Are Evidence-Based Medicine and Public Health Incompatible?

Essay by Michael Schulson: “It’s a familiar pandemic story: In September 2020, Angela McLean and John Edmunds found themselves sitting in the same Zoom meeting, listening to a discussion they didn’t like.

At some point during the meeting, McLean — professor of mathematical biology at the Oxford University, dame commander of the Order of the British Empire, fellow of the Royal Society of London, and then-chief scientific adviser to the United Kingdom’s Ministry of Defense — sent Edmunds a message on WhatsApp.

“Who is this fuckwitt?” she asked.

The message was evidently referring to Carl Heneghan, director of the Center for Evidence-Based Medicine at Oxford. He was on Zoom that day, along with McLean and Edmunds and two other experts, to advise the British prime minister on the Covid-19 pandemic.

Their disagreement — recently made public as part of a British government inquiry into the Covid-19 response — is one small chapter in a long-running clash between two schools of thought within the world of health care.

McLean and Edmunds are experts in infectious disease modeling; they build elaborate simulations of pandemics, which they use to predict how infections will spread and how best to slow them down. Often, during the Covid-19 pandemic, such models were used alongside other forms of evidence to urge more restrictions to slow the spread of the disease. Heneghan, meanwhile, is a prominent figure in the world of evidence-based medicine, or EBM. The movement aims to help doctors draw on the best available evidence when making decisions and advising patients. Over the past 30 years, EBM has transformed the practice of medicine worldwide.

Whether it can transform the practice of public health — which focuses not on individuals, but on keeping the broader community healthy — is a thornier question…(More)”.

Community views on the secondary use of general practice data: Findings from a mixed-methods study

Paper by Annette J. Braunack-Mayer et al: “General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research.Using general practice data is particularly important in countries where general practitioners (GPs) are the first and principal source of health care for most people.

Although there is broad public support for the secondary use of health data, there are good reasons to question whether this support extends to general practice settings. GP–patient relationships may be very personal and longstanding and the general practice health record can capture a large amount of information about patients. There is also the potential for multiple angles on patients’ lives: GPs often care for, or at least record information about, more than one generation of a family. These factors combine to amplify patients’ and GPs’ concerns about sharing patient data….

Adams et al. have developed a model of social licence, specifically in the context of sharing administrative data for health research, based on an analysis of the social licence literature and founded on two principal elements: trust and legitimacy.In this model, trust is founded on research enterprises being perceived as reliable and responsive, including in relation to privacy and security of information, and having regard to the community’s interests and well-being.

Transparency and accountability measures may be used to demonstrate trustworthiness and, as a consequence, to generate trust. Transparency involves a level of openness about the way data are handled and used as well as about the nature and outcomes of the research. Adams et al. note that lack of transparency can undermine trust. They also note that the quality of public engagement is important and that simply providing information is not sufficient. While this is one element of transparency, other elements such as accountability and collaboration are also part of the trusting, reflexive relationship necessary to establish and support social licence.

The second principal element, legitimacy, is founded on research enterprises conforming to the legal, cultural and social norms of society and, again, acting in the best interests of the community. In diverse communities with a range of views and interests, it is necessary to develop a broad consensus on what amounts to the common good through deliberative and collaborative processes.

Social licence cannot be assumed. It must be built through public discussion and engagement to avoid undermining the relationship of trust with health care providers and confidence in the confidentiality of health information…(More)”

How Health Data Integrity Can Earn Trust and Advance Health

Article by Jochen Lennerz, Nick Schneider and Karl Lauterbach: “Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.

Imagine a scenario in which Mary, an individual with a rare disease, has agreed to share her medical records for a research project aimed at finding better treatments for genetic disorders. Mary’s consent is grounded in trust that her data will be handled with the utmost care, protected from unauthorized access, and used according to her wishes. 

It may sound simple, but meeting these standards comes with myriad complications. Whose job is it to weigh the risk that Mary might be reidentified, even if her information is de-identified and stored securely? How should that assessment be done? How can data from Mary’s records be aggregated with patients from health systems in other countries, each with their own requirements for data protection and formats for record keeping? How can Mary’s wishes be respected, both in terms of what research is conducted and in returning relevant results to her?

From electronic medical records to genomic sequencing, health care providers and researchers now have an unprecedented wealth of information that could help tailor treatments to individual needs, revolutionize understanding of disease, and enhance the overall quality of health care. Data protection, privacy safeguards, and cybersecurity are all paramount for safeguarding sensitive medical information, but much of the potential that lies in this abundance of data is being lost because well-intentioned regulations have not been set up to allow for data sharing and collaboration. This stymies efforts to study rare diseases, map disease patterns, improve public health surveillance, and advance evidence-based policymaking (for instance, by comparing effectiveness of interventions across regions and demographics). Projects that could excel with enough data get bogged down in bureaucracy and uncertainty. For example, Germany now has strict data protection laws—with heavy punishment for violations—that should allow de-identified health insurance claims to be used for research within secure processing environments, but the legality of such use has been challenged…(More)”.

Data and density: Two tools to boost health equity in cities

Article by Ann Aerts and Diana Rodríguez Franco: “Improving health and health equity for vulnerable populations requires addressing the social determinants of health. In the US, it is estimated that medical care only accounts for 10-20% of health outcomes while social determinants like education and income account for the remaining 80-90%.

Place-based interventions, however, are showing promise for improving health outcomes despite persistent inequalities. Research and practice increasingly point to the role of cities in promoting health equity — or reversing health inequities — as 56% of the global population lives in cities, and several social determinants of health are directly tied to urban factors like opportunity, environmental health, neighbourhoods and physical environments, access to food and more.

Thus, it is critical to identify both true drivers of good health and poor health outcomes so that underserved populations can be better served.

Place-based strategies can address health inequities and lead to meaningful improvements for vulnerable populations…

Initial data analysis revealed a strong correlation between cardiovascular disease risk in city residents and social determinants such as higher education, commuting time, access to Medicaid, rental costs and internet access.

Understanding which data points are correlated with health risks is key to effectively tailoring interventions.

Determined to reverse this trend, city authorities have launched a “HealthyNYC” campaign and are working with the Novartis Foundation to uncover the behavioural and social determinants behind non-communicable diseases (NCDs) (e.g. diabetes and cardiovascular disease), which cause 87% of all deaths in New York City…(More)”

AI cannot be used to deny health care coverage, feds clarify to insurers

Article by Beth Mole: “Health insurance companies cannot use algorithms or artificial intelligence to determine care or deny coverage to members on Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) clarified in a memo sent to all Medicare Advantage insurers.

The memo—formatted like an FAQ on Medicare Advantage (MA) plan rules—comes just months after patients filed lawsuits claiming that UnitedHealth and Humana have been using a deeply flawed AI-powered tool to deny care to elderly patients on MA plans. The lawsuits, which seek class-action status, center on the same AI tool, called nH Predict, used by both insurers and developed by NaviHealth, a UnitedHealth subsidiary.

According to the lawsuits, nH Predict produces draconian estimates for how long a patient will need post-acute care in facilities like skilled nursing homes and rehabilitation centers after an acute injury, illness, or event, like a fall or a stroke. And NaviHealth employees face discipline for deviating from the estimates, even though they often don’t match prescribing physicians’ recommendations or Medicare coverage rules. For instance, while MA plans typically provide up to 100 days of covered care in a nursing home after a three-day hospital stay, using nH Predict, patients on UnitedHealth’s MA plan rarely stay in nursing homes for more than 14 days before receiving payment denials, the lawsuits allege…(More)”

We urgently need data for equitable personalized medicine

Article by Manuel Corpas: “…As a bioinformatician, I am now focusing my attention on gathering the statistics to show just how biased medical research data are. There are problems across the board, ranging from which research questions get asked in the first place, to who participates in clinical trials, to who gets their genomes sequenced. The world is moving toward “precision medicine,” where any individual can have their DNA analyzed and that information can be used to help prescribe the right drugs in the right dosages. But this won’t work if a person’s genetic variants have never been identified or studied in the first place.

It’s astonishing how powerful our genetics can be in mediating medicines. Take the gene CYP2D6, which is known to play a vital role in how fast humans metabolize 25 percent of all the pharmaceuticals on the market. If you have a genetic variant of CYP2D6 that makes you metabolize drugs more quickly, or less quickly, it can have a huge impact on how well those drugs work and the dangers you face from taking them. Codeine was banned from all of Ethiopia in 2015, for example, because a high proportion of people in the country (perhaps 30 percent) have a genetic variant of CYP2D6 that makes them quickly metabolize that drug into morphine, making it more likely to cause respiratory distress and even death…(More)”

Outpacing Pandemics: Solving the First and Last Mile Challenges of Data-Driven Policy Making

Article by Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto, and Alessandro Vespignani: “As society continues to emerge from the legacy of COVID-19, a dangerous complacency seems to be setting in. Amidst recurrent surges of cases, each serving as a reminder of the virus’s persistence, there is a noticeable decline in collective urgency to prepare for future pandemics. This situation represents not just a lapse in memory but a significant shortfall in our approach to pandemic preparedness. It dramatically underscores the urgent need to develop novel and sustainable approaches and responses and to reinvent how we approach public health emergencies.

Among the many lessons learned from previous infectious disease outbreaks, the potential and utility of data, and particularly non-traditional forms of data, are surely among the most important lessons. Among other benefits, data has proven useful in providing intelligence and situational awareness in early stages of outbreaks, empowering citizens to protect their health and the health of vulnerable community members, advancing compliance with non-pharmaceutical interventions to mitigate societal impacts, tracking vaccination rates and the availability of treatment, and more. A variety of research now highlights the particular role played by open source data (and other non-traditional forms of data) in these initiatives.

Although multiple data sources are useful at various stages of outbreaks, we focus on two critical stages proven to be especially challenging: what we call the first mile and the last mile.

We argue that focusing on these two stages (or chokepoints) can help pandemic responses and rationalize resources. In particular, we highlight the role of Data Stewards at both stages and in overall pandemic response effectiveness…(More)”.

The story of the R number: How an obscure epidemiological figure took over our lives

Article by Gavin Freeguard: “Covid-19 did not only dominate our lives in April 2020. It also dominated the list of new words entered into the Oxford English Dictionary.

Alongside Covid-19 itself (noun, “An acute respiratory illness in humans caused by a coronavirus”), the vocabulary of the virus included “self-quarantine”, “social distancing”, “infodemic”, “flatten the curve”, “personal protective equipment”, “elbow bump”, “WFH” and much else. But nestled among this pantheon of new pandemic words was a number, one that would shape our conversations, our politics, our lives for the next 18 months like no other: “Basic reproduction number (R0): The average number of cases of an infectious disease arising by transmission from a single infected individual, in a population that has not previously encountered the disease.”


“There have been many important figures in this pandemic,” wrote The Times in January 2021, “but one has come to tower over the rest: the reproduction rate. The R number, as everyone calls it, has been used by the government to justify imposing and lifting lockdowns. Indeed while there are many important numbers — gross domestic product, parliamentary majorities, interest rates — few can compete right now with R” (

Descriptions of it at the start of the pandemic made R the star of the disaster movie reality we lived through. And it wasn’t just a breakout star of the UK’s coronavirus press conferences; in Germany, (then) Chancellor Angela Merkel made the most of her scientific background to explain the meaning of R and its consequences to the public (

But for others, the “obsession” (Professor Linda Bauld, University of Edinburgh) with “the pandemic’s misunderstood metric” ( has been “a distraction”, an “unhelpful focus”; as the University of Edinburgh’s Professor Mark Woolhouse told one parliamentary select committee, “we’ve created a monster”.

How did this epidemiological number come to dominate our discourse? How useful is it? And where does it come from?…(More)”.

AI for Good: Applications in Sustainability, Humanitarian Action, and Health

Book by Juan M. Lavista Ferres, and William B. Weeks: “…delivers an insightful and fascinating discussion of how one of the world’s most recognizable software companies is tackling intractable social problems with the power of artificial intelligence (AI). In the book, you’ll see real in-the-field examples of researchers using AI with replicable methods and reusable AI code to inspire your own uses.

The authors also provide:

  • Easy-to-follow, non-technical explanations of what AI is and how it works
  • Examples of the use of AI for scientists working on mitigating climate change, showing how AI can better analyze data without human bias, remedy pattern recognition deficits, and make use of satellite and other data on a scale never seen before so policy makers can make informed decisions
  • Real applications of AI in humanitarian action, whether in speeding disaster relief with more accurate data for first responders or in helping address populations that have experienced adversity with examples of how analytics is being used to promote inclusivity
  • A deep focus on AI in healthcare where it is improving provider productivity and patient experience, reducing per-capita healthcare costs, and increasing care access, equity, and outcomes
  • Discussions of the future of AI in the realm of social benefit organizations and efforts…(More)”

Collective action for responsible AI in health

OECD Report: “Artificial intelligence (AI) will have profound impacts across health systems, transforming health care, public health, and research. Responsible AI can accelerate efforts toward health systems being more resilient, sustainable, equitable, and person-centred. This paper provides an overview of the background and current state of artificial intelligence in health, perspectives on opportunities, risks, and barriers to success. The paper proposes several areas to be explored for policy-makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all.

The areas to be explored relate to trust, capacity building, evaluation, and collaboration. This recognises that the primary forces that are needed to unlock the value from artificial intelligence are people-based and not technical…(More)”