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KEY POINTS\ud In Canada, commercial data brokers collect deidentified patient data from pharmacies, private drug insurers, the federal government and medical clinics without patient consent.\ud \ud Although pharmaceutical companies are the data brokers’ primary customers, academics and nonprofit and public entities also use commercial data sets, given the absence of a coordinated public approach to collecting these data across Canada.\ud \ud Risks of commercialized patient data include loss of anonymity, surveillance and marketing, discrimination and violation of Indigenous data sovereignty.\ud \ud Coordinated infrastructure for the collection and coordination of health data across Canada and updated privacy legislation would protect individuals and communities and enable appropriate data uses.

The adoption of genetically engineered (GE) crops has led to economic and environmental benefits. However, there are regulatory and environmental concerns regarding the potential movement of transgenes beyond cultivation. These concerns are greater for GE crops with high outcrossing frequencies to sexually compatible wild relatives and those grown in their native region. Newer GE crops may also confer traits that enhance fitness, and introgression of these traits could negatively impact natural populations. Transgene flow could be lessened or prevented altogether through the addition of a bioconfinement system during transgenic plant production. Several bioconfinement approaches have been designed and tested and a few show promise for transgene flow prevention. However, no system has been widely adopted despite nearly three decades of GE crop cultivation. Nonetheless, it may be necessary to implement a bioconfinement system in new GE crops or in those where the potential of transgene flow is high. Here, we survey such systems that focus on male and seed sterility, transgene excision, delayed flowering, as well as the potential of CRISPR/Cas9 to reduce or eliminate transgene flow. We discuss system utility and efficacy, as well as necessary features for commercial adoption.

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

Objectives: Electronic health records (EHRs) contain rich information for understanding health conditions and their treatment. A large proportion of clinical information in EHRs is stored in narrative free text. This text is currently under-utilised due to privacy concerns, as it is harder to remove patient identifiers from text than from structured data. Automated de-identification of clinical text is now possible using heuristic or machine-learning-based systems. We conducted a review of the literature on patient and public understanding and attitudes towards the use of patients’ medical data for research, particularly seeking views on free text. The aim was to inform and develop a governance framework for the de-identification and use of medical free text for research, and to instigate a wider discussion on the topic. Approach: We undertook a systematic search in Web of Science and ScienceDirect with terms such as “public attitudes” and “electronic health records”. 3480 results were sifted by title, abstract and full text. Forty-two articles were retained for review, these reported on studies of patient and public perceptions, understanding and attitudes towards the use of patients’ medical data in research. Results: Research participants were positively inclined towards information in records being used in research “for the greater good”. However, no clear patterns by age, ethnicity, education level or SES emerged as to who was more favourable to data use. Participants generally trusted health care professionals and public sector researchers with de-identified medical data, whereas government health agencies and commercial entities were not trusted. No explicitly feared harms associated with data use were articulated. However the general objections appeared to be a dislike of personal data being exploited for commercial gain, and a dislike of personal data being moved around and used without personal knowledge or consent. Notably the use of EHR medical text for research did not emerge as a specific patient/public concern. De-identification was important to participants but text was not identified as a distinct privacy risk. Conclusion: This review demonstrates that transparency about data usage, and working “for the greater good” rather than financial gain, appear to be the most important public concerns to be addressed when using patients’ medical data. Governance frameworks for using EHRs must now be enhanced to provide for the use of medical text. This will involve informing both regulators and the public about the current capabilities of automated de-identification, and developing other assurances to safeguard patients’ privacy.

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform ethical and privacy debates.Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis.Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data.Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data

Background: Use of medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public opinion and understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public opinions of medical data use in research, critically analysing such opinions though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform ethical and privacy debates.Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the wider public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a Framework Analysis.Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share EHRs for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns through a variety of hypothetical examples. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data.Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which data guardians should address when evaluating proposals for the secondary use of data.