Can Real-Time Metrics Fill China’s Data Gap?

Curated on Posted on by Stefaan Verhulst

Case-study by Danielle Goldfarb: “After Chinese authorities abruptly reversed the country’s zero-COVID policy in 2022, global policymakers needed a clear and timely picture of the economic and health fallout.

China’s economy is the world’s second largest and the country has deep global links, so an accurate picture of its trajectory mattered for global health, growth and inflation. Getting a solid read was a challenge, however, since official health and economic data not only were not timely, but were widely viewed as unreliable.

There are now vast amounts and varied types of digital data available, from satellite images to social media text to online payments; these, along with advances in artificial intelligence (AI), make it possible to collect and analyze digital data in ways previously impossible.

Could these new tools help governments and global institutions refute or confirm China’s official picture and gather more timely intelligence?..(More)”.

Artificial intelligence for modelling infectious disease epidemics

Curated on Posted on by Stefaan Verhulst

Paper by Moritz U. G. Kraemer et al: “Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI…(More)”.

Announcing the Youth Engagement Toolkit for Responsible Data Reuse: An Innovative Methodology for the Future of Data-Driven Services

Curated on Posted on by Stefaan Verhulst

Blog by Elena Murray, Moiz Shaikh, and Stefaan G. Verhulst: “Young people seeking essential services — whether mental health support, education, or government benefits — often face a critical challenge: they are asked to share their data without having a say in how it is used or for what purpose. While the responsible use of data can help tailor services to better meet their needs and ensure that vulnerable populations are not overlooked, a lack of trust in data collection and usage can have the opposite effect.

When young people feel uncertain or uneasy about how their data is being handled, they may adopt privacy-protective behaviors — choosing not to seek services at all or withholding critical information out of fear of misuse. This risks deepening existing inequalities rather than addressing them.

To build trust, those designing and delivering services must engage young people meaningfully in shaping data practices. Understanding their concerns, expectations, and values is key to aligning data use with their preferences. But how can this be done effectively?

This question was at the heart of a year-long global collaboration through the NextGenData project, which brought together partners worldwide to explore solutions. Today, we are releasing a key deliverable of that project: The Youth Engagement Toolkit for Responsible Data Reuse:

Based on a methodology developed and piloted during the NextGenData project, the Toolkit describes an innovative methodology for engaging young people on responsible data reuse practices, to improve services that matter to them…(More)”.

To Stop Tariffs, Trump Demands Opioid Data That Doesn’t Yet Exist

Curated on Posted on by Stefaan Verhulst

Article by Josh Katz and Margot Sanger-Katz: “One month ago, President Trump agreed to delay tariffs on Canada and Mexico after the two countries agreed to help stem the flow of fentanyl into the United States. On Tuesday, the Trump administration imposed the tariffs anyway, saying that the countries had failed to do enough — and claiming that tariffs would be lifted only when drug deaths fall.

But the administration has seemingly established an impossible standard. Real-time, national data on fentanyl overdose deaths does not exist, so there is no way to know whether Canada and Mexico were able to “adequately address the situation” since February, as the White House demanded.

“We need to see material reduction in autopsied deaths from opioids,” said Howard Lutnick, the commerce secretary, in an interview on CNBC on Tuesday, indicating that such a decline would be a precondition to lowering tariffs. “But you’ve seen it — it has not been a statistically relevant reduction of deaths in America.”

In a way, Mr. Lutnick is correct that there is no evidence that overdose deaths have fallen in the last month — since there is no such national data yet. His stated goal to measure deaths again in early April will face similar challenges.

But data through September shows that fentanyl deaths had already been falling at a statistically significant rate for months, causing overall drug deaths to drop at a pace unlike any seen in more than 50 years of recorded drug overdose mortality data.

The declines can be seen in provisional data from the Centers for Disease Control and Prevention, which compiles death records from states, which in turn collect data from medical examiners and coroners in cities and towns. Final national data generally takes more than a year to produce. But, as the drug overdose crisis has become a major public health emergency in recent years, the C.D.C. has been publishing monthly data, with some holes, at around a four-month lag…(More)”.

The Preventative Shift: How can we embed prevention and achieve long term missions

Curated on Posted on by Stefaan Verhulst

Paper by Demos (UK): “Over the past two years Demos has been making the case for a fundamental shift in the purpose of government away from firefighting in public services towards preventing problems arriving. First, we set out the case for The Preventative State, to rebuild local, social and civic foundations; then, jointly with The Health Foundation, we made the case to change treasury rules to ringfence funding for prevention. By differentiating between everyday spending, and preventative spending, the government could measure what really matters.

There has been widespread support for this – but also concerns about both the feasibility of measuring preventative spending accurately and appropriately but also that ring-fencing alone may not lead to the desired improvements in outcomes and value for money.

In response we have developed two practical approaches, covered in two papers:

  • Our first paper, Counting What Matters, explores the challenge of measurement and makes a series of recommendations, including the passage of a “Public Investment Act”, to show how this could be appropriately achieved.
  • This second paper, The Preventative Shift, looks at how to shift the culture of public bodies to think ‘prevention first’ and target spending at activities which promise value for money and improve outcomes…(More)”.

Data Sovereignty and Open Sharing: Reconceiving Benefit-Sharing and Governance of Digital Sequence Information

Curated on Posted on by Stefaan Verhulst

Paper by Masanori Arita: “There are ethical, legal, and governance challenges surrounding data, particularly in the context of digital sequence information (DSI) on genetic resources. I focus on the shift in the international framework, as exemplified by the CBD-COP15 decision on benefit-sharing from DSI and discuss the growing significance of data sovereignty in the age of AI and synthetic biology. Using the example of the COVID-19 pandemic, the tension between open science principles and data control rights is explained. This opinion also highlights the importance of inclusive and equitable data sharing frameworks that respect both privacy and sovereign data rights, stressing the need for international cooperation and equitable access to data to reduce global inequalities in scientific and technological advancement…(More)”.

Patients’ Trust in Health Systems to Use Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Paige Nong and Jodyn Platt: “The growth and development of artificial intelligence (AI) in health care introduces a new set of questions about patient engagement and whether patients trust systems to use AI responsibly and safely. The answer to this question is embedded in patients’ experiences seeking care and trust in health systems. Meanwhile, the adoption of AI technology outpaces efforts to analyze patient perspectives, which are critical to designing trustworthy AI systems and ensuring patient-centered care.

We conducted a national survey of US adults to understand whether they trust their health systems to use AI responsibly and protect them from AI harms. We also examined variables that may be associated with these attitudes, including knowledge of AI, trust, and experiences of discrimination in health care….Most respondents reported low trust in their health care system to use AI responsibly (65.8%) and low trust that their health care system would make sure an AI tool would not harm them (57.7%)…(More)”.

Trump’s shocking purge of public health data, explained

Curated on Posted on by Stefaan Verhulst

Article by Dylan Scott: “In the initial days of the Trump administration, officials scoured federal websites for any mention of what they deemed “DEI” keywords — terms as generic as “diverse” and “historically” and even “women.” They soon identified reams of some of the country’s most valuable public health data containing some of the targeted words, including language about LGBTQ+ people, and quickly took down much of it — from surveys on obesity and suicide rates to real-time reports on immediate infectious disease threats like bird flu.

The removal elicited a swift response from public health experts who warned that without this data, the country risked being in the dark about important health trends that shape life-and-death public health decisions made in communities across the country.

Some of this data was restored in a matter of days, but much of it was incomplete. In some cases, the raw data sheets were posted again, but the reference documents that would allow most people to decipher them were not. Meanwhile, health data continues to be taken down: The New York Times reported last week that data from the Centers for Disease Control and Prevention on bird flu transmission between humans and cats had been posted and then promptly removed…

It is difficult to capture the sheer breadth and importance of the public health data that has been affected. Here are a few illustrative examples of reports that have either been tampered with or removed completely, as compiled by KFF.

The Behavioral Risk Factor Surveillance System (BRFSS), which is “one of the most widely used national health surveys and has been ongoing for about 40 years,” per KFF, is an annual survey that contacts 400,000 Americans to ask people about everything from their own perception of their general health to exercise, diet, sexual activity, and alcohol and drug use.

That in turn allows experts to track important health trends, like the fluctuations in teen vaping use. One recent study that relied on BRFSS data warned that a recent ban on flavored e-cigarettes (also known as vapes) may be driving more young people to conventional smoking, five years after an earlier Yale study based on the same survey led to the ban being proposed in the first place. The Supreme Court and the Trump administration are currently revisiting the flavored vape ban, and the Yale study was cited in at least one amicus brief for the case.

This survey has also been of particular use in identifying health disparities among LGBTQ+ people, such as higher rates of uninsurance and reported poor health compared to the general population. Those findings have motivated policymakers at the federal, state and local levels to launch new initiatives aimed specifically at that at-risk population.

As of now, most of the BRFSS data has been restored, but the supplemental materials that make it legible to lay people still has not…(More)”.

Empowering open data sharing for social good: a privacy-aware approach

Curated on Posted on by Stefaan Verhulst

Paper by Tânia Carvalho et al: “The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have facilitated access to daily cases to support research initiatives, fostering collaboration between organisations and making such data available to the public through open data platforms. Despite the several advantages of data sharing, one of the major concerns before releasing health data is its impact on individuals’ privacy. Such a sharing process should adhere to state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a Covid-19 data set from Portugal’s second-largest hospital to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. Our goal is to demonstrate how knowledge exchange in multidisciplinary teams of healthcare practitioners, data privacy, and data science experts is crucial to co-developing strategies that ensure high utility in de-identified data…(More).”

Cities, health, and the big data revolution

Curated on Posted on by Stefaan Verhulst

Blog by Harvard Public Health: “Cities influence our health in unexpected ways. From sidewalks to crosswalks, the built environment affects how much we move, impacting our risk for diseases like obesity and diabetes. A recent New York City study underscores that focusing solely on infrastructure, without understanding how people use it, can lead to ineffective interventions. Researchers analyzed over two million Google Street View images, combining them with health and demographic data to reveal these dynamics. Harvard Public Health spoke with Rumi Chunara, director of New York University’s Center for Health Data Science and lead author of the study.

Why study this topic?

We’re seeing an explosion of new data sources, like street-view imagery, being used to make decisions. But there’s often a disconnect—people using these tools don’t always have the public health knowledge to interpret the data correctly. We wanted to highlight the importance of combining data science and domain expertise to ensure interventions are accurate and impactful.

What did you find?

We discovered that the relationship between built environment features and health outcomes isn’t straightforward. It’s not just about having sidewalks; it’s about how often people are using them. Improving physical activity levels in a community could have a far greater impact on health outcomes than simply adding more infrastructure.

It also revealed the importance of understanding the local context. For instance, Google Street View data sometimes misclassifies sidewalks, particularly near highways or bridges, leading to inaccurate conclusions. Relying solely on this data, without accounting for these nuances, could result in less effective interventions…(More)”.