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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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.”

graphic

“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” (tinyurl.com/v7j6cth9).

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 (tinyurl.com/mva7urw5).

But for others, the “obsession” (Professor Linda Bauld, University of Edinburgh) with “the pandemic’s misunderstood metric” (Nature: tinyurl.com/y3sr6n6m) 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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”

Medical AI could be ‘dangerous’ for poorer nations, WHO warns

Curated on Posted on by Stefaan Verhulst

Article by David Adam: “The introduction of health-care technologies based on artificial intelligence (AI) could be “dangerous” for people in lower-income countries, the World Health Organization (WHO) has warned.

The organization, which today issued a report describing new guidelines on large multi-modal models (LMMs), says it is essential that uses of the developing technology are not shaped only by technology companies and those in wealthy countries. If models aren’t trained on data from people in under-resourced places, those populations might be poorly served by the algorithms, the agency says.

“The very last thing that we want to see happen as part of this leap forward with technology is the propagation or amplification of inequities and biases in the social fabric of countries around the world,” Alain Labrique, the WHO’s director for digital health and innovation, said at a media briefing today.

The WHO issued its first guidelines on AI in health care in 2021. But the organization was prompted to update them less than three years later by the rise in the power and availability of LMMs. Also called generative AI, these models, including the one that powers the popular ChatGPT chatbot, process and produce text, videos and images…(More)”.

How can Mixed Reality and AI improve emergency medical care?

Curated on Posted on by Stefaan Verhulst

Springwise: “Mixed reality (MR) refers to technologies that create immersive computer-generated environments in which parts of the physical and virtual environment are combined. With potential applications that range from education and engineering to entertainment, the market for MR is forecast to record revenues of just under $25 billion by 2032. Now, in a ground-breaking partnership, Singapore-based company Mediwave is teaming up with Sri Lanka’s 1990 Suwa Seriya to deploy MR and artificial intelligence (AI) to create a fully connected ambulance.

1990 Suwa Seriya is Sri Lanka’s national pre-hospital emergency ambulance service, which boasts response times that surpass even some services in developed countries. The innovative ambulance it has deployed uses Mediwave’s integrated Emergency Response Suite, which combines the latest communications equipment with internet-of-things (IoT) and AR capabilities to enhance the efficiency of the emergency response eco-system.

The connected ambulance ensures swift response times and digitises critical processes, while specialised care can be provided remotely through a Microsoft HoloLens. The technology enables Emergency Medical Technicians (EMTs) – staff who man ambulances in Sri Lanka – to connect with physicians at the Emergency Command and Control Centre. These physicians help the EMTs provide care during the so-called ‘golden hour’ of medical emergencies – the concept that rapid clinical investigation and care within 60 minutes of a traumatic injury is essential for a positive patient outcome…

Other applications of extended reality in the Springwise library include holograms that are used to train doctors, virtual environments for treating phobias, and an augmented reality contact lens…(More)”.

A synthesis of evidence for policy from behavioral science during COVID-19

Curated on Posted on by Stefaan Verhulst

Paper by Kai Ruggeri et al: “Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization…(More)”