Conflicts of interest in clinical guidelines, advisory committee reports, opinion pieces, and narrative reviews: associations with recommendations.

BACKGROUND: Treatment and diagnostic recommendations are often made in clinical guidelines, reports from advisory committee meetings, opinion pieces such as editorials, and narrative reviews. Quite often, the authors or members of advisory committees have industry ties or particular specialty interests which may impact on which interventions are recommended. Similarly, clinical guidelines and narrative reviews may be funded by industry sources resulting in conflicts of interest. OBJECTIVES: To investigate to what degree financial and non-financial conflicts of interest are associated with favourable recommendations in clinical guidelines, advisory committee reports, opinion pieces, and narrative reviews. SEARCH METHODS: We searched PubMed, Embase, and the Cochrane Methodology Register for studies published up to February 2020. We also searched reference lists of included studies, Web of Science for studies citing the included studies, and grey literature sources. SELECTION CRITERIA: We included studies comparing the association between conflicts of interest and favourable recommendations of drugs or devices (e.g. recommending a particular drug) in clinical guidelines, advisory committee reports, opinion pieces, or narrative reviews. DATA COLLECTION AND ANALYSIS: Two review authors independently included studies, extracted data, and assessed risk of bias. When a meta-analysis was considered meaningful to synthesise our findings, we used random-effects models to estimate risk ratios (RRs) with 95% confidence intervals (CIs), with RR > 1 indicating that documents (e.g. clinical guidelines) with conflicts of interest more often had favourable recommendations. We analysed associations for financial and non-financial conflicts of interest separately, and analysed the four types of documents both separately (pre-planned analyses) and combined (post hoc analysis). MAIN RESULTS: We included 21 studies analysing 106 clinical guidelines, 1809 advisory committee reports, 340 opinion pieces, and 497 narrative reviews. We received unpublished data from 11 studies; eight full data sets and three summary data sets. Fifteen studies had a risk of confounding, as they compared documents that may differ in other aspects than conflicts of interest (e.g. documents on different drugs used for different populations). The associations between financial conflicts of interest and favourable recommendations were: clinical guidelines, RR: 1.26, 95% CI: 0.93 to 1.69 (four studies of 86 clinical guidelines); advisory committee reports, RR: 1.20, 95% CI: 0.99 to 1.45 (four studies of 629 advisory committee reports); opinion pieces, RR: 2.62, 95% CI: 0.91 to 7.55 (four studies of 284 opinion pieces); and narrative reviews, RR: 1.20, 95% CI: 0.97 to 1.49 (four studies of 457 narrative reviews). An analysis combining all four document types supported these findings (RR: 1.26, 95% CI: 1.09 to 1.44). One study investigating specialty interests found that the association between including radiologist guideline authors and recommending routine breast cancer screening was RR: 2.10, 95% CI: 0.92 to 4.77 (12 clinical guidelines). AUTHORS' CONCLUSIONS: We interpret our findings to indicate that financial conflicts of interest are associated with favourable recommendations of drugs and devices in clinical guidelines, advisory committee reports, opinion pieces, and narrative reviews. However, we also stress risk of confounding in the included studies and the statistical imprecision of individual analyses of each document type. It is not certain whether non-financial conflicts of interest impact on recommendations.

[Digital data for more efficient prevention: ethical and legal considerations regarding potentials and risks]. / Digitale Daten für eine effizientere Prävention: Ethische und rechtliche Überlegungen zu Potenzialen und Risiken.

Digitization offers considerable potential for strengthening prevention in the healthcare system. Data from various clinical and nonclinical sources can be collected in a structured way and systematically processed using algorithms. Prevention needs can thus be identified more quickly and precisely, and interventions can be planned, implemented, and evaluated for specific target groups. At the same time, however, it is necessary that data processing not only meets high technical but also ethical standards and legal data protection regulations in order to avoid or minimize risks.This discussion article examines the potentials and risks of digital prevention first from a "data perspective," which deals with the use of health-related data for the purpose of prevention, and second from an "algorithm perspective," which focuses on the use of algorithmic systems, including artificial intelligence, for the assessment of needs and evaluation of preventive measures, from an ethical and legal point of view. Finally, recommendations are formulated for framework conditions that should be created to strengthen the further development of prevention in the healthcare system.

Patient data and patient rights: Swiss healthcare stakeholders' ethical awareness regarding large patient data sets - a qualitative study.

BACKGROUND: There is a growing interest in aggregating more biomedical and patient data into large health data sets for research and public benefits. However, collecting and processing patient data raises new ethical issues regarding patient's rights, social justice and trust in public institutions. The aim of this empirical study is to gain an in-depth understanding of the awareness of possible ethical risks and corresponding obligations among those who are involved in projects using patient data, i.e. healthcare professionals, regulators and policy makers. METHODS: We used a qualitative design to examine Swiss healthcare stakeholders' experiences and perceptions of ethical challenges with regard to patient data in real-life settings where clinical registries are sponsored, created and/or used. A semi-structured interview was carried out with 22 participants (11 physicians, 7 policy-makers, 4 ethical committee members) between July 2014 and January 2015. The interviews were audio-recorded, transcribed, coded and analysed using a thematic method derived from Grounded Theory. RESULTS: All interviewees were concerned as a matter of priority with the needs of legal and operating norms for the collection and use of data, whereas less interest was shown in issues regarding patient agency, the need for reciprocity, and shared governance in the management and use of clinical registries' patient data. This observed asymmetry highlights a possible tension between public and research interests on the one hand, and the recognition of patients' rights and citizens' involvement on the other. CONCLUSIONS: The advocation of further health-related data sharing on the grounds of research and public interest, without due regard for the perspective of patients and donors, could run the risk of fostering distrust towards healthcare data collections. Ultimately, this could diminish the expected social benefits. However, rather than setting patient rights against public interest, new ethical approaches could strengthen both concurrently. On a normative level, this study thus provides material from which to develop further ethical reflection towards a more cooperative approach involving patients and citizens in the governance of their health-related big data.

The care.data consensus? A qualitative analysis of opinions expressed on Twitter.

BACKGROUND: Large, integrated datasets can be used to improve the identification and management of health conditions. However, big data initiatives are controversial because of risks to privacy. In 2014, NHS England launched a public awareness campaign about the care.data project, whereby data from patients' medical records would be regularly uploaded to a central database. Details of the project sparked intense debate across a number of platforms, including social media sites such as Twitter. Twitter is increasingly being used to educate and inform patients and care providers, and as a source of data for health services research. The aim of the study was to identify and describe the range of opinions expressed about care.data on Twitter for the period during which a delay to this project was announced, and provide insight into the strengths and flaws of the project. METHODS: Tweets with the hashtag #caredata were collected using the NCapture tool for NVivo. Methods of qualitative data analysis were used to identify emerging themes. Tweets were coded and analysed in-depth within and across themes. RESULTS: The dataset consisted of 9895 tweets, captured over 18 days during February and March 2014. Retweets (6118, 62%) and spam (240, 2%) were excluded. The remaining 3537 tweets were posted by 904 contributors, and coded into one or more of 50 sub-themes, which were organised into 9 key themes. These were: informed consent and the default 'opt-in', trust, privacy and data security, involvement of private companies, legal issues and GPs' concerns, communication failure and confusion about care.data, delayed implementation, patient-centeredness, and potential of care.data and the ideal model of implementation. CONCLUSIONS: Various concerns were raised about care.data that appeared to be shared by those both for and against the project. Qualitatively analysing tweets enabled us to identify a range of concerns about care.data and how these might be overcome, for example, by increasing the involvement of stakeholders and those with expert knowledge. Our findings also highlight the risks of not considering public opinion, such as the potential for patient safety failures resulting from a lack of trust in the healthcare system. However, caution is advised if using Twitter as a stand-alone data source, as contributors may lie more heavily on one side of a debate than another. A mixed-methods approach would have enabled us to complement this data with a more representative overview.

[The digital looking glass: ethical aspects of large data collections]. / Digitale Spiegelbilder - Ethische Aspekte großer Datensammlungen.

The increase in the quality and quantity of health-related data stored and analyzed in different contexts is leading to new challenges to the protection of privacy. To demonstrate the major challenges, a description of the main categories of large data collections is given, followed by a short analysis of their current data security demands. The rising possibilities and risks that emerge from the increasing potential of big data methods of aggregation and the analysis of the huge amount of health-related data are discussed in light of the question: should new ways be found to further ensure the right to privacy?

The impact of commercial health datasets on medical research and health-care algorithms.

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Big data in forensic genetics.

The potential and difficulties of the application of genome wide data in forensics are analyzed. We argue that, besides statistical, computational, ethical, economic and technical validation problems, the state of the art of population genetics theory is insufficient to deal with the forensic use of this type of data. In order to keep the current standards of quantifying and reporting genetic evidence, namely in kinship analyses and identification, substantial improvement in the theoretical framework should be reached, since to obtain genome-wide results is to provide the experts with data that they cannot quantify the corresponding evidentiary value. Therefore, while a satisfactory, generalized theoretical and biostatistical modelling is not achieved, it may well be wiser to improve the already established approaches to a limited, pre-defined number of validated genetic markers, amenable to a consensual handling and reporting. Whole genome population analyses will prove extremely useful in selecting the best suited and most efficient of those markers.

Ethics and Epistemology in Big Data Research.

Biomedical innovation and translation are increasingly emphasizing research using "big data." The hope is that big data methods will both speed up research and make its results more applicable to "real-world" patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.

Health data research in New Zealand: updating the ethical governance framework.

Demand for health data for secondary research is increasing, both in New Zealand and worldwide. The New Zealand government has established a large research database, the Integrated Data Infrastructure (IDI), which facilitates research, and an independent ministerial advisory group, the Data Futures Partnership (DFP), to engage with citizens, the private sector and non-government organisations (NGOs) to facilitate trusted data use and strengthen the data ecosystem in New Zealand. We commend these steps but argue that key strategies for effective health-data governance remain absent in New Zealand. In particular, we argue in favour of the establishment of: (1) a specialist Health and Disability Ethics Committee (HDEC) to review applications for secondary-use data research; (2) a public registry of approved secondary-use research projects (similar to a clinical trials registry); and (3) detailed guidelines for the review and approval of secondary-use data research. We present an ethical framework based on the values of public interest, trust and transparency to justify these innovations.

Ethics and Epistemology of Big Data.

In this Symposium on the Ethics and Epistemology of Big Data, we present four perspectives on the ways in which the rapid growth in size of research databanks-i.e. their shift into the realm of "big data"-has changed their moral, socio-political, and epistemic status. While there is clearly something different about "big data" databanks, we encourage readers to place the arguments presented in this Symposium in the context of longstanding debates about the ethics, politics, and epistemology of biobank, database, genetic, and epidemiological research.

Potential of big data analytics in the French in vitro diagnostics market.

The new paradigm of the big data raises many expectations, particularly in the field of health. Curiously, even though medical biology laboratories generate a great amount of data, the opportunities offered by this new field are poorly documented. For better understanding the clinical context of chronical disease follow-up, for leveraging preventive and/or personalized medicine, the contribution of big data analytics seems very promising. It is within this framework that we have explored to use data of a Breton group of laboratories of medical biology to analyze the possible contributions of their exploitation in the improvement of the clinical practices and to anticipate the evolution of pathologies for the benefit of patients. We report here three practical applications derived from routine laboratory data from a period of 5 years (February 2010-August 2015): follow-up of patients treated with AVK according to the recommendations of the High authority of health (HAS), use of the new troponin markers HS and NT-proBNP in cardiology. While the risks and difficulties of using algorithms in the health domain should not be underestimated - quality, accessibility, and protection of personal data in particular - these first results show that use of tools and technologies of the big data repository could provide decisive support for the concept of "evidence based medicine".