fruit', and may be challenging current regulatory thinking. We outline future research to consider the societal, psycho-social and moral issues relating to 'molecularly unstratified' patients, and the impact of the drive towards personalisation on the research, funding, and regulatory ecosystem.